Progress 03/30/16 to 03/29/21
Outputs
PROGRESS REPORT Objectives (from AD-416): Objective 1: Advance development and validation of on-line automated whole-surface inspection systems for simultaneous safety and quality inspection of fresh produce in high-throughput commercial processing operations. Objective 2: Develop and validate user-friendly analytical sensing methods and technologies for targeted and non-targeted rapid screening of foods for microbial, chemical, and biological contaminants in laboratory, field, and/or industrial environments. Objective 3: Advance development of portable spectral imaging technologies to allow identification and detection of food contaminants, and develop sampling and inspection protocols for implementation of the developed technologies in industry and regulatory applications. Objective 4: Advance development, test and validate an automated system for detecting contaminants in produce fields, and investigate cost and sensitivity trade-offs of different potential system components and configurations with regard to production of a cost-effective commercial system. Approach (from AD-416): Because cross-contamination may occur at many points throughout the production, processing, and distribution chains, our research targets reduction of food safety risks at multiple points, including both upstream (pre-harvest) processing and subsequent stages. The inspection point at single-layer processing is not intended to be a comprehensive inspection on its own but is key for some packaged fresh products. The ARS approach includes both pre- and post-harvest risk reduction measures that collectively can mitigate food safety concerns related to foodborne illnesses. The whole-surface sample presentation/imaging technologies developed in the previous project cycle along with the multitask imaging technology will be integrated on conveyor/processing systems to develop two automated whole-surface inspection platforms to simultaneously inspect produce for safety and quality attributes such as contaminants and defects. The two stand-alone commercial-grade prototype processing- inspection platforms will be transportable to produce processing facilities for testing and demonstration of the whole-surface inspection efficacies, with an ultimate goal of technology transfer. The proposed whole-surface fruit inspection will complement current industry sorting⿿based on quality attributes such as color and size⿿by the addition of safety inspection and will be used immediately after conventional color and size sorting. The proposed leafy green inspection will be used for inspection immediately prior to ⿿value-added processing, e.g., washing for packaged fresh-cut products. This is the final report for project 1245-42000-020-00D, which was terminated in March 2021. Significant progress has been made for all objectives of the project, which fall under National Program 108. For objective 1, ARS scientists in Beltsville, Maryland, integrated image processing algorithms into the prototype inspection system for round fruits, enabling real-time generation of a 2-dimensional map representing the entire surface of a spherical fruit. The prototype system for inspection of leafy greens was used in experiments for simultaneous fecal contamination inspection and defect detection on spinach and romaine lettuce. Prototype development for whole-surface inspection systems to perform bulk processing and safety inspection was completed, and the technologies were demonstrated to commercial entities. In January 2021, exclusive licensing was granted to a Cooperative Research and Development Agreement (CRADA) partner for the ARS-patented multitask inspection technology (U.S. Patent No. 7,787,111, Simultaneous acquisition of fluorescence and reflectance imaging techniques with a single imaging device for multitask inspection). For objective 2, ARS scientists in Beltsville, Maryland, successfully used a newly developed 1064 nm Raman system to detect chemical contaminants in spice powders and also used Fourier transform infrared (FT-IR) spectroscopy for quantitative detection. The work showed that Raman spectral imaging is a suitable technique for nondestructive detection of contaminants in heterogeneous powder samples but not for qualitative analysis due to false-positive detections. Unlike Raman measurement, FT-IR measurement requires prior sample preparation, but IR can perform quantitative analysis. While either technique alone may provide incomplete molecular information, both IR and Raman spectra together at the same measurement site can provide a morecomplete diagnostic set of vibrational modes, reducing false positive/negative detection. Following the customized Raman imaging systems already developed to detect contaminants in food powders, a customized point-scan IR system to collect spectra across the entire surface area of a sample will be developed next. On a shared platform, the point-scan IR and Raman systems will operate simultaneously for a sample. The combined spectra can be used to develop chemometric models to estimate adulterant concentrations in many types of mixtures. In collaboration with National Agricultural Products Quality Management Service, South Korea, ARS scientists in Beltsville, Maryland, developed a transportable multimodal optical sensing system for automated and intelligent biological and chemical assessment in food safety applications. The system uses two pairs of point lasers and spectrometers, at 785 and 1064 nm, to conduct dual-band Raman sensing, which can be used for samples generating low- and high-fluorescence interference signals, respectively. The system capability was demonstrated by an example application for the rapid identification of five common foodborne bacteria. Using a machine-learning model based on a linear support vector machine, over 98% classification accuracy was achieved using spectra automatically collected from bacterial colonies for the five species grown on nonselective agar in Petri dishes. ARS scientists provided additional data in a system methodology manuscript to support a patent disclosure that was suspended in 2020 for insufficient data. In collaboration with NASA Kennedy Space Center (KSC), ARS scientists in Beltsville, Maryland, continued to develop next-generation hyperspectral imaging technology suitable for plant health and food safety monitoring in fresh food production systems for the future spaceflight. ARS scientists finished the development and testing of a compact hyperspectral system equipped with Visible Near Infrared (VNIR) and UVA light for reflectance and fluorescence measurements. The prototype system was installed in a plant growth chamber at KSC for experiments on pick- and-eat salad crops. Hyperspectral reflectance and fluorescence images were collected from Dragoon lettuce, pak choi, and mizuna grown by KSC scientists under normal and abiotic stress conditions (e.g., drought and overwatering). ARS scientists are developing image processing and classification procedures to analyze the data. In collaboration with a CRADA partner, ARS scientists in Beltsville, Maryland, continued work to develop fish authentication methods and systems based on multimode hyperspectral imaging techniques to address issues of species mislabeling and fraud the freshness of fish fillets. In this continuing study, three ARS in-house developed line-scan hyperspectral systems were utilized to acquire four types of images from fresh and frozen-thawed fish fillet samples obtained from local and online vendors. Imaging experiments and DNA tests were completed for over 50 major fish species. A hyperspectral database with DNA barcoding species labels was established. Machine learning AI models and spectral and image fusion algorithms were developed to classify fish species and compare the performance of individual and combined imaging data. The results will be used to design and develop portable smart sensing devices for industrial on-site fish inspection applications. For Objective 3, ARS scientists in Beltsville, Maryland, worked with multiple collaborators to test and commercial ARS portable multispectral imaging technology for contamination and sanitization inspection. Based on an exclusive license for the ARS-patented (.S.U.S. patent no. US 8,310, 544) handheld fluorescence imaging technology granted to a CRADA partner in January 2021, a commercial Contamination Sanitization Inspection and Disinfection (CSI-D) device was developed and marketed in 2021. The handheld CSI-D device provides an innovative solution for infection prevention in food preparation and serving facilities, including visualization of contamination via UVA fluorescence imaging, disinfection via UVC illumination, and documentation of cleanliness. Commercial CSI-D (UVA/UVC) and prototype CSI (UVA) devices have been obtained for experiments, testing, and improvement of their integrated touchscreen user App. Cooperative Agreements have been established with three land- grant universities and a private research university to develop embedded imaging AI and real-time machine learning inspection techniques and determine optimal device and experiment settings for detection and disinfection foodborne pathogens on food-contact surfaces. In addition, ARS scientists developed two enclosed bench-top UV sensing systems to evaluate the effectiveness of UVB and UVC for germicidal applications. The first system consists of a 305 nm UVB LED module with a cooling fan, a height-adjustable sample holder, a single-board computer with a touchscreen monitor, and a safety trigger. This system will be used to measure UVB irradiance at varying distances and pulse rates to determine optimal parameters for antimicrobial experiments on foodborne bacteria grown in Petri dishes. The second system includes 305 nm UVB, 275 nm UVC, mixed UVB-UVC LED modules, a programmable linear stage, a depth and RGB camera for a seedling plant, a physical data logger, a single-board computer with a touchscreen monitor, and a safety trigger. This system will be used to establish optimal UVB and UVC parameters along with efficacy, safety, and functionality, for nonchemical control of plant pathogens and insect pests. With the resulting data, commercial CSI-D devices will be optimized (e.g., light intensity, pulse rate, and working distance) for eventual use in field testing and experiments. For objective 4, ARS scientists in Beltsville, Maryland, expanded research to develop preharvest imaging of field crops to detect animal intrusion and fecal contamination. Although the lead scientist for developing the ground-based prototype retired, the remaining scientists began transitioning from the ground-based imaging approach to a new drone- based implementation for higher speed imaging of larger field areas and also for potential joint operation with drone-based monitoring of the microbial quality of irrigation water. The new (already approved) project includes developing a small drone with multimode imaging technologies for these purposes. Design of the field-level ground truth measurement platform has been initiated. In collaboration with the water quality team, a hyperspectral imaging system mounted on a GPS-equipped motorized floating platform was developed and used to acquire line-scan water images on a 3m x 191m irrigation pond while corresponding water samples at reference points were collected for measurement of chlorophyll-a concentration, a water quality indicator. Models using the image data yielded predicted concentrations highly correlated to the measured concentrations, showing that low-altitude spectral imaging and data mapping can provide valuable information about water quality. ACCOMPLISHMENTS 01 Evaluation of irrigation pond water quality using hyperspectral reflectance imaging from a multipurpose floating platform. Irrigation water is a potential vehicle for spreading human infections via agricultural produce. Poor microbiological water quality can lead to severe hemorrhagic and gastrointestinal diseases in humans. One symptom of degraded water quality condition is the increase of algae biomass as measured by the concentration of chlorophyll-a. ARS scientists in Beltsville, Maryland, developed a hyperspectral imaging method to measure the chlorophyll-a concentration for evaluating irrigation pond water quality. A hyperspectral system was mounted on a motor-driven multipurpose floating platform for water sampling and acquisition of visible/near-infrared reflectance images. Spectral and image processing algorithms were developed to analyze 80,000 sample images collected in an excavated irrigation pond at the University of Maryland Wye Research Center. Spectral intensities at selected key wavelengths were used as inputs to mathematical models for predicting the concentration of chlorophyll-a, resulting in the best determination coefficient (R2) of 0.83. The developed approach can be used as a rapid and precise method for evaluating the quality of irrigation water . 02 Detection of fish bones in fillets using hyperspectral Raman imaging. Fish bone fragments are a serious hazard that must be strictly controlled in fish products. New detection techniques are increasingly needed to detect fish bones effectively. This study developed a novel fish bone detection method based on line-scan macro-scale hyperspectral Raman imaging technology to improve the detection accuracy and realize automated inspection. Raman spectral differences between fish bone and fish meat were investigated, and the optimal band information was selected. A classification model was developed using the selected band information to realize automated detection of the fish bones. Experiments on the fish bones from grass carp fillets showed that the method could effectively detect fish bones at depths up to 2.5 mm in the fillet and yielded a detection accuracy of 90.5%. The technique developed in this study opens new possibilities in automated fish bone detection in fish or fish fillet products. 03 Nondestructive freshness evaluation of intact prawns using spatially offset Raman spectroscopy. Technical difficulties exist in accurately evaluating the internal quality of prawns without destroying their shells. This study developed a nondestructive method to detect the quality of the prawns with shells intact based on a laser Raman spectroscopy subsurface sensing technique combined with a data modeling analysis method. Line-scan Raman scattering image data were acquired from intact prawn samples spanning zero-day to seven-day storage (24 hours between sampling intervals) using a line-scan Raman imaging system. Feature selection methods were used to identify important bands, and prediction models were developed based on the selected bands to evaluate the freshness of the prawns. The results demonstrated that the nondestructive sensing method meets the accuracy requirements of the seafood industry for the freshness evaluation of intact prawns. The use of the technique would benefit the seafood industry in ensuring the quality and safety of shrimp products and help regulatory agencies, such as FDA and USDA FSIS, with interest in enforcing quality and safety standards for shrimp products. 04 Nondestructive evaluation of pork meat freshness using shortwave infrared hyperspectral reflectance imaging. Monitoring and maintaining the freshness of pork is important to ensure safe supply of meat for consumption. However, methods for grading freshness and safety of pork lack on-site inspection. ARS scientists in Beltsville, Maryland, developed a shortwave infrared hyperspectral reflectance imaging method to determine total volatile basic nitrogen (TVB-N) content as a reference for the pork freshness. This study developed algorithms to select optimal wavelengths and evaluate pork freshness using multivariable models. The predictions from the optimal model exhibited high-accuracy results. Moreover, this research showed that visualization of TVB-N as an indicator of the pork freshness provides an intuitive way to interpret spatial information of meat samples. The method developed for rapid and nondestructive assessment of pork freshness is feasible in online inspection systems as an effective substitute for traditional evaluation methods. 05 Rapid nondestructive detection method for chemical contaminants in foods. The use of veterinary drugs such as Tetracycline (TC) and the grassland fertilizer Dicyandiamide (DCD) can result in residual contamination in milk products. Rapid, nondestructive detection methodologies to detect these contaminants are necessary to avoid human health risks from tainted dairy foods. ARS scientists in Beltsville, Maryland, have developed a 785-nm point scan Raman imaging method and apparatus for rapid nondestructive detection of chemical contaminants in food materials. Spectroscopic analysis required first the development of a silver nanoparticle layer on an aluminum oxide surface. TC and DCD can then be detected on this surface layer by a technology called Surface Enhanced Raman Spectroscopy (SERS). Detection levels were found to be 1 ÿ 10-9 M and 1 ÿ 10-7 M, for TC and DCS, respectively. This study demonstrated the new sensing method's effectiveness in providing a practical and reliable platform for rapid contaminant detection in milk products. 06 GTRS-based screening methods for temperature-dependent interactions of food ingredients and contaminants. ARS scientists in Beltsville, Maryland, have developed a gradient temperature Raman spectroscopy (GTRS) method and apparatus for research addressing food integrity concerns arising from adulteration or contamination. The GTRS technology (U.S. patent no: U.S. 9,963,882 B2, 2018) has been applied to a wide variety of sample types, including amines, peptides, herbicides, and polyunsaturated lipids. Utilizing the GTRS system, state-of-the-art Raman vibrational mode reference data has been published for 15 unsaturated lipids. The GTRS contour plots are highly diagnostic, tracing the spectroscopy from the solid phase through melting to the liquid phase. Presentations on GTRS technology to instrumentation companies have been made to enable GTRS instrumentation to be commercially available. 07 Applying GTRS specificity to distinguish between commercial fish oil supplements. Dietary consumption of fish and fish oils lowers the risks of multiple health-related disorders. One fish oil component, eiscosapentaenoic acid ethyl ester, is an FDA-approved drug for the treatment of cardiovascular disease. The fish oil market in 2019 had sales of $4 billion. The composition of fish oil products, however, is far from uniform. Fish oil from different fish may not have an identical chemical composition, and reformulations for commercial products may not be evident. ARS scientists in Beltsville, Maryland, discovered that differences among the chemical composition of fish oil types could be quickly and accurately discerned spectroscopically when data is collected in a mild temperature gradient. The Gradient Temperature Raman Spectroscopy (GTRS) contour plot for a fish oil product can be used as a fingerprint to match with fingerprints of known component lipids. The GTRS technology can be used to determine if a specific fish oil product has been reformulated.
Impacts
(N/A)
Publications
- Baek, I., Lee, H., Cho, B., Mo, C., Chan, D.E., Kim, M.S. 2020. Shortwave infrared hyperspectral imaging system coupled with multivariable method for TVB-N measurement in pork. Food Control. 124, 107854. https://doi.org/ 10.1016/j.foodcont.2020.107854.
- Baek, I., Qin, J., Cho, B., Kim, M.S. 2021. Quality evaluation of agro- products by imaging and spectroscopy. Bentham Science Publishers. p. 27-48. https://doi.org/10.2174/97898114858001210101.
- Broadhurst, C.L., Schmidt, W.F., Qin, J., Chao, K., Kim, M.S. 2018. Continuous gradient temperature Raman spectroscopy of fish oils provides detailed vibrational analysis and rapid, nondestructive graphical product authentication. Molecules. 23(12):3293. https://doi.org/10.3390/ molecules23123293.
- Crawford, M.A., Schmidt, W.F., Broadhurst, C.L., Thabet, M., Wang, Y. 2021. Lipids in the origin of intracellular detail and speciation in the Cambrian epoch and the significance of the last double bond of docosahexaenoic acid in cell signaling. Prostaglandins Leukotrienes and Essential Fatty Acids. https://doi.org/10.1016/j.plefa.2020.102230.
- Delwiche, S.R., Baek, I., Kim, M.S. 2021. Effect of curvature on hyperspectral reflectance images of cereal seed-sized objects. Biosystems Engineering. 202: 55-65. https://doi.org/10.1016/j.biosystemseng.2020.11. 004.
- Hassoun, A., Mage, I., Schmidt, W.F., Temiz, H., Li, L., Kim, H., Nilsen, H., Biancolillo, A., Ait-Kaddour, A., Sikorski, M., Sikorski, E., Grassi, S., Cozzolino, D. 2020. Fraud in animal origin food products: advances in emerging detection methods over the past five years. Foods. 9(8), 1069. https://doi.org/10.3390/foods9081069.
- Hwang, C., Mo, C., Seo, Y., Lim, J., Baek, I., Kim, M.S. 2021. Development of fluorescence imaging technique to detect fresh-cut food organic residue on processing equipment surface. Applied Sciences. 11(1), 458. https://doi. org/10.3390/app11010458.
- Liu, Z., Huang, M., Zhu, Q., Qin, J., Kim, M.S. 2021. Non-destructive freshness evaluation of intact prones using line-scan spatially offset Raman spectroscopy. Food Control. 126:108054. https://doi.org/10.1016/j. foodcont.2021.108054.
- Muhammad, M., Yan, B., Yao, G., Chao, K., Zhu, G., Huang, Q. 2020. Surface- enhanced Raman spectroscopy for trace detection of tetracycline and dicyandiamide in milk using transparent substrate of Ag nanoparticle arrays. American Chemical Society Applied Nano Materials. https://doi.org/ 10.1021/acsanm.0c01389.
- Kim, G., Baek, I., Stocker, M., Smith, J., Van Tessel, A., Qin, J., Chan, D.E., Pachepsky, Y.A., Kim, M.S. 2020. Hyperspectral imaging from a multipurpose floating platform to estimate chlorophyll-a concentrations in irrigation pond water. Remote Sensing. 13(12):2070. https://doi.org/doi:10. 3390/rs12132070.
- Schmidt, W.F., Chen, F., Broadhurst, C.L., Crawford, M. 2020. Liquid molecular model explains discontinuity between site uniformity among three N-3 fatty acids and their 13C and 1H NMR spectra. Journal of Molecular Liquids. 314:113376. https://doi.org/10.1016/j.molliq.2020.113376.
- Kandpal, L., Lee, J., Bae, H., Kim, M.S., Baek, I., Cho, B. 2020. Near- Infrared Transmittance Spectral Imaging for Nondestructive Measurement of Internal Disorder in Korean Ginseng. Sensors. 20:273. https://doi.org/10. 3390/s20010273.
- Barnaby, J.Y., Huggins, T.D., Lee, H., McClung, A.M., Pinson, S.R., Oh, M., Bauchan, G.R., Tarpley, L., Lee, K., Kim, M.S., Edwards, J. 2020. Vis/NIR hyperspectral imaging distinguishes sub-population, production environment, and physicochemical properties in rice. Scientific Reports. https://doi. org/10.1038/s41598-020-65999-7.
- Faqeerzada, M., Lohumo, S., Joshi, R., Kim, M.S., Baek, I., Cho, B. 2020. Non-targeted detection of adulterants in almond powder using spectroscopic techniques combined with chemometrics. Foods. 9(7), 976. https://doi.org/ doi:10.3390/foods9070876.
- Lu, Y., Saeys, W., Kim, M.S., Peng, Y., Lu, R. 2020. Hyperspectral imaging technology for quality and safety evaluation of horticultural products: A review and celebration of the past 20-year progress. Postharvest Biology and Technology. 170. Article 111318. https://doi.org/10.1016/j.postharvbio. 2020.111318.
- Kim, M., Lim, J., Kwon, S.W., Kim, G., Kim, M.S., Cho, B., Baek, I., Lee, S.H., Seo, Y., Mo, C. 2020. Geographical origin discrimination of white rice based on image pixel size using hyperspectral fluorescence imaging analysis. Applied Sciences. 10(17), 6794. https://doi.org/doi:10.3390/ app10175794.
- Faqeerzada, M., Snatosh, S., Joshi, R., Lee, H., Kim, G., Kim, M.S., Cho, B. 2020. Hyperspectral shortwave infrared image analysis for detection of adulterants in almond powder with one-class classification method. Sensors. 20(20), 5855. https://doi.org/doi:10.3390/s20205855.
- Ahmed, M.R., Yasmin, J., Park, E., Kim, G., Kim, M.S., Wakholi, C., Mo, C., Cho, B. 2020. Classification of watermelon seeds using morphological patterns of X-ray imaging: A comparison of conventional machine learning and deep learning. Sensors. 23(20), 6753. https://doi.org/doi:10.3390/ s20236753.
- Seo, Y., Kim, G., Lim, J., Lee, A., Kim, B., Jang, J., Mo, C., Kim, M.S. 2021. Non-destructive detection pilot study of vegetable organic residues using VNIR hyperspectral imaging and deep learning techniques. Sensors. 21(9):2899. https://doi.org/10.3390/s21092899.
- Joshi, R., Joshi, R., Kim, G., Faqeerzada, M.A., Amanah, H., Kim, J., Kim, M.S., Cho, B. 2021. Quantitative analysis of glycerol concentration in red wine combining Fourier transform infrared spectroscopy and multivariate analysis. Korean Journal of Agricultural Science. 48:299-310. https://doi. org/10.7744/kjoas.20210023.
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Progress 10/01/19 to 09/30/20
Outputs
Progress Report Objectives (from AD-416): Objective 1: Advance development and validation of on-line automated whole-surface inspection systems for simultaneous safety and quality inspection of fresh produce in high-throughput commercial processing operations. Objective 2: Develop and validate user-friendly analytical sensing methods and technologies for targeted and non-targeted rapid screening of foods for microbial, chemical, and biological contaminants in laboratory, field, and/or industrial environments. Objective 3: Advance development of portable spectral imaging technologies to allow identification and detection of food contaminants, and develop sampling and inspection protocols for implementation of the developed technologies in industry and regulatory applications. Objective 4: Advance development, test and validate an automated system for detecting contaminants in produce fields, and investigate cost and sensitivity trade-offs of different potential system components and configurations with regard to production of a cost-effective commercial system. Approach (from AD-416): Because cross-contamination may occur at many points throughout the production, processing, and distribution chains, our research targets reduction of food safety risks at multiple points, including both upstream (pre-harvest) processing and subsequent stages. The inspection point at single-layer processing is not intended to be a comprehensive inspection on its own but is key for some packaged fresh products. The ARS approach includes both pre- and post-harvest risk reduction measures that collectively can mitigate food safety concerns related to foodborne illnesses. The whole-surface sample presentation/imaging technologies developed in the previous project cycle along with the multitask imaging technology will be integrated on conveyor/processing systems to develop two automated whole-surface inspection platforms to simultaneously inspect produce for safety and quality attributes such as contaminants and defects. The two stand-alone commercial-grade prototype processing- inspection platforms will be transportable to produce processing facilities for testing and demonstration of the whole-surface inspection efficacies, with an ultimate goal of technology transfer. The proposed whole-surface fruit inspection will complement current industry sorting⿿based on quality attributes such as color and size⿿by the addition of safety inspection and will be used immediately after conventional color and size sorting. The proposed leafy green inspection will be used for inspection immediately prior to ⿿value-added processing, e.g., washing for packaged fresh-cut products. Significant progress has been made for all objectives of the project, which fall under National Program 108. For Objective 1, enhanced line- scan image-processing algorithms to represent the whole surface of a rotating round fruit were developed and integrated with the prototype inspection system. The line-scan imaging prototype system generates whole- surface images of round fruits, visualizing in real-time a two- dimensional ⿿map of the entire surface of a spherical fruit. The prototype system for multispectral line-scan inspection of leafy greens was upgraded and used to conduct inspection experiments that simultaneously performed fecal contamination inspection and defect detection on leafy greens such as spinach, and romaine lettuce. Prototype development of the on-line whole-surface inspection systems for bulk processing and safety inspection of round fruits and of leafy greens has been completed. The systems have been demonstrated to industry, including a Cooperative Research and Development Agreement (CRADA) partner who has expressed interest in obtaining licensing for the ARS-patented multitask inspection technology (U.S. Patent No. 7,787,111, ⿿Simultaneous acquisition of fluorescence and reflectance imaging techniques with a single imaging device for multitask inspection). For Objective 2, ARS scientists in Beltsville, Maryland, in collaboration with National Agricultural Products Quality Management Service (NAQS), South Korea, developed a portable multimodal optical sensing system and method for automated chemical and biological assessment. The system includes a white ring light and RGB camera for color imaging, a UV-A ring light and monochromatic camera for fluorescence imaging, a dual Raman system with 785 and 1064 nm lasers for spectroscopy and imaging, and a programmable X-Y translation stage with back-illuminated sample holder. The system can conduct dual-band Raman spectroscopy and imaging measurements using the 785 and 1064 nm point-lasers for food and biological materials. Spectral and spatial classification models can be developed using multivariate analysis and/or artificial intelligence approaches (e.g., machine learning and deep learning techniques) for integration into the system software for real-time identification of chemical and biological contaminants (e.g., adulterants and bacteria) in food and agricultural products. Built on a 30 cm ÿ 45 cm aluminum breadboard, the compact and portable system is suitable for rapid on-site analysis of food or other sample materials for chemical or biological contaminants. A patent disclosure was submitted in 2020. In addition, ARS scientists in Beltsville, Maryland, continued to collaborate with a CRADA partner to develop fish authentication methods and systems based on multimodal hyperspectral imaging techniques, to address deceptive labeling and substitution of fish fillets. Two major fraudulent practices in the seafood industry are the substitution of inexpensive fish for higher-priced species and the substitution of frozen- thawed product for never-frozen fresh product. This continuing study used three line-scan hyperspectral systems developed in-house by ARS scientists to collect four types of hyperspectral image data from fillet samples. The continuing work includes analysis of combinations of feature extraction and selection techniques and exhaustive data search, optimization, and fusion to determine the most important features needed to perform fish authentication using the different imaging modes. The results can be used to design and develop customized systems for industrial fish inspection applications. ARS scientists in Beltsville, Maryland, continued to collaborate with NASA Kennedy Space Center (KSC) to develop hyperspectral imaging systems to monitor plant growth/health and food safety in fresh food production systems to be used in spaceflight. ARS scientists designed and developed a compact hyperspectral imaging system prototype equipped with Vis/NIR and UV lights for reflectance and fluorescence measurements and with LabView- based control software developed in-house. The prototype system was integrated into a KSC growth chamber for ground testing and verification under lab conditions. Preliminary hyperspectral reflectance and fluorescence images of pick-and-eat salad crops grown by KSC scientists were acquired to evaluate system performance and develop real-time image processing algorithms. For Objective 2, ARS scientists in Beltsville, Maryland, continued experiments to develop spectroscopy- or imaging-based methods for nondestructive detection of contaminants or other food safety risks which food industries are seeking to manage or prevent with the use of better tools. A 1064-nm point-scan Raman imaging system and an infrared (IR) spectroscopic system were used to investigate spectral detection of Sudan Red and white turmeric (a toxic dye and a botanical additive, respectively) mixed into yellow turmeric, a common culinary seasoning and health supplement. Sudan Red was effectively detected using either Raman or IR spectra. White turmeric mixed into yellow turmeric was effectively identified by a distinct IR peak, but could not be detected by Raman spectroscopy due to overlapping peaks. Quantitative models developed using IR spectra for each mixture type estimated Sudan Red and white turmeric concentrations with correlation coefficients of 0.97 and 0.95, respectively. A new method based on airflow and laser ranging technique was developed to nondestructively evaluate beef freshness, enabling in situ on-line nondestructive testing. The spectral data collected with this method makes possible the identification of deformation characteristics from loss in freshness and distinguishing them from process parameters which are not measures of food safety freshness. Fipronil, a broad spectrum insecticide often used to kill lice and fleas, was analyzed using the 785-nm point-scan Raman system, and a new surface- enhanced Raman spectroscopy (SERS) method was developed in collaboration with Hefei Institute of Physical Science, China. A SERS material selective for fipronil enabled detection at concentrations as low as 0.1 ppm on chicken egg membranes. For Objective 3, ARS scientists in Beltsville, Maryland, continued work with multiple collaborators for application-specific testing of ARS portable handheld multispectral imaging technology for sanitation and contamination inspection, such as detection of bacterial biofilms for sanitation inspection of KSC plant growth chambers. ARS scientists have continued discussions with US Army Natick Soldier Center regarding improvements and advancements needed for effective use in sanitation inspection of commercial or military-contracted food processing facilities. In early 2020, an exclusive licensing request for the portable handheld imaging technology (US patent no. US 8,310,544) was granted to a CRADA partner for commercialization. Two ARS prototypes were also transferred, and ARS scientists are cooperating on ongoing development of a user-friendly App for image enhancement functions and inspection management. For Objective 4, ARS scientists in Beltsville, Maryland, expanded the research to determine the feasibility of using a drone-based sensing platform to detect in-field animal intrusion and fecal contamination. Although the SY leading the development of the ground-based laser-induced fluorescence-imaging prototype retired, the remaining SYs have continued with steps to begin moving the imaging techniques from ground-based implementation to drone-based implementation that will allow for faster imaging across larger field areas to be monitored and potentially enable joint operation with drone-based monitoring of irrigation water quality. For the new ARS research project, the group proposed to develop and validate an autonomous unmanned aerial vehicle with multimode imaging technologies for pre-harvest inspection of produce fields for animal intrusion and fecal contamination, and for irrigation water quality monitoring. The researchers also initiated design of the field-level ground truth measurement platform. In collaboration with the microbial water quality project team, a line-scan hyperspectral imaging camera system mounted on the bow of a GPS-equipped multipurpose floating platform (MFP) was developed and tested on an irrigation pond. A field study acquired about 80,000 near-infrared/red spectral line-scan images of the water for correlation to measurements of chlorophyll-a content, a water quality indicator, for water samples collected during imaging. Models developed to predict chlorophyll-a concentrations showed results highly correlated to the measured concentrations. This work shows that low-altitude hyperspectral imaging via MFP can provide valuable information about water quality through spatial mapping for data visualization. The hyperspectral imaging method for water quality can be further improved by additional research addressing variation arising from floating debris, aquatic organisms, and changes in sunlight intensity and cloud movement, as well as by considering other indicator measurements such as suspended solids, colored dissolved organic matter, bacteria, nutrient concentrations, and turbidity. This research will help researchers develop and optimize models and methods that can be used in field production by the fresh produce industry to help meet federal mandates for irrigation water quality. Accomplishments 01 Rapid identification of potential adulterants in commercial spice powders. ARS scientists in Beltsville, Maryland, have developed a 1064- nm dispersive Raman imaging system which successfully detected chemical contaminants in spice powders, such as Sudan Red in turmeric powder and both Sudan-I and metanil yellow in curry powder, with greatly reduced fluorescence interference. Raman spectral imaging is a suitable technique for non-destructive detection in heterogenous samples. No prior preparation is required for Raman measurement of the powder samples. With powder placed directly in a flat sample holder, Raman spectra are acquired over the entire surface area. The distribution of the contaminant particles can then be visualized in binary images. Given the widespread distribution of many powdered ingredients throughout food processing supply lines nationally and worldwide, this method will benefit food processors and food safety regulators seeking to ensure safety and quality of food powders. 02 A rapid method for detecting chemical contaminants in foods. ARS scientists in Beltsville, Maryland, have developed a Raman imaging method and apparatus for rapid, nondestructive detection of chemical contaminants in food materials. This rapid screening technology provides a direct and practical method to detect the animal feed chemical called ractopamine in meats without any need for chemical extraction. Now, this is a method to screen for animal feed chemicals or other veterinary drugs in animal meat products. 03 A method to detect fake eggs. Although some fake or imitation food materials produced for economic fraud contain lower quality or cheaper alternative ingredients, they are safe for consumption. Others containing non-edible or hazardous ingredients are not, including fake eggs made with harmful ingredients such as sodium alginate, tartrazine dye, gypsum powder, and paraffin wax. ARS scientists in Beltsville, Maryland, determined how to differentiate between fake and real chicken eggs. The results showed that using the Raman imaging technique, they could separate fake eggs from real eggs. The food industry can use this method to ensure that food products are safe and protect consumer health while combating fraud. 04 Detection of mislabeled fish fillet. A recent survey by the nonprofit organization Oceana found that 21 percent of fish sold in the United States were mislabeled. Fish fillets are easy to mislabel. Mixing inexpensive species with high-priced species and substituting frozen- thawed fillets for fresh fillets are two major fraudulent practices in the seafood industry. ARS scientists in Beltsville, Maryland, used the Raman imaging machine to differentiate fish fillets of different species and freshness conditions. The new handheld imaging machine can be used by regulatory agencies and the seafood industry to verify fish and other seafood products. 05 Method for automated imaging of whole-surface of round-shape agricultural products. The lack of a method to effectively and efficiently image the entire surfaces of round agro-products has been a longstanding obstacle to developing practical automated imaging-based food safety inspection technology to detect surface defects and contamination on round fruits such as apples and oranges. Traditional single-camera machine vision systems cannot effectively view all sides of a round object, while systems using multiple cameras or complex object manipulation increase the operational and instrumentation costs associated with practical implementation. ARS scientists in Beltsville, Maryland, developed a novel hyperspectral imaging system that uniquely incorporates an external optical assembly of three mirrors to view a round object from two sides and acquire images of the object while it is rotated on rollers, to construct a whole-surface image of the round object similar to a world-map projection. A whole-surface image processing algorithm was first developed using wooden spheres of different sizes and marked with ⿿defect spots at six surface locations, and then tested using 101 apples of various sizes and similarly marked with simulated defect spots. The results showed that the novel system accurately showed all six defect spots in 78% of the apple images, missed one of the six spots in only 4%, and showed seven spots in 18% due to partial duplication of an image area. This new system and image processing technique provide the basis for developing an effective whole-surface spectral imaging-based inspection system for round fruits and vegetables that will benefit fresh food processors seeking to ensure product safety and optimize quality-based sorting of their products.
Impacts
(N/A)
Publications
- Mukasa, P., Wahkoli, C., Mohammad, A., Park, E., Lee, J., Suh, H., Mo, C., Lee, H., Baek, I., Kim, M.S., Cho, B. 2020. Determination of the viability of retinispora (Hinoki cypress) seeds using shortwave infrared hyperspectral imaging spectroscopy. Journal of Near Infrared Spectroscopy.
- Li, Y., Wang, W., Long, Y., Peng, Y., Li, Y., Chao, K., Tang, X. 2019. A feasibility study of rapid nondestructive detection of total volatile basic nitrogen (TVB-N) content in beef based on airflow and laser ranging technique. Meat Science. 145:367-374.
- Muhammad, M., Yao, G., Zhong, J., Chao, K., Aziz, M., Huang, Q. 2020. A facile and label-free SERS approach for inspection of fipronil in chicken eggs using SiO2@Au core/shell nanoparticles. Talanta. 207:120324.
- Chao, K., Dhakal, S., Schmidt, W.F., Qin, J., Kim, M.S., Peng, Y., Huang, Q. 2020. Raman and IR spectroscopic modality for authentication of turmeric powder. Journal of Food Chemistry. 320:126567.
- Qin, J., Vasefi, F., Hellberg, R.S., Akhbardesh, A., Issacs, R.B., Yilmaz, A., Hwang, C., Baek, I., Schmidt, W.F., Kim, M.S. 2020. Detection of fish fillet substitution and mislabeling using multimode hyperspectral imaging techniques. Food Control. 114:107234.
- Joshi, R., Lohumi, S., Joshi, R., Kim, M.S., Qin, J., Baek, I., Cho, B. 2019. Raman spectral analysis for non-invasive detection of external and internal parameters of fake eggs. Sensors and Actuators B: Chemical. 303:127243.
- Pyo, J., Hong, S., Kwon, Y., Kim, M.S., Cho, K. 2020. Estimation of heavy metals using deep neural network with visible and infrared spectroscopy of soil. Journal of Hazardous Materials.
- Yasmin, J., Ahmed, M., Lohumi, S., Wakholi, C., Kim, M.S., Cho, B. 2019. Classification method for viability screening of naturally aged watermelon seeds using FT-NIR spectroscopy. Sensors. 19(5):1190.
- Song, S., Liu, X., Huang, M., Zhu, Q., Qin, J., Kim, M.S. 2019. Detection of fish bones in fillets by Raman hyperspectral imaging technology. Journal of Food Engineering. 272:109808.
- Jeon, D., Stocker, M.D., Sokolova, E., Lee, H., Baek, I., Kim, M.S., Pachepsky, Y.A. 2020. Accounting for the three-dimensional distribution of E. coli concentrations in pond water in simulations assessment of microbial quality of water withdrawn for irrigation. Irrigation Science. 12(6):1708.
- Yasmin, J., Lohumi, S., Ahmed, M.R., Kandpal, L.M., Faqeerzada, M.A., Kim, M.S., Cho, B. 2020. Improvement in purity of healthy tomato seeds using an image-based one-class classification method. Sensors. 20(9):2690.
- Joshi, R., Joshi, R., Mo, C., Faqeezada, M., Amanah, H., Masithoh, R., Kim, M.S., Cho, B. 2020. Raman spectral analysis for quality determination of grignard reagent. Applied Sciences. 10(10):3545.
- Pyo, J., Duan, H., Baek, S., Kim, M.S., Jeon, T., Kwon, Y., Lee, H., Cho, K. 2020. A convolutional neural network regression for quantifying harmful cyanobacteria using hyperspectral imagery. Remote Sensing of Environment. 233:111350.
- Morgan, B.J., Stocker, M.D., Valdes-Avellan, J., Kim, M.S., Pachepsky, Y.A. 2019. Drone-based imaging to assess the microbial water quality in an irrigation pond: a pilot study. Science of the Total Environment. 716:135757.
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Progress 10/01/18 to 09/30/19
Outputs
Progress Report Objectives (from AD-416): Objective 1: Advance development and validation of on-line automated whole-surface inspection systems for simultaneous safety and quality inspection of fresh produce in high-throughput commercial processing operations. Objective 2: Develop and validate user-friendly analytical sensing methods and technologies for targeted and non-targeted rapid screening of foods for microbial, chemical, and biological contaminants in laboratory, field, and/or industrial environments. Objective 3: Advance development of portable spectral imaging technologies to allow identification and detection of food contaminants, and develop sampling and inspection protocols for implementation of the developed technologies in industry and regulatory applications. Objective 4: Advance development, test and validate an automated system for detecting contaminants in produce fields, and investigate cost and sensitivity trade-offs of different potential system components and configurations with regard to production of a cost-effective commercial system. Approach (from AD-416): Because cross-contamination may occur at many points throughout the production, processing, and distribution chains, our research targets reduction of food safety risks at multiple points, including both upstream (pre-harvest) processing and subsequent stages. The inspection point at single-layer processing is not intended to be a comprehensive inspection on its own but is key for some packaged fresh products. The ARS approach includes both pre- and post-harvest risk reduction measures that collectively can mitigate food safety concerns related to foodborne illnesses. The whole-surface sample presentation/imaging technologies developed in the previous project cycle along with the multitask imaging technology will be integrated on conveyor/processing systems to develop two automated whole-surface inspection platforms to simultaneously inspect produce for safety and quality attributes such as contaminants and defects. The two stand-alone commercial-grade prototype processing- inspection platforms will be transportable to produce processing facilities for testing and demonstration of the whole-surface inspection efficacies, with an ultimate goal of technology transfer. The proposed whole-surface fruit inspection will complement current industry sorting⿿based on quality attributes such as color and size⿿by the addition of safety inspection and will be used immediately after conventional color and size sorting. The proposed leafy green inspection will be used for inspection immediately prior to ⿿value-added processing, e.g., washing for packaged fresh-cut products. Significant progress has been made for all objectives of the project, which fall under National Program 108. For Objective 1, a newly developed image processing algorithm to represent the whole surface of a round fruit was integrated with the prototype inspection system for round fruits. Using the new algorithm, the multispectral line-scan imaging system can generate whole-surface images of round fruits, allowing for real-time visualization of a two-dimensional ⿿map of the entire surface of a spherically shaped fruit. The system for multispectral line-scan inspection of leafy greens was used to conduct inspection experiments that simultaneously performed fecal contamination inspection and defect detection on salad greens (spinach, romaine lettuce). Cooperative Research and Development Agreement (CRADA) partner submitted an application for licensing the ARS-patented multitask inspection technology (U.S. Patent No. 7,787,111, Simultaneous acquisition of fluorescence and reflectance imaging techniques with a single imaging device for multitask inspection). For Objective 2, ARS scientists in Beltsville, Maryland, conducted multiple experiments to develop spectroscopy- or imaging-based methods for nondestructive detection of contaminants or other food safety risks which food industries are seeking to manage or prevent with the use of better tools. Mathematical models using infrared spectroscopy and imaging were developed for detecting and quantifying concentrations of adulterants in samples of turmeric powder⿿concentrations of Sudan Red G (a toxic dye) and white turmeric (a botanical additive) could be estimated with accuracies over 97% and 92%, respectively, and an image processing method was developed to visualize adulterant pixels present in a sample of turmeric powder covering a surface area of ten square millimeters. A rapid and nondestructive technique was developed using visible/near-infrared transmittance spectroscopy and demonstrated 95% accuracy in identifying unfertilized duck eggs, which, if undetected among fertilized eggs, can present a food safety contamination risk due to breakage during incubation. The chemical structure and spectral fingerprints of fipronil, a commonly used insecticide banned in Europe for use in food production, were analyzed and identified and are now being used to develop a rapid method to detect fipronil-tainted eggs. A Raman spectral imaging method was developed to detect veterinary drug residues in pork and demonstrated detection accuracies of 98%, 99%, and 98% for detecting ofloxacin, chloramphenicol and sulfadimidine residues, respectively, showing feasibility for detecting drug residues in muscle foods. For Objective 2, ARS scientists in Beltsville, Maryland, developed a transportable dual-band laser Raman spectroscopy and imaging system for automated, in-field or on-site food safety inspection in food processing operations. The system acquires Raman measurements of food/agricultural products using point lasers at two wavelengths (either 785 nm or 1064 nm, selected as needed to address fluorescence interference signals) along with two integrated laser probes and two miniature Raman spectrometers. In addition to taking point-scan images across a sample up to 10 cm x 10 cm in area, the system can also perform flexible and automated Raman measurements for targets in a predetermined arrangement or in a scattered distribution, such as liquids/powders in a well plate or bacteria colonies in a 100-mm petri dish. Automated sample counting, positioning, sampling, and synchronization functions are performed by machine vision and motion control techniques implemented via LabView-based software developed in-house. With a 30 cm ÿ 45 cm footprint, the compact system is suited for in-field and on-site use in food processing operations to rapidly inspect samples of foods and agricultural products for ingredient authentication and contaminant detection. For Objective 2, ARS researchers in Beltsville, Maryland, initiated a new study with a CRADA partner to develop fish authentication methods and systems based on multimodal hyperspectral imaging techniques, to address deceptive labeling and substitution of fish fillets. Two major fraudulent practices in the seafood industry are the substitution of inexpensive fish for higher-priced species and the substitution of frozen-thawed product for never-frozen fresh product. This continuing study uses three line-scan hyperspectral systems developed in-house by ARS scientists to collect four types of hyperspectral image data from fillet samples⿿(1) visible and near-infrared reflectance images (VNIR: 400⿿1000 nm) light, (2) fluorescence images obtained using 365-nm ultraviolet light, (3) short-wave infrared reflectance images (SWIR: 1000-2500 nm) light , and (4) Raman chemical images obtained with 785-nm line-laser excitation⿿and also used DNA-testing to verify the species of each sample. The continuing work includes analysis of combinations of feature extraction and selection techniques and exhaustive data search, optimization, and fusion to determine the most important features needed to perform fish authentication using the different imaging modes. This process will help identify the image mode (or combination of modes) that will have the highest impact and classification accuracies, and which can be used to design and build future customized systems for industrial fish inspection applications. The CRADA partner also submitted applications for licensing the ARS-patented Raman line-scan imaging technology (U.S. Patent No. 9, 927,364, ⿿Line-scan Raman imaging method and system for sample evaluation) and handheld multispectral inspection imaging device (U.S. Patent No. 8,310,544, ⿿Hand-held inspection tool and method). For Objective 2, ARS scientists in Beltsville, Maryland, established a new interagency agreement with NASA Kennedy Space Center (KSC) to develop hyperspectral imaging systems to monitor plant growth/health and food safety in fresh food production systems to be used in spaceflight. In the first step of this collaborative research, ARS scientists designed and developed a new hyperspectral imaging system prototype suitable for leafy- green inspection in KSC growth chambers, utilizing a miniature line-scan hyperspectral camera, line-lights to provide broadband Vis/NIR and UV light for reflectance and fluorescence measurements, and a linear translation stage that moves the camera and lights above the growing plants to conduct line-scan hyperspectral imaging from overhead. LabView- based control software was developed in-house. The next step in this research will be to integrate the prototype into a KSC growth chamber for ground testing and verification under lab conditions, and then to acquire hyperspectral reflectance and fluorescence images for pick-and-eat salad crops grown by KSC scientists to evaluate system performance and develop real-time image processing algorithms. For Objective 3, ARS scientists in Beltsville, Maryland, are working with multiple collaborators regarding application-specific testing and development of ARS portable handheld multispectral imaging technology. ARS and KSC scientists have begun testing ARS portable handheld multispectral imaging devices for sanitation and contamination inspection in plant growth chambers and in astronaut working/living spaces⿿of specific interest is the evaluation of the imagers for use in monitoring water quality and equipment sanitation for the plant growth chambers, such as for the detection of bacterial biofilms, in addition to contamination/sanitation inspection in other areas of spacecraft. ARS scientists have continued discussions with USDA FSIS and U.S. Army Natick Soldier Center regarding improvements and advancements needed for effective use in sanitation inspection in commercial or military- contracted food processing facilities, and have also discussed potential cooperation with NASA for developing real-time image processing capabilities to be implement via smartphone app to enable user-friendly non-expert operation. ARS is also working with a new CRADA partner to develop a handheld hyperspectral imaging device using the partner⿿s proprietary hyperspectral imaging hardware, and to develop real-time image processing capabilities to maximize the options for user-friendly data analysis that would be enabled with hyperspectral data. For Objective 4, ARS scientists in Beltsville, Maryland, demonstrated the feasibility of laser-induced fluorescence (LIF) imaging for field monitoring to detect animal intrusion and fecal contamination with a ground-based prototype motorized imaging system. Although the SY leading the development of the ground-based LIF prototype retired, the remaining project SYs have continued the work with steps to begin moving the imaging techniques from ground-based implementation to drone-based implementation that will allow for higher speed imaging across larger field areas to be monitored and potentially enable joint operation with drone-based monitoring of irrigation water quality. Accomplishments 01 Rapid detection and quantification of adulterants in commercial turmeric powder. Yellow turmeric powder is popular worldwide as a dietary supplement, and consequently, incidence of turmeric adulteration by chemical dyes and botanical additives have increased. ARS scientists in Beltsville, Maryland, investigated a light absorption sensing method to identify and quantify chemical contaminants and botanical additives in commercial yellow turmeric powder. The results show that the method can quantify Sudan Red and white turmeric adulteration in yellow turmeric powder with high accuracy. Given the widespread distribution of many powdered ingredients through food processing supply lines nationally and worldwide, this method will benefit food processors and food safety regulators seeking to ensure safety and quality of food powders. 02 Light scattering imaging technique to detect mixed veterinary drug residues in pork. Current methods to detect veterinary drug residues in meats are time-consuming, labor-intensive, sample-destructive, and require pre-treatment procedures. A line-scan light-scattering imaging system was developed and used for the first time for nondestructive quantitative analysis of ofloxacin, chloramphenicol, and sulfadimidine residues in pork. These drugs are commonly used to treat bacterial infection. Light-scattering images of pork containing mixtures of the three drugs were acquired and analyzed. The results indicate that the imaging technique can precisely identify and quantify the drug residues in pork. This approach can serve as a potential method for nondestructive real-time inspection of muscle foods for food safety issues. 03 New method of Raman imaging for improved sensitivity for powdered food analysis. Raman imaging has been shown to be a powerful analytical technique for the characterization and visualization of chemical components in a range of products, particularly in the food and pharmaceutical industries. The conventional backscattering Raman imaging technique for the spatial analysis of a sample of significant thickness or including subsurface variations can suffer from the presence of intense fluorescence and Raman signals from the surface layer that can mask weaker subsurface signals. ARS scientists in Beltsville, Maryland, in collaboration with cooperators from Chungnam National University, demonstrated the application of a new reflection- amplifying method using a background mirror in a sample holder to increase the signals that can be detected from subsurface layers. Results showed that when bilayer samples placed on a mirror are scanned, the average signal for the subsurface layer material increases two- fold. The method was then applied successfully to noninvasively detect the presence of small polystyrene pieces buried under a 2-mm thick layer of food, which would have been undetectable via conventional backscattering Raman imaging. This method can potentially be used to noninvasively evaluate materials of non-uniform constituent compositions not visible at the surface.
Impacts
(N/A)
Publications
- Mo, C., Lim, J., Kwon, S., Lim, D., Kim, M.S., Kim, G., Kang, H., Kwon, K., Cho, B. 2018. Hyperspectral imaging and partial least square discriminant analysis for geographical origin discrimination of white rice. Journal of Biosystems Engineering.
- Hong, J., Qin, J., Van Kessel, J.S., Oh, M., Dhakal, S., Lee, H., Kim, D., Kim, M.S., Cho, H. 2018. Evaluation of SERS nanoparticles for detection of Bacillus cereus and Bacillus thuringiensis. Biosystems Engineering. 43:394- 400.
- Lohumi, S., Lee, H., Kim, M.S., Qin, J., Cho, B. 2019. Raman hyperspectral imaging and spectral similarity analysis for quantitative detection of multiple adulterants in wheat flour. Biosystems Engineering. 181:103-113.
- Lim, J., Kim, G., Mo, C., Oh, K., Kim, G., Ham, H., Kim, S., Kim, M.S. 2018. Application of near infrared reflectance spectroscopy for rapid and non-destructive discrimination of hulled barley, naked barley, and wheat contaminated with Fusarium. Sensors. 18(1):113.
- Qin, J., Kim, M.S., Chao, K., Dhakal, S., Cho, B., Lohumi, S., Mo, C., Peng, Y., Huang, M. 2019. Recent advances in VIS/NIR/IR technologies for measurement of postharvest quality. Postharvest Biology and Technology. 149:101-117.
- Liu, Z., Huang, M., Zhu, Q., Qin, J., Kim, M.S. 2019. Packaged food detection method based on the generalized Gaussian model for line-scan Raman scattering images. Journal of Food Engineering. 258:9-17.
- Dhakal, S., Schmidt, W.F., Kim, M.S., Tang, X., Peng, Y., Chao, K. 2019. Detection of additives and chemical contaminants in turmeric powder using FT-IR spectroscopy. Foods. 8(5):143.
- Li, Y., Peng, Y., Qin, J., Chao, K. 2019. A correction method of mixed pesticide content prediction in apple by using Raman spectra. Applied Sciences. 9(8):1699.
- Wang, W., Zhai, C., Peng, Y., Chao, K. 2019. A nondestructive detection method for mixed veterinary drugs in pork using line-scan Raman chemical imaging technology. Journal of Food Analytical Methods. 12(3):658-667.
- Dong, J., Dong, X., Li, Y., Peng, Y., Chao, K., Gao, C., Tang, X. 2019. Identification of unfertilized duck eggs before hatching using visible/ near infrared transmittance spectroscopy. Computers and Electronics in Agriculture. 157:471-478.
- Qin, J., Kim, M.S., Chao, K., Bellato, L., Schmidt, W.F., Cho, B., Huang, M. 2018. Inspection of maleic anhydride in starch powder using line-scan hyperspectral Raman chemical imaging technique. International Journal of Agricultural and Biological Engineering. 11(6):120⿿125.
- Lim, J., Kim, G., Mo, C., Oh, K., Yoo, H., Ham, H., Kim, M.S. 2017. Classification of Fusarium-infected Korean husked barley using near- infrared reflectance spectroscopy and partial least squares discriminant analysis. Sensors. 17(10):2258.
- Baek, I., Kusumaningrum, D., Kandpal, L., Lohumi, S., Mo, C., Kim, M.S., Cho, B. 2019. Rapid measurement of soybean seed viability using kernel- based multispectral imaging analysis. Sensors. 19(2):271.
- Baek, I., Kim, M.S., Cho, B., Mo, C., Barnaby, J.Y., McClung, A.M., Oh, M. 2019. Selection of optimal hyperspectral wavebands for detection of discolored, diseased rice seeds. Applied Sciences. 9:1027.
- Lohumi, S., Kim, M.S., Qin, J., Cho, B. 2019. Improving sensitivity in Raman imaging for thin layered and powdered food analysis utilizing a reflection mirror. Sensors. 19(12):2698.
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Progress 10/01/17 to 09/30/18
Outputs
Progress Report Objectives (from AD-416): Objective 1: Advance development and validation of on-line automated whole-surface inspection systems for simultaneous safety and quality inspection of fresh produce in high-throughput commercial processing operations. Objective 2: Develop and validate user-friendly analytical sensing methods and technologies for targeted and non-targeted rapid screening of foods for microbial, chemical, and biological contaminants in laboratory, field, and/or industrial environments. Objective 3: Advance development of portable spectral imaging technologies to allow identification and detection of food contaminants, and develop sampling and inspection protocols for implementation of the developed technologies in industry and regulatory applications. Objective 4: Advance development, test and validate an automated system for detecting contaminants in produce fields, and investigate cost and sensitivity trade-offs of different potential system components and configurations with regard to production of a cost-effective commercial system. Approach (from AD-416): Because cross-contamination may occur at many points throughout the production, processing, and distribution chains, our research targets reduction of food safety risks at multiple points, including both upstream (pre-harvest) processing and subsequent stages. The inspection point at single-layer processing is not intended to be a comprehensive inspection on its own but is key for some packaged fresh products. The ARS approach includes both pre- and post-harvest risk reduction measures that collectively can mitigate food safety concerns related to foodborne illnesses. The whole-surface sample presentation/imaging technologies developed in the previous project cycle along with the multitask imaging technology will be integrated on conveyor/processing systems to develop two automated whole-surface inspection platforms to simultaneously inspect produce for safety and quality attributes such as contaminants and defects. The two stand-alone commercial-grade prototype processing- inspection platforms will be transportable to produce processing facilities for testing and demonstration of the whole-surface inspection efficacies, with an ultimate goal of technology transfer. The proposed whole-surface fruit inspection will complement current industry sorting�based on quality attributes such as color and size�by the addition of safety inspection and will be used immediately after conventional color and size sorting. The proposed leafy green inspection will be used for inspection immediately prior to �value-added� processing, e.g., washing for packaged fresh-cut products. Significant progress has been made for all objectives during the third year of the project. For Objective 1, integrated individual components into assembled prototype for commercial whole-surface inspection platform for round fruits. Designed and developed customized critical components for the prototype using in-house 3-D printer, including optical mounts for upgraded two-view angle optics and housing for illumination sources. Developed an enhanced version of the software interface to generate whole- surface fruit images from multispectral line-scan imaging to allow for real-time visualization of a two-dimensional �map� of the entire surface of a spherically shaped fruit. In addition, interface control software was developed for operation of the prototype round-fruit inspection system. System testing with oranges and apples purchased from retail markets confirmed imaging of entire fruit surface through 360-degree rotation with effective view of polar regions. For the leafy green whole- surface inspection system, an upgraded interface software was developed to allow integrated control of the multitask imaging module and illumination, conveyor speed, and sample ejection mechanism. The software enhancements included incorporation of real-time image processing and multispectral algorithms for fecal contamination detection on the surfaces of leafy green samples. For Objective 2, a U.S. patent (U.S. Patent #9,927,364, March 27, 2018) was issued for the recently developed technology for macro-scale line- scan Raman chemical imaging. This technology can be used to evaluate sample materials faster�by three orders of magnitude�than conventional Raman methods, and is suitable for use in commercial food processing plants for rapid authentication of food ingredients. In addition, a new 1064 nm hyperspectral Raman imaging system was developed and tested for rapid screening of chemical hazards in foods, using toxic dyes (that have been occurred in instances of hazardous food fraud) mixed into turmeric powder and curry powder that were imaged as leveled samples surfaces. The system was demonstrated effective for qualitative single-contaminant detection using hyperspectral Raman images of turmeric samples mixed at different concentrations with metanil yellow. Effective quantitative models for assessing contaminant concentration were developed via imaging of curry powder samples prepared across a range of concentrations for metanil yellow and (separately) Sudan I. Additionally, a detection algorithm for simultaneous multiple-contaminant detection, developed via imaging of curry powder samples mixed with metanil yellow and Sudan I together, was able to identify individual image pixels corresponding to the two different contaminants present. The hyperspectral Raman imaging system was also modified to enable subsurface detection of contaminants, which was demonstrated effectively for detection of metanil yellow mixed into turmeric powder contained within multiple layers of gelatin capsules. A transparent surface-enhanced Raman scattering (SERS) nanoparticle substrate was fabricated with collaborators at the Hefei Institutes of Physical Sciences, China, for use with a 785 nm Raman spectrometer, which was able to detect ractopamine in solution at a very low concentration in preliminary tests and tetracycline residues in milk at 0.01 ppm. The novel gradient temperature Raman spectroscopy (GTRS) system previously developed in-house (U.S. patent # 9,863,882, issued January 09, 2018) was used for multiple lipid investigations. A library of the highest quality Raman spectra currently available for long-chain polyunsaturated fatty acids (LC-PUFA) in fish and shellfish was produced and published, notably filling a previous void in the literature. Commercial fish and krill oil samples were characterized and the resulting spectral contour plots were used to provide a simple, rapid, and highly accurate graphical means to authenticate oils of a given source. This information/method will be used for future work in an ongoing cooperative project to investigate rapid methods for nondestructive determination of freshness, origin, and quality of fish filets, for which a CRADA was established in April 2018 with a safety- sensing development company. The LC-PUFA data also provided significant contributions to further understanding of the biophysics of the brain and retina. The compiled data from this work for LC-PUFA, which are critical to the mammalian brain, retina, and heart and are highly preventive of chronic inflammatory conditions, also provided significant contributions to improved understanding of the biophysics of the brain and retina. With meat and bone meal samples provided by cooperators at the University of Cordoba, Spain, investigations were conducted into fluorescence, reflectance, and Raman techniques as potential rapid methods to determine species origin of meat and bone meal, which are critical ingredients in the animal feed industry. Because the potentially useful Raman vibrational modes observed in meat and bone meal samples arise from the lipid content, GTRS in the 0 to 30�C range (easily attainable in the field) was used to analyze crude lipids extracted from a suite of pork, chicken, and mixed-origin samples. Developed in-house was the 20-minute crude-lipid extraction method for meat and bone meal that requires no toxic/expensive solvents and can be adapted to field use. For Objective 3, research on ARS portable imaging technologies for contamination and sanitation inspection applications was discussed with USDA-Food Safety and Inspection Service (FSIS) and with commercial and U. S. military collaborators. ARS researchers again met with USDA-FSIS Office of Policy and Program Development, Risk, Innovations, and Management Staff, to discuss past field tests of the current ARS portable handheld imaging device, device modifications needed to improve the device to better fit in-plant FSIS inspection needs, and opportunities for additional in-plant testing at food processing facilities. In-plant field testing of the current device was also conducted in cooperation with U.S. Army Natick Soldier Center collaborators at a large-scale commercial baking facility that manufactures products for the military as well as for private sector food and restaurant companies, to investigate the use of the handheld imaging device for inspection of surfaces in conjunction with a novel surface-sampling method developed by the collaborators that may be incorporated into routine procedures. To increase both breadth and specificity of detection targets as well as user-friendliness of the device, plans were established to incorporate hyperspectral imaging and additional image analysis capabilities into the device. A formal discussion of cooperative research and development agreement was initiated with the optics division of an industry-leading advanced materials science company to address the hyperspectral imaging requirements. Development and testing of customized structural improvements to the device�s design, to address ergonomic properties and practicalities of in-plant use, continued via in-house 3D printing of the outer shell. For Objective 4, a laser-induced hyperspectral fluorescence imaging technology critical for developing an autonomous field inspection platform to detect fecal contamination in production fields for fresh produce was developed and tested. However, to address growers� request for the initial use of a manned vehicle rather than an autonomous vehicle in their fields, an agreement with was initiated with the University of Arizona to develop a manned vehicle for use as an imaging platform for the laser-induced hyperspectral fluorescence imaging system. Because the most significant obstacle for using any vehicle in produce fields immediately prior to harvest is the fact of nearly nonexistent space between rows due to plant growth, the cooperators undertook the primary modification task of reducing wheel and tire width to minimize potential damage to field plants. For the potential future use of an autonomous vehicle, guidance methods and algorithms and controls were studied and specifically examined for use with a robotic field vehicle. To address the relatively high expense of the hyperspectral imaging system components�a gated, intensified camera and pulsed laser for illumination�originally envisioned for use at the start of this project, an alternative approach and conditions were evaluated to enable potential use of a standard monochrome camera with a fixed filter and LED UV light source for detecting fecal contamination in produce fields. Testing of factors including measurement wavelength, degree of shading, and ambient light intensity as a function of time of day, determined that measurements made at dusk or in the evening, using a 520-nm filter, could be used to reliably detect fecal materials with no false positives. Accomplishments 01 Raman sensing technology for chemical hazard detection in foods. ARS scientists in Beltsville, Maryland, have developed a line-scan high- throughput Raman imaging method and apparatus for rapid nondestructive detection of chemical contaminants in food materials. The system can directly and rapidly analyze a full petri dish of sample powder in only ten minutes, compared to conventional instruments that might take hours for the same analysis. The system has imaged a variety of food powders mixed with chemical additives and results indicate that the system can provide quantitative measurement of chemical adulterants. This technology (U.S. patent no: US 9,927,364) provides a useful screening tool to address chemical contamination and adulteration of food products. 02 Raman sensing technique for food ingredient authentication. ARS scientists in Beltsville, Maryland, have recently developed a new Raman imaging system for chemical contaminant detection and food ingredient authentication. Because some food powders such as turmeric and curry powder, and chemical contaminants such as metanil yellow and Sudan-I, emit overwhelming autofluorescence, the Raman light scattering signal of these food powders and chemical contaminants cannot be directly measured by Raman instruments in the visible wavelengths of light. However, the autofluorescence background can be eliminated using 1064 nm (near-infrared) laser excitation. Given the widespread distribution of many powdered ingredients through food processing supply lines nationally and worldwide, use of this near-infrared Raman system will benefit food processors and food safety regulators seeking to ensure safety and quality of ingredients ultimately consumed by the public. 03 Inspection of starch powder for maleic anhydride using line-scan hyperspectral Raman chemical imaging technique. Since starch accounts for a large proportion of the carbohydrates found in staple foods worldwide and is the most common carbohydrate consumed by humans, starch safety and quality is critical for public health. Starch production that uses excessive amounts of maleic anhydride, a chemical additive that can improve chewiness, glutinosity, and water retention of some food products, is potentially harmful for consumers� health. This study developed a high-throughput Raman chemical imaging method for direct detection of maleic anhydride mixed into corn starch. Chemical images were generated to identify and map the maleic anhydride particles. This Raman-image-based screening method can be used by regulatory agencies and food processors to authenticate starch powder as well as other powdered food materials. 04 Fluorescence sensing technology for inspecting produce fields for animal fecal materials. ARS scientists in Beltsville, Maryland, developed a motorized, imaging platform that uses a hyperspectral imaging system to detect fecal contamination on fresh produce in the field. Field contamination is the primary source of contaminated produce, and current practice uses trained observers walking around fields looking for signs of animal intrusion. Scientists developed and tested a novel method for generating a line illumination source using a high energy pulsed laser. This new imaging platform has the potential to allow the survey of 100% of produce fields, which should increase the efficacy of inspections and greatly reduce the risk of foodborne illness in the population at large. 05 Detection of multiple-species fecal contamination on produce. A large portion of past outbreaks of foodborne illnesses involving E. coli and Salmonella are associated with trends of increased consumption of raw produce as part of healthful lifestyles and the growing popularity of ready-to-eat leafy green products. Because the primary sources of those bacteria are animal feces, this research used feces samples from dairy cattle, pigs, chickens, and sheep to simulate contamination scenarios. Spots of both undiluted and diluted feces were applied to romaine lettuce leaves and hyperspectral fluorescence images of the leaves were acquired. The images were analyzed and algorithms were developed to detect each fecal species as well as all four simultaneously. The results show that fluorescence imaging methods are effective in detecting fecal contamination on green produce surfaces. This research will benefit the fresh produce industry and food safety regulators by providing science-based tools to help ensure the safety of fresh vegetables consumed by the public. 06 Rapid nondestructive assessment of seed and grain bacterial infection. Bacterial infection is an important seed quality factor that can greatly reduce crop yields, especially when infections not only affect the eventual seedlings but also can spread to other nearby seedlings in fields or greenhouses. The infection of grains can also result in food safety problems for humans and livestock, and in quality problems that are of significant economic impact for grain producers and processors. Conventional inspection methods for bacterial infection of seed and grain are time-consuming and labor-intensive. ARS researchers in Beltsville, Maryland, in collaboration with colleagues at the Chungnam National University, South Korea, developed near-infrared spectroscopic methods to accurately discriminate between normal barley grains and grains infected with Fusarium. The methods developed and demonstrated in this research can be used to develop nondestructive seed and grain quality and safety inspection systems.
Impacts
(N/A)
Publications
- Wakholi, C., Kandpal, L., Lee, H., Bae, H., Seo, Y., Kim, M.S., Mo, C., Lee, W., Cho, B. 2018. Rapid assessment of corn seed viability using short wave infrared line-scan hyperspectral imaging and chemometrics. Sensors and Actuators B: Chemical. 255:498-507.
- Colm, E., Kim, M.S., Lee, H. 2016. Assessment of a handheld fluorescence imaging device as a visual-aid for detection of food residues on processing surfaces. Food Control. 59:243-249.
- Qin, J., Kim, M.S., Chao, K., Gonzalez, M., Cho, B. 2017. Quantitative detection of benzoyl peroxide in wheat flour by line-scan macro-scale Raman chemical imaging. Applied Spectroscopy. doi:10.1177/0003702817706690.
- Everard, C.D., Kim, M.S., Siemens, M., Cho, H., Lefcourt, A.M., Odonnel, C. 2018. A multispectral imaging system using solar illumination to distinguish fecal matter on leafy greens and soils. Biosystems Engineering. 171:258-264.
- Cho, H., Kim, M.S., Kim, S., Lee, H., Oh, M., Chung, S. 2018. Hyperspectral determination of fluorescence wavebands for multispectral imaging detection of multiple animal fecal species contamination on romaine lettuce. Food and Bioprocess Technology. 11:774-784.
- Lee, H., Kim, M.S., Qin, J., Park, E., Song, Y., Oh, C., Cho, B. 2017. Raman hyperspectral imaging for detection of watermelon seeds infected with acidovorax avenae subsp. citrulli. Sensors. 17(10):2188.
- Lee, H., Kim, M.S., Cho, B. 2018. Detection of melamine in milk powder using MCT-based shortwave infrared hyperspectral imaging system. Journal of Food Additives & Contaminants. 35(6):1027-1037.
- Broadhurst, L., Schmidt, W.F., Kim, M.S., Nguyen, J.K., Qin, J., Chao, K., Bauchan, G.R., Shelton, D.R. 2016. Continuous gradient temperature Raman spectroscopy of oleic and linoleic acids from -100 to 50�C. Journal of Lipids. 51:1289-1302.
- Ambrose, A., Kandpal, L., Kim, M.S., Lee, W., Cho, B. 2016. High speed measurement of corn seed viability using hyperspectral imaging. Infrared Physics and Technology. 75:171-179.
- Kandpal, L., Lohomi, S., Kim, M.S., Cho, B. 2016. Estimation of germination ability of muskmelon seeds using hyperspectral imaging technique with variable selection and chemometrics. Sensors and Actuators B: Chemical. 229:534-544.
- Kusumaningrum, D., Lee, H., Kim, M.S., Cho, B. 2017. Nondestructive technique for determining the viability of soybean (Glycine max) seeds using FT-NIR spectroscopy. Journal of the Science of Food and Agriculture. 98:1734-1742.
- Lohumi, S., Lee, H., Kim, M.S., Qin, J., Cho, B. 2018. Through-packaging analysis of butter adulteration using line-scan spatially offset Raman spectroscopy technique. Analytical and Bioanalytical Chemistry.
- Lohumi, S., Lee, H., Kim, M.S., Qin, J., Cho, B. 2018. Raman imaging for detection of adulterants in paprika powder: A comparison of data analysis methods. Applied Sciences. 8:485.
- Ma, Y., He, H., Wu, J., Wang, C., Chao, K., Huang, Q. 2018. Assessment of polysaccharides from mycelia of genus Ganoderma by mid-infrared and near- infrared spectroscopy. Scientific Reports. 8:10.
- Lohumi, S., Lee, H., Kim, M.S., Qin, J., Kandpal, L., Bae, H., Rahman, A., Cho, B. 2018. Calibration and testing of a Raman hyperspectral imaging system to reveal powdered food adulteration. PLoS One. 13(4):e0195253.
- Rahman, A., Kandpal, L.M., Lohomi, S., Kim, M.S., Lee, H., Mo, C., Cho, B. 2017. Nondestructive estimation of moisture content, pH and soluble solid contents in in intact tomatoes using hyperspectral imaging. Applied Sciences. 7(1):109.
- Dhakal, S., Chao, K., Huang, Q., Kim, M.S., Schmidt, W.F., Qin, J., Broadhurst, C.L. 2018. A simple surface-enhanced Raman spectroscopic method for on-site screening of tetracycline residue in whole milk. Sensors. 18(2):424.
- Chao, K., Dhakal, S., Qin, J., Kim, M.S., Peng, Y. 2018. A 1064 nm dispersive Raman spectral imaging system for food safety and quality evaluation. Applied Sciences. 8(3):431.
- Tewey, K., Lefcourt, A.M., Tasch, U., Shilts, P., Kim, M.S. 2018. Hyperspectral, time-resolved, fluorescence imaging system for large sample sizes: Part II. Detection of fecal contamination on spinach. Transactions of the ASABE. 61(2):391-398.
- Tewey, K., Lefcourt, A.M., Shilts, P., Tasch, U., Kim, M.S. 2018. Hyperspectral, time-resolved, fluorescence imaging system for large sample sizes: Part I. Development of high energy line illumination source. Transactions of the ASABE. 61(2):381-389.
- Delwiche, S.R., Qin, J., Graybosch, R.A., Rausch, S.R., Kim, M.S. 2018. Near-infrared hyperspectral imaging of blends of conventional and waxy hard wheats. Journal of Spectral Imaging. 7(a2):1-13.
- Dhakal, S., Chao, K., Schmidt, W.F., Qin, J., Kim, M.S., Huang, Q. 2018. Detection of azo dyes in curry powder using a 1064-nm dispersive hyperspectral Raman imaging system. Applied Sciences. 8(4):564.
- Lee, H., Huy, T., Park, E., Bae, H., Baek, I., Kim, M.S., Mo, C., Cho, B. 2017. Machine vision technique for rapid vigor measurement of soybean seed. Journal of Biosystems Engineering.
- Broadhurst, C.L., Schmidt, W.F., Nguyen, J.K., Qin, J., Chao, K., Aubuchon, S.R., Kim, M.S. 2017. Continuous gradient temperature Raman spectroscopy and differential scanning calorimetry of N-3DPA and DHA from -100 to 10�C. Chemistry and Physics of Lipids. 204:94-104.
- Lim, J., Mo, C., Oh, K., Kim, G., Yoo, H., Ham, H., Kim, Y., Kim, S., Kim, M.S. 2017. Rapid and nondestructive discrimination of Fusarium asiaticum and Fusarium graminearum in hulled barley (Hordeum vulgare L.) using near- infrared spectroscopy. Journal of Biosystems Engineering.
- Bonadies, S., Smith, N., Niewoehner, N., Lee, A.S., Lefcourt, A.M., Gadsden, A. 2018. Development of PID and fuzzy control strategies for navigation in agricultural environments. Journal of Dynamic Systems, Measurement, and Control. 140(6):061007.
- Lefcourt, A.M., Siemans, M. 2017. Interactions of isolation and shading on ability to use fluorescence imaging to detect fecal contaminated spinach. Applied Sciences. 7(10):1041.
- Lee, H., Kim, M.S., Lee, W., Cho, B. 2017. Determination of the total volatile basic nitrogen (TVB-N) content in pork meat using hyperspectral fluorescence imaging. Sensors and Actuators B: Chemical. 259:532-539.
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Progress 10/01/16 to 09/30/17
Outputs
Progress Report Objectives (from AD-416): Objective 1: Advance development and validation of on-line automated whole-surface inspection systems for simultaneous safety and quality inspection of fresh produce in high-throughput commercial processing operations. Objective 2: Develop and validate user-friendly analytical sensing methods and technologies for targeted and non-targeted rapid screening of foods for microbial, chemical, and biological contaminants in laboratory, field, and/or industrial environments. Objective 3: Advance development of portable spectral imaging technologies to allow identification and detection of food contaminants, and develop sampling and inspection protocols for implementation of the developed technologies in industry and regulatory applications. Objective 4: Advance development, test and validate an automated system for detecting contaminants in produce fields, and investigate cost and sensitivity trade-offs of different potential system components and configurations with regard to production of a cost-effective commercial system. Approach (from AD-416): Because cross-contamination may occur at many points throughout the production, processing, and distribution chains, our research targets reduction of food safety risks at multiple points, including both upstream (pre-harvest) processing and subsequent stages. The inspection point at single-layer processing is not intended to be a comprehensive inspection on its own but is key for some packaged fresh products. The ARS approach includes both pre- and post-harvest risk reduction measures that collectively can mitigate food safety concerns related to foodborne illnesses. The whole-surface sample presentation/imaging technologies developed in the previous project cycle along with the multitask imaging technology will be integrated on conveyor/processing systems to develop two automated whole-surface inspection platforms to simultaneously inspect produce for safety and quality attributes such as contaminants and defects. The two stand-alone commercial-grade prototype processing- inspection platforms will be transportable to produce processing facilities for testing and demonstration of the whole-surface inspection efficacies, with an ultimate goal of technology transfer. The proposed whole-surface fruit inspection will complement current industry sorting�based on quality attributes such as color and size�by the addition of safety inspection and will be used immediately after conventional color and size sorting. The proposed leafy green inspection will be used for inspection immediately prior to �value-added� processing, e.g., washing for packaged fresh-cut products. Significant progress has been made for all objectives during the second year of the project. For Objective 1, upgraded multitask imaging module including the two-view angle optics and illumination sources for the whole-surface round fruit inspection technology along with an automated software to generate whole-surface fruit images was developed. In addition, developed upgraded leafy green multitask imaging module, sample flipping module, and ejection module along with two-conveyor system. Integrated individual modules for development of the commercial prototype leafy-green wholes surface inspection platform. For Objective 2.1 concerning the determination and evaluation critical parameters for Raman chemical imaging systems for effective commodity- and contaminant- specific analysis, we conducted experiments using tapioca starch, milk powder, and wheat flour to determine the depth of laser penetration into the food powders, the power of the laser, and the spatial resolution needed for effective quantitative imaging-based detection of contaminant particles. Optimal parameters were then used to develop a Raman chemical imaging method for identification and quantification of multiple components present in complex food powder mixtures. The method was developed by analyzing ten samples of powdered non-dairy creamer that were prepared at ten different mixture concentrations with vanillin, melamine, and sugar (all three components added together in equal amounts by weight). Individual pixels corresponding to each of the three components could be detected in Raman chemical images of the thinly spread sample mixtures, and their numbers were found to be strongly correlated with the actual sample concentrations (correlation coefficient of 0.99 for all components), indicating that this method can be used for simultaneous identification of multiple components and estimation of their concentrations for food powder authentication or quantitative inspection purposes. Under Objective 2.2, a variety of food powders with chemical additives/ residues were imaged using the refined line-scan high-throughput Raman imaging system and the images were analyzed using a simple thresholding method based on key "fingerprint" peaks of the chemicals of interest. The first investigation used milk powder mixed with melamine and urea contaminants and found that the method used could detect melamine and urea at concentrations as low as 50 ppm in milk powder, with high correlation between of the image-based pixel determinations to the actual sample concentrations. The second investigation found that benzoyl peroxide (flour bleaching agent) could be detected in wheat flour at concentrations as low as 50 ppm; this is on the same level as industry regulatory standards. The third investigation found that maleic anhydride could be detected in cornstarch at 100 ppm. Under Objective 2.3, a multipurpose line-scan Raman platform was developed for food safety and quality research. The platform was designed so that it can be configured for either (1) line-laser Raman chemical imaging mode for surface evaluation or (2) point-laser spatially offset Raman spectroscopy (SORS) mode for nondestructive subsurface evaluation. To validate and confirm the new SORS functionality, we conducted an investigation to develop a SORS method for subsurface sample analysis. The method successfully analyzed samples of urea, ibuprofen, and acetaminophen powders through up to eight layers of nested pharmaceutical gelatin capsules. The Gradient Temperature Raman Spectroscopy (GTRS) technique was expanded to elucidate conformation and phase transitions of natural Poly Unsaturated Fatty Acids (PUFA) from one to six double bonds. The techniques was used to discovered properties with an even number of double bond were similar and discretely different from those with an odd number of double bond. Further, comparison of GTRS results for two cell membrane phospholipids biochemically critical to brain function (1-stearoyl-2-docosahexaenoyl (DHA) phosphotidylcholine and 1-stearoyl-2-arachidonoly (AA) phosphotidylcholine) identified the specific (and different in each) elastic molecular site that could be responsible for their unique role in brain chemistry. For Objective 3, research on ARS portable imaging technologies for contamination and sanitation inspection applications was discussed with the USDA-FSIS Office of Policy and Program Development (OPPD), Risk, Innovations, and Management Staff (RIMS). Seeking suitable new measures with strong scientific basis to use in modernizing inspection and enforcement policies, OPPD/RIMS consulted EMFSL on the potential use of the handheld imaging device and challenges of in-plant testing and evaluation. Joint EMFSL-RIMS site visits were planned for preliminary prototype testing at meat processing plants, to provide greater insight on in-plant inspection needs and situational considerations of processing environments, and to solicit feedback from FSIS inspection staff who are the potential end-users of the device for development of user protocol. Preliminary testing was conducted during one site visit to a Ready-to-Eat (RTE) deli meat processor and another to a beef packing plant. Initial results suggest more effective use might be found in meat slaughtering operations compared to RTE meat processing, and highlighted ergonomic aspects of the device and consideration of methods to safely and objectively evaluate device performance for real-world residue detection without imposition upon commercial processing operations. In-house 3D printing has been used to improve and test device balance and shape; ongoing efforts are intended to improve these ergonomic properties as well as device ruggedness for practical in-plant use. A user-friendly touchscreen interface is under development for ease of use and for easy on/off detection modes to implement targeted spectral image processing algorithms. For Objective 4, a second self-propelled vehicle for mounting the hyperspectral imaging system was developed, tested, and shipped to Yuma, Arizona, for use in commercial produce fields. This unit uses larger wheels compared to the first unit, allowing the vehicle to traverse the relatively rough terrain in produce fields. The optics system is moved between the USDA laboratory in Beltsville, Maryland, and Yuma as needed. The optics mounting system allows the optics to be calibrated in the laboratory and then transferred to the vehicle. Mounting the optics on the vehicle takes about 1 hr. Accomplishments 01 Sensing techniques for food ingredient authentication and food contaminant detection. ARS researchers in Beltsville, Maryland, have developed point-scan and line-scan Raman chemical imaging systems and analysis methods for non-destructive detection of chemical contaminants in powdered food materials. Without any special sample preparation, the newest system can analyze a 30-ml sample (two tablespoons) of dry powder within 10 minutes, more quickly and efficiently than conventional Raman instruments that might require 24 hours to analyze the same sample. The system can perform quantitative contaminant detection down to concentrations as low as 50 parts per million of benzoyl peroxide (used for bleaching) in wheat flour or melamine in milk powder, for example. Currently under patent review, this system will provide a useful screening tool to help detect contaminated food products and prevent their distribution and use, which is now a global concern due to the potential for widespread illnesses and even deaths, such as those that occurred with over 135 documented cases of melamine- adulterated skim milk powder between 1984 and 2012 worldwide. 02 Rapid quantitative detection of benzoyl peroxide in wheat flour. Quality and safety of wheat flour is an important issue worldwide due to the routine use of wheat flour in many staple foods. Use of benzoyl peroxide (BPO), a flour bleaching agent, at levels exceeding regulatory standards can destroy nutrients in the flour and may cause health concerns for consumers. A line-scan high-throughput Raman chemical imaging method was used for direct non-destructive sample analysis to create images visualizing BPO particles that were mixed into samples of wheat flour. BPO was detectable at 50 parts per million, which is on the same level as regulatory standards for acceptable use. High correlation between BPO pixel concentrations in the detection images and BPO mass concentrations in the prepared flour samples suggests that the method can be used for quantitative detection. This Raman imaging inspection method can be used by regulatory agencies and food processors to inspect wheat flour and other food powders and ingredients for adulteration and authentication screening purposes. 03 Detection and quantification of adulterants in milk powder. Past incidents of melamine-contaminated milk have illustrated the grave public health threat posed by economically motivated adulteration of milk. ARS researchers in Beltsville, Maryland, developed a new method for authenticating milk powder, using line-scan Raman chemical imaging to visualize particle identification, spatial distribution, and morphological features of two chemical adulterants (melamine and urea) mixed into milk powder samples, effectively detecting adulterant concentrations as low as 50 parts per million, which is a much lower concentration than those reported in some real-life incidents of adulteration (e.g., thousands of parts per million). The high correlation between the percentages of adulterant pixels in the images and the mass concentrations of the adulterants in the prepared samples suggests that the method can be effectively used for quantitative detection of adulterants in milk powder. This non-destructive Raman method is faster than conventional Raman methods and can be used to help regulatory agencies and food processors authenticate milk powder and other powdered foods subject to economic adulteration or fraud. 04 Subsurface food inspection using line-scan spatially offset Raman spectroscopy technique. Non-destructive subsurface food inspection is challenging due to complex interactions between light and heterogeneous or layered sample materials. ARS researchers in Beltsville, Maryland, developed a new line-scan-based technique to perform spatially offset Raman spectroscopy (SORS) for food and agricultural products. Unlike conventional Raman spectroscopy that detects signals dominated by the material nearest to the sample surface at point of measurement, SORS laterally separates the point laser source and the detector on the sample surface, thereby retrieving subsurface signals in the light that has passed through a deeper region of the sample before reaching the detector. The new line-scan SORS technique is more flexible and efficient than the traditional optical fiber probe approach in that a single image exposure can collect a series of Raman spectra all at once across a broad offset range with a narrow spatial interval. The new technique can be used for rapid and nondestructive subsurface inspection applications such as authentication of food ingredients or detection of contaminants in heterogeneous mixtures and layers or through coatings, films, or plastic packaging, and evaluation of internal attributes of fruits and vegetables, to the benefit of food processors seeking to ensure the safety and quality of their food products as well as regulatory agencies (e.g., FDA and USDA FSIS) that develop and enforce standards of food safety and quality. 05 Fecal detection system for produce fields. A system to take high- resolution visible/near-infrared hyperspectral images in outdoor fields using either natural ambient lighting for reflectance images or a pulsed ultraviolet laser for fluorescence images was designed, built, and tested by ARS researchers in Beltsville, Maryland. Components of the system include a semi-autonomous cart, a gated-intensified camera, a spectral adapter, a frequency-triple Nd:YAG (Neodymium-doped Yttrium Aluminum Garnet) laser, and optics to convert the Gaussian laser beam into a line-illumination source. The laser and camera are mounted on a removable plate that allows for laboratory optics calibration followed by installation on the cart for field use. The front wheels of the cart are independently powered by stepper motors that support stepping or continuous motion. When stepping, a spreadsheet is used to program imaging parameters for each step such as setting acquisition delays, acquisition time, and laser attenuation, a functionality that allows for specialized imaging such as establishing parameters for measuring fluorescence decay-curve characteristics. The system was validated by acquiring images of fluorescence responses of spinach leaves and dairy manure. These developments can be incorporated into future field imaging systems to detect fecal contamination and prevent cross- contamination during harvest operations. 06 Identification of parasite membrane vulnerability for chemical water treatments. Bleach is a commonly used disinfectant but can be ineffective in killing the infectious parasite Cryptosportium parva, which sickened ten school children in Litchfield, Minnesota, in May 2016. This parasite can cause typically mild yet treatable illness as well as severe and even fatal illness in people with weakened immune systems, and can easily be spread by contaminated drinking water or recreational aquatic environments. Raman mapping of the parasite�s protective membrane layer in its environmentally durable oocyst life stage found significant concentrations of calcium ions and magnesium ions on the oocyst�s spherical surface and at the edges of opened / broken surfaces. This suggests chemical mechanisms to be investigated for effective water treatment to protect against and prevent future outbreaks. 07 Fluorescence imaging for detecting fecal contamination of soil and assessing compost maturity. Serious outbreaks of food-borne illness can result from consumption of fresh produce contaminated by pathogens such as E. coli and salmonella that originate from animal or human fecal matter, particularly if the produce is mishandled at temperatures that encourage pathogen growth. Sources/pathways of contamination can include the excrement of wildlife or livestock, immature manure composts used as soil amendments, contaminated irrigation water or field tools, and poor health or hygiene of field workers. Although food safety standards exist regarding use of mature manure composts and prevention of fecal contamination in produce fields, verification in field production environments remains challenging since neither compost maturity nor fecal traces are easily identified by eye. ARS researchers investigated hyperspectral fluorescence imaging techniques to determine spectral characteristics of fecal samples from four species (dairy cows, pigs, chickens, and sheep) to test detection of animal feces and identification of species origin in soil-feces mixtures, and to evaluate use for assessing maturity of manure-based composts and results found fluorescence features that could be used to detect feces on soil and showed that identifying animal species origin is feasible. Furthermore, the fluorescence features could be used via simpler single- waveband imaging techniques, instead of more complex and costly full- spectrum hyperspectral imaging methods, for assessing compost maturity. These findings can be incorporated into field-use tools to help prevent contamination or harvesting of contaminated produce.
Impacts
(N/A)
Publications
- Qin, J., Kim, M.S., Chao, K., Schmidt, W.F., Cho, B., Delwiche, S.R. 2017. Line-scan Raman imaging and spectroscopy platform for surface and subsurface evaluation of food safety and quality. Journal of Food Engineering. 198:17-27.
- Chao, K., Dhakal, S., Qin, J., Schmidt, W.F., Kim, M.S., Chan, D.E. 2017. A spatially offset Raman spectroscopy method for non-destructive detection of gelatin-encapsulated powders. Sensors. 17(3):618.
- Dhakal, S., Chao, K., Qin, J., Kim, M.S., Chan, D.E. 2017. Identification and evaluation of composition in food powder using point-scan Raman spectral imaging. Applied Sciences. 7(1):1.
- Dhakal, S., Qin, J., Kim, M.S., Chao, K. 2017. Raman spectroscopy. In: Franca, A.S., Nollet, L.M.L., editors. Spectroscopic Methods in Food Analysis. Boca Raton, FL: CRC Press. p. 111-142.
- Chen, Z., Peng, Y., Li, Y., Chao, K. 2017. Extraction and identification of mixed pesticides� Raman signal and establishment of their prediction models. Raman Spectroscopy. 48(3):494-500.
- Schmidt, W.F., Hapeman, C.J., McConnell, L., Mookherji, S., Rice, C., Nguyen, J.K., Qin, J., Chao, K., Kim, M.S., Broadhurst, C.L., Shelton, D. R. 2017. Using torsional forces to explain the gradient temperature Raman spectra of endosulfan isomers and its irreversible isomerization. Journal of Molecular Spectroscopy. 1139:43-51.
- Broadhurst, L., Schmidt, W.F., Kim, M.S., Nguyen, J.K., Qin, J., Chao, K., Bauchan, G.R., Shelton, D.R. 2016. Continuous gradient temperature Raman spectroscopy of n-6 DPA and DHA from -100 C to 20�C. Chemistry and Physics of Lipids. 200:1-10.
- Mo, C., Kim, M.S., Kim, G., Lim, J., Delwiche, S.R., Chao, K., Lee, H., Cho, B. 2017. Spatial assessment of soluble solid contents on apple slices using hyperspectral imaging. Biosystems Engineering. 159:10-21.
- Mo, C., Kim, G., Kim, M.S., Lim, J., Lee, S.H., Lee, H., Kang, J., Cho, B. 2017. Discrimination methods of biological contamination on fresh-cut lettuce based on VNIR and NIR hyperspectral imaging. Infrared Physics and Technology. 85:1-12.
- Mo, C., Kim, G., Kim, M.S., Lim, J., Cho, H., Barnaby, J.Y., Cho, B. 2017. Fluorescence hyperspectral imaging technique for the foreign substance detection on fresh-cut lettuce. Journal of the Science of Food and Agriculture. 97(12):3985-3993.
- Qin, J., Chao, K., Schmidt, W.F., Dhakal, S., Cho, B., Peng, Y., Huang, M., Lee, H., Kim, M.S. 2017. Subsurface inspection of food safety and quality using line-scan spatially offset Raman spectroscopy technique. Food Control. 75:246-254.
- Qin, J., Kim, M.S., Chao, K., Chan, D.E., Delwiche, S.R., Cho, B. 2017. Line-scan hyperspectral imaging techniques for food and agricultural applications. Applied Sciences. 7(20):125.
- Qin, J., Kim, M.S., Chao, K., Dhakal, S., Lee, H., Cho, B. 2017. Detection and quantification of adulterants in milk powder using high-throughput Raman chemical imaging technique. Food Additives & Contaminants. 34(2):152- 161.
- Lohumi, S., Joshi, R., Kandpal, L.M., Kim, M.S., Cho, H., Mo, C., Seo, Y., Rahman, A., Cho, B. 2017. Quantitative analysis of Sudan dye adulteration in paprika powder using FTIR spectroscopy. Journal of Food Additives & Contaminants. 34(5):678-686.
- Scholl, P., Bergana, M., Yakes, B., Zbylut, S., Downey, G., Mossoba, M., Jablonski, J., Margaletta, R., Holroyd, S.E., Buehler, M., Qin, J., Xie, Z. , Hurst, W., Laponte, J.H., Roberts, D., Zrybko, C., Mackey, A., Holton, J. D., Israelson, G.A., Payne, A., Kim, M.S., Chao, K., Moore, J. 2017. Effects of the adulteration technique on the near-infrared detection of melamine in milk powder. Journal of Agricultural and Food Chemistry. 65:5799-5809.
- Lefcourt, A.M., Kistler, R., Gadsden, A., Kim, M.S. 2016. Automated cart with VIS/NIR hyperspectral reflectance and fluorescence imaging capabilities. Applied Sciences. 7(1):3.
- Qin, J., Kim, M.S., Chao, K., Cho, B. 2017. Raman chemical imaging technology for food and agricultural applications. Journal of Biosystems Engineering. 42(3):170-189.
- Lohumi, S., Kim, M.S., Qin, J., Cho, B. 2017. Raman imaging from microscopy to macroscopy: Quality and safety control of biological materials. Trends in Analytical Chemistry. 93:183-198.
- Cho, H., Lee, H., Kim, S., Kim, D., Park, H., Lefcourt, A.M., Chan, D.E., Kim, M.S. 2016. Hyperspectral fluorescence imaging of animal feces and soil: potential use of fluorescence imaging for assessment of soil fecal contamination and compost maturity. Applied Sciences. 6:246.
- Everard, C., Kim, M.S., O'Donnell, C. 2016. Distinguishing bovine fecal matter on spinach leaves using field spectroscopy. Applied Sciences. 6:246- 254.
- Mo, C., Kim, K., Kim, M.S., Lim, J., Lee, K., Lee, W., Cho, B. 2017. On- line fresh-cut lettuce quality measurement system using hyperspectral imaging. Biosystems Engineering. 156:38-50.
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Progress 10/01/15 to 09/30/16
Outputs
Progress Report Objectives (from AD-416): Objective 1: Advance development and validation of on-line automated whole-surface inspection systems for simultaneous safety and quality inspection of fresh produce in high-throughput commercial processing operations. Objective 2: Develop and validate user-friendly analytical sensing methods and technologies for targeted and non-targeted rapid screening of foods for microbial, chemical, and biological contaminants in laboratory, field, and/or industrial environments. Objective 3: Advance development of portable spectral imaging technologies to allow identification and detection of food contaminants, and develop sampling and inspection protocols for implementation of the developed technologies in industry and regulatory applications. Objective 4: Advance development, test and validate an automated system for detecting contaminants in produce fields, and investigate cost and sensitivity trade-offs of different potential system components and configurations with regard to production of a cost-effective commercial system. Approach (from AD-416): Because cross-contamination may occur at many points throughout the production, processing, and distribution chains, our research targets reduction of food safety risks at multiple points, including both upstream (pre-harvest) processing and subsequent stages. The inspection point at single-layer processing is not intended to be a comprehensive inspection on its own but is key for some packaged fresh products. The ARS approach includes both pre- and post-harvest risk reduction measures that collectively can mitigate food safety concerns related to foodborne illnesses. The whole-surface sample presentation/imaging technologies developed in the previous project cycle along with the multitask imaging technology will be integrated on conveyor/processing systems to develop two automated whole-surface inspection platforms to simultaneously inspect produce for safety and quality attributes such as contaminants and defects. The two stand-alone commercial-grade prototype processing- inspection platforms will be transportable to produce processing facilities for testing and demonstration of the whole-surface inspection efficacies, with an ultimate goal of technology transfer. The proposed whole-surface fruit inspection will complement current industry sorting�based on quality attributes such as color and size�by the addition of safety inspection and will be used immediately after conventional color and size sorting. The proposed leafy green inspection will be used for inspection immediately prior to �value-added� processing, e.g., washing for packaged fresh-cut products. With most of the proof-of-concept for the critical technologies and fundamental research already established previously, significant progress has been made for all objectives in advancing the sensing and instrumentation technologies for use in food production, processing and distribution chains. For Objective 1, various hardware components for on- line automated whole-surface inspection systems have been designed and developed in cooperation with University of Maryland, Baltimore County, Maryland. This collaboration has resulted in 3-d printing of custom- designed sensing and optical support structures to build commercial prototypes of inspection conveyor systems for round fruits and for relatively flat leafy greens. Significant progress has been made for Objective 2, targeting detection of microbial, chemical, and biological contaminants using fluorescence, Raman, and hyperspectral imaging technologies. Animal feed prepared with the inclusion of meat and bone meal (MBM) has been the source of bovine spongiform encephalopathy (BSE) in cattle and other livestock animals. Many countries have banned the use MBM as an animal feed ingredient. Spectral imaging techniques have shown potential for rapid assessment and authentication of various food and feed ingredients. ARS researchers in Beltsville, Maryland, initiated a cooperative agreement with the University of Cordoba, Spain, to develop rapid and accurate spectral imaging methods for assessing MBM in animal feed. A preliminary investigation of hyperspectral fluorescence and Raman chemical imaging techniques for differentiating poultry and pork MBM exhibited promising results. The Gradient Temperature Raman Spectroscopy (GTRS) technique, developed by the scientists in Beltsville, Maryland, can detect molecular and macromolecular changes such as phase transitions and protein denaturation in situ. The temperature range of GTRS was extended to analyze samples under cryogenic conditions. This enabled characterizing phase transitions unique to polyunsaturated lipids and those unique to or shared in common by structurally different omega-3 fatty acids docosapentaenoic acid (DPA, 22:5n-6) and docosahexaenoic acid (DHA, 22:6n-3). Research will continue to investigate changes in phospholipid folding/unfolding in a temperature gradient as a portal to detecting temperature-associated changes in cell walls and lipid bilayer membranes of microbes. Recent Raman imaging experiments on intact biofilms generated on stainless steel metal identified bioflim marker peaks corresponding to their carbohydrate and protein structures. Different microbes result in different sets of marker peaks. Films on stainless steel are detectable by Raman imaging at concentrations lower than those that can be detected visually, and biofilms from more than one microbe can be distinguished from each other. Further research is planned to also investigate biofilm formation on surfaces of other metals. Cryptosporidium parva is an infectious parasite that sickened ten school children in Litchfield, Minnesota, in May 2016. Bleach is an ineffective treatment because of the spherical oocysts� membrane layer which protects the parasite. Raman microimaging enables identification of the chemical structures characteristic of this morphological site and of chemo-markers for sites of vulnerability in this membrane. The oocysts must have sites of non-uniformity; otherwise, the parasites would never �hatch� from inside the membrane. From this information, effective chemical treatments can be designed and optimized to protect against and prevent future outbreaks. In addition, ARS researchers in Beltsville, Maryland, have developed a new line-scan hyperspectral imaging system to acquire shortwave infrared (SWIR) images for biological sample evaluation. The system uses a mercury- cadmium-telluride focal plane array detector, which extends the effective spectral imaging region beyond the 400 � 1000 nm and 900 � 1700 nm regions that are typically imaged by systems using more common silicon- based CCD cameras and Indium-Gallium-Arsenide CCD cameras, respectively. Previously, agricultural applications of infrared imaging were limited to near-infrared wavelengths up to 1700 nm; the new system extends infrared imaging from 900 to 2500 nm. The new imaging technique and system can be used in non-destructive evaluation for food inspection, contaminant detection, and ingredient authentication. For Objective 3, significant design changes were made for the handheld contamination and sanitation inspection devices to extend testing and validation of the technology for use as visual-aid inspection tools and to develop standard protocols for end-users. The upgraded design incorporated a detachable rechargeable battery and improvement in weight distributions of the internal components to enhance ergonomics. An interagency agreement with the U.S. Army Natick Soldier Research Development and Engineering Center was established to further test and validate the imaging devices for use in the U.S. Army food safety audit programs. For Objective 4, efforts to develop methods and instrumentation for detecting fecal material and signs of animal intrusion in produce fields prior to harvest were continued in the new project. An ARS scientist in Beltsville, Maryland, began development of a semiautonomous cart to serve as the vehicle for mounting the imaging platform. The cart will enhance the potential capabilities of the imaging system by allowing imaging at night and also allowing detection using time-resolved imaging techniques, and will also reduce net operating costs by relieving the need for a human operator. Vehicle design specifications were established, and a collaboration with a local university established. The imaging system itself was modified, including development of a new mounting system for optics and integration of a new intensified charge-coupled device (CCD) camera to facilitate conversion of the laboratory system to field use. Accomplishments 01 Handheld fluorescence imaging device for meat safety inspection in slaughter plants. Current meat inspection in slaughter plants for food safety and quality attributes, including potential fecal contamination, is conducted through visual examination by human inspectors working under conditions that are poorly suited to conventional fluorescence detection methods that require ambient darkness. ARS researchers in Beltsville, Maryland, developed a handheld fluorescence-based imaging device (HFID) to highlight contaminated food and equipment surfaces on a display monitor during use under ambient lighting in food processing plants. This study investigated the effectiveness of the HFID in enhancing visual detection of fecal contamination on red meat, fat, and bone surfaces of beef under varying ambient luminous intensities (0, 10, 30, 50 and 70 foot-candles). Overall, diluted feces on fat, red meat and bone surfaces of beef under ambient light ranging from 0- to 50- foot-candles were detectable in the 670-nm single-band fluorescence images. As an assistive tool, this technology will support and improve meat safety inspection programs as implemented by U.S. processors and regulatory inspectors. 02 Evaluation of turmeric powder adulterated with metanil yellow using FT- Raman and FT-IR spectroscopy. Turmeric (Curuma long L.), an herbaceous root commonly used for food seasoning, is also valued for medicinal properties which arise from its natural content of curcumin, a yellow pigment with anti-inflammatory, anti-cancer, antioxidant, and wound healing attributes. However, economically driven adulteration of turmeric has occurred repeatedly, such as the addition of metanil yellow, a known carcinogen, to increase yellow color and product weight. ARS researchers in Beltsville, Maryland used Fourier Transform Raman (FT-Raman) spectroscopy and Fourier Transform Infrared (FT-IR) spectroscopy for detection of metanil yellow in turmeric powder. FT- Raman and FT-IR spectra of metanil yellow, turmeric, and curcumin were acquired and analyzed. Spectral analysis of metanil yellow mixed into turmeric at 8 different concentrations showed that the FT-Raman method was able to detect metanil yellow at 1% concentration, while detection by the FT-IR method was limited to 5% concentration. With the increasing popularity of turmeric as a health food additive, these techniques are a potential tool for food safety inspection that could greatly benefit the food industry, safety regulators, and consumers worldwide. 03 Raman chemical imaging system for detecting low levels of food adulterants. ARS researchers in Beltsville, Maryland, developed a line- scan Raman chemical imaging system to detect adulterants in milk powder, using a 5-W 785-nm line laser (240 mm long and 1 mm wide) as the Raman excitation source. Hyperspectral Raman images were acquired in the wavenumber range of 103�2881 cm-1 for samples of skim milk powder mixed with two nitrogen-rich adulterants, melamine and urea, at eight concentrations ranging from 50 to 10,000 parts per million. Chemical detection images to visualize identification, spatial distribution, and morphological features of the two adulterants in the milk powder were generated by combining individual binary images of melamine and urea. With the limits of detection for both melamine and urea estimated at an order of 50 parts per million, this line-scan Raman imaging system can be used for rapid, nondestructive, and quantitative measurement of melamine and other chemical adulterants that can pose risk of illness and even death when present in dry powdered food ingredients as has been illustrated by various instances of economically-driven adulteration around the world. 04 Surface and subsurface inspection of food safety and quality using a line-scan Raman system. ARS researchers in Beltsville, Maryland, developed a line-scan Raman imaging platform that can perform either Raman chemical imaging (RCI) for macro-scale surface imaging using a 785 nm line laser up to 24 cm long for push-broom imaging, or Spatially Offset Raman spectroscopy (SORS) for subsurface inspection over an offset range of 0-36 mm with a spatial interval of 0.07 mm using one CCD exposure. Large-field-of-view (230 mm wide) and high-spatial- resolution (0.07 mm/pixel) settings of the RCI mode were tested by analyzing fluorescence corrected images at select Raman peaks to view Raman-active analytes (fat on pork shoulder surface and carotenoids across carrot). Testing of the SORS mode showed that the carrot and melamine spectra acquired across a 5-mm thick carrot slice laid atop a layer of melamine powder could be effectively resolved using self- modeling mixture analysis. By using a shared detection module covering a Raman shift range from -674 to 2865 cm-1 for both RCI and SORS modes, the line-scan Raman imaging and spectroscopy platform provides a new tool that may be used for a wider range of food materials for surface and subsurface inspection for safety and quality attributes.
Impacts
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Publications
- Dhakal, S., Chao, K., Schmidt, W.F., Qin, J., Kim, M.S., Chan, D.E. 2016. Evaluation of turmeric powder adulterated with metanil yellow using FT- Raman and FT-IR spectroscopy. Foods. 5(2):36-51.
- Huang, M., Kim, M.S., Chao, K., Qin, J., Mo, C., Esquerre, C., Delwiche, S. R., Zhu, Q. 2016. Penetration depth measurement of near-infrared hyperspectral imaging light for milk powder. Sensors. 16(4):441.
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