Progress 09/19/04 to 09/19/09
Outputs
Progress Report Objectives (from AD-416) The overall research goal is to develop new and/or improved sensing technologies for rapid, nondestructive assessment and grading of quality and condition of pickling cucumbers and tree fruits, primarily apples and cherries, during preharvest, harvest, and postharvest handling, sorting and processing. Specific objectives are to: 1) develop new techniques and systems for assessing and grading quality and maturity of apples; 2) develop an optical technique for rapid detection of pits and pit fragments in processed tart cherries; and 3) develop nondestructive sensing methods and techniques for detecting and segregating defective and inferior cucumbers to assure the keeping and processing quality of pickling cucumbers. Approach (from AD-416) Imaging spectroscopy will be used for determining the spectral absorption and scattering properties of apple fruit and computer simulations will be developed to quantify light propagation and scattering in apple fruit and its relationship with fruit quality attributes. A prototype sensing system will be developed, using multispectral imaging technique, for grading apple fruit for firmness and soluble solids content. A new technique of combining hyperspectral reflectance and fluorescence will be developed to measure apple fruit maturity. A rapid sensing technique and system, using hyperspectral and/or multispectral transmission imaging, will be developed for detecting pits and pit fragments in processed tart cherries. Hyperspectral and/or multispectral reflectance and fluorescence imaging techniques will be used to detect defective cucumbers resulting from mechanical stress, temperature chilling, physiological disorder and diseases. Image processing algorithms, using artificial neural networks and statistical classification methods, will be developed and incorporated into a hyperspectral/multispectral imaging system for real- time sorting of defective pickling cucumbers. Near-infrared spectroscopy and/or imaging spectroscopy in transmission and reflectance modes will be used for measuring the keeping and processing quality of pickling cucumbers. Significant Activities that Support Special Target Populations Absorption and scattering are two basic phenomena when light interacts with horticultural and food products. Measurement of the spectral absorption and scattering properties will enable us to gain knowledge about light interaction with plant materials and provide a new means for accurate determination of product quality attributes. Researchers at East Lansing, Michigan designed and assembled an optical property measurement prototype for automatic measurement of the absorption and scattering properties from fruits and food products for the visible and near- infrared region of 400-1000 nm. In addition, this prototype also can be used to acquire spectral images at wavelengths in the visible and near- infrared region. Upon completion of the system calibration and algorithms integration, this instrument will provide a new means for optical property characterization and quality assessment of fruits and other food and agricultural products. Currently, optical techniques (e.g., near-infrared spectroscopy and spectral scattering) are used for nondestructive detection of internal quality of fruits like apple. Accurate optical measurement of fruit flesh would require minimizing or eliminating the skin effect. A new method is being developed of minimizing or eliminating the fruit skin effect on the optical measurement of fruit flesh. Computer simulations and experimental evaluation with the model samples of known optical properties were carried out to validate the method. The results showed that the method is promising for separately measuring the optical properties of fruit flesh and skin. With further improvement in the hardware and algorithm, the method could open a new avenue for better characterization and assessment of fruits and food products.
Impacts
(N/A)
Publications
- Qin, J., Lu, R. 2009. Monte Carlo Simulation for Quantification of Light Transport Features in Apples. Computers and Electronics in Agriculture. 68(1):44-51.
- Kavdir, I., Burukcan, M., Lu, R., Kocabiyik, H., Seker, M. 2009. Prediction of Olive Quality Using FT-NIR Spectroscopy in Reflectance and Transmittance Modes. Biosystems Engineering. 103(3):304-312.
- Lu, R. 2009. Spectroscopic Technique for Measuring the Texture of Horticultural Products: Spatially Resolved Approach. In: Zude, M. Optical Monitoring of Fresh and Processed Agricultural Crops. Boca Raton, FL: CRC Press. p. 391-423.
- Lu, R., Tipper, N.C. 2009. A Portable Device for the Bioyield Detection to Measure Apple Firmness. Applied Engineering in Agriculture. 25(4):517-523.
- Qin, J., Lu, R., Peng, Y. 2009. Prediction of Apple Internal Quality Using Spectral Absorption and Scattering Properties. Transactions of the ASABE. 52(2):499-507.
- Ariana, D., Lu, R. 2008. Detection of internal defect in pickling cucumbers using hyperspectral transmittance imaging. Transactions of the ASABE. 51(2):705-713.
- Ariana, D., Lu, R. 2008. Quality evaluation of pickling cucumbers using hyperspectral reflectance and transmittance imaging. Part 1. Development of a prototype. Sensing and Instrumentation for Food Quality and Safety. DOI 10.1007/s11694-008-9057-x. Available: www.springerlink. com/content/w44j431683101493/?.
- Ariana, D., Lu, R. 2008. Quality evaluation of pickling cucumbers using hyperspectral reflectance and transmittance imaging. Part 2. Performance of a prototype. Sensing and Instrumentation for Food Quality and Safety. DOI 10.1007/s11694-008-9058-9. Available: www.springerlink. com/content/r3715505p0155360/?.
- Lu, R., Peng, Y. 2007. Development of a Multispectral Imaging Prototype for Real-Time Detection of Apple Fruit Firmness. Optical Engineering. 46(12):123201.
- Mizrach, A., Lu, R., Rubino, M. 2008. Gloss evaluation of curved-surface fruits and vegetables. Food and Bioprocess Technology. DOI 10.1007/s11947- 008-0083-9. Available: www.springerlink.com/content/a223w2330w876341/?.
- Peng, Y., Lu, R. 2008. Analysis of Spatially-Resolved Hyperspectral Scattering Images for Assessing Apple Fruit Firmness and Soluble Solids Content. Postharvest Biology and Technology. 48(1):52-62.
- Qin, J., Lu, R. 2008. Measurement of the optical properties of fruits and vegetables using spatially resolved hyperspectral diffuse reflectance imaging technique. Postharvest Biology and Technology. 49(3):355-365.
- Lu, R. 2007. Quality evaluation of fruit by hyperspectral imaging. In: Sun, D.W., editor. Computer Vision Technology for Food Quality Evaluation. Burlington, MA: Elsevier. p. 319-348.
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Progress 10/01/06 to 09/30/07
Outputs
Progress Report Objectives (from AD-416) The overall research goal is to develop new and/or improved sensing technologies for rapid, nondestructive assessment and grading of quality and condition of pickling cucumbers and tree fruits, primarily apples and cherries, during preharvest, harvest, and postharvest handling, sorting and processing. Specific objectives are to: 1) develop new techniques and systems for assessing and grading quality and maturity of apples; 2) develop an optical technique for rapid detection of pits and pit fragments in processed tart cherries; and 3) develop nondestructive sensing methods and techniques for detecting and segregating defective and inferior cucumbers to assure the keeping and processing quality of pickling cucumbers. Approach (from AD-416) Imaging spectroscopy will be used for determining the spectral absorption and scattering properties of apple fruit and computer simulations will be developed to quantify light propagation and scattering in apple fruit and its relationship with fruit quality attributes. A prototype sensing system will be developed, using multispectral imaging technique, for grading apple fruit for firmness and soluble solids content. A new technique of combining hyperspectral reflectance and fluorescence will be developed to measure apple fruit maturity. A rapid sensing technique and system, using hyperspectral and/or multispectral transmission imaging, will be developed for detecting pits and pit fragments in processed tart cherries. Hyperspectral and/or multispectral reflectance and fluorescence imaging techniques will be used to detect defective cucumbers resulting from mechanical stress, temperature chilling, physiological disorder and diseases. Image processing algorithms, using artificial neural networks and statistical classification methods, will be developed and incorporated into a hyperspectral/multispectral imaging system for real- time sorting of defective pickling cucumbers. Near-infrared spectroscopy and/or imaging spectroscopy in transmission and reflectance modes will be used for measuring the keeping and processing quality of pickling cucumbers. Significant Activities that Support Special Target Populations Detection of pits in tart cherries. The presence of pits or pit fragments in processed cherry products poses potential hazard to consumers and could make the industry liable for economic loss. Current pitting technology is generally effective for removal of pits from tart cherries, but it still cannot assure 100% pit-free products. This research was aimed at developing an effective pit detection technology for inspection of pitted cherry products. Two optical sensing configurations were assembled and tested for detection of pits in tart cherries. Data were collected from intact (with pits) and pitted cherries during the 2007 harvest season, using the two sensing configurations. Preliminary data analysis showed that one sensing configuration looked promising for detection of pits in tart cherries. Algorithms are being developed to segregate cherries with pits from those without pits. Based on the results for the two sensing configurations, a prototype will be developed for automatic detection and segregation of cherries with pits. The technology under development could offer the food industry a new, effective means for inspecting processed cherry products to assure that they are free from pits or pit fragments. Measurement of the gloss of apples. Gloss is an important outward characteristic of apple fruit and consumers often prefer glossy or shinning fruit to dull or less shinning fruit. Hence, artificial gloss is often added to the surface of apples to enhance their appeal to consumers and also extend fruit shelf life. However, fruit packers in Michigan have found that the quality of waxing on apples can vary greatly due to various factors related to application procedure, fruit condition, and environmental condition (e.g., humidity and temperature). This research was conducted in collaboration with Michigan State University to address the issue of enhancing and evaluating apple fruit appearance and thus quality. A commercial glossmeter was evaluated for several types of fruits and vegetable (apple, nectarine, plum, tomato). Tests showed that gloss measurements could vary greatly, and great care and skill was needed in using the commercial glossmeter. Based on the review of literature and the evaluation of the commercial glossmeter, an automatic gloss measurement prototype integrated with imaging and motorized positioning capabilities was assembled and tested for apples. The prototype automatically found appropriate locations on the fruit for gloss measurement; its gloss readings were nonlinearly related to standard measurements. The prototype achieved a repeatability value of 5% for 50% of the measurements and 16% for 90% of the measurements, as measured by the range of replicated measurements divided by the mean value expressed in percent. With further improvements in hardware and software, the prototype could be used for automatic gloss measurement of apples and other horticultural products, thus providing researchers and packers with an objective means for assessing fruit appearance/quality. Accomplishments a. Measurement of the optical properties of horticultural products for quality assessment. Until now, no appropriate methods/techniques are available for measuring the optical properties of fruits and vegetables, which are needed in the quantification of light interaction with plant tissues and the development of effective optical techniques for quality assessment and grading. The optical properties of horticultural products (apple, kiwifruit, peach, pear, plum, cucumber, squash and tomato) were measured using a novel hyperspectral imaging method developed at the ARS East Lansing, MI location, which filled the literature void. Computer simulation models were developed, providing quantitative information on light absorption and scattering patterns and light penetration depths in apple fruit. Good correlations were established between the optical property data and the firmness and sugar content of apples. The method/technique opened a new avenue for assessing quality of fruits and other agricultural products. Researchers can now use the method to measure the optical properties of a wide range of food and agricultural products, and instrumentation engineers can use the optical property data to assist in the design of sensing configurations for quality assessment. This accomplishment addresses the first component of NP 306 - Quality Characterization, Preservation, and Enhancement, Problem Area 1b - Methods to Evaluate and Predict Quality. b. Automatic detection of quality/defect of pickling cucumbers. Good, consistent pickling cucumbers free from external and internal defect are critical to high quality pickled products. A prototype of imaging system along with detection algorithms was developed and evaluated for online inspection of fresh pickling cucumbers for both external and internal quality characteristics including skin and flesh color, fruit firmness, and defect (bruises and hollow hearts). The prototype achieved an overall accuracy of 90% in segregating normal cucumbers from defective cucumbers; it also provided relatively good measurement of skin and flesh color and flesh firmness. The technology will be suitable for online inspection of pickling cucumbers and pickled products to ensure their quality and consistency, and it is also potentially useful for inspecting other agricultural products. This accomplishment addresses the first component of NP 306 - Quality Characterization, Preservation, and Enhancement, Problem Area 1b - Methods to Evaluate and Predict Quality and Problem Area 1d - Preservation and/or Enhancement of Quality and Marketability. c. Assessing internal quality of apples by spectral scattering technique. Modern packinghouses can sort and grade fruit for color and size; some of them are now even capable of sorting fruit for soluble solids (sugar). However, it is still challenging to sort apples for firmness, not to mention both firmness and soluble solids. A new method/technique was developed for assessing fruit firmness and soluble solids content, which is based on the measurement of light scattering in apples at selected wavelengths or for a spectral region. Mathematical models were proposed and compared for prediction of fruit firmness and soluble solids content. An improved sensing configuration was tested on the prototype developed earlier for real time measurement of light scattering over the visible and near-infrared region, which showed promising results in measuring both firmness and soluble solids content. The spectral scattering technology will provide the industry a new capability for delivering better quality fruit to the marketplace. The method has been used by other researchers for assessing quality of other horticultural and food products. This accomplishment addresses the first component of NP 306 � Quality Characterization, Preservation, and Enhancement, and it addresses Problem 1b � Methods to Evaluate and Predict Quality. Technology Transfer Number of Non-Peer Reviewed Presentations and Proceedings: 8 Number of Newspaper Articles,Presentations for NonScience Audiences: 9
Impacts
(N/A)
Publications
- Kavdir, I., Lu, R., Ariana, D., Ngouajio, M. 2007. Visible and near- infrared spectroscopy for nondestructive quality assessment of pickling cucumbers. Postharvest Biology and Technology. 44(2):165-174.
- Lu, R. 2007. Nondestructive measurement of firmness and soluble solids content for apple fruit using hyperspectral scattering images. Sensing and Instrumentation for Food Quality and Safety. 1(1):19-27.
- Noh, H., Lu, R. 2007. Hyperspectral laser-induced fluorescence imaging for assessing apple quality. Postharvest Biology and Technology. 43(2):193-201.
- Noh, H., Peng, Y., Lu, R. 2007. Integration of Hyperspectral Reflectance and Fluorescence Imaging for Assessing Apple Maturity. Transactions of the ASABE. 50(3):963-971.
- Peng, Y., Lu, R. 2007. Prediction of apple fruit firmness and soluble solids content using characteristics of multispectral scattering images. Journal of Food Engineering. 82(2):142-152.
- Qin, J., Lu, R. 2006. Hyperspectral diffuse reflectance for rapid, noncontact determination of the optical properties of turbid materials. Applied Optics. 45(32):8366-8373.
- Qin, J., Lu, R. 2007. Measurement of the Absorption and Scattering Properties of Turbid Liquid Foods Using Hyperspectral Imaging. Applied Spectroscopy. 61(4):388-396.
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Progress 10/01/05 to 09/30/06
Outputs
Progress Report 1. What major problem or issue is being resolved and how are you resolving it (summarize project aims and objectives)? How serious is the problem? Why does it matter? This CRIS project is aligned with National Program 306, Quality and Utilization of Agricultural Products. Apple maturity determines harvest time and directly affects postharvest handling/storage regimes and thus fruit quality. Maturity measurements include skin and flesh color, fruit firmness, soluble solids content, acid content, starch level, and ethylene production. The maturity determination methods are destructive, time consuming or inefficient, and prone to operational error. After harvest, fruit are sorted and graded based on color and size or weight. However, sorting for color or size cannot guarantee the eating quality of individual fruit. Poor, inconsistent fruit quality continues to be a major concern for the fruit industry. There is a critical need for appropriate sensor
technology to sort and grade fruit based on internal quality attributes such as firmness, soluble solids content, and acid. Reliable and accurate sensor technology would allow the industry to better manage harvest time, implement more appropriate postharvest handling procedures and provide consistent, superior quality fruit to the consumer. Approximately 95% of the tart cherry crop produced in the U.S. is pitted and processed into canned, frozen, and dried fruit, or made into juice. The presence of pits or pit fragments in processed cherry products presents a potential safety hazard to the consumer and makes the industry liable for economic losses. Several pit detection techniques have been researched and developed, but they cannot meet the stringent requirement set by the industry. This has adversely affected the cherry industry in providing value-added processed cherry products to the consumer. Thus, an effective pit detection technique is needed to ensure the end products free of
pits and keep the cherry industry competitive and profitable. Defective and inferior pickling cucumbers/vegetables will lead to poor quality, inconsistent pickled products, and this in turn can cause significant economic losses to growers and processors. Methods currently available for measuring and grading quality of cucumbers and other vegetables are either ineffective or time consuming. New and/or improved technologies are needed to assess, inspect and grade fresh pickling cucumbers rapidly and accurately for internal and external quality characteristics so that raw products can be directed to, or removed from, appropriate processing or marketing avenues. This will minimize postharvest losses of food that has already been produced and ensure high quality, consistent final products and end-user satisfaction. The research at East Lansing, Michigan is focused on developing new and/or improved sensors and sensing technologies for rapid, nondestructive assessment and grading of
postharvest quality and condition of tree fruits (apples, peaches and cherries) and pickling cucumbers. Imaging and spectroscopy, especially hyper-/multi-spectral imaging and near-infrared spectroscopy, will be used in developing new or improved sensors and sensing systems. The research will greatly expand our understanding of the optical properties of fruits and vegetables and lead to new sensing technologies for assessing and grading quality and condition of apples, cucumbers and other horticultural products and for ensuring pit-free processed cherry products. This would help the horticultural industry deliver consistent, superior quality products to the marketplace, increase consumer confidence and satisfaction, and improve industry competitiveness and profitability. 2. List by year the currently approved milestones (indicators of research progress) Milestone 1: Assessing Quality/Maturity of Apples: A. Set up a hyperspectral imaging system for measuring the optical properties of
apples and other horticultural products. Develop algorithms for determining optical properties from hyperspectral scattering images. Measure optical properties of apples. Develop simulation models to study light propagation in apples and factors affecting it. Measure optical properties of other horticultural products. Establish an optical properties database for selected horticultural products. (40 months) B. Assemble a prototype for real time detection of fruit firmness and soluble solids content. Develop an improved algorithm relating light scattering features to fruit firmness and soluble solids content. Develop a computer program and integrate it into the prototype for real time sensing. Test and refine the prototype. Seek partnership for technology transfer. (40 months) C. Set up a hyperspectral reflectance and fluorescence imaging system and a reflectance and UV-induced fluorescence spectroscopic system for measuring apple fruit maturity. Collect image and spectroscopic data
from different apple cultivars. Develop calibration methods for estimating fruit maturity. Refine and test the imaging and spectroscopic systems. Develop a portable device for measuring fruit maturity. Field test and refine the portable device. (60 months) Milestone 2: Detecting Pits in Cherries: Set up a hyperspectral/multispectral transmission imaging system for detecting pits in tart cherries. Develop image processing algorithms for identifying pits from cherry samples. Assemble a prototype for rapid detection of pits in cherries. Develop algorithms for real time pit detection. Integrate computer program into the prototype. Test and evaluate the prototype. (60 months) Milestone 3: Detecting Quality/Defects of Pickling Cucumbers: Test and evaluate a hyperspectral imaging system for detecting defective pickling cucumbers in reflectance and transmission modes. Test and evaluate near-infrared spectroscopy for measuring quality of cucumbers including firmness, skin and flesh color, and
dry matter. Develop algorithms and identify appropriate wavelengths for defects detection. Develop a prototype for detection and segregation of inferior or defective cucumbers. Refine and evaluate the prototype. (60 months) 4a List the single most significant research accomplishment during FY 2006. Hyperspectral imaging for detecting defective pickling cucumbers. This accomplishment is aligned with the first component of NP 306-Quality Characterization, Preservation, and Enhancement, and it addresses Problem 1b-Methods to Evaluate and Predict Quality. Rapid, nondestructive detection of defective pickling cucumbers is critical to ensuring consistent, high quality pickled products. Research was performed in the ARS facilities at Michigan State University, East Lansing, Michigan on developing a nondestructive optical technique to detect surface and internal defects of pickling cucumbers. A hyperspectral imaging system was assembled and tested for acquiring reflectance and transmittance
images from pickling cucumbers. Image processing algorithms were developed for automated classification of defective and normal cucumbers. An overall classification accuracy of 95% was achieved for surface and internal defects (bruises and damaged or hollow carpel) caused by mechanical stress. The research represents the first effort of using hyperspectral imaging in transmittance mode for detecting defects in pickling cucumbers. The technique can be implemented in real time, which would provide the pickle industry a rapid, objective means for automated sorting and grading of pickling cucumbers and products. 4b List other significant research accomplishment(s), if any. Hyperspectral imaging for measuring optical properties of fruits. This accomplishment is aligned with the first component of NP 306 - Quality Characterization, Preservation, and Enhancement, and it addresses Problem 1b - Methods to Evaluate and Predict Quality. Measurement of optical properties can help us better
understand the interaction of light with fruit tissue and design more effective optical systems for sorting and grading horticultural products. Research was performed at the East Lansing, MI location on the development of a novel hyperspectral imaging technique for rapid determination of optical properties of apples and other food products. System calibration procedures and mathematical methods were developed for analyzing hyperspectral images to determine the optical properties of apples and other turbid food products. Compared to other techniques currently available, the hyperspectral imaging technique developed in this research is faster, simpler, and especially suitable for measuring horticultural and food products. This technique offers researchers a new means for studying and measuring the optical properties of a large class of solid and liquid foods and will help instrumentation engineers in designing effective optical systems for quality evaluation of food products.
Multispectral scattering for assessing apple fruit quality. This accomplishment is aligned with the first component of NP 306 - Quality Characterization, Preservation, and Enhancement, and it addresses Problem 1b - Methods to Evaluate and Predict Quality. The ARS East Lansing, MI location made good progress on the development of a novel multispectral scattering technique for assessing apple fruit firmness, which is completely different from conventional nondestructive techniques. Improvements in mathematical analyses and system calibration led to better, more consistent prediction of fruit firmness. A multispectral scattering prototype has been designed and tested for real time detection of apple fruit quality. With further improvements, the prototype will be suitable for sorting and grading of apples for firmness and soluble solids. The research represents an important advancement in nondestructive sensing of fruit quality, and the technology would provide the fruit industry new
capabilities for delivering better quality fresh products to the consumer. An integrated optical technique for measuring apple maturity. This accomplishment is aligned with the first component of NP 306 - Quality Characterization, Preservation, and Enhancement, and it addresses Problem 1b - Methods to Evaluate and Predict Quality. Apple maturity measurements are routinely performed with destructive methods. Reflectance and fluorescence are two nondestructive optical techniques that could be complementary in assessing multiple maturity/quality parameters. Research was performed at the ARS East Lansing, Michigan location to develop an optical technique integrating reflectance and fluorescence for nondestructive measurement of maturity parameters for apples. Based on research from the previous year, an improved technique that enabled fast measurement of both reflectance and fluorescence was developed. Mathematical methods were refined for integrating reflectance and fluorescence data to
predict individual maturity parameters. The integrated technique gave better measurement of maturity parameters over individual sensing modes. This technique could potentially replace current multiple destructive maturity measurement methods and hence would be valuable to researchers and growers in determining optimal harvest time and implementing appropriate postharvest handling, storage, and marketing strategies. 4d Progress report. Multispectral transmittance data collected from tart cherries with or without pits were analyzed. Preliminary experiments were performed to explore the potential of fluorescence technique for differentiating cherries with or without pits. Preliminary analyses showed that cherry flesh either did not fluoresce or it had a very low level of fluorescence under UV or blue light illumination. Cherry pits, on the other hand, showed much higher fluorescence. Hence, fluorescence could be potentially useful for differentiating cherry flesh from pits; however, the
challenge lies in improving light penetration into cherry tissue. Our studies on hyper- and multi-spectral imaging and fluorescence techniques provide information needed for the further development of an effective technique for rapid detection of pits or pit fragments from cherries. 5. Describe the major accomplishments to date and their predicted or actual impact. The accomplishments described below are aligned with the first component of NP 306 - Quality Characterization, Preservation, and Enhancement, and it addresses Problem 1b Methods to Evaluate and Predict Quality.- Hyperspectral imaging for measuring optical properties of fruit. Measurement of the optical properties of fruit can help us better understand the interaction of light with fruit tissue and design more effective optical systems for quality evaluation. A hyperspectral imaging technique coupled with a theoretical model was developed for rapid determination of the optical properties of turbid foods. This new technique
is faster, simpler and easier to use for measuring turbid food and agricultural products, compared to other techniques currently available. Optical property data were collected from intact apples, selected fruit and vegetable juices and milk, and applications of the data for quality evaluation of fruit and food products were demonstrated. These data will help researchers and instrumentation engineers in designing and constructing more effective optical systems for quality inspection of agricultural and food products. The technique developed in this research provides researchers an effective means for measuring optical properties of turbid food and agricultural products. Multispectral scattering for assessing apple fruit quality. Firmness and sugar are important quality attributes for apples and many other fruits. Technology that can sort and grade fruit for these quality attributes is critical for ensuring consistent, high quality fruit delivered to the marketplace. A novel concept
was proposed in this research for measuring light scattering at multiple wavelengths as a means to assess fruit firmness and soluble solids. Spectral scattering images of apples were captured with a multispectral imaging system, and a generalized mathematical model was proposed for describing spectral scattering features from apple fruit. Optimal wavelengths for predicting fruit firmness were identified. System calibration method and procedures were established and the fruit size effect on scattering measurements was quantified by a mathematical equation. The improvements in data analysis and hardware design enhanced performance of the multispectral scattering imaging system for prediction of apple fruit firmness and soluble solids. A prototype has been assembled and tested and several equipment manufacturers have expressed interest in the technology. This technology will provide the fruit industry new capabilities for delivering better quality fresh products to the marketplace.
Bioyield tester for measuring apple fruit firmness. A compact, portable fruit firmness tester has been built and tested for measuring apple fruit firmness nondestructively. The tester measures the bioyield point of apple fruit as an indication of fruit firmness. The portable device records fruit firmness automatically through an on-board chip or via computer. The tester correlates well with the standard destructive firmness tester, and it does not degrade the fruit. The tester is useful for field measurement of apple fruit firmness and is also suitable for laboratory and packinghouse uses for measuring and/or monitoring fruit firmness during postharvest handling and storage. The device is being used by other researchers to test and monitor apple firmness during and after harvest. Integrated optical technique for assessing apple maturity. Fruit maturity determines harvest time and directly affects postharvest storage regimes and thus fruit quality. Currently, maturity measurements are
largely destructive; they include skin and flesh color, fruit firmness, soluble solids content, starch level, acid content, and ethylene production. A technique integrating reflectance and fluorescence was developed for measuring multiple maturity parameters; it allows for rapid acquisition of both reflectance and fluorescence spectra from apples. Results showed that the integrated technique improved maturity predictions over individual sensing modes of reflectance and fluorescence. The technique looks promising for nondestructive measurement of multiple maturity parameters for apples, which would enable researchers and growers to better determine harvest time and implement more appropriate postharvest handling, storage and marketing strategies. Optical techniques for detecting pits in tart cherries. The majority of tart cherries produced in US are pitted and processed, and the lack of an effective pit detecting technology has been a major factor limiting the tart cherry industry in
increasing its production capacity and profitability. Hyper- and multi-spectral transmittance imaging coupled with artificial neural networks was applied to detect pits in tart cherries. An overall pit detection accuracy of 97% was achieved. Preliminary experiments were also performed on using fluorescence spectroscopy to detect cherry pits. Cherry flesh and pits are different in their fluorescence properties; pits have considerably higher fluorescence than cherry flesh, which could be used for detecting pits or pit fragments in cherries. However, poor light penetration presents a major obstacle for detecting pits inside the cherry fruit. The research provides a basis for the further development of an effective pit detection system. The successful development of such technology would mean improved production capacity and profitability for the U.S. tart cherry industry. Hyperspectral imaging for detecting defective pickling cucumbers. Currently, fresh pickling cucumbers are sorted and
graded for size and shape before being further processed. Technology that enables to detect both surface and internal defects is needed to ensure consistent pickle product quality. Research was performed on using hyperspectral imaging in reflectance and transmittance mode for detecting defective cucumbers (bruises and damaged or defective carpel) resulting from mechanical stress. Image processing algorithms were developed for automated classification of defective and normal cucumbers. Superior results were obtained for detecting bruises and damaged carpel; an overall detection accuracy of 95% was achieved. A prototype hyperspectral imaging system was assembled and tested for real time detection of defective pickling cucumbers. This ongoing research will lead to the development of a practical technology for sorting and grading pickling cucumbers and pickled products. The technology can help the pickle industry in automated inspection and grading of individual pickling cucumbers to
assure better quality final pickle products. 6. What science and/or technologies have been transferred and to whom? When is the science and/or technology likely to become available to the end- user (industry, farmer, other scientists)? What are the constraints, if known, to the adoption and durability of the technology products? Research findings and activities were presented at industry conferences and workshops. The hyperspectral imaging technique for measuring optical properties of fruit has been successfully developed and published, enabling researchers to use the technique for studying the optical properties of food and agricultural products. Our research on spectral scattering for firmness measurement has been published in a series of scientific papers, and it has stimulated considerable interests in US and abroad. Several research institutes and a private company are applying the method/technique or conducting similar research for other agricultural and food products. The
bioyield firmness tester is being used by researchers to test and monitor apple fruit during and after harvest. 7. List your most important publications in the popular press and presentations to organizations and articles written about your work. (NOTE: List your peer reviewed publications below). Lu, R., Beaudry, R.M. 2005. Hyperspectral reflectance and fluorescence scattering for assessing apple fruit maturity. Washington Tree Fruit Research Commission - 2005 Apple Postharvest Research Review. p. 186-191. Ariana, D.P., Lu, R. 2006. Detection of bruises on pickling cucumbers using near-infrared hyperspectral imaging. 2006 Pickling Cucumber Research Report Session, Michigan State University, p. 44-49. Lu, R., Noh, H. 2006. Assessing the maturity of apples by integrating hyperspectral reflectance and fluorescence imaging techniques. 2006 Michigan State University Controlled Atmosphere Clinic, Michigan State University Extension, 5(2): 209-217. "Near-infrared hyperspectral imaging for
detection of mechanical injury on pickling cucumbers." 2005 Pickle Packers International (PPI) Annual Meeting, Chicago, IL, October 2005. "Michigan update: current activities, concepts, and integrated engineering capabilities." Washington Tree Fruit Research Commission workshop "Role of technology on high-value perennial fruiting system." Prosser, WA, November 2005. "Real-time detection of apple fruit firmness by a laser-based multispectral imaging system." 2005 Washington State Horticultural Society Annual Meeting, Wenatchee, WA, December 2005. "A low-cost, portable bioyield tester for nondestructive measurement of apple fruit firmness." 2005 Washington State Horticultural Society Annual Meeting, Wenatchee, WA, December 2005.
Impacts
(N/A)
Publications
- Qin, J., Lu, R. 2005. Hyperspectral diffuse reflectance for determination of the optical properties of milk and fruit and vegetable juice. Proceedings of SPIE. 5996:59960Q.
- Ariana, D.P., Lu, R. 2006. Visible/near-infrared hyperspectral transmittance imaging for detection of internal mechanical injury in pickling cucumbers. ASABE Annual International Meeting. Paper No. 063039.
- Guyer, D., Ariana, D., Shrestha, B., Lu, R. 2006. Opto-electric determination of insect presence in fruit. ASABE Annual International Meeting. Paper No. 066061.
- Lu, R., Qin, J., Peng, Y. 2006. Measurement of the optical properties of apples by hyperspectral imaging for assessing fruit quality. ASABE Annual International Meeting. Paper No. 066179.
- Peng, Y., Lu, R. 2006. New approaches of analyzing multispectral scattering profiles for predicting apple fruit firmness and soluble solids content. ASABE Annual International Meeting. Paper No. 066234.
- Qin, J., Lu, R. 2006. Measurement of the optical properties of apples using hyperspectral diffuse reflectance imaging. ASABE Annual International Meeting. Paper No. 063037.
- Noh, H., Peng, Y., Lu, R. 2006. Integration of hyperspectral reflectance and laser-induced fluorescence imaging for assessing apple maturity. ASABE Annual International Meeting. Paper No. 066182.
- Lu, R., Peng, Y. 2006. Hyperspectral scattering for assessing peach fruit firmness. Biosystems Engineering. 93(2):161-171.
- Peng, Y., Lu, R. 2006. Improving apple fruit firmness predictions by effective correction of multispectral scattering images. Postharvest Biology and Technology. 41(3):266-274.
- Qin, J., Lu, R. 2005. Detection of pits in tart cherries by hyperspectral transmission imaging. Transactions of the ASAE. Volume 48(5):1963-1970.
- Lu, R., Srivastava, A., Beaudry, R. 2005. A new bioyield tester for measuring apple fruit firmness. Applied Engineering in Agriculture. 21(5) :893-900.
- Peng, Y., Lu, R. 2006. A LCTF based multispectral imaging system for estimation of apple fruit firmness: Part I: acquisition and characterization of scattering images. Transactions of the ASABE. 49(1) :259-267.
- Peng, Y., Lu, R. 2006. A LCTF based multispectral imaging system for estimation of apple fruit firmness: Part II: selection of optimal wavelengths and development of prediction models. Transactions of the ASABE. 49(1):269-275.
- Lu, R., Srivastava, A.J., Ababneh, H.A. 2006. Finite element analysis and experimental evaluation of bioyield probes for measuring apple fruit firmness. Transactions of the ASABE. 49(1):123-131.
- Ariana, D., Lu, R., Guyer, D.E. 2006. Hyperspectral reflectance imaging for detection of bruises on pickling cucumbers. Computers and Electronics in Agriculture. 53(1):60-70.
- Ariana, D., Lu, R., Guyer, D. 2005. Detection of mechanical injury on pickling cucumbers using near-infrared hyperspectral imaging. Proceedings of SPIE. 5996:59960P.
- Lu, R., Peng, Y. 2005. A laser-based multispectral imaging system for real- time detection of apple fruit firmness. Proceedings of SPIE. 5996:59960F.
- Noh, H., Lu, R. 2005. UV/blue light-induced fluorescence for assessing apple maturity. Proceedings of SPIE. 5996:59960I.
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Progress 10/01/04 to 09/30/05
Outputs
1. What major problem or issue is being resolved and how are you resolving it (summarize project aims and objectives)? How serious is the problem? What does it matter? The maturity or ripeness of apples is measured by multiple quality parameters; they include skin and flesh color, fruit firmness, soluble solids content, acid content, starch level, and ethylene production. The maturity determination methods are destructive, time consuming or inefficient, and prone to operational error. After harvest, fruit are sorted and graded based on color and size or weight. However, sorting for color or size cannot guarantee the eating quality of individual fruit. Poor, inconsistent fruit quality continues to be a major concern for the fruit industry. There is a critical need for appropriate sensor technology to grade and sort fruit based on internal quality attributes such as firmness, soluble solids content, and acid. Reliable and accurate sensor technology would allow the
industry to better manage harvest time, implement more appropriate postharvest handling procedures and provide consistent, superior quality fruit to the consumer. Approximately 95% of the tart cherry crop produced in the U.S. is pitted and processed into canned, frozen, and dried fruit, or made into juice. The presence of pits or pit fragments in processed cherry products presents a potential safety hazard to the consumer and makes the industry liable for economic losses. Several pit detection techniques have been researched and developed, but they cannot meet the stringent requirement set by the industry. This has adversely affected the cherry industry in providing value-added processed cherry products to the consumer. An effective pit detection technique is needed to ensure the end products free of pits and keep the cherry industry competitive and profitable. Defective and inferior cucumbers/vegetables will lead to poor quality, inconsistent pickled products and can cause
significant economic losses to growers and processors. Methods currently available for measuring and grading quality of cucumbers and other vegetables are either ineffective or time consuming. New and/or improved technologies are needed to assess, inspect and grade fresh cucumbers rapidly and accurately for various internal and external quality characteristics so that raw products can be directed to, or removed from, appropriate processing or marketing avenues. This will minimize postharvest losses of food that has already been produced and ensure high quality, consistent final products and end-user satisfaction. The research project at East Lansing, Michigan is focused on developing new and/or improved sensors and sensing technologies for rapid, nondestructive assessment and grading of postharvest quality and condition of tree fruits (apples, peaches and cherries) and pickling cucumbers. The research will greatly expand our understanding of the optical properties of fruits and
vegetables and lead to new sensing technologies for assessing and grading quality and condition of apples, cucumbers and other horticultural products and for ensuring pit-free processed cherry products. This would help the horticultural industry deliver consistent, superior quality products to the marketplace, increase consumer confidence and satisfaction, and improve industry competitiveness and profitability. 2. List the milestones (indicators of progress) from your Project Plan. This CRIS project resulted from the merging of two previous CRIS projects "Sensor technologies for assessing and grading quality and condition of tree fruits" and "Postharvest sanitation of cucumbers", which was completed in October 2004. The milestones listed here reflect the changes in this new CRIS project. Milestone 1 - Assessing Quality/Maturity of Apples: * Set up an imaging spectroscopy system for measuring the optical properties of apples and other horticultural products. Develop algorithms for
determining optical properties from hyperspectral scattering images. Measure optical properties of apples. Develop simulation models to study light propagation in apples and factors affecting it. Measure optical properties of other horticultural products. Establish an optical properties database for selected horticultural products. (40 months) * Assemble a prototype for real time detection of fruit firmness and soluble solids content. Develop an improved algorithm relating light scattering features to fruit firmness and soluble solids content. Develop a computer program and integrate it into the prototype for real time sensing. Test and refine the prototype. Seek partnership for technology transfer. (40 months) * Set up a hyperspectral reflectance and laser-induced fluorescence imaging system and a reflectance and UV-induced fluorescence spectroscopic system for measuring apple fruit maturity. Collect image and spectroscopic data from different apple cultivars. Develop calibration
methods for estimating fruit maturity. Refine and test the imaging and spectroscopic systems. Develop a portable device for measuring fruit maturity. Field test and refine the portable device. (60 months) Milestone 2 - Detecting Pits in Cherries: Set up a hyperspectral/multispectral transmission imaging system for detecting pits in tart cherries. Develop image processing algorithms for identifying pits from cherry samples. Assemble a prototype for rapid detection of pits in cherries. Develop algorithms for real time pit detection. Integrate computer program into the prototype. Test and evaluate the prototype. (60 months) Milestone 3 - Detecting Quality/Defects of Pickling Cucumbers: Test and evaluate a hyperspectral imaging system for detecting defects of pickling cucumbers in both reflectance and transmission modes. Test and evaluate near-infrared spectroscopy for measuring quality of cucumbers including firmness, skin and flesh color, and dry matter. Develop algorithms and identify
appropriate wavelengths for defects detection. Develop a prototype for detection and segregation of inferior or defective cucumbers. Refine and evaluate the prototype. (60 months) 3a List the milestones that were scheduled to be addressed in FY 2005. For each milestone, indicate the status: fully met, substantially met, or not met. If not met, why. 1. Assessing Quality/Maturity of Apples Milestone Fully Met 2. Detecting Pits in Cherries Milestone Fully Met 3. Detecting Quality/Defects of Pickling Cucumbers Milestone Fully Met 3b List the milestones that you expect to address over the next 3 years (FY 2006, 2007, and 2008). What do you expect to accomplish, year by year, over the next 3 years under each milestone? FY 2006: Milestone 1: Develop algorithms for accurate determination of both spectral absorption and scattering properties of apples and other horticultural products. Measure the optical properties of apples. Develop and validate simulation models for describing light
propagation in apples. Develop an improved computer program and integrate it into the prototype for real time sensing. Design an improved light source for better measurement of light scattering in apples. Improve mathematical methods for describing light scattering features. Refine and test the prototype for predicting fruit firmness and soluble solids content. Improve the hyperspectral reflectance and laser-induced fluorescence imaging system and the reflectance and UV-induced fluorescence spectroscopic system for faster and more reliable acquisition of reflectance and fluorescence data from apples. Develop improved algorithms of integrating reflectance and fluorescence data for predicting multiple maturity parameters. Milestone 2: Continue research on using the hyperspectral/multispectral imaging system to collect images from cherries with and without pits. Develop improved image processing algorithms for detecting pits in cherries. Milestone 3: Develop algorithms for processing
hyperspectral images for detecting defects of pickling cucumbers resulting from mechanical stress, temperature chilling, physiological disorder or diseases. Analyze near- infrared spectroscopic data for predicting firmness, skin and flesh color, and dry matter of pickling cucumbers. Identify appropriate wavelengths for detecting cucumber defects. Assemble and test a multispectral imaging system for detecting defects of cucumbers. FY 2007: Milestone 1: Develop computer models to study light propagation in apple fruit and the factors affecting it. Measure optical properties of other horticultural products. Establish an optical properties database for selected horticultural products. Refine and test the prototype to improve its speed and performance consistency. Investigate and develop improved calibration procedures. Seek partnership for technology transfer. Improve methods of analyzing reflectance and fluorescence data. Improve the hyperspectral imaging system and the spectroscopic
system for better measurement of fruit maturity. Milestone 2: Assemble a prototype for real time detection of cherry pits. Develop improved algorithms and computer program for integration into the prototype. Test and evaluate the prototype. Milestone 3: Develop computer program for real time detection of inferior and defective cucumbers. Perform system integration to improve the prototype performance and speed. Test and evaluate the prototype for real time detection of defective or inferior pickling cucumbers. FY 2008: Milestone 1: Complete the optical properties database and make it available to the public. Work with a partner to further improve the prototype for sorting and grading apples and other fruits for firmness and soluble solids content. Refine the reflectance and fluorescence systems. Develop a portable maturity measurement device. Develop an improved calibration method for predicting maturity parameters. Milestone 2: Test and evaluate the prototype with cherry samples
from hand harvest and commercial processing plant. Improve hardware and software for better pit detection. Milestone 3: Improve defects detection algorithms. Test and refine the prototype. 4a What was the single most significant accomplishment this past year? Nondestructive sensing of fruit firmness is critical for delivering superior, consistent fruit to the marketplace. Research was performed in the ARS facilities at Michigan State University, East Lansing, Michigan on developing a novel multispectral scattering technique to measure fruit firmness. A multispectral imaging system coupled with a tunable filter was built for measuring light scattering at wavelengths in the visible and near-infrared region. New methods were proposed for improving the scattering signal processing and incorporating the fruit size effect in the mathematical modeling of light scattering. A set of optimal wavelengths was identified for measuring fruit firmness. These improvements lead to significantly better
predictions of fruit firmness by the multispectral imaging system, which can meet the requirement of sorting apple fruit into two or three firmness grades. 4b List other significant accomplishments, if any. A low-cost portable fruit firmness tester was designed and constructed, which measures the bioyield point of apple fruit, a phenomenon that results in the cell failure without the tissue rupture during compression of the fruit by a mechanical probe. The tester uses a specially designed probe to enhance bioyield measurements. The tester correlates well with the standard destructive firmness tester and does not degrade the fruit. It can operate either as a standalone device or via computer for providing automated data recording during the firmness testing. The portable tester is useful for measuring and monitoring the firmness of fruit on the tree in the orchard and it can also be used to inspect or monitor apple fruit firmness during postharvest handling, storage, and retailing. A
hyperspectral reflectance and laser-induced fluorescence imaging system was assembled and tested for measuring both reflectance and fluorescence scattering from apple fruit. Experiments were performed on collecting hyperspectral reflectance and fluorescence images from two apple cultivars. Algorithms were developed on combining both reflectance and fluorescence scattering to predict multiple maturity parameters for apple fruit. Reflectance was found to give better predictions of fruit maturity parameters than fluorescence. Combination of reflectance and fluorescence improved overall predictions of fruit firmness, soluble solids, and acid. A hyperspectral imaging technique in transmission mode was developed for detecting pits in tart cherries. Computer algorithms using artificial neural networks and image processing methods were developed to detect cherries with and without pits. The hyperspectral imaging system achieved an overall classification error of 3.3%, which is better than
other reported pit detection techniques. The technique is potentially useful for detecting pits or pit fragments in processed cherry products. 4d Progress report. Milestone 1: An imaging spectroscopy system was set up for collecting hyperspectral scattering data from simulation turbid samples and fruit juice samples. Mathematical models and computer algorithms were developed and validated for determining the absorption and scattering properties of turbid samples. Results were presented at international conferences. A multispectral imaging prototype was tested for real time measurement of apple fruit firmness. Different sample holding methods and light source settings were evaluated for measuring fruit firmness. Optimal wavelengths were identified by using a compact multispectral imaging system equipped with a tunable filter. Improved mathematical methods were proposed for estimating fruit firmness. Results were published and presented at conferences. Two systems that are able to
measure reflectance and fluorescence from apples were assembled. Reflectance and fluorescence data were collected from two apple cultivars. Preliminary analyses were performed, indicating that the technique of integrating reflectance and fluorescence is promising for measuring multiple quality parameters of apples. Findings were reported at an international conference. Milestone 2: Algorithms for processing hyperspectral transmission images collected from intact and pitted cherries were developed, which achieved high accuracies for differentiating cherries with and without pits. Multispectral image data were also collected from cherries with and without pits. Results were published in a scientific journal. Milestone 3: Hyperspectral reflectance and transmission images were collected from both good and defective cucumbers resulting from mechanical stress and temperature chilling. Near-infrared spectra were collected from pickling cucumbers under different postharvest storage conditions
for estimating firmness and moisture content. 5. Describe the major accomplishments over the life of the project, including their predicted or actual impact. Optical techniques such as machine vision and near-infrared spectroscopy are being increasingly used for quality evaluation of fruits and other agricultural products. But there is dearth of optical properties (absorption and scattering) data in the literature, which are needed for developing effective optical systems for quality inspection of fruits and other agricultural products. An imaging spectroscopy technique along with an appropriate mathematical method was developed for determining the spectral absorption and scattering properties of fruits. The technique gave good estimate of the scattering properties of turbid samples. With further improvements in the mathematical analyses, the technique can measure both scattering and absorption properties of apples and other horticultural products simultaneously. This will provide an
important optical properties database for researchers and instrumentation engineers in designing and constructing more effective optical systems for quality inspection of agricultural and food products. An improved multispectral imaging system for measuring light scattering from apple fruit was developed for predicting fruit firmness. A set of optimal wavelengths were identified for two apple cultivars. Based upon previous progress, new and/improved methods were proposed for better analyzing spectral scattering features from apple fruit. The new data analysis methods significantly improved the multispectral imaging system for accurate prediction of apple fruit firmness. A compact, portable fruit firmness tester has been built and tested for measuring apple fruit firmness. The tester measures the bioyield point of apple fruit as an indication of fruit firmness. The portable device records fruit firmness automatically through an on-board chip or via computer. The tester correlates well
with the standard destructive firmness tester, and it does not degrade the fruit. The tester is useful for field testing of apple fruit firmness and is also suitable for laboratory uses for monitoring fruit firmness during postharvest handling and storage. Fruit maturity is directly related to postharvest quality of apple fruit. Currently, fruit maturity is measured using multiple destructive methods; they include skin and flesh color, fruit firmness, soluble solids content, starch level, acid content, and ethylene production. A hyperspectral imaging technique of combining reflectance and fluorescence was investigated for measuring multiple maturity parameters. Preliminary research showed that the integrated technique improved predictions of fruit firmness, soluble solids content, and acid content. The technique looks promising for measuring multiple maturity parameters for apples. 6. What science and/or technologies have been transferred and to whom? When is the science and/or
technology likely to become available to the end- user (industry, farmer, other scientists)? What are the constraints, if known, to the adoption and durability of the technology products? Research findings and activities were timely conveyed to the industry and stakeholders (including growers, packers, shippers, and equipment manufacturers) through presentations and participations at industry workshops and meetings, and non-technical publications. Important research findings were presented to scientists and engineers at major scientific conferences and published on scientific journals. The novel scattering technique developed from this research has been well received and recognized by researchers and engineers. Research information was also disseminated by providing technical consultation for industry people and academic researchers. 7. List your most important publications in the popular press and presentations to organizations and articles written about your work. (NOTE: List
your peer reviewed publications below). Publications: Lu, R., Peng Y. 2005. Multispectral Scattering Measure Fruit Texture. Laser Focus World. April 2005. Presentations: "Nondestructive measures for fruit quality" at the USDA-MOST (Ministry of Science and Technology), China Workshop on Agricultural Products Processing in Monterey Park, CA. July 2004. "Sensor technology for assessing and grading postharvest quality and condition of pickling cucumbers" at the 2005 Michigan State University Pickling Cucumber Research Reporting Session. January 2005. "Hyperspectral reflectance and fluorescence for assessing the maturity of apples" at the Washington Tree Fruit Research Commission's Annual Research Review Meeting. July 2005. Articles written about CRIS project: Comis, D. 2005. Apple Quality's More Than Skin Deep. USDA/ARS Agricultural Research, May 2005. Christopher, A. 2005. The Apple of an Electronic Eye. Wired News, May 2005. Hewett, J. 2005. Lasers Test the Sweetness of Fruit. Photonics
Ireland, June 2005. Hogan, H. 2005. Tasting Apples Without Taking A Bite. Photonics Spectra, July 2005.
Impacts
(N/A)
Publications
- Peng, Y., Lu, R. 2004. A liquid crystal tunable filter based multispectral imaging system for prediction of apple fruit firmness. Proceedings of SPIE. 5587:91-100.
- Lu, R., Peng, Y. 2005. Assessing peach firmness by multispectral scattering. Near Infrared Spectroscopy Journal. 13(1):27-36.
- Lu, R., Peng, Y. 2005. Comparison of multispectral scattering with visible/NIR spectroscopy for predicting apple fruit firmness. 7th Fruit, Nut, and Vegetable Production Engineering International Symposium. Paper No. 62.
- Peng, Y., Lu, R. 2005. Modeling multispectral scattering profiles for predction of apple fruit firmness. Transactions of the ASAE. Volume 48(1): 235-242.
- Lu, R., Srivastiva, A., Beaudry, R. 2005. A bioyield tester for measuring apple fruit firmness. ASAE Annual International Meeting. Paper No. 056171.
- Noh, H., Lu, R. 2005. Hyperspectral reflectance and fluorescence for assessing apple quality. ASAE Annual International Meeting. Paper No. 053069.
- Peng, Y., Lu, R. 2005. An improved multispectral imaging system for apple fruit firmness prediction. ASAE Annual International Meeting. Paper No. 056172.
- Qin, J., Lu, R. 2005. Determination of the optical properties of turbid materials by hyperspectral diffuse reflectance. ASAE Annual International Meeting Proceedings. Paper No. 053068.
- Qin, J., Lu, R. 2004. Detecting pits in tart cherries by hyperspectral transmission imaging. Proceedings of SPIE. 5587:153-162.
- Lu, R., Bailey, B.B. 2005. NIR measurement of apple fruit soluble solids content and firmness as affected by postharvest storage. ASAE Annual International Meeting. Paper No. 056070.
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