HANDHELD SURFACE-ENHANCED RAMAN SCATTERING (SERS) SENSORS FOR FIELD MONITORING OF CHEMICAL CONTAMINANTS

• Sponsoring Institution: National Institute of Food and Agriculture
• Project Status: COMPLETE
• Funding Source: HATCH
• Reporting Frequency: Annual
• Accession No.: 1021117
• Project Start Date: Oct 25, 2019
• Project End Date: Sep 30, 2024
• Program Code: [(N/A)]- (N/A)

Recipient Organization
OHIO STATE UNIVERSITY
1680 MADISON AVENUE
WOOSTER,OH 44691

Performing Department
Food Science & Technology

Non Technical Summary
Unintentionally chemical contaminants in food, such as environmental, process contaminants and natural toxins, can pose public health concerns if their concentrations are not kept at their appropriately low levels, as the health risks from dietary exposure cannot be fully avoided. Their potential harm to human health is that they may cause chronic diseases from the long-term low-level dietary exposures. Effective surveillance and response systems are required to prevent chemical hazards from entering the food supply and posing harm to the public. Current methods for testing chemical contaminants rely on chromatography combined with mass spectroscopy (GC-MS and LC-MS/MS) offering analytical sensitivity, selectivity, specificity and reliability, but they are time-consuming, expensive, labor-intensive, require complex procedures of sample pretreatment and well-trained technicians to operate the instrumentation, and do not allow field deployment. Thus, it is of paramount importance to develop simpler, quicker, sensitive, and cost-effective methods for detection of chemical contaminants in foods. We propose to develop highly sensitive SERS substrates for label free detection of trace levels of contaminants (mycotoxins and food process contaminants) using a handheld Raman system, enabling analyses to be conducted in the field. SERS active surfaces have been prepared by such methods as electrochemical roughening, metal island films, electron-beam lithography, chemical etching, and the deposition or growth of metal nanostructures. Nanostructures, such as spheres or nanopyramids, support enhancements on the order of 107 by maximizing the enhancements of molecules residing in ''hotspots''. Our proposed sensing device would enable detection chemical food contaminants via unique spectral signature profiles, permitting real-time and field-based capabilities providing immediately evidence of potential hazard rather than having to wait for laboratory test results. Implementation by the industry and regulatory agencies of rapid testing procedures based on Raman technology would help to streamline food safety. The end-product would be a simple and automated system that would allow for "near real-time" analysis of aflatoxin and acrylamide levels to protect the health of consumers and help the industry to effectively implement mitigating approaches.

Animal Health Component

50%

Research Effort Categories

Basic

50%

Applied

50%

Developmental

(N/A)

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
723	2299	2000	100%

Knowledge Area
723 - Hazards to Human Health and Safety;

Subject Of Investigation
2299 - Miscellaneous and new crops, general/other;

Field Of Science
2000 - Chemistry;

Keywords

contaminants

detection

surface enhanced raman spectroscopy (sers)

handheld systems

Goals / Objectives
Evaluate vapor deposition of silver and gold onto a porous anodized aluminum oxide template to produce SERS substrates that would give rise to distinctive and enhanced Raman signals for trace analyte detection.Develop sensing protocols for detection of chemical hazards (aflatoxins, acrylamide) based on their unique Raman fingerprinting in complex food matrices.Technology Transfer interfacing - Implementation of sensors allowing for the routine analysis

Project Methods
The experimental design will include establishing a comprehensive reference spectroscopic profile for materials/ingredients, model design and analysis of samples using Raman spectroscopy. Aflatoxin contamination will be evaluated on peanuts using an in-vitro study by infecting them with Aspergillus flavus strains. We will follow the protocol described by Korani et al. (2017). The peanuts are hydrated to about 20% moisture content. Peanuts are placed in 5 petri plates, containing 10 seeds randomly distributed on the plate. Each plate is considered a replication. We will test different strains of toxigenic A. flavus and also the non-toxigenic strain isolate AF36. Acrylamide will be evaluated on commercially available snack chips (potato and sweet potatoes and other vegetable) will be obtained from local grocery stores in Columbus (OHIO) and from retailers available in the internet.SERS substrates were prepared by thermal evaporation of either silver or gold onto a commercial anodized aluminum oxide filter with 0.1 mm pores as described by Asiala and Schultz (2013). Raman spectra will be collected by using a handheld Metrohm Instant Raman Analysers (Mira) equipped with a 785nm excitation laser. The acrylamide and aflatoxin will be extracted using QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) procedure and analyzed by using uHPLC-MS/MS (Roach et al., 2003).Spectral analysis will be carried out by supervised chemometric methods for sample classification (SIMCA) and quantitative (PLSR) analysis. SIMCA consists of assigning training data sets to classes and then a principal component model is created for each class with different confidence regions. Probability clouds (95% CI) are built around the clusters based on PCA scores, allowing SIMCA to be used as a predictive modeling system. This step also involves determining the lower limits of adulterant concentration that each technology can detect. For each chemical entity, triplicate samples will be prepared at each level of contamination. Quantitative models based on infrared spectra will be generated by Partial Least Squares regression (PLSR) using the spiked levels confirmed by using a reference method. Using published data on fundamental vibrations of specific functional groups and available standards will correlate the Raman spectral signals. Classification and regression models will be used to generate prediction models and the accuracy and ability of these models will be examined with an independent test set representative of the classes modeled with the training set. Blind samples (the researcher will not have access to its identity before prediction) will be included to test the ability of the models to predict the identity and levels of a potential chemical contaminant.Method sensitivity and quantification of target chemical contaminant will be accomplished by spiking target foods with different levels to determine level of detection. An aliquot of the spiked sample will be analyzed by Raman spectroscopy. For each chemical entity, triplicate samples will be prepared at each level of contamination. Quantitative models based on infrared spectra will be generated by Partial Least Squares regression (PLSR) using the spiked levels confirmed by using a reference method. PLSR uses the variance-covariance matrix to reduce the dimensionality of multivariate data sets by determining the principal components that best explain the systematic variation. A cross-validation algorithm is then used to determine the number of principal components that yields the minimum prediction error. Calibration models will be internally validated using full cross-validation (CV) (leave-one-out approach) and externally validated with an independent set. Independent validation study will be conducted using approximately 75 % of the all set to generate calibration models and about 25 % of the all set to serve as independent validation set. The performance of the models will be evaluated in terms of loading vectors, standard error of prediction (SEP), coefficient of determination (R2), and F-value. The SEP is an estimate of the standard error of prediction (magnitude of error expected when independent samples are predicted using the model) and will provide information regarding method sensitivity. The coefficient of determination gives the proportion of variability of the property that is described by the model. The F-value can be viewed as a measure of the signal to noise ratio in the model as it determines whether the property variance is significantly better than the residual property variance.Results from the validation testing set will be used to determine the sensitivity, specificity and positive predictive value of the patterns. Sensitivity is defined as the true-positive test results (samples above the maximum residue level (MRL) expressed as a percentage of all tested samples. Specificity describes the true-negative results (samples that test negative for contamination) expressed as a percentage of all tested authentic samples.Deployability of protocols/models: We will consider existing technologies such as newly developed portable optical systems for chemical identification which bring the analytical precision of spectroscopy to field applications with spectral resolution equivalent to bench-top instruments. We will also find ways to improve or expand upon existing technologies (front-end sampling, purpose-built sensor systems and detection algorithms, spectral libraries) using our expertise to make the technologies more applicable.

Progress 10/25/19 to 09/30/20

Outputs
Target Audience:The results from our Hatch project have been disseminated internationally and nationally through events that target scientific and industrial audiences interested in rapid alternatives for monitoring quality parameters and detection of food contaminants. These events provided a perfect setting for exchanging ideas with experts in the field, discuss current work, receive feedback from potential end-users, develop professional and industrial partnerships and disseminate knowledge through multidisciplinary collaborations. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Students are developing skills in spectral collection, chromatographic analysis and data analysis. Our Lab has trained 5 OSU Food Science undergraduate students, Hannah Smith, Yingcen Xie, JIngwen Huang, Thania Ortiz and Kelly Pan. The students are working on authentication, quality monitoring and food safety. Kelly Pan was finalist in the undergraduate research competition at the 2020 SHIFT Conference with her work on "Untargeted Approach for In-Situ Screening of Pesticide Residues in Cacao Beans Theobroma cacao L. By Portable Infrared Spectroscopy". We continue key collaborations with Thermo Nicolet, Rigaku, Si-Ware, Hamamatsu, Wasatch and Agilent to evaluate the applications of state-of-the-art portable handheld/portable spectrometers for real-time sensing of food. Graduate students have presented at: Handheld Raman for Rapid Screening for Oil Type Used in Potato Chip Manufacturing. SIYU YOA and Luis E. Rodriguez-Saona. Food Chemistry Poster Session. 2020 SHIFT Annual Meeting and Food Expo Rapid Analysis of Agrochemical Contaminants in Cocoa (Theobroma Cacao L.) by Vibrational Spectroscopy. PATRICIA GLORIO PAULET, Eudes Lopez Villanueva, Gregory Sigurdson, M. Monica Giusti and Luis E. Rodriguez. Food Chemistry Poster Session. 2020 SHIFT Annual Meeting and Food Expo. Real-Time Screening of Acrylamide of Potato Products (Chips and Fries) Using Handheld Near Infrared Spectroscopy Devices. ALEJANDRA URTUBIA and Luis E. Rodriguez-Saona. Toxicology & Safety Evaluation Poster Session. 2020 SHIFT Annual Meeting and Food Expo. In-Field Phenotyping of Soybeans with High Oleic Traits using a Handheld NIR Sensor. KUANRONG ZHOU and Luis E. Rodriguez-Saona. Quality Assurance Poster Session. 2020 SHIFT Annual Meeting and Food Expo. Field Detection of Adulteration of Fresh Cheese in Market Samples by Handheld Infrared Devices. Fanny E. Ludena-Urquizo, Beatriz A. Hatta-Sakoda, Walter F. Salas Valerio, Luis E. Rodriguez-Saona. International Poster Session. 2020 SHIFT Annual Meeting and Food Expo. How have the results been disseminated to communities of interest?The results have been disseminated at the IFT, Pittcon and SCIX events that target scientific and industrial audiences interested in vibrational spectroscopy and food applications of the technology to monitor quality parameters and detect economic adulteration. In addition, I have been invited to present our technology to various companies that have interest in deploying these handheld/portable devices for in-field data collection. What do you plan to do during the next reporting period to accomplish the goals?We will continue our efforts of developing real-time detection methods for acrylamide, mycotoxins and pesticides in high-risk food products. We will evaluate the capabilities of handheld Raman technology for detection of contaminants using SERS substrates for enhancing the Raman features and minimize fluorescence interference.

Impacts
What was accomplished under these goals? We have established collaboration with Dr. Zachary Schultz, Associate Professor at the Department of chemistry and Biochemistry - The Ohio State University. Dr. Schultz's research focuses on developing new tools for identifying molecules relevant to biomedical diagnostics and other applications and has vast expertise in Raman spectroscopy and techniques such as surface enhanced Raman spectroscopy and tip-enhanced Raman scattering (TERS). We are collaborating in signal enhancements incorporated into our measurements of food contaminants such as acrylamide and mycotoxins. We are working in understanding nanomaterials with plasmonic properties interacting with light to alter the response from nearby contaminant molecules. We have acquired several compact (Wasatch Photonics WP 1064) and handheld (Progeny 1064nm by Rigaku Corporation and Mira DS by Metrohm) Raman devices for early detection and characterization of potential microbial and chemical food contaminants via unique spectral signature profiles, permitting real-time and field-based analysis to enhance safety and brand equity. This year we have focused our efforts on developing Raman strategies for analysis of high-risk foods for adulteration. We evaluated the Progeny Raman system for the to authentication of potato chip frying oils based on their unique Raman signatures combined with the pattern recognition analysis. Potato chips samples (n=118) were collected from local grocery stores, and the oil was extracted by a hydraulic press and characterized by their fatty acid profile determined by the GC-FAME method. Supervised classification by SIMCA, clustered the samples with a 100% sensitivity based on a validation set and allowed to identify potato chips (n=13) that indicated the use of a single oil in the label but were mislabeled. Our data supported the new generation of portable vibrational spectroscopy devices provided an effective tool for rapid in-situ identification of oil type of potato chips in the market and surveillance of the accurate labeling of the products. Similarly, we evaluated a global set of honey samples for authentication based on their spectra generated by the Progeny 1064nm Raman system, allowing to collect spectral data through clear glass/plastic containers to monitor their contents without perturbing the sample. Our results showed that by using an excitation laser of 1064nm minimized the effect of fluorescence and provided sharp and well-resolved bands for identification of potential tampered samples. We identified the prevalence of adulteration in the market with 23% of store-bought samples labeled as "pure" were tainted with other ingredients. Our results were in accordance with reports from JRC Round Table and American Bee Keeping Association that approximately 14-23% of honeys in the market are adulterated. We are working with Universidad Nacional Agraria la Molina (UNALM) (Lima, Peru) and the industry on developing in-situ Raman methods for monitoring methanol and ethanol in Pisco spirits allowing for rapid (~1 min), non-invasive and in-situ screening of counterfeited brandies (Pisco) by dilution or substituting with cheaper alcohols. We are evaluating the use of handheld Raman and NIR spectrometers for screening acrylamide in frozen French Fries. The food industry is modifying processing operations to limit acrylamide formation as consumer concerns increase as a result of new scientific evidence or federal/state regulations. We are evaluating the feasibility of using Raman techniques (SERS) to establish reliable monitoring program(s) for acrylamide levels. SERS can provide detection and quantification of acrylamide levels through spectral signature profiles enabling for high-throughput measurements for controlling the product stream and addressing risk management.

Publications

• Type: Book Chapters Status: Published Year Published: 2020 Citation: Ayvaz H, Akpolat H, Sezer B, Boyac? I, Rodriguez-Saona, L. 2020. Vibrational Spectroscopy in Food Traceability. In: Cifuentes, A. (Ed.), Comprehensive Foodomics, vol. 3. Elsevier, pp. 322339.
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Rodriguez-Saona LE, Aykas DP, Rodrigues Borba K, Urtubia, A. 2020. Miniaturization of optical sensors and their potential for high-throughput screening of foods. Current Opinion in Food Science, 31:136-150.
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Aykas, DP, Shotts, ML, Rodriguez-Saona, LE. 2020. Authentication of commercial honeys based on Raman fingerprinting and pattern recognition analysis. Food Control, 117, 107346
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Aykas DP, Karaman AD, Keser B, Rodriguez-Saona L. 2020. Non-Targeted Authentication Approach for Extra Virgin Olive Oil. Foods, 9(2): 221
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Akpolat H, Barineau M, Jackson KA, Akpolat MZ, Francis DM, Chen YJ, Rodriguez-Saona LE. 2020. High-Throughput Phenotyping Approach for Screening Major Carotenoids of Tomato by Handheld Raman Spectroscopy Using Chemometric Methods. Sensors (Basel). 20(13):3723.
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Hackshaw KV, Miller JS, Aykas DP, and Rodriguez-Saona L. 2020. Vibrational Spectroscopy for Identification of Metabolites in Biologic Samples. Molecules 25(20):4725
• Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Handheld and Portable Devices for In-Situ Screening of Food Adulteration and Chemical Contaminants. In Economically Motivated Adulteration of Food and Analytical Methodologies to Monitor Food Safety and Quality Session. Pittcon Conference and Expo 2020. Room W179B Session Number: 2-30-3. Tuesday, March 03, 2020: 2:45 PM - 3:20 PM. McCormick Place, Chicago, Illinois, USA
• Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Miniaturization of Optical Sensors and their Potential for High-Throughput Screening of Foods. In Rapid, Spectroscopic and Spectrometric Methods Session. Virtual 2020 AOCS Annual Meeting and Expo. Tuesday, June 30, 2020.
• Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: AAF-OD2 Trends and Emerging Approaches for Authentication of Food Ingredients  The Handheld Spectroscopy Revolution. In Food Forensics. SCIX 2020 The Great Virtual Exchange. October 12-15, 2020.