Research Project: Sensing Technologies for the Detection and Characterization of Microbial, Chemical, and Biological Contaminants in Foods

Location: Environmental Microbial & Food Safety Laboratory

2021 Annual Report

Objectives
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
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.

Progress Report
This is the final report for project 8042-42000-020-00D, 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
1. 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 .

2. 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.

3. 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.

4. 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.

5. 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 × 10-9 M and 1 × 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.

6. 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.

7. 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.

Review 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.