Location: Environmental Microbial & Food Safety Laboratory
2019 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
Significant progress has been made for all objectives of the project, which fall under National Program 108. For Objective 1, a newly developed image processing algorithm to represent the whole surface of a round fruit was integrated with the prototype inspection system for round fruits. Using the new algorithm, the multispectral line-scan imaging system can generate whole-surface images of round fruits, allowing for real-time visualization of a two-dimensional “map” of the entire surface of a spherically shaped fruit. The system for multispectral line-scan inspection of leafy greens was used to conduct inspection experiments that simultaneously performed fecal contamination inspection and defect detection on salad greens (spinach, romaine lettuce). Cooperative Research and Development Agreement (CRADA) partner submitted an application for licensing the ARS-patented multitask inspection technology (U.S. Patent No. 7,787,111, Simultaneous acquisition of fluorescence and reflectance imaging techniques with a single imaging device for multitask inspection”).
For Objective 2, ARS scientists in Beltsville, Maryland, conducted multiple experiments to develop spectroscopy- or imaging-based methods for nondestructive detection of contaminants or other food safety risks which food industries are seeking to manage or prevent with the use of better tools. Mathematical models using infrared spectroscopy and imaging were developed for detecting and quantifying concentrations of adulterants in samples of turmeric powder—concentrations of Sudan Red G (a toxic dye) and white turmeric (a botanical additive) could be estimated with accuracies over 97% and 92%, respectively, and an image processing method was developed to visualize adulterant pixels present in a sample of turmeric powder covering a surface area of ten square millimeters. A rapid and nondestructive technique was developed using visible/near-infrared transmittance spectroscopy and demonstrated 95% accuracy in identifying unfertilized duck eggs, which, if undetected among fertilized eggs, can present a food safety contamination risk due to breakage during incubation. The chemical structure and spectral fingerprints of fipronil, a commonly used insecticide banned in Europe for use in food production, were analyzed and identified and are now being used to develop a rapid method to detect fipronil-tainted eggs. A Raman spectral imaging method was developed to detect veterinary drug residues in pork and demonstrated detection accuracies of 98%, 99%, and 98% for detecting ofloxacin, chloramphenicol and sulfadimidine residues, respectively, showing feasibility for detecting drug residues in muscle foods.
For Objective 2, ARS scientists in Beltsville, Maryland, developed a transportable dual-band laser Raman spectroscopy and imaging system for automated, in-field or on-site food safety inspection in food processing operations. The system acquires Raman measurements of food/agricultural products using point lasers at two wavelengths (either 785 nm or 1064 nm, selected as needed to address fluorescence interference signals) along with two integrated laser probes and two miniature Raman spectrometers. In addition to taking point-scan images across a sample up to 10 cm x 10 cm in area, the system can also perform flexible and automated Raman measurements for targets in a predetermined arrangement or in a scattered distribution, such as liquids/powders in a well plate or bacteria colonies in a 100-mm petri dish. Automated sample counting, positioning, sampling, and synchronization functions are performed by machine vision and motion control techniques implemented via LabView-based software developed in-house. With a 30 cm × 45 cm footprint, the compact system is suited for in-field and on-site use in food processing operations to rapidly inspect samples of foods and agricultural products for ingredient authentication and contaminant detection.
For Objective 2, ARS researchers in Beltsville, Maryland, initiated a new study with a CRADA partner to develop fish authentication methods and systems based on multimodal hyperspectral imaging techniques, to address deceptive labeling and substitution of fish fillets. Two major fraudulent practices in the seafood industry are the substitution of inexpensive fish for higher-priced species and the substitution of frozen-thawed product for never-frozen fresh product. This continuing study uses three line-scan hyperspectral systems developed in-house by ARS scientists to collect four types of hyperspectral image data from fillet samples—(1) visible and near-infrared reflectance images (VNIR: 400–1000 nm) light, (2) fluorescence images obtained using 365-nm ultraviolet light, (3) short-wave infrared reflectance images (SWIR: 1000-2500 nm) light , and (4) Raman chemical images obtained with 785-nm line-laser excitation—and also used DNA-testing to verify the species of each sample. The continuing work includes analysis of combinations of feature extraction and selection techniques and exhaustive data search, optimization, and fusion to determine the most important features needed to perform fish authentication using the different imaging modes. This process will help identify the image mode (or combination of modes) that will have the highest impact and classification accuracies, and which can be used to design and build future customized systems for industrial fish inspection applications. The CRADA partner also submitted applications for licensing the ARS-patented Raman line-scan imaging technology (U.S. Patent No. 9,927,364, “Line-scan Raman imaging method and system for sample evaluation”) and handheld multispectral inspection imaging device (U.S. Patent No. 8,310,544, “Hand-held inspection tool and method”).
For Objective 2, ARS scientists in Beltsville, Maryland, established a new interagency agreement with NASA Kennedy Space Center (KSC) to develop hyperspectral imaging systems to monitor plant growth/health and food safety in fresh food production systems to be used in spaceflight. In the first step of this collaborative research, ARS scientists designed and developed a new hyperspectral imaging system prototype suitable for leafy-green inspection in KSC growth chambers, utilizing a miniature line-scan hyperspectral camera, line-lights to provide broadband Vis/NIR and UV light for reflectance and fluorescence measurements, and a linear translation stage that moves the camera and lights above the growing plants to conduct line-scan hyperspectral imaging from overhead. LabView-based control software was developed in-house. The next step in this research will be to integrate the prototype into a KSC growth chamber for ground testing and verification under lab conditions, and then to acquire hyperspectral reflectance and fluorescence images for pick-and-eat salad crops grown by KSC scientists to evaluate system performance and develop real-time image processing algorithms.
For Objective 3, ARS scientists in Beltsville, Maryland, are working with multiple collaborators regarding application-specific testing and development of ARS portable handheld multispectral imaging technology. ARS and KSC scientists have begun testing ARS portable handheld multispectral imaging devices for sanitation and contamination inspection in plant growth chambers and in astronaut working/living spaces—of specific interest is the evaluation of the imagers for use in monitoring water quality and equipment sanitation for the plant growth chambers, such as for the detection of bacterial biofilms, in addition to contamination/sanitation inspection in other areas of spacecraft. ARS scientists have continued discussions with USDA FSIS and U.S. Army Natick Soldier Center regarding improvements and advancements needed for effective use in sanitation inspection in commercial or military-contracted food processing facilities, and have also discussed potential cooperation with NASA for developing real-time image processing capabilities to be implement via smartphone app to enable user-friendly non-expert operation. ARS is also working with a new CRADA partner to develop a handheld hyperspectral imaging device using the partner’s proprietary hyperspectral imaging hardware, and to develop real-time image processing capabilities to maximize the options for user-friendly data analysis that would be enabled with hyperspectral data.
For Objective 4, ARS scientists in Beltsville, Maryland, demonstrated the feasibility of laser-induced fluorescence (LIF) imaging for field monitoring to detect animal intrusion and fecal contamination with a ground-based prototype motorized imaging system. Although the SY leading the development of the ground-based LIF prototype retired, the remaining project SYs have continued the work with steps to begin moving the imaging techniques from ground-based implementation to drone-based implementation that will allow for higher speed imaging across larger field areas to be monitored and potentially enable joint operation with drone-based monitoring of irrigation water quality.
Accomplishments
1. Rapid detection and quantification of adulterants in commercial turmeric powder. Yellow turmeric powder is popular worldwide as a dietary supplement, and consequently, incidence of turmeric adulteration by chemical dyes and botanical additives have increased. ARS scientists in Beltsville, Maryland, investigated a light absorption sensing method to identify and quantify chemical contaminants and botanical additives in commercial yellow turmeric powder. The results show that the method can quantify Sudan Red and white turmeric adulteration in yellow turmeric powder with high accuracy. Given the widespread distribution of many powdered ingredients through food processing supply lines nationally and worldwide, this method will benefit food processors and food safety regulators seeking to ensure safety and quality of food powders.
2. Light scattering imaging technique to detect mixed veterinary drug residues in pork. Current methods to detect veterinary drug residues in meats are time-consuming, labor-intensive, sample-destructive, and require pre-treatment procedures. A line-scan light-scattering imaging system was developed and used for the first time for nondestructive quantitative analysis of ofloxacin, chloramphenicol, and sulfadimidine residues in pork. These drugs are commonly used to treat bacterial infection. Light-scattering images of pork containing mixtures of the three drugs were acquired and analyzed. The results indicate that the imaging technique can precisely identify and quantify the drug residues in pork. This approach can serve as a potential method for nondestructive real-time inspection of muscle foods for food safety issues.
3. New method of Raman imaging for improved sensitivity for powdered food analysis. Raman imaging has been shown to be a powerful analytical technique for the characterization and visualization of chemical components in a range of products, particularly in the food and pharmaceutical industries. The conventional backscattering Raman imaging technique for the spatial analysis of a sample of significant thickness or including subsurface variations can suffer from the presence of intense fluorescence and Raman signals from the surface layer that can mask weaker subsurface signals. ARS scientists in Beltsville, Maryland, in collaboration with cooperators from Chungnam National University, demonstrated the application of a new reflection-amplifying method using a background mirror in a sample holder to increase the signals that can be detected from subsurface layers. Results showed that when bilayer samples placed on a mirror are scanned, the average signal for the subsurface layer material increases two-fold. The method was then applied successfully to noninvasively detect the presence of small polystyrene pieces buried under a 2-mm thick layer of food, which would have been undetectable via conventional backscattering Raman imaging. This method can potentially be used to noninvasively evaluate materials of non-uniform constituent compositions not visible at the surface.
Review Publications
Mo, C., Lim, J., Kwon, S., Lim, D., Kim, M.S., Kim, G., Kang, H., Kwon, K., Cho, B. 2018. Hyperspectral imaging and partial least square discriminant analysis for geographical origin discrimination of white rice. Journal of Biosystems Engineering. https://doi.org/10.5307/JBE.2017.42.4.293.
Hong, J., Qin, J., Van Kessel, J.S., Oh, M., Dhakal, S., Lee, H., Kim, D., Kim, M.S., Cho, H. 2018. Evaluation of SERS nanoparticles for detection of Bacillus cereus and Bacillus thuringiensis. Biosystems Engineering. 43:394-400. https://doi.org/10.5307/JBE.2018.43.4.394.
Lohumi, S., Lee, H., Kim, M.S., Qin, J., Cho, B. 2019. Raman hyperspectral imaging and spectral similarity analysis for quantitative detection of multiple adulterants in wheat flour. Biosystems Engineering. 181:103-113.
Lim, J., Kim, G., Mo, C., Oh, K., Kim, G., Ham, H., Kim, S., Kim, M.S. 2018. Application of near infrared reflectance spectroscopy for rapid and non-destructive discrimination of hulled barley, naked barley, and wheat contaminated with Fusarium. Sensors. 18(1):113. https://doi.org/10.3390/s18010113.
Qin, J., Kim, M.S., Chao, K., Dhakal, S., Cho, B., Lohumi, S., Mo, C., Peng, Y., Huang, M. 2019. Advances in Raman spectroscopy and imaging techniques for quality and safety inspection of horticultural products. Postharvest Biology and Technology. 149:101-117. https://doi.org/10.1016/j.postharvbio.2018.11.004.
Liu, Z., Huang, M., Zhu, Q., Qin, J., Kim, M.S. 2019. Packaged food detection method based on the generalized Gaussian model for line-scan Raman scattering images. Journal of Food Engineering. 258:9-17.
Dhakal, S., Schmidt, W.F., Kim, M.S., Tang, X., Peng, Y., Chao, K. 2019. Detection of additives and chemical contaminants in turmeric powder using FT-IR spectroscopy. Foods. 8(5):143.
Li, Y., Peng, Y., Qin, J., Chao, K. 2019. A correction method of mixed pesticide content prediction in apple by using Raman spectra. Applied Sciences. 9(8):1699.
Wang, W., Zhai, C., Peng, Y., Chao, K. 2019. A nondestructive detection method for mixed veterinary drugs in pork using line-scan Raman chemical imaging technology. Journal of Food Analytical Methods. 12(3):658-667.
Dong, J., Dong, X., Li, Y., Peng, Y., Chao, K., Gao, C., Tang, X. 2019. Identification of unfertilized duck eggs before hatching using visible/near infrared transmittance spectroscopy. Computers and Electronics in Agriculture. 157:471-478.
Qin, J., Kim, M.S., Chao, K., Bellato, L., Schmidt, W.F., Cho, B., Huang, M. 2018. Inspection of maleic anhydride in starch powder using line-scan hyperspectral Raman chemical imaging technique. International Journal of Agricultural and Biological Engineering. 11(6):120–125. https://doi.org/10.25165/j.ijabe.20181106.4339.
Lim, J., Kim, G., Mo, C., Oh, K., Yoo, H., Ham, H., Kim, M.S. 2017. Classification of Fusarium-infected Korean husked barley using near-infrared reflectance spectroscopy and partial least squares discriminant analysis. Sensors. 17(10):2258. https://doi.org/10.3390/s17102258.
Baek, I., Kusumaningrum, D., Kandpal, L., Lohumi, S., Mo, C., Kim, M.S., Cho, B. 2019. Rapid measurement of soybean seed viability using kernel-based multispectral imaging analysis. Sensors. 19(2):271. https://doi.org/10.3390/s19020271.
Baek, I., Kim, M.S., Cho, B., Mo, C., Barnaby, J.Y., McClung, A.M., Oh, M. 2019. Selection of optimal hyperspectral wavebands for detection of discolored, diseased rice seeds. Applied Sciences. 9:1027. https://doi.org/10.3390/app9051027.
Lohumi, S., Kim, M.S., Qin, J., Cho, B. 2019. Improving sensitivity in Raman imaging for thin layered and powdered food analysis utilizing a reflection mirror. Sensors. 19(12):2698. https://doi.org/10.3390/s19122698.
