1a. Objectives (from AD-416):
Objective 1: Advance development and validation of on-line automated whole-surface inspection systems for simultaneous safety and quality inspection of fresh produce in high-throughput commercial processing operations. Objective 2: Develop and validate user-friendly analytical sensing methods and technologies for targeted and non-targeted rapid screening of foods for microbial, chemical, and biological contaminants in laboratory, field, and/or industrial environments. Objective 3: Advance development of portable spectral imaging technologies to allow identification and detection of food contaminants, and develop sampling and inspection protocols for implementation of the developed technologies in industry and regulatory applications. Objective 4: Advance development, test and validate an automated system for detecting contaminants in produce fields, and investigate cost and sensitivity trade-offs of different potential system components and configurations with regard to production of a cost-effective commercial system.
1b. Approach (from AD-416):
Because cross-contamination may occur at many points throughout the production, processing, and distribution chains, our research targets reduction of food safety risks at multiple points, including both upstream (pre-harvest) processing and subsequent stages. The inspection point at single-layer processing is not intended to be a comprehensive inspection on its own but is key for some packaged fresh products. The ARS approach includes both pre- and post-harvest risk reduction measures that collectively can mitigate food safety concerns related to foodborne illnesses. The whole-surface sample presentation/imaging technologies developed in the previous project cycle along with the multitask imaging technology will be integrated on conveyor/processing systems to develop two automated whole-surface inspection platforms to simultaneously inspect produce for safety and quality attributes such as contaminants and defects. The two stand-alone commercial-grade prototype processing-inspection platforms will be transportable to produce processing facilities for testing and demonstration of the whole-surface inspection efficacies, with an ultimate goal of technology transfer. The proposed whole-surface fruit inspection will complement current industry sorting—based on quality attributes such as color and size—by the addition of safety inspection and will be used immediately after conventional color and size sorting. The proposed leafy green inspection will be used for inspection immediately prior to “value-added” processing, e.g., washing for packaged fresh-cut products.
3. Progress Report:
Significant progress has been made for all objectives during the third year of the project. For Objective 1, integrated individual components into assembled prototype for commercial whole-surface inspection platform for round fruits. Designed and developed customized critical components for the prototype using in-house 3-D printer, including optical mounts for upgraded two-view angle optics and housing for illumination sources. Developed an enhanced version of the software interface to generate whole-surface fruit images from multispectral line-scan imaging to allow for real-time visualization of a two-dimensional “map” of the entire surface of a spherically shaped fruit. In addition, interface control software was developed for operation of the prototype round-fruit inspection system. System testing with oranges and apples purchased from retail markets confirmed imaging of entire fruit surface through 360-degree rotation with effective view of polar regions. For the leafy green whole-surface inspection system, an upgraded interface software was developed to allow integrated control of the multitask imaging module and illumination, conveyor speed, and sample ejection mechanism. The software enhancements included incorporation of real-time image processing and multispectral algorithms for fecal contamination detection on the surfaces of leafy green samples. For Objective 2, a U.S. patent (U.S. Patent #9,927,364, March 27, 2018) was issued for the recently developed technology for macro-scale line-scan Raman chemical imaging. This technology can be used to evaluate sample materials faster—by three orders of magnitude—than conventional Raman methods, and is suitable for use in commercial food processing plants for rapid authentication of food ingredients. In addition, a new 1064 nm hyperspectral Raman imaging system was developed and tested for rapid screening of chemical hazards in foods, using toxic dyes (that have been occurred in instances of hazardous food fraud) mixed into turmeric powder and curry powder that were imaged as leveled samples surfaces. The system was demonstrated effective for qualitative single-contaminant detection using hyperspectral Raman images of turmeric samples mixed at different concentrations with metanil yellow. Effective quantitative models for assessing contaminant concentration were developed via imaging of curry powder samples prepared across a range of concentrations for metanil yellow and (separately) Sudan I. Additionally, a detection algorithm for simultaneous multiple-contaminant detection, developed via imaging of curry powder samples mixed with metanil yellow and Sudan I together, was able to identify individual image pixels corresponding to the two different contaminants present. The hyperspectral Raman imaging system was also modified to enable subsurface detection of contaminants, which was demonstrated effectively for detection of metanil yellow mixed into turmeric powder contained within multiple layers of gelatin capsules. A transparent surface-enhanced Raman scattering (SERS) nanoparticle substrate was fabricated with collaborators at the Hefei Institutes of Physical Sciences, China, for use with a 785 nm Raman spectrometer, which was able to detect ractopamine in solution at a very low concentration in preliminary tests and tetracycline residues in milk at 0.01 ppm. The novel gradient temperature Raman spectroscopy (GTRS) system previously developed in-house (U.S. patent # 9,863,882, issued January 09, 2018) was used for multiple lipid investigations. A library of the highest quality Raman spectra currently available for long-chain polyunsaturated fatty acids (LC-PUFA) in fish and shellfish was produced and published, notably filling a previous void in the literature. Commercial fish and krill oil samples were characterized and the resulting spectral contour plots were used to provide a simple, rapid, and highly accurate graphical means to authenticate oils of a given source. This information/method will be used for future work in an ongoing cooperative project to investigate rapid methods for nondestructive determination of freshness, origin, and quality of fish filets, for which a CRADA was established in April 2018 with a safety-sensing development company. The LC-PUFA data also provided significant contributions to further understanding of the biophysics of the brain and retina. The compiled data from this work for LC-PUFA, which are critical to the mammalian brain, retina, and heart and are highly preventive of chronic inflammatory conditions, also provided significant contributions to improved understanding of the biophysics of the brain and retina. With meat and bone meal samples provided by cooperators at the University of Cordoba, Spain, investigations were conducted into fluorescence, reflectance, and Raman techniques as potential rapid methods to determine species origin of meat and bone meal, which are critical ingredients in the animal feed industry. Because the potentially useful Raman vibrational modes observed in meat and bone meal samples arise from the lipid content, GTRS in the 0 to 30°C range (easily attainable in the field) was used to analyze crude lipids extracted from a suite of pork, chicken, and mixed-origin samples. Developed in-house was the 20-minute crude-lipid extraction method for meat and bone meal that requires no toxic/expensive solvents and can be adapted to field use. For Objective 3, research on ARS portable imaging technologies for contamination and sanitation inspection applications was discussed with USDA-Food Safety and Inspection Service (FSIS) and with commercial and U.S. military collaborators. ARS researchers again met with USDA-FSIS Office of Policy and Program Development, Risk, Innovations, and Management Staff, to discuss past field tests of the current ARS portable handheld imaging device, device modifications needed to improve the device to better fit in-plant FSIS inspection needs, and opportunities for additional in-plant testing at food processing facilities. In-plant field testing of the current device was also conducted in cooperation with U.S. Army Natick Soldier Center collaborators at a large-scale commercial baking facility that manufactures products for the military as well as for private sector food and restaurant companies, to investigate the use of the handheld imaging device for inspection of surfaces in conjunction with a novel surface-sampling method developed by the collaborators that may be incorporated into routine procedures. To increase both breadth and specificity of detection targets as well as user-friendliness of the device, plans were established to incorporate hyperspectral imaging and additional image analysis capabilities into the device. A formal discussion of cooperative research and development agreement was initiated with the optics division of an industry-leading advanced materials science company to address the hyperspectral imaging requirements. Development and testing of customized structural improvements to the device’s design, to address ergonomic properties and practicalities of in-plant use, continued via in-house 3D printing of the outer shell. For Objective 4, a laser-induced hyperspectral fluorescence imaging technology critical for developing an autonomous field inspection platform to detect fecal contamination in production fields for fresh produce was developed and tested. However, to address growers’ request for the initial use of a manned vehicle rather than an autonomous vehicle in their fields, an agreement with was initiated with the University of Arizona to develop a manned vehicle for use as an imaging platform for the laser-induced hyperspectral fluorescence imaging system. Because the most significant obstacle for using any vehicle in produce fields immediately prior to harvest is the fact of nearly nonexistent space between rows due to plant growth, the cooperators undertook the primary modification task of reducing wheel and tire width to minimize potential damage to field plants. For the potential future use of an autonomous vehicle, guidance methods and algorithms and controls were studied and specifically examined for use with a robotic field vehicle. To address the relatively high expense of the hyperspectral imaging system components—a gated, intensified camera and pulsed laser for illumination—originally envisioned for use at the start of this project, an alternative approach and conditions were evaluated to enable potential use of a standard monochrome camera with a fixed filter and LED UV light source for detecting fecal contamination in produce fields. Testing of factors including measurement wavelength, degree of shading, and ambient light intensity as a function of time of day, determined that measurements made at dusk or in the evening, using a 520-nm filter, could be used to reliably detect fecal materials with no false positives.
1. Raman sensing technology for chemical hazard detection in foods. ARS scientists in Beltsville, Maryland, have developed a line-scan high-throughput Raman imaging method and apparatus for rapid nondestructive detection of chemical contaminants in food materials. The system can directly and rapidly analyze a full petri dish of sample powder in only ten minutes, compared to conventional instruments that might take hours for the same analysis. The system has imaged a variety of food powders mixed with chemical additives and results indicate that the system can provide quantitative measurement of chemical adulterants. This technology (U.S. patent no: US 9,927,364) provides a useful screening tool to address chemical contamination and adulteration of food products.
2. Raman sensing technique for food ingredient authentication. ARS scientists in Beltsville, Maryland, have recently developed a new Raman imaging system for chemical contaminant detection and food ingredient authentication. Because some food powders such as turmeric and curry powder, and chemical contaminants such as metanil yellow and Sudan-I, emit overwhelming autofluorescence, the Raman light scattering signal of these food powders and chemical contaminants cannot be directly measured by Raman instruments in the visible wavelengths of light. However, the autofluorescence background can be eliminated using 1064 nm (near-infrared) laser excitation. Given the widespread distribution of many powdered ingredients through food processing supply lines nationally and worldwide, use of this near-infrared Raman system will benefit food processors and food safety regulators seeking to ensure safety and quality of ingredients ultimately consumed by the public.
3. Inspection of starch powder for maleic anhydride using line-scan hyperspectral Raman chemical imaging technique. Since starch accounts for a large proportion of the carbohydrates found in staple foods worldwide and is the most common carbohydrate consumed by humans, starch safety and quality is critical for public health. Starch production that uses excessive amounts of maleic anhydride, a chemical additive that can improve chewiness, glutinosity, and water retention of some food products, is potentially harmful for consumers’ health. This study developed a high-throughput Raman chemical imaging method for direct detection of maleic anhydride mixed into corn starch. Chemical images were generated to identify and map the maleic anhydride particles. This Raman-image-based screening method can be used by regulatory agencies and food processors to authenticate starch powder as well as other powdered food materials.
4. Fluorescence sensing technology for inspecting produce fields for animal fecal materials. ARS scientists in Beltsville, Maryland, developed a motorized, imaging platform that uses a hyperspectral imaging system to detect fecal contamination on fresh produce in the field. Field contamination is the primary source of contaminated produce, and current practice uses trained observers walking around fields looking for signs of animal intrusion. Scientists developed and tested a novel method for generating a line illumination source using a high energy pulsed laser. This new imaging platform has the potential to allow the survey of 100% of produce fields, which should increase the efficacy of inspections and greatly reduce the risk of foodborne illness in the population at large.
5. Detection of multiple-species fecal contamination on produce. A large portion of past outbreaks of foodborne illnesses involving E. coli and Salmonella are associated with trends of increased consumption of raw produce as part of healthful lifestyles and the growing popularity of ready-to-eat leafy green products. Because the primary sources of those bacteria are animal feces, this research used feces samples from dairy cattle, pigs, chickens, and sheep to simulate contamination scenarios. Spots of both undiluted and diluted feces were applied to romaine lettuce leaves and hyperspectral fluorescence images of the leaves were acquired. The images were analyzed and algorithms were developed to detect each fecal species as well as all four simultaneously. The results show that fluorescence imaging methods are effective in detecting fecal contamination on green produce surfaces. This research will benefit the fresh produce industry and food safety regulators by providing science-based tools to help ensure the safety of fresh vegetables consumed by the public.
6. Rapid nondestructive assessment of seed and grain bacterial infection. Bacterial infection is an important seed quality factor that can greatly reduce crop yields, especially when infections not only affect the eventual seedlings but also can spread to other nearby seedlings in fields or greenhouses. The infection of grains can also result in food safety problems for humans and livestock, and in quality problems that are of significant economic impact for grain producers and processors. Conventional inspection methods for bacterial infection of seed and grain are time-consuming and labor-intensive. ARS researchers in Beltsville, Maryland, in collaboration with colleagues at the Chungnam National University, South Korea, developed near-infrared spectroscopic methods to accurately discriminate between normal barley grains and grains infected with Fusarium. The methods developed and demonstrated in this research can be used to develop nondestructive seed and grain quality and safety inspection systems.
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Qin, J., Kim, M.S., Chao, K., Gonzalez, M., Cho, B. 2017. Quantitative detection of benzoyl peroxide in wheat flour by line-scan macro-scale Raman chemical imaging. Applied Spectroscopy. doi:10.1177/0003702817706690.
Everard, C.D., Kim, M.S., Siemens, M., Cho, H., Lefcourt, A.M., Odonnel, C. 2018. A multispectral imaging system using solar illumination to distinguish fecal matter on leafy greens and soils. Biosystems Engineering. 171:258-264. https://doi.org/10.1016/j.biosystemseng.2018.05.001.
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Lee, H., Kim, M.S., Qin, J., Park, E., Song, Y., Oh, C., Cho, B. 2017. Raman hyperspectral imaging for detection of watermelon seeds infected with acidovorax avenae subsp. citrulli. Sensors. 17(10):2188. https://doi.org/10.3390/s17102188.
Lee, H., Kim, M.S., Cho, B. 2018. Detection of melamine in milk powder using MCT-based shortwave infrared hyperspectral imaging system. Journal of Food Additives & Contaminants. 35(6):1027-1037. https://doi.org/10.1080/19440049.2018.1469050.
Broadhurst, L., Schmidt, W.F., Kim, M.S., Nguyen, J.K., Qin, J., Chao, K., Bauchan, G.R., Shelton, D.R. 2016. Continuous gradient temperature Raman spectroscopy of oleic and linoleic acids from -100 to 50°C. Journal of Lipids. 51:1289-1302.
Ambrose, A., Kandpal, L., Kim, M.S., Lee, W., Cho, B. 2016. High speed measurement of corn seed viability using hyperspectral imaging. Infrared Physics and Technology. 75:171-179.
Kandpal, L., Lohomi, S., Kim, M.S., Cho, B. 2016. Estimation of germination ability of muskmelon seeds using hyperspectral imaging technique with variable selection and chemometrics. Sensors and Actuators B: Chemical. 229:534-544.
Kusumaningrum, D., Lee, H., Kim, M.S., Cho, B. 2017. Nondestructive technique for determining the viability of soybean (Glycine max) seeds using FT-NIR spectroscopy. Journal of the Science of Food and Agriculture. 98:1734-1742. https://doi.org/10.1002/jsfa.8646.
Lohumi, S., Lee, H., Kim, M.S., Qin, J., Cho, B. 2018. Through-packaging analysis of butter adulteration using line-scan spatially offset Raman spectroscopy technique. Analytical and Bioanalytical Chemistry. https://doi.org/10.1007/s00216-018-1189-1.
Lohumi, S., Lee, H., Kim, M.S., Qin, J., Cho, B. 2018. Raman imaging for detection of adulterants in paprika powder: A comparison of data analysis methods. Applied Sciences. 8:485. https://doi.org/10.3390/app8040485.
Ma, Y., He, H., Wu, J., Wang, C., Chao, K., Huang, Q. 2018. Assessment of polysaccharides from mycelia of genus Ganoderma by mid-infrared and near-infrared spectroscopy. Scientific Reports. 8:10.
Lohumi, S., Lee, H., Kim, M.S., Qin, J., Kandpal, L., Bae, H., Rahman, A., Cho, B. 2018. Calibration and testing of a Raman hyperspectral imaging system to reveal powdered food adulteration. PLoS One. 13(4):e0195253. https://doi.org/10.1371/journal.pone.0195253.
Rahman, A., Kandpal, L.M., Lohomi, S., Kim, M.S., Lee, H., Mo, C., Cho, B. 2017. Nondestructive estimation of moisture content, pH and soluble solid contents in in intact tomatoes using hyperspectral imaging. Applied Sciences. 7(1):109. https://doi:10.3390/app7010109.
Dhakal, S., Chao, K., Huang, Q., Kim, M.S., Schmidt, W.F., Qin, J., Broadhurst, C.L. 2018. A simple surface-enhanced Raman spectroscopic method for on-site screening of tetracycline residue in whole milk. Sensors. 18(2):424.
Chao, K., Dhakal, S., Qin, J., Kim, M.S., Peng, Y. 2018. A 1064 nm dispersive Raman spectral imaging system for food safety and quality evaluation. Applied Sciences. 8(3):431.
Tewey, K., Lefcourt, A.M., Tasch, U., Shilts, P., Kim, M.S. 2018. Hyperspectral, time-resolved, fluorescence imaging system for large sample sizes: Part II. Detection of fecal contamination on spinach. Transactions of the ASABE. 61(2):391-398. https://doi.org/10.13031/trans.11571.
Tewey, K., Lefcourt, A.M., Shilts, P., Tasch, U., Kim, M.S. 2018. Hyperspectral, time-resolved, fluorescence imaging system for large sample sizes: Part I. Development of high energy line illumination source. Transactions of the ASABE. 61(2):381-389. https://doi.org/10.13031/trans.11570.
Delwiche, S.R., Qin, J., Graybosch, R.A., Rausch, S.R., Kim, M.S. 2018. Near-infrared hyperspectral imaging of blends of conventional and waxy hard wheats. Journal of Spectral Imaging. 7(a2):1-13.
Dhakal, S., Chao, K., Schmidt, W.F., Qin, J., Kim, M.S., Huang, Q. 2018. Detection of azo dyes in curry powder using a 1064-nm dispersive hyperspectral Raman imaging system. Applied Sciences. 8(4):564.
Lee, H., Huy, T., Park, E., Bae, H., Baek, I., Kim, M.S., Mo, C., Cho, B. 2017. Machine vision technique for rapid vigor measurement of soybean seed. Journal of Biosystems Engineering. https://doi.org/10.5307/JBE.2017.42.3.227.
Broadhurst, C.L., Schmidt, W.F., Nguyen, J.K., Qin, J., Chao, K., Aubuchon, S.R., Kim, M.S. 2017. Continuous gradient temperature Raman spectroscopy and differential scanning calorimetry of N-3DPA and DHA from -100 to 10°C. Chemistry and Physics of Lipids. 204:94-104.
Lim, J., Mo, C., Oh, K., Kim, G., Yoo, H., Ham, H., Kim, Y., Kim, S., Kim, M.S. 2017. Rapid and nondestructive discrimination of Fusarium asiaticum and Fusarium graminearum in hulled barley (Hordeum vulgare L.) using near-infrared spectroscopy. Journal of Biosystems Engineering. https://doi.org/10.5307/JBE.2017.42.4.301.
Bonadies, S., Smith, N., Niewoehner, N., Lee, A.S., Lefcourt, A.M., Gadsden, A. 2018. Development of PID and fuzzy control strategies for navigation in agricultural environments. Journal of Dynamic Systems, Measurement, and Control. 140(6):061007.
Lefcourt, A.M., Siemans, M. 2017. Interactions of isolation and shading on ability to use fluorescence imaging to detect fecal contaminated spinach. Applied Sciences. 7(10):1041.
Lee, H., Kim, M.S., Lee, W., Cho, B. 2017. Determination of the total volatile basic nitrogen (TVB-N) content in pork meat using hyperspectral fluorescence imaging. Sensors and Actuators B: Chemical. 259:532-539.