1a. Objectives (from AD-416):
The first objective is to develop and validate multitask in-line real-time inspection technologies for small to large processors that simultaneously detect contaminants and defects of fruits and vegetables. 1a) Evaluate visible/near-infrared reflectance and fluorescence imaging techniques for whole-surface detection of fecal material, damage, defects, and spoilage artifacts on fruits and vegetables. 1b) Identify multispectral wavebands and develop detection algorithms and image segmentation procedures for whole-surface inspection of produce that can be utilized for multitask screening for safety and quality concerns. Integrate and test methods for use in in-line multitask inspection systems. 1c) Develop and evaluate methods to facilitate whole-surface line-scan imaging of fruits and vegetables for in-line inspection. 1d) Develop and evaluate two prototype multitask inspection systems, one for fruits such as apples and tomatoes and a second for leafy green vegetables such as spinach and lettuce. The second objective is to develop and validate portable optical sensing technologies for detecting the presence of chemical and biological contaminants on food products and processing surfaces. 2a) Evaluate fluorescence, reflectance, and Raman spectral and imaging technologies for use in rapid sample analysis to detect fecal contamination, organic residues, bacterial biofilms, and food adulterants. 2b) Develop and validate a portable Raman-based hyperspectral imaging platform that can be used for macro-scale imaging of food samples as large as intact fruits and vegetables. 2c) Develop and validate handheld imaging devices for contamination and sanitation inspection in processing environments. 2d) Develop and validate imaging platform for in-field detection of fecal contamination.
1b. Approach (from AD-416):
The previous project included four patents (pending/issued) for methods and technologies developed: multitask line-scan imaging inspection, macro-scale laser-induced fluorescence imaging, Raman spectral detection of melamine adulteration, and image-based portable handheld sanitation inspection devices. This new project will build upon these previous accomplishments to develop prototype devices for commercialization. Rapid line-scan imaging technologies developed during the previous project cycle will be used to construct prototype whole-surface in-line inspection systems for simultaneously detecting surface contamination and defects using a single camera. This research focuses primarily on fresh fruits and vegetables, such as leafy greens, apples, and tomatoes, and on the detection of defects and of fecal contamination (a recognized source of human pathogens associated with fresh fruits and vegetables). Two prototype whole-surface in-line inspection systems will be developed, one for flat leafy produce such as Romaine lettuce and baby spinach, and a second for round-shaped produce such as apples and tomatoes. These systems will incorporate multitasking capabilities that allow users to select desired inspection criteria, and to optimize wavelengths and thresholds to address changes in produce characteristics on-the-fly. To detect chemical and biological substances of food safety interest, and to address the needs of the fruit and vegetable industries for evaluation or inspection tools for rapid on-site or in situ assessment of food safety risks, portable NIR (1000 to 2200 nm) hyperspectral imaging and Raman hyperspectral macro-scale imaging systems will be developed and validated . These enhanced capabilities will improve the existing toolbox of available imaging technologies for addressing unforeseen biological/chemical contamination problems in a timely manner. To enhance existing survey methods in produce processing plants, a previously developed handheld imaging device for inspecting poultry processing areas will serve as the basis for the development of a similar system for inspecting produce processing surfaces. The handheld inspection devices are intended as assistive tools for human inspectors to use during off-line inspection of processing equipment surfaces. To address the industry-identified need to survey produce fields for fecal contamination, technology to detect feces in produce fields will be developed based on a previously patented laser-induced fluorescence imaging technique. The proposed field imaging platform will assist industry in addressing in-field in situ detection of fecal contamination. As an applied engineering research project, the effective outcome of this work should be commercialization of the technologies developed. Critical to this end is collaboration with industry partners. Thus, this project will continue strategic partnerships with four companies with whom Cooperative Research and Development Agreements (CRADAs) have been established.
3. Progress Report:
Significant progress in the development of multitask in-line inspection technologies for detection of contaminants and defects on fruits and vegetables was made. From hyperspectral imaging and analysis, multispectral imaging algorithms using visible/near-infrared reflectance and fluorescence, respectively, were developed to detect cracks and frass residues--known vectors for pathogenic contamination of fresh produce--on tomatoes. These spectral image-based methods can be implemented into our in-line inspection technologies for rapid inspection on fruit processing lines. In addition, separate methods and prototype device/conveyor systems for whole-surface inspection of round fruits and of relatively flat leafy-greens were developed; currently, no such whole-surface online imaging inspection technologies exist for industry use. The whole-surface imaging methods will allow thorough safety/quality inspection of round fruits and leafy greens on commercial processing lines. Noteworthy progress was also made in improving the imaging platforms’ fluorescence lighting with new high-power violet light-emitting-diodes; these are better suited for processing line applications for fruit and vegetable inspection as compared to conventional ultraviolet fluorescence lighting. With the improved lighting, a multispectral imaging algorithm can detect apples contaminated with animal fecal matter with over 99% success. Significant advances have been made in developing sensing technologies to detect the presence of chemical and biological contaminants on food products and processing surfaces. New macro-scale hyperspectral Raman chemical imaging and near-infrared (1000-1700 nm) hyperspectral imaging platforms were developed, expanding the current suite of portable sensing capabilities into the near-infrared and Raman realms. For sanitation inspection in food processing environments, a handheld hyperspectral imaging system was developed; experimental trials were conducted at two commercial fresh-cut produce processing plants to examine the efficacy of routine sanitation and cleaning procedures and identified a number of problematic steps in procedures. In response, cleaning crews successfully revised procedures at no additional cost in time or materials. To assist industry in addressing in-field detection of fecal contamination, a line-scan hyperspectral imaging system that uses a 355-nm pulsed-laser illumination was developed. This system allows automated acquisition of hyperspectral fluorescence responses including time-dependent fluorescence decay curves. This novel laser-based imaging technology is a precursor for the system slated for in-field use during pre-harvest produce inspection. Rapid Raman methods were developed for simultaneous detection of multiple adulterants in dry milk; potential chemical adulterants including ammonium sulfate, dicyandiamide, melamine, and urea, were mixed together with dry milk powder and evaluated. Raman chemical images allowed identification and visualization of spatial distribution of the multiple adulterant particles in the dry milk.
Kim, M.S., Delwiche, S.R., Chao, K., Lefcourt, A.M., Chan, D.E. 2012. Visible to SWIR hyperspectral imaging for produce safety and quality evaluation. Sensing and Instrumentation for Food Quality and Safety. 5(5):155-164.
Qin, J., Chao, K., Kim, M.S. 2012. Nondestructive evaluation of internal maturity of tomatoes using spatially offset Raman spectroscopy. Postharvest Biology and Technology. 71:21-31.
Chao, K. 2010. Hyperspectral Imaging for Food Quality Analysis and Control. London: Academic Press, Elsevier. p. 241-272.
Yang, C., Kim, M.S., Kang, S., Cho, B., Chao, K., Lefcourt, A.M., Chan, D.E. 2011. Red to far-red multispectral fluorescence image fusion for detection of fecal contamination on apples. Journal of Food Engineering. 108:312-319.