Hyperspectral Determination of Fluorescence Wavebands for Multispectral Imaging Detection of Multiple Animal Fecal Species Contaminations on Romaine Lettuce

Joint Diagnostic Method of Tumor Tissue Based on Hyperspectral Spectral-Spatial Transfer Features

In order to improve the clinical application of hyperspectral technology in the pathological diagnosis of tumor tissue, a joint diagnostic method based on spectral-spatial transfer features was established by simulating the actual clinical diagnosis process and combining micro-hyperspectral imaging with large-scale pathological data. In view of the limited sample volume of medical hyperspectral data, a multi-data transfer model pre-trained on conventional pathology datasets was applied to the classification task of micro-hyperspectral images, to explore the differences in spectral-spatial transfer features in the wavelength of 410-900 nm between tumor tissues and normal tissues. The experimental results show that the spectral-spatial transfer convolutional neural network (SST-CNN) achieved a classification accuracy of 95.46% for the gastric cancer dataset and 95.89% for the thyroid cancer dataset, thus outperforming models trained on single conventional digital pathology and single hyperspectral data. The joint diagnostic method established based on SST-CNN can complete the interpretation of a section of data in 3 min, thus providing a new technical solution for the rapid diagnosis of pathology. This study also explored problems involving the correlation between tumor tissues and typical spectral-spatial features, as well as the efficient transformation of conventional pathological and transfer spectral-spatial features, which solidified the theoretical research on hyperspectral pathological diagnosis.

Nondestructive classification of soft rot disease in napa cabbage using hyperspectral imaging analysis

Identification of soft rot disease in napa cabbage, an essential ingredient of kimchi, is challenging at the industrial scale. Therefore, nondestructive imaging techniques are necessary. Here, we investigated the potential of hyperspectral imaging (HSI) processing in the near-infrared region (900–1700 nm) for classifying napa cabbage quality using nondestructive measurements. We determined the microbiological and physicochemical qualitative properties of napa cabbage for intercomparison of HSI information, extracted HSI characteristics from hyperspectral images to predict and classify freshness, and established a novel approach for classifying healthy and rotten napa cabbage. The second derivative Savitzky–Golay method for data preprocessing was implemented, followed by wavelength selection using variable importance in projection scores. For multivariate data of the classification models, partial least square discriminant analysis (PLS-DA), support vector machine (SVM), and random forests were used for predicting cabbage conditions. The SVM model accurately distinguished the cabbage exhibiting soft rot disease symptoms from the healthy cabbage. This study presents the potential of HSI systems for separating soft rot disease-infected napa cabbages from healthy napa cabbages using the SVM model, especially under the most effective wavelengths (970, 980, 1180, 1070, 1120, and 978 nm), prior to processing. These results are applicable to industrial multispectral images.

Combining deep learning and fluorescence imaging to automatically identify fecal contamination on meat carcasses

Food safety and foodborne diseases are significant global public health concerns. Meat and poultry carcasses can be contaminated by pathogens like E. coli and salmonella, by contact with animal fecal matter and ingesta during slaughter and processing. Since fecal matter and ingesta can host these pathogens, detection, and excision of contaminated regions on meat surfaces is crucial. Fluorescence imaging has proven its potential for the detection of fecal residue but requires expertise to interpret. In order to be used by meat cutters without special training, automated detection is needed. This study used fluorescence imaging and deep learning algorithms to automatically detect and segment areas of fecal matter in carcass images using EfficientNet-B0 to determine which meat surface images showed fecal contamination and then U-Net to precisely segment the areas of contamination. The EfficientNet-B0 model achieved a 97.32% accuracy (precision 97.66%, recall 97.06%, specificity 97.59%, F-score 97.35%) for discriminating clean and contaminated areas on carcasses. U-Net segmented areas with fecal residue with an intersection over union (IoU) score of 89.34% (precision 92.95%, recall 95.84%, specificity 99.79%, F-score 94.37%, and AUC 99.54%). These results demonstrate that the combination of deep learning and fluorescence imaging techniques can improve food safety assurance by allowing the industry to use CSI-D fluorescence imaging to train employees in trimming carcasses as part of their Hazard Analysis Critical Control Point zero-tolerance plan.

Interactions between Microbial Food Safety and Environmental Sustainability in the Fresh Produce Supply Chain

Improving the environmental sustainability of the food supply chain will help to achieve the United Nations Sustainable Development Goals (SDGs). This environmental sustainability is related to different SDGs, but mainly to SDG 2 (Zero Hunger), SDG 12 (Responsible Production and Consumption), SDG 13 (Climate Action), and SDG 15 (Life on Land). The strategies and measures used to improve this aspect of the food supply chain must remain in balance with other sustainability aspects (economic and social). In this framework, the interactions and possible conflicts between food supply chain safety and sustainability need to be assessed. Although priority must be given to safety aspects, food safety policies should be calibrated in order to avoid unnecessary deleterious effects on the environment. In the present review, a number of potential tensions and/or disagreements between the microbial safety and environmental sustainability of the fresh produce supply chain are identified and discussed. The addressed issues are spread throughout the food supply chain, from primary production to the end-of-life of the products, and also include the handling and processing industry, retailers, and consumers. Interactions of fresh produce microbial safety with topics such as food waste, supply chain structure, climate change, and use of resources have been covered. Finally, approaches and strategies that will prove useful to solve or mitigate the potential contradictions between fresh produce safety and sustainability are described and discussed. Upon analyzing the interplay between microbial safety and the environmental sustainability of the fresh produce supply chain, it becomes clear that decisions that are taken to ensure fresh produce safety must consider the possible effects on environmental, economic, and social sustainability aspects. To manage these interactions, a global approach considering the interconnections between human activities, animals, and the environment will be required.