高光谱特征的人造肉中低色度差异物检测

人造植物肉在其原料运输、制糜和包装等加工环节时有发生异物污染事件,误食异物会严重损害人的身体健康.常规食品异物检测方法容易检测出如金属、石头等坚硬、深色异物,而软质、浅色、透明异物却是食品异物污染事件中的主要来源且是检测的难点.根据异物和人造肉各自化学组成成分的差异,提出了一种人造肉中低色度差异物的高光谱成像检测方法,...

Detection of Low Chromaticity Difference Foreign Matters in Soy Protein Meat Based on Hyperspectral Imaging Technology

Incidents of foreign matter contamination in the processing links of soy protein meat occur frequently. Consumers’ accidental ingestion of foreign matters will seriously damage human health. Conventional foreign matter detection methods can easily detect hard and dark foreign matters such as metals and stones. Therefore, soft, light-colored, and transparent foreign matters have become the main source of foreign matters in food foreign body contamination incidents and are difficult to detect. Based on the inconsistency of the chemical composition of the foreign matter and the soy protein meat, this study proposes a hyperspectral imaging detection method for the low-contrast foreign matter in the soy protein meat. According to the difference in the spectral information of the foreign matter and the soy protein meat, a pattern recognition model was established to perform soy protein meat and finally combined with digital image processing technology to visualize the spatial distribution of foreign objects. Five kinds of low-contrast foreign matters: polycarbonate (PC), polyester resin (PET), polyvinyl chloride (PVC), silica gel, and glass were selected as the foreign matter in this study. Collecting foreign matter and soy protein meat region of interest (ROI) reflectance hyperspectral data, using SG, SNVT, MSC, VN, 1ST and 2ND six different spectral preprocessing methods to preprocess the original spectral data, and then use principal component analysis (PCA) to reduce the dimension of the preprocessed spectral data, and use successive projections algorithm (SPA) to extract soy protein meat Characteristic wavelength. Using the raw spectrum, characteristic wavelength and principal component variables as the input variables of the pattern recognition model, try to compare the accuracy of the four pattern recognition models: LDA, KNN, BP-ANN, and LS-SVM, and select the best qualitative recognition model. Set the output variable of the foreign matter category of the optimal model to 1, the category of soy protein meat is 0, generate a binary image, and then combine the digital image processing technology to realize the visualization of the low-contrast foreign matter distribution in the soy protein meat, to realize the recognition of the low-contrast foreign matter in the soy protein meat. The results show that the spectrum after SG pretreatment is better than other pretreatment methods in noise reduction. The SPA method optimized 10 characteristic wavelengths of soy protein meat. The detection effect of the whole band principal component variables combined with the BP-ANN model is the best, with an accuracy rate of 98.33%.