多光谱成像无损识别冻融猪肉中危害级碎骨

冻融猪肉作为肉制品加工原料，被广泛应用于无骨肉制品加工。该原料中的危害级碎骨（1～2.5 cm）对后期加工及食用安全均有较大风险。因此，开展多光谱成像技术（405～970 nm）快速无损识别冻融猪肉中碎骨的可行性研究十分必要。将195块肉片制备成65个无骨肉样、65个碎骨表面嵌入式肉样和65个碎骨内部嵌入式肉样，经冻融处理后采集其多光谱图像；再利用经典判别分析（CDA）进行图像分割，获得两类感兴趣区域（ROIs-1和ROIs-2），并提取相应光谱和图像信息；最后运用支持向量机（SVM）和神经网络（NN）建立冻融猪肉危害级碎骨识别模型。结果显示：ROIs-2全光谱比ROIs-1全光谱有更好的识别能力，SVM和NN模型的精度均为100%，表明区域分割与模型精度密切相关。基于连续投影算法（SPA）筛选出六个关键波长（505，590，700，850，890和970 nm），所提取的ROIs-2特征光谱可实现样品碎骨高精度识别，准确率为100%，进一步提升了识别效率。利用图像信息既能建立优越的SVM和NN碎骨识别模型，准确率分别为93.8%和93.33%，又能实现结果可视化，体现出优良的技术优势，但精度低于光谱识别模型。综上所述，多光谱成像技术可实现冻融猪肉危害级碎骨的高精度识别，为工业在线检测提供理论基础。

Non-Destructive Identification of Hazardousbone Fragments Embedded in the Frozen-Thawed Pork Based on Multispectral Imaging

Frozen-thawed pork was widely used as a raw material for processing boneless meat products. The hazardous bone fragment (1~2.5 cm) embedded in the pork can risk processing equipment and consumer health. Therefore, it is necessary to study the feasibility of multispectral imaging technology (405~970 nm) for rapid and non-destructive identification of the bone fragments embedded in frozen-thawed pork. In this work, 195 lean pork slices (LPSs) were prepared into 65 samples of boneless LPSs, 65 samples of bone fragments embedded in the surface of LPSs and 65 samples of bone fragments embedded in the inner of LPSs, and then the multispectral images of them were captured after freeze-thaw treatment. These images were segmented by canonical discriminant analysis (CDA) and converted into two types of regions of interest(ROIs-1 and ROIs-2), then extracted their spectral and image information. Finally, the identification models of hazardous bone fragments embedded in the frozen-thawed LPSs were established by support vector machine (SVM) and neural network (NN). The results showed that the whole spectra extracted from ROIs-2 had better identification ability of bone fragments than that extracted from ROIs-1 and could be used to establish SVM and NN models with 100% accuracy, indicating that the region segmentation was closely related to model accuracy. The bone fragments in pork could be identified with 100% accuracy using the spectra extracted from ROIs-2 at six key wavelengths (505, 590, 700, 850, 890 and 970 nm) that were selected by successive projection algorithm (SPA), implying that the testing efficiency was further improved. The image information had a significant advantage because it could establish the SVM model with 93.8% accuracy and the NN model with 93.33% accuracy for identifying the bone fragments that were lower than those established by the spectral information and obtain visible results. In conclusion, the bone fragments embedded in the frozen-thawed pork could be precisely identified based on multispectral imaging technology, which would provide a theoretical basis for industrial online detection.