TY - JOUR A2 - Cozzolino，丹尼尔AU - 张，陶AU - 王，Biyao AU - 颜，澎AU - 王昆仑AU - 张，徐AU - 王，慧慧AU - 吕燕PY - 2018 DA - 2018 /12/26 TI - 鲑搀杂的无损检测中的应用的高光谱数据SP水 - 1809297 VL - 2018 AB - 为了鉴定鲑掺假的注水，提出了一种基于高光谱图像无损鉴别方法。The hyperspectral images of salmon fillets in visible and near-infrared ranges (390–1050 nm) were obtained with a system. The original hyperspectral data were processed through the principal-component analysis (PCA). According to the image quality and PCA parameters, a second principal-component (PC2) image was selected as the feature image, and the wavelengths corresponding to the local extremum values of feature image weighting coefficients were extracted as feature wavelengths, which were 454.9, 512.3, and 569.1 nm. On this basis, the color combined with spectra at feature wavelengths, texture combined with spectra at feature wavelengths, and color-texture combined with spectra at feature wavelengths were independently set as the input, for the modeling of salmon adulteration identification based on the self-organizing feature map (SOM) network. The distances between neighboring neurons and feature weights of the models were analyzed to realize the visualization of identification results. The results showed that the SOM-based model, with texture-color combined with fusion features of spectra at feature wavelengths as the input, was evaluated to possess the best performance and identification accuracy is as high as 96.7%. SN - 0146-9428 UR - https://doi.org/10.1155/2018/1809297 DO - 10.1155/2018/1809297 JF - Journal of Food Quality PB - Hindawi KW - ER -