Food Refrigeration and Computerised Food Technology, School of Agriculture, Food Science & Veterinary Medicine, University College Dublin, National University of Ireland, Agriculture & Food Science Centre, Belfield, Dublin 4, Ireland.
Meat Sci. 2012 Jan;90(1):259-68. doi: 10.1016/j.meatsci.2011.07.011. Epub 2011 Jul 21.
In this study, a hyperspectral imaging technique was developed to achieve fast, accurate, and objective determination of pork quality grades. Hyperspectral images were acquired in the near-infrared (NIR) range from 900 to 1700 nm for 75 pork cuts of longissimus dorsi muscle from three quality grades (PSE, RFN and DFD). Spectral information was extracted from each sample and six significant wavelengths that explain most of the variation among pork classes were identified from 2nd derivative spectra. There were obvious reflectance differences among the three quality grades mainly at wavelengths 960, 1074, 1124, 1147, 1207 and 1341 nm. Principal component analysis (PCA) was carried out using these particular wavelengths and the results indicated that pork classes could be precisely discriminated with overall accuracy of 96%. Algorithm was developed to produce classification maps of the tested samples based on score images resulting from PCA and the results were compared with the ordinary classification method. Investigation of the misclassified samples was performed and showed that hyperspectral based classification can aid in class determination by showing spatial location of classes within the samples.
本研究旨在开发一种高光谱成像技术，以实现快速、准确、客观地测定猪肉品质等级。采集了来自三个品质等级（PSE、RFN 和 DFD）的 75 块背最长肌猪肉的近红外（NIR）范围内 900 至 1700nm 的高光谱图像。从二阶导数光谱中提取了每个样本的光谱信息，并确定了 6 个能解释猪肉类间大部分变化的显著波长。三个品质等级之间的反射率存在明显差异，主要在 960nm、1074nm、1124nm、1147nm、1207nm 和 1341nm 处。使用这些特定波长进行主成分分析（PCA），结果表明，猪肉类可以被精确区分，总体准确率为 96%。基于 PCA 产生的得分图像，开发了一种算法来生成测试样本的分类图，并将结果与普通分类方法进行比较。对误分类样本进行了调查，结果表明，基于高光谱的分类可以通过显示样本内类别的空间位置来辅助类别的确定。
