Abstract：
The purpose of this work was to assess the potential of 2T2D COS PLS-DA (two-trace two-dimensional correlation spectroscopy and partial least squares discriminant analysis) in conjunction with Visible Near infrared multispectral imaging (MSI) as a quick, non-destructive, and precise technique for classifying three beef muscles -Longissimus thoracis, Semimembranosus, and Biceps femoris- obtained from three breeds - the Blonde d\'Aquitaine, Limousine, and Aberdeen Angus. The experiment was performed on 240 muscle samples. Before performing PLS-DA, spectra were extracted from MSI images and processed by SNV (Standard Normal Variate), MSC (Multivariate Scattering Correction) or AREA (area under curve equal 1) and converted in synchronous and asynchronous 2T2D COS maps. The results of the study highlighted that combining synchronous and asynchronous 2T2D COS maps before performing PLS-DA was the best strategy to discriminate between the three muscles (100% of classification accuracy and 0% of error).
摘要：
这项工作的目的是评估2T2DCOSPLS-DA（两道二维相关光谱和偏最小二乘判别分析）与可见近红外多光谱成像（MSI）相结合的潜力，非破坏性的，以及对三种牛肉肌肉进行分类的精确技术-胸背肌，半膜，和股二头肌-从三个品种-金发碧眼的阿基坦，豪华轿车,还有阿伯丁安格斯.在240个肌肉样品上进行实验。在执行PLS-DA之前，从MSI图像中提取光谱，并通过SNV（标准正态变量）进行处理，MSC（多变量散射校正）或AREA（曲线下面积等于1），并在同步和异步2T2DCOS图中转换。研究结果强调，在执行PLS-DA之前结合同步和异步2T2DCOS图是区分三种肌肉的最佳策略（分类精度为100％，误差为0％）。
