Université Clermont Auvergne, INRAE, VetAgro Sup, UMRF, Lempdes F-63370, France; Laboratory of Food Chemistry, Department of Food Technology, Universitas Padjadjaran, Bandung, Indonesia.
Université Clermont Auvergne, INRAE, VetAgro Sup, UMRF, Lempdes F-63370, France.
Meat Sci. 2024 Aug;214:109533. doi: 10.1016/j.meatsci.2024.109533. Epub 2024 May 7.
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).
这项工作的目的是评估 2T2D COS PLS-DA（双迹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，作为一种快速、无损、精确的技术，用于对来自三个品种的三种牛肉肌肉——长肌胸肌、半膜肌和股二头肌——进行分类：阿基坦 Blonde d'Aquitaine、利穆赞和阿伯丁安格斯。实验在 240 个肌肉样本上进行。在进行 PLS-DA 之前，从 MSI 图像中提取光谱，并通过 SNV（标准正态变量）、MSC（多元散射校正）或 AREA（曲线下面积等于 1）进行处理，并转换为同步和异步 2T2D COS 图谱。研究结果表明，在进行 PLS-DA 之前，结合同步和异步 2T2D COS 图谱是区分三种肌肉的最佳策略（100%的分类准确率和 0%的错误率）。
