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多光谱食品品质可视化：基于双道二维相关光谱（2t2d Cos）和多快照可见近红外多光谱成像的牛肉肌肉鉴别新方法
发表时间：2023-02-15 14:50:45点击：667
摘要
本研究的目的是评估可见-近红外多光谱成像结合2T2D COS PLS-DA（双道二维相关光谱和偏最小二乘判别分析）的能力，可见-近红外多光谱成像作为一种快速、无损和准确的方法，根据牛肉的品种来源和肌肉类型对其进行分类。该实验是从三个品种（Aberdeen Angus、Limousine和Blonde d’Aquitaine）获得的三种类型（胸最长肌、半膜肌和股二头肌）的240块肌肉上进行的。在执行PLS-DA之前，肌肉多光谱图像光谱通过SNV（标准正态变量）、MSC（多变量散射校正）或AREA（曲线下面积等于1）进行处理，并通过2T2D COS进行变换，以计算同步和异步2T2D图。研究结果着重指出，在执行PLS-DA之前，结合未预处理的同步和异步2T2D图是获得肌肉之间的高辨别精度（100%的分类精度和0%的误差）和繁殖类（100%的归类精度和0%误差）的最佳策略。
关键词：牛肉、品种、肌肉、鉴别、多光谱成像、2T2D COS、PLS-DA
A New Approach of Beef Muscle Discrimination Based on Two-Trace Two-Dimensional Correlation Spectroscopy (2t2d Cos) Combined with Multi-Snapshot Visible-Near Infrared Multispectral Imaging
Abstract
The aim of this study was to evalsuate the ability of Visible Near infrared multispectral imaging coupled with 2T2D COS PLS-DA (two-trace two-dimensional correlation spectroscopy and partial least squares discriminant analysis) as a rapid, non-destructive, and accurate methodology to classify beef muscles based on their breed origin and muscle type. The experiment was performed on 240 muscles of three types (Longissimus thoracis, Semimembranosus, and Biceps femoris) obtained from three breeds (Aberdeen Angus, Limousine, and Blonde d’Aquitaine). Before performing PLS-DA, the muscle multispectral images spectra were processed by SNV (standard normal variate), MSC (multivariate scattering correction) or AREA (area under curve equal 1) and transformed by 2T2D COS in order to calculate synchronous and asynchronous 2T2D maps. The results of the study highlighted that combining non-preprocessed synchronous and asynchronous 2T2D maps before performing PLS-DA was the best strategy to obtain a high discrimination accuracy between muscles (100% of classification accuracy and 0% of error) and breeds classes (100% of classification accuracy and 0% of error).
Keywords: beef, breed, Muscle, discrimination, Multispectral imaging, 2T2D COS, PLS-DA
Sign and position (in nm) of identified cross-peaks
450 | 470 | 505 | 565 | 590 | 630 | 645 | 660 | 850 | 890 | 910 | 920 | 940 | 950 | |
565 | (+) | |||||||||||||
630 | (+) | (-)(+) | ||||||||||||
645 | (+) | (+) | (+) | |||||||||||
660 | (-) | |||||||||||||
850 | (+)(+) | (+) | (+)(+) | |||||||||||
870 | ||||||||||||||
890 | (-) | (-) | ||||||||||||
900 | ||||||||||||||
910 | (+) | (+) | (+)(-) | (-) | (+) | |||||||||
920 | (+) | (-) | (-) | |||||||||||
940 | (+) | (+) | (+)(-) | (-) | (+) | |||||||||
950 | (-) | (-)(+) | (-) | (-) | (+) | (-) | (-)(-) | (-) | ||||||
970 | (-) | (-) | (-) | (-) | (-) | (-) | (-) |
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