Abderrahmane Aït-Kaddour , Mohammed Loudiyi , Oumayma Boukria , Jasur Safarov , Shaxnoza Sultanova , Donato Andueza , Anne Listrat , Yana Cahyana
{"title":"基于双踪迹二维相关光谱(2T2D COS)和快照可见-近红外多光谱成像的牛肉肌肉鉴别技术","authors":"Abderrahmane Aït-Kaddour , Mohammed Loudiyi , Oumayma Boukria , Jasur Safarov , Shaxnoza Sultanova , Donato Andueza , Anne Listrat , Yana Cahyana","doi":"10.1016/j.meatsci.2024.109533","DOIUrl":null,"url":null,"abstract":"<div><p>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 -<em>Longissimus thoracis, Semimembranosus, and Biceps femoris</em>- 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).</p></div>","PeriodicalId":389,"journal":{"name":"Meat Science","volume":null,"pages":null},"PeriodicalIF":7.1000,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0309174024001104/pdfft?md5=6aadc5fcf7362d3d0694d11aa3ad9067&pid=1-s2.0-S0309174024001104-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Beef muscle discrimination based on two-trace two-dimensional correlation spectroscopy (2T2D COS) combined with snapshot visible-near infrared multispectral imaging\",\"authors\":\"Abderrahmane Aït-Kaddour , Mohammed Loudiyi , Oumayma Boukria , Jasur Safarov , Shaxnoza Sultanova , Donato Andueza , Anne Listrat , Yana Cahyana\",\"doi\":\"10.1016/j.meatsci.2024.109533\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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 -<em>Longissimus thoracis, Semimembranosus, and Biceps femoris</em>- 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).</p></div>\",\"PeriodicalId\":389,\"journal\":{\"name\":\"Meat Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.1000,\"publicationDate\":\"2024-05-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0309174024001104/pdfft?md5=6aadc5fcf7362d3d0694d11aa3ad9067&pid=1-s2.0-S0309174024001104-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Meat Science\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0309174024001104\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Agricultural and Biological Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Meat Science","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0309174024001104","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Agricultural and Biological Sciences","Score":null,"Total":0}
引用次数: 0
摘要
这项工作的目的是评估 2T2D COS PLS-DA(双向二维相关光谱和偏最小二乘法判别分析)与可见近红外多光谱成像(MSI)结合作为一种快速、非破坏性和精确技术的潜力、非破坏性的精确技术,用于对三种牛肉肌肉(胸长肌、半膜肌和股二头肌)进行分类,这三种肌肉分别来自三个品种--阿基坦金牛、利木赞牛和阿伯丁安格斯。实验在 240 块肌肉样本上进行。在执行 PLS-DA 之前,先从 MSI 图像中提取光谱,并通过 SNV(标准正态变异)、MSC(多变量散射校正)或 AREA(曲线下面积等于 1)进行处理,然后转换为同步和异步 2T2D COS 地图。研究结果表明,在进行 PLS-DA 之前将同步和异步 2T2D COS 图结合起来是区分三块肌肉的最佳策略(分类准确率为 100%,误差为 0%)。
Beef muscle discrimination based on two-trace two-dimensional correlation spectroscopy (2T2D COS) combined with snapshot visible-near infrared multispectral imaging
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).
期刊介绍:
The aim of Meat Science is to serve as a suitable platform for the dissemination of interdisciplinary and international knowledge on all factors influencing the properties of meat. While the journal primarily focuses on the flesh of mammals, contributions related to poultry will be considered if they enhance the overall understanding of the relationship between muscle nature and meat quality post mortem. Additionally, papers on large birds (e.g., emus, ostriches) as well as wild-captured mammals and crocodiles will be welcomed.