Classification of frozen-thawed pork loins based on the freezing conditions and thawing losses using the hyperspectral imaging system

IF 7.1 1区 农林科学 Q1 Agricultural and Biological Sciences
Seul-Ki-Chan Jeong , Kyung Jo , Seonmin Lee , Hayeon Jeon , Yun-Sang Choi , Samooel Jung
{"title":"Classification of frozen-thawed pork loins based on the freezing conditions and thawing losses using the hyperspectral imaging system","authors":"Seul-Ki-Chan Jeong ,&nbsp;Kyung Jo ,&nbsp;Seonmin Lee ,&nbsp;Hayeon Jeon ,&nbsp;Yun-Sang Choi ,&nbsp;Samooel Jung","doi":"10.1016/j.meatsci.2024.109716","DOIUrl":null,"url":null,"abstract":"<div><div>This study investigated the suitability of a hyperspectral imaging (HSI) system for the classification of frozen-thawed pork loins according to their quality properties. The pork loin slices were frozen at −20, −50, and −70 °C for 1, 2, and 3 months (the 9 freezing conditions). After thawing pork loins at 2 °C, the hyperspectral image was obtained. The photomicrographs of the loins showed that the extracellular spaces were the biggest in the loins frozen at −20 °C for 3 months. The denaturation of myofibrillar proteins measured by the intrinsic tryptophan intensity and surface hydrophobicity was higher in the loins frozen at −20 °C than that of loins frozen at −50 and −70 °C for 2 and 3 months (<em>P</em> &lt; 0.05). The highest and lowest thawing loss was observed in loins frozen at −20 °C for 3 months (9.1 %) and at −70 °C for 1 month (3.6 %), respectively. The classification by the HSI system for 10-class (the 9 freezing conditions and the 1 fresh loin) showed that the highest correct classification (CC%) rates were 83.20 % and 81.82 % in the calibration and prediction sets, respectively, when partial least squares discriminant analysis (PLS-DA) with pre-processing by baseline offset and second derivative was used. In addition, 93.36 % and 91.92 % of CC in the calibration and prediction sets, respectively, were found in the classification of 4-class (the 3 thawing losses and the 1 fresh loin) with the PLS-DA and read-once-write-many-columnar. This study demonstrates that the HSI system can be used to present information on the quality of frozen-thawed pork loin.</div></div>","PeriodicalId":389,"journal":{"name":"Meat Science","volume":"221 ","pages":"Article 109716"},"PeriodicalIF":7.1000,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Meat Science","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0309174024002936","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

Abstract

This study investigated the suitability of a hyperspectral imaging (HSI) system for the classification of frozen-thawed pork loins according to their quality properties. The pork loin slices were frozen at −20, −50, and −70 °C for 1, 2, and 3 months (the 9 freezing conditions). After thawing pork loins at 2 °C, the hyperspectral image was obtained. The photomicrographs of the loins showed that the extracellular spaces were the biggest in the loins frozen at −20 °C for 3 months. The denaturation of myofibrillar proteins measured by the intrinsic tryptophan intensity and surface hydrophobicity was higher in the loins frozen at −20 °C than that of loins frozen at −50 and −70 °C for 2 and 3 months (P < 0.05). The highest and lowest thawing loss was observed in loins frozen at −20 °C for 3 months (9.1 %) and at −70 °C for 1 month (3.6 %), respectively. The classification by the HSI system for 10-class (the 9 freezing conditions and the 1 fresh loin) showed that the highest correct classification (CC%) rates were 83.20 % and 81.82 % in the calibration and prediction sets, respectively, when partial least squares discriminant analysis (PLS-DA) with pre-processing by baseline offset and second derivative was used. In addition, 93.36 % and 91.92 % of CC in the calibration and prediction sets, respectively, were found in the classification of 4-class (the 3 thawing losses and the 1 fresh loin) with the PLS-DA and read-once-write-many-columnar. This study demonstrates that the HSI system can be used to present information on the quality of frozen-thawed pork loin.
利用高光谱成像系统根据冷冻条件和解冻损失对冷冻解冻猪里脊肉进行分类
本研究调查了高光谱成像(HSI)系统是否适合根据冷冻解冻猪里脊肉的质量特性对其进行分类。猪里脊肉片分别在 -20、-50 和 -70 °C 下冷冻 1、2 和 3 个月(9 种冷冻条件)。在 2 ℃ 下解冻猪里脊肉后,获得高光谱图像。里脊肉的显微照片显示,在-20 °C下冷冻3个月的里脊肉细胞外空隙最大。用色氨酸固有强度和表面疏水性测量的肌纤维蛋白变性程度,在-20 °C冷冻2个月和3个月的腰肉高于在-50 °C和-70 °C冷冻的腰肉(P <0.05)。在-20°C冷冻3个月(9.1%)和-70°C冷冻1个月(3.6%)的里脊的解冻损失分别最高和最低。HSI 系统对 10 个类别(9 种冷冻条件和 1 种新鲜里脊肉)的分类结果表明,当使用偏最小二乘判别分析(PLS-DA)并通过基线偏移和二次导数进行预处理时,校准集和预测集的最高正确分类率(CC%)分别为 83.20% 和 81.82%。此外,在使用 PLS-DA 和 "一次读写多列 "对 4 类(3 个解冻损失和 1 个新鲜里脊)进行分类时,校准集和预测集中的 CC 分别为 93.36 % 和 91.92 %。这项研究表明,人脸识别系统可用于呈现冷冻解冻猪里脊肉的质量信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Meat Science
Meat Science 工程技术-食品科技
CiteScore
12.60
自引率
9.90%
发文量
282
审稿时长
60 days
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信