基于中红外光谱的乳清蛋白中单一和多种植物蛋白掺假物鉴别方法

IF 0.8 4区 化学 Q4 SPECTROSCOPY
Yuduan Lin, Honghao Cai, Shihao Lin, Hui Ni
{"title":"基于中红外光谱的乳清蛋白中单一和多种植物蛋白掺假物鉴别方法","authors":"Yuduan Lin,&nbsp;Honghao Cai,&nbsp;Shihao Lin,&nbsp;Hui Ni","doi":"10.1007/s10812-025-01863-8","DOIUrl":null,"url":null,"abstract":"<p>With the rising popularity of whey protein as a dietary supplement, ensuring its quality has become imperative for consumer protection. Unscrupulous merchants sometimes adulterate whey protein with inexpensive vegetable protein to boost profits. Despite the criticality of this concern, reliable studies and relevant practical detection methods are currently limited. To fill this gap, this study adopted an integrated technique combining mid-infrared spectroscopy with machine learning to rapidly and accurately identify both single and multiple vegetable protein adulterants in whey protein. First, various recognition models were trained using AdaBoost-support vector classification (AdaBoost-SVC), AdaBoost-decision tree, K-nearest neighbor, SVC, and Gaussian Naive Bayes. Ten-fold cross-validation was subsequently used to determine the optimal spectra pre-processing combination, which included standard normal variate, first derivative, and Savitzky–Golay smoothing. Feature selection was then performed using the successive projection algorithm, principal component analysis, genetic algorithm (GA), and interval partial least squares with GA (iPLS-GA). The classification results revealed that the iPLS-GA–AdaBoost-SVC achieved the best performance on both the training and prediction sets, demonstrating the ability of the iPLS-GA to improve model stability and robustness. Overall, our findings underscore the potential applicability of the proposed method as an accurate and practical tool for improving the quality control of whey protein.</p>","PeriodicalId":609,"journal":{"name":"Journal of Applied Spectroscopy","volume":"91 6","pages":"1378 - 1386"},"PeriodicalIF":0.8000,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Mid-Infrared-Spectroscopy-Based Method for Identifying Single and Multiple Vegetable Protein Adulterants in Whey Protein\",\"authors\":\"Yuduan Lin,&nbsp;Honghao Cai,&nbsp;Shihao Lin,&nbsp;Hui Ni\",\"doi\":\"10.1007/s10812-025-01863-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>With the rising popularity of whey protein as a dietary supplement, ensuring its quality has become imperative for consumer protection. Unscrupulous merchants sometimes adulterate whey protein with inexpensive vegetable protein to boost profits. Despite the criticality of this concern, reliable studies and relevant practical detection methods are currently limited. To fill this gap, this study adopted an integrated technique combining mid-infrared spectroscopy with machine learning to rapidly and accurately identify both single and multiple vegetable protein adulterants in whey protein. First, various recognition models were trained using AdaBoost-support vector classification (AdaBoost-SVC), AdaBoost-decision tree, K-nearest neighbor, SVC, and Gaussian Naive Bayes. Ten-fold cross-validation was subsequently used to determine the optimal spectra pre-processing combination, which included standard normal variate, first derivative, and Savitzky–Golay smoothing. Feature selection was then performed using the successive projection algorithm, principal component analysis, genetic algorithm (GA), and interval partial least squares with GA (iPLS-GA). The classification results revealed that the iPLS-GA–AdaBoost-SVC achieved the best performance on both the training and prediction sets, demonstrating the ability of the iPLS-GA to improve model stability and robustness. Overall, our findings underscore the potential applicability of the proposed method as an accurate and practical tool for improving the quality control of whey protein.</p>\",\"PeriodicalId\":609,\"journal\":{\"name\":\"Journal of Applied Spectroscopy\",\"volume\":\"91 6\",\"pages\":\"1378 - 1386\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2025-01-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Applied Spectroscopy\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10812-025-01863-8\",\"RegionNum\":4,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"SPECTROSCOPY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Spectroscopy","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1007/s10812-025-01863-8","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"SPECTROSCOPY","Score":null,"Total":0}
引用次数: 0

摘要

随着乳清蛋白作为一种膳食补充剂的日益普及,确保其质量已成为消费者保护的当务之急。不择手段的商人有时会在乳清蛋白中掺入廉价的植物蛋白以增加利润。尽管这一问题至关重要,但目前可靠的研究和相关的实用检测方法有限。为了填补这一空白,本研究采用中红外光谱与机器学习相结合的集成技术,快速准确地鉴定乳清蛋白中的单一和多种植物蛋白掺杂物。首先,使用adaboost -支持向量分类(AdaBoost-SVC)、adaboost -决策树、k近邻、SVC和高斯朴素贝叶斯训练各种识别模型。随后采用十重交叉验证确定最佳光谱预处理组合,包括标准正态变量、一阶导数和Savitzky-Golay平滑。然后使用连续投影算法、主成分分析、遗传算法(GA)和带遗传算法的区间偏最小二乘(iPLS-GA)进行特征选择。分类结果表明,iPLS-GA - adaboost - svc在训练集和预测集上都取得了最好的性能,表明iPLS-GA能够提高模型的稳定性和鲁棒性。总的来说,我们的研究结果强调了所提出的方法作为改进乳清蛋白质量控制的准确和实用工具的潜在适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mid-Infrared-Spectroscopy-Based Method for Identifying Single and Multiple Vegetable Protein Adulterants in Whey Protein

With the rising popularity of whey protein as a dietary supplement, ensuring its quality has become imperative for consumer protection. Unscrupulous merchants sometimes adulterate whey protein with inexpensive vegetable protein to boost profits. Despite the criticality of this concern, reliable studies and relevant practical detection methods are currently limited. To fill this gap, this study adopted an integrated technique combining mid-infrared spectroscopy with machine learning to rapidly and accurately identify both single and multiple vegetable protein adulterants in whey protein. First, various recognition models were trained using AdaBoost-support vector classification (AdaBoost-SVC), AdaBoost-decision tree, K-nearest neighbor, SVC, and Gaussian Naive Bayes. Ten-fold cross-validation was subsequently used to determine the optimal spectra pre-processing combination, which included standard normal variate, first derivative, and Savitzky–Golay smoothing. Feature selection was then performed using the successive projection algorithm, principal component analysis, genetic algorithm (GA), and interval partial least squares with GA (iPLS-GA). The classification results revealed that the iPLS-GA–AdaBoost-SVC achieved the best performance on both the training and prediction sets, demonstrating the ability of the iPLS-GA to improve model stability and robustness. Overall, our findings underscore the potential applicability of the proposed method as an accurate and practical tool for improving the quality control of whey protein.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
1.30
自引率
14.30%
发文量
145
审稿时长
2.5 months
期刊介绍: Journal of Applied Spectroscopy reports on many key applications of spectroscopy in chemistry, physics, metallurgy, and biology. An increasing number of papers focus on the theory of lasers, as well as the tremendous potential for the practical applications of lasers in numerous fields and industries.
×
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