Weizhe Wang , Ruirui Liu , Yufang Su , Suozai Ren , Yanmei Xi , Yun Huang , Juan Wang , Lixiang Lan , Xuelu Chi , Baoguo Sun , Nasi Ai
{"title":"Characterization and classification of odorous raw milk: Volatile profiles and algorithm model perspectives","authors":"Weizhe Wang , Ruirui Liu , Yufang Su , Suozai Ren , Yanmei Xi , Yun Huang , Juan Wang , Lixiang Lan , Xuelu Chi , Baoguo Sun , Nasi Ai","doi":"10.1016/j.jfca.2024.107030","DOIUrl":null,"url":null,"abstract":"<div><div>Odorous raw milk poses a growing threat to dairy product quality, negatively impacting both producers and consumers. However, methods for effectively identifying odorous raw milk have not been systematically established. In this study, 30 raw milk samples (RMSs) collected from different pastures were classified into 18 fresh RMSs and 12 odorous RMSs through sensory evaluation. Quantitative datasets of volatile compounds in RMSs were obtained using HS-SPME–GC–MS. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models identified nine key differential volatile compounds distinguishing fresh from odorous RMSs. Among these, hexanal and octanal were identified as potent odorants contributing to the off-odor in raw milk. Based on nine key difference volatile compounds, support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF) algorithmic models were successfully constructed to classify RMSs to identify odorous RMSs. All models achieved classification accuracy exceeding 0.9, with the RF model performing the best, achieving an accuracy of 1.0. This work provides a reference and available workflow for identifying and labeling odorous RMSs.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"138 ","pages":"Article 107030"},"PeriodicalIF":4.0000,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Food Composition and Analysis","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0889157524010640","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
引用次数: 0
Abstract
Odorous raw milk poses a growing threat to dairy product quality, negatively impacting both producers and consumers. However, methods for effectively identifying odorous raw milk have not been systematically established. In this study, 30 raw milk samples (RMSs) collected from different pastures were classified into 18 fresh RMSs and 12 odorous RMSs through sensory evaluation. Quantitative datasets of volatile compounds in RMSs were obtained using HS-SPME–GC–MS. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models identified nine key differential volatile compounds distinguishing fresh from odorous RMSs. Among these, hexanal and octanal were identified as potent odorants contributing to the off-odor in raw milk. Based on nine key difference volatile compounds, support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF) algorithmic models were successfully constructed to classify RMSs to identify odorous RMSs. All models achieved classification accuracy exceeding 0.9, with the RF model performing the best, achieving an accuracy of 1.0. This work provides a reference and available workflow for identifying and labeling odorous RMSs.
期刊介绍:
The Journal of Food Composition and Analysis publishes manuscripts on scientific aspects of data on the chemical composition of human foods, with particular emphasis on actual data on composition of foods; analytical methods; studies on the manipulation, storage, distribution and use of food composition data; and studies on the statistics, use and distribution of such data and data systems. The Journal''s basis is nutrient composition, with increasing emphasis on bioactive non-nutrient and anti-nutrient components. Papers must provide sufficient description of the food samples, analytical methods, quality control procedures and statistical treatments of the data to permit the end users of the food composition data to evaluate the appropriateness of such data in their projects.
The Journal does not publish papers on: microbiological compounds; sensory quality; aromatics/volatiles in food and wine; essential oils; organoleptic characteristics of food; physical properties; or clinical papers and pharmacology-related papers.