基于中红外光谱的掺伪牛奶非靶向检测方法研究

doi:10.3964/j.issn.1000-0593(2023)10-3009-06

摘要
参考文献
相关文章 (15)

全文: PDF (2290 KB)
输出: BibTeX | EndNote (RIS)

摘要：随着生活和消费水平提高，消费者对于乳制品食品安全及品质的要求越来越高。原料奶质量直接影响乳制品的生产与消费安全，在牛奶收储及生产环节都需要对各种非法添加物进行严格检测，以保证产品质量。目前常用的检测方法主要以化学法与仪器分析方法为主，需要针对不同类型添加物设计前处理步骤，过程繁琐，检测效率低，无法满足实时在线需要。针对多种类掺假异常牛奶样品实时在线检测需要，研究了基于中红外光谱的非靶向检测方法。实验样品选择蒙牛公司六个奶质稳定的奶源地收集到的天然原奶样品，并配制含有多种掺假物的异常牛奶样品集。采集样品中红外光谱，并针对在线检测过程中的干扰来源，选择平滑滤波、多元散射校正、基线校正及归一化等预处理方法，提高光谱信噪比与一致性。为了提高非靶向模型识别准确度及稳健性，根据牛奶样品中红外光谱特征，选择无信息变量消除（MC-UVE）、无变量信息消除-连续投影（UVE-SPA）与竞争自适应重加权采样（CARS）三种方法，筛选原始光谱中的特征波长变量。在得到的不同特征波长变量组合的基础上，分别建立基于偏最小二乘判别（PLS-DA）及支持向量机（SVM）的鉴别模型，对多种掺假物异常牛奶样品进行非靶向鉴别。实验结果表明，SVM模型鉴别准确度优于PLS-DA，CARS方法筛选得到的变量组合应用于不同鉴别模型的效果均较优，与SVM模型结合对训练集与测试集的分类准确率分别达到97.84%与94.55%。分析特征波长变量分布可知，CARS方法筛选出的变量主要集中在异常牛奶样品光谱特征比较明显的区域。样品误分类结果表明，该模型组合可以较为准确识别异常牛奶样品，具有较好的特异性。研究结果表明，基于红外光谱技术建立非靶向鉴别模型可以实现多种异常牛奶样品快速准确识别，为牛奶掺假及生产过程在线检测提供了支持。

关键词：中红外光谱；非靶向检测；变量选择；判别模型

Abstract：For instance, an increase in living and consumption level has significantly led to an increase in demand for food safety and quality of milk and its products. The quality of milk affects the production and consumption of dairy products. In order to ensure the quality of dairy products, methods and procedures have been developed to detect various milk adulterants in the collection, storage and production procedure. Most current analytical methods, such as chemical and instrumental analysis, are targeted detection methods, which require pre-treatment steps designed for adulterants and are cumbersome and time-consuming. In this paper, we proposed a non-targeted method based on mid-infrared spectroscopy developed for the identification of abnormal milk samples. The natural raw milk samples were collected from six pastures of the Mengniu company, and abnormal milk samples were prepared by adding multiple adulterants. Then the mid-infrared spectrum was measured and pre-processed with smoothing, multiple scattering correction, baseline correction and normalization. In order to improve the accuracy and robustness of models, Three different variable selection methods were implemented, such as uninformative variables elimination (MC-UVE), uninformative variables elimination-successive projections algorithm (UVE-SPA) and competitive adaptive reweighted sampling(CARS). Then, two classification algorithms, partial least squares discriminant analysis(PLS-DA) and support vector machine (SVM), were employed and compared in the discrimination models. The results indicated that SVM is the better classification algorithm achieving higher identifying accuracy, and CARS method screening performs better with PLS-DA and SVM classification models. The accuracy of the -SVM-CARS discrimination model achieved 97.84% and 94.55% for validation and prediction, respectively. The variables screened by the CARS method were mainly concentrated in the regions where the spectral features of the anomalous milk samples were more obvious. Further analysis of the misclassified sample showed that the model combination could more accurately identify the abnormal milk samples. These results demonstrate that abnormal milk can be identified successfully using mid-infrared spectroscopy with discriminant analysis, suggesting our techniques to provide an efficient and practical reference for milk adulteration and on-line detection of the production process.

Key words：Mid-infrared spectroscopy; Untargeted detection; Variable selection; Discriminant models

收稿日期: 2022-06-27 修订日期: 2022-09-26

中图分类号:

O433.4

基金资助: 国家自然科学基金项目（81971657)，呼和浩特市科技计划项目（2021-社-5），呼和浩特市科技计划项目（2016-高新-4，2021-农-重-1），国家重点研发计划项目（2019YFC1606505）资助

通讯作者: 高安平 E-mail: gaoanping@mengniu.cn

作者简介: 刘伯扬，1978年生，内蒙古蒙牛乳业（集团）股份有限公司高级工程师 e-mail: liuboyang@mengniu.cn

引用本文:

刘伯扬，高安平，杨戬，高永亮，白鹏，特日格乐，马利军，赵三军，李雪晶，张慧萍，康俊巍，李慧，王慧，杨斯，李晨曦，刘蓉. 基于中红外光谱的掺伪牛奶非靶向检测方法研究[J]. 光谱学与光谱分析, 2023, 43(10): 3009-3014.
LIU Bo-yang, GAO An-ping, YANG Jian, GAO Yong-liang, BAI Peng, Teri-gele, MA Li-jun, ZHAO San-jun, LI Xue-jing, ZHANG Hui-ping, KANG Jun-wei, LI Hui, WANG Hui, YANG Si, LI Chen-xi, LIU Rong. Research on Non-Targeted Abnormal Milk Identification Method Based on Mid-Infrared Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3009-3014.

链接本文:

https://www.gpxygpfx.com/CN/10.3964/j.issn.1000-0593(2023)10-3009-06 或 https://www.gpxygpfx.com/CN/Y2023/V43/I10/3009