机器学习辅助批量拉曼光谱快速、同时定量和判别分析白酒质量参数:协同仪器升级和化学计量优化

IF 5.6 1区 农林科学 Q1 FOOD SCIENCE & TECHNOLOGY
Wenguang Liu , Xiaohong Liang , Songgui He , Zhuangwei Shi , Baoyan Cen , Wangqiao Chen , Hai Bi , Chenhui Wang
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引用次数: 0

摘要

本研究以一种商品白酒为研究对象,利用批量拉曼光谱系统和化学计量学,研究了两种关键白酒质量参数——酒精含量和整体感官质量的快速、同时评估。通过自主设计的12比皿电动托盘,对34批标准白酒进行了稳定高效的光谱采集,并补充了酒精含量调整和整体感官不合格的样品,形成了两个独立的数据集,用于基于多变量分析和机器学习的化学计量方法的优化和评估。降维主成分分析(PCA)和非线性核支持向量回归(SVR)相结合的方法在酒精含量预测和感官不合格品识别方面表现出较好的效果。扩大训练集的酒精含量范围增强了PCA-SVR模型的量化能力,对所测标准白酒的酒精含量预测相对准确(±0.15% vol)。建立的PCA-SVR感官质量分级模型对成分与标准白酒相似的不合格白酒样品的识别精度平均为93%。基于仪器和化学计量学的协同优化,本文提出的机器学习辅助批量拉曼光谱系统为白酒生产提供了一种快速、可靠、集成的质量控制工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rapid and simultaneous quantitative and discriminative analyses of liquor quality parameters with machine learning-assisted batch Raman spectroscopy: Synergistic instrumental upgrade and chemometric optimization

This study investigates the rapid and simultaneous assessments of two key liquor quality parameters – alcohol content and overall sensory quality – utilizing a batch Raman spectroscopic system and chemometrics, with a commercial baijiu (Chinese liquor) as the subject. An in-house designed motorized 12-cuvette tray facilitated stable and efficient spectral acquisition from 34 production batches of standard baijiu, supplemented by alcohol content-adjusted and overall sensory-disqualified samples, to form two separate datasets for the optimization and evaluation of chemometric approaches based on multivariate analysis and machine learning. The combination of dimension reduction with principal component analysis (PCA) and support vector regression (SVR) with a nonlinear kernel showed superior performances for predicting alcohol content and identifying sensory-disqualified samples. Expanding the alcohol content range of the training set enhanced the quantification capacity of the PCA-SVR model and yielded a relatively accurate alcohol content prediction (±0.15 % v/v) for the tested standard baijiu. The PCA-SVR model built for sensory quality grading achieved an average precision of 93% for identifying disqualified baijiu samples that compositionally resemble standard ones. Based on the synergistic instrumental and chemometric optimization, the proposed machine learning-assisted batch Raman spectroscopic system offers a rapid, reliable, and integrated quality control tool for liquor production.

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来源期刊
Food Control
Food Control 工程技术-食品科技
CiteScore
12.20
自引率
6.70%
发文量
758
审稿时长
33 days
期刊介绍: Food Control is an international journal that provides essential information for those involved in food safety and process control. Food Control covers the below areas that relate to food process control or to food safety of human foods: • Microbial food safety and antimicrobial systems • Mycotoxins • Hazard analysis, HACCP and food safety objectives • Risk assessment, including microbial and chemical hazards • Quality assurance • Good manufacturing practices • Food process systems design and control • Food Packaging technology and materials in contact with foods • Rapid methods of analysis and detection, including sensor technology • Codes of practice, legislation and international harmonization • Consumer issues • Education, training and research needs. The scope of Food Control is comprehensive and includes original research papers, authoritative reviews, short communications, comment articles that report on new developments in food control, and position papers.
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