食品与发酵工业 >

2024 , Vol. 50 >Issue 1: 272 - 278;285

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.035300

分析与检测

基于傅立叶变换中红外光谱技术的浓香型基酒快速检测

  • 韩云翠 ,
  • 吕志远 ,
  • 刘玉涛 ,
  • 张梦梦 ,
  • 张晨曦 ,
  • 卢春玲 ,
  • 邱振清 ,
  • 汪俊卿
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  • 1(齐鲁工业大学 生物工程学院,山东 济南,250353)
    2(济南趵突泉酿酒有限责任公司,山东 济南,250115)
第一作者:硕士,中级工程师(汪俊卿副教授为通信作者,E-mail:wjqtt.6082@163.com)

收稿日期: 2023-02-28

  修回日期: 2023-04-23

  网络出版日期: 2024-01-31

基金资助

山东省重点研发计划(重大科技创新工程)项目(2022CXGC020206);齐鲁工业大学(山东省科学院)科教产重大创新专项(2022JBZ01-06)

收起

Research on rapid detection of strong-flavor base Baijiu by Fourier transform mid-infrared spectroscopy

  • HAN Yuncui ,
  • LYU Zhiyuan ,
  • LIU Yutao ,
  • ZHANG Mengmeng ,
  • ZHANG Chenxi ,
  • LU Chunling ,
  • QIU Zhenqing ,
  • WANG Junqing
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  • 1(Bio-engineering Institute, Qilu University of Technology, Jinan 250353, China)
    2(Jinan Baotuquan Brewery Co. Ltd., Jinan 250115, China)

Received date: 2023-02-28

  Revised date: 2023-04-23

  Online published: 2024-01-31

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摘要

为在线监测浓香型白酒酿造过程中不同馏分的质量情况,以摘酒工艺过程中的基酒为研究对象,利用傅立叶变换中红外光谱结合偏最小二乘法建立了基酒中乙醇、乙酸乙酯、丁酸乙酯、己酸乙酯、乳酸乙酯、乙酸、己酸、丁酸8种化合物的快速检测模型。酒精度、乙酸乙酯、乳酸乙酯预测模型的决定系数R2为0.99,己酸乙酯分析模型的R2为0.95,丁酸乙酯、乙酸、己酸检测模型的R2约为0.90,丁酸模型的R2为0.79。8种化合物的红外光谱预测结果与化学值均有良好的线性相关性,并且化合物含量范围越宽,含量分布越均匀,模型的拟合度越好。不同季节温差大会引起基酒红外吸收光谱波动,导致模型检测准确性降低,通过温度补偿全局校正的方法建立的检测模型解决了这一问题,为实现在线自动化摘酒提供了一种可行性方案。

关键词： 傅立叶变换中红外光谱; 偏最小二乘法; 浓香型基酒; 温度补偿全局校正; 检测模型

本文引用格式

韩云翠 , 吕志远 , 刘玉涛 , 张梦梦 , 张晨曦 , 卢春玲 , 邱振清 , 汪俊卿 . 基于傅立叶变换中红外光谱技术的浓香型基酒快速检测[J]. 食品与发酵工业, 2024 , 50(1) : 272 -278;285 . DOI: 10.13995/j.cnki.11-1802/ts.035300

Abstract

To monitor the quality of different distillates in the brewing process of strong-flavor Baijiu online, rapid detection models of eight compounds from strong-flavor base Baijiu in the process of picking were established through Fourier transform mid-infrared spectroscopy combined with partial least square method. The eight compounds include ethanol, ethyl acetate, ethyl butyrate, ethyl caproate, ethyl lactate, acetic acid, caproic acid and butyrate. The determination coefficients R2 of the prediction model of alcohol content, ethyl acetate and ethyl lactate were 0.99, that of ethyl caproate was 0.95, that of ethyl butyrate, acetic acid caproic acid are about 0.90, and that of butyrate was 0.79. The predicted results of infrared spectra on eight compounds had good linear correlation with chemical values, and the wider range of compound content, the more uniform content distribution, the better fitting degree of the model. The temperature difference in different seasons caused the fluctuation of infrared absorption spectrum of base Baijiu, which will reduce the accuracy of model detection. The detection model established by the method of temperature compensation global correction can solve this problem and provide a feasible scheme for realizing automatic online Baijiu picking.

Key words： Fourier transform mid-infrared spectroscopy; partial least square method; strong-flavor base Baijiu; temperature compensation global correction; detection model

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