食品与发酵工业 >

2025 , Vol. 51 >Issue 17: 150 - 158

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.041171

研究报告

清蒸高粱糊化度检测及其蒸粮效果评价方法研究

  • 李晨阳 ,
  • 杨平 ,
  • 秦辉 ,
  • 杨峰 ,
  • 蔡小波 ,
  • 曾智娟 ,
  • 冉弋凡 ,
  • 董亮
展开
  • 1(四川轻化工大学 生物工程学院,四川 宜宾,644000)
    2(泸州老窖股份有限公司,四川 泸州,646000)
    3(国家固态酿造工程技术研究中心,四川 泸州,646000)
第一作者:硕士研究生(董亮研究员为通信作者,E-mail:410898720@qq.com)

收稿日期: 2024-09-28

  修回日期: 2024-12-25

  网络出版日期: 2025-09-29

基金资助

酿酒生物技术及应用四川省重点实验室项目(NJ2021-05);四川轻化工大学教学改革项目(JG-2359);四川轻化工大学研究生创新基金项目(D10501592);四川轻化工大学大学生创新创业项目(S202410622037,S202210622077,202310622021,CX2023199);四川轻化工大学研究生教学建设项目(AL202309);基于应用创新导向的食品质量安全检测新技术进展案例库建设研究项目(YJGXM24-C045)

收起

Detection of gelatinization degree of steamed sorghum and evaluation method of its effect on steamed grain

  • LI Chenyang ,
  • YANG Ping ,
  • QIN Hui ,
  • YANG Feng ,
  • CAI Xiaobo ,
  • ZENG Zhijuan ,
  • RAN Yifan ,
  • DONG Liang
Expand
  • 1(College of Bioengineering, Sichuan University of Science & Engineering, Yibin 644000, China)
    2(Luzhou Laojiao Co.Ltd., Luzhou 646000, China)
    3(National Engineering Research Center for Solid State Brewing, Luzhou 646000, China)

Received date: 2024-09-28

  Revised date: 2024-12-25

  Online published: 2025-09-29

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摘要

基于近红外偏最小二乘法(near-infrared partial least squares,NIR-PLS)开发了一种近红外光谱快速检测模型,用于快速、无损检测清蒸高粱糊化度。同时分析了白酒酿造生产关键指标相关性,以清蒸高粱糊化度为基础,结合清蒸高粱感官评价、出入窖糟醅理化、出酒率、基础酒理化及评分,利用自适应权重算法(adaptive weights,AW)建立了一种清蒸高粱蒸粮效果评价方法。该检测模型在预测清蒸高粱糊化度方面具有较高的相关性,模型预测值与建模参考值之间的决定系数R2=0.977 1。此外,蒸粮效果评价方法验证结果与经验丰富的酿酒师的感官评定相吻合。当75≤糊化度检测值≤80时,清蒸高粱糊化效果评价为优级;65≤糊化度检测值＜75时,评价为一级;55＜糊化度检测值＜65或80＜糊化度检测值＜90时,评价为二级;糊化度检测值≥90或糊化度检测值≤55时,评价为不合格。该评价方法量化了白酒生产中清蒸高粱蒸粮效果的评定,将依赖经验的感官评估方式转化为标准化、数字化的手段,有助于加强生产过程和产品质量的可控性,减少粮食损耗。

关键词： 近红外光谱; 快速检测模型; 自适应权重算法; 清蒸高粱蒸粮效果; 评价方法

本文引用格式

李晨阳 , 杨平 , 秦辉 , 杨峰 , 蔡小波 , 曾智娟 , 冉弋凡 , 董亮 . 清蒸高粱糊化度检测及其蒸粮效果评价方法研究[J]. 食品与发酵工业, 2025 , 51(17) : 150 -158 . DOI: 10.13995/j.cnki.11-1802/ts.041171

Abstract

Based on the near-infrared partial least squares method (NIR-PLS), a rapid detection model of near-infrared spectroscopy was developed for rapid and non-destructive detection of the gelatinization degree of steamed sorghum.At the same time, the correlation of key indicators of Baijiu brewing and production was analyzed, and based on the gelatinization degree of steamed sorghum, combined with the sensory evaluation of steamed sorghum, the physicochemical and physical chemistry of the cellar residue, the wine yield, the physicochemical and scoring of the basic baijiu, and the adaptive weights algorithm (AW) was used to establish an evaluation method for the effect of steamed sorghum on grain steaming.The detection model had a high correlation in predicting the gelatinization degree of steamed sorghum, and the coefficient of determination between the predicted value of the model and the modeling reference value was R2=0.977 1.In addition, the validation results of the evaluation method for the effect of steamed grains were consistent with the sensory evaluation of experienced winemakers.When the detection value of the gelatinization degree was [75,80], the gelatinization effect of the steamed sorghum was evaluated as excellent, when the gelatinization detection value was [65,75), it was evaluated as level 1, gelatinization detection value was (55,65) or the gelatinization detection value was (80,90), the evaluation was grade 2, when the gelatinization test value ≥90 or the gelatinization test value ≤55, it was evaluated as unqualified.This evaluation method quantifies the evaluation of the effect of steamed sorghum in baijiu production and transforms the sensory evaluation method based on experience into a standardized and digital means, which is helpful to strengthen the controllability of the production process and product quality and reduce food loss.

Key words： near-infrared spectroscopy; rapid detection of models; adaptive weighting algorithm; effect of steamed sorghum on grain; evaluation methods

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