分析与检测

基于机器视觉及电子鼻的天麻微波热风干燥品质预测

  • 邓明磊 ,
  • 李静 ,
  • 包凯铖 ,
  • 夏新宇 ,
  • 孙亮 ,
  • 周健 ,
  • 刘莉 ,
  • 宋春芳 ,
  • 李臻峰
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  • 1(江南大学 智能制造学院,江苏 无锡,214122)
    2(山东碧海机械科技有限公司,山东 临沂,276600)
第一作者:硕士研究生(李臻峰教授为通信作者,E-mail:1290500137@qq.com)

收稿日期: 2025-01-17

  修回日期: 2025-02-26

  网络出版日期: 2025-11-21

Prediction of Gastrodia elata drying quality based on machine vision and electronic nose technology

  • DENG Minglei ,
  • LI Jing ,
  • BAO Kaicheng ,
  • XIA Xinyu ,
  • SUN Liang ,
  • ZHOU Jian ,
  • LIU Li ,
  • SONG Chunfang ,
  • LI Zhenfeng
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  • 1(School of Intelligent Manufacturing, Jiangnan University, Wuxi 214122, China)
    2(Shandong Bihai Machinery Technology Co.Ltd., Linyi 276600, China)

Received date: 2025-01-17

  Revised date: 2025-02-26

  Online published: 2025-11-21

摘要

该研究针对天麻干燥品质快速检测问题,构建了基于机器视觉与电子鼻技术的实时监测系统,在线采集天麻在不同干燥条件下的视觉及气味特征信息。同时测定干燥过程中天麻总酚类、总黄酮(total flavonoid content,TFC)、多糖、天麻素、对羟基苯甲醇(4-hydroxybenzyl alcohol,HBA)的含量变化,并分析其与特征变量的相关性。结果表明,视觉特征L*a*b*和ΔE与多糖相关性分别为0.93、-0.93、0.90和0.98,电子鼻R6传感器与TFC和HBA相关性分别为-0.96和-0.91,均具有显著相关性。基于粒子群优化的最小二乘支持向量机建立品质预测模型,TFC、HBA和多糖的预测模型精度R2分别为0.903 4、0.918 7和0.933 7,模型精度较高,实现了对天麻品质的精准评价。

本文引用格式

邓明磊 , 李静 , 包凯铖 , 夏新宇 , 孙亮 , 周健 , 刘莉 , 宋春芳 , 李臻峰 . 基于机器视觉及电子鼻的天麻微波热风干燥品质预测[J]. 食品与发酵工业, 2025 , 51(21) : 392 -399 . DOI: 10.13995/j.cnki.11-1802/ts.042152

Abstract

This study focused on the rapid detection of Gastrodia elata drying quality by developing a real-time monitoring system that integrated machine vision and electronic nose technologies.The system collected odor and visual feature information of G.elata under various drying conditions.During the drying process, the study measured changes in total phenolic content, total flavonoid content (TFC), polysaccharides, gastrodin, and 4-hydroxybenzyl alcohol (HBA) and analyzed their correlations with characteristic variables.Results showed that the visual features L, a, b, and ΔE were significantly correlated with polysaccharides, with correlation coefficients of 0.93, -0.93, 0.90, and 0.98 respectively.Additionally, the correlations between the electronic nose R6 sensor and TFC and HBA were -0.96 and -0.91, respectively.A quality prediction model was constructed using a particle swarm optimization-based least squares support vector machine.The model achieves prediction accuracies (R2) of 0.903 4, 0.918 7, and 0.933 7 for TFC, HBA, and polysaccharides, respectively, demonstrating its high precision and effectiveness in evaluating G.elata quality.

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