食品与发酵工业 >

2023 , Vol. 49 >Issue 16: 283 - 290

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.033224

分析与检测

基于便携式近红外光谱仪的蓝莓热风干燥过程模型的建立

  • 黄积微 ,
  • 李洋 ,
  • 袁迪 ,
  • 张欣硕 ,
  • 李国庆
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  • (东北林业大学 工程技术学院,黑龙江 哈尔滨,150040)
第一作者:硕士研究生(李洋副教授为通信作者,E-mail:378918917@qq.com)

收稿日期: 2022-08-08

  修回日期: 2022-09-08

  网络出版日期: 2023-09-12

基金资助

黑龙江省自然科学基金项目(LH2021C016);中央高校基本科研业务费专项资金项目(2572017CB05)

收起

Moisture monitoring model of blueberry during hot air drying process based on portable near-infrared spectrometer

  • HUANG Jiwei ,
  • LI Yang ,
  • YUAN Di ,
  • ZHANG Xinshuo ,
  • LI Guoqing
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  • (College of Engineering and technology, Northeast Forestry University, Harbin 150040, China)

Received date: 2022-08-08

  Revised date: 2022-09-08

  Online published: 2023-09-12

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摘要

该文利用便携式近红外光谱技术采集蓝莓干燥过程光谱,以“蓝丰”蓝莓为研究对象,构建蓝莓热风干燥过程含水率快速检测模型。通过主成分分析法对蓝莓干燥阶段进行定性监测,分析比较竞争性自适应重加权采样法(competitive adaptive reweighting sampling,CARS)、移动窗口偏最小二乘法(moving window partial least squares,MWPLS)和蒙特卡洛无信息变量消除法算法对蓝莓红外光谱特征变量的选取影响,通过相关系数(R2)和均方根误差(root mean square error,RMSE)等参数对模型进行评价,得到最优蓝莓含水率近红外预测模型。各特征变量选取算法均能在减少参与建模波长变量基础上,提升模型拟合度及预测能力。其中,CARS-PLS建模方法共选取11个特征变量参与建模,其模型校正相关系数为0.951 0,校正均方根误差为0.042 9,预测相关系数为0.946 5,预测均方根误差为0.047 3。最后建立基于CARS特征变量的蓝莓干燥过程含水率PLS模型,能有效对蓝莓干燥过程含水率进行准确、快速、稳定的无损检测,为果蔬干燥特性及相关食品开发研究提供理论和技术支撑,有利于推动果蔬近红外无损检测应用至其他品质指标。

关键词： 蓝莓; 含水率; 近红外光谱; 偏最小二乘法; 特征变量

本文引用格式

黄积微 , 李洋 , 袁迪 , 张欣硕 , 李国庆 . 基于便携式近红外光谱仪的蓝莓热风干燥过程模型的建立[J]. 食品与发酵工业, 2023 , 49(16) : 283 -290 . DOI: 10.13995/j.cnki.11-1802/ts.033224

Abstract

Portable near-infrared spectroscopy was used to collect the spectrum of the blueberry drying process. The “Lanfeng” blueberry was taken as the research object, establishing a rapid detection model of moisture content in the blueberry hot air drying process. The principal component analysis (PCA) was used to qualitatively monitor the drying stage of blueberries. This study analyzed and compared the selection of characteristic variables of the blueberry infrared spectrum by competitive adaptive reweighting sampling (CARS), moving window partial least squares (MWPLS), and Monte Carlo uninformative variable elimination (MCUVE), evaluated the PLS model by error parameters such as correlation coefficient (R2) and root mean square error (RMSE), and the optimal near-infrared prediction model of blueberry moisture content was obtained. Each characteristic variable selection algorithm could improve model fitting degree and prediction ability based on reducing the wavelength variables involved in modelling. Among them, the CARS-PLS modelling was selected, and a total of 11 characteristic variables were selected to participate in the modelling. The model correction correlation coefficient was 0.951 0, the corrected root mean square error was 0.042 9, the prediction correlation coefficient was 0.946 5, and the prediction root mean square error was 0.047 3. The moisture content of the blueberry drying process established based on the CARS characteristic variable selection method could effectively carry out accurate, rapid and stable nondestructive testing of moisture content of the blueberry drying process, provide theoretical and technical support for the drying characteristics of fruits and vegetables and related food development research, and promote the application of near-infrared nondestructive testing of fruits and vegetables to other quality indexes.

Key words： blueberry; moisture content; near-infrared spectroscopy; partial least square method; characteristic variable

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