食品与发酵工业 >

2021 , Vol. 47 >Issue 23: 214 - 220

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.026939

分析与检测

基于便携式近红外光谱仪的樱桃番茄糖分快速分析模型

  • 孙阳 ,
  • 刘翠玲 ,
  • 孙晓荣 ,
  • 闻世震
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  • (北京工商大学 人工智能学院,食品安全大数据技术北京市重点实验室,北京,100048)
硕士研究生(刘翠玲教授为通讯作者,E-mail: lclbtbu@163.com)

收稿日期: 2021-01-31

  修回日期: 2021-03-12

  网络出版日期: 2021-12-31

基金资助

国家自然科学基金(61807001);北京市自然科学基金项目(4182017)

收起

Rapid analysis model of sugar content in cherry tomatoes based on portable near infrared spectrometer

  • SUN Yang ,
  • LIU Cuiling ,
  • SUN Xiaorong ,
  • WEN Shizhen
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  • (College of Artificial Intelligence Academy,Beijing Technology and Business University, Beijing Key Laboratory of Food Safety Big Data Technology,Beijing 100048,China)

Received date: 2021-01-31

  Revised date: 2021-03-12

  Online published: 2021-12-31

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摘要

为实现对樱桃番茄糖分的现场快速无损检测,应用便携式近红外光谱仪器AMBER Ⅱ对所采集樱桃番茄的近红外光谱数据进行建模分析。实验样本共172个,利用Kennard-Stone(K-S)算法以3∶1比例划分样本集。光谱预处理选用Savitzky-Golay卷积平滑和标准归一化(standard normal variate,SNV),分别使用无信息变量消除法(uniformative variable elimination,UVE)、连续投影算法(successive projections algorithm,SPA)和无信息变量消除结合连续投影算法(UVE-SPA)3种算法进行特征波长提取,用偏最小二乘(partial least squares,PLS)方法建模,最终使用UVE-SPA算法提取得到12个特征波长点进行PLS建模的结果最佳,建模集和预测集的决定系数R2分别为0.938 5和0.934 7,建模集和预测集的均方根误差分别为0.130 5和0.174 4,相对分析误差(relative percent deviation,RPD)为2.81。研究表明,利用以上方法提取的特征波长点所建立的模型预测效果较好,说明便携式近红外光谱仪器可以应用于对樱桃番茄糖分的现场快速无损检测。

关键词： 樱桃番茄; 糖分检测; 便携式近红外光谱仪; 偏最小二乘; 特征波长提取

本文引用格式

孙阳 , 刘翠玲 , 孙晓荣 , 闻世震 . 基于便携式近红外光谱仪的樱桃番茄糖分快速分析模型[J]. 食品与发酵工业, 2021 , 47(23) : 214 -220 . DOI: 10.13995/j.cnki.11-1802/ts.026939

Abstract

To realize the rapid and non-destructive detection of sugar content in cherry tomato, a portable near infrared spectroscopy instrument amber II was used to build model and analyze the near infrared spectroscopy data of cherry tomato. A total of 172 experimental samples were divided into 3∶1 scale by Kennard stone (K-S) algorithm. Savitzky-Golay convolution smoothing and standard normal variable (SNV) were used for spectral preprocessing. Three algorithms, namely, uniform variable elimination (UVE), successive projections algorithm (SPA) and UVE-SPA, were used to extract characteristic wavelengths, and partial least squares (PLS) method was used to set model. Finally, 12 characteristic wavelength points extracted by UVE-SPA algorithm were used for PLS modeling, the determination coefficients of modeling set and prediction set were 0.938 5 and 0.934 7 respectively. The root mean square error of modeling set and prediction set were 0.130 5 and 0.174 4, the relative percent deviation was 2.81. The results showed that the model based on the extracted characteristic wavelength points had a good prediction effect, and the portable NIR spectrometer can be used for the rapid and nondestructive detection of sugar content in cherry tomato.

Key words： cherry tomatoes; sugar detection; portable near infrared spectrometer; partial least squares; characteristic wavelength extraction

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