食品与发酵工业 >

2020 , Vol. 46 >Issue 10: 264 - 270

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.023489

分析与监测

基于近红外高光谱技术快速检测冷鲜猪肉酸价

  • 何鸿举 ,
  • 王魏 ,
  • 王洋洋 ,
  • 马汉军 ,
  • 陈复生 ,
  • 朱明明 ,
  • 赵圣明 ,
  • 康壮丽
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  • 1(河南科技学院 食品学院,河南 新乡,453003)
    2(河南科技学院博士后研发基地,河南 新乡,453003)
    3(河南工业大学 粮油食品学院,河南 郑州,450001)
博士,教授(本文通讯作者,E-mail:hongju_he007@126.com)

收稿日期: 2020-02-03

  网络出版日期: 2020-06-17

基金资助

河南省科技攻关项目(182102310060);河南省重大科技专项项目(161100110600);中国博士后科学基金(2018M632767);河南省青年人才托举工程项目(2018HYTP008);河南省博士后科研项目(001801021);河南科技学院高层次人才引进项目(2015015);河南科技学院重大科研培育项目(2016ZD03)

收起

NIR hyperspectral imaging technology for rapid detection of acid value in fresh chilled pork

  • HE Hongju ,
  • WANG Wei ,
  • WANG Yangyang ,
  • MA Hanjun ,
  • CHEN Fusheng ,
  • ZHU Mingming ,
  • ZHAO Shengming ,
  • KANG Zhuangli
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  • 1(School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China)
    2(Postdoctoral Research Base, Henan Institute of Science and Technology, Xinxiang 453003, China)
    3(College of Grain, Oil and Food, Henan University of Technology, Zhengzhou 450001, China)

Received date: 2020-02-03

  Online published: 2020-06-17

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摘要

基于近红外(near infrared,NIR)高光谱成像技术(900~1 700 nm)对0~4 ℃冷藏条件下猪肉的酸价变化进行快速无损检测研究。通过采集新鲜猪肉样品的高光谱图像,提取图像中感兴趣区域内的反射光谱信息,再经移动平均值平滑、卷积平滑、中值滤波平滑、高斯滤波平滑、标准化校正、多元散射校正、基线校正、标准正态变量变换等8种方式预处理原始光谱(raw extracted spectra,RAW),利用偏最小二乘(partial least squares,PLS)算法建立酸价预测模型。结果显示,基于RAW光谱(rP=0.824,RMSEP=0.594 mg/g)和BC光谱(rP=0.825,RMSEP=0.587 mg/g)构建的全波段PLS模型(RAW-PLS和BC-PLS)预测酸价效果较好。使用回归系数法(regression coefficient,RC)和连续投影算法筛选最优波长优化模型。结果显示,基于RC法从RAW光谱中筛选的28个最优波长构建的RAW-RC-PLS模型预测猪肉酸价效果最好(rP=0.846,RMSEP=0.569 mg/g)。研究表明,利用NIR高光谱成像技术构建PLS模型可潜在实现猪肉酸价的快速无损评价。

关键词： 高光谱成像技术; 酸价; 偏最小二乘; 回归系数; 连续投影算法

本文引用格式

何鸿举 , 王魏 , 王洋洋 , 马汉军 , 陈复生 , 朱明明 , 赵圣明 , 康壮丽 . 基于近红外高光谱技术快速检测冷鲜猪肉酸价[J]. 食品与发酵工业, 2020 , 46(10) : 264 -270 . DOI: 10.13995/j.cnki.11-1802/ts.023489

Abstract

The aim of this study was to detect acid value of pork stored at 0-4 ℃ by using near infrared (NIR) hyperspectral technique (900-1 700 nm) in a rapid and nondestructive way. The hyperspectral images of pork samples were collected, and the reflectance spectral information within the region of interest of the images was extracted. Then eight methods including moving average smoothing (MAS), Savitzky Golay convolution smoothing (SGCS), median filtering smoothing (MFS), Gaussian filter smoothing (GFS), normalization correction (NC), multiplicative scatter correction (MSC), baseline correction (BC) and standard normal variate (SNV) were applied to preprocess the raw extracted spectra. Partial least squares (PLS) algorithm was used to establish the model for predicting the acid value of pork. The results showed that the full-band PLS models based on the raw spectra and BC spectra had better performance in prediction of acid value, with rP of 0.824, RMSEP of 0.594 mg/g in RAW-PLS model and rP of 0.825, RMSEP of 0.587 mg/g in BC-PLS model. Regression coefficient method (RC) and successive projections algorithm (SPA) were used to select the optimal wavelengths for PLS model optimization. The results showed that the RAW-RC-PLS model built with 28 optimal wavelengths selected from raw spectra by RC method had best prediction performance, resulting in rP of 0.846 and RMSEP of 0.569 mg/g. The whole study indicated that it is potential to realize the rapid and nondestructive detection of acid value of pork by NIR hyperspectral imaging technology.

Key words： hyperspectral imaging technology; acid value; partial least squares; regression coefficient; successive projections algorithm

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