| 二维相关可见-近红外光谱结合支持向量机评价猪肉新鲜度 |
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| 引用本文： | 王文秀,彭彦昆,孙宏伟,魏文松,郑晓春. 二维相关可见-近红外光谱结合支持向量机评价猪肉新鲜度[J]. 食品科学, 2018, 39(18): 273-279. DOI: 10.7506/spkx1002-6630-201818042 |
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| 作者姓名： | 王文秀 彭彦昆 孙宏伟 魏文松 郑晓春 |
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| 作者单位： | （中国农业大学工学院，国家农产品加工技术装备研发分中心，北京 100083） |
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| 基金项目： | “十三五”国家重点研发计划重点专项（2016YFD0401205）；国家农产品质量安全风险评估项目（GJFP201701504） |
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| 摘 要： | 为探究二维相关同步光谱优选生鲜肉新鲜度特征变量的可行性，采集生鲜猪肉在1～15 d共58 个样品的可见-近红外反射光谱信息，并参照国标方法测定其挥发性盐基氮值（total volatile basic nitrogen，TVB-N）。然后，以TVB-N为“外界扰动”，选择10 条代表性光谱并进行包络线去除，结合光谱差异选取了7?个子区间。通过对每个子区间作二维相关分析，解析其二维相关同步谱和自相关谱，获取与TVB-N变化密切相关的敏感变量。最后，利用所选特征变量，分别基于原始、标准正态变量变换预处理和归一化预处理的光谱，建立猪肉新鲜度的支持向量机（support vector machine，SVM）判别模型。结果表明，利用二维相关光谱分析共提取到17?个特征波长，仅占总变量个数的1.61%，建立的SVM模型总体正确率分别为94.83%、98.28%和98.28%。这表明所建立的模型具有较好的判别效果，也说明二维相关分析用于筛选与生鲜肉新鲜度相关特征变量的方法是可行的。这有利于解析生鲜肉在腐败变质过程中的光谱特征信息变化，也为近红外光谱分析中变量筛选提供了一种新的思路。
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| 关 键 词： | 可见-近红外光谱 二维相关光谱 新鲜度 生鲜肉 判别模型 |
Evaluation of Pork Freshness Using Two-Dimensional Correlation Visible/Near-Infrared Spectroscopy Combined with Support Vector Machine |
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| WANG Wenxiu,PENG Yankun,SUN Hongwei,WEI Wensong,ZHENG Xiaochun. Evaluation of Pork Freshness Using Two-Dimensional Correlation Visible/Near-Infrared Spectroscopy Combined with Support Vector Machine[J]. Food Science, 2018, 39(18): 273-279. DOI: 10.7506/spkx1002-6630-201818042 |
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| Authors: | WANG Wenxiu PENG Yankun SUN Hongwei WEI Wensong ZHENG Xiaochun |
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| Affiliation: | (National Research and Development Center for Agro-processing Equipment, College of Engineering, China Agricultural University, Beijing 100083, China) |
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| Abstract: | In order to explore the feasibility of two-dimensional correlation synchronous spectra to select feature variables for meat freshness, visible/near infrared reflectance spectral information and total volatile basic nitrogen (TVB-N) content of 58 pork samples stored for 1–15 days were obtained. Then TVB-N content was employed as “external disturbance” and 10 representative spectra were selected for continuum removal. Seven spectral subregions were chosen according to the spectral difference and used for two-dimensional correlation analysis. By analyzing the synchronization spectra and the autocorrelation spectra, sensitive variables, which were closely related to TVB-N content, were obtained. Finally, using the selected variables, support vector machine (SVM) models for discrimination of pork freshness were established based on the original, standard normal variate preprocessed and normalized spectra, respectively. The results showed that 17 characteristic wavelengths, which accounted for only 1.61% of the total variables, were extracted by two-dimensional correlation spectral analysis, and that the overall accuracy rates of the SVM models were 94.83%, 98.28% and 98.28% respectively, indicating that the models performed well. Hence two-dimensional correlation analysis can be used to screen out the characteristic variables related to meat freshness. The research will be helpful for analyzing the change of spectral characteristics during meat spoilage and also provide new insights into variables selection in near infrared spectroscopy analysis. |
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| Keywords: | visible/near-infrared spectroscopy two-dimensional correlation spectrum freshness pork discrimination model |
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