徐富斌,黄星奕,丁然,顾海洋,姚丽娅,戴煌.基于近红外光谱的大黄鱼新鲜度评价模型[J].食品安全质量检测学报,2012,3(6):644-648
基于近红外光谱的大黄鱼新鲜度评价模型
Freshness evaluation model of Pseudosciaena crocea based on near-infrared spectra
投稿时间:2012-11-13  修订日期:2012-11-27
DOI:
中文关键词:  大黄鱼  近红外光谱  挥发性盐基氮  菌落总数  新鲜度  向后区间偏最小二乘
英文关键词:Pseudosciaena crocea  near-infrared spectroscopy  total volatile basic nitrogen  aerobic plate count  freshness  backward interval partial least squares
基金项目:公益性行业(农业)科研专项(201003008)
作者单位
徐富斌 江苏大学食品与生物工程学院 
黄星奕 江苏大学食品与生物工程学院 
丁然 江苏大学食品与生物工程学院 
顾海洋 江苏大学食品与生物工程学院 
姚丽娅 江苏大学食品与生物工程学院 
戴煌 江苏大学食品与生物工程学院 
AuthorInstitution
XU Fu-Bin School of Food and Biological Engineering, Jiangsu University 
HUANG Xing-Yi School of Food and Biological Engineering, Jiangsu University 
DING Ran School of Food and Biological Engineering, Jiangsu University 
GU Hai-Yang School of Food and Biological Engineering, Jiangsu University 
YAO Li-Ya School of Food and Biological Engineering, Jiangsu University 
DAI Huang School of Food and Biological Engineering, Jiangsu University 
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中文摘要:
      目的 探索定量评价大黄鱼新鲜度的方法。方法 在整鱼背部采集近红外光谱, 将原始光谱预处理后分别与挥发性盐基氮(TVB-N)、菌落总数建立偏最小二乘(PLS)模型、区间偏最小二乘(iPLS)模型、向后区间偏最小二乘(biPLS)模型和联合区间偏最小二乘(siPLS)模型。结果 biPLS模型的精度最高、预测性能最佳。TVB-N的biPLS模型的校正集和预测集相关系数分别为0.8371和0.7652; 菌落总数的biPLS模型的校正集和预测集相关系数分别为0.878和0.7009。结论 大黄鱼的近红外光谱信息与其TVB-N、菌落总数间都存在较高的相关性, 所建模型可以快速、无损地定量评价大黄鱼的新鲜度。
英文摘要:
      Objective To investigate a method for the quantitatively freshness evaluation of Pseudosciaena crocea. Methods Near-infrared spectra of the whole back of fish was adopted and preprocessed. Quantitative models of total volatile basic nitrogen (TVB-N) content and aerobic plate count were built with the processed spectra, respectively. The partial least squares (PLS), interval PLS (iPLS), backward interval partial least squares (biPLS) and synergy interval partial least squares (siPLS) algorithms were used for modeling. Results biPLS model had the highest accuracy and predicted the best performance. The optimal biPLS model of TVB-N was achieved with correlation coefficient (Rc=0.8371) in calibration set and correlation coefficient (Rp=0.7652) in prediction set. The optimal biPLS model of aerobic plate count was achieved with correlation coefficient (Rc=0.878) in calibration set and correlation coefficient (Rp=0.7009) in prediction set. Conclusion There is a high correlation between near-infrared spectra and TVB-N or aerobic plate count. Near-infrared spectroscopy with biPLS can be successfully applied as an accurate and non-destructive method for the determination of freshness of Pseudosciaena crocea.
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