穆丽君,黄星奕,姚丽娅,戴 煌.基于可视传感器阵列的鳊鱼新鲜度评价模型研究[J].食品安全质量检测学报,2012,3(6):649-653
基于可视传感器阵列的鳊鱼新鲜度评价模型研究
Evaluating model of Parabramis pekinensis’s freshness based on visual sensor arrays
投稿时间:2012-11-13  修订日期:2012-11-28
DOI:
中文关键词:  鳊鱼  可视传感器阵列  挥发性盐基氮  BP神经网络  联合间隔偏最小二乘法
英文关键词:Parabramis pekinensis  vsiual sensor arrays  total volatile basic nitrogen  BP neural networks  synergy interval partial least squares
基金项目:公益性行业(农业)科研专项(201003008)
作者单位
穆丽君 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 
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
MU Li-Jun School of Food and Biological Engineering, Jiangsu University 
HUANG Xing-Yi 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 建立基于可视传感器阵列的鳊鱼新鲜度评价模型。方法 研究采用可视传感阵列与鱼体进行无接触式反应, 提取阵列反应前后的颜色变化信息来表征鱼的气味特征; 同时根据行业标准SC/T3032-2007测得表征鱼新鲜度的挥发性盐基氮(TVB-N)含量; 将可视传感技术所得的特征信息与TVB-N指标含量进行关联, 分别建立基于可视传感技术鱼新鲜度评价的定性模型BP神经网络和联合间隔偏最小二乘法(siPLS)定量模型。结果 BP神经网络模型精度较高, 训练集正确率为86.79%, 预测集正确率为86.43%; siPLS模型次之, 模型校正集和预测集的正确率分别为82.52%和80.67%。结论 可视传感器新技术所测得指标与TVB-N相关性较大, 可快速预测出鱼在储藏期间TVB-N的变化从而能够快速、无损地评价鱼类新鲜度。
英文摘要:
      Objective To establish evaluating models of Parabramis pekinensis’s freshness based on visual sensor arrays. Methods The visual sensor array was developed to react with fish odors without contact. The color changes of the sensor array before and after exposure to fish odors were used to represent the characteristic of fish’s odor. Meanwhile, the content of total volatile basic nitrogen (TVB-N) of fish samples were measured according to SC/T3032-2007 standard. The freshness evaluating models were set up including qualitative model BP neural networks and synergy interval partial least squares (siPLS) quantitative model based on the color information obtained from visual sensor array and TVB-N measurements. Results BP neural networks model was more precise, with correct rate of 86.43% for training set, 86.07% for prediction set. The correct rate of siPLS model for calibration set and prediction set was 82.52% and 80.67%, respectively. Conclusion The visual sensor array could rapidly predict TVB-N content in fish because of the tight relationship between detection values of sensor arrays and TVB-N contents. It is feasible to evaluate the freshness of fish rapidly and non-destructively in this way.
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