蓝蔚青,孙雨晴,张楠楠,周大鹏,胡潇予,谢 晶.基于近红外光谱建立大黄鱼新鲜度预测模型[J].包装工程,2020,41(17):1-6.
LAN Wei-qing,SUN Yu-qing,ZHANG Nan-nan,ZHOU Da-peng,HU Xiao-yu,XIE Jing.Prediction Model for Freshness of Large Yellow Croaker Based on Near Infrared Spectroscopy[J].Packaging Engineering,2020,41(17):1-6. |
| 基于近红外光谱建立大黄鱼新鲜度预测模型 |
| Prediction Model for Freshness of Large Yellow Croaker Based on Near Infrared Spectroscopy |
| 投稿时间：2020-06-26 修订日期：2020-09-10 |
| DOI：10.19554/j.cnki.1001-3563.2020.17.001 |
| 中文关键词: 近红外光谱 大黄鱼 冰藏 菌落总数 偏最小二乘法 |
| 英文关键词: near-infrared spectroscopy Pseudosciaena crocea ice storage total viable count Partial Least Squares |
| 基金项目:“十三五”国家重点研发计划重点专项（2019YFD0901602）；现代农业产业技术体系建设专项（CARS-47-G26）；上海水产品加工及贮藏工程技术研究中心能力提升项目（19DZ2284000） |
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| 中文摘要: |
| 目的 通过近红外光谱技术对不同贮藏时间下冰鲜大黄鱼的鲜度进行评价。方法 以菌落总数为鲜度评价指标，基于均值中心化、标准正态变量变换、趋近归一化法（Normalization by Closure, Ncl）、多元散射校正、一阶导数和二阶导数等预处理方法，运用偏最小二乘法（Partial Least Squares, PLS）建模，比较所建模型的定标集与验证集间的相关系数和标准偏差，构建大黄鱼冰藏期间菌落总数的定量模型，以期快速预测其新鲜度。结果 Ncl比其它预处理方法可以更好地消除光谱噪音，提高模型的预测能力。经Ncl光谱预处理，利用PLS建模，可达到最佳的建模效果，其定标集相关系数为0.9095，校正标准偏差相关系数为0.5872，验证集相关系数为0.8858，预测标准偏差为0.6615。模型相关系数>0.9；结论 表明该模型预测精度较好，在大黄鱼新鲜度检测和品质评价方面应用前景良好。 |
| 英文摘要: |
| The work aims to evaluate the freshness of large yellow croaker (Pseudosciaena crocea) by near infrared spectroscopy (NIRS) during ice storage for different time. Total viable counts (TVC) were used as an index to evaluate the freshness and the partial least squares (PLS) were adopted to construct the model based on the pretreatment methods like Mean Center (MC), Normalization by Closure (Ncl), Standard Normal Variate (SNV), Derivative 1st (Db1), Derivative 2nd (Db2) and Multiplicative Scatter Correction (MSC). By comparing the correlation coefficient and standard deviation of calibration set and validation set, the best TVC model for rapidly predicting the freshness of large yellow croaker (Pseudosciaena crocea) in ice storage was established. Ncl could eliminate the spectral noise better and improve the prediction ability of the model. After pretreatment by Ncl, the best modeling effects could be achieved by PLS. The correlation coefficient of calibration set was 0.9095, the correlation coefficient of standard error for calibration (SEC) was 0.5872, the correlation coefficient of verification set was 0.8858, and the standard error of prediction (SEP) was 0.6615. The correlation coefficient of model was higher than 0.9. The result shows that the model has good prediction accuracy, and has a good application prospect in the freshness detection and quality evaluation of large yellow croaker (Pseudosciaena crocea). |
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