%0 Journal Article
%T 近红外光谱技术快速预测团头鲂新鲜度<br>Using near infrared reflectance spectroscopy to quickly predict the freshness of Megalobrama amblycephala
%A 周娇娇
%A 吴潇扬
%A 陈周
%A 熊善柏
%J 华中农业大学学报
%D 2019
%X 采用近红外光谱技术和化学计量学方法，探究团头鲂新鲜度指标快速检测方法。选取不同的季节、产地、规格、贮藏时间的150个团头鲂样品，采集样品1 000～1 799 nm范围的近红外光谱数据，应用偏最小二乘法建立pH、TVB N、TBA和 K值的新鲜度指标定量分析模型，经不同光谱预处理方法优化和CARS挑选特征波长后，模型的相关系数分别为0.961、0.881、0.955和0.946，交叉验证均方根误差分别为0.049、1.659、0.047和2.558，模型具有较好的预测能力，为淡水鱼新鲜度快速无损检测提供了一种有效的方法。<br>To investigate the potential of near infrared reflectance spectroscopy (NIRS) and chemometrics methods to quickly predict the freshness of Megalobrama amblycephala,NIR spectra of 150 samples from different seasons,different origins,different specifications and different storage durations were recorded in the range of 1 000 1 799 nm. The spectra were preprocessed and then calculated using CARS for wavelength variable selection for establishing quantitative models to predict pH,TVB N,TBA and K values with partial least squares regression (PLSR) method. The results showed that correlation coefficients of the models were 0.961,0.881,0.955 and 0.946,and root mean square errors of cross validation (RMSECV) were 0.049,1.659,0.047 and 2.558,respectively. The models have good prediction ability,which will provide an effective method for predicting the freshness of freshwater fish quickly and non destructively
%K 近红外光谱 团头鲂 新鲜度 偏最小二乘法 无损检测<br>near infrared spectroscopy Megalobrama amblycephala freshness partial least squares regression nondestructive testing
%U http://hnxbl.cnjournals.net/hznydxzr/ch/reader/view_abstract.aspx?file_no=20190416&flag=1