利用近红外光谱技术建立数学模型,预测南美白对虾鲜度变化时挥发性盐基氮(TVB-N)和菌落总数(TBC)的含量。对42个虾糜样品进行扫描,获得950~1 650 nm光谱信息,运用Unscrambler10.3软件进行计算,光谱经过一阶导数、Savisky-Golay(SG)平滑和标准正态变化(SNV)预处理,运用偏最小二乘法(PLS)建立TVB-N及TBC模型,并对模型进行验证。结果表明:TVB-N模型中定标集和预测集相关系数分别为0.980和0.923,交叉验证标准方差(RMSECV)和预测均方根误差(RMSEP)分别为1.189和2.179;TBC模型中定标集和预测集相关系数分别为0.991和0.943,RMSECV和RMSEP分别为0.136和0.603,表明所建模型取得了较好的预测效果,可以很好的判别虾新鲜度。

The mathematical model was established to determinate the Penaeus vannamei Boone's content of volatile base nitrogen( TVB-N) and total bacterial count( TBC) by near infrared spectroscopy. There were 42 samples scanned by a spectroradiometer,the spectra was obtained range from 950 to 1650nm. The raw spectra were pretreated by first derivative、Savisky-Golay( SG) smoothing and Standard Normal Variate( SNV) using the Unscrambler 10. 3. The partial least square regression( PLSR) was used to build TVB-N and TBC model,and validating model. The results showed that the correlation coefficients of calibration set and prediction set of TVB-N model were 0. 980 and 0. 923 respectively. Root Mean Standard Error of Cross-Validation( RMSECV) and Root Mean Square Errors of Prediction( RMSEP) were 1. 189 and 2. 179 respectively; The correlation coefficients of calibration set and prediction set of TBC model were 0. 991and 0. 943 respectively. RMSECV and RMSEP were 0. 136 and 0. 603. The results indicated that these models were better to predict TVB-N and TBC in Penaeus vannamei Boone,and could determine shrimp freshness.