基于高光谱技术的香肠亚硝酸盐快速检测方法

选取7个不同储藏时期的香肠分别进行亚硝酸盐含量检测和对应的光谱数据采集,并用Savitzky-Golary法进行光谱数据预处理,以减少光谱数据的噪声;在预处理后的光谱数据基础上,用偏最小二乘回归系数法提取出29个特征波长;对比分析了特征波长和全波长下香肠中亚硝酸盐含量预测模型的检测精度。结果表明:全波长下的回归模型预测结果均高于特征波长下,且全波长下偏最小二乘回归模型优于主成分回归模型,表征偏最小二乘回归模型精度的决定系数和均方根误差分别为0.9829和0.0592。说明全波长下的光谱信息更适用于香肠储藏过程中亚硝酸盐含量高光谱检测模型的构建。

Rapid detection method of sausage nitrite based on hyperspectral technology

Seven different storage period sausages were selected for nitrite content detection and corresponding spectral data collection,and uses Savitzky-Golary method to preprocess spectral data to reduce the noise of spectral data.Then based on the pre-processed spectral data, 29 characteristic wavelengths were extracted by partial least squares regression coefficient method.Finally, the detection accuracy of the prediction model of nitrite in sausages at characteristic wavelength and full wavelength were analyzed.The results showed that the prediction results of the regression model based on full wavelength were all higher than that based on characteristic wavelength, and the full-wavelength partial least squares regression model was superior to that of the principal component regression model, and the coefficient of determination of the accuracy of the partial least squares regression model was determined.The R 2 and root mean square errors were 0.982 9 and 0.059 2 , respectively.The dissertation studies show that the spectral information at full wavelength is more suitable for the construction of hyperspectral detection model of nitrite content in sausage storage.