摘要:
以整块鸡胸肉为研究对象,利用在线近红外光谱系统采集其900~1650 nm波长范围内的光谱信息,探究光谱信息与细菌菌落总数(Total Viable Count,TVC)之间的定量关系。对采集的原始光谱信息进行高斯滤波平滑(Gaussian Filter Smoothing,GFS)等五种预处理后,建立全波段偏最小二乘(Partial Least Squares,PLS)回归模型。采用回归系数法(Regression Coefficient,RC)和连续投影算法(Successive Projections Algorithm,SPA)筛选最优波长,构建优化的PLS模型和多元线性回归(Multiple Linear Regression,MLR)模型。结果表明,基于全波段GFS光谱构建的GFS-PLS模型预测鸡胸肉TVC效果最佳(rP=0.964,RMSEP=0.806 lg CFU/g)。基于SPA法从GFS光谱中筛选出的25个最优波长(907.0、913.7、923.8、927.2、937.2、947.3、974.0、987.3、997.3、1007.3、1040.4、1080.1、1099.9、1132.9、1155.9、1185.5、1215.0、1241.2、1270.6、1358.2、1380.8、1403.3、1419.3、1578.9和1615.2 nm),建立的SPA-GFS-MLR模型预测性能(rP=0.944,RMSEP=1.022 lg CFU/g)最接近GFS-PLS模型。基于在线近红外光谱系统可实现对大批量整块鸡胸肉细菌总数含量的快速无接触检测。
Abstract:
Taking the whole chicken breast as the research object,an online near-infrared spectroscopy(NIR)system was used to collect the spectra in the wavelength range of 900~1650 nm to explore the quantitative relationship between the spectral information and the total viable count(TVC). The collected original spectral information was preprocessed using five pretreatments including Gaussian filter smoothing(GFS)correction to construct partial least squares(PLS)models. The regression coefficients(RC)and successive projections algorithm(SPA),respectively,were used to select optimal wavelengths to build optimized PLS model and multiple linear regression(MLR)model. The results showed that the GFS-PLS model based on GFS spectra performed best in predicting TVC with rP of 0.964 and RMSEP of 0.806 lg CFU/g. SPA-GFS-MLR model based on the 25 optimal wavelengths(907.0,913.7,923.8,927.2,937.2,937.2,947.3,974.0,987.3,997.3,1007.3,1040.4,1080.1,1099.9,1132.9,1155.9,1185.5,1215.0,1241.2,1270.6,1358.2,1380.8,1403.3,1419.3,1578.9 and 1615.2nm)selected by SPA had similar accuracy and stability,with rP of 0.944 and RMSEP of 1.022 lg CFU/g,compared with GFS-PLS model. It is demonstrated that the on-line NIR system can be used to detect the TVC in the whole chicken breast in a rapid way.
百度学术
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