摘要:
基于NIR高光谱成像技术快速评估鸡肉热杀索丝菌含量。通过采集新鲜鸡肉高光谱图像并提取样本反射光谱信息(900~1699 nm),再采用多元散射校正(Multiplicative Scatter Correction,MSC)、基线校正(Baseline Correction,BC)和标准正态变量校正(Standard Normal Variable Correction,SNV)三种方法预处理原始光谱,分别利用偏最小二乘(Partial Least Squares,PLS)、多元线性回归(Multiple Linear Regression,MLR)挖掘光谱信息与鸡肉热杀索丝菌参考值之间的定量关系。同时采用PLS-β系数法、Stepwise算法和连续投影算法(Successive Projections Algorithm,SPA)筛选最优波长简化全波段模型(F-PLS)提高预测效率。结果显示,经BC预处理的全波段光谱(485个波长)构建的F-PLS模型预测热杀索丝菌效果较好,相关系数RP为0.973,误差RMSEP为0.295 lg CFU/g。基于PLS-β法从BC预处理光谱中筛选出25个最优波长构建的PLS-β-PLS(RP=0.931,RMSEP=0.434 lg CFU/g)模型预测较好。本试验表明,利用近红外高光谱成像技术可潜在实现鸡肉热杀索丝菌含量的快速评估。
Abstract:
NIR hyperspectral imaging technology was investigated to rapidly evaluate Brochothrix thermosphacta in chilled chicken. After collecting hyperspectral images of fresh chicken and extracting reflective spectral information in the range of 900~1699 nm, the original spectra were pretreated by three methods including multivariate scattering correction (MSC), baseline correction (BC) and standard normal variable (SNV) correction. The quantitative relationship between spectral information and the reference value of Brochothrix thermosphacta in chicken samples was established by partial least squares (PLS) and multivariate linear regression (MLR). The optimal wavelengths were selected by PLS-β method, stepwise method and successive projection algorithm (SPA), respectively. The results showed that the full-band PLS regression model (F-PLS) built with BC spectra (485 wavelengths) performed better in predicting Brochothrix thermosphacta with RP of 0.973 and RMSEP of 0.295 lg CFU/g.Twenty five optimal wavelengths were selected by PLS-β method to optimize the full wavelength PLS, resulting in the better performance with RP of 0.931 and RMSEP of 0.434 lg CFU/g. In conclusion, NIR hyperspectral imaging technology had a great potential and could be used for rapid evaluation of the content of Brochothrix thermosphacta in chicken.