NIR高光谱成像技术联用SPA算法快速检测五花肉的过氧化值

NIR Hyperspectral Imaging Combined with SPA Algorithm for the Rapid Detection of Peroxidation Value of Pork Belly

利用近红外(NIR)高光谱成像技术结合连续投影算法(SPA)快速、无损检测五花肉的过氧化值。通过高光谱成像系统采集样品的光谱图像,提取其反射光谱信息,经过基线校正(BC)、高斯滤波平滑(GFS)、中值滤波平滑(MFS)、卷积平滑(SGS)、移动平均值平滑(MAS)、标准正态变量变换(SNV)、多元散射校正(MSC)七种预处理后,利用偏最小二乘(PLS)建立预测模型。使用SPA筛选最优波长,重新预算,构建优化的PLS模型和多元线性回归(MLR)模型。结果显示,经过BC预处理(RP=0.960,RMSEP=5.15×10-4 g/100 g)和原始数据RAW(RP=0.960,RMSEP=4.89×10-4 g/100 g)的全波段PLS模型(F-PLS)预测过氧化值效果较好。优化结果显示,RAW的MLR模型(RP=0.968,RMSEP=4.12×10-4 g/100 g)预测效果更好。研究表明,NIR高光谱成像技术联用SPA算法可潜在实现对五花肉过氧化值的快速无损检测。

The peroxidation value of pork belly was rapidly and nondestructively detected by near infrared(NIR)hyperspectral technique combined with continuous projection algorithm(SPA).Spectral images of samples were acquired through the high spectral spectroscopy imaging system,and their reflection spectrum information was extracted,after seven kinds of pretreatment,including baseline correction(BC),gaussian filter smoothing(GFS),and median filtering smoothing(MFS),savitzky golay smoothing(SGS),moving average smoothing(MAS),standard normal variate(SNV),and multiplicative scatter correction(MSC),partial least squares(PLS)were used to establish the prediction model,and used the regression coefficient method(RC)and SPA to select the optimal wavelength,multiple linear regression(MLR)and PLS optimization model.The results showed that the full-band PLS model(F-PLS)with BC preconditioning(RP=0.960,RMSEP=5.15×10-4 g/100 g)and RAW data(RP=0.960,RMSEP=4.89×10-4 g/100 g)had better prediction effect on peroxidation value,and the optimization showed that the MLR optimization model of RAW(RP=0.968,RMSEP=4.12×10-4 g/100 g)had better prediction effect.The results showed that this method can predict the changes of pork under different refrigeration conditions.