基于高光谱成像快速检测牛肉糜中大豆分离蛋白掺入量

采用近红外高光谱成像技术(900~1700 nm)结合化学计量学算法快速定量预测牛肉糜中大豆分离蛋白掺入量。首先按照2%~30%(w/w),掺入间隔1%的浓度梯度,制备不同大豆分离蛋白掺入浓度的牛肉糜样品,然后采集样品的高光谱图像并提取光谱数据,最后运用偏最小二乘回归(Partial least squares regression,PLSR)和多元线性回归(Multiple linear regression,MLR)算法建立预测模型。为了减少模型的高维共线性问题,采用回归系数法(Regression coefficients,RC)和连续投影算法(Successive projection algorithm,SPA)筛选最优波长,优化全波段预测模型。结果显示基于RC法筛选的22个最优波长构建的RC-PLSR模型和RC-MLR模型预测效果优于基于SPA法筛选的21个最优波长构建的SPA-PLSR模型和SPA-MLR模型。其中,RC-PLSR模型预测效果最接近全波段PLSR模型,r_P为0.95,RMSEP为2.73%,RPD为3.32。试验结果表明近红外高光谱成像技术结合化学计量学方法可快速预测牛肉糜中大豆分离蛋白的掺入量。

Rapid Detection of Soy Protein Isolate Concentration in Minced Beef by Hyperspectral Imaging Technology

Near-infrared hyperspectral imaging(900~1700 nm)combined with chemometrics algorithms was investigated to rapidly predict the concentration of soy protein isolate in minced beef.The minced beef samples with different concentration of soy protein isolate(2%~30%,at interval of 1%,w/w)were firstly prepared. The hyperspectral images of the samples were collected and the spectral data within the images were then extracted. Based on the spectral information,the predicted models were established by partial least square regression(PLSR)and multiple linear regression(MLR). To improve model efficiency and reduce the collinearity of spectral data,optimal wavelengths were selected by regression coefficients(RC)and successive projection algorithm(SPA). The results demonstrated that the RC-PLSR model and RC-MLR model built with 22 optimal wavelengths selected by RC method performed better than the SPA-PLSR model and SPA-MLR model built with 21 optimal wavelengths selected by SPA method. The RC-PLSR model had best performance with r_P of 0.95,RMSEP of 2.73% and RPD of 3.32.The whole study revealed that near-infrared hyperspectral imaging combined with chemometrics could be used for rapid and non-destructive detection of the soy protein isolate concentration in minced beef.