基于近红外光谱的大米蛋白质含量快速检测

Rapid detection of protein contentin rice based on near infrared spectroscopy

针对国标化学检测方法耗时耗力、成本昂贵的问题,分析了近红外光谱(NIRS)结合化学计量学进行大米蛋白质含量检测的可行性。基于变量选择、特征提取和非线性建模的策略,将反向区间偏最小二乘(BiPLS)与主成分分析(PCA)和支持向量机(SVM)相结合,构建了BiPLS-PCA-SVM模型,用于提高蛋白质回归模型的性能。在BiPLS-PCA-SVM模型中,将蒙特卡罗交叉验证与预测残差平方和相结合进行最佳主成分个数的选择,通过遗传模拟退火算法对模型参数进行优化。为了评估BiPLS-PCA-SVM模型的性能,建立了Full-PLS、BiPLS和BiPLS-SVM 3种模型,并系统分析了上述模型的预测精度和鲁棒性。BiPLS-PCA-SVM模型在预测蛋白质含量方面显示的性能高于其他模型,使用最佳主成分和优化后的SVM参数建立的模型具有更高的鲁棒性和准确性。对于BiPLS-PCA-SVM模型,验证集的决定系数、均方根误差和剩余预测偏差分别为0.9289、0.1967%和4.0246。结果表明,NIRS与BIPLS-PCA-SVM模型相结合,可作为替代策略实现大米中蛋白质含量的快速检测。

In view of the time-consuming,labor-intensive,and costly problem of chemical detection methods in the national standard,the feasibility of near-infrared spectroscopy(NIRS)combined with chemometrics for rapid detection of rice protein was investigated.Based on strategies of variable selection,feature extraction and nonlinear modeling,BiPLS-PCA-SVM was constructed by combining reverse interval partial least squares(BiPLS)with principal component analysis(PCA)and support vector machine(SVM)to improve the performance of the protein regression model.In BiPLS-PCA-SVM,the optimal number of principal components(PCs)was selected by combining Monte Carlo cross validation with the predicted residual sum of squares,and the model parameters were optimized by genetic simulated annealing algorithm.To evaluate the performance of BiPLS-PCA-SVM,three different models,including Full-PLS,BiPLS and BiPLS-SVM,were established,and the prediction accuracy and model robustness of all models were systematically analyzed.The performance of BiPLS-PA-SVM model in predicting protein content was higher than that of other models,and the model established by using the optimal number of PCs and optimized SVM parameters had higher robustness and accuracy.For BiPLS-PCA-SVM,the determination coefficient,root-mean square error and residual predictive deviation of the validation set were 0.9289,0.1967%and 4.0246,respectively.The results showed that NIRS combined with BiPLS-PCA-SVM model could be used as a reliable alternative strategy to realize the rapid detection of protein content in rice.