潜变量机器学习方法在咖啡NIR定量分析中的应用

采用近红外（NIR）光谱快检技术实现对咖啡蛋白质的定量检测，研究支持向量机（SVM）和极限学习机（ELM）等机器学习方法在建模分析中的实用性。结合潜变量分析技术，建立潜变量SVM（LV-SVM）模型和潜变量ELM（LV-ELM）模型，通过调试潜变量个数和机器学习关键参数的联合优选，实现数据降维和机器学习关键参数的同过程优化。运用定标-验证-测试机制，利用定标集样本建立咖啡蛋白质的NIR分析模型，随参数变动形成三维随动优选结构的建模预测结果，结合验证集样本对模型进行联合优选，然后将优化模型应用于测试集样本进行模型评价。LV-SVM建模优选的验证集预测均方根误差为6.797，对应的测试集预测均方根误差为8.384。LV-ELM建模优选的验证集预测均方根误差为6.118，对应的测试集预测均方根误差为7.837。与常规偏最小二乘（PLS）方法相比较，LV-SVM和LV-ELM方法均取得更好的预测结果，验证了潜变量机器学习方法在近红外定量分析中的应用优势，该方法有望应用于不同类型的咖啡各成分含量检测。

Latent Variable Machine Learning Methods Applied for NIR Quantitative Analysis of Coffee

Near-infrared(NIR)spectroscopy rapid detection technology was used to determine protein content in instant coffee.Support vector machine(SVM)and extreme learning machine(ELM)was applied for validating their practicality in modeling analysis.We proposed the latent variable SVM(LV-SVM)and latent variable ELM(LV-ELM)methods combined with latent variable analysis technique.Thetuning of latent variables and the optimization of the key parameters in machines were joint in-one so that the data dimension reduction and the selection of machine parameters can be both accomplished in one single modeling process.The calibrating-validating-testing mechanism was used for sample division.The NIR analytical models were trained based on the calibrating sample set.The model prediction results were generated and saved as a 3 Dbox as they were determined by the simultaneous tuning of the latent variable and the machine parameter.Then the joint optimization of model parameters was selected in the way of predicting the validating samples.Further,the optimal model was evaluated by the testing samples.The optimal LV-SVM model gave the validating root mean square error as 6.797;the corresponding testing root mean square error as 8.384.The optimal LV-ELM model obtained the validating root mean square error as 6.118.The corresponding testing root means square error as 7.837.Compared with the common partial least square method,the LV-SVM and LV-ELM methods have better prediction results,which verified the application advantages of the latent variable machine learning method in near-infrared quantitative analysis.This proposed method is expected for further application in content detection of other kinds of coffee.