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首页> 外文期刊>配管·装置·プラント技術 >Non-linear modeling of water content in processed green tea using ANN and PCA: application of the hyphenated PCA-ANN regression model to the absorbance obtained by NIRS
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Non-linear modeling of water content in processed green tea using ANN and PCA: application of the hyphenated PCA-ANN regression model to the absorbance obtained by NIRS

机译：ANN和PCA加工绿茶中水含量的非线性建模：用Hypenated PCA-ANN回归模型应用于NIRS获得的吸光度

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摘要

For determination of moisture of the processed Japanese green tea, Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) were applied to the absorbance that were provided with near infrared spectoroscopy (NIRS). The 3-3-1hierarchy type PCA-ANN (principal component analysis - artificial neural networks) regression model used the first three PCA scores as input signal, and the water content as output signal ,showed that the standard error of prediction (SEP) and thecorrelation coefficient (r) between the actual value and the predicted value of water were 1.547%w.b. and 0.998 in prediction set respectively. This value of SEP was decreasing to about 30~73%, compared to the conventional multiple linear regression(MLR), principal component regression (PCR) and partial least squares (PLS) regression model, and this model performed with a high accuracy of prediction in prediction set. The efficiency of the combined model of PCA-ANN regression becomes clear as themodeling for multicollinearity in variables and non-linearity between the moisture content covering a wide range in tea processing and the absorbance provided with NIRS.
机译：为了测定加工日本绿茶的水分，主要成分分析（PCA）和人工神经网络（ANN）应用于具有近红外施椎（NIRS）的吸光度。 3-3-1比级型PCA-ANN（主成分分析 - 人工神经网络）回归模型使用前三个PCA分数作为输入信号，以及作为输出信号的水分，显示预测的标准误差（SEP）和实际值与水的预测值之间的围类系数（R）为1.547％WB分别预测集0.998。与传统的多元线性回归（MLR），主成分回归（PCR）和局部最小二乘（PCR）和偏最小二乘（PCR）和偏最小二乘（PCR）回归模型相比，SEP的该值下降至约30〜73％，并且该模型以高精度的预测执行在预测集中。 PCA-ANN回归的组合模型的效率变得清楚，因为在覆盖茶处理宽范围的覆盖范围内的含水量和带内联的吸光度之间的变量中的多含量和非线性的主题。

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