近红外光谱结合极限学习机和GA-PLS算法检测普洱茶茶多酚含量

Determination of Tea Polyphenols Content in Puerh Tea Using NearInfrared Spectroscopy Combined with Extreme Learning Machine and GA-PLS Algorithm

利用近红外光谱技术检测普洱茶中茶多酚的含量,首先通过遗传偏最小二乘法(GA-PLS)筛选出表征茶多酚含量的特征波数点,并进行主成分分析,然后建立极限学习机(ELM)预测模型。研究得到的最佳ELM预测模型涉及40个变量,主成分分析后以第1、第2主成分作为输入,以Sigmoidal函数为隐含层神经元激励函数,隐含层神经元个数确定为13。模型的交互验证均方根误差值、预测集均方根误差值和预测集相关系数R2分别为1.0109、1.6686和0.9705,预测性能明显优于全光谱偏最小二乘模型和遗传偏最小二乘模型。说明利用近红外光谱技术结合极限学习机和遗传偏最小二乘法可以很好地预测普洱茶中茶多酚的含量。

To determine the tea polyphenols content in Puerh tea by near infrared spectroscopy, genetic algorithm combined with partial least square （GA-PLS） is used to select those wave-numbers carrying information that are highly related to the tea polyphenols of Puerh tea. As a result, 40 wave numbers are selected from the spectral range of 10001～4000 cm-1. Then principal component analysis is applied for these selected wave numbers, and the first two principal components are achieved to input into the extreme learning machine （ELM）. Using sigmoidal function as active function, the ELM is trained with the number of hidden-layer neurons varying from 1 to 50. Result shows that it will reach the minimum root mean square error of prediction （RMSEP） set and get the optimal ELM model when there are 13 hidden-layer neurons. The optimal ELM model gives the correlation coefficient of prediction set R2 of 0.9705, with root mean square error of cross-validation （RMSECV） of 1.0109 and RMSEP of 1.6686. This model uses only 40 wave numbers and 2 input-layer neurons, but obtains better performance, compared with the global PLS model with 1557 wave numbers and GA-PLS model with 40 wave numbers. Results show that the tea polyphenols content in Puerh tea can be determined with high precision by using near-infrared spectroscopy combined with genetic algorithm and ELM.