中文摘要：

针对近红外光谱应用,提出了一种基于高斯过程（GP）模型的波长选择算法,即联合区间高斯过程（synergy interval gaussian process,siGP）算法。首先将全光谱区域划分为一系列无重复且间距相等的区间,再选取最优的若干个区间联合建立GP模型,由于GP模型具有非线性处理能力,因此该方法可以减少非线性的影响。以红曲菌固态发酵过程中过程参数水分含量和pH值的检测为例,新算法对水分含量、pH值的预测集相关系数（rp）分别为0.956 4和0.977 3,预测均方根误差（RMSEP）分别为0.012 7和0.161 0,参与建模的数据点由全谱的1 500个分别减少到225个、375个,在对独立样本的预测上,表现出较好的精度。与传统联合区间偏最小二乘（siPIS）波长选择算法对比,siGP算法预测效果更好：对水分含量和pH值,rp在GP模型预测时提高了3.37%和3.51%,RMSEP在GP模型预测时提高了29.4%和34.8%。表明siGP结合GP模型能够有效选择波长区间以及提高近红外模型的准确性,对进一步实现近红外光谱技术在线检测具有参考价值。

英文摘要：

Based on Gaussian Process（GP）,a wavelength selection algorithm named Synergy Interval Gaussian Process（siGP）model is proposed in this paper by using near infrared spectroscopy technology.Full spectrum is divided into a series of unique and equal spacing intervals,before selecting optimal several intervals to establish GP model.Due to the GP model with nonlinear processing ability,the method reduces the disadvantages of nonlinear factor.Taking the near infrared spectrum data of moisture content and pH in solid-state fermentation of monascus as performance verification object of this new algorithm,the prediction correlation coefficient（Rp） of moisture content and pH are 0.956 4 and 0.977 3,respectively.The root mean square errors for prediction set（RMSEP） are 0.012 7 and 0.161 0,respectively.Data points participating in modeling decrease respectively from the original 1 500 to 225 and 375.In the prediction for independent samples,it shows good accuracy.Comparing with traditional synergy interval partial least squares（siPLS） algorithm,the results show that the siGP achieves the best prediction result.The prediction correlation coefficient of moisture content and pH in new algorithm has increased respectively by 3.37%and 3.51%under the model of Gaussian Process,with increases of 29.4%and 34.8%in the root mean square errors for prediction set.This study shows that the combination of siGP and GP model can select wavelength effectively and improves the prediction accuracy of the NIR model.This method is reference for realizing the online detection and optimization control.