基于集群方法(ER)的近红外光谱转移集优化法(英文)

Using Ensemble Refinement(ER)Method to Optimize Transfer Set of Near-Infrared Spectra

近红外光谱因为具有小成本、易操作、低耗时等优点,所以广泛用于食品领域。作为一种间接的检测方法,近红外光谱检测需要建立光谱和浓度之间的统计模型。但是,一种条件下建立的模型在另一种检测条件下会失效。针对此问题,重新建模可以加以解决,但是重新建立光谱与浓度之间的模型非常繁琐耗时。此时,模型转移可以在避免重新建模的情况下,通过光谱校正,保证预测精度。在模型转移中,已经建立好模型的光谱称为主光谱(A),不用建立模型,而只用主光谱模型预测的光谱称为从光谱(B)。模型转移方法的步骤是,先在校正集中选择一些样本作为主光谱的转移集(A_(t)),然后选择从光谱中浓度和A_(t)相同的光谱,以此作为从光谱的转移集(B_(t))。通过A_(t)和B_(t)构建模型转移矩阵。最后将需要校正的从光谱(B_(v))乘以上述的转移矩阵中,即可获得校正后的从光谱(B_(new))。此时,B_(new)就可以用主光谱的模型来直接预测。在模型转移中,转移集样本的选择对模型校正至关重要。目前,转移集的样本通常从光谱之间的距离而非模型转移误差获得。但是,转移误差对模型转移结果的验证至关重要,故该研究出了基于集群分析的集群优化法(ER)并将其用于优化KS方法产生的转移集样本。ER先用随机方法建立转移集的多个子集合,并计算每个子集合的转移误差。然后,对某一个样本,计算包含这个样本的子集合转移误差均值。最后,选择转移误差均值较低的样本作为新转移集样本进行模型转移。以玉米数据测试了ER算法。结果显示,对于典型相关分析-有信息成分提取法(CCA-ICE)、直接校正法(DS)、分段直接校正法(PDS)、光谱空间转化法(SST)这些常见的模型转移方法,相比于KS样本选择方法,ER方法可以找出重要的转移集样本,进而显著降低模型转移误差。

The near-infrared spectra has been widely used in the food region with advantages of low measurement cost,easy operation,and fast analysis rate.An indirect analytical method should calibrate a feasible model between spectra and concentrations.However,the model calibrated under a specific condition may be invalid for the spectra measured under another condition.Recalibration is a solution to this problem.However,recalibrating the model between spectra and concentration cost much time and workforce.Thus,calibration transfer can correct the spectral deviation to keep the precision of prediction and avoid the expense of recalibration.In calibration transfer,the spectra used for calibrating model are called primary spectra(A),while those not calibrate model but only use the model of primary spectra are called secondary spectra(B).The procedure of calibration transfer is selecting samples as transfer set of primary spectra(A_(t))from the calibration set,while choosing the samples of secondary spectra as transfer-set of secondary spectra(B_(t))who share the same concentrations of At.Then the transfer matrix can be constructed through Atand Bt.After that,the corrected secondary spectra(Bnew)can be obtained by validating a set of secondary spectra(B_(v))multiplying the transfer matrix.Finally,the Bnew can be substituted for the primary spectra model for prediction.In calibration transfer,generating a transfer set is an important procedure.Selecting samples of transfer set is commonly based on the distances of spectra rather than validation errors.However,the transfer errors are important to estimate the power of calibration transfer.Hence,in this paper,ensemble refinement(ER)based on model population analysis has been proposed to refine further the transfer set generated by the KS method.Initially,the ER generates several subsets of a transfer set and then computes the validation errors of each subset.Subsequently the average error of subsets that includes the sample can be obtained for each sample.Finally,the samples with low average errors can be selected as a transfer set for calibration transfer.The corn dataset is used to examine this method.The results exhibited that in calibration transfer methods such as canonical correlation analysis combined with informative components extraction(CCA-ICE),direct standardization(DS),piecewise direct standardization(PDS)and spectral space transformation(SST),ER can select key samples for calibration transfer to reduce the errors,compared with KS method significantly.