基于集群方法(ER)的近红外光谱转移集优化法

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...

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

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 A_t. Then the transfer matrix can be constructed through A_t and B_t. After that, the corrected secondary spectra (B_new) can be obtained by validating a set of secondary spectra (B_v) multiplying the transfer matrix. Finally, the B_new 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.