Myles Anthony J, Zimmerman Tyler A, Brown Steven D
Laboratory for Chemometrics, Department of Chemistry and Biochemistry, University of Delaware, Newark, Delaware 19716, USA.
Appl Spectrosc. 2006 Oct;60(10):1198-203. doi: 10.1366/000370206778664581.
Analogous to the situation found in calibration, a classification model constructed from spectra measured on one instrument may not be valid for prediction of class from spectra measured on a second instrument. In this paper, the transfer of multivariate classification models between laboratory and process near-infrared spectrometers is investigated for the discrimination of whole, green Coffea arabica (Arabica) and Coffea canefora (Robusta) coffee beans. A modified version of slope/bias correction, orthogonal signal correction trained on a vector of discrete class identities, and model updating were found to perform well in the preprocessing of data to permit the transfer of a classification model developed on data from one instrument to be used on another instrument. These techniques permitted development of robust models for the discrimination of green coffee beans on both spectrometers and resulted in misclassification errors for the transfer process in the range of 5-10%.
类似于在校准中发现的情况，从一台仪器上测量的光谱构建的分类模型可能不适用于根据另一台仪器上测量的光谱来预测类别。本文研究了多元分类模型在实验室近红外光谱仪和过程近红外光谱仪之间的转移，用于鉴别完整的绿色阿拉比卡咖啡豆（Arabica）和卡内弗拉咖啡豆（Robusta）。发现一种经过修改的斜率/偏差校正、基于离散类别标识向量训练的正交信号校正以及模型更新，在数据预处理中表现良好，能够使基于一台仪器数据开发的分类模型转移到另一台仪器上使用。这些技术使得能够在两台光谱仪上开发出用于鉴别生咖啡豆的稳健模型，并且转移过程中的误分类误差在5%至10%的范围内。
