When Process Analysis Technology Meets Transfer Learning: A Model Transfer Strategy Between Different Spectrometers for Quantitative Analysis

With the increase in the number of types of spectrometers in use, calibration models cannot be shared among different instruments, however, this problem can be solved via calibration transfer (CT). In this study, a variety of modern process analysis technology (PAT) data are taken as the research object. After preprocessing the spectra data using principal component analysis (PCA) and cubic spline interpolation, the TrAdaBoost algorithm in transfer learning combined with extreme learning machine (ELM), i.e., TrAdaBoost-ELM, is used to transfer the master model to slave instruments and to make comparisons with the Transfer via an Extreme learning machine Auto-encoder Method (TEAM) and the semi-supervised parameter free framework for calibration enhancement (SS-PFCE) method. After the master model is transferred by the TrAdaBoost-ELM algorithm for the prediction dataset of slave instruments, the mean coefficient of determination of prediction (R _p ² ) increases from 0.7843 to 0.8707, and the mean root mean square error of prediction (RMSEP) decreases from 2.7508 to 2.3112. Furthermore, variable combination population analysis (VCPA) in combination with a genetic algorithm (VCPA-IGA) were used to select characteristic wavelengths in molecular and atomic spectra, respectively. For the same type of laser-induced breakdown spectroscopy (LIBS) instruments K1 and K2, after processing by the VCPA-IGA algorithm, the LIBS calibration model established on K1 was transferred successfully to K2, and for the major elements, the mean R _p ² = 0.9563 and the mean RMSEP = 1.3796. After processing by the VCPA algorithm, the near-infrared (NIR) model for instrument L was transferred to a different instrument J, and the prediction results were R _p ² = 0.9110 and RMSEP = 0.4044 °Brix. The results demonstrated that an appropriate variable selection method combined with the TrAdaBoost-ELM algorithm can be effectively used for CT for spectrometers of the same and different types, thus achieving model sharing between different spectrometers.