An interpretable deep learning approach for calibration transfer among multiple near-infrared instruments

Spectroscopic techniques have been widely applied in agricultural applications. The development of calibration transfer is promising for the robust analysis of spectral data collected by varying instruments. The reliance on standard samples for standardization approaches remains a critical challenge for on-site applications. In this study, a deep learning approach, named DeepTranSpectra (DTS), is proposed to transfer convolutional neural network models among multiple near-infrared spectrometers with different types. The proposed DTS approach effectively avoids the requirements for standard samples by using labeled samples of slave instruments. The calibration transfer analysis is investigated on a soybean meal and one wheat dataset for predicting moisture and crude protein contents. The developed DTS approach demonstrates improved transfer performance compared with three popular standardization approaches, including piecewise direct standardization (PDS), canonical correlation analysis (CCA), and slope and bias correction (SBC). A feature visualization method is leveraged to interpret the transfer mechanism of the DTS approach. The interpretation results show that the DTS approach refines the model parameters to adapt to slave devices based on critical features of the master calibration. The DTS approach provides advanced reliability under different sample selections in Monte-Carlo cross-validation. The integration of deep learning approaches with calibration transfer analysis facilitates agricultural applications for emerging deep learning-based chemometric analysis.