Integrating progressive screening strategy-based continuous wavelet transform with EfficientNetV2 for enhanced near-infrared spectroscopy.

Near-infrared (NIR) spectroscopy has gained wide acceptance across various fields as a result of advances in portable equipment that can record spectra on site or at production lines. Continuous wavelet transform (CWT) can transform traditional one-dimensional (1D) NIR spectra into more informative two-dimensional (2D) spectrograms, thus enhancing the analysis and interpretation of spectral information. This study introduces a high-efficiency 2D CWT-EfficientNetV2 regression model to optimize NIR spectroscopy applications. A novel progressive screening strategy is employed to select the optimal wavelet functions and scales for CWT, which are then used to transform the features into wavelet coefficient matrices. Direct digital mapping (DDM) with Gray colormap generates 2D spectrograms from matrices, significantly preserving the representation of wavelet coefficients. The 2D CWT-EfficientNetV2 model was used to predict the content of five polyphenols in tobacco leaf samples with superior performance compared to partial least squares regression (PLSR) and other high-efficiency models. Moreover, to further validate the robustness and reliability of the proposed method, two additional public NIR spectral datasets were included in this study. The model achieves lower root mean square error of prediction (RMSEP), as well as higher coefficient of determination of prediction (R) and the ratio of the standard error of prediction to the standard deviation of the reference values (RPD) on the test datasets. These results demonstrate that the 2D CWT-EfficientNetV2 model is a robust and efficient approach for the accurate quantification of various target compounds utilizing NIR spectroscopy.