Enhancing the robustness of a near-infrared (NIR) model for determining the blending proportion of cut tobacco by accounting for variations in moisture content

Abstract

The blending proportion of cut tobacco significantly affects the intrinsic quality of the cigarette product. Variability in the moisture content of cut tobacco markedly influences its near-infrared (NIR) spectral signature and the accuracy of blending proportion prediction models. To address this critical challenge, spectral data were systematically collected from tobacco samples at various moisture levels. Four partial least squares regression (PLSR)-based correction methods were implemented to mitigate the moisture effect: global correction, orthogonal signal correction (OSC), generalized least squares weighting (GLSW), and dynamic orthogonal projection (DOP). The results indicate that moisture content strongly influences the diffuse transmission spectrum of cut tobacco. A model calibrated at a fixed 12.15% moisture content achieves satisfactory prediction accuracy under that specific condition but exhibits substantial errors when applied to samples with different moisture levels. This underscores the necessity of correcting for moisture effects to establish a more robust and generalizable blending proportion prediction model. Among the correction methods, DOP yielded the most promising performance, enhancing the coefficient of determination for prediction (R_p) from 0.39 to 0.90 and decreasing the root mean square error of prediction (RMSEP) from 5.50% to 2.22% compared to the uncorrected model. These findings have significant practical implications for advancing the application of blending proportion prediction in the tobacco industry.