Quantitative determination of blended proportions in tobacco formulations using near-infrared spectroscopy and transfer learning

Résumé fourni par la source

Accurate detection of blending proportions in tobacco formulations is crucial for ensuring the quality consistency and flavor stability of cigarette products. In recent years, modeling approaches based on near-infrared spectroscopy (NIRS) have attracted significant attention for the quantitative analysis of tobacco blending. However, due to variations in tobacco composition and spectral characteristics across different cigarette brands, the generalization ability of NIRS-based models often declines when applied to cross-brand prediction tasks. To address this issue, this study takes the detection of blending proportions of tobacco silk in tobacco formulations as the research focus, and investigates transfer learning strategies aimed at enhancing the cross-brand adaptability of NIRS-based models. A partial least squares regression (PLSR) model was first developed using NIRS data from four different tobacco brands, achieving high prediction accuracy on the combined dataset (RMSEP = 1.20%). However, when the model trained on a single brand was applied to predict other brands, the prediction performance decreased notably. To improve model adaptability, three approaches were explored: Transfer Component Analysis (TCA), Correlation Alignment (Coral), and model updating. The results show that TCA-PLSR achieved substantial reductions in prediction error in most transfer tasks involving large discrepancies in feature distributions. Coral-PLSR demonstrated superior performance in transfer tasks involving similar spectral feature distributions. Additionally, in transfer tasks characterized by substantial distribution differences, the Updated-TCA-PLSR model, which incorporates a small proportion of target domain samples into the source domain before domain adaptation, yielded accurate predictions of tobacco silk blending proportions. These findings demonstrate that transfer learning and model updating offer practical, flexible, and robust approaches for enhancing the performance of NIRS-based models, supporting more accurate and consistent quality control in industrial-scale formulated tobacco production.

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