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ORIGINAL RESEARCH article

Front. Plant Sci.

Sec. Technical Advances in Plant Science

Volume 16 - 2025 | doi: 10.3389/fpls.2025.1617958

Quantitative Determination of Blended Proportions in Tobacco Formulations Using Near-Infrared Spectroscopy and Transfer Learning

Provisionally accepted
Qinlin  XiaoQinlin Xiao1,2Ruifang  GuRuifang Gu1Li  LiLi Li1Jing  WenJing Wen1Xixiang  ZhangXixiang Zhang1Yi  ShenYi Shen1Yang  LiuYang Liu1Lan  XiaoLan Xiao1Qinqin  TangQinqin Tang1Jun  YangJun Yang1Yong  HeYong He2Juan  YangJuan Yang1*
  • 1China Tobacco Sichuan Industrial Co.,Ltd., Chengdu, Sichuan, China
  • 2College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang Province, China

The final, formatted version of the article will be published soon.

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.

Keywords: near-infrared spectroscopy, Transfer Learning, blended proportions, tobacco silk, Quantitative detection

Received: 25 Apr 2025; Accepted: 18 Jul 2025.

Copyright: © 2025 Xiao, Gu, Li, Wen, Zhang, Shen, Liu, Xiao, Tang, Yang, He and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Juan Yang, China Tobacco Sichuan Industrial Co.,Ltd., Chengdu, 610094, Sichuan, China

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