Rapid Detection of Physicochemical Indicators of Tobacco Flavorings Using Fourier-Transform Near Infrared Spectroscopy with Chemometrics and Machine Learning.

Journal: ACS omega
Published Date: May 6, 2025

Abstract

Timely and rapid monitoring of the quality of tobacco flavorings is crucial for the accurate quality management of cigarette products. In this study, FT-NIR spectroscopy combined with chemometrics and machine learning was used to detect physicochemical indicators of tobacco flavorings. FT-NIR spectra of 1,608 flavoring samples, encompassing 145 categories and 90 production batches from actual industrial scenarios, were collected. The physicochemical indicators, including the acid value, relative density, and refractive index, were accurately measured. The effect of different spectral preprocessing methods (standard normal variate transformation (SNV), multiplicative scatter correction (MSC), and normalization) was compared. The least angle regression (LAR), successive projection algorithm (SPA), and random frog (RF) were used to select characteristic wavelengths. Partial least-squares regression (PLSR), decision tree (DT), least-squares-support vector machine (LSSVM), and convolutional neural network regression (CNNR) were applied to establish detection models. For acid value, the normalization-SPA-LSSVM model achieved the best performance, reaching an Rp of 0.929, RMSEP of 1.155, and an RPD of 3.741. For relative density, the MSC-LAR-LSSVM model performed best, with an Rp of 0.951, RMSEP of 0.018, and an RPD of 4.481. For the refractive index, the SNV-SPA-LSSVM model obtained satisfactory results, with an Rp at 0.955, an RMSEP at 0.004, and an RPD of 4.664. The results illustrated that FT-NIR spectroscopy is an effective approach for detecting physicochemical indicators of large-scale industrial tobacco flavorings and holds promise for accurate quality assessment of tobacco flavoring products. Also, the performance of the CNNR model is not consistently superior to that of conventional models, especially in situations when the number of features used for building models is relatively limited.

Authors

  • Qinlin Xiao
    Technology Center, China Tobacco Sichuan Industrial Co., Ltd., Chengdu 610066, China.
  • Jian Zheng
    Biospheric Assessment for Waste Disposal Team, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, 4-9-1 Anagawa, Inage, Chiba 263-8555, Japan; Fukushima Project Headquarters, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, 4-9-1 Anagawa, Inage, Chiba 263-8555, Japan. Electronic address: zheng.jian@qst.go.jp.
  • Jing Wen
    Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA.
  • Fada Deng
    Technology Center, China Tobacco Sichuan Industrial Co., Ltd., Chengdu 610066, China.
  • Ruifang Gu
    Technology Center, China Tobacco Sichuan Industrial Co., Ltd., Chengdu 610066, China.
  • Li Li
    Department of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
  • Yong He
    College of Biosystems Engineering and Food Science, Zhejiang Univ., Hangzhou, 310058, China.
  • Juan Yang
    Department of Neurology, Shanghai Pudong New Area People's Hospital, Shanghai, China.

Keywords

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