A new paradigm in cigarette smoke detection: Rapid identification technique based on ATR-FTIR spectroscopy and GhostNet-[formula omitted]

Abstract

In the tobacco field, the analysis and classification of cigarette smoke are crucial for ensuring product quality, protecting consumer health, and driving industry innovation. These analyses provide a comprehensive understanding of tobacco products and serve as a scientific basis for production standards and regulatory policies. Traditional infrared spectroscopy techniques for tobacco quality analysis often suffer from limitations such as time consumption, high costs, and complexity. To achieve efficient detection of cigarette smoke and quality control of cigarettes, this study developed a cigarette mainstream smoke detection system based on Attenuated Total Reflectance Fourier transform infrared (ATR-FTIR) spectroscopy technology, aimed at rapidly acquiring the mid-infrared (MIR) spectral characteristics of cigarette smoke and combining deep learning for rapid cigarette type identification. Five different brands of cigarettes were selected for this study, and a total of 400 MIR spectra of cigarette smoke aerosols were collected. Using three spectral preprocessing methods and two-dimensional correlation spectroscopy (2DCOS), we extracted the fingerprint region (1850–650 cm-1) to generate 4800 2DCOS images. We then optimized the GhostNet network architecture and constructed a more compact and efficient deep learning model, GhostNet-α. A mixed-precision training strategy further enhanced model training speed and computational efficiency. Results show that Multiple Scatter Correction (MSC)-preprocessed synchronous 2DCOS images achieved 100 % classification accuracy. This study validates GhostNet-α’s efficiency and accuracy in cigarette smoke recognition, offering a new technical approach for quality control and rapid detection in the tobacco industry, significantly enhancing its competitiveness and consumer health protection.