Detection of the proportion of tobacco flavoring based on near-infrared hyperspectral and machine learning

To address the issues of complex operation, long time consumption, low efficiency, and difficulty in online monitoring of traditional methods for detecting the proportion of flavoring in tobacco shreds, this study proposes a non-destructive detection method for the proportion of flavoring in tobacco shreds based on near-infrared hyperspectral imaging technology combined with machine learning. In the research, a hyperspectral dataset of tobacco shreds with various flavoring proportions was constructed, and the collected near-infrared hyperspectral data were preprocessed using improved Direct Orthogonal Signal Correction (DOSC) and other algorithms to remove noise interference and enhance signal features. On this basis, multiple models such as Partial Least Squares Regression (PLS), Support Vector Regression (SVR), and Random Forest Regression (RF) were constructed to predict and analyze tobacco shred samples with different flavoring proportions. The results show that the data preprocessed by DOSC have the highest prediction accuracy on the PLS model, especially when the flavoring proportion is 0.6% or higher, with a prediction accuracy of 91.8% and a root mean square error of 0.010. This method provides an effective means for rapid and non-destructive detection of the proportion of flavoring in tobacco shreds and has good industrial application prospects.