Research on detection of tobacco blend ratio based on hyperspectral imaging

No rapid method existed for detecting blend ratios on production lines, hyperspectral imaging technology and machine learning methods were used to collect spectral data from mixed tobacco with different blend ratios. The effects of single and combined preprocessing techniques on model performance were explored. Regression models were established using partial least squares regression ( PLSR) and support vector machine regression ( SVR). Feature wavelength selection was performed with least angle regression (LARS), successive projections algorithm (SPA), competitive adaptive reweighted sampling ( CARS), and genetic algorithm ( GA) to build simplified models. The results showed that preprocessing methods, either individually or combined, affected model accuracy. The combined wavelet transform and SG filtering ( Wave + SG) method reduced mean absolute percentage error (MAPE) by 1. 2 percentage points compared to raw data. The Wave+SG-GA-PLSR model performed best, with MAPE of 1. 415% and 1. 531% for the training and test sets of two-component blends, respectively. This method was also applicable to multi-component blends, with MAPE in three-component and four-component blends below 8. 3615%. Hyperspectral imaging combined with machine learning accurately predicted the proportions of components in mixed tobacco, providing a reference for online monitoring and quality control in cigarette production.