Cigarette brand identification model based on PPF projection algorithm and hyperspectral technology

ZHU Yakun; MEI Jifan; GUO Wenmeng; SU Ziqi; LI Jiakang; XU Dayong; DU Jinsong; LI Hui

doi:10.12187/2024.04.015

Abstract：Addressing the complexity of cigarette ingredients and the challenges in brand identification,a cigarette brand identification model based on Projection Pursuit based on Fisher·s Criterion(PPF)and hyperspectral technology was proposed.Firstly,the PPF projection algorithm was employed to conduct spectral similarity analysis on the original spectra as well as those processed with Multiplicative Scatter Correction(MSC),first-order derivative(1st D),and second-order derivative(2nd D).Secondly,feature wavelength selection was carried out using Successive Projections Algorithm(SPA)and Genetic Algorithm(GA)to enhance the classification performance in subsequent steps.Finally,cigarette brand identification models were established using Support Vector Machine(SVM)and Extreme Gradient Boosting(XGBoost)algorithms,and the personality of the model was evaluated.The results indicated that,in the combination of hyperspectral technology and the PPF projection algorithm,the second-order derivative(2nd D)served as the optimal band processing method,significantly enhancing the spectral separability.The recognition model established based on 2nd D-SPA-SVM emerged as the best model,achieving an overall classification accuracy of 94.58%and 92.50%for the training and test sets respectively.Moreover,a greater difference in the proportion of leaf silk and expanded silk among different brand formulations led to a lower similarity value between categories and a higher recognition accuracy.This model can provide a new efficient,fast,accurate,and non-destructive classification and discrimination method for cigarettes of different grades.It offered theoretical support for the application of hyperspectral technology in cigarette brand maintenance,tobacco blend formulation design,and other scenarios.