Qualitative Discrimination of Intact Tobacco Leaves Based on Near-Infrared Technology

Mengyao Lu,Tian’En Chen,Junhui Li,Qiang Zhou,Dong Chen,Cong Wang,Qin Gao,Shuwen Jiang

doi:10.1155/2021/8807199

Abstract

To explore the application of near-infrared (NIR) technology to the quality analysis of raw intact tobacco leaves, a nondestructive discrimination method based on NIR spectroscopy is proposed. A “multiregion + multipoint” NIR spectrum acquisition method is developed, allowing 18 NIR diffuse reflectance spectra to be collected from an intact tobacco leaf. The spectral characteristics and spectral preprocessing methods of intact tobacco leaves are analyzed, and then different spectra (independent or average spectra) and different algorithms (discriminant partial least-squares (DPLS) and Fisher’s discriminant algorithms) are used to construct discriminant models for verifying the feasibility of intact leaf modeling and determining the optimal model conditions. Qualitative discrimination models based on the position, green-variegated (GV), and the grade of intact tobacco leaves are then constructed using the NIR spectra. In the application and verification stage, a multiclassification voting mechanism is used to fuse the results of multiple spectra from a single tobacco leaf to obtain the final discrimination result for that leaf. The results show that the position-GV discrimination model constructed using independent spectra and the DPLS algorithm and the grade discrimination model constructed using independent spectra and Fisher’s algorithm achieve optimal results with intact leaf NIR wavenumbers from 5006–8988 cm−1 and the first-derivative and standard normal variate transformation preprocessing method. Finally, when applied to new tobacco leaves, the position-GV model and the grade model achieve discrimination accuracies of 95.18% and 92.77%, respectively. This demonstrates that the two models have satisfactory qualitative discrimination ability for intact tobacco leaves. This study has established a feasible method for the nondestructive qualitative discrimination of the position, GV, and grade of intact tobacco leaves based on NIR technology.

Highlights

Enhanced requirements in the Chinese cigarette industry regarding the quality of flue-cured tobacco raw materials mean that improving and maintaining the quality of tobacco leaves in the production area is increasingly important in the tobacco processing industry [1, 2]
Fisher’s algorithm combined with the independent spectra and average spectra preprocessed with 1st derivative + standard normal variate transformation (SNV) was used to construct tobacco discrimination models
We described the development of a qualitative discrimination method for the position-GV and grade of intact tobacco leaves using NIR technology

Summary

Introduction

Enhanced requirements in the Chinese cigarette industry regarding the quality of flue-cured tobacco raw materials mean that improving and maintaining the quality of tobacco leaves in the production area is increasingly important in the tobacco processing industry [1, 2]. In the management of tobacco quality, the position and grade of tobacco are the most important key factors [3]. According to the different growing positions of the tobacco on the tobacco stalk, tobacco positions are divided into three major positions (i.e., upper(B), middle(C), and lower(X)). In accordance with the Chinese national standard for “Flue-cured tobacco” (GB2635-1992) [5], based on the differences of tobacco characteristics, such as tissue structure, oil content, thickness, maturity, and injury degree, tobacco leaves in the same position can be further divided into 1, 2, 3, and 4 grades. B2F refers to the tobacco leaf of upper, 2 grades, and orange.

Methods

Results

Conclusion