Near-infrared-spectrum-based tobacco leaf part feature extraction and discrimination method

Abstract

The invention discloses a near-infrared-spectrum-based tobacco leaf part feature extraction and discrimination method, which comprises: providing K tobacco leaf samples, collecting the spectrums of the samples through a near-infrared spectrometer, and pre-treating; sampling N times, and calculating the correlation p between each wavenumber point and the part in the sample and the ratio d of the distance in the same part to the distance between the parts in each wavenumber point sample; recording the correlation matrix P and the distance matrix D after the n sampling, and calculating the average values of P and D and the standard deviations Pm, Ps, Dm and Ds; determining the wavenumber points meeting threshold conditions by giving thresholds t1, t2 and t3; combining the wavenumber points meeting threshold conditions as feature points, and modeling by using the spectrums of the feature points and the part labels; and collecting the near-infrared spectrum of the sample to be determined, predicting by using the established model, and determining the tobacco leaf part of the sample to be determined. According to the present invention, the modeling is performed by screening the feature wavenumber points related to the tobacco leaf part in the spectrum so as to identify the part of the tobacco leaf in the same production place.

Description

technical field [0001] The invention belongs to the technical field of tobacco leaf processing and detection, and in particular relates to a method for feature extraction and discrimination of tobacco leaf parts based on near-infrared spectrum. Background technique [0002] Part is the most important attribute to measure the quality of tobacco leaves. The physical properties, chemical components and smoking characteristics of different tobacco leaf parts are quite different. Generally speaking, tobacco leaves are divided into upper, middle and lower leaves. The middle leaves have sufficient aroma, moderate vigor, low miscellaneous gas stimulation, and the highest quality; the upper leaves have sufficient aroma, relatively strong vigor, and relatively large stimulation; the lower leaves have low aroma and low stimulation. Differences in tobacco leaf parts will lead to huge differences in quality. Therefore, in tobacco leaf processing, the degree of conformity of parts is on...

AI Technical Summary

Problems solved by technology

[0004] However, the existing methods have low detection accuracy for parts and cannot meet the needs of online identification of tobacco leaf parts.

In the actual processing of tobacco leaves, tobacco leaves of different grades in the same province are generally used, and their part characteristics are relatively close. In the above research, only the full spectrum of the spectrum was modeled, and the part prediction of the tobacco leaf samples from the same origin is poor, which cannot meet the detection accuracy of actual production.

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