Fast measuring method of tobacco shred mixing ratio

Abstract

The invention discloses a fast measuring method of tobacco shred mixing ratio, which takes a light absorbency value corresponding to a 5500 to 4200cm near-infrared spectrum of a diffuse reflection near-infrared spectrogram of finished tobacco shred samples to be measured, a diffuse reflection near-infrared spectrogram of leaf-silk samples to be measured, a diffuse reflection near-infrared spectrogram of cut stem samples to be measured and a diffuse reflection near-infrared spectrogram of slice silk samples to be measured as an independent variable matrix; the maxing ratio of leaf silks, cut stems and slice silks used for producing the finished tobacco shreds forms a dependent variable matrix; and a multivariate curve resolution-alternating least square method is used for calculating the matrix in the third step, thus obtaining the actual mixing ratio of the leaf silks, the cut stems and the slice silks in the tobacco shred samples to be measured. The method overcomes the defects of relatively complicated model building process, relatively small model application range, large work load and the like existing in the prior art, is a method that uses less measuring instruments, has simple process and can realize fast onsite measurement, and can be widely applied to the tobacco industry.

Description

technical field [0001] The invention relates to a detection method of shredded tobacco, in particular to a detection method of the proportion of shredded tobacco. Background technique [0002] There are many factors affecting the stability of the quality of cigarette products, among which the stability of the blending ratio of shredded leaves, cut stems and shredded slices in shredded tobacco is one of the important factors affecting the stability of the inherent quality of cigarette products; The stability of the blend ratio of silk and thin slices and the conformity of the formula ratio are also important indicators for evaluating the capability of the cigarette production process. significance. There are two common methods at present: one is the manual selection method, which has the disadvantages of heavy workload and poor accuracy of results; the other is the near-infrared spectrum-partial least squares model prediction method, which is similar to Compared with the ma...

AI Technical Summary

Problems solved by technology

There are two common methods at present: one is the manual selection method, which has the disadvantages of heavy workload and poor accuracy of results; the other is the near-infrared spectrum-partial least squares model prediction method, which is similar to Compared with the manual selection method, the accuracy of the results has been greatly improved, but there are disadvantages such as a complicated modeling process, a narrow scope of application of the model, and a large workload, which makes it impossible to achieve rapid on-site detection

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