Near infrared spectrum classification model training method and system and classification method and system

Abstract

The invention relates to a near infrared spectrum classification model training method and system, and a classification method and system, and the method comprises the steps of converting each training near infrared spectrum sequence into a training two-dimensional image through an MTF, and obtaining a training data set; and constructing an initial classification model, and training the initial classification model by using the training data set to obtain a classification model. A near-infrared spectrum sequence is integrally converted into an image by introducing an MTF method, the dependency of an original spectrum sequence on wavelength and the integrity of characteristics are reserved, a one-dimensional spectrum sequence is encoded into an image, the powerful advantage of machine vision image processing can be applied to classification and recognition of one-dimensional near-infrared spectrums, and the classification accuracy of the classification model can be obviously improved. By using the classification model, the near infrared spectrum sequence can be classified more accurately.

Description

technical field [0001] The present invention relates to the technical field of one-dimensional near-infrared spectrum data analysis and processing, in particular to a classification model training method, system and method for near-infrared spectrum based on MTF (Markov Transition Field) image coding and residual network. Classification methods and systems. Background technique [0002] Due to its advantages of simple operation, high detection efficiency, simultaneous detection of multiple indicators, no damage to the sample, low cost and no pollution in the experiment, near-infrared spectroscopy gradually stands out among many detection technologies mainly based on chemical inspection. The key to classification, recognition and detection of near-infrared spectroscopy is to use the rich spectral information in the near-infrared spectral region to establish a mathematical model with classification ability, and use the generalization ability of the model to detect the spectrum...

AI Technical Summary

Problems solved by technology

[0003] However, near-infrared spectroscopy has a wide spectral range, a lot of noise interference, and weak useful information. The traditional qualitative model must combine a large number of spectral preprocessing, feature extraction, and dimensionality reduction.

The feature change brought about by this feature extraction and dimensionality reduction is often achieved through data compression, which will destroy the integrity of the spectrum, lose useful information, and fail to ensure the accuracy of the classification results.

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