A meat freshness detection method based on multi-source sensory information fusion

Abstract

The invention provides a meat product freshness detection method based on multisource perceptual information fusion. According to the method, a near infrared spectrometer, a computer vision system and an electronic nose are used for acquiring internal and external information of a sample simultaneously and respectively, the feature extraction and fusion treatment are performed on the acquired information, and the freshness grade of a meat product is evaluated according to a freshness grade evaluation standard of the meat product. The method comprises the steps of establishing the freshness grade evaluation standard of the meat product, establishing a freshness grade forecast model of the meat product, and performing freshness grade evaluation on the sample to be detected. The method comprehensively uses the spectrum, image and smell fingerprint information, comprehensively determines the internal and external quality of the meat product, and can realize quick, simple, convenient and objective grade evaluation of the freshness of the meat product.

Description

technical field [0001] The invention relates to a method for detecting the freshness of meat products, in particular to a method for detecting the freshness of meat products based on fusion of multi-source perception information. Background technique [0002] There are many methods for detecting the freshness of meat, mainly including artificial sensory evaluation and conventional chemical analysis methods. Artificial sensory evaluation requires trained personnel to evaluate, and the evaluation results are subject to a certain degree; conventional chemical analysis methods have a long detection cycle and cumbersome steps, and the detection results depend on the technical level of the detection personnel. That is to say, these traditional detection methods have limitations such as low detection accuracy or too long time-consuming, and inability to timely and accurately feed back meat freshness information. [0003] With the improvement of living standards, the quality and sa...

AI Technical Summary

Problems solved by technology

Artificial sensory evaluation requires trained personnel to conduct evaluation, and the evaluation results are somewhat subjective; conventional chemical analysis methods have a long detection cycle and cumbersome steps, and the detection results depend on the technical level of the detection personnel

That is to say, these traditional detection methods have limitations such as low detection accuracy or too long time-consuming, and inability to timely and accurately feed back meat freshness information.

[0003] With the improvement of living standards, the quality and safety control of meat and its products has become more stringent in countries all over the world. People's demand for meat has gradually changed from quantity to quality. Traditional meat quality testing methods can no longer meet people's needs. demand, and the demand for accurate, rapid and sensitive detection methods is becoming more and more urgent

[0004] At present, most of the detection methods are more traditional signal preprocessing technology and pattern recognition method, which often have a better information response to one or two indicators, but it seems powerless to make full use of multiple information for comprehensive evaluation.

Meat spoilage is a complicated process, including changes in internal chemical components and sensory indicators such as external odors. A non-destructive testing method that makes full use of the above-mentioned various information for comprehensive and comprehensive evaluation has not yet appeared. , which is the shortcoming of the existing technology

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