Preserved meat quality detection method

Abstract

The invention discloses a preserved meat quality detection method. The method includes steps: simultaneously acquiring hyperspectral image characteristic wavelength information and hyperspectral nitrite content spectral information of preserved meat, and acquiring microscopic image information of colony count of the preserved meat to obtain image characteristic wavelength, nitrite content and colony count of the preserved meat; taking the image characteristic wavelength, the nitrite content and the colony count of the preserved meat as input of a radial basis function artificial neural network multi-data fusion predication model to obtain preserved meat quality output values according to a genetic optimization method; judging preserved meat quality according to the preserved meat quality output values, and performing quality grading predication. By adoption of the preserved meat quality detection method, quickness and accuracy in quality detection and grading of the preserved meat can be realized, and whether the preserved meat is safe to eat or not can be identified more conveniently.

Description

technical field [0001] The invention relates to the field of food detection, in particular to a method for detecting the quality of bacon. Background technique [0002] As an emerging detection technology, Hyper-spectral detection technology integrates advanced technologies in optoelectronics, optics, electronic information processing, computer science and other fields, and organically integrates traditional near-infrared spectroscopy technology and 2D image technology combined. In the visible and near-infrared regions, hyperspectral sensors have dozens to hundreds of bands, and their spectral resolution is very high. In the near-infrared 780-2526nm wavelength range, the spectral resolution is generally less than 10nm, usually up to 2- 3nm. Therefore, in order to improve the detection accuracy, the application of hyperspectral detection technology to the quality and safety detection of agricultural products, livestock meat products, and food has great application potential...

AI Technical Summary

Problems solved by technology

However, the results of sensory inspection are not easy to quantify, and are subjective and one-sided. Even if the inspectors have enough experience, it is difficult to draw correct conclusions in many cases. The meat quality is measured by the increase of substances, changes in conductivity, viscosity, and water retention; chemical inspection is to use qualitative or quantitative methods to determine protein decomposition products, such as ammonia, amines, TVB-N (volatile base nitrogen), trimethylamine (TMA) ), indole, etc., to measure the degree of deterioration of meat

Among them, TVB-N is the national standard for testing the freshness of meat in my country. The TVB-N value can regularly reflect changes in the freshness of meat. The difference between fresh meat, sub-fresh meat and spoiled meat is very significant, and it is consistent with sensory changes. , is a relatively objective indicator, but the method requires complex equipment, cumbersome steps, and a long detection cycle, making it difficult to quickly detect o

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