Multi-feature fusion-based meat freshness hyperspectral image visual detection

Abstract

The invention discloses a multi-feature fusion-based meat freshness hyperspectral image visual nondestructive detection method, aiming at overcoming the defects that the traditional nondestructive detection method is poor in detection accuracy stability and reliability. According to the method, the technical scheme comprises the steps of a. acquiring hyperspectral reflection image of a meat sample; b. extracting the light intensity mean value, the image entropy and the average energy feature of the hyperspectral reflection image under the different wave bands; c. respectively establishing partial least squares prediction models of TVB-N, which have three features and are obtained by instrument destructive testing, and obtaining an unweighted fusion prediction model related to the TVB-N; e. acquiring the hyperspectral image of the meat sample to be tested, and inputting the image into the established unweighted fusion prediction model to obtain the TVB-N prediction results of all pixels and realize the visual detection for the decay degree and region of the meat sample. After the method is adopted, the rapid meat freshness visual detection can be realized under the condition that most meat samples are not damaged; the method has the advantages of being simple, rapid in speed, high in prediction accuracy and good in robustness.

Description

technical field [0001] The method relates to a nondestructive detection method for meat freshness, in particular to a method for visual nondestructive detection of meat freshness using hyperspectral image technology combined with a multi-feature fusion method. Background technique [0002] Meat is rich in protein, fat, minerals, etc., which can provide rich operating substances to the human body, and is an important part of human dietary structure. [0003] Meat is prone to be affected by enzymes and microorganisms during storage, transportation, and processing, resulting in spoilage. Rotten meat not only changes in business value and taste, but also produces toxic substances, which endangers health and causes safety accidents. In recent years, the circulation volume and circulation distance of my country's meat products have been increasing; followed by the increasing risk of meat spoilage, as well as the resulting food safety accidents, so it is urgent to solve the proble...

AI Technical Summary

Problems solved by technology

[0007] Compared with traditional images, hyperspectral images have rich image information and spectral information, but the huge amount of data brings certain difficulties to data processing. How to mine some useful information and make full use of this information has always been a difficult problem

Traditional hyperspectral image non-destructive testing methods generally only extract a single feature information to construct a non-destructive testing model, which not only wastes the rich information brought by hyperspectral images, but also a single feature is often affected by the outside world and cannot fully reflect the measured Substance properties, resulting in limited accuracy and robustness of the detection model

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