A discriminant analysis method of food quality based on dynamic and static data fusion

Abstract

The invention discloses a food quality discriminant analysis method based on dynamic and static data fusion, which respectively collects the static data and the dynamic data of the food, fuses the static data and the dynamic data by adopting a weighted similarity algorithm, obtains a correlation coefficient, and thereby discriminates the food quality. Static data refers to the characterization data of food measured under a certain condition, and dynamic data refers to the characterization data under the changing state of food system, that is, under certain disturbance conditions (such as time,temperature, concentration and chemical reaction), the data of food system change sequence spectrum. The invention can combine the static data and the dynamic data obtained in the food quality detection process, overcomes the one-sidedness of the traditional simple use of the static data or the dynamic data, greatly improves the information utilization rate, reaches 100%, and realizes more accurate and comprehensive food quality discrimination analysis.

Description

technical field [0001] The invention belongs to the technical fields of food quality control and quality discrimination analysis, and in particular relates to a food quality discrimination analysis method based on dynamic and static data fusion. Background technique [0002] The issue of food quality and safety is one of the hot issues that researchers and the public are generally concerned about at this stage. Food quality and safety evaluation based on instrumental analysis methods is currently a commonly used method for food quality discrimination. Commonly used testing instruments include spectrum, chromatography, mass spectrometry, etc. The test results are spectrogram data. The x-axis of the data indicates wavelength, wave number, retention time or mass-to-charge ratio, and the y-axis of the data indicates the intensity value corresponding to the signal at each x-coordinate. , with different chemical connotations, but the basic form of data output is two-dimensional po...

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Problems solved by technology

[0009] The aforementioned research shows that data fusion analysis can improve the efficiency of data utilization and the accuracy of discrimination. However, the existing research either focuses on the research of mining algorithms for static data or the research of acquisition strategies for dynamic data, and there are still problems of one-sided evaluation. Combining static data and dynamic data to achieve a more comprehensive food quality judgment has rarely been reported

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