Detection of Sheep-derived Components in Sheep Plasma Using DNA Electrophoresis Image Analysis Based on the Python/RGB Module
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Abstract:
A novel method for determining the molecular weight and content of DNA in foods was developed using Python, and an image analysis method for DNA gels was established. Agarose gel electrophoresis was performed using DNA markers of different molecular weights and DNA standard samples, and gel images were captured for analysis with a selfdeveloped Python program. Image optimization was then performed using grayscale image conversion, Gaussian blurring, image thresholding, and contour detection, and the linear relationship between DNA concentration and RGB value was explored using the contour average, contour centerline, global data average, and global data integration methods, and the optimal processing method was selected. Pixel migration distance, RGB-grayscale value, RGB-vector, and RGB-brightness were used to determine the molecular weight and DNA content. A method for analyzing the molecular weight and content in DNA gel electrophoresis was established based on the Python/RGB color system. The relatively small error in the detection results demonstrates the feasibility of using Python/RGB to obtain information about DNA molecular weight and analyze content. Application of the gel image analysis method to detect sheep-derived components in the sheep plasma indicated 296 bp of the target protein, while 294 bp was obtained using the DNA detection method, indicating an error of only 0.99%. The results therefore indicate that the method may serve as a novel means of detecting meat-derived components.
