By using traditional chemical methods and infrared spectroscopy technology to study mushrooms of different species and geographical regions, contrast, chemical methods were destructive, time-consuming, and expensive. Through comparison, it is found that the nonlinear model was more suitable for Gannan tea oil adulteration recognition. ![]() By comparing different pretreatment and principal component analysis methods, combined with linear and nonlinear modeling methods, the oil adulteration recognition model was established. ![]() The recognition of Gannan tea oil adulteration was studied based on near-infrared spectroscopy. Fourier transform infrared spectroscopy and partial least squares discriminant analysis, were combined to recognize fungus. The multispectral technology has been widely used to acquire crop phenotypic characteristics due to the advantages of rapid, non-destructive, and non-pollution, , which provided a theoretical basis and technical reference for detecting Auricularia auricula fruiting body varieties. The methods used by these scholars to recognize fungus species and species generally have shortcomings such as complicated operation, long time-consuming, expensive, and destructive. By using the method of grey relation analysis, the agronomic characters of Auricularia auricula varieties from different sources were analyzed, and the varieties with good quality were selected. strain provenance was identified by DNA sequencing, and the best nutritional conditions for mycelial growth were screened. The reliability of Auricularia cornea var. The differences between Auricularia auricula polysaccharides in different varieties and producing areas were compared, and a new method for the determination of Auricularia auricula polysaccharides was established. Anthrone sulfuric acid method was used to determine the content of polysaccharides in Auricularia auricula and Auricularia auricula from different producing areas. At present, the recognition of fruit body mainly depends on traditional chemical analysis methods, naked-eye observation, and artificial experience. Among them, Auricularia auricula, the fourth largest cultivated edible fungus globally, the third largest edible fungus in China, has high nutritional and health care value. The rapid recognition of phenotypic characteristics of their fruit body is of great significance, theoretical and practical value for the classification, evaluation of germplasm resources, breeding, and cultivation of edible fungus. This achievement established a rapid, efficient, and accurate method for the recognition of Auricularia auricula varieties, which provided a theoretical basis and technical support for the rapid of Auricularia auricula varieties for the classification of edible fungus and evaluation of germplasm resources, breeding, and cultivation.Įdible fungus is a large fungus with edible and medicinal value. The research results showed that the accuracy of DT-CARS-RBF model for recognizing Auricularia auricula varieties was 99.32%, the root mean square error value was 0.0908, and the running time was 0.000956 s. Finally, the radial basis function (RBF) neural network of type 131-12-4 was constructed to recognize Auricularia auricula varieties. Then the competitive adaptive reweighted sampling (CARS) algorithm was applied to extract 131 effective characteristic wavenumbers from the pre-processed spectral data. Second, the detrend(DT)method was used to pre-process the raw spectral data. First, the four Auricularia auricula varieties of Cheng D, Hei Feng, Hei Shan, and Xu 1 were taken as the research objects, the near-infrared spectral data of Auricularia auricula were scanned by Fourier transform near-infrared spectrometer (Tango). Therefore, a rapid recognition method of Auricularia auricula varieties was proposed based on the characteristics of near-infrared spectroscopy. At present, the operation process of DNA molecular marker technology is complex, time-consuming, laborious, and expensive, moreover, the traditional artificial judgment of fruit body varieties based on naked-eye observation and experience easily led to errors and inaccuracy. The existing morphological-physiological phenotype detection for edible fungus’ growth process is not systematic enough and lack of rapid and accurate detection methods for the fruit body varieties of Auricularia auricula. ![]() The rapid recognition of fruit body phenotypic characteristics is of great significance, theoretical and practical value for the classification of edible fungus, evaluation of germplasm resources, breeding, and intelligent cultivation. Edible fungus is one of the significant foods for human beings.
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