This study collected multidimensional feature data such as spectra, texture, and component contents of Polygonati Rhizoma from different origins and varieties (Polygonatum kingianum Coll. et Hemsl from Yunnan and Guizhou; Polygonatum cyrtonema Hua from Anhui and Jiangxi; Polygonatum sibiricum Red from Hunan). Multivariate statistical analysis was used to select 39 characteristic factors for distinguishing PR origins and 14 characteristic factors for discriminating PR varieties (VIP > 1 and P < 0.05). In addition, by combining multivariate statistical analysis with a deep belief network (DBN) classification algorithm, a novel artificial intelligence algorithm was developed and optimized. Compared to traditional discriminant analysis methods, the accuracy of this new approach was significantly improved, achieving a 100% discrimination rate for PR varieties and a 100% accuracy rate for tracing the origin of PR. This research provides a reference and data support for constructing intelligent algorithms based on multidimensional data fusion, to achieve food variety discrimination and origin tracing.
Keywords: DBN classification algorithm; Multivariate statistics; Polygonati Rhizoma; Traceability; Visible spectrum.
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