On-site analysis and rapid identification of citrus herbs by miniature mass spectrometry and machine learning.

Abstract

Natural medicines present a considerable analytical challenge due to their diverse botanical origins and complex multi-species composition. This inherent complexity complicates their rapid identification and analysis. Tangerine peel, a product of the Citrus species from the Rutaceae family, is widely used both as a culinary ingredient and in traditional Chinese medicine. It is classified into two primary types in China: Citri Reticulatae Pericarpium (CP) and Citri Reticulatae Pericarpium Viride (QP), differentiated by harvest time. A notable price disparity exists between CP and another variety, Citri reticulatae "Chachi" (GCP), with differences being based on the original variety. This study introduces an innovative method using portable miniature mass spectrometry for swift on-site analysis of QP, CP, and GCP, requiring less than a minute per sample. And combined with machine learning to differentiate the three types on site, the method was used to try to distinguish GCP from different storage years. This novel method using portable miniature mass spectrometry for swift on-site analysis of tangerine peels enabled the characterization of 22 compounds in less than one minute per sample. The method simplifies sample processing and integrates machine learning to distinguish between the CP, QP, and GCP varieties. Moreover, a multiple-perceptron neural network model is further employed to specifically differentiate between CP and GCP, addressing the significant price gap between them. The entire analytical time of the method is about 1minute, and samples can be analyzed on site, greatly reducing the cost of testing. Besides, this approach is versatile, operates independently of location and environmental conditions, and offers a valuable tool for assessing the quality of natural medicines.