Rapid classification of coffee origin by combining mass spectrometry analysis of coffee aroma with deep learning

•Directly analyze coffee aroma by SACDI-MS with no pretreatment.•Developing an ACSD module to improve the detection throughput to 1 s/sample.•Construct a deep learning algorithm to process the MS data for origin identification.•The strategy enables non-destructive, accurate, and high-throughput assay. Mislabeling the geographical origin of coffee is a prevalent form of fraud. In this study, a rapid, nondestructive, and high-throughput method combining mass spectrometry (MS) analysis and intelligence algorithms to classify coffee origin was developed. Specifically, volatile compounds in coffee aroma were detected using self-aspiration corona discharge ionization mass spectrometry (SACDI–MS), and the acquired MS data were processed using a customized deep learning algorithm to perform origin authentication automatically. To facilitate high-throughput analysis, an air curtain sampling device was designed and coupled with SACDI–MS to prevent volatile mixing and signal overlap. An accuracy of 99.78% was achieved in the classification of coffee samples from six origins at a throughput of 1 s per sample. The proposed approach may be effective in preventing coffee fraud owing to its straightforward operation, rapidity, and high accuracy and thus benefit consumers.