Predicting Indonesian coffee origins using untargeted SPME − GCMS - based volatile compounds fingerprinting and machine learning approaches

Aroma is a fundamental property of coffee and is modulated by volatile compounds (VCs). The VC fingerprint based on untargeted SPME-GC/MS can be used for coffee origin prediction. Coffee of Indonesian origin has been attracting considerable research interest in recent years. However, data analysis of the untargeted VC analysis remains one of the greatest challenges for the prediction of origins. Therefore, this study aimed to investigate the ability of untargeted SPME-GC/MS-based volatile fingerprinting to predict the origins of Indonesian coffee using machine learning (ML) approaches. Indonesian coffee samples from economically precious origins were studied. An SPME arrow was used to extract the VCs from the coffee headspace with optimised temperatures coupled to a GC/MS system. Several machine learning models were compared to obtain the most accurate origin prediction. This study found that adsorption at 70 °C for 10 min extracted the most reliable VCs for coffee origin prediction. The untargeted headspace-SPME volatile fingerprint of coffee employing 200 samples identified 224 features out of 656 detected signals. In the exploratory dataset, RF and PLS-DA models are comparable in predicting Indonesian coffee origins with accuracies of 97% and 95.2%, respectively. They also reached an AUC of 100% and 95.8% in the validation dataset, respectively. Furthermore, both models indicated promising results in selecting the important features. These features illustrate a clear classification in the visualisation using unsupervised models. Overall, the results of the study demonstrate the reliability of the current workflow for the predictive modelling of Indonesian coffees. This study contributes to the advancement of coffee origin prediction and classification for further authentication.