Novel method for the detection of adulterants in coffee and the determination of a coffee's geographical origin using near infrared spectroscopy complemented by an autoencoder

Coffee authenticity is a foundational aspect of quality when considering coffee's market value. This has become important given frequent adulteration and mislabelling for economic gains. Therefore, this research aimed to investigate the ability deep autoencoder neural network detect adulterants in roasted coffee determine geographical origin (roasted) using near infrared (NIR) spectroscopy. Arabica was adulterated with robusta or chicory at levels ranging from 2.5% 30% increments light, medium dark roast levels. First, trained pure arabica before being used presence samples. Furthermore, it coffee. All samples were detectable by all In case robusta-adulterated samples, detection possible above 7.5% roasts. Additionally, differentiate different origins. PCA analysis showed grouping based on type concentration adulterant. conclusion, an conjunction NIR spectroscopy could be reliable technique ensure authenticity.