Prediction of coffee aroma from single roasted coffee beans by hyperspectral imaging

Coffee aroma is critical for consumer liking and enables price differentiation of coffee. This study applied hyperspectral imaging (1000-2500 nm) to predict volatile compounds in single roasted coffee beans, as measured by Solid Phase Micro Extraction-Gas Chromatography-Mass Spectrometry and Gas Chromatography-Olfactometry. Partial least square (PLS) regression models were built for individual volatile compounds and chemical classes. Selected key aroma compounds were predicted well enough to allow rapid screening (R-2 greater than 0.7, Ratio to Performance Deviation (RPD) greater than 1.5), and improved predictions were achieved for classes of compounds - e.g. aldehydes and pyrazines (R-2 similar to 0.8, RPD similar to 1.9). To demonstrate the approach, beans were successfully segregated by HSI into prototype batches with different levels of pyrazines (smoky) or aldehydes (sweet). This is industrially relevant as it will provide new rapid tools for quality evaluation, opportunities to understand and minimise heterogeneity during production and roasting and ultimately provide the tools to define and achieve new coffee flavour profiles.

Automated summary

In this study, hyperspectral imaging was used to predict volatile compounds in roasted coffee beans, allowing for rapid screening of key aroma compounds. The approach showed improved predictions for classes of compounds such as aldehydes and pyrazines. This method provides industrial relevance by offering new tools for quality evaluation and minimizing heterogeneity during production and roasting.