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

Nicola Caporaso,Martin B Whitworth,Ian D Fisk

doi:10.1016/j.foodchem.2021.131159

Abstract

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 (R2 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 (R2 ∼ 0.8, RPD ∼ 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.

Highlights

Coffee is a highly traded agricultural commodity
Whilst many attempts have been made to link the complex pattern of coffee volatile compounds to its sensory quality, the selection of “key aroma compounds” is most commonly used to identify compounds that contribute to the overall aroma (Sunarharum, Williams, & Smyth, 2014)
Ferreira, & Salva (2011) applied cup testing to bulk ground and roasted coffee and built NIR models (1100–2500 nm) to predict sensory parameters such as acidity, bitterness, flavour, cleanliness, body and “overall quality” while Este ban-Díez et al (2004) reported PLSR models for the sensory attributes of body, acidity, bitterness and appearance. Both of these studies webre carried out on batches of roast and ground coffee, which has limitations that were overcome in our research as we have demonstrated that volatile compounds that represent coffee aroma can be predicted for individual beans with sufficient accuracy to allow screening

Summary

Introduction

Coffee is a highly traded agricultural commodity. Its price and quality are strongly dependent on the aroma obtained after roasting and brewing. Coffee aroma is of great commercial interest and important for the enjoyment of the product by the consumer. The vol atile composition of roasted coffee is complex, with more than 800 compounds identified. Whilst many attempts have been made to link the complex pattern of coffee volatile compounds to its sensory quality, the selection of “key aroma compounds” is most commonly used to identify compounds that contribute to the overall aroma (Sunarharum, Williams, & Smyth, 2014). Coffee batches are not homogeneous and, in many cases, contain both within specification coffee beans and beans with unique flavour profiles

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