Potential Aroma Chemical Fingerprint of Oxidised Coffee Note by HS-SPME-GC-MS and Machine Learning.

This study examines the volatilome of good and oxidised coffee samples from two commercial coffee species (i.e., Coffea arabica (arabica) and Coffea canephora (robusta)) in different packagings (i.e., standard with aluminium barrier and Eco-caps) to define a fingerprint potentially describing their oxidised note, independently of origin and packaging. The study was carried out using HS-SPME-GC-MS/FPD in conjunction with a machine learning data processing. PCA and PLS-DA were used to extrapolate 25 volatiles (out of 147) indicative of oxidised coffees, and their behaviour was compared with literature data and critically discussed. An increase in four volatiles was observed in all oxidised samples tested, albeit to varying degrees depending on the blend and packaging: acetic and propionic acids (pungent, acidic, rancid), 1-H-pyrrole-2-carboxaldehyde (musty), and 5-(hydroxymethyl)-dihydro-2(3H)-furanone.

Chemometric Modeling of Coffee Sensory Notes through Their Chemical Signatures: Potential and Limits in Defining an Analytical Tool for Quality Control.

Aroma is a primary hedonic aspect of a good coffee. Coffee aroma quality is generally defined by cup tasting, which however is time-consuming in terms of panel training and alignment and too subjective. It is challenging to define a relationship between chemical profile and aroma sensory impact, but it might provide an objective evaluation of industrial products. This study aimed to define the chemical signature of coffee sensory notes, to develop prediction models based on analytical measurements for use at the control level. In particular, the sensory profile was linked with the chemical composition defined by HS-SPME-GC-MS, using a chemometric-driven approach. The strategy was found to be discriminative and informative, identifying aroma compounds characteristic of the selected sensory notes. The predictive ability in defining the sensory scores of each aroma note was used as a validation tool for the chemical signatures characterized. The most reliable models were those obtained for woody, bitter, and acidic properties, whose selected volatiles reliably represented the sensory note fingerprints. Prediction models could be exploited in quality control, but compromises must be determined if they are to become complementary to panel tasting.

Coffee aroma: Chemometric comparison of the chemical information provided by three different samplings combined with GC-MS to describe the sensory properties in cup.

This study is part of a wider project aiming to correlate the chemical composition of the coffee volatile fraction to its sensory properties with the end-goal of developing an instrumental analysis approach complementary to human sensory profiling. The proposed investigation strategy compares the chemical information concerning coffee aroma and flavor obtained with HS-SPME of the ground coffee and in-solution SBSE/SPME sampling combined with GC-MS to evaluate their compatibility with the cupping evaluation for quality control purposes. Roasted coffee samples with specific sensory properties were analyzed. The chemical results obtained by the three samplings were compared through multivariate analysis, and related to the samples' sensory attributes. Despite the differences between the three sampling approaches, data processing showed that the three methods provide the same kind of chemical information useful for sample discrimination, and that they could be used interchangeably to sample the coffee aroma and flavor.

Non-separative headspace solid phase microextraction-mass spectrometry profile as a marker to monitor coffee roasting degree.

This study describes a non-separative headspace solid phase microextraction-mass spectrometry (HS-SPME-MS) approach, in view of its application to online monitoring of a roasting process. The system can quickly provide representative and diagnostic fingerprints of the volatile fraction of samples and, in combination with appropriate chemometric pattern recognition and regression techniques, can successfully be applied to characterize, discriminate, and/or correlate patterns with the roasting process. Eighty coffee samples of different varieties, geographical origins, and blends were analyzed. The experimental HS-SPME-MS results show that the TIC fingerprint can be used to discriminate the degree of roasting; diagnostic ion abundance(s) or ratios were closely correlated with the roasting process; both could successfully be used as markers or analytical decision makers, to monitor roasting processes online, and to define quality and safety of roasted coffee.

A further tool to monitor the coffee roasting process: aroma composition and chemical indices.

Coffee quality is strictly related to its flavor and aroma developed during the roasting process, that, in their turn, depend on variety and origin, harvest and postharvest practices, and the time, temperature, and degree of roasting. This study investigates the possibility of combining chemical (aroma components) and physical (color) parameters through chemometric approaches to monitor the roasting process, degree of roasting, and aroma formation by analyzing a suitable number of coffee samples from different varieties and blends. In particular, a correlation between the aroma composition of roasted coffee obtained by HS-SPME-GC-MS and degree of roasting, defined by the color, has been researched. The results showed that aroma components are linearly correlated to coffee color with a correlation factor of 0.9387. The study continued looking for chemical indices: 11 indices were found to be linearly correlated to the color resulting from the roasting process, the most effective of them being the 5-methylfurfural/2-acetylfuran ratio (index).