How can science help to create new value in coffee?

A Systematic Mapping Study of Coffee Quality throughout the Production-to-Consumer Chain

Coffee is one of the most consumed beverages in the world and is crucial in the economy of many developing countries. The search to improve coffee quality comes from many fronts, as do the many ways to measure quality and the factors that affect it. Several techniques are used to measure the different metrics to assess coffee quality, across different types of coffee samples and species, and throughout the entire process from farm to cup. In this work, we conducted a systematic mapping study of 1,470 articles to identify the aspects of quality that are the most important in the scientific literature to evaluate coffee throughout the processing chain. The study revealed that cup quality and biochemical composition are the most researched quality attributes. The main objective of the reviewed studies is the correlation between different quality measurements. The most used techniques are the analytical chemistry methods. The most studied species is Coffea arabica. The most used sample presentation is green coffee. The postharvest stage is the most researched, in which quality control receives more attention. In the preharvest stage, management practices stand out. Finally, the most used type of research was the evaluation research.

HS-SPME-MS-Enose Coupled with Chemometrics as an Analytical Decision Maker to Predict In-Cup Coffee Sensory Quality in Routine Controls: Possibilities and Limits

The quality assessment of the green coffee that you will go to buy cannot be disregarded from a sensory evaluation, although this practice is time consuming and requires a trained professional panel. This study aims to investigate both the potential and the limits of the direct headspace solid phase microextraction, mass spectrometry electronic nose technique (HS-SPME-MS or MS-EN) combined with chemometrics for use as an objective, diagnostic and high-throughput technique to be used as an analytical decision maker to predict the in-cup coffee sensory quality of incoming raw beans. The challenge of this study lies in the ability of the analytical approach to predict the sensory qualities of very different coffee types, as is usual in industry for the qualification and selection of incoming coffees. Coffees have been analysed using HS-SPME-MS and sensory analyses. The mass spectral fingerprints (MS-EN data) obtained were elaborated using: (i) unsupervised principal component analysis (PCA); (ii) supervised partial least square discriminant analysis (PLS-DA) to select the ions that are most related to the sensory notes investigated; and (iii) cross-validated partial least square regression (PLS), to predict the sensory attribute in new samples. The regression models were built with a training set of 150 coffee samples and an external test set of 34. The most reliable results were obtained with acid, bitter, spicy and aromatic intensity attributes. The mean error in the sensory-score predictions on the test set with the available data always fell within a limit of ±2. The results show that the combination of HS-SPME-MS fingerprints and chemometrics is an effective approach that can be used as a Total Analysis System (TAS) for the high-throughput definition of in-cup coffee sensory quality. Limitations in the method are found in the compromises that are accepted when applying a screening method, as opposed to human evaluation, in the sensory assessment of incoming raw material. The cost-benefit relationship of this and other screening instrumental approaches must be considered and weighed against the advantages of the potency of human response which could thus be better exploited in modulating blends for sensory experiences outside routine.

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.