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

Machine learning models for terroir classification and blend similarity prediction: A proof-of-concept to enhance cocoa quality evaluation

Flavour is a key quality attribute of cocoa, essential for industry standards and consumer preferences. Automated methods for assessing flavour quality support industrial laboratories in achieving high sample throughput. Targeted and untargeted HS-SPME-GC-MS chromatographic fingerprints of cocoa volatiles from fermented beans and liquors, combined with machine learning (ML), are used for terroir qualification, enabling effective origin classification with both approaches. The targeted method, which aims to identify chemical patterns associated with sensory attributes is used for flavour comparison of origin with a reference. The similarity analysis successfully identified the most suitable origin to create new blends with a similar flavour to the industry standard. The resulting ML, model based on odorants distribution, enabled the prediction of similarity of blends to the industrial reference with an accuracy of 88 %, a sensitivity of 90 % and a specificity of 84 %.

Prediction of coffee traits by artificial neural networks and laser-assisted rapid evaporative ionization mass spectrometry

BACKGROUND

Coffee is an important commodity in the worldwide economy and smart technologies are important for accurate quality control and consumer-oriented product development. Sensory perception is probably the most important information since it is directly related to product acceptance. However, sensory analysis is imprecise and present large deviation related to subjectivity and relying exclusively on the sensory panel. Thus, practical technologies may be developed to assist in making accurate decisions.

RESULTS

This study presents a new method applying laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS) coupled with high-resolution mass spectrometry to fingerprint coffee samples. Predictive models have estimated sensory properties with accuracies between 87 and 96 % for test samples. The complex relationship between the MS profiles and modelled properties, artificial neural networks (ANN) outperformed partial least square-discriminant analysis (PLS-DA) on estimation of coffee properties. Tentatively identified compounds such as sugars, chlorogenic, and fatty acids were the ones that most affected coffee sensory properties according to a novel approach to evaluate ANN weights.

SIGNIFICANCE

The proposed method could analyse coffee samples with minimal sample preparation using an automated device. Predictive models can be applied to assist sensory panel on making decision due to accuracies up to 96 % Additionally, a novel algorithm for evaluate m/z importance in ANN models were presented, paving the way for a higher-level of interpretation by using this algorithm.

Refermentation with yeast and lactic acid bacteria isolates: a strategy to improve the flavor of green coffee beans

BACKGROUND

Yeast and lactic acid bacteria (LAB) play an important part in the post-harvest fermentation of coffee. This study applied lab-scale fermentation to commercial green coffee beans using dry coffee pulp as the substrate, with the aim of modifying coffee-bean flavor. In addition to spontaneous fermentation, yeast and LAB isolated from coffee beans and dried coffee pulp were added during fermentation.

RESULTS

Co-inoculation of yeast and LAB showed a significant effect on the chlorogenic acid content after between 24 and 72 h of fermentation. Acetic, citric, malic, lactic, and quinic acids were shown to be affected significantly (P < 0.05) by fermentation and inoculation. Gas chromatography detected that esters, alcohols, aldehydes, furans, and pyrazines were the primary compounds in the coffee beans. Certain volatile groups were present in greater concentrations and broader varieties within the inoculated beans. The highest cupping scores were given to beans that had been co-inoculated with yeast and LAB.

CONCLUSION

Overall, the use of yeasts and LAB starters showed potential to create coffee beverages with desirable characteristics by standardized fermentation. © 2024 Society of Chemical Industry.

Medium Roasting and Brewing Methods Differentially Modulate Global Metabolites, Lipids, Biogenic Amines, Minerals, and Antioxidant Capacity of Hawai'i-Grown Coffee (Coffea arabica)

In the United States, besides the US territory Puerto Rico, Hawai'i is the only state that grows commercial coffee. In Hawai'i, coffee is the second most valuable agricultural commodity. Health benefits associated with moderate coffee consumption, including its antioxidant capacity, have been correlated to its bioactive components. Post-harvest techniques, coffee variety, degree of roasting, and brewing methods significantly impact the metabolites, lipids, minerals, and/or antioxidant capacity of brewed coffees. The goal of our study was to understand the impact of roasting and brewing methods on metabolites, lipids, biogenic amines, minerals, and antioxidant capacity of two Hawai'i-grown coffee (Coffea arabica) varieties, "Kona Typica" and "Yellow Catuai". Our results indicated that both roasting and coffee variety significantly modulated several metabolites, lipids, and biogenic amines of the coffee brews. Furthermore, regardless of coffee variety, the antioxidant capacity of roasted coffee brews was higher in cold brews. Similarly, total minerals were higher in "Kona Typica" cold brews followed by "Yellow Catuai" cold brews. Hawai'i-grown coffees are considered "specialty coffees" since they are grown in unique volcanic soils and tropical microclimates with unique flavors. Our studies indicate that both Hawai'i-grown coffees contain several health-promoting components. However, future studies are warranted to compare Hawai'i-grown coffees with other popular brand coffees and their health benefits in vivo.

Can Volatiles Fingerprints be an Alternative to Isotope Ratio Mass Spectrometry in the Botanical Origin Determination of Spirits?

Mass spectrometry based quasi-electronic nose using solid-phase microextraction to introduce volatiles directly to mass spectrometer without chromatographic separation (HS-SPME-MS) was used to discriminate 45 raw spirits produced from C3 (potato, rye, wheat) and C4 (corn, sorghum) plants. The samples were also subjected to isotope ratio mass spectrometry (IRMS), which unequivocally distinguished C3 from C4 samples; however, no clear differentiation was observed for C3 samples. On the contrary, HS-SPME-MS, which uses unresolved volatile compounds "fingerprints" in a form of ions of a given m/z range and various intensities provided excellent sample classification and prediction after OPLS-DA data processing verified also by the artificial neural network (ANN).

Acids in coffee: A review of sensory measurements and meta-analysis of chemical composition

Coffee contains a variety of organic acids (OAs) and chlorogenic acids (CGAs) that contribute to overall sensory properties. Large variations in preparation and measurement methodology across the literature complicate interpretation of general trends. Here, we perform a systematic review and meta-analysis of the published literature to elucidate the concentrations of OAs and CGAs in both Coffea arabica (arabica) and Coffea canephora (robusta), for both green coffee and roasted coffee at multiple roast levels. A total of 129 publications were found to report acid concentration measurements, yielding 8,634 distinct data points. Analysis of the full data set reveals several trends. First, roasted robusta has considerably more acidic compounds than arabica with 2 to 5 times as much total OAs, and much larger amounts of formic and acetic acid. As for CGAs, in both arabica and robusta 5-CQA is the major component, and progressive roasting decreases the concentration of all CGAs. The total amount of CGA present was more dependent on roast level than the type of coffee (arabica vs. robusta). Overall, this meta-analysis suggests that the increases in certain OAs with roast level might play more of a role in the sensory profile of dark roast coffees than previously suspected.

Analysis of Volatiles in Food Products

The evaluation of volatiles in food is an important aspect of food production. It gives knowledge about the quality of foods and their relationship to consumers’ choices. Alcohols, aldehydes, acids, esters, terpenes, pyrazines, and furans are the main chemical groups that are involved in aroma formation. They are products of food processing: thermal treatment, fermentation, storage, etc. Food aroma is a mixture of varied molecules. Because of this, the analysis of aroma composition can be challenging. The four main steps can be distinguished in the evaluation of the volatiles in the food matrix as follows: (1) isolation and concentration; (2) separation; (3) identification; and (4) sensory characterization. The most commonly used techniques to separate a fraction of volatiles from non-volatiles are solid-phase micro-(SPME) and stir bar sorptive extractions (SBSE). However, to study the active components of food aroma by gas chromatography with olfactometry detector (GC-O), solvent-assisted flavor evaporation (SAFE) is used. The volatiles are mostly separated on GC systems (GC or comprehensive two-dimensional GCxGC) with the support of mass spectrometry (MS, MS/MS, ToF–MS) for chemical compound identification. Besides omics techniques, the promising part could be a study of aroma using electronic nose. Therefore, the main assumptions of volatolomics are here described.

Chromatographic Fingerprinting Strategy to Delineate Chemical Patterns Correlated to Coffee Odor and Taste Attributes

Coffee cupping includes both aroma and taste, and its evaluation considers several different attributes simultaneously to define flavor quality and therefore requires complementary data from aroma and taste. This study investigates the potential and limits of a data-driven approach to describe the sensory quality of coffee using complementary analytical techniques usually available in routine quality control laboratories. Coffee flavor chemical data from 155 samples were obtained by analyzing volatile (headspace-solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS)) and nonvolatile (liquid chromatography-ultraviolet/diode array detector (LC-UV/DAD)) fractions, as well as from sensory data. Chemometric tools were used to explore the data sets, select relevant features, predict sensory scores, and investigate the networks between features. A comparison of the Q model parameter and root-mean-squared error prediction (RMSEP) highlights the variable influence that the nonvolatile fraction has on prediction, showing that it has a higher impact on describing acid, bitter, and woody notes than on flowery and fruity. The data fusion emphasized the aroma contribution to driving sensory perceptions, although the correlative networks highlighted from the volatile and nonvolatile data deserve a thorough investigation to verify the potential of odor-taste integration.

Cocoa smoky off-flavour: A MS-based analytical decision maker for routine controls

Cocoa smoky off-flavour is generated from an inappropriate artificial drying applied on beans to speeding up the post-harvest process and it can affect the quality of the chocolate. The sensory tests are time-consuming, and at present, a fast analytical method to detect this defect in raw materials is not yet available. This study applies a HS-SPME-MS-enose in combination with chemometrics to obtain diagnostic mass-spectral patterns to detect smoked samples and/or as analytical decision maker. SIMCA models provide the best classification results, compared to PLS-DA, with sensitivities exceeding 90% and a high class specificity range of 89-100% depending on the matrix investigated (beans or liquors). Resulting diagnostic ions were related to phenolic derivatives. The discrimination ability of the method has been confirmed by a quantitative analysis through HS-SPME-GC-MS. HS-SPME-MS-enose turned out to be a fast, cost-effective and objective approach for high throughput analytical screening to discard defective cocoa samples.