Single Origin Coffee Aroma: From Optimized Flavor Protocols and Coffee Customization to Instrumental Volatile Characterization and Chemometrics.

In this study, the aroma profile of 10 single origin Arabica coffees originating from eight different growing locations, from Central America to Indonesia, was analyzed using Headspace SPME-GC-MS as the analytical method. Their roasting was performed under temperature-time conditions, customized for each sample to reach specific sensory brew characteristics in an attempt to underline the customization of roast profiles and implementation of separate roastings followed by subsequent blending as a means to tailor cup quality. A total of 138 volatile compounds were identified in all coffee samples, mainly furan (~24-41%) and pyrazine (~25-39%) derivatives, many of which are recognized as coffee key odorants, while the main formation mechanism was the Maillard reaction. Volatile compounds' composition data were also chemometrically processed using the HCA Heatmap, PCA and HCA aiming to explore if they meet the expected aroma quality attributes and if they can be an indicator of coffee origin. The desired brew characteristics of the samples were satisfactorily captured from the volatile compounds formed, contributing to the aroma potential of each sample. Furthermore, the volatile compounds presented a strong variation with the applied roasting conditions, meaning lighter roasted samples were efficiently differentiated from darker roasted samples, while roasting degree exceeded the geographical origin of the coffee. The coffee samples were distinguished into two groups, with the first two PCs accounting for 73.66% of the total variation, attributed mainly to the presence of higher quantities of furans and pyrazines, as well as to other chemical classes (e.g., dihydrofuranone and phenol derivatives), while HCA confirmed the above results rendering roasting conditions as the underlying criterion for differentiation.

Methoxypyrazine analysis and influence of viticultural and enological procedures on their levels in grapes, musts, and wines.

This review discusses the factors that affect the concentrations of methoxypyrazines (MPs) and the techniques used to analyze MPs in grapes, musts, and wines. MPs are commonly studied pyrazines in food science due to their contribution of aroma and flavor to numerous vegetables such as peas and asparagus. They are described as highly odorous compounds with a very low olfactory threshold. The grape varietals that exhibit green or herbaceous aromas that are characteristic of MPs are predominantly Vitis vinifera cv. Cabernet Sauvignon and Sauvignon Blanc, but include others. The most extensively studied MPs include 3-isobutyl-2-methoxypyrazine, 3-isopropyl-2-methoxypyrazine, and 3-sec-butyl-2-methoxypyrazine. It outlines the significance of methoxypyrazines in grapes, musts, and wines in terms of the concentrations that are capable of contributing their sensory characteristics to wines. This review discusses methods for analyzing MPs including gas chromatography-mass spectroscopy (one or two dimension) and high-performance liquid chromatography, the appropriate extraction techniques, and the efficacy of these methods. Additionally, this review explores factors that affect pyrazine content of grapes, must, and wines, such as the effects of different viticultural practices, effects of light exposure and grape maturation, climate, soil, the multi-colored Asian lady beetle and the effects of different vinification processes.

Discovery and development of second-generation proteasome inhibitors.

Proteasome inhibition is a validated therapeutic strategy for the treatment of B-cell neoplasms. The peptide boronate based inhibitor bortezomib has become an important tool in the armamentarium for the treatment of multiple myeloma (MM) and has spurred the development of new agents that target the catalytic activities of the proteasome. Five of these agents, representing three distinct chemical classes, have reached clinical testing. These compounds have properties similar to and distinct from bortezomib. Here, the preclinical activity and clinical development of these agents are reviewed with special attention given to comparisons with bortezomib.

Bisindole alkaloids of the topsentin and hamacanthin classes from a marine sponge Spongosorites sp.

Seven new (1 and 3-8) and seven known (2 and 9-14) bisindole alkaloids of the topsentin and hamacanthin classes were isolated from the MeOH extract of a marine sponge Spongosorites sp. by bioactivity-guided fractionation. The structure of compound 7 is a revision from our previous report. The planar structures were established on the basis of NMR and MS spectroscopic analyses. Configurations of these compounds were defined by NMR spectroscopy and optical rotation. It is noteworthy that both R and S isomers were isolated for the hamacanthins (1-4, 9, 10, 15, and 16), while a single stereoisomer was isolated for dihydrohamacanthins (5, 11-14, 17, and 18). Compounds 1-4, 6, and 8-14 showed marginal cytotoxicity against five human solid tumor cell lines, and compound 2 showed weak antibacterial activity against clinically isolated methicillin-resistant strains.

Bayesian neural networks for aroma classification.

Bayesian Neural Networks (BNNs) are investigated to test their potential to distinguish between different aroma impressions. Special attention is thereby drawn on mixed aroma impressions, resulting from the flavor description of a single compound with more than one aroma quality. The structures of 133 pyrazine-derived aroma compounds as well as their aroma descriptions are selected for comparison. The information fed into the neural networks is based on molecular descriptors calculated from the geometrically optimized chemical structures. While in the case of the Probabilistic Neural Network (PNN) the networks' output consists of a categorical variable, the output for the General Regression Neural Network (GRNN) is defined in a numerical way. The best models attain comparable performance with a correct prediction of 90.8% of the cases for PNN and 89.9% for GRNN, respectively. Comparison of the BNN results to those obtained by Multiple Linear Regression (MLR) points out that the nonlinear methods work significantly better on the studied problem and that BNNs can be applied to multiple-category problems in structure-flavor relationships with good accuracy.

Aroma quality differentiation of pyrazine derivatives using self-organizing molecular field analysis and artificial neural network.

The encoding of various aroma impressions and the distinction between different aroma qualities are unsolved problems, as differences between aroma impressions can be described only in a qualitative but not in a quantitative manner. As a consequence, classifications of various aroma qualities cannot easily be performed by standard QSAR methods. To find a proper way to encode aroma impressions for SAR studies, a total of 50 pyrazine-based aroma compounds showing the aroma quality of earthy, green-earthy, or green are analyzed. Special attention is thereby turned on the mixed aroma impression green-earthy. Classifications on the whole data set as well as on smaller subsets are calculated using self-organizing molecular field analysis (SOMFA) and artificial neural networks (ANNs). SOMFA classifies between two or three aroma impressions, leading to models satisfying in predictive power. ANN analysis using multilayer perceptron network architecture with one hidden layer and nominal output as well as genetic regression neural network) with two hidden layers and numerical output both lead to a rather good performance rate of 94%.

Prediction of the aroma quality and the threshold values of some pyrazines using artificial neural networks.

An artificial neural network is used to predict both the classification of aroma compounds and their flavor impression threshold values for a series of pyrazines. The classification set consists of 98 compounds (32 green, 43 bell-pepper, and 23 nutty smelling pyrazines), and the regression sets consist of 24 green and 37 bell-pepper odorous pyrazines. The best classification of the three aroma impressions (93.7%) is obtained by using a multilayer perceptron network architecture. To predict the threshold values of bell-pepper fragrance, a standard Pearson R correlation coefficient of 0.936 for the training set, 0.912 for the verification set, and 0.926 for the test set is received with two hidden layers consisting of two and one neurons. The network for the threshold prediction of the class of green-smelling pyrazines with one hidden layer containing three neurons turns out to be the best with a standard Pearson R correlation coefficient of 0.859 for the training, 0.918 for the verification, and 0.948 for the test set. These good correlations show that artificial neural networks are versatile tools for the classification of aroma compounds.