Cocoa origin classifiability through LC-MS data: A statistical approach for large and long-term datasets

Key molecular compounds for simultaneous origin discrimination and sensory prediction of cocoa: An UHPLC-HRMS sensomics approach

Cocoa-based and chocolate mono-origin products are increasingly gaining market share because they are perceived by consumers as more valuable and high quality. A comprehensive characterization of the sensory profile of a specific geographical area is complex and different analytical and sensorial strategies have been adopted. This study focused on identifying molecular markers capable of discriminating between different origins and, at the same time, predicting their sensory attributes adopting a sensomics approach. The aim is to provide a useful tool for chocolate producers to effectively screen the origins of cocoa, controlling and optimizing the gustative properties and processing flow. An untargeted method was adopted, based on the coupling of UHPLC-HRMS, followed by the application of chemometric tools for the selection of 71 discriminating molecular markers for six origins. These markers, via OPLS-Regressions, also demonstrated a strong global correlation with the sensory descriptors, evaluated by trained assessors, allowing their prediction.

Children of Nature: Thoughts on Targeted and Untargeted Analytical Approaches to Decipher Polyphenol Reactivity in Food Processing and Metabolism

Following 25 years of polyphenol research in our laboratory, the astonishing chemical and metabolic reactivity of polyphenols resulting in considerable chemical diversity has emerged as the most remarkable attribute of this class of natural products. To illustrate this concept, we will present selected data from black tea and coffee chemistry. In black tea chemistry, enzymatic fermentation converts six catechin derivatives into an estimated 30 000 different polyphenolic compounds via a process we have termed the oxidative cascade process. In coffee roasting, around 45 chlorogenic acids are converted into an estimated 250 novel derivatives following a series of diverse chemical transformations. Following ingestion by humans, these dietary polyphenols, whether genuine secondary metabolites or food processing products, encounter the microorganisms of the gut microbiota, converting them into a myriad of novel structures. In the case of coffee, only two out of 250 chlorogenic acids are absorbed intact, with most others being subject to gut microbial metabolism. Modern mass spectrometry (MS) has been key in unravelling the true complexity of polyphenols subjected to food processing and metabolism. We will accompany this assay with a short overview on analytical strategies developed, including ultrahigh-resolution MS, tandem MS, multivariate statistics, and molecular networking that allow an insight into the fascinating chemical processes surrounding dietary polyphenols. Finally, experimental results studying biological activity of polyphenols will be presented and discussed, highlighting a general promiscuity of this class of compounds associated with nonselective protein binding leading to loss of enzymatic function, another noteworthy general property of many dietary polyphenols frequently overlooked.

Authentication of Cocoa Products Based on Profiling and Fingerprinting Approaches: Assessment of Geographical, Varietal, Agricultural and Processing Features

Cocoa and its derivative products, especially chocolate, are highly appreciated by consumers for their exceptional organoleptic qualities, thus being often considered delicacies. They are also regarded as superfoods due to their nutritional and health properties. Cocoa is susceptible to adulteration to obtain illicit economic benefits, so strategies capable of authenticating its attributes are needed. Features such as cocoa variety, origin, fair trade, and organic production are increasingly important in our society, so they need to be guaranteed. Most of the methods dealing with food authentication rely on profiling and fingerprinting approaches. The compositional profiles of natural components -such as polyphenols, biogenic amines, amino acids, volatile organic compounds, and fatty acids- are the source of information to address these issues. As for fingerprinting, analytical techniques, such as chromatography, infrared, Raman, and mass spectrometry, generate rich fingerprints containing dozens of features to be used for discrimination purposes. In the two cases, the data generated are complex, so chemometric methods are usually applied to extract the underlying information. In this review, we present the state of the art of cocoa and chocolate authentication, highlighting the pros and cons of the different approaches. Besides, the relevance of the proposed methods in quality control and the novel trends for sample analysis are also discussed.

Shades of Fine Dark Chocolate Colors: Polyphenol Metabolomics and Molecular Networking to Enlighten the Brown from the Black

High-quality dark chocolates (70% cocoa content) can have shades from light to dark brown color. This work aimed at revealing compounds that discriminate black and brown chocolates. From 37 fine chocolate samples from years 2019 and 2020 provided by Valrhona,8 dark black samples and 8 light brown samples were selected. A non-targeted metabolomics study was performed based on ultra-high performance liquid chromatography-high resolution mass spectrometry/mass spectrometry experiments, univariate, multivariate, and feature-based molecular networking analyses. Twenty-seven overaccumulated discriminating compounds were found for black chocolates. Among them, glycosylated flavanols including monomers and glycosylated A-type procyanidin dimers and trimers were highly representative. Fifty overaccumulated discriminating compounds were found for brown chocolates. Most of them were B-type procyanidins (from trimers to nonamers). These phenolic compounds may be partially related to the chocolate colors as precursors of colored compounds. This study increases the knowledge on the chemical diversity of dark chocolates by providing new information about the phenolic profiles of black and brown chocolates.

Cocoa bean fingerprinting via correlation networks

Cocoa products have a remarkable chemical and sensory complexity. However, in contrast to other fermentation processes in the food industry, cocoa bean fermentation is left essentially uncontrolled and is devoid of standardization. Questions of food authenticity and food quality are hence particularly challenging for cocoa. Here we provide an illustration how network science can support food fingerprinting and food authenticity research. Using a large dataset of 140 cocoa samples comprising three cocoa fermentation/processing stages and eight countries, we obtain correlation networks between the cocoa samples by computing measures of pairwise correlation from their liquid chromatography-mass spectrometry (LC-MS) profiles. We find that the topology of correlation networks derived from untargeted LC-MS profiles is indicative of the fermentation and processing stage as well as the origin country of cocoa samples. Progressively increasing the correlation threshold firstly reveals network clusters based on processing stage and later country-based clusters. We present both, qualitative and quantitative evidence through network visualization, network statistics and concepts from machine learning. In our view, this network-based approach for classifying mass spectrometry data has broad applicability beyond cocoa.