Cocoa quality: Chemical relationship of cocoa beans and liquors in origin identitation.

In this study, HS-SPME-GC-MS was applied in combination with machine learning tools to the identitation of a set of cocoa samples of different origins. Untargeted fingerprinting and profiling approaches were tested for their informative, discriminative and classification ability provided by the volatilome of the raw beans and liquors inbound at the factory in search of robust tools exploitable for long-time studies. The ability to distinguish the country of origin on both beans and liquors is not so obvious due to processing steps accompanying the transformation of the beans, but this capacity is of particular interest to the chocolate industry as both beans and liquors can enter indifferently into the processing of chocolate. Both fingerprinting (untargeted) and profiling (targeted) strategies enable to decipher of the information contained in the complex dataset and the cross-validation of the results, affording to discriminate between the origins with effective classification models.

Bacterial and Fungal Communities Are Specifically Modulated by the Cocoa Bean Fermentation Method.

Cocoa bean fermentation is carried out in different production areas following various methods. This study aimed to assess how the bacterial and fungal communities were affected by box, ground or jute fermentation methods, using high-throughput sequencing (HTS) of phylogenetic amplicons. Moreover, an evaluation of the preferable fermentation method was carried out based on the microbial dynamics observed. Box fermentation resulted in higher bacterial species diversity, while beans processed on the ground had a wider fungal community. Lactobacillus fermentum and Pichia kudriavzevii were observed in all three fermentation methods studied. Moreover, Acetobacter tropicalis dominated box fermentation and Pseudomonas fluorescens abounded in ground-fermented samples. Hanseniaspora opuntiae was the most important yeast in jute and box, while Saccharomyces cerevisiae prevailed in the box and ground fermentation. PICRUST analysis was performed to identify potential interesting pathways. In conclusion, there were noticeable differences between the three different fermentation methods. Due to its limited microbial diversity and the presence of microorganisms that guarantee good fermentation, the box method was found to be preferable. Moreover, the present study allowed us to thoroughly explore the microbiota of differently treated cocoa beans and to better understand the technological processes useful to obtain a standardized end-product.

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.

Cocoa smoky off-flavor: Chemical characterization and objective evaluation for quality control.

Cocoa smoky off-flavor is due to inappropriate post-harvest processing and cannot be removed in the subsequent chocolate-manufacturing steps. To date, no reliable analytical method to detect key-analytes responsible for smoky off-flavor in incoming raw material is available. This study aims to develop an analytical method, suitable for quality control, to detect smoky markers. The cocoa volatilome was first profiled by headspace solid phase microextration combined with comprehensive two-dimensional gas chromatography-mass spectrometry from a set of representative smoky and non-smoky samples; advanced fingerprinting revealed the chemicals responsible for the off-flavor. The results served to develop a 1D-GC method suitable for routine application. Ten identified smoky markers were subjected to accurate quantification, thereby defining operative ranges to accept/reject incoming bean samples. On average, these markers are present in smoky samples at 7 to 125 fold concentrations vs. those in non-smoky beans, ranging from 32.5 ng/g for naphtalene to 721.8 ng/g for phenol.

Detailed analyses of the bacterial populations in processed cocoa beans of different geographic origin, subject to varied fermentation conditions.

The quality of chocolate is influenced by several parameters, one of which is bacterial diversity during fermentation and drying; a crucial factor for the generation of the optimal cocoa flavor precursors. Our understanding of the bacterial populations involved in chocolate fermentation can be improved by the use of high-throughput sequencing technologies (HTS), combined with PCR amplification of the 16S rRNA subunit. Here, we have conducted a high-throughput assessment of bacterial diversity in four processed samples of cocoa beans from different geographic origins. As part of this study, we also assessed whether different DNA extraction methods could affect the quality of our data. The dynamics of microbial populations were analyzed postharvest (fermentation and sun drying) and shipment, before entry to the industrial process. A total of 691,867 high quality sequences were obtained by Illumina MiSeq sequencing of the two bacterial 16S rRNA hypervariable regions, V3 and V4, following paired-read assembly of the raw reads. Manual curation of the 16S database allowed us to assign the correct taxonomic classifications, at species level, for 83.8% of those reads. This approach revealed a limited biodiversity and population dynamics for both the lactic acid bacteria (LAB) and acetic acid bacteria (AAB), both of which are key players during the acetification and lactic acid fermentation phases. Among the LAB, the most abundant species were Lactobacillus fermentum, Enterococcus casseliflavus, Weissella paramesenteroides, and Lactobacillus plantarum/paraplantarum. Among the AAB, Acetobacter syzygii, was most abundant, then Acetobacter senegalensis and Acetobacter pasteriuanus. Our results indicate that HTS approach has the ability to provide a comprehensive view of the cocoa bean microbiota at the species level.