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Cocoa beans (Theobroma cacao L.) can be used for craft chocolate production, which arouses consumer interest due to their perceived better quality. This study aimed to evaluate the chemical profile of 80% artisanal chocolate samples produced with cocoa beans subjected to different maturation conditions. In the first maturation process, beans were matured under no-oxygen conditions, and in the second, the toasted beans were matured in oak barrels. The volatile compounds of the chocolate samples were extracted by the solid-phase microextraction method in headspace mode and analyzed by gas chromatography/mass spectrometer. The non-volatile compounds were extracted with methanol and analyzed through paper spray mass spectrometry. Overall, 35 volatile compounds belonging to different chemical classes (acids, alcohols, aldehydes, ketones, esters, and pyrazines) were identified, such as propanoic acid and butane-2,3-diol. In addition, 37 non-volatile compounds, such as procyanidin A pentoside and soyasaponin B, were listed. Tannins, flavonoids, and phenylpropanoids were the main chemical classes observed, varying between the two samples analyzed. Therefore, it was possible to verify that maturation conditions affected the metabolomic profile of the 80% artisanal chocolate samples, being able to influence the sensory characteristics and bioactive compounds profile. Given these results, the sensory evaluation of these chocolates is suggested as the next step. [ABSTRACT FROM AUTHOR]

Fires are one of the main sources of disturbance in fire-sensitive ecosystems such as the Amazon. Any attempt to characterize their impacts and establish actions aimed at combating these events presupposes the correct identification of the affected areas. However, accurate mapping of burned areas in humid tropical forest regions remains a challenging task. In this paper, we evaluate the performance of four operational BA products (MCD64A1, Fire_cci, GABAM and MapBiomas Fogo) on a regional scale in the southwestern Amazon and propose a new approach to BA mapping using fraction images extracted from data cubes of the Brazilian orbital sensors CBERS-4/WFI and CBERS-4A/WFI. The methodology for detecting burned areas consisted of applying the Linear Spectral Mixture Model to the images from the CBERS-4/WFI and CBERS-4A/WFI data cubes to generate shadow fraction images, which were then segmented and classified using the ISOSEG non-supervised algorithm. Regression and similarity analyses based on regular grid cells were carried out to compare the BA mappings. The results showed large discrepancies between the mappings in terms of total area burned, land use and land cover affected (forest and non-forest) and spatial location of the burned area. The global products MCD64A1, GABAM and Fire_cci tended to underestimate the area burned in the region, with Fire_cci underestimating BA by 88%, while the regional product MapBiomas Fogo was the closest to the reference, underestimating by only 7%. The burned area estimated by the method proposed in this work (337.5 km2) was 12% higher than the reference and showed a small difference in relation to the MapBiomas Fogo product (18% more BA). These differences can be explained by the different datasets and methods used to detect burned areas. The adoption of global products in regional studies can be critical in underestimating the total area burned in sensitive regions. Our study highlights the need to develop approaches aimed at improving the accuracy of current global products, and the development of regional burned area products may be more suitable for this purpose. Our proposed approach based on WFI data cubes has shown high potential for generating more accurate regional burned area maps, which can refine BA estimates in the Amazon. [ABSTRACT FROM AUTHOR]