Combining rapid evaporative ionization mass spectrometry and chemometrics for the differentiation of cocoa bean quality

Abstract

Ensuring the high and consistent quality of cocoa beans presents a significant challenge, driven by concerns related to food safety, economic profitability, and overall quality. Recently, functional microbial cultures have been developed by selecting specific microbial strains that enhance the cocoa bean fermentation process and improve the quality of the fermented and dried beans. These selection processes require extensive and time-consuming screening of numerous microbial strains. To address this, the present study explored a rapid, untargeted, metabolite-based approach to distinguish cocoa beans fermented with specific microbial cultures. This was achieved using rapid evaporative ionization mass spectrometry (REIMS) combined with chemometric analysis. Metabolite fingerprints of cocoa beans fermented with 21 antifungal (AF) and/or pectinolytic (P) microbial cultures were analyzed using REIMS. The fermented beans were differentiated based on their metabolite profiles using LiveID software, which is integrated with the REIMS system. Subsequently, six classification models were compared in detail to evaluate their performance, and tentatively extended to classify metabolite fingerprints from independently fermented beans. Initially, LiveID combined with PCA-LDA successfully distinguished metabolite fingerprints based on single-strain microbial cultures and the expected AF or AF&P functionalities of co-cultures, achieving an accuracy of 80 %. Further analysis of the six classification models demonstrated the strong performance of gradient boosting machines, random forests, and neural networks in differentiating metabolite fingerprints based on the functionality of microbial co-cultures, with accuracy estimates of 85 %, 84 %, and 81 %, respectively. Finally, optimized random forest models were tested on an independent dataset, achieving 70-85 % accuracy for the two-class models. The performance of these models on independent data highlights their potential for broader applications, such as differentiating cocoa beans at the lab scale or in on-farm settings to support the development of functional microbial cultures for the production of cocoa beans with consistently high quality.

Keywords: Antifungal; Cocoa bean fermentation; Metabolite fingerprinting; Microbial culture; Pectinolytic; Rapid evaporative ionization mass spectrometry.

Graphical abstract

Fig. 1

Representative mass spectrum of cocoa beans acquired by REIMS where each peak is an ion characterized by its mass-to-charge ratio (x-axis) and relative intensity (y-axis).

Fig. 2

PCA (a) and PCA-LDA (b) models for the differentiation of cocoa beans fermented with single-strain cultures of Saccharomyces cerevisiae (H290, H017, H354), Limosilactobacillus fermentum (223, M017, 193), and Wickerhamomyces pijperi (H312 and H403) based on the chemical fingerprints.

Fig. 3

PCA (a) and PCA-LDA (b) models for the differentiation of cocoa beans according to the functionalities of the microbial co-cultures using LiveID (AF = antifungal; P = pectinolytic).

Fig. 4

The first ten most important ions involved in the random forest models differentiating the metabolite fingerprints of: cocoa beans fermented with AF, and AF&P co-cultures and control (a), cocoa beans fermented with AF co-culture and control (b), cocoa beans fermented with AF&P co-culture and control (c), cocoa beans fermented with AF, and AF&P co-cultures (d) according to the mean decrease accuracy (AF = antifungal, P = pectinolytic).

Fig. 5

The relative intensities of the most important ions contributing to the differentiation of the cocoa beans fermented with AF microbial co-cultures (a), AF&P microbial co-cultures (b), and Control (c) (AF = antifungal, P = pectinolytic).