Abstract
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.
Highlights
Food fingerprinting has been discussed from a multitude of technological perspectives[1,2,3]
We illustrate – using a large data set of cocoa liquid chromatography coupled to mass spectroscopy (LC-MS) data from eight countries of origin and in three processing stages – how a network-based analysis can reveal the underlying patterns, embedded in interconnected biochemical and chemical reaction grids
We introduce a network-based approach for quality control in cocoa research by classifying cocoa bean samples based on their high-throughput LC-MS profiles
Summary
Food fingerprinting has been discussed from a multitude of technological perspectives[1,2,3]. We illustrate – using a large data set of cocoa liquid chromatography coupled to mass spectroscopy (LC-MS) data from eight countries of origin and in three processing stages (unfermented, fermented, liquor) – how a network-based analysis can reveal the underlying patterns, embedded in interconnected biochemical and chemical reaction grids. In its most basic form, a network-based interpretation of highthroughput or alternative complex data offers a dimensionality reduction of the raw data, dramatically facilitating further data analysis steps and increasing the statistical significance of results[10,11] This basic idea has revolutionized the way we analyze diverse biological and medical data and serves as a foundation of what is called today Systems Biology and Systems Medicine[12,13,14]. The functional hierarchy of structures in this family of networks is revealed
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