The impact of cocoa lipid content on chocolate quality has been extensively described. Nevertheless, few studies have elucidated the cocoa lipid composition and their bioactive properties, focusing only on specific lipids. In the present study the lipidome of fine-flavor cocoa fermentation was analyzed using LC-MS-QTOF and a Machine Learning model to assess potential bioactivity was developed. Our results revealed that the cocoa lipidome, comprised mainly of fatty acyls and glycerophospholipids, remains stable during fine-flavor cocoa fermentations. Also, several Machine Learning algorithms were trained to explore potential biological activity among the identified lipids. We found that K-Nearest Neighbors had the best performance. This model was used to classify the identified lipids as bioactive or non-bioactive, nominating 28 molecules as potential bioactive lipids. None of these compounds have been previously reported as bioactive. Our work is the first untargeted lipidomic study and systematic effort to investigate potential bioactivity in fine-flavor cocoa lipids.

Saffron is a spice revered for its unique flavor and health attributes often subjected to fraudulence. In this study, molecular networking as a visualization tool for UPLC/MS dataset of saffron and its common substitutes i.e. safflower and calendula (n = 21) was employed for determining genuineness of saffron and detecting its common substitutes i.e. safflower and calendula. Saffron was abundant in flavonol-O-glycosides and crocetin esters versus richness of flavanones/chalcones glycosides in safflower and cinnamates/terpenes in calendula. OPLS-DA identified differences in UPLC/MS profiles of different saffron accessions where oxo-hydroxy-undecenoic acid-O-hexoside was posed as saffron authentication marker and aided in discrimination between Spanish saffron of high quality from its inferior grade i.e. Iranian saffron along with crocetin di-O-gentiobiosyl ester and kaempferol-O-sophoroside. Kaempferol-O-neohesperidoside and N,N,N,-p-coumaroyl spermidine were characteristic safflower metabolites, whereas, calendulaglycoside C and di-O-caffeoyl quinic acid were unique to calendula. UV/VIS fingerprint spectral regions of picrocrocin (230–260 nm) and crocin derivatives (400–470 nm) were posed as being discriminatory of saffron authenticity and suggestive it can replace UPLC/MS in saffrom quality determination.

With a favored taste and various bioactivities, coffee has been consumed as a daily beverage worldwide. The current study presented a multi-faceted comparative metabolomics approach dissecting commercially available coffee products in the Middle East region for quality assessment and functional food purposes using NMR and GC/MS platforms. NMR metabolites fingerprinting led to identification of 18 metabolites and quantification (qNMR) of six prominent markers for standardization purposes. An increase of β-ethanolamine (MEA) reported for the first time, 5-(hydroxymethyl) furfural (5-HMF), concurrent with a reduction in chlorogenic acid, kahweol, and sucrose levels post roasting as revealed using multivariate data analyses (MVA). The diterpenes kahweol and cafestol were identified in green and roasted Coffea arabica, while 16-O-methyl cafestol in roasted C. robusta. Moreover, GC/MS identified a total of 143 metabolites belonging to 15 different chemical classes, with fructose found enriched in green C. robusta versus fatty acids abundance, i.e., palmitic and stearic acids in C. arabica confirming NMR results. These potential results aided to identify novel quality control attributes, i.e., ethanolamine, for coffee in the Middle East region and have yet to be confirmed in other coffee specimens.