Comprehensive lipid analysis of green Arabica coffee beans by LC-HRMS/MS

Comparative lipidomics analysis reveals changes in lipid profile of Arabica coffee at different maturity

This study aimed to investigate the effects of maturity on the changes in major lipid metabolites of coffee and their associated pathways. UPLC-ESI-MS/MS was used to compare the lipidomic profiles of coffee beans at five different maturity stages. A total of 516 lipid metabolites across 26 subclasses were identified, with 111 showing significant differences. Glycerolipids (GL) and fatty acyls (FA) were the most abundant, followed by glycerophospholipids (GP), sphingolipids (SP) and prenol lipids (PR). PCA and OPLS-DA analyses demonstrated significant changes in coffee lipids during maturation. Glycerophospholipid metabolism and glycerolipid metabolism were identified as key metabolic pathways, with phosphatidic acid (PA), lysophosphatidic acid (LPA) and diacylglycerol (DG) as key lipid metabolites in these pathways during coffee maturation. Lipids in immature and overripe beans were significantly different from those in mature coffee beans. This study provides a foundational understanding of lipid transformation and flavor profile formation during coffee maturation.

Revealing the dynamic changes of lipids in coffee beans during roasting based on UHPLC-QE-HR-AM/MS/MS

Coffee is popular worldwide and its consumption is increasing in recent years. Although mass spectrometry-based lipidomics approaches have been prevalent, their application in studies related to detailed information and dynamic changes in lipid composition during coffee bean roasting is still limited. The aim of this study was to investigate the dynamic changes in coffee bean lipids during the roasting process. The lipid classes and lipid molecular species in coffee beans were characterized by lipidomic analysis combined with chemometrics. A total of 12 lipid classes and 105 lipid molecular species were identified and quantified. Triacylglycerols (TAG) was the most abundant lipid class in both green beans and roasted beans. The content of phosphatidylethanolamine (PE) and lysophosphatidylethanolamine (LPE) in green beans was obviously higher than that in roasted beans. Other phospholipids, such as phosphatidylinositol (PI), lysophosphatidylinositol (LPI), phosphatidylcholine (PC), lysophophatidylcholine (LPC) and phosphatidic acid (PA), showed a tendency to increase at the beginning of roasting, then decreased gradually. Several differential lipid molecule species, for instance, PE (16:0_18:2), PC (18:2_18:2) were significantly down-regulated, and PI (18:1_18:2) was significantly up-regulated. This study provided a scientific basis for the change of coffee bean lipids during the roasting process.

An overview of food lipids toward food lipidomics

Increasing evidence regarding lipids' beneficial effects on human health has changed the common perception of consumers and dietary officials about the role(s) of food lipids in a healthy diet. However, lipids are a wide group of molecules with specific nutritional and bioactive properties. To understand their true nutritional and functional value, robust methods are needed for accurate identification and quantification. Specific analytical strategies are crucial to target specific classes, especially the ones present in trace amounts. Finding a unique and comprehensive methodology to cover the full lipidome of each foodstuff is still a challenge. This review presents an overview of the lipids nutritionally relevant in foods and new trends in food lipid analysis for each type/class of lipids. Food lipid classes are described following the LipidMaps classification, fatty acids, endocannabinoids, waxes, C8 compounds, glycerophospholipids, glycerolipids (i.e., glycolipids, betaine lipids, and triglycerides), sphingolipids, sterols, sercosterols (vitamin D), isoprenoids (i.e., carotenoids and retinoids (vitamin A)), quinones (i.e., coenzyme Q, vitamin K, and vitamin E), terpenes, oxidized lipids, and oxylipin are highlighted. The uniqueness of each food group: oil-, protein-, and starch-rich, as well as marine foods, fruits, and vegetables (water-rich) regarding its lipid composition, is included. The effect of cooking, food processing, and storage, in addition to the importance of lipidomics in food quality and authenticity, are also discussed. A critical review of challenges and future trends of the analytical approaches and computational methods in global food lipidomics as the basis to increase consumer awareness of the significant role of lipids in food quality and food security worldwide is presented.

Authentication of Coffee Blends by 16-O-Methylcafestol Quantification Using NMR Spectroscopy

In 2019, a coffee chain in Taiwan was found to be mixing relatively cheap Robusta beans into products marketed as 100% Arabica. Many studies show 16-OMC is a remarkable marker to distinguish Robusta from Arabica beans, and nuclear magnetic resonance (NMR) is a convenient and efficient technique for 16-OMC quantification. Here, a 500 MHz NMR was employed to determine the content of 16-OMC in coffee for adulterate evaluation. A total of 118 samples were analyzed including products from the coffee chain, raw materials (single coffee beans), and other commercial products. The contents of 16-OMC in single Robusta beans were between 1005.55 and 3208.32 mg/kg and were absent from single Arabica beans. The surveillance results indicate that 17 out of 47 blend products claiming to contain 100% Arabica had 16-OMC quantifications in the range of 155.74–784.60 mg/kg. Furthermore, all 17 products were produced by the same coffee chain. We confirmed that coffee chain adulterated Arabica with Robusta in parts of their products, which claimed to include 100% Arabica. Moreover, this work highlights the free form of 16-OMC was esterified by coffee instantly. The decomposition products of 16-OMC were observed obviously in green Robusta while the mechanisms remain unclear. Future research should focus more on these aspects to further increase our understanding of these mechanisms.

Medium Roasting and Brewing Methods Differentially Modulate Global Metabolites, Lipids, Biogenic Amines, Minerals, and Antioxidant Capacity of Hawai'i-Grown Coffee (Coffea arabica)

In the United States, besides the US territory Puerto Rico, Hawai'i is the only state that grows commercial coffee. In Hawai'i, coffee is the second most valuable agricultural commodity. Health benefits associated with moderate coffee consumption, including its antioxidant capacity, have been correlated to its bioactive components. Post-harvest techniques, coffee variety, degree of roasting, and brewing methods significantly impact the metabolites, lipids, minerals, and/or antioxidant capacity of brewed coffees. The goal of our study was to understand the impact of roasting and brewing methods on metabolites, lipids, biogenic amines, minerals, and antioxidant capacity of two Hawai'i-grown coffee (Coffea arabica) varieties, "Kona Typica" and "Yellow Catuai". Our results indicated that both roasting and coffee variety significantly modulated several metabolites, lipids, and biogenic amines of the coffee brews. Furthermore, regardless of coffee variety, the antioxidant capacity of roasted coffee brews was higher in cold brews. Similarly, total minerals were higher in "Kona Typica" cold brews followed by "Yellow Catuai" cold brews. Hawai'i-grown coffees are considered "specialty coffees" since they are grown in unique volcanic soils and tropical microclimates with unique flavors. Our studies indicate that both Hawai'i-grown coffees contain several health-promoting components. However, future studies are warranted to compare Hawai'i-grown coffees with other popular brand coffees and their health benefits in vivo.

Dissecting coffee seeds metabolome in context of genotype, roasting degree, and blending in the Middle East using NMR and GC/MS techniques

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.

Untargeted LC-QTOF-MS/MS based metabolomics approach for revealing bioactive components in probiotic fermented coffee brews

Amidst trends in non-dairy probiotic foods and functional coffees, we recently developed a fermented coffee brew containing high live counts of the probiotics Lacticaseibacillus rhamnosus GG and Saccharomyces boulardii CNCM-I745. However, probiotic fermentation did not alter levels of principal coffee bioactive components based on targeted analyses. Here, to provide therapeutic justification compared to other non-fermented coffee brews, we aimed to discover postbiotics in coffee brews fermented with L. rhamnosus GG and/or S. boulardii CNCM-I745. By using an untargeted LC-QTOF-MS/MS based metabolomics approach coupled with validated multivariate analyses, 37 differential metabolites between fermentation treatments were putatively annotated. These include the production of postbiotics such as 2-isopropylmalate by S. boulardii CNCM-I745, and aromatic amino acid catabolites (indole-3-lactate, p-hydroxyphenyllactate, 3-phenyllactate), and hydroxydodecanoic acid by L. rhamnosus GG. Overall, LC-QTOF based untargeted metabolomics can be an effective approach to uncover postbiotics, which may substantiate additional potential functionalities of probiotic fermented foods compared to their non-fermented counterparts.