Metabolite Profiling of Premium Civet Luwak Bio-Transformed Coffee Compared with Conventional Coffee Types, as Analyzed Using Chemometric Tools

Valorization potential of Egyptian mango kernel waste product as analyzed via GC/MS metabolites profiling from different cultivars and geographical origins

Increasing attention has been given to mango (Mangifera indica) fruits owing to their characteristic taste, and rich nutritional value. Mango kernels are typically discarded as a major waste product in mango industry, though of potential economic value. The present study aims to outline the first comparison of different mango kernel cvs. originated from different localities alongside Egypt, e.g., Sharqia, Suez, Ismailia, and Giza. Gas chromatography-mass spectroscopy (GC-MS) post silylation analysis revealed that sugars were the major class being detected at 3.5-290.9 µg/mg, with some kernels originating from Sharqia province being the richest amongst other cvs. In consistency with sugar results, sugar alcohols predominated in Sharqia cvs. at 1.3-38.1 µg/mg represented by ribitol, iditol, pinitol, and myo-inositol. No major variation was observed in the fatty acids profile either based on cv. type or localities, with butyl caprylate as a major component in most cvs. identified for the first time in mango. Regarding phenolics, Sedeeq cv. represented the highest level at 18.3 µg/mg and showing distinct variation among cvs. posing phenolics as better classification markers than sugars. Multivariate data analyses (MVA) confirmed that the premium cvs "Aweis and Fons" were less enriched in sugars, i.e., fructose, talose, and glucose compared to the other cvs. Moreover, MVA of Zabdeya cv. collected from three localities revealed clear segregation to be chemically distinct. Sharqia originated mango kernels were rich in sugars (e.g., glucose and fructose), whilst sarcosine esters predominated in other origins.

Averrhoa carambola L. fruit and stem metabolites profiling and immunostimulatory action mechanisms against cyclosporine induced toxic effects in rat model as analyzed using UHPLC/MS-MS-based chemometrics and bioassays

The Averrhoa carambola L. tree encompasses a myriad of phytochemicals contributing to its nutritional and health benefits. The current study aims at investigating the A. carambola L. the metabolite profile grown in tropical and temperate regions represented by fruit and stem, for the first time using UPLC/MS-based molecular networking and chemometrics. Asides, assessment of the immunostimulatory effect of ripe fruit and stem, was compared in relation to metabolite fingerprints. Eighty metabolites were identified, 8 of which are first-time to be reported including 3 dihydrochalcone-C-glycosides, 4 flavonoids, and one phenolic. Multivariate data analysis revealed dihydrochalcones as origin-discriminating metabolites between temperate and tropical grown fruits. Further, an in vivo immunomodulatory assay in a cyclosporine A-induced rat model revealed a potential immune-enhancing effect as manifested by down-regulation of inflammatory markers (IL-6, INF-γ, IL-1, TLR4, and ESR) concurrent with the up-regulation of CD4 level and the CD4/CD8 ratio. Moreover, both extracts suppressed elevation of liver and kidney functions in serum as well as reduction in oxidative stress with concurrent increased levels of T-protein, albumin, globulin, and A/G ratio. This study pinpoints differences in secondary metabolite profiles amongst A. carambola L. accessions from different origins and organ type and its immunomodulatory action mechanisms.

Analysis and classification of coffee beans using single coffee bean mass spectrometry with machine learning strategy

Coffee is a daily essential, with prices varying based on taste, aroma, and chemical composition. However, distinguishing between different coffee beans is challenging due to time-consuming and destructive sample pretreatment. This study presents a novel approach for directly analyzing single coffee beans through mass spectrometry (MS) without the need for sample pretreatment. Using a single coffee bean deposited with a solvent droplet containing methanol and deionized water, we generated electrospray to extract the main species for MS analysis. Mass spectra of single coffee beans were obtained in just a few seconds. To showcase the effectiveness of the developed method, we used palm civet coffee beans (kopi luwak), one of the most expensive coffee types, as model samples. Our approach distinguished palm civet coffee beans from regular ones with high accuracy, sensitivity, and selectivity. Moreover, we employed a machine learning strategy to rapidly classify coffee beans based on their mass spectra, achieving 99.58% accuracy, 98.75% sensitivity, and 100% selectivity in cross-validation. Our study highlights the potential of combining the single-bean MS method with machine learning for the rapid and non-destructive classification of coffee beans. This approach can help to detect low-priced coffee beans mixed with high-priced ones, benefiting both consumers and the coffee industry.