Dietary modulation of the gut microbiota impacts human health. Here we investigated the hitherto unknown effects of resistant starch type 4 (RS4) enriched diet on gut microbiota composition and short-chain fatty acid (SCFA) concentrations in parallel with host immunometabolic functions in twenty individuals with signs of metabolic syndrome (MetS). Cholesterols, fasting glucose, glycosylated haemoglobin, and proinflammatory markers in the blood as well as waist circumference and % body fat were lower post intervention in the RS4 group compared with the control group. 16S-rRNA gene sequencing revealed a differential abundance of 71 bacterial operational taxonomic units, including the enrichment of three Bacteroides species and one each of Parabacteroides, Oscillospira, Blautia, Ruminococcus, Eubacterium, and Christensenella species in the RS4 group. Gas chromatography–mass spectrometry revealed higher faecal SCFAs, including butyrate, propionate, valerate, isovalerate, and hexanoate after RS4-intake. Bivariate analyses showed RS4-specific associations of the gut microbiota with the host metabolic functions and SCFA levels. Here we show that dietary RS4 induced changes in the gut microbiota are linked to its biological activity in individuals with signs of MetS. These findings have potential implications for dietary guidelines in metabolic health management.

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A new, rapid Fourier transform near infrared (FT-NIR) spectroscopic procedure is described to screen for the authenticity of extra virgin olive oils (EVOO) and to determine the kind and amount of an adulterant in EVOO. To screen EVOO, a partial least squares (PLS1) calibration model was developed to estimate a newly created FT-NIR index based mainly on the relative intensities of two unique carbonyl overtone absorptions in the FT-NIR spectra of EVOO and other mixtures attributed to volatile (5280 cm(-1)) and non-volatile (5180 cm(-1)) components. Spectra were also used to predict the fatty acid (FA) composition of EVOO or samples spiked with an adulterant using previously developed PLS1 calibration models. Some adulterated mixtures could be identified provided the FA profile was sufficiently different from those of EVOO. To identify the type and determine the quantity of an adulterant, gravimetric mixtures were prepared by spiking EVOO with different concentrations of each adulterant. Based on FT-NIR spectra, four PLS1 calibration models were developed for four specific groups of adulterants, each with a characteristic FA composition. Using these different PLS1 calibration models for prediction, plots of predicted vs. gravimetric concentrations of an adulterant in EVOO yielded linear regression functions with four unique sets of slopes, one for each group of adulterants. Four corresponding slope rules were defined that allowed for the determination of the nature and concentration of an adulterant in EVOO products by applying these four calibration models. The standard addition technique was used for confirmation.

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