Monitoring of the Rioja red wine production process by 1 H-NMR spectroscopy.

BACKGROUND: As an inherently quantitative and unbiased analytical technique, proton nuclear magnetic resonance (1 H-NMR) provides an excellent method to monitor the quality of food and beverages, and a sensitive and informative tool to study the winemaking process. RESULTS: By using NMR, it is possible to monitor quantitative changes in wine metabolites (amino acids, organic acids and some phenolic compounds) during the winemaking process, including wine ageing. This study shows an increase in the concentration of the phenols at the beginning of alcoholic fermentation, as well as a stabilization and slight increase in gallic acid and a slight decrease in resveratrol during the oak barrel ageing step. CONCLUSION: This study demonstrates the potential of NMR as a process analytical technology (PAT) tool in the wine industry, by monitoring amino acids, organic acids and three polyphenols - gallic acid, catechin and resveratrol - during the winemaking process. This study of the time course evolution of wine has been conducted in a commercial winery rather than an experimental laboratory, demonstrating the capacity of this technique in commercial winemaking production. © 2021 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Three different Fourier-transform mid-infrared sampling techniques to characterize bio-organic samples.

In recent years, there has been a surge in the number of applications of Fourier-transform mid-infrared (FTIR) spectroscopy for the characterization of environmental samples and prediction of some of their properties whose measurement has traditionally involved time-consuming and costly methods. However, there are several different mid-infrared techniques available, and there is a gap in knowledge regarding the best-suited technique for recording informative spectra of different types of environmental samples. This study compared the three most widespread FTIR techniques using solid and liquid samples. A total of 11 environmental samples belonging to four categories were analyzed with attenuated total reflectance (ATR), photoacoustic (PAS), and diffuse reflectance (DR) FTIR spectroscopy. Overall, PAS-FTIR was the best technique, providing a greater amount of information, especially for opaque samples (i.e., organic waste, biochar, and soil), than ATR-FTIR and DR-FTIR spectroscopy. Attenuated total reflectance FTIR provided the best spectra for soft samples, such as plant materials, probably due to their ability to achieve good optical contact with the ATR crystal. Finally, DR-FTIR performed relatively well for most samples but was found to be more sensitive to moisture in the samples, resulting in noise in specific areas, and was less sensitive in bond vibrations related to Si.

Untargeted GC-MS Metabolomics Reveals Changes in the Metabolite Dynamics of Industrial Scale Batch Fermentations of Streptoccoccus thermophilus Broth.

An industrial scale biomass production using batch or fed-batch fermentations usually optimized by selection of bacterial strains, tuning fermentation media, feeding strategy, and temperature. However, in-depth investigation of the biomass metabolome during the production may reveal new knowledge for better optimization. In this study, for the first time, the authors investigated seven fermentation batches performed on five Streptoccoccus thermophilus strains during the biomass production at Chr. Hansen (Denmark) in a real life large scale fermentation process. The study is designed to investigate effects of batch fermentation, fermentation time, production line, and yeast extract brands on the biomass metabolome using untargeted GC-MS metabolomics. Processing of the raw GC-MS data using PARAFAC2 revealed a total of 90 metabolites out of which 64 are identified. Partitioning of the data variance according to the experimental design was performed using ASCA and revealed that batch and fermentation time effects and their interaction term were the most significant effects. The yeast extract brand had a smaller impact on the biomass metabolome, while the production line showed no effect. This study shows that in-depth metabolic analysis of fermentation broth provides a new tool for advanced optimization of high-volume-low-cost biomass production by lowering the cost, increase the yield, and augment the product quality.

A comprehensive and comparative GC-MS metabolomics study of non-volatiles in Tanzanian grown mango, pineapple, jackfruit, baobab and tamarind fruits.

Tropical fruits contribute significantly to the total fruit intake worldwide. However, their metabolomes have not yet been investigated comprehensively, as most previous studies revealed only volatile and bulk compositions. This study compares non-volatile metabolites of five fruits grown in Tanzania. A new methodology is developed for broad-spectrum GC-MS metabolomics in fruits using a new derivatization and a two dimensional peak deconvolution techniques. A total of 92 peaks were detected from fruits of which 45 were identified. Jackfruits contained the highest amount of carbohydrates, while baobab contained the highest amount of fatty acids. The highest content of organic acids was detected in tamarind. Principal component analysis revealed insights into metabolic differences and similarities, while hierarchical cluster analysis correctly grouped the fruits according to their relationships in plants' phylogenetic tree. The developed methodology could potentially be applied in large-scale studies on fruit quality, authenticity/variety, optimization of post-harvest processing and storage.

Simultaneous quantification of the boar-taint compounds skatole and androstenone by surface-enhanced Raman scattering (SERS) and multivariate data analysis.

This study investigates the feasibility of using surface-enhanced Raman scattering (SERS) for the quantification of absolute levels of the boar-taint compounds skatole and androstenone in porcine fat. By investigation of different types of nanoparticles, pH and aggregating agents, an optimized environment that promotes SERS of the analytes was developed and tested with different multivariate spectral pre-processing techniques, and this was combined with variable selection on a series of analytical standards. The resulting method exhibited prediction errors (root mean square error of cross validation, RMSECV) of 2.4 × 10(-6) M skatole and 1.2 × 10(-7) M androstenone, with a limit of detection corresponding to approximately 2.1 × 10(-11) M for skatole and approximately 1.8 × 10(-10) for androstenone. The method was subsequently tested on porcine fat extract, leading to prediction errors (RMSECV) of 0.17 µg/g for skatole and 1.5 µg/g for androstenone. It is clear that this optimized SERS method, when combined with multivariate analysis, shows great potential for optimization into an on-line application, which will be the first of its kind, and opens up possibilities for simultaneous detection of other meat-quality metabolites or pathogen markers. Graphical abstract Artistic rendering of a laser-illuminated gold colloid sphere with skatole and androstenone adsorbed on the surface.

Measurement of boar taint in porcine fat using a high-throughput gas chromatography-mass spectrometry protocol.

This work outlines an optimized gas chromatrography-mass spectrometry (GC-MS) based protocol for screening of the presence of the three boar-taint-producing compounds indole, skatole (3-methylindole), and androstenone (5α-androst-16-en-3-one) in porcine fat. The study shows that an accuracy suitable for sample screening can be achieved even when speed and ease of sample handling is prioritized and without the use of internal standards. The method provides levels of detection of 82 ng/g for indole, 97 ng/g for skatole, and 623 ng/g for androstenone (in a 2 g natural backfat matrix). A least-squares approach of predicting the sample analyte mass from instrument response is shown to have prediction errors (root-mean-square error) of 96 ng/g for indole, 94 ng/g for skatole, and 331.3 ng/g for androstenone. The method is intended for discrimination of samples in risk groups, and a scheme is proposed to turn the GC-MS response of the boar-taint analytes into risk classes.