Microbial technologies for acetic acid production using fruit waste

Tripathi, Neha Mani; Prinsi, None; Sharma, Deepansh; Ranga, Poonam; Aseri, Gajender Kumar; Singh, Deepti

doi:10.1016/b978-0-323-90590-9.00006-7

Cited by 2 publications

(1 citation statement)

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“…Vinegar must also be filtered to remove mycoderma suspended vinegars before the commercialization. Therefore, vinegar can be produced from any sugar source convertible in an alcoholic matrix [7]. AAB more utilized are members of the genus Acetobacter and characterized by their ability to convert ethyl alcohol (CH 3 CH 2 OH) into acetic acid (CH 3 COOH) by oxidation.…”

Section: Introductionmentioning

confidence: 99%

Real Time Monitoring of Wine Vinegar Supply Chain through MOX Sensors

Genzardi

Greco

Núñez-Carmona

et al. 2022

Sensors

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Vinegar is a fermented product that is appreciated world-wide. It can be obtained from different kinds of matrices. Specifically, it is a solution of acetic acid produced by a two stage fermentation process. The first is an alcoholic fermentation, where the sugars are converted in ethanol and lower metabolites by the yeast action, generally Saccharomyces cerevisiae. This was performed through a technique that is expanding more and more, the so-called “pied de cuve”. The second step is an acetic fermentation where acetic acid bacteria (AAB) action causes the conversion of ethanol into acetic acid. Overall, the aim of this research is to follow wine vinegar production step by step through the volatiloma analysis by metal oxide semiconductor MOX sensors developed by Nano Sensor Systems S.r.l. This work is based on wine vinegar monitored from the grape must to the formed vinegar. The monitoring lasted 4 months and the analyses were carried out with a new generation of Electronic Nose (EN) engineered by Nano Sensor Systems S.r.l., called Small Sensor Systems Plus (S3+), equipped with an array of six gas MOX sensors with different sensing layers each. In particular, real-time monitoring made it possible to follow and to differentiate each step of the vinegar production. The principal component analysis (PCA) method was the statistical multivariate analysis utilized to process the dataset obtained from the sensors. A closer look to PCA graphs affirms how the sensors were able to cluster the production steps in a chronologically correct manner.

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Section: Introductionmentioning

confidence: 99%