Papers by Keyword: Vinegar

Paper TitlePage

Authors: Duongruitai Nicomrat
Abstract: Fresh fruit vinegar fermentation is well known for the activities of diverse groups of microorganisms at two stages of the fermentation process. Their species diversity depend on the raw materials fermented. In the study, at the first step of high sugar production, less culturable acetic acid bacterial species but more Aspergillus spp. and yeasts, non-Saccharomyces were detected. At the end, the vinegar production step, the fermented broth showed only dominant acetic acid bacteria. In the study, yeasts and fungi were isolated and inoculated to the juice. The results showed that these consortium could help increase high alcohol and later more acetic acid production when compared with the control fruit vinegar fermentation.
Authors: Hua Dong Xie, Li Jun Bu, Xiang Wei Peng, Zhi Xi Li
Abstract: An inexpensive and simple approach using ultraviolet-spectroscopy to distinguish vinegar samples was developed. Vinegar samples were diluted with distilled water(water/vinegar was 6/1, v/v), then distilled with rotary evaporator at 45°C. The distilled liquid was introduced into the UV-Vis 2550 spectrophotometer to scan the UV spectrum from 245 nm to 330 nm, distilled water was used as reference solution. Once spectra were collected, principal components analysis (PCA) and artificial neural network (ANN) were employed for the exploratory analysis and the development of classification models, respectively. The criteria for discrimination were various raw materials and the different fermentation process of vinegars. The correct rate of the classification according to the production process was more than 90% and it was 100% according to the raw materials. The ANN model also could be used to class vinegar samples according to the raw materials, the correct rate was 80.95% in this research.
Authors: Xue Mei Yang, Long Zhang, Zhi Xi Li
Abstract: In this paper, we proposed a new method to identify vinegar. First, we obtained the ultraviolet spectrum curves(samples) of five kinds of vinegar by ultraviolet spectrum scanning technology. Then, the samples of five kinds of vinegar were trained and tested by improved Multi-class Support Vector Machines(MSVM) for identification. The experimental results indicate that the method of combining ultraviolet spectrum technology and improved multi-class support vector machines is effective and accurate. The identification accuracy is 100%.
Showing 1 to 3 of 3 Paper Titles