Comparison of Volatile Compounds and Sensory Profiles of Low-Alcohol Pear Beverages Fermented with Saccharomyces Cerevisiae and Different Non-Saccharomyces Cerevisiae
Food Microbiology(2024)
Abstract
This study aimed to assess the impact of Saccharomyces cerevisiae and different non-Saccharomyces cerevisiae (Zygosaccharomyces bailii, Hanseniaspora opuntiae and Zygosaccharomyces rouxii) on the volatile compounds and sensory properties of low-alcohol pear beverages fermented from three varieties of pear juices (Korla, Laiyang and Binzhou). Results showed that all three pear juices were favorable matrices for yeasts growth. Non-Saccharomyces cerevisiae exhibited a higher capacity for acetate ester production compared to Saccharomyces cerevisiae, resulting in a significant enhancement in sensory complexity of the beverages. PCA and sensory analysis demonstrated that pear varieties exerted a stronger influence on the crucial volatile components and aroma characteristics of the fermented beverages compared to the yeast species. CA results showed different yeast strains exhibited suitability for the fermentation of specific pear juice varieties.
MoreTranslated text
Key words
Saccharomyces cerevisiae,Non,Low-alcohol pear beverages,Volatile compounds,Sensory properties
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2005
被引用86 | 浏览
2005
被引用32 | 浏览
2003
被引用58 | 浏览
2013
被引用47 | 浏览
2015
被引用64 | 浏览
2014
被引用11 | 浏览
2015
被引用26 | 浏览
2017
被引用115 | 浏览
2018
被引用81 | 浏览
2017
被引用130 | 浏览
2018
被引用85 | 浏览
2017
被引用38 | 浏览
2019
被引用44 | 浏览
2020
被引用27 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper