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- Title
AI-based forecasting of ethanol fermentation using yeast morphological data.
- Authors
Kaori Itto-Nakama; Shun Watanabe; Naoko Kondo; Shinsuke Ohnuki; Ryota Kikuchi; Toru Nakamura; Wataru Ogasawara; Ken Kasahara; Yoshikazu Ohya
- Abstract
Several industries require getting information of products as soon as possible during fermentation. However, the trade-off between sensing speed and data quantity presents challenges for forecasting fermentation product yields. In this study, we tried to develop AI models to forecast ethanol yields in yeast fermentation cultures, using cell morphological data. Our platform involves the quick acquisition of yeast morphological images using a nonstaining protocol, extraction of high-dimensional morphological data using image processing software, and forecasting of ethanol yields via supervised machine learning .We found that the neural network algorithm produced the best performance, which had a coefficient of determination of >0.9 even at 30 and 60 min in the future. The model was validated using test data collected using the CalMorph-PC(10) system, which enables rapid image acquisition within 10 min. AI-based forecasting of product yields based on cell morphology will facilitate the management and stable production of desired biocommodities.
- Subjects
IMAGE processing software; FERMENTATION; ETHANOL; FORECASTING; YEAST culture; YEAST
- Publication
Bioscience, Biotechnology & Biochemistry, 2022, Vol 86, Issue 1, p125
- ISSN
0916-8451
- Publication type
Article
- DOI
10.1093/bbb/zbab188