ArticleFood chemistry: X2024
Quantitative modelling of Plato and total flavonoids in Qingke wort at mashing and boiling stages based on FT-IR combined with deep learning and chemometrics.
Article in Food chemistry: X, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
- Multisource spectral fusion combined with variable selection for rapid geographical origin discrimination ofFrontiers in chemistry · 2025Article
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10 authors.
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Abstract
Craft beer brewers need to learn process control strategies from traditional industrial production to ensure the consistent quality of the finished product. In this study, FT-IR combined with deep learning was used for the first time to model and analyze the Plato degree and total flavonoid content of Qingke beer during the mashing and boiling stages and to compare the effectiveness with traditional chemometrics methods. Two deep learning neural networks were designed, the effect of variable input methods on the effectiveness of the models was discussed. The experimental results showed that the CARS-LSTM model had the best predictive performance, not only as the best quantitative model for Plato in the mashing (R
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