Species identification and strain discrimination of fermentation yeasts <i>Saccharomyces cerevisiae</i> and <i>Saccharomyces uvarum</i> using Raman spectroscopy and convolutional neural networks
Bibliographic record
Abstract
ABSTRACT Reliable typing of yeast strains is of great importance to the alcoholic beverage industry to ensure a reliable fermentation process and high-quality products. Saccharomyces cerevisiae is the most used yeast species in wine, sake, and ale beer fermentation, whereas Saccharomyces uvarum is more commonly used for cider fermentation and, due to its cryotolerance, white wine production. We propose a promising method for species identification and strain discrimination of S. cerevisiae and S. uvarum using Raman spectroscopy in combination with convolutional neural networks (CNNs). Raman spectra collected from various S. cerevisiae and S. uvarum strains were accurately classified at the species level using random forest. Cultivation time and temperature did not significantly affect the spectral reproducibility and discrimination capability. An overall accuracy of 91.9% was achieved to discriminate 27 yeast isolates at the strain level using a CNN model. Raman-CNN further identified eight yeast isolates spiked in grape juice with an accuracy of 98.1%. Raman spectral signatures derived from diverse protein and lipid compositions may contribute to this discrimination. The proposed approach also precisely predicted the concentration of a specific yeast strain within a yeast mixture with an R 2 of 0.9913 and an average error of 4.09%. The entire analysis can be completed within 1 hour following cultivation and only requires simple sample preparation and low consumable cost. Taken together, Raman spectroscopy coupled with CNN is a robust, accurate, and reliable approach for typing of fermentation yeast strains. IMPORTANCE The use of S. cerevisiae and S. uvarum yeast starter cultures is a common practice in the alcoholic beverage fermentation industry. As yeast strains from different or the same species have variable fermentation properties, rapid and reliable typing of yeast strains plays an important role in the final quality of the product. In this study, Raman spectroscopy combined with CNN achieved accurate identification of S. cerevisiae and S. uvarum isolates at both the species and strain levels in a rapid, non-destructive, and easy-to-operate manner. This approach can be utilized to test the identity of commercialized dry yeast products and to monitor the diversity of yeast strains during fermentation. It provides great benefits as a high-throughput screening method for agri-food and the alcoholic beverage fermentation industry. This proposed method has the potential to be a powerful tool to discriminate S. cerevisiae and S. uvarum strains in taxonomic, ecological studies and fermentation applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".