Application of Fourier Transform Raman Spectroscopy for Prediction of Bitterness of Peptides
Bibliographic record
Abstract
The potential application of Fourier transform (FT) Raman spectroscopy to predict the bitterness of peptides was investigated. FT-Raman spectra were measured for the amino acid Phe and 9 synthetic di-, tri-, and tetra peptides composed of Phe, Gly, and Pro. Partial least squares regression (PLS)-1 analysis was applied to correlate the FT-Raman spectra with bitterness intensity values (R(caf) and log 1/T) reported in the literature. Using full cross-validation, Model 1 based on the single spectral data set for the nine peptides yielded a high correlation coefficient for calibration (R = 0.99), but a low correlation coefficient for prediction (R = 0.56). Two models were constructed using the data sets including replicate spectra for the calibrations and were validated using full cross-validation. Using leave-one-sample-set-out calibrations, Model 2, which was developed with the data for the peptides as well as Phe, yielded a low correlation coefficient (R = 0.533) for the prediction of the bitterness, while Model 3 developed with only the peptide data provided better correlation coefficients (R = 0.807 and 0.724 for R(caf) and log 1/T values, respectively). The correlation coefficients for prediction were 0.975 (R(caf) values) and 0.874 (log 1/T values) for Model 4, which was developed using subtracted spectral data (spectra of peptides with higher R(caf) values minus spectra of peptides with lower R(caf) values). Examination of the PLS regression coefficients at wavenumbers most highly correlated with bitterness revealed the importance of hydrophobicity and peptide length on bitterness. This study indicates the potential of FT-Raman spectroscopy as a useful tool for predicting bitterness of peptides and amino acids.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".