Rapid detection of quality of Japanese fermented soy sauce using near-infrared spectroscopy
Why this work is in the frame
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Bibliographic record
Abstract
This study investigated the feasibility of rapidly evaluating the final quality of Japanese fermented soy sauce (shoyu) using NIR spectroscopy and partial least-squares (PLS) regression. In total, 110 shoyu samples that had been entered in the annual soy sauce competition from 2016 to 2018 were collected and analyzed. The transmittance spectra (400-1800 nm) and the transflectance spectra (680-2500 nm) of these samples were acquired and processed by different pre-treatments. PLS regression was applied to the raw and processed spectra to construct models based on a calibration set (76 shoyu samples from 2016 and 2017) and to evaluate these models using a validation set (34 shoyu samples from 2018), according to their values for bias and root mean square error of prediction (RMSEP). The results showed that the models constructed using the full spectra of transflectance performed better than those using transmittance spectra. Comparing the influence of different regions in the transflectance spectra enabled the accuracy of the models to be improved. The model constructed from transflectance spectra from the 1800 to 2500 nm region using pre-treatment of second derivative was superior to the other models, with a bias value of -2 and the lowest RMSEP value of 13 in the validation set. To further narrow the wavelength range, the models constructed using the spectral region from 2050 to 2400 nm also showed a better performance for predicting the sensory quality of soy sauce products. This study has demonstrated that the NIR spectroscopy technique could be used as an alternative routine quality control procedure, which can rapidly and economically classify the quality of soy sauce products.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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 it