Prediction of pea composites physicochemical traits and techno-functionalities using FTIR spectroscopy
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Bibliographic record
Abstract
This study aimed to test the feasibility of applying FTIR spectroscopy in order to create models that can predict quality attributes of pea composites. FTIR spectroscopy data were captured from three types of samples namely pea flour (PF), pea concentrate (PC) and pea isolate (PI). The FTIR spectral data along with partial least square (PLS) regression were used to build predictive models for physicochemical (moisture content, protein content, starch content, fiber content, bulk density, color), structural (particle size distribution, surface openings, fractal dimension), thermal (denaturization temperature, enthalpy), and techno-functional traits (water holding capacity, foaming capacity, foam stability, solubility, least gelation concentration) of pea composites. The FTIR spectra were subjected to different spectral pretreatment where 2nd derivative pretreatment provided the most suitable model for prediction of the studied parameters. The values of pea composite's traits determined through non-destructive FTIR spectroscopy coupled with partial least squares regression analysis, were very closed to the values obtained by the destructive standard methods. Performance (correlation, root mean square error) of the developed models were attribute-specific. For most of the studied attributes, correlation coefficient (r) were higher than 0.82 in the calibration step, and 0.71 in the prediction step. Pea composites have shown distinctive functionalities both as individual entity and in formulating meat analogs. Overall, it could be recommended that FTIR spectroscopy could be used in pea processing industry for developing robust models, to non-destructively assess the pea composite's properties and techno-functionalities. • Physicochemical characteristics of pea composites vary between types (flour, concentrate, isolate). • Physicochemical and techno-functional properties were predicted using FTIR spectra coupled with PLS-R analysis. • Water-oil holding capacity, solubility, foaming capacity-stability, and least gelation concentration of peas were modeled. • Pea composites are capable to develop heat-induced meat analogs.
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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 | 0.000 | 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 it