Estimating Thermodynamic Properties of Pure Triglyceride Systems Using the Triglyceride Property Calculator
Bibliographic record
Abstract
Abstract To date, the most comprehensive model for predicting thermodynamic properties of pure triglycerides was presented by Wesdorp in “Liquid‐multiple solid phase equilibria in fats: theory and experiments” (1990). In this paper, we present (1) corrections to the published model, as well as (2) a software implementation of the model for numerical assessment. The software tool, Triglyceride Property Calculator (TPC), uses a semi‐empirical model to estimate the enthalpy of fusion and melting temperature for a given triglyceride based on its molecular composition and polymorphic form. These estimates are compared to experimentally collected data when available. The web application is available at http://www.crcfoodandhealth.com (under research tools) and through the AOCS Lipid Library. The quality of estimates is characterized according to defined counting metrics and presented for TAG subcategories. Additionally, the extrapolative value of the TPC is assessed by checking for consistency with underlying thermodynamic constraints. The current TPC implementation is effective in describing experimentally collected melting point data, with greater than 91% of the fitted values falling within 10% of the actual data. The TPC is also very good at describing collected enthalpy data. The underlying semi‐empirical model and parameter set perform well in ensuring enthalpy predictions are thermodynamically consistent, however, extrapolated melting temperatures appear unreliable. Developing models and parameter sets that ensure thermodynamic consistency is a priority with future TPC iterations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".