Effect of model selection approach obtained by machine learning tools on predicting the volume reduction of plant-based dehydrated foods
Bibliographic record
Abstract
Previous research has identified eight key factors—product type, initial moisture content, pretreatment method, drying technology, temperature, pressure, microwave power, and sample thickness—that influence the shrinkage coefficient in dehydrated foods. Despite this understanding, there is a lack of comprehensive studies exploring the effect of model selection on predicting shrinkage. This study aims to evaluate the impact of different artificial intelligence (AI) model selection approaches on the prediction accuracy of the shrinkage coefficient for dehydrated foods. Specifically, we compare the performance of three AI techniques: Extreme Learning Machine (ELM), Evolutionary Polynomial Regression (EPR), and Group Method of Data Handling (GMDH). Our findings provide valuable insights into the role of model selection in enhancing the predictive accuracy of AI-driven models for food dehydration processes. To achieve this, two model selection approaches were considered: 1) prioritizing higher accuracy and minimizing error, and 2) conducting a comprehensive evaluation of the overall performance of the input variables. The obtained results showed that product type, drying technology, and temperature were consistently involved in the most effective model combinations, underscoring their critical relevance in accurately estimating the shrinkage coefficient. However, the most accurate model involved five inputs (product, technology, temperature, initial moisture content, and pretreatment), was developed with ELM, and produced a correlation coefficient of 0.8713, a root mean square error of 0.0897, and a Nash-Sutcliffe efficiency of 0.7534. EPR and GMDH models had simpler structures than ELM but lower accuracy. An external validation of the models suggested that selecting input combinations according to a comprehensive assessment of the inputs’ global performances lead to models being more generalizable. Therefore, the model selection approach was found to be critical for predicting shrinkage. The developed models can be used for quick estimation of volume reduction in foods during drying and thus can help in process design and optimization for improved structural properties of dried products. • The method of selecting input combinations influences final model performance. • Product type, drying method, and temperature are critical factors for food shrinkage. • Model generalization can be improved after a thorough input performance assessment.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".