Application of multivariate adaptive regression splines (MARS) to study the colorization occurring in the process of lactulose production following lactose electro-activation
Bibliographic record
Abstract
Lactulose is widely used in the food and pharmaceutical industries and is obtained from the chemical and enzymatic isomerization processes of lactose. Recently, electro-activation, a chemical-free process, has been effectively used for lactulose production at ambient temperature. However, the electro-activated (EA) isomerization process produces colored lactose solutions like the conventional chemical isomerization processes, which becomes a concerning issue. In this context, artificial intelligence-based machine learning tools can be pivotal. The current study observed the color development due to the EA isomerization process and established a model using the multivariate adaptive regression spline (MARS). The color development was expressed in terms of total color difference (ΔE ab ) using the seven input parameters, such as reaction type, reaction time, relaxation time, relaxation temperature, L* , a* , and b* . A total of 127 configurations resulting from the combination of input numbers (1-7) were employed to understand the color development using MARS. The K-fold (K=10) cross-validation technique was applied to select the best-fit combination for each input configuration that underscored the non-linear and complex nature of the color development, offering insights about the input parameters. The reaction type, reaction time, a *, and b* were identified as the key parameters of the color development. The corresponding basis functions of the best-fit configurations reflected the interactions and connected the nonlinearities across the input parameters to understand their impact on the color development using the MARS algorithm.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 teacher head, 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".