Systematic Statistical-Based Approach for Product Design: Application to Disinfectant Formulations
Bibliographic record
Abstract
Product formulation development is a difficult and challenging task. The challenges include modeling complex systems and chemicals. Various tests are performed, often on a trial and error basis, to evaluate the performance of the prototypes in the product development process. These tests can be very expensive and time-consuming. A methodology is presented to shorten the product development time and reduce the costs given a database of historical data. It is based on augmenting the existing data set through designed experiments. An empirical model is first developed by analyzing the augmented data set using least-squares regression analysis. The model is then inverted by using an optimization technique, and the product formulation can be predicted on the basis of the desired product specifications. An iterative, sequential approach is employed in which the knowledge gained at each stage is applied in a systematic manner to design further experiments so that the future efforts will need fewer trials. This methodology is illustrated by a case study of disinfectant formulations and is proven to be superior to conventional formulation design methods. These disinfectant formulations consist of primarily water and small amounts of surfactants, oxidizing agents, chelating agents, pH buffers, and pH adjusters and consequently the resulting products are clear liquids. Although the methodology is illustrated on disinfectant product development, it is introduced in this paper in a general way and can be implemented in other applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".