INTERDISCIPLINARY EDUCATION THROUGH THE DEVELOPMENT OF A COST-EFFECTIVE PHOTOMETRIC pH METER SENSOR USING NATURAL PIGMENTS
Bibliographic record
Abstract
There is a trend of development of analytical methodologies and technologies that allow in situ analysis, producing accurate information in real time, at low cost, using homemade experiments and devices to increase interest in scientific knowledge with a constructive approach in an interdisciplinary perspective in Chemistry Education. In this context, a photometric-chemometric method of analysis was developed to measure the pH of solutions using easily accessible and low-cost material based on the use of natural pigments found in red cabbage (Brassica oleracea L.). Calibrations and determinations were performed by RGB measurements of pigment coloration in solution at different pH values using the free app Photometrix, converted to HSV, YCbCr and YUV color spaces and processed by Partial Least Squares regression (PLS). The best PLS model found was HSV with mean central scale obtained pH measurements with RMSEP 0.98, R2 0.97 and bias close to zero. In addition, experimental data were statistically validated. Analyzes of predicted pH from three independents experiments revealed high recoveries (95-103%) and low relative standard deviations. Thus, the PhotoMetrix app was viable for colorimetric pH determinations using the low-cost photometric pH meter sensor and a smartphone, improving accessibility and applicability in Chemistry Education.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 0.002 |
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".