Determining the accuracy of colorimetric pH testing compared to potentiometric methods
Bibliographic record
Abstract
Background: Bacterial growth in foods can be prevented by applying various controls to the food product, including adjusting the acidity of the food. Research has indicated that a pH level of 4.6 or lower will be effective to prevent most bacterial growth. In order to verify this level has been achieved pH test strips (colorimetric) or a digital calibrated pH meter (potentiometric) can be used. This study attempted to quantify the degree of accuracy that pH test strips have compared to the calibrated pH meter. Method: MColorpHastTM pH indicator strips with a pH range of 0-14 were tested against a calibrated Extech pH100 meter. In this study 40 samples of rice were acidified to varying levels. Each sample was measured with both colorimetric and potentiometric method. Results were compared to determine the level of accuracy of the pH test strips. As well, test strips were used to measure pH in a variety of different coloured preserves. Results: A two-tailed test showed that there was a statistically significant difference between the readings from the pH test strips and the digital pH meter (P=0.0003). Conclusion: Based on the results, it can be concluded that both methods of measurement are not equally accurate. A calibrated pH meter will give more accurate readings of pH levels and should be used in most cases to confirm food safety with a high degree of confidence. In testing dark coloured jellies and preserves, pH test strips should not be relied on as they will be stained by the food, making the colorimetric reading difficult to determine accurately.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".