Portable analogue-based electronic moisture meter for root-crop chips
Bibliographic record
Abstract
Moisture content regulation of root crops is crucial in post-harvest processing operations, not only in the price stipulation but also to avoid aflatoxin contamination. To prolong their storage life, they are processed into dried chips to extend their usability in feed formulations and starches. In this study, we use the capacitance-based method to evaluate the performance of an analogue-based electronic meter for the cassava, sweet potato, and taro chips. The meter was calibrated against the oven-drying method, yielding high R2 values of the different root crops. The established calibration models were validated and revealed high R2 values with 0.9580 for the cassava, 0.9958 for the sweet potato, and 0.9798 for the taro. The trendline equations are y = 59.44x0.56, y = 54.38x0.47, and y = 52.94x0.62, respectively. The results revealed that the moisture meter is capable of reading the moisture content on a weight basis (% MCwb) with accuracy and reliability at specified limits of 8% < x < 69% for the cassava, 15% < x < 59% for the sweet potato, and 9%<x < 57% for the taro. This study presents the performance of a portable analogue-based moisture meter as a reliable and accessible solution to small-scale operations, especially for farmers, offering an on-site rapid moisture content measurement in root crop processing.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".