Optimization of Postharvest Handling of Eggplant Using the Taguchi Technique
Bibliographic record
Abstract
By quantifying and predicting postharvest quality losses in response to different environmental factors, the study offers valuable insights into enhancing postharvest management practices and minimizing quality loss of horticultural commodities like eggplant. Additionally, the manuscript highlights the potential of the Taguchi method as a robust design tool for postharvest quality in various crops, suggesting broader applicability beyond eggplant to other fruits and vegetables exposed to diverse environmental conditions. Taguchi technique was used to optimize the postharvest handling process to minimize quality loss of fresh eggplant (Solanum melongena L.). To date Taguchi approach has been widely used in various subject areas, but no application to postharvest quality has been reported until the present time with the exception of some previous work conducted by the same authors in 2015. Measuring postharvest losses is an essential operational strategy to enhance postharvest management and to curtail quality loss of fresh horticultural commodities. In this study, the Taguchi approach was able to quantify the quality for all combinations of environmental factors/levels (T, RH, Light & time) used in this experiment and were expressed in terms of Signal-to-Noise ratios. The highest ratio—which correlated with the least variability of the noise factors around the desired objective of this characteristic—was found for each quality attribute. The Taguchi technique has proven to be an effective tool for estimating and forecasting postharvest quality losses resulting from various combinations of environmental parameters. It has also been shown to identify the ideal conditions for handling and storing eggplant. As a result, this technique can enhance postharvest quality management from field to fork and alleviate quality loss of fresh fruits and vegetables. This technique could, therefore be recommended as a robust design of quality in postharvest technology and could be applied to many other crops exposed to various environmental conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".