Non-destructive approaches for quality evaluation of eggplant («Solanum melongena» L. cv. Traviata)
Bibliographic record
Abstract
The goal of this study was to examine the use of non-destructive compression tests, and hyperspectral imaging for determining physical quality attributes of eggplant (Solanum melongena L. cv.Traviata).The effects of source (farms where samples came from), light and temperature conditions during storage were studied.Nondestructive compression tests were conducted to obtain surface stiffness (S) parameter whereas the hyperspectral-imaging technique was used in the near infrared (NIR) spectral range (900-1,700 nm), to indicate the peel gloss (G) of fresh and stored eggplants.Eggplant density (D) was computed by dividing its weight (W) by its volume (V).Laboratory tests were carried out to develop a freshness index for an American variety of eggplant fruit (Solanum melongena L. cv.Traviata).It was determined that source (farm) was a highly significant factor on quantitative and qualitative quality parameters.Light and temperature conditions had as well significant impact on such parameters, and their respective ratios, at different storage periods.Surface stiffness and peel gloss decreased during postharvest storage.On the contrary, density increased significantly over time.Based on this, a freshness index (I f ) of eggplant was defined as the product of surface stiffness and peel gloss ratios divided by the density ratio.I f was then plotted vs. mass, density, surface stiffness, and peel gloss ratios, and storage period (h) in order to apply kinetic approaches to fit models with which it was possible to predict such index.The values of correlation coefficient (CC) and Mean Square Error (MSE) were assessed to see how well the curves fitted.I f as a function of surface stiffness loss (SL) was the best model; it showed the highest CC (0.99) and the lowest MSE values (0.0014).The least accurate predictor was I f as a function of peel gloss loss (GL).Stepwise regressions were performed to determine which wavelengths were significant for I f .The models built with such wavelengths for fruits stored at 10 and 27C showed CC values of about 0.73 and 0.90, respectively.Freshness index is a concept that may be helpful to avoid over or underestimations of eggplant quality and, hence, to fix fair prices.Nevertheless, more varieties of eggplant should be studied to validate this concept.More temperature conditions should be studied in the future in order to apply the Arrhenius' equation.hours on the statistical analyses, and Dr. Akinbode Adedeji for correcting and editing this piece of work.My dear friend, Hui Huang, thank you very much for teaching me part of what you knew about the hyperspectral-imaging technique.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".