Evaluation of Carrot Quality Using Visible-Near Infrared Spectroscopy and Multivariate Analysis
Bibliographic record
Abstract
Carrot has a relatively high content of Vitamin C and it is a major source of natural carotenoids. However, carrot has a short shelf-life and is better consumed fresh. A quick assessment of its quality attributes is important to preserving its freshness. The objective of this study was to apply Vis-NIR spectroscopy to noninvasively assess and predict the various quality attributes of carrot (cv. Nectar), namely color (L*a*b*), moisture content (MC), total soluble solids (TSS), firmness, Vitamin C, and β-carotene. Two spectroscopic sensors (400-1,000 nm and 900-1,700 nm) were utilized and samples included whole root and 25.4 mm thick sliced disc. The best prediction models using partial least squares regression yielded correlation coefficient, r, and ratio of performance to deviation or r(RPD) of 0.50(0.73), 0.84(0.88), 0.86(2.07), 0.69(0.66), 0.97(1.44), 0.90(1.49), 0.47(1.47), and 0.92(1.76) for color indices, L* a* b*, firmness, MC, TSS, Vitamin C, and β-carotene, respectively. However, using only the wavelengths selected by interval partial least squares, the r(RPD) values for the aforementioned attributes improved and are presented as follows: 0.92(1.97), 0.96(2.83), 0.98(5.85), 0.99(6.65), 0.98(3.91), 0.99(5.93), 0.98(4.16), and 0.98(4.43), respectively. Generally, Vis-NIR region had higher prediction performance than NIR region, and whole roots had similar prediction performance as sliced samples. This study shows that rapid determination of quality parameters of carrot is possible through non-destructive Vis-NIR sensing, which could be useful for quality tracking during carrot supply chain. Moreover, results of this study could be improved using a larger sample size.
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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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".