Challenges and potential solutions to utilization of carrot rejects and waste in food processing
Bibliographic record
Abstract
Purpose Of the global carrot production, 20–30% is outgraded as carrot rejects and waste (CRW) at the primary processing level, which is partially used toward animal feed and the remaining ends in the landfills. This study was undertaken to identify the hurdles and seek potential solutions for using CRW in food processing. Design/methodology/approach CRW were procured from the processing unit in Ontario, Canada, as (1) outgraded carrots (OGCs) and (2) processed discards (PDs). The physical parameters of CRW, imperfections responsible for their separation from the graded carrots and shelf-life studies were recorded. Findings A significant difference with p ≤ 0.05 was recorded for both the physical parameters and the nature of imperfections in CRW. Discolored carrots (42.37 ± 3.59%) and the presence of vertical splits (52.71 ± 3.18%) were among the top defects in the OGCs. In contrast, the presence of broken tips (54.83 ± 2.52%) and vertical splits (40.56 ± 2.65%) were among the primary cause for the generation of PDs. In total, five percent of CRW were initially infected, which later increased to 30% during the seven days storage period. Research limitations/implications The limitation of the study was that only two varieties of carrots were considered and these were procured from one processor (the authors’ industry partner) at different time intervals of the year. Microbiological analysis could not be completed and reported due to prevailing coronavirus disease 2019 (COVID-19) situation but is included for future studies. Practical implications Development of specialized post-harvest packaging and handling protocols and separation of infected fragments are essential before suggesting the use of CRW in food processing. Originality/value Numerous studies report on the post-harvest management and processing of graded carrots, but limited to no studies are published on the usage of CRW in food processing.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".