A Comprehensive Review on Pre- and Post-Harvest Perspectives of Potato Quality and Non-Destructive Assessment Approaches
Bibliographic record
Abstract
Potato (Solanum tuberosum) is an important crop globally, being a starchy, energy-dense food source rich in several micronutrients and bioactive compounds. Achieving food security for everyone is highly challenging in the context of growing populations and climate change. As a highly adaptable crop, potatoes can significantly contribute to food security for vulnerable populations and have outstanding commercial relevance. Specific pre- and post-harvest parameters influence potato quality. It is vital to understand how these factors interact to shape potato quality, minimizing post-harvest losses, ensuring consumer safety, and enhancing marketability. This review highlights how pre-harvest (cultivation approaches, agronomic conditions, biotic and abiotic stresses) and post-harvest factors impact tuber’s microbial stability, physiological behaviour, nutritional, functional attributes and frying quality. Quality parameters, such as moisture content, dry matter, starch, sugar, protein, antioxidants, and color, are typically measured using both traditional and modern assessment methods. However, advanced non-destructive techniques, such as imaging and spectroscopy, enable rapid, high-throughput quality inspection from the field to storage. This review integrates recent advancements and specific findings to identify factors that contribute to substantial quality degradation or enhancement, as well as current challenges. It also examines how pre- and post-harvest factors collectively impact potato quality. It proposes future directions for quality maintenance and enhancement across the field and storage, highlighting research gaps in the pre- and post-harvest linkage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".