Bibliographic record
Abstract
Tomato is originated in Peru of South America and name of crop came from the Aztec word ‘Tomato’. Botanical name of tomato is Solanum lycopersicum and family is Solanaceae. Tomato is widely cultivated and consumed fruit that is often treated as vegetable. The significance of processing tomatoes into value added products focusing on quality enhancement and waste reduction. The main aim of tomato processing is to extend the shelf life of tomatoes, allowing for long term storage and reducing post-harvest losses. The superior quality of winter tomatoes attributed to higher total solids prompts strategic processing to minimize losses during peak seasons. Tomatoes undergo a transformation into value added products such as paste/puree, juice, ketchup in countries like USA, Canada and Australia. Key principles for producing high quality tomato products include selecting uniformly ripened, red tomatoes, avoiding prolonged heating and employing non-reactive equipment. Chemical composition of tomatoes influenced by factors like variety and growing conditions play an important role in determining the quality of both raw and processed products. The transition from insoluble components to simple sugars during tomato ripening impacts total solid content and flavour. Different value added products prepared from tomato are juice, ketchup, puree/paste, chutney, pickle, cocktail, powder, soup, chutney, canned tomatoes. This abstract delves into the key stages of tomato processing and highlights the significance of each in producing diverse and widely consumed tomato based products
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.180 | 0.109 |
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".