Evaluation of Wines Rating Based on Sensory Characteristics Using Neural Networks
Bibliographic record
Abstract
Wine is an agricultural product with very high commerce price variation, which is highly affected by quality ratings.Therefore, quality rating is particularly important for both industry and consumers.However, absence of clear concepts on what constitutes wine quality makes the perception of quality highly subjective, and it is usual for tasters to disagree on the quality rating of a specific wine.For this purpose, a Feedforward Neural Network (FNN) could be trained in order to predict wine quality.In this study, a new FNN method was developed to predict the accurate wine quality based on major sensory characteristics as FNN inputs, and to improve the ability of a taster, groups of tasters, or consumers, to rate wine by taking into account previous decisions.Specifically, five principle sensory characteristics of wines were used as inputs (Aging in Barrel, Aroma Intensity, Body, Astringency, and Acidity) in a rating range 1-3.As outputs, the quality ratings of wines in a range 70-100 were considered.The FNN was created in MATLAB with 1 hidden layer, 5 neurons and 1 output layer.For ratings divided in 5 categories the accuracy was 53% with the use of the FNN, as opposed to the accuracy of 36% achieved by Multiple Linear Regression.For ratings divided in 9 categories the accuracy was 90%.This method may allow each individual or group of tasters to introduce their own data to produce a more objective rating by taking into account previous decisions (subjective) that have accumulated in the database.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".