A PIR Sensor-Based System for Real-Time Alcohol Monitoring and Automated Preservative Control in Fruit Wine Fermentation: Accuracy, Usability, and Adoption Assessment
Bibliographic record
Abstract
Accurate and continuous measurement of alcohol concentration during fermentation is crucial for maintaining quality, ensuring safety, and ensuring regulatory compliance in fruit wine production. Traditional methods, such as manual hydrometry and sensory-based evaluation, are often limited by subjectivity, measurement variability, and a lack of real-time responsiveness. This study introduces a novel sensor-integrated system that utilizes Passive Infrared (PIR) technology to dynamically monitor alcohol levels and automate potassium sorbate dosing during the fermentation of fruit wines. The proposed system combines a repurposed PIR sensor with a hydrometer-actuated mechanical switch to estimate alcohol by volume (ABV) in real-time, achieving a validated accuracy of 94.09% when benchmarked against gas chromatography (GC) standards. Integrated control logic enables automatic preservative application aligned with ABV thresholds of 220 mg/L for 9% v/v and 50 mg/L for 14% v/v alcohol, thus ensuring microbial stability and compliance with enological standards. Experimental trials involving pineapple, mango, and grape wines demonstrated the system’s capability to capture both pre-fermentation and post-fermentation alcohol values with minimal error margins (<2%). A user experience study conducted with 20 professional winemakers and 380 broader respondents revealed high satisfaction scores across usability, observation ability, and simplicity of use, with Likert-scale ratings averaging 4.50 or higher. Statistical validation using Structural Equation Modeling (SEM) confirmed the positive influence of user experience factors on adoption intention (R² = 0.533, p < 0.001). These findings highlight the PIR-based system’s potential to modernize artisanal winemaking by offering a non-invasive, accurate, and user-friendly tool for real-time fermentation monitoring and control.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".