Whisky: Systematic Review with ☸️SAIMSARA.



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Review Stats
Identification of studies via EPMC (titles/abstracts) Identification Screening Included Records identified:n=642Records excluded:n=0 Records assessed for eligibilityn=642Records excluded:n=275 Studies included in reviewn=367 PRISMA Diagram generated by ☸️ SAIMSARA
⛛OSMA Triangle Effect-of Predictor → Outcome whisky  →  Outcome Beneficial for patients ΣN=717 (3%) Harmful for patients ΣN=7168 (33%) Neutral ΣN=13945 (64%) 0 ⛛OSMA Triangle generated by ☸️SAIMSARA
Outcome-Sentiment Meta-Analysis (OSMA): (LLM-only)
Frame: Effect-of Predictor → Outcome • Source: Europe PMC
Outcome: Outcome Typical timepoints: 45-y, 60-day. Reported metrics: %, CI, p.
Common endpoints: Common endpoints: mortality, complications, functional.
Predictor: whisky — exposure/predictor. Doses/units seen: 67 mg, 0 mg, 35 ml, 5.46 kg, 500 ml, 30 ml…. Routes seen: topical, oral, intravenous, inhaled. Typical comparator: original ones, control, no treatment, fasted state conditions….




1) Introduction
Whisky, a globally significant distilled alcoholic beverage, is a subject of extensive scientific inquiry spanning its complex chemical composition, production processes, sensory attributes, authentication, and diverse impacts on human health and the environment. Recent research highlights the evolving landscape of whisky, from the emergence of new regional varieties like Chinese whisky [1] to advanced analytical techniques for quality control and counterfeit detection [4, 102]. Understanding the intricate interplay of raw materials, fermentation, maturation, and consumption patterns is crucial for both the industry and public health. This paper synthesizes current research to delineate key themes and identify critical gaps in the scientific understanding of whisky.

2) Aim
To systematically review and synthesize recent academic literature concerning whisky, extracting key findings related to its composition, production, analysis, health implications, and environmental aspects, and to identify emergent research topics and future directions.

3) Methods
Systematic review with multilayer AI research agent: keyword normalization, retrieval & structuring, and paper synthesis (see SAIMSARA About section for details).


4) Results
4.1 Study characteristics:
The studies predominantly utilized mixed designs, often integrating chemical analysis with other methodologies, and frequently lacked specified directionality. Cohort and cross-sectional studies were also observed, particularly in human health and environmental contexts. Populations varied widely, encompassing yeast strains during fermentation [3, 5], commercial whisky samples [4, 11], human adults [7, 10, 16], rats and mice [10, 63, 105], and specific barley cultivars [19, 20]. Follow-up periods were largely not specified, or varied from short durations (e.g., 2 hours for alcohol metabolism [76]) to long-term observations (e.g., 36 months for spirit aging [23], up to 43 years for maturation impact [56]).

4.2 Main numerical result aligned to the query:
Machine learning models and various spectroscopic and mass spectrometric techniques demonstrated high accuracy in whisky identification, classification, and authentication. The median accuracy reported across these diverse methods was 97.86% [8, 15], with a range spanning 92% [41] to 100% [54] for distinguishing between brands, origins, or authentic versus counterfeit samples. For instance, portable Raman spectroscopy achieved over 99% accuracy in brand identification [4], while sensory evaluation and analytical procedures distinguished Scotch from American whiskies with 97.86% and 96.94% accuracy, respectively [8, 15]. A bimetallic nanoplasmonic tongue differentiated whiskies with over 99.7% accuracy [102], and SWIR hyperspectral imaging achieved 99.8% accuracy for phenolic compound levels in peated barley malt [113].

4.3 Topic synthesis:


5) Discussion
5.1 Principal finding:
Diverse analytical techniques, including machine learning models, spectroscopy, and mass spectrometry, consistently achieve high accuracy in whisky identification, classification, and authentication, with a median accuracy of 97.86% [8, 15] and a range of 92% [41] to 100% [54].

5.2 Clinical implications:


5.3 Research implications / key gaps:


5.4 Limitations:


5.5 Future directions:


6) Conclusion
Diverse analytical techniques, including machine learning models, spectroscopy, and mass spectrometry, consistently achieve high accuracy in whisky identification, classification, and authentication, with a median accuracy of 97.86% [8, 15] and a range of 92% [41] to 100% [54]. These findings are generally applicable to commercial whisky products and various geographical origins, supporting quality control and consumer protection. However, the heterogeneity in study designs and populations represents the most significant limitation affecting the certainty and generalizability of some findings. Therefore, future research should prioritize the development of standardized adulteration benchmarks to ensure public safety and product integrity globally.

References
SAIMSARA Session Index — session.json

Figure 1. Publication-year distribution of included originals
Figure 1. Publication-year distribution of included originals

Figure 2. Study-design distribution of included originals
Figure 2. Study-design distribution

Figure 3. Study-type (directionality) distribution of included originals
Figure 3. Directionality distribution

Figure 4. Main extracted research topics
Figure 4. Main extracted research topics (Results)

Figure 5. Limitations of current studies (topics)
Figure 5. Limitations of current studies (topics)

Figure 6. Future research directions (topics)
Figure 6. Future research directions (topics)