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April 7, 2026Future Foods0 citationsOpen Access

Brewing for Beyond Earth: Assessing How Simulated Space Environment Alters Beer Perception, with AI-Driven Sensory Prediction from E-Nose Data

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CVClaudia Gonzalez ViejoRVRaúl Villarreal-LaraAMArturo A. Mayorga-Martinez

Key Points

  • The aim is to evaluate how sensory perceptions of beer differ in Earth-like and space-like conditions.
  • Produced three pale ale beers with varied silicon levels and sound treatments.
  • Evaluated samples by 59 regular beer consumers in simulated environments.
  • Collected self-reported and biometric data.
  • Applied ANOVA and principal components analysis.
  • Developed two machine learning models to predict sample acceptability.
  • Significant differences in sensory acceptability between space and Earth-simulated environments (p<0.01).
  • Higher emotional responses and acceptability in the space-simulated environment.
  • Machine learning models achieved 95% accuracy and a correlation of R=0.98 for predictions.

Abstract

• Space-like environment enhances sensory acceptability of beer • Sonicated beers evoke positive emotional responses in simulated space conditions • Silicon-enriched adjuncts show potential for improving space food perception • ML models with e-nose inputs accurately predict acceptance in space and Earth-like settings Understanding how conditions in space alter sensory perception of food and beverages in astronauts is a great challenge. This study aimed to assess differences in sensory acceptability and perception of beer tasted under Earth- and space-like immersive environments. Three pale ale beers infused with dried coriander and basil were produced with different foliar-stage silicon supplementation levels and exposed to 20-70 Hz sound treatments. Fifty-nine regular beer consumers evaluated the samples in simulated space and Earth environments; self-reported and contactless biometrics were collected. A low-cost electronic nose was used to analyze the samples. ANOVA and principal components analysis (PCA) were applied. Two machine learning (ML) models were developed to predict sample treatments (Model 1) and acceptability in both simulated environments (Model 2). ANOVA showed significant differences (p<0.01) between samples in space and Earth-simulated environments. PCA (60% explained variability) showed higher sensory acceptability and positive emotional differentiation in samples evaluated in space-simulated environment than in Earth-simulated environment. Both ML models showed very high accuracies (Model 1: 95%; Model 2: R=0.98). This study provides a relevant contribution to sensory knowledge in space-simulated environments, aiming the development of specialized foods and beverages for long-term Moon and Mars missions and space tourism.

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Cite This Study

Viejo et al. (2026) studied this question.

synapsesocial.com/papers/69d49f8ab33cc4c35a227ed4https://doi.org/10.1016/j.fufo.2026.101009
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