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A Multisensor Data Fusion Approach for Predicting Consumer Acceptance of Food Products.

Víctor M Álvarez-Pato1, Claudia N Sánchez1, Julieta Domínguez-Soberanes2

  • 1Facultad de Ingeniería, Universidad Panamericana, Aguascalientes 20290, Mexico.

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Summary

Combining facial emotion recognition (FER) with physiological signals like galvanic skin response (GSR) improves food acceptance prediction. This novel sensory analysis system enhances consumer studies for new food products.

Keywords:
consumer acceptance predictiondata fusionemotion recognitionfacial expression recognitiongalvanic skin responsemachine learningneural networkssensory analysis

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Area of Science:

  • Consumer behavior research
  • Neuroscience and sensory science
  • Machine learning applications in food science

Background:

  • Sensory experiences significantly influence consumer decisions and loyalty towards food products.
  • Accurate assessment of consumer response is crucial for successful new food product launches.
  • Current methods may lack precision in capturing the full spectrum of consumer reactions.

Purpose of the Study:

  • To introduce a novel sensory analysis system integrating facial emotion recognition (FER), galvanic skin response (GSR), and cardiac pulse.
  • To determine consumer acceptance of food samples by analyzing combined physiological and facial data.
  • To predict consumer acceptance without continuous reliance on self-reported liking scores.

Main Methods:

  • Conducted taste and smell experiments with 120 participants.
  • Recorded facial images, biometric signals (GSR, cardiac pulse), and self-reported liking.
  • Utilized data fusion and machine learning models for acceptance prediction.

Main Results:

  • Facial emotion recognition (FER) alone is insufficient for accurate consumer acceptance prediction.
  • Combining FER with galvanic skin response (GSR) significantly improves acceptance prediction accuracy.
  • Cardiac pulse signals offer a lesser, but still beneficial, contribution when combined with FER and GSR.

Conclusions:

  • A multi-modal sensory analysis system integrating facial and physiological responses offers a more precise method for assessing food acceptance.
  • This approach can reduce the need for continuous subjective liking scores in consumer studies.
  • Findings contribute to understanding the interplay between facial expressions and physiological reactions in non-rational decision-making for food products.