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A Machine Learning Approach Investigating Consumers' Familiarity with and Involvement in the Just Noticeable Color

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Summary
This summary is machine-generated.

Understanding dry-cured ham color is key. This study found consumer involvement and age affect color perception, establishing a Just Noticeable Color Difference (JNCD) of 6.2. Machine learning improved color analysis.

Keywords:
JNCDJNDconsumerdelta Edifferencejust-noticeablemachine learning

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Area of Science:

  • Food Science
  • Sensory Science
  • Colorimetry

Background:

  • Visual color perception is crucial for food quality assessment.
  • Instrumental color measurements provide objective data but require interpretation in relation to human perception.
  • Consumer perception of food color is influenced by various factors, including familiarity and involvement.

Purpose of the Study:

  • To determine the relationship between instrumental color and visual color perception of dry-cured ham.
  • To establish the Just Noticeable Color Difference (JNCD) for dry-cured ham color.
  • To investigate the influence of consumer involvement and familiarity on color perception and JNCD.

Main Methods:

  • Instrumental color analysis of dry-cured ham slices.
  • Consumer surveys assessing color scoring, matching, involvement, and familiarity.
  • Clustering consumers based on involvement levels.
  • Calculation of JNCD for different consumer clusters.
  • Application of interpretable machine learning to correlate visual and instrumental color data.

Main Results:

  • A JNCD of ΔEab* = 6.2 was determined for dry-cured ham color.
  • JNCD was found to be lower for younger consumers.
  • Consumer involvement significantly influenced color perception and JNCD.
  • Machine learning models incorporating psychographic data outperformed multiple linear regression.
  • L* and hab were identified as the most influential color variables in the machine learning model.

Conclusions:

  • Consumer involvement and familiarity significantly impact the perception of dry-cured ham color.
  • Objective color measurements (L*, hab) combined with machine learning can effectively model visual color perception.
  • The established JNCD provides a benchmark for quality control and product development in the dry-cured ham industry.