Data on the application of Functional Data Analysis in food fermentations

Area of Science:

• Food Science
• Statistical Analysis
• Agricultural Science

Background:

• Table olive fermentation is crucial for quality and safety.
• Traditional and acidified curing methods are widely used.
• Aloreña de Málaga is a significant olive cultivar.

Purpose of the Study:

• To assess and compare five different table olive fermentation systems for Aloreña de Málaga olives.
• To apply Functional Data Analysis (FDA) for a novel comparison of fermentation processes.
• To analyze key fermentation parameters including pH, titratable acidity, and yeast count.

Main Methods:

• Utilized Functional Data Analysis (FDA) on fermentation data (pH, titratable acidity, yeast count, area) over 380 days.
• Transformed raw data into smooth curves for statistical comparison.
• Applied advanced statistical tools including functional derivatives and regression analysis using R software.

Main Results:

• FDA revealed significant differences in fermentation dynamics across the five tested systems.
• Specific fermentation parameters showed distinct patterns under different treatments.
• The study identified variations in yeast counts and acidity development.

Conclusions:

• Functional Data Analysis provides a powerful framework for evaluating table olive fermentation processes.
• The findings offer insights into optimizing fermentation strategies for Aloreña de Málaga olives.
• This methodology can be applied to other fermented food products.