Assessment of table olive fermentation by functional data analysis.

Area of Science:

• Food Microbiology
• Statistical Analysis
• Agricultural Science

Background:

• Traditional analysis of table olive fermentation provides limited insights.
• Assessing the impact of various treatments on olive fermentation requires advanced analytical methods.

Purpose of the Study:

• To apply functional data analysis (FDA) for the first time to evaluate treatments affecting Aloreña de Málaga table olive fermentations.
• To analyze the evolution of yeast populations and physicochemical properties during fermentation.
• To compare the efficacy of FDA with conventional methods in extracting meaningful data.

Main Methods:

• Functional data analysis (FDA) was employed to transform microbial and physicochemical data into smooth curves.
• Statistical tools including functional pointwise estimation, derivatives, regression, and F and t-tests were utilized.
• Yeast population dynamics, pH, and titratable acidity were monitored throughout the fermentation process.

Main Results:

• FDA demonstrated consistent trends in yeast population evolution across all tested treatments.
• Significant differences were observed in pH and titratable acidity profiles among treatments.
• FDA provided more comprehensive information compared to conventional analytical approaches.

Conclusions:

• Functional data analysis is a powerful and valuable tool for studying table olive fermentations.
• FDA offers deeper insights into microbial and physicochemical changes during food fermentations.
• This approach has broad applicability in the field of food microbiology.