Multi-block data analysis using ComDim for the evaluation of complex samples: Characterization of edible oils.

Area of Science:

• Analytical Chemistry
• Chemometrics
• Food Science

Background:

• Vegetable oil characterization requires robust analytical methods.
• Integrating data from diverse techniques (spectroscopy, chromatography) presents challenges.
• Chemometrics offers tools for multi-block data analysis.

Purpose of the Study:

• To evaluate the ComDim chemometrics method for multi-block analysis of vegetable oil samples.
• To demonstrate the method's ability to integrate data from Near Infrared (NIR), Ultraviolet-Visible (UV-Vis) spectroscopy, and Gas Chromatography with Flame Ionization Detection (GC-FID).
• To infer similarities and differences among vegetable oil samples based on spectral and fatty acid profiles.

Main Methods:

• Application of the ComDim chemometrics method for unsupervised pattern recognition.
• Analysis of 32 vegetable oil samples using NIR and UV-Vis spectroscopy.
• Determination of fatty acid composition using GC-FID.

Main Results:

• ComDim successfully extracted information and generated informative graphs (scores, saliences, loadings).
• The method revealed relationships between samples based on spectral absorption and fatty acid profiles.
• Similarities and differences among vegetable oil samples were successfully inferred.

Conclusions:

• The ComDim method is applicable for characterizing samples using multiple analytical techniques.
• This chemometric approach effectively discriminates samples based on their diverse characteristics and compositions.
• ComDim provides a powerful framework for integrating and interpreting multi-block analytical data.