Date of Publication

8-13-2024

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Chemistry

Subject Categories

Chemistry

College

College of Science

Department/Unit

Chemistry

Thesis Advisor

Emmanuel V. Garcia

Defense Panel Member

Mariafe N. Calingacion
Maria Carmen S. Tan

Abstract/Summary

Along with the rise of cafe culture, the popularity of coffee has been increasing over the years. However, one consequence of such popularity is the increased occurrence of food fraud or the purposeful manipulation of food products. The study aimed to determine a method that is both accurate and efficient for tracing the regional and barangay origin of Philippine arabica green coffee beans using multi-elemental analysis by portable X-ray fluorescence (pXRF) and energy-dispersive X-ray fluorescence (ED-XRF). The data collected was analyzed using one-way ANOVA, Tukey’s HSD, and Random Forest models. The study analyzed 29 samples of C. arabica green coffee beans sourced from five different regions across the Philippines: Region II, Region X, Region XI, CAR, and BARMM. The study successfully developed models using both ED-XRF and pXRF data, achieving accurate classification of samples into their respective regions. It was found that manganese and potassium were significant elements for regional classification, while manganese alone was the most significant factor for barangay classification The constructed Random Forest models had 100% accuracy for both regional and barangay classifications. The study was also able to demonstrate the potential of multi-elemental analysis, which when accompanied with machine learning is a valuable tool for the traceability and authentication of Philippine arabica green coffee beans.

Abstract Format

html

Language

English

Format

Electronic

Keywords

Coffee--Philippines; X-ray spectroscopy; Machine learning

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Embargo Period

8-16-2024

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