Date of Publication

12-2023

Document Type

Master's Thesis

Degree Name

Master of Science in Chemistry

Subject Categories

Chemistry

College

College of Science

Department/Unit

Chemistry

Thesis Advisor

Emmanuel V. Garcia

Defense Panel Chair

Marissa G. Noel

Defense Panel Member

Anna Karen C. Laserna
Angel T. Bautista, VII

Abstract/Summary

Multi-element and stable isotope ratio (SIR) profiling with chemometrics and machine learning techniques can provide a means to differentiate roasted coffee beans based on their species (Arabica and Robusta) and geographical origin. This approach can help mitigate food fraud and secure the geographical indication (GI) of Philippine coffee. Cultivation practices, post-harvest processes, and environmental factors such as soil composition, precipitation, temperature, and altitude influence the chemical composition of a coffee bean. A total of fifty-six (56) roasted coffee bean samples were collected from the participants of the 2022 Philippine Coffee Quality Competition (PCQC). Eight (8) commercially available roasted coffee beans were also collected. XRF-based multi-element and stable isotope ratio profiles from these two sets of samples were subjected to principal component analysis (PCA), linear discriminant analysis (LDA), and random forest (RF). Samples are categorized based on regions. The concentrations of P, S, K, Ca, Mn, Fe, Cu, Zn, Rb, Sr, δ13C, and δ15N were utilized as predictors to differentiate and classify samples based on species and geographical origin. RF provides higher accuracy than LDA on species classification (98.21% vs. 94.64%). On the other hand, region-based geographical origin classification accuracy is higher in LDA than in RF (74.07% vs. 64.82%). Including SIRs (δ13C and δ15N) as explanatory variables for classification increases the accuracy of the LDA model by 3.70% and the RF model by 3.71% in geographical origin classification. Based on the generated models, δ13C is a better predictor than δ15N in discriminating coffee based on geographical origin. XRF-based multi-element profiling can be used for high-throughput screening of species and the geographical origin of coffee. XRF is a fast, cost-efficient, and reliable instrumental method for multi-elemental analysis. LDA and RF are viable statistical tools for utilizing XRF-based multi-element and SIR profiles to accurately classify coffee based on species and geographical origin.

Abstract Format

html

Language

English

Format

Electronic

Keywords

Coffee--Philippines

Upload Full Text

wf_yes

Embargo Period

12-10-2024

Download

Share

COinS