Regional and barangay traceability of Philippine green coffee beans through multi-elemental analysis using portable x-ray fluorescence and energy dispersive x-ray fluorescence

Clicks: 2
ID: 284996
2024
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #2,814 of 3,757 articles by views in Malay Journal

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 3,757 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
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.
Reference Key
persistent_1760654313_68f173e90eda2 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Timoteo, Hannah Franchesca D.
Journal Malay Journal
Year 2024
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/etdb_chem/49
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.