Regional and varietal traceability of arabica (C. arabica) and robusta (C. canephora) Philippine green coffee beans using x-ray fluorescence spectroscopy based elemental profiling with machine learning

Clicks: 4
ID: 284983
2025
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 #317 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
The Philippine coffee industry faces several challenges when it comes to the authenticity of the coffee products being sold in the market. The lack of an accessible geographical identification tool hinders the industry in ensuring that the products being sold are authentic based on their geographical origin. With that, the study aimed to determine the multi-elemental composition of local coffee beans through the use of X-Ray Fluorescence spectroscopy in order to identify its geographic origin using statistical methods and machine learning. The study analyzed the 46 samples of green coffee beans grown in the Philippines, specifically in Regions VI, X, XI, and XII. The samples collected were from the annual Philippine Coffee Quality Competition held in 2024. The data obtained was analyzed using One-Way Analysis of Variance, Post Hoc Tukey’s HSD test, and Welch’s two-sample t-test. With that, it was found that the elements Manganese and Strontium were highly significant elements in regional classification. While Potassium, Manganese, and Copper were the highly significant elements in the varietal classification. The Random Forest model exhibited a 80.85% and 95.74% accuracy for the regional and varietal classification, respectively. With that, the method of integrating multi-elemental analysis with machine learning was successful in determining the geographical and varietal origin of the coffee samples, providing a non-invasive method for coffee classification.
Reference Key
persistent_1760654275_68f173c307003 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Aranton, Enrique Manuel Kong
Journal Malay Journal
Year 2025
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/etdb_chem/63
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.