Date of Publication

12-11-2023

Document Type

Master's Thesis

Degree Name

Master of Science in Chemistry

Subject Categories

Chemistry

College

College of Science

Department/Unit

Chemistry

Thesis Advisor

Drexel H. Camacho

Defense Panel Chair

Emmanuel V. Garcia

Defense Panel Member

Mariafe N. Calingacion
Rhowell N. Tiozon, Jr.

Abstract/Summary

The Philippine carabao mango is a high-value agricultural product. It is, however, affected by rising concerns about food fraud and origin-mislabeling resulting in consumer distrust, decline in production, and exportation. There is a need for a long-term, strategic research and development initiatives especially using innovative technologies that would help enhance its competitiveness in the local and international markets. We report herein the use of modern technology such as artificial intelligence to classify and authenticate mango samples. In this work, the geographical origin of 70 mango samples collected from Guimaras and Zambales was determined using ionomics technique and machine learning. Seventeen ionomes were analyzed using a validated method of Inductively-coupled Plasma Mass Spectrometry (ICP-MS) and were subjected to multivariate analysis, including correlation, principal component analysis (PCA), and partial least square–discriminant analysis (PLS-DA). PCA and PLS-DA clearly discriminated the samples between two locations. Both variable importance of projection (VIP) scores and F-scores agreed that the ionomes contributed significantly to the origin discrimination identifying the Ni, V, Mn, Ca, Ba, Fe, and Cu ions as chemical markers. These ionomes were used to develop machine learning classification models namely: Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) or Multi-layer Perceptron (MLP). The prediction accuracy of RF, SVM, and ANN/MLP models reached 87.5%, 100%, and 100%, respectively, allowing for the reliable authentication of mango origin from Guimaras and Zambales. This study contributes to the body of knowledge about the applications of both ionomics and machine learning for the determination of fruit’s geographical traceability, which can be used for controlling the geographical origin of mango by the government authorities and protecting consumers from improper labeling and unfair trade.

Abstract Format

html

Language

English

Format

Electronic

Keywords

Mango--Philippines

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Embargo Period

12-10-2024

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Available for download on Tuesday, December 10, 2024

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