Detection of Adulteration in Coconut Milk using Infrared Spectroscopy and Machine Learning

TL;DR

This study presents a machine learning system using infrared spectroscopy data to accurately detect adulteration in coconut milk, achieving over 93% accuracy in classification.

Contribution

It introduces a novel combination of spectral preprocessing, LDA feature extraction, and KNN classification for coconut milk adulteration detection.

Findings

Achieved 93.33% cross-validation accuracy.

Effective discrimination between authentic and adulterated samples.

Demonstrated feasibility of infrared spectroscopy with machine learning for food safety.

Abstract

In this paper, we propose a system for detecting adulteration in coconut milk, utilizing infrared spectroscopy. The machine learning-based proposed system comprises three phases: preprocessing, feature extraction, and classification. The first phase involves removing irrelevant data from coconut milk spectral signals. In the second phase, we employ the Linear Discriminant Analysis (LDA) algorithm for extracting the most discriminating features. In the third phase, we use the K-Nearest Neighbor (KNN) model to classify coconut milk samples into authentic or adulterated. We evaluate the performance of the proposed system using a public dataset comprising Fourier Transform Infrared (FTIR) spectral information of pure and contaminated coconut milk samples. Findings show that the proposed method successfully detects adulteration with a cross-validation accuracy of 93.33%.