TARU PUBLICATIONS
Journal of Information and Optimization Sciences cover

Journal of Information and Optimization Sciences

(Since 1980)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

Powered by:Powered by

Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
submissions@tarupublications.com
Taru Online First Current IssueSpecial IssuesBrowse all Issues
Journal of Information and Optimization Sciences
Open Access Research Article

Efficient machine learning models for the detection of coconut milk adulteration

, * ,

* Corresponding author · click or hover a name for details

pp. 215–233Vol. 45Issue 2March 2024DOI: 10.47974/JIOS-1546XML
Published Online:
17 Mar 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1546
Pages:
215–233

Abstract

Coconut milk adulteration detection involves the use of analytical methods, including machine learning, to detect the presence of impurities or additives in coconut milk. Adulteration can occur when substances such as water or other cheaper ingredients are added to coconut milk, compromising its quality, nutritional value, and authenticity. Identifying adulteration is crucial for ensuring consumer safety, maintaining product quality, and upholding industry standards. The aim of this study was to propose a machine learning model to detect adulteration in coconut milk. To implement the proposed work, a coconut milk adulteration dataset is collected from a standard source. The amount of available data is limited; hence, synthetic data is generated by applying the CTGAN algorithm. The proposed framework employs three different Feature Extraction (FE) strategies, i.e., Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Autoencoder (AE). Then, the feature-extracted dataset is classified through four effective machine learning algorithms, such as Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The machine learning algorithms outcomes are evaluated using four performance metrics, i.e., Accuracy, Precision, Recall, and F1-score. RF provides the highest accuracy of 99.17%, precision value of 97.29%, recall value of 96.71%, and F1 Score of 97% for the LDA techniques.

Keywords

Food adulterationCoconut milkMachine learningInfrared spectraFeature extraction

Subject Classifications

Primary 93A30Secondary 49K15

References

[1] Saha, D., & Manickavasagan, A., Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review. Current Research in Food Science, 4, 28-44 (2021). 
[2] Othman, S., Mavani, N. R., Hussain, M. A., Abd Rahman, N., & Ali, J. M., Artificial intelligence-based techniques for adulteration and defect detections in food and agricultural industry: A review. Journal of Agriculture and Food Research, 100590 (2023).
[3] Rachineni, K., Kakita, V. M. R., Awasthi, N. P., Shirke, V. S., Hosur, R. V., & Shukla, S. C., Identifying type of sugar adulterants in honey: Combined application of NMR spectroscopy and supervised machine learning classification. Current Research in Food Science, 5, 272-277 (2022).
[4] Asefa, B. G., Hagos, L., Kore, T., & Emire, S. A., Computer vision based detection and quantification of extraneous water in raw milk (2021).
[5] Q. Li et al., Low risk of category misdiagnosis of rice syrup adulteration in three botanical origin honey by ATR-FTIR and general model, vol. 332. Elsevier Ltd. (2020). doi: 10.1016/j.foodchem.2020.127356.
[6] Noviyanto and W. H. Abdulla, “Honey Dataset Standard Using Hyperspectral Imaging for Machine Learning Problems,” pp. 503–507 (2017).
[7] M. Hussain Khan, Z. Saleem, M. Ahmad, A. Sohaib, H. Ayaz, and M. Mazzara, “Hyperspectral imaging for color adulteration detection in red chili,” Appl. Sci., vol. 10, no. 17 (2020), doi: 10.3390/app10175955.
[8] S. P. S. Brighty, G. S. Harini, and N. Vishal, “Detection of Adulteration in Fruits Using Machine Learning,” pp. 2021–2024 (2021).
[9] M. Pérez-Calabuig, S. Pradana-López, A. Ramayo-Muñoz, J. C. Cancilla, and J. S. Torrecilla, “Deep quantification of a refined adulterant blended into pure avocado oil,” Food Chem., vol. 404, (no. September 2022, 2023), doi: 10.1016/j.foodchem.2022.134474.
[10] P. kumar Goyal, Kashish, “Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain,” Food Eng. Rev., pp. 596–616 (2022), doi: 10.1007/s12393-022-09322-2.
[11] Sitorus, A., Muslih, M., Cebro, I. S., & Bulan, R., Dataset of adulteration with water in coconut milk using FTIR spectroscopy. Data in Brief, 36, 107058 (2021).

Views: 324Downloads: 83Citations: 1