Non-Halal Gelatin Prediction - A Comparative Machine Learning Analysis between OPLS–DA and ANN Models

Aug 3, 2026

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Gelatin, a collagen-derived food ingredient commonly sourced from bovine or porcine materials, poses halal-status concerns for consumers observing dietary restrictions. Conventional identification approaches such as qPCR and LC–MS face limitations related to DNA reliability and gelatin’s complex composition. This study applies AI-based machine learning to predict non-halal gelatin using amino acid composition, analyzing chromatographic peak areas of 18 amino acids across 210 gelatin samples from 3,780 data points. OPLS–DA achieves 100% accuracy with R2 = 0.997 and RMSE = 0.130, while ANN shows 98.5% accuracy, R2 = 0.913, and RMSE = 0.244. Results indicate both models can complement existing halal analytical methods to support product integrity and halal compliance.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Abstract

Introduction

Materials and Methods

Results and Discussion

Conclusion

Sains Malaysiana 54(8)(2025): 1913-1925 [http://doi.org/10.17576/jsm-2025-5408-04](http://doi.org/10.17576/jsm-2025-5408-04) Non-Halal Gelatin Prediction: A Comparative Machine Learning Analysis between OPLS–DA and ANN Models (Ramalan Gelatin Tidak Halal: Perbandingan Analisis Pembelajaran Mesin antara Model OPLS-DA dan ANN) MOHD HAFIS YUSWAN1,*, NORAZLINA ALI2, SYAIFUL IZWAN ISMAIL2, BASYIRAH MUDA2, MOHAMAD HABEEB HELMY IDRIS2, MAZIDAH MD NOR2, NUR SUHADAH NAWI2, MUHAMAD SHIRWAN ABDULLAH SANI3 & LAI KOK SONG4 1Halal Products Research Institute, Universiti Putra Malaysia, 43400 UPMSerdang, Selangor, Malaysia 2Malaysia Halal Analysis Centre (MyHAC), Department of Islamic Development Malaysia, No. 1 Persiaran Teknologi 1, Lebuh Enstek, 71760 Bandar Baru Enstek, Negeri Sembilan, Malaysia 3International Institute for Halal Research and Training, International Islamic University Malaysia, Jalan Gombak, 53100 Kuala Lumpur, Malaysia 4Health Sciences Division, Abu Dhabi Women’s College, Higher Colleges of Technology, 41012 Abu Dhabi, United Arab Emirates Received: 17 February 2025/Acccepted: 23 June 2025 ABSTRACT Gelatin is derived from animal collagen, sourced primarily from bovine or porcine, and finds widespread application within the food industry. These issues raise concern over its halal status, particularly among Muslims and Jews, as they adhere to dietary laws prohibiting the consumption of pork and its derivatives. Conventional methods like quantitative Polymerase Chain Reaction (qPCR) and liquid chromatography–mass spectrometry (LC–MS) have limitations due to the deoxyribonucleic acid (DNA)’s reliability and the gelatin’s complex composition, respectively. Therefore, this study aimed to explore the application of artificial intelligence (AI)–based machine learning, focusing on amino acid composition for non-halal gelatin prediction. A set of 3,780 data points enabled the analysis of the chromatographic peak areas of 18 amino acids in 210 gelatin samples. Orthogonal partial least squares discriminant analysis (OPLS–DA) and artificial neural network (ANN) compared their performance in machine learning models. The ANN employed resilient backpropagation algorithms that demonstrated high accuracy (98 .5%) and regression (R2) of 0 .913, with a slightly higher Root Mean Square Error (RMSE) of 0.244. However, OPLSDA demonstrated the best accuracy (100%), R2 of 0 .997, and lower RMSE (0 . 130) compared to the ANN model. The ANN’s robustness against outliers and direct output results provided practical advantages, while OPLS–DA offered comprehensive insights and robust discrimination. This study demonstrates the potential of AIbased machine learning in non-halal gelatin prediction, with both models showing the same capability. These findings can be integrated with existing analytical methods to complement the halal analysis, thus ensuring product integrity and upholding halal sanctity. Keywords: Artificial neural network; gelatin; halal; machine learning; OPLS–DA ABSTRAK Gelatin diperoleh daripada kolagen haiwan dan biasanyadiperoleh daripadalembu ataukhinzir. Gelatin inidigunakan secarameluas dalam industri makanan. Hal ini menimbulkan kebimbangan mengenai status halal, terutamanya dalam kalanganumat Islam dan Yahudi, kerana mereka terikat kepada undang-undang pemakanan yang melarang pengambilan daging babi dan sumbernya. Kaedah analisis seperti tindak balas rantaian polimerase kuantitatif (qPCR) dan kromatografi cecair– spektrometri jisim (LC–MS) mempunyai had kerana kebolehpercayaanasid deoksiribonukleik (DNA) dankomposisi gelatin yang kompleks. Oleh itu, kajian ini bertujuan untuk meneroka penggunaan pembelajaran mesin berasaskan kecerdasan buatan (AI), dengan memberi tumpuankepada komposisi asid amino untuk ramalan gelatin tidak halal. Set data yang terdiridaripada 3,780 data membolehkan analisis kawasan kromatografi bagi 18 asid amino dalam 210 sampel gelatin. Analisis diskriminan–kuasa dua sepa

FAQs

Why are traditional methods like qPCR and LC–MS limited for gelatin halal analysis?

qPCR is constrained by DNA reliability, while LC–MS is affected by gelatin’s complex composition, which reduces dependable identification for halal assessment.

What data and features were used for non-halal gelatin prediction?

A dataset of 3,780 points was used to analyze chromatographic peak areas of 18 amino acids across 210 gelatin samples.

How do OPLS–DA and ANN compare in predictive performance?

OPLS–DA outperformed ANN with 100% accuracy, R2 = 0.997, and RMSE = 0.130. ANN achieved 98.5% accuracy, R2 = 0.913, and RMSE = 0.244.

Aug 3, 2026

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