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Employing a discriminant model is advantageous since it can identify the critical biomarkers for the halal authentication of each critical ingredient and product . For instance , discriminant analysis has been used to authenticate fish , bovine , and porcine gelatine sources .
The Department of Standard Malaysia has initiated the development of Malaysian Standards focusing on discriminant analysis for halal authentication .
As the Malaysian Standard is commonly referred to by the Standards and Metrology Institute for Islamic Countries ( SMIIC ) and upgraded into international standards , worldwide halal certification bodies and enforcement agencies could leverage discriminant data science analysis insights to curtail food fraud ’ s proliferation .
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PREDICTIVE MAINTENANCE IN HALAL FACILITIES
The halal assurance system requires establishing the process flow of halal manufacturing and identifying and monitoring halal control points .
These activities include monitoring the operation of facilities via manual or real-time sensor readings , where production executives record the maintenance information of the machines , conveyors , storage , etc . and keep the information as an internal facility database .
Data scientists evaluate the occurrence of machinery failures in the database and train predictive models to detect subtle anomalies and deviations that may signal underlying issues concerning machinery performance .
Thus , the predictive models could accurately forecast potential failures by discerning patterns within halal facilities . They can also be implemented as predictive maintenance to guard against unexpected equipment failures and ensure uninterrupted production while upholding halal standards .
These predictions empower maintenance teams to proactively address looming issues before they escalate into costly downtime or compromise product quality .
By maintaining machinery proactively , companies mitigate the risk of production disruptions that could jeopardise halal certification and consumer trust .
Companies that adopt the incorporated predictive maintenance and data science can reduce downtime on critical production lines by up to 30 per cent ( Moore , 2024 ), a 20 per cent decrease in maintenance costs , achieving a 15 per cent increase in asset lifespan , a 10 per cent reduction in replacement costs and a 10 per cent increase in overall production uptime ( Elkateb et al ., 2024 ; Uzoigwe , 2024 ).
This effort shifts from reactive to proactive maintenance strategies in the halal facilities .
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ment , the laboratory can validate and verify the analytical methods for various halal ingredients , especially the critical ones , e . g . protein and fat-based ingredients .
For products , the laboratory can work to establish analytical methods to detect alcohol in fermented food and beverages , as well as the presence of porcine in leather and hairbased products .
Various critical reviews divide the analytical methods into protein , fat and alcohol-based analytical techniques , and analysts can employ these analytical methods to analyse the ingredients and products that undergo the application for halal certification .
Then , analysts compile the results and segregate them into sources . This compilation becomes the database , which grows with the continuous addition of new ingredients and product results .
When compiling the results , all ingredients and products with porcine present can be flagged and kept in the database . These results furnish invaluable insights into the halal status of products , serving as a cornerstone for data-driven decision-making .
The database could be expanded to store genetic information , the biochemical composition of plant - and animal-based sources , and information on the presence or absence of non-halal components .
The biochemical compositions of synthetic ingredients can also be kept for reference .
The authorities related to halal could emulate the effort of establishing a food composition database ( https :// myfcd . moh . gov . my /) by adding the manufacturers ’ names and addresses , halal certificate from worldwide halal certification bodies , certificate of analysis , material safety data sheet and process flow of the ingredients , which are part and parcel of the raw material master list as stated in the Malaysian Halal Management System 2020 .
Meanwhile , the enforcement agencies conducting surveillance audits of the halal supply chain can collect all ingredients and submit them for laboratory testing to update the database .
By integrating this disparate information ,
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data scientists construct a holistic view of the halal supply chain , enhancing transparency and accountability .
FRAUD PATTERN DETECTION
From the established halal food database , the data scientists carry out a meticulous analysis involving leveraging advanced analytics to address and mitigate the issue of detecting fraudulent practices that could compromise the integrity of halal products .
Data scientists discern subtle anomalies within the ingredient lists and product labelling via sophisticated machine-learning algorithms trained on the established halal food database .
These algorithms can identify intricate patterns indicative of fraudulent behaviour . For instance , consider the case of a halal meat supplier whose products exhibit inconsistent labelling across different batches .
By scrutinising these discrepancies , data scientists can uncover potential food fraud cases , empowering halal certification bodies to safeguard consumer interests and ensuring the authenticity of halal products .
Likewise , halal certification bodies with access to the halal food database can employ laboratory test results to identify the possible adulteration occurrence . Discriminant analysis and machine learning approaches have been proven to assist with authentication . It can also be used to analyse halal food databases and classify the halal status of ingredients and products into their sources , i . e . plant , animal , synthetic , etc .
In the process , the database is divided into training , validation , and testing datasets . Based on the training dataset , a discriminant model is then established .
Data scientists evaluate the efficacy of the discriminant model to differentiate between the animal , plant or synthetic sources in the validation and testing datasets , where the discriminant model is improved until the ability to discriminate the sources achieves 100 per cent correct classification .
Then , this discriminant model can be used to identify and predict the potential occurrence of food fraud .
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TRANSFORMATIVE FORCE
While data science emerges as a transformative force fortifying the integrity of the halal industry , it is essential to acknowledge its potential challenges and limitations .
The successful implementation of data science in halal food compliance requires the willingness of the halal certification bodies to establish the halal food database , utilise the data science for halal certification , develop and maintain a robust data infrastructure , nurture skilled data scientists , and live a culture of data-driven decision-making .
Data privacy and security are crucial when handling sensitive information such as genetic data and supply chain records . Despite these challenges , the strategic integration of data science is critical to ensuring the authenticity and credibility of halal products globally .
The data science bytes shall assure one to consume the halal products with complete trust … Bismillah … –
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“ When compiling the results , all ingredients and products with porcine present can be flagged and kept in the database . These results furnish invaluable insights into the halal status of products , serving as a cornerstone for datadriven decisionmaking .” |