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Assessing synthetic milk through the detection of anionic detergents in milk: a concise review

Authors:
Norliza Julmohammad at Universiti of Malaysia Sabah
Norliza Julmohammad
E Tan
E Tan
E Tan
  • This person is not on ResearchGate, or hasn't claimed this research yet.
W Y Koh
W Y Koh
W Y Koh
  • This person is not on ResearchGate, or hasn't claimed this research yet.
S Soloi
S Soloi
S Soloi
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Abstract

The emergence of synthetic milk as a deceptive method for economic gains involves the incorporation of adulterants such as starch, urea, inexpensive vegetable oils, glucose detergent, water, and preservatives. This fraudulent mixture, seamlessly integrated with pure milk, deceives consumers by maintaining a natural taste and appearance. However, this unethical practice poses significant health risks, particularly to vulnerable demographics like children, pregnant women, and individuals with compromised immune systems. Despite being prevalent in both developed and developing countries, this deceptive practice could extend to other regions, given the global demand for milk and dairy products. Various methods exist for detecting synthetic milk, and one prominent approach involves identifying anionic detergent, which serves as a key component in the production of synthetic milk, acting as an emulsifier for vegetable oils and other ingredients. Therefore, this paper aims to provide an in-depth review of the role of detergent in synthetic milk production, highlighting the evolution of detection technologies. Additionally, a comparative analysis of the strengths and limitations of the method for identifying anionic detergent in synthetic milk is presented.
Available via license: CC BY 4.0
Content may be subject to copyright.
IOP Conference Series: Earth and
Environmental Science
PAPER • OPEN ACCESS
Assessing synthetic milk through the detection of
anionic detergents in milk: a concise review
To cite this article: N Julmohammad
et al
2024
IOP Conf. Ser.: Earth Environ. Sci.
1377 012028
View the article online for updates and enhancements.
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Published under licence by IOP Publishing Ltd
The 2nd International Conference on Food and Agricultural Sciences 2023
IOP Conf. Series: Earth and Environmental Science 1377 (2024) 012028
IOP Publishing
doi:10.1088/1755-1315/1377/1/012028
1
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The 2nd International Conference on Food and Agricultural Sciences 2023
IOP Conf. Series: Earth and Environmental Science 1377 (2024) 012028
IOP Publishing
doi:10.1088/1755-1315/1377/1/012028
2
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The 2nd International Conference on Food and Agricultural Sciences 2023
IOP Conf. Series: Earth and Environmental Science 1377 (2024) 012028
IOP Publishing
doi:10.1088/1755-1315/1377/1/012028
3
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The 2nd International Conference on Food and Agricultural Sciences 2023
IOP Conf. Series: Earth and Environmental Science 1377 (2024) 012028
IOP Publishing
doi:10.1088/1755-1315/1377/1/012028
4
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The 2nd International Conference on Food and Agricultural Sciences 2023
IOP Conf. Series: Earth and Environmental Science 1377 (2024) 012028
IOP Publishing
doi:10.1088/1755-1315/1377/1/012028
5
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The 2nd International Conference on Food and Agricultural Sciences 2023
IOP Conf. Series: Earth and Environmental Science 1377 (2024) 012028
IOP Publishing
doi:10.1088/1755-1315/1377/1/012028
6
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The 2nd International Conference on Food and Agricultural Sciences 2023
IOP Conf. Series: Earth and Environmental Science 1377 (2024) 012028
IOP Publishing
doi:10.1088/1755-1315/1377/1/012028
7
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The 2nd International Conference on Food and Agricultural Sciences 2023
IOP Conf. Series: Earth and Environmental Science 1377 (2024) 012028
IOP Publishing
doi:10.1088/1755-1315/1377/1/012028
8
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78I8?BC<A: 6BHAGE<8F J<G; J84>8E E8:H?4GBEL 5B7<8F 78@4A7F 6BA68EG87 899BEGF GB @<G<:4G8 G;<F F8E<BHF
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The 2nd International Conference on Food and Agricultural Sciences 2023
IOP Conf. Series: Earth and Environmental Science 1377 (2024) 012028
IOP Publishing
doi:10.1088/1755-1315/1377/1/012028
9
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The 2nd International Conference on Food and Agricultural Sciences 2023
IOP Conf. Series: Earth and Environmental Science 1377 (2024) 012028
IOP Publishing
doi:10.1088/1755-1315/1377/1/012028
10
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The 2nd International Conference on Food and Agricultural Sciences 2023
IOP Conf. Series: Earth and Environmental Science 1377 (2024) 012028
IOP Publishing
doi:10.1088/1755-1315/1377/1/012028
11
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Application of ATR-FTIR Incorporated with Multivariate Data Analysis for Discrimination and Quantification of Urea as an Adulterant in UHT Milk
Article
Full-text available
  • Jul 2023
Urea is naturally present in milk, yet urea is added intentionally to increase milk’s nitrogen content and shelf life. In this study, a total of 50 Ultra heat treatment (UHT) milk samples were spiked with known urea concentrations (0–5 w/v%). Attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy with principal component analysis (PCA), discriminant analysis (DA), and multiple linear regression (MLR) were used for the discrimination and quantification of urea. The PCA was built using 387 variables with higher FL > 0.75 from the first PCA with cumulative variability (90.036%). Subsequently, the DA model was built using the same variables from PCA and demonstrated the good distinction between unadulterated and adulterated milk, with a correct classification rate of 98% for cross-validation. The MLR model used 48 variables with p-value < 0.05 from the DA model and gave R2 values greater than 0.90, with RMSE and MSE below 1 for cross-validation and prediction. The DA and MLR models were then validated externally using a test dataset, which shows 100% correct classification, and the t-test result (p > 0.05) indicated that the MLR could determine the percentage of urea in UHT milk within the permission limit (70 mg/mL). In short, the wavenumbers 1626.63, 1601.98, and 1585.5534 cm−1 are suitable as fingerprint regions for detecting urea in UHT milk.
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Key Milk Adulterants in India and their Detection Techniques: a Review
Article
Full-text available
  • Dec 2022
  • FOOD ANAL METHOD
Adulteration in most food products is a rising challenge and a matter of concern in front of the authorities of countries, especially the developing ones. Even though the problem with milk contamination exists in developed countries too, yet the underdeveloped nations suffer more because they do not have adequate screening and regulating infrastructure to detect point-to-point adulteration. Besides this, another big issue that boosts malpractice is the lack of awareness regarding the maintenance of food standards. Among all the food products, milk is most vulnerable to adulteration and can cause serious health issues in consumers. Adulterated milk in circulation and consumption poses a bigger threat to regulators, consumers, and the milk industry equally in the implementation of food safety standards. There are numerous detection methods and techniques available globally which are in practice to analyze and differentiate adulterated milk. However, most of them are complex, expensive, and lab-based techniques that limit their application. In this review paper, various adulterants, their health consequences, and available detection techniques have been discussed. This paper will focus on types of milk adulterants and their purposes of mixing, consequences of mixing, and different available detection techniques including methods based on electrical technologies.
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3D Paper-based milk adulteration detection device
Article
Full-text available
  • Aug 2022
Milk adulteration is a common problem in developing countries, and it can lead to fatal diseases in humans. Despite several studies to identify different adulterants in milk samples, the effects of multiple adulterants remain unexplored. In this work, a three-dimensional (3D) paper-based microfluidic device is designed and fabricated to simultaneously detect multiple chemical adulterants in milk. This device comprises a top cover, a bottom cover, and a middle layer composed of transportation and a detection zone. By making cuts on the middle layer’s support, the device’s flow path is characterised by optimum and uniform velocity. For the first time, seven adulterants (urea, detergents, soap, starch, hydrogen peroxide, sodium-hydrogen-carbonate, and salt) are detected in the milk sample simultaneously with specificity evaluation and detailed color interference analysis. Only 1–2 mL of sample volume is required to detect 7 adulterants at one time. We have used only 10 μL of the reagent’s volume for the colorimetric reaction and found the results within a few seconds. Observation reveals that the limit of detection (LOD) of the adulterants lies in the range between 0.05% (vol./vol.) to 0.2% (vol./vol.) using the colorimetric detection technique. The unknown quantity of the added adulterants is measured using the calibration curves obtained from the experiments results. The repeatability and reproducibility of the process, sensitivity, and the linear range of detection of the calibration curves and the statistical study of the color intensity data are thoroughly analysed herein. In any resource-limited setting, this simple, portable, and user-friendly 3D microfluidic device is expected to be used for testing liquid foods before consumption.
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A Study on Milk Adulteration and methods of detection of various Chemical Adulterants qualitatively
Article
Full-text available
  • Feb 2022
The food Adulteration now becomes a worldwide issue as the emergent nations are at very high risk associated with this Adulteration. The milk is an important part of our daily life and is consumed on the daily basis but, now a days milk is being adulterated by several chemicals due to lack of monitoring, unhygienic conditions and for financial gain. Unfortunately, various chemical Adulterants can pose danger health risks that lead to many lethal diseases including heart problems, diarrhoea, CNS disorders, irritation and gastrointestinal disorders. Various common Adulterants like benzoic acid, urea, water, skimmed milk powder, sugar, detergent were detected in the pure milk samples which concludes that the milk samples doesn’t meet the expected quality accepted as standard. Many studies have been carried out on milk Adulteration and detection techniques. This Present work gives a clear view of the quality of milk. This article gives a comprehensive review of the common Adulterants added in milk along with the various methods of detection qualitatively.
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Recent Analytical Method for Detection of Chemical Adulterants in Herbal Medicine
Article
Full-text available
Herbal medicine has become popular in recent years as an alternative medicine. The problem arises when herbal medicines contain an undeclared synthetic drug that is illegally added, since it is a natural product that does not contain any chemical drugs due to the potential cause of harmful effects. Supervision of herbal medicines is important to ensure that these herbal medicines are still safe to use. Thus, developing a reliable analytical technique for the determination of adulterated drugs in herbal medicine is gaining interest. This review aims to provide a recent analytical method that has been used within the past 5 years (2016–2021) for the determination of chemical adulterants in herbal medicine.
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Paper-based Microfluidic Devices for Food Adulterants: Cost-Effective Technological Monitoring Systems
Article
  • May 2022
  • FOOD CHEM
The field of analytical sciences has witnessed emergent techniques for efficient detection of clinical and industrial food adulterants. In this review, the contributions made by the paper-based devices are highlighted for an efficient and rapid detection of food adulterants and additives, which is the need of the hour and how different categories of techniques have been developed in the past decade for upgrading the performance for point-of-care testing. A simple strategy with an arrangement for the detection of specific adulterant is followed by the addition of samples to obtain well-defined qualitative or quantitative signal for confirming the presence of target species. The paper-based microfluidics-based technology advances and prospects for food adulterant’s detection are discussed in view of the high-demand from the food sectors as well as to serve as a valued technology for food researchers working in interdisciplinary technological frontiers.
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Near-Infrared Spectroscopy and Machine Learning-Based Classification and Calibration Methods in Detection and Measurement of Anionic Surfactant in Milk
Article
  • Sep 2021
  • J FOOD COMPOS ANAL
The monitoring of milk and milk-based products are crucial in the dairy industry because of their global consumption, fraudulent practice applied to mask the quality parameters for gaining more profit. Non-destructive near-infrared (NIR) spectroscopy and machine learning (ML) methods like interval partial least squares (iPLS), principal components analysis (PCA), and soft independent modeling of class analogy (SIMCA) were used to classify and discriminate pure milk samples from adulterated with an anionic surfactant. The surfactant’s model was sodium dodecylbenzene sulfate (SDBS) in different concentrations (1.94-19.4 gkg⁻¹). ML models were assessed by sensitivity, specificity and efficiency rates as classification figures of merit. As a result, iPLS regression method based on standard normal variate (SNV) pre-processed spectra provided better performance compared to partial least squares (PLS) regression with a full-spectrum dataset. The root mean square error of cross-validation (RMSECV) was 0.123 and 0.135 for the iPLS and full-spectrum models, respectively. PCA as a qualitative grouping analysis demonstrated separate clusters of samples in terms of their concentration level of SDBS. For the iPLS dataset in SIMCA model, all classification figures of merit were recorded 100% for pure milk samples. In contrast, the average sensitivity, specificity and efficiency rates were 97.6%, 93%, and 94.9% for adulterated samples.
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