Detection Of Milk Adulteration Research Articles

Predicting food adulterants in milk using Support Vector Machine (SVM)

Milk adulteration is a significant global issue, particularly in emerging nations, where inadequate monitoring and unhygienic conditions prevail. The adulteration of milk with various chemicals, such as urea, water, skimmed milk powder, sugar, and detergent, poses serious health risks, including heart problems, diarrhea, CNS disorders, irritation, and gastrointestinal disorders. Traditional detection methods are labor-intensive and require sophisticated equipment, which limits their practical application. This study aims to develop a machine learning-based approach to detect milk adulteration using attributes like Solids- Not-Fat (SNF), fat, Corrected Lactometer Reading (CLR), Total Solids (TS), temperature, and protein content. Various machine learning models were employed and evaluated for their performance, including Logistic Regression, Decision Trees, SVM, and Random Forests. The findings demonstrate that machine learning can effectively identify adulteration types, providing a foundation for the dairy industry’s practical and automated detection systems. This research comprehensively reviews common milk adulterants and highlights advanced detection methods to ensure milk quality and safety.

Study on detection of adulteration in milk and some milk products.

Study on detection of adulteration in milk and some milk products.

Urea Concentration Detection in Milk Using Microscopic Image Processing Algorithm under Ultraviolet Light Approach

The adulteration of milk is a pressing concern for the citizens of India and people all around the globe. Due to a lack of regulation compliance and insufficient surveillance infrastructure, it is noticeably worse in emerging and slow-growing nations. One of the most common and dangerous adulterants in milk is urea. If the permissible quantity of urea in milk is surpassed, it could have a major negative impact on people's health. All existing methods of urea detection require time, expertise, costly chemicals, and enzymes, along with exorbitant instruments and instrument-specific expertise. The key to overcoming this challenge is having the infrastructure to detect adulterated milk. This study aims to identify a cost- effective and largely implementable system for quantitative detection of urea content to identify adulterated milk primarily for milk distribution centers in India. The proposed milk adulteration detection system, dubbed the MADS, entails a cost-effective, rapid, accurate, precise, and completely novel method for the quantitative computation of urea levels in adulterated milk. It is a device that detects the concentration of particles of urea in milk using a microscopic image processing algorithm under ultraviolet light. Using ultraviolet light and a proprietary program in Python, the isolation of the urea particle from the rest of the milk solids is done and the area concentration, as an average of the value calculated in each of the frames of the video captured through the microscopic camera, is computed. This gives the final urea concentration in milk, which can be used to check whether the concentration follows the government guidelines and exceeds the legal limit.

Photonic Dipstick Immunosensor to Detect Adulteration of Ewe, Goat, and Donkey Milk with Cow Milk through Bovine κ-Casein Detection.

The quality and authenticity of milk are of paramount importance. Cow milk is more allergenic and less nutritious than ewe, goat, or donkey milk, which are often adulterated with cow milk due to their seasonal availability and higher prices. In this work, a silicon photonic dipstick sensor accommodating two U-shaped Mach-Zehnder Interferometers (MZIs) was employed for the label-free detection of the adulteration of ewe, goat, and donkey milk with cow milk. One of the two MZIs of the chip was modified with bovine κ-casein, while the other was modified with bovine serum albumin to serve as a blank. All assay steps were performed by immersion of the chip side where the MZIs are positioned into the reagent solutions, leading to a photonic dipstick immunosensor. Thus, the chip was first immersed in a mixture of milk with anti-bovine κ-casein antibody and then in a secondary antibody solution for signal enhancement. A limit of detection of 0.05% v/v cow milk in ewe, goat, or donkey milk was achieved in 12 min using a 50-times diluted sample. This fast, sensitive, and simple assay, without the need for sample pre-processing, microfluidics, or pumps, makes the developed sensor ideal for the detection of milk adulteration at the point of need.

A novel refractive index based-fiber optic sensor for milk adulteration detection

Milk adulteration poses a significant risk to public health and consumer trust, compromising its nutritional value. To address this, we've developed an optical fiber-based sensor using a Lossy Mode Resonance (LMR) approach. Antimony Oxide (Sb2O3) serves as the sensing layer, deposited onto the optical fiber's unclad core using Radio Frequency Magnetron sputtering. The sensing layer underwent comprehensive characterization, including X-Ray diffraction (XRD), UV–Vis spectroscopy, Ellipsometer, Atomic Force Microscopy (AFM), EDAX, and Field Emission Scanning Microscopy (FESEM). The sensor was tested for formaldehyde and water adulteration across normal milk type 1, full-fat milk type 2, vendor milk type 3, and, with concentrations ranging from 0 % to 10 %. Observations included resonance wavelength shifts and sensing parameters such as sensor response, sensitivity, and repeatability. For Sb2O3-coated sensor probes, responses to formaldehyde and water adulteration for type 1 milk is 2.05 % and 2.22 %, type 2 milk is 2.17 % and 2.4 %, and type 3 milk is 1.90 % and 1.95 %. Sensitivity for Sb2O3-coated probe to formaldehyde are 2.2 nm/v% (type 1 milk),1.9 nm/v% (type 2 milk) and 1.5 nm/v% (type 3 milk). For water in milk, the sensitivities are 1.7 nm/v%, 1.5 nm/v%, and 1.2 nm/v%. Furthermore, a prototype sensor probe has been developed for continuous milk adulteration monitoring, interfaced with Arduino and Thing Speak.

Analysis of milk adulteration by means of a potentiometric electronic tongue

The adulteration of milk presents significant challenges in the food industry, promoting the need for efficient detection methods. This study introduces a potentiometric electronic tongue for rapid and accurate milk adulteration detection. Utilizing polymeric membranes integrated with various additives, the electronic tongue distinguishes between different milk types and detects common adulterants. Experimental results demonstrate its effectiveness in discriminating raw, pasteurized, and medicated cow milk, as well as goat milk. Moreover, it successfully identifies adulterants like water and bovine milk in goat milk samples. Chemometric analyses, including Principal Component Analysis and Partial Least Squares regression, correlate sensor responses with traditional milk parameters such as fat, protein, and lactose content with up to a 0.97 R2 on the validation step. Strong correlations validate the electronic tongue's potential for rapid milk quality assessment. This innovative approach offers a cost-effective, reliable solution for detecting milk adulteration in contrast with current techniques that require numerous, time consuming experiments.

Image-Based Detection of Adulterants in Milk Using Convolutional Neural Network.

Adulteration of milk poses a severe human health hazard. Existing methods for detecting adulterants such as water, urea, ammonium sulfate (AmS), oils, and surfactants in milk are selective, expensive, and often challenging to implement in rural areas. The present work shows the potential of machine learning to detect milk adulterants using patterns of evaporative milk deposits. The final deposit patterns obtained after evaporation of the adulterated milk droplets are used to create an image data set. This data set is used to develop a deep learning model that deploys a convolutional neural network (CNN/ConvNet) to classify the distinct evaporation patterns obtained for different types and concentrations of adulterants. Further, we apply implicit and explicit regularization and compare their accuracies. The models trained with different regularization optimization schemes demonstrate that a CNN can be successfully implemented to detect adulterants in milk. Additionally, we experimentally determine how the type and concentration of milk adulterants, including ammonium sulfate (AmS), urea, oil, and surfactants, affect milk evaporative deposition. Added AmS and urea in milk crystallizes during evaporation to produce recognizable patterns that can be used for their detection. The method is capable of detecting AmS added in excess of 2.4% and urea in excess of 5% in diluted milk (20 wt %) due to the crystallization of AmS and urea, respectively. In the case of milk adulterated with vegetable oil, evaporation leads to the separation and accumulation of oil at the top of the deposit, leading to the detection of oil present in excess of 2% in 20% diluted milk. Furthermore, a minimum individual amount of 5% urea, 2.4% AmS, and 2% oil concentration in diluted milk (20%) is shown to be individually detected by evaporation pattern-based technique when milk is adulterated with all the adulterants (water, urea, AmS, and oil + surfactant) together. When subjected to different regularization optimization schemes, the CNN gives varying degrees of accuracy for successful detection. The use of implicit regularization in the form of data augmentation gives the best results with a testing average accuracy of 98%, showing that a CNN can be successfully deployed to classify and detect adulterants in milk.

Zein/MnO2 Nanosheet Composites Integrated with a Smartphone for Colorimetric Sensors for On-Site Detection of Adulterants in Milk

Zein/MnO₂ Nanosheet Composites Integrated with a Smartphone for Colorimetric Sensors for On-Site Detection of Adulterants in Milk

Rapid detection and quantification of milk adulterants using a nanoclusters-based fluorescent optical tongue

A paper-based sensor array consisting of eight nanoclusters (NCs) combined with multivariate analysis was used as a rapid method for the determination of animal sources of milk; goat, camel, sheep and cow. It was also used to detect and quantify three adulterants including sodium hypochlorite, hydrogen peroxide and formaldehyde in milk. The changes in fluorescence intensity of the NCs were quantified using a smartphone when the sensor array was immersed in the milk samples. The device generated a specific colorimetric signature for milk samples from different animals and for different adulterants. This allowed simultaneous identification of animal and adulterant sources with 100% accuracy. The device was found to be capable of accurately measuring the level of contaminants with a detection limit as low as 0.01% using partial least squares regression. In conclusion, a paper-based optical tongue device has been developed for the detection of adulterants in milk with point-of-need capability.

Detection of milk adulteration using coffee ring effect and convolutional neural network

A low-cost and effective method is reported to identify water and synthetic milk adulteration of cow’s milk using coffee ring patterns. The cow’s milk samples were diluted with tap water (TW), distilled water (DW) and mineral water (MW) and drop cast onto glass slides to observe coffee ring patterns. The area of the ring, total particle area and average particle diameter were extracted from these patterns. For each ring, the ratio of total particle area versus total ring area was calculated. The area ratio, regardless of water adulterants, follows an exponential model with respect to average particle diameter. Unlike TW, the ratio for DW and MW adulterated milk are clustered and classified together with respect to the particle diameter. These results were independent of dilution level and are used for adulterant classification. The ring of milk adulterated using synthetic milk gave multiple concentric rings, flower-like structures, and oil globules throughout the dilution level. An Alexnet model was used to classify water and synthetic milk adulterants in authentic milk. The trained model could achieve 96.7% and 95.8% accuracy for binary and tertiary classification respectively. These results enable us to distinguish synthetic milk from pure milk and segregate DW and MW with respect to TW adulterated milk.

Role of biosensors and nanomaterials in the detection of adulterants (melamine and urea) in milk and milk products

PurposeMilk is often referred to as the ultimate food because it meets the nutritional needs of infants, children and adults alike. It is a rich source of protein, fat, sweetness, vitamins and minerals. Because of its widespread usage as a healthy dairy product, the issue of milk adulteration is of global significance. The increasing frequency of fraudulent methods in the dairy business raises concerns about its purity and quality.Design/methodology/approachA study was conducted and reviewed that looked at several approaches for detecting milk adulteration during the past 15 years. This study examines the current state of research and analyzes recent advances in development.FindingsThere are ways and technology available that can effectively put an end to the abhorrent practice of milk adulteration.Originality/valueThis research takes a unique approach, focusing on the application of milk adulteration. It provides an overview of milk adulteration detection and investigates the effectiveness of biosensors in identifying common milk adulterants.

Молекулярные методы аутентификации молочного сырья в сыре: обзор

The modern dairy industry needs authentic raw milk in cheese production to provide high-quality product control and consumer protection. Unfortunately, unreliable cheese producers sometimes replace goat’s or sheep’s milk with cow’s milk. This process is associated with seasonality and the high cost of milk from small ruminants. The article reviews the methods molecular genetics used to detect milk adulteration in cheese making. It highlights the problems and prospects of applying molecular genetics to complex food matrices, such as cheese. Modern PCR methods prove quite efficient in identifying species-specific sequences by using mitochondrial DNA (mtDNA) as markers. Multiplex PCR, quantitative PCR (qPCR), and digital droplet PCR (ddPCR) have their advantages and shortcomings. The multiplex PCR method presupposes a simultaneous amplification of multiple DNA targets in a single tube while the qPCR method provides a quantitative profile. These methods make it possible to maintain high industry standards by ensuring the authenticity of cheeses and cheese products. In addition, molecular methods provide supply chain stability, increase consumer confidence, and advance dairy technology.

Innovative sensor design for reliable detection of formaldehyde adulteration in milk

This research presents a straightforward and cost-effective sensor design for the detection of harmful chemical adulterants in raw milk, with a focus on formaldehyde, a common preservative. The proposed sensor design involves a minimal sample volume of 4 ml and utilizes a low-cost assembly with two nickel electrodes within a plastic cuvette. Electrochemical Impedance Spectroscopy (EIS) was conducted with an LCR meter to record three key electrical parameters: impedance (Z), phase angle (θ), and parallel plate capacitance (Cp). Testing was conducted in a controlled laboratory environment at temperatures between 25 to 27 °C for both unadulterated and adulterated milk samples. The results reveal that Z is the most sensitive parameter, capable of detecting formaldehyde adulteration as low as 0.5%. Conversely, θ and Cp effectively detected adulteration levels exceeding 3%. A noteworthy finding is a consistent and proportional increase in Z ranging from 2.2% to 26.4% as the formaldehyde concentration increases from 0.5% to 10% across a wide frequency range from 10 kHz to 200 kHz. This method offers a dependable and efficient approach to quality control, ensuring the safety and integrity of milk products.

XAI-empowered IoT multi-sensor system for real-time milk adulteration detection

In response to the critical issue of milk adulteration jeopardizing both the nutritional integrity of milk and the health of consumers, this paper presents an innovative Artificial Intelligence (AI) enabled Internet of Things (IoT) based multi-sensor system. The escalating consumption of milk as a pivotal nutritional source necessitates robust measures to ensure its safety and quality. Traditional methods of detecting adulteration have shown limitations, prompting the development of an automated and advanced approach. The proposed system integrates various sensors capable of real-time measurement, including pH, electrical conductivity (EC), temperature, gas parameters, and Volatile Organic Compounds (VOC) parameters. This comprehensive approach extends to measuring key constituents of milk samples like Fat, Protein, Solids Not Fat (SNF), Lactose, and Gravity values. To address specific adulterants—Urea, Starch, Sodium Bicarbonate, Maltodextrin, and Formaldehyde—a machine learning-based ensemble technique is employed for classification. This ensemble method outperforms conventional algorithms like RF, Light GBM, and Extra Trees Classifiers, achieving an impressive 96% accuracy rate in detecting adulterants within the milk dataset. The pivotal contribution of this study lies in the development of an IoT-based data acquisition device that seamlessly integrates with the sensor system, enabling efficient and precise measurements. Additionally, XAI is used to analyse the results obtained by the proposed model. For this, a framework called SHAP (SHapley Additive exPlanations) analysis is employed to elucidate the decision-making process of the ensemble model, enhancing the interpretability of results. By virtue of its real-time monitoring capabilities and accurate classification, the AI-enabled IoT-based multi-sensor system emerges as a promising solution for addressing milk adulteration. This innovation holds the potential to bolster milk quality control measures in the dairy industry. The system's ability to swiftly detect and categorize adulterants underscores its significance in combating the pervasive issue of compromised milk quality, thereby ensuring consumer safety and fostering industry integrity.

Casein, whey protein and non-protein nitrogen content of milk to identify water, sugar and flour adulterated milk

This study was aimed to assess the potential of using milk's nitrogen distribution pattern, specifically casein, whey protein, and non-protein nitrogen content, as a method for detecting milk adulteration with water, sugar, and flour. Whole milk samples were collected from the Bangladesh Agricultural University Dairy Farm, BAU, Mymensingh, and subjected to adulterations, including 15%, 20%, and 25% water additions and subsequent sugar or flour adjustments to match the fresh milk's specific gravity. The samples were analyzed for specific gravity, fat content, and nitrogen distribution. Results indicated that while specific gravity remained consistent across samples adulterated with sugar or flour, it varied significantly (P<0.05) in those diluted with water. Fat content was significantly (P<0.05) reduced in samples adulterated with water and sugar, particularly at the 25% water addition level. Though total protein, true protein, and casein contents were significantly lower (P<0.05) in the 25% water-added milk compared to fresh milk, they were not significantly different (P>0.05) across the other adulterated samples when compared to fresh milk. Whey protein and non-protein nitrogen levels were statistically consistent (P>0.05) across all the samples. The results indicate that the nitrogen distribution pattern, in its current state, cannot be used to detect milk adulteration effectively. Further research with a larger dataset considering various factors affecting nitrogen distribution in milk is recommended for conclusive results. Bangladesh Journal of Animal Science 53 (1): 23-30, 2024

Qualitative Assessment for Milk Adulteration: Extent, Common Adulterants, and Utility of Rapid Tests.

The adulteration of milk is an important issue that needs to be addressed because of its deleterious effects on health. Detection of adulterants in milk can be done in laboratories or by purchasing adulteration detection kits. There are few home-based rapid tests to detect common milk adulterants, which are less known by our community. Objectives of this study were to estimate the proportion of milk adulteration and to identify common adulterants, to determine the association of milk adulteration with gastrointestinal and renal diseases; and also to validate rapid home-based tests by comparing its result with standard milk adulteration detection kit. A community-based study was conducted on 330 medical and paramedical staff. Collected milk samples during family visits were tested by standard milk adulteration detection tests and rapid home-based tests. Among 330 tested milk samples, 233 samples were adulterated with one or more than one adulterant. The most common adulteration observed was the addition of water to milk, followed by detergent, urea, and neutralizer. The specificity of the rapid tests was found to be almost 100% in the detection of water and detergent in milk and accuracy was above 80%; with "substantial agreement" (kappa = 0.7) between rapid tests and standard tests on kappa statistics. The present study has identified a large extent of milk adulteration and common adulterants, which may have harmful effects on health. The utility of home-based rapid tests is clearly proven to detect common adulterants.

A Novel Inexpensive Capacitive Sensor for Instant Milk Adulteration Detection

A Novel Inexpensive Capacitive Sensor for Instant Milk Adulteration Detection

Efficient machine learning models for the detection of coconut milk adulteration

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.

A simple smartphone-assisted paper-based colorimetric biosensor for the detection of urea adulteration in milk based on an environment-friendly pH-sensitive nanocomposite

Urea is a common milk adulterant that falsely increases its protein content. Excessive consumption of urea is harmful to the kidney, liver, and gastrointestinal system. The conventional methods for urea detection in milk are time-consuming, costly, and require highly skilled operators. So, there is an increasing demand for the development of rapid, convenient, and cost-efficient methods for the detection of urea adulteration in milk. Herein, we report a novel colorimetric paper-based urea biosensor, consisting of a novel environment-friendly nanocomposite of halloysite nanotubes (HNT), that urease enzyme and an anthocyanin-rich extract, as a natural pH indicator are simultaneously immobilized into its internal and external surfaces. The biosensing mechanism of this biosensor is based on anthocyanin color change, which occurs due to urease-mediated hydrolysis of urea and pH increment of the environment. The colorimetric signal of this biosensor is measured through smartphone-assisted analysis of the mean RGB (Red-Green-Blue) intensity of samples and is capable of detecting urea with a detection limit of 0.2 mM, and a linear range from 0.5 to 100 mM. This biosensor has demonstrated promising results for the detection of urea in milk samples, in the presence of other milk adulterants and interferents.

Enzymatic construction Au NPs-rGO based MIP electrochemical sensor for adulteration detection of bovine-derived allergen in camel milk

In this work, a high-performance molecularly imprinted polymer (MIP) sensor for the determination of β-lactoglobulin (β-LG) was fabricated by using trypsin as a template removal reagent. Gold nanoparticles (Au NPs) and reduced graphene oxide (rGO) designed for electrode modification accelerate the heterogeneous electron transfer rate to enhance the sensitivity of the prepared sensor. With enzymatic hydrolysis, β-LG templates were effectively digested into short peptides without damage to the MIP so that the imprinted cavities of the MIP were preserved with a complete spatial structure exhibiting high selectivity. Based on the optimization of the protein removal time and pH, the prepared MIP electrochemical sensor could recognize β-LG in the range of 4–100 ng/mL with a low detection limit (3.58 ng/mL). The sensor also expressed excellent selectivity and was successfully applied to real sample detection. The results demonstrate that the proposed MIP electrochemical sensor may be a promising candidate for camel milk adulteration detection.