Authentication Of Milk Research Articles

Simultaneous Detection of Eight Dairy-Derived Components Using Double-Tube Multiplex qPCR Based TaqMan Probe.

The authentication of milk and dairy products has great significance for food fraud. The present investigation entailed the development of a novel method that amalgamates the double-tube approach with multiplex real-time polymerase chain reaction (PCR) technology, incorporating TaqMan probes, to facilitate the high-throughput screening and detection of animal-derived constituents within milk and dairy products. Eight dairy-derived animal-specific primers and probes were designed for the mitochondrial cytochrome b (Cytb) gene of eight dairy products, including cow, buffalo, yak, goat, sheep, horse, donkey, and camel. Through the developed double-tube detection assays, the above eight targets could be simultaneously identified with a detection limit of 0.00128-0.0064 ng/μL. The multiplex qPCR assay was effectively validated using simulated adulterated samples with different mixing ratios and demonstrated a detection limit of 0.1%. Upon analysis of 54 commercially available dairy products, a mislabeling rate of 33% was revealed. This method affords an efficacious means of detecting dairy product ingredients, thereby offering robust technical backing for market oversight and regulatory enforcement of milk and dairy products.

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.

Floral-core terahertz photonic crystal fiber bio-sensor: An exclusive approach to recognizing milk from various animal species

In this article, an advanced-level type of Photonic Crystal Fiber (PCF) sensor, having a flower shape core with a unique hexagonal cladding, is proposed. Performance for detecting is evaluated in various animal's milk, such as camel, cow, and buffalo milk, with the suggested PCF sensor. The maximum Relative Sensitivities (RS) of the PCF sensor are 88.2 %, 88.5 %, and 88.8 % for detecting camel milk, cow milk, and buffalo milk, respectively. Moreover, it has a negligible Confinement Loss (CL) of 2.04018402 × 10−14 dB/m and Total Loss (TL) of 2.1520000 × 10−01 dB/m and Effective Material Loss (EML) of 0.21520 cm−1. Since Milk is as great source of nutrients such as Calcium, Vitamin D, Vitamin B12, Phosphorus and Protein. The nutrient composition of different animals' milks and their respective quantities are determined by set values at the beginning and differ among the types of milk. As all of them hold the same tone, it becomes challenging to identify them. Our suggested PCF sensor comes with its remarkable detection capacity to identify them. Therefore, the use of suggested PCF sensor will be helpful for both the industry and consumers. For the industry, it aids in ensuring the quality and composition of animal milk. For consumers, it means they can trust that the milk they purchase has been accurately tested and verified for its nutrient content and safety. With the implementation of the PCF sensor, the industry can better ensure the quality and authenticity of animal milk, reducing the risk of fraud and helping consumers make more informed choices about the milk they consume. This contributes to better assurance of proper nutrient intake.

Development of a Gold Nanoparticle-Based Sensor for Authentication of Organic Milk Based on Differential Levels of miRNA.

Dairy production systems significantly impact environmental sustainability, animal welfare, and human health. Intensive farming maximizes output through high-input practices, raising concerns about environmental degradation, animal welfare, and health risks from antibiotic residues. Conversely, organic farming emphasizes sustainable practices, animal welfare, and minimal synthetic inputs, potentially enhancing biodiversity, soil health, and milk quality. MicroRNAs (miRNAs), non-coding RNAs regulating gene expression, are promising biomarkers due to their response to various conditions. In this study, miRNAs bta-miR-103 and bta-miR-155, which are abundant in milk from pasture-fed cows, were selected. Additionally, bta-miR-215, which is abundant in milk fat from intensive systems, was also studied, in order to differentiate dairy production systems. A novel, cost-effective gold nanoparticle (AuNP)-based sensor was developed for miRNA detection, leveraging the unique plasmonic properties of AuNPs for visual detection. The method involves functionalizing AuNPs with complementary RNA probes and detecting miRNA-induced aggregation through colorimetric changes. This rapid, results in 30 min, and sensitive, visual limit of detection of 200 nM, assay requires minimal instrumentation and can be easily interpreted, offering significant advantages for field implementation in characterizing dairy production systems. This study demonstrates the successful application of this sensor in detecting miRNAs in 350 nM miRNA spiked raw milk, highlighting its potential for in situ dairy industry applications.

Green synthesis of N,S dual heteroatom-doped fluorescent carbon dots (N,S-CDs) and their applications in gentamicin sensing and dual-switch anti-counterfeiting encryption

Herein, we report a facile one-step solvothermal synthesis of nitrogen and sulfur co-doped fluorescent carbon dots (N,S-CDs) using benzoic acid as the carbon source and sulfonamide as the doping agent. The synthesized N,S-CDs exhibit stable photoluminescence with an impressive fluorescence quantum yield of 16.8 %. These N,S-CDs maintain their luminescent properties under various challenging conditions, including high salt concentrations, elevated temperatures, UV radiation exposure, acidic or alkaline environments, long-term storage, and different solvents. Notably, the photoluminescence of N,S-CDs is excitation-wavelength dependent, with emission wavelengths tunable from 470 nm to 585 nm. Furthermore, the N,S-CDs display distinct fluorescent hues in aqueous and ethanolic media, presenting a novel dual-switch anti-counterfeiting ink system. Leveraging the sensitivity of N,S-CDs to gentamicin, we have developed a facile and rapid detection method for this antibiotic, which has been successfully applied to the analysis of authentic milk samples. This research contributes a novel and efficient protocol for the fabrication of high-performance carbon dots, thereby expanding their potential applications in food analysis and anti-counterfeiting technologies.

Discrimination of cow, goat, and sheep milk by femtosecond and nanosecond laser-induced breakdown spectroscopy

In the present work, femtosecond Laser Induced Breakdown Spectroscopy, fs-LIBS, is applied for the first time for the analysis of the inorganic content and the identification of the animal origin of milk samples (i.e., cow, goat, and sheep). The fs-LIBS spectra were analyzed by different machine learning algorithms (i.e., multivariate statistical analysis), and their performance was assessed. The obtained classification accuracies of the milk samples based on the animal origin were found to attain values up to 95 %, depending on the algorithm used. For comparison purposes, LIBS experiments were also carried out employing nanosecond laser pulses, ns-LIBS. The obtained results from both the fs- and ns-LIBS experiments were found to be in excellent agreement between them. The present findings, using fs laser excitation, demonstrate and extend the capabilities of LIBS technique, for the identification of the animal origin of milk samples, thus making fs- and/or ns-LIBS technique a powerful and useful tool for milk authentication, quality, and safety control related research.

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.

Multiplex PCR for detection of camel milk adulteration with cattle and goat milk

Determination of adulteration in raw milk has become increasingly important from the viewpoint of food safety and quality. The present study aimed to detect the adulteration of camel milk with cattle and goat milk by multiplex PCR technique based on amplification of the Cytochrome b gene. The developed technique successfully amplified the target fragment of 208 bp (Camel), 274 bp (cattle), and 174 bp (goat) of the gene. The amplified products were sequenced for further verification and submitted to NCBI GenBank. The limit of quantification (LOQ) for camel, cattle and goat were 2 × 10−6 ng μL−1, 2 × 10−7 ng μL−1 and 2 × 10−2 ng μL−1 respectively. The limit of detection (LOD) of cattle and goat milk in camel milk was found to be 10% and 5%, respectively. Results of the present study indicated that the multiplex PCR analysis is a useful technique for authentication of camel milk and detection of its adulteration with cattle and goat milk.

Amplification-free quantitative detection of genomic DNA using lateral flow strips for milk authentication

With the globalization and complexity of the food supply chain, the market is becoming increasingly competitive and food fraudulent activities are intensifying. The current state of food detection faced two primary challenges. Firstly, existing testing methods were predominantly laboratory-based, requiring complex procedures and precision instruments. Secondly, there was a lack of accurate and efficient quantitative detection methods. Taking cow's milk as an example, this study introduced a novel method for nucleic acid quantification in dairy products, based on lateral flow strips (LFS). The core idea of this method is to design single-stranded DNA (ssDNA) probes to hybridize with mitochondrial genes, which are abundant, stable, and species-specific in dairy products, as detection targets. Drawing inspiration from the principles of nucleic acid amplification, this research innovatively established a new DNA hybridization method, named LAMP-Like Hybridization (HybLAMP-Like). Leveraging the denaturation and DNA polymerization functions of the bst enzyme, efficient binding of the probe and template strand was achieved. This method eliminated the need for nucleic acid amplification, simplifying the procedure and mitigating aerosol contamination, thereby ensuring the accuracy of the detection results. The method exhibited exceptional sensitivity, capable of detecting extremely low to 12.5 ng in visual inspection and 3.125 ng when using a reader. In terms of practicality, it could achieve visual detection of cow's milk content as low as 1% in adulterated dairy products. When combined with a portable LFS reader, it also enabled precise quantitative analysis of milk adulteration.

HS-GC-IMS and HS-SPME/GC-MS coupled with E-nose and E-tongue reveal the flavors of raw milk from different regions of China

Milk authentication requires identification of the origin and assessment of the aroma characteristics. In this study, we analyzed 24 raw milk samples from different regions of China by profiling volatile flavors using headspace solid phase microextraction-gas chromatography-mass spectrometry, headspace gas chromatography-ion mobility spectrometry, and intelligent sensory technology (E-tongue and E-nose). The flavor of raw milk in Southern and Northern China had evident differences based on the intelligent sensory technology. However, the differences among the samples from the northeast, northwest, and central regions were not significant. Correlations between milk origin and volatile compounds based on variable importance prediction > 1 and principal component analysis results revealed differential compounds including pyridine, nonanal, dodecane, furfural, 1-decene, octanoic acid, and 1,3,5,7-cyclooctatetraene. Our study findings provided a deeper understanding of the geographical differences in raw milk volatile compounds in China.

Identification of Milk Adulteration in Camel Milk Using FT-Mid-Infrared Spectroscopy and Machine Learning Models.

Camel milk, esteemed for its high nutritional value, has long been a subject of interest. However, the adulteration of camel milk with cow milk poses a significant threat to food quality and safety. Fourier-transform infrared spectroscopy (FT-MIR) has emerged as a rapid method for the detection and quantification of cow milk adulteration. Nevertheless, its effectiveness in conveniently detecting adulteration in camel milk remains to be determined. Camel milk samples were collected from Alxa League, Inner Mongolia, China, and were supplemented with varying concentrations of cow milk samples. Spectra were acquired using the FOSS FT6000 spectrometer, and a diverse set of machine learning models was employed to detect cow milk adulteration in camel milk. Our results demonstrate that the Linear Discriminant Analysis (LDA) model effectively distinguishes pure camel milk from adulterated samples, maintaining a 100% detection rate even at cow milk addition levels of 10 g/100 g. The neural network quantitative model for cow milk adulteration in camel milk exhibited a detection limit of 3.27 g/100 g and a quantification limit of 10.90 g/100 g. The quantitative model demonstrated excellent precision and accuracy within the range of 10-90 g/100 g of adulteration. This study highlights the potential of FT-MIR spectroscopy in conjunction with machine learning techniques for ensuring the authenticity and quality of camel milk, thus addressing concerns related to food integrity and consumer safety.

Establishing authenticity of hay milk: Detection of silage feeding through cyclopropane fatty acids analysis using 1H NMR spectroscopy

Cyclopropane fatty acids (CPFAs) serve as indicators of silage feeding, verifying the authenticity of hay milk where silage feeding is forbidden. In this study, the authenticity of hay milk was determined by detecting CPFAs using proton nuclear magnetic resonance (1H NMR) spectroscopy. 245 milk samples were collected in South Tyrol (Italy), categorized as follows: 98 from grass silage-fed cows, 98 from maize silage-fed cows, and 49 authentic hay milk. The limit of detection of CPFAs was 12 µM, corresponding to 70 mg/kg of freeze-dried milk. The CPFAs were absent in all of the hay milk samples, verifying their authenticity. In contrast, 97 % of maize silage and 77 % of grass silage samples exhibited distinct CPFAs signals. These findings were further corroborated by gas chromatography-mass detector (GC–MS) analysis. The study highlights 1H NMR as a robust, and rapid technique for hay milk authentication, supporting alpine dairy production and increasing consumer trust in food authenticity.

Исследование методов экстракции ДНК из сырого молока

This article presents the results of work on the study of the effectiveness of various methods of DNA isolation for use in the analysis of biosafety of milk and dairy products by PCR. Some components of milk, such as proteases and calcium ions, can significantly reduce the effectiveness of PCR and lead to distortion of the results. We have given quantitative (DNA concentration) and qualitative assessments (amplification followed by gel electrophoresis) to commercial DNA purification kits «DNA-sorb-S-M» and «DNA-Extran-2». The results obtained indicate the applicability of the selected kits for the isolation of deoxyribonucleic acids from raw milk, the quality and quantity of which allows them to be used for the successful subsequent molecular genetic analysis of the safety and authenticity of milk.

Analytical strategies based on untargeted and targeted metabolomics for the accurate authentication of organic milk from Jersey and Yak.

Organic milk has a high risk of food fraud as it can easily be adulterated with non-organic milk. This study aimed to identify metabolite markers for assessing the authenticity of organic milk from Jersey and Yak. In the untargeted strategy, ultra-high performance liquid chromatography-Q Exactive HF-X mass spectrometer coupled with chemometrics analysis was used to screen and identify tentative markers of organic milk from Jersey and Yak. In the targeted strategy, a quick and easy method of ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was developed to quantify three markers. The peptide of Thr-Ala-Val and D-biotin were determined to be metabolite markers for distinguishing organic and non-organic Jersey milk, whereas trimethylamine N-oxide was determined to be a metabolite marker for distinguishing organic and non-organic Yak milk. These findings provide critical information to facilitate assessments of organic milk authenticity.

Applications of Solution NMR Spectroscopy in Quality Assessment and Authentication of Bovine Milk.

Nuclear magnetic resonance (NMR) spectroscopy is emerging as a promising technique for the analysis of bovine milk, primarily due to its non-destructive nature, minimal sample preparation requirements, and comprehensive approach to untargeted milk analysis. These inherent strengths of NMR make it a formidable complementary tool to mass spectrometry-based techniques in milk metabolomic studies. This review aims to provide a comprehensive overview of the applications of NMR techniques in the quality assessment and authentication of bovine milk. It will focus on the experimental setup and data processing techniques that contribute to achieving accurate and highly reproducible results. The review will also highlight key studies that have utilized commonly used NMR methodologies in milk analysis, covering a wide range of application fields. These applications include determining milk animal species and feeding regimes, as well as assessing milk nutritional quality and authenticity. By providing an overview of the diverse applications of NMR in milk analysis, this review aims to demonstrate the versatility and significance of NMR spectroscopy as an invaluable tool for milk and dairy metabolomics research and hence, for assessing the quality and authenticity of bovine milk.

Virtual Multiplexing Chamber-Based Digital PCR for Camel Milk Authentication Applications.

In this work, we proposed a chamber-based digital PCR (cdPCR) microfluidic device that is compatible with fluorescence imaging systems for milk adulteration detection. The device enables the digitalization of PCR reagents, which are loaded into microchambers, and subsequent thermocycling for DNA amplification. Then, fluorescence images of the microchambers are captured and analyzed to obtain the total number of positive chambers, which is used to calculate the copy numbers of the target DNA, enabling accurate quantitative detections to determine intentional milk adulteration from accidental contaminations. The validation of this device is performed by camel milk authentication. We performed 25,600-chamber virtual multiplexing cdPCR tests using 40 × 40 chamber devices for the detection of DNA templates extracted from pure or mixed milk with different dilutions. Then, the cdPCR chip was used to authenticate blind milk samples, demonstrating its efficacy in real biotechnical applications.

Recent Advances in the Determination of Milk Adulterants and Contaminants by Mid-Infrared Spectroscopy.

The presence of chemical contaminants, toxins, or veterinary drugs in milk, as well as the adulteration of milk from different species, has driven the development of new tools to ensure safety and quality. Several analytical procedures have been proposed for the rapid screening of hazardous substances or the selective confirmation of the authenticity of milk. Mid-infrared spectroscopy and Fourier-transform infrared have been two of the most relevant technologies conventionally employed in the dairy industry. These fingerprint methodologies can be very powerful in determining the trait of raw material without knowing the identity of each constituent, and several aspects suggest their potential as a screening method to detect adulteration. This paper reviews the latest advances in applying mid-infrared spectroscopy for the detection and quantification of adulterants, milk dilution, the presence of pathogenic bacteria, veterinary drugs, and hazardous substances in milk.

Lipidomics of coconut, almond and soybean milks - Comprehensive characterization of triacylglycerol class and comparison with bovine milk

Nowadays, plant-based milk consumption, as part of a healthy diet, is continuously increasing. In this paper, for the first time a lipidomic analysis on molecular species of triacylglycerol (TG) fraction of plant-based beverages (almond, soy, coconut) was performed by liquid chromatography quadrupole time-of-flight mass spectrometry. A total of 557 TG molecular species was measured, showing significantly different profiles between milk alternatives, compared with bovine milk. The most abundant TG molecular species were TG 18:1_18:1_18:1 and 18:1_18:1_18:2 for almond, TG 18:2_18:2_18:2 and 16:0_18:2_18:2 for soy, TG 12:0_10:0_12:0 and 12:0_12:0_14:0 for coconut. Unconventional fatty acids were detected in almond and soy. The main TG with ethereal linkage were TG-O 56:2, TG-O 56:4, and TG-O 56:5, while the main oxygenated TG was TG 54:5;1O. A total of 30 molecular species were identified as biomarkers for milk differentiation by principal component analysis, providing an interesting support for milk authentication and detection of adulteration on a larger sampling.

Development of a non-targeted approach using three handheld spectrometers combined with ensemble classifiers for authentication of bovine milk

In this study, three Vis/NIR handheld spectrometers coupled with four machine learning methods were developed for the analysis of the authentic bovine milk samples and adulterated samples with hydrogen peroxide and sodium hypochlorite. On this matter, ensemble bagged tree (EBT), random under sampling-boosted ensemble (RUS-BE), random subspace discriminant ensemble (RSDE), and random subspace ensemble k-nearest neighbor (RSE-kNN) were proposed and compared. The experiments were performed in two different scenarios. In the first scenario, pure milk and adulterated samples with one adulterant were analyzed separately with three handheld spectrometers. By comparing the results of ensemble classifiers for three spectrometers, RSE-KNN represented the best results for the accuracy and Youden index (96%, and 93% respectively). Interestingly, the results of RSE-kNN were much better than kNN as base learner. In the second scenario, the mixtures of two adulterants were analyzed with three handheld spectrometers. Among them, one presented the best results, with the predictive accuracy and Youden index of 100% for EBT, RSDE and RSE-kNN.

Identification of Characteristic Peptides of Casein in Cow Milk Based on MALDI-TOF MS for Direct Adulteration Detection of Goat Milk

It is widely acknowledged that casein is an important allergenic protein in milk which may cause danger to customers. The identification and confirmation of caseins through mass spectrometry requires the selection of suitable characteristic peptides. In this study, by means of matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS), the three most representative specific peptides of caseins in cow milk were screened out with mass-to-charge ratios (m/z) of 830, 1195, and 1759, respectively. By comparing 2,5-dihydroxybenzoic acid (DHB) and α-cyano-4-hydroxycinnamic acid (CHCA) MALDI matrices, it was found that DHB was more suitable for peptide detection with the limits of detection (LODs) of 0.1 mg/L for α, β-casein. Furthermore, on the basis of verifying the characteristic peptides of casein from cow milk, this protocol was applied to goat milk authentication. Cow milk addition in goat milk was investigated by using the screened specific peptides. The results showed that the adulteration could be identified when the proportion of cow milk was 1% or more. When applied to inspect adulteration in five brands of commercial goat milk, specific peptides of bovine casein were detected in four of them. The method has the advantages of strong reliability, high throughput, simple preprocessing, and fast speed, which can provide powerful help for prewarning dairy allergen.