Recent advances on determination of milk adulterants

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.

Highly sensitive biofunctionalized nanostructures for paper-based colorimetric sensing of hydrogen peroxide in raw milk

Hydrogen Peroxide (H2O2) is a highly hazardous, toxic, and carcinogenic chemical compound utilised in various industries-based applications. Despite strict restriction, they are deliberately added to food items such as milk as preservatives to increase its shelf life. Herein, we have formulated a green rapid colorimetric nanosensor for detection of H2O2 in milk using cotton leaves as both reducing and functionalizing agent for synthesis of silver nanoparticles (AgNPs). UV-Vis spectra exhibit a strong plasmonic peak at around 434 nm. X-Ray Diffraction (XRD) analysis was performed to determine the crystallinity of the nanoparticles. Field Emission Scanning Electron Microscope (FESEM) and Transmission Electron Microscope (TEM) characterizations revealed spherical morphology with size approximately ∼16 nm. This functionalized nanoparticle could colorimetrically sense presence of H2O2 in milk samples both in liquid media and on paper substrates with Limit of Detection (LOD) of 8.46 ppm even in presence of other interfering substances in milk. This inexpensive route will pave the way for in depth research.

Application of MXene composites for target gas detection in food safety

Food contamination has long plagued agriculture, posing significant health risks to consumers. The use of volatile gases for food safety detection has proven highly effective, with composite gas sensors that leverage the two-dimensional material MXene exhibiting notable advancements in detecting various target gases. This paper reviews the progress of MXene-based composite gas sensors in the detection of food safety-related gases. The review begins by examining MXene material synthesis methods and then presents an overview of techniques aimed at enhancing MXene-based sensor detection capabilities. Recently, advancements in MXene composite gas sensors tailored for food safety gases have been highlighted. Finally, challenges encountered in gas-sensing applications of MXene-based composites are outlined, alongside predictions for their future development, aiming to offer insights for the application and advancement of intelligent gas sensors for target gases in food safety.

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.

Rapid detection and quantification of melamine, urea, sucrose, water, and milk powder adulteration in pasteurized milk using Fourier transform infrared (FTIR) spectroscopy coupled with modern statistical machine learning algorithms

There is an evident requirement for a rapid, efficient, and simple method to screen the authenticity of milk products in the market. Fourier transform infrared (FTIR) spectroscopy stands out as a promising solution. This work employed FTIR spectroscopy and modern statistical machine learning algorithms for the identification and quantification of pasteurized milk adulteration. Comparative results demonstrate modern statistical machine learning algorithms will improve the ability of FTIR spectroscopy to predict milk adulteration compared to partial least square (PLS). To discern the types of substances utilized in milk adulteration, a top-performing multiclassification model was established using multi-layer perceptron (MLP) algorithm, delivering an impressive prediction accuracy of 97.4 %. For quantification purposes, bayesian regularized neural networks (BRNN) provided the best results for the determination of both melamine, urea and milk powder adulteration, while extreme gradient boosting (XGB) and projection pursuit regression (PPR) gave better results in predicting sucrose and water adulteration levels, respectively. The regression models provided suitable predictive accuracy with the ratio of performance to deviation (RPD) values higher than 3. The proposed methodology proved to be a cost-effective and fast tool for screening the authenticity of pasteurized milk in the market.

One-Pot Interference-Based Colorimetric Detection of Melamine in Raw Milk via Green Tea-Modified Silver Nanostructures

Detection of melamine has proven to be a challenge, requiring the use of complex analytical techniques. This study introduces an innovative, straightforward one-pot technique for qualitative assessment of the milk adulterant melamine. Originally, silver nanoparticles (AgNPs) were synthesized by utilizing green tea extract, which acted as both a reducing and sensing element. The synthesized AgNPs were characterized using UV-vis spectroscopy, X-ray diffraction, zeta potential, transmission electron microscopy, field emission scanning electron microscopy, and Fourier transform infrared spectroscopy. Melamine, rich in -NH2 groups, interacts with the biopolyphenols of green tea extract through hydrogen bonding. This interaction inhibits the formation of nanoparticles, resulting in a noticeable colorimetric response. The data obtained were confirmed by a standard UV-vis spectrophotometer and validated by the high-performance liquid chromatography technique. The limit of detection achieved by this scheme was quite low, falling below the permissible levels recommended by government bodies, e.g., the Food Safety and Standards Authority of India (FSSAI).

Rapid and green discrimination of bovine milk according to fat content, thermal treatment, brand and manufacturer via colloidal fingerprinting

Addressing food safety and detecting food fraud while fulfilling greenness requisites for analysis is a challenging but necessary task. The use of sustainable techniques, with limited pretreatment, non-toxic chemicals, high throughput results, is recommended. A combination of Field Flow Fractionation (FFF), working in saline carrier and with minimal preprocessing, and chemometrics was for the first time applied to bovine milk grouping. A set of 47 bovine milk samples was analyzed: a single analysis yielded a characteristic multidimensional colloidal dataset, that once processed with multivariate tools allowed simultaneously for different discriminations: fat content, thermal treatment, brand and manufacturing plant. The analytical methodology is fast, green, simple, and inexpensive and could offer great help in the field of quality control and frauds identification. This work represents also the first attempt to identify milk sub-typologies based on colloidal profiles, and the most complete study concerning multivariate analysis of FFF fingerprint.

Graphene-based biosensors in milk analysis: A review of recent developments

Cow's milk, an excellent source of fat, protein, amino acids, vitamins and minerals, is currently one of the most consumed products worldwide. Contaminations originating from diverse sources, such as biological, chemical, and physical, cause dairy product quality problems and thus dairy-related disorders, raising public health issues. For this reason, legal authorities have deemed it necessary to classify certain contaminations in commercial milk and keep them within particular limitations; therefore, it is urgent to develop next-generation detection systems that can accurately identify just the contaminants of concern to human health. This review presents a detailed investigation of biosensors based on graphene and its derivatives, which offer superior sensitivity and selectivity, by classifying the contaminants under the headings biological, chemical, and physical, in cow's milk according to their sources. We reviewed the current status of graphene-based biosensor (GBs) technology for milk or dairy analysis, highlighting its strengths and weaknesses with the help of comparative studies, tables, and charts, and we put forward a novel perspective to handle future challenges.

Perceptions of rural and urban residents in Borana pastoral and agro-pastoral areas in Ethiopia related to milk adulteration

Milk is a nutritious food that plays a great role in the diets of a society largely dependent on livestock production. On the other hand, contaminants can enter milk naturally or intentionally, causing a negative impact on the health of consumers. Milk adulteration is a wide concern in the dairy industry in many countries, including Ethiopia, with a subsequent negative impact on its nutritive value and potentially affecting the health of consumers. This study was designed to assess the perceptions of rural and urban residents in Borana pastoral and agro-pastoral areas in Ethiopia related to milk adulteration. It was also aimed at identifying the potential reasons for milk adulteration in the area. A semi-structured questionnaire and focus group discussions (FGDs) were used to collect quantitative and qualitative data, respectively, focusing on the types of substances added to milk and the reasons for the addition of the substances. In rural and urban areas, 73.1% and 91.7% of respondents reported suspicion of the addition of nonmilk substances or milk of other animal species to cow's milk before selling, respectively. According to the qualitative data, most reported adulterants were water and 'pasta or rice water' (a murky fluid left after boiling rice or pasta). Respondents mentioned that they identify adulterated milk by observation or tasting. Economic gain was the primary perceived reason to adulterate the milk according to the study participants. The respondents had concerns about the quality and safety of milk associated with adulteration in the area. The weak enforcement of regulations related to milk quality and marketing as well as the inadequacy of capacity for the detection of adulteration were mentioned as gaps toward mitigating the problems. Awareness creation about the negative impacts of milk adulteration among the community supported by strategies for regulation, such as improving regular testing of milk and taking actions on adulterated milk, is recommended to tackle consumer concerns around milk adulteration in the area.

Carboxyl-functionalized two-dimensional MXene-Au nanocomposites were prepared as SERS substrates for the detection of melamine in dairy products

In the present study, we address the limitations of conventional surface-enhanced Raman scattering (SERS) techniques for sensitive and stable detection of melamine in food products, especially dairy. To overcome these challenges, we developed a novel SERS-active substrate by incorporating gold nanoparticles (AuNPs) onto carboxyl-functionalized two-dimensional (2D) MXene material doped with nitrides, specifically Au-Ti2N-COOH. Our strategy leverages the unique physicochemical properties of MXene, a class of atomically thin, 2D transition metal carbides/nitrides, with tunable surface functionalities. By modifying the MXene surface with AuNPs and introducing carboxyl groups (-COOH), we successfully enhanced the interaction between the substrate and melamine molecules. The carboxyl groups form hydrogen bonds with the amino groups on the melamine's triazine ring, facilitating the adsorption of melamine molecules within the 'hotspot' regions responsible for SERS signal amplification. A series of characterization methods were used to confirm the successful synthesis of Au-Ti2N-COOH composites.Using Au-Ti2N-COOH as the SERS substrate, we detected melamine in spiked dairy product samples with significantly enhanced sensitivity and stability compared to nitride-doped MXene alone. The detection limit in liquid milk stands at 3.7008 μg kg-1, with spike recovery rates ranging from 99.84% to 107.55% and an approximate RSD of 5%. This work demonstrates the effectiveness of our approach in designing a label-free, rapid, and robust SERS platform for the accurate quantitation of melamine contamination in food, thereby mitigating health risks associated with melamine adulteration.

Bacillus sp. alone or combined with salicylic acid inhibited Trichoderma spp. infection on harvested white Hypsizygus marmoreus

Introduction

White Hypsizygus marmoreus is a popular edible mushroom. It is rich in nutrition and flavor but vulnerable to fungal disease, resulting in nutrient loss and aging.

Methods

In this study, the pathogenic fungus Trichoderma spp. BBP-6 and its antagonist Bacillus sp. 1-23 were isolated and identified. The negative effects caused by this pathogen were judged by detecting a series of changes in the infected white H. marmoreus. The effects of Bacillus sp. 1-23 on Trichoderma spp. BBP-6 and the infected white H. marmoreus were detected. The effect of Bacillus sp. 1-23 treatment combined with salicylic acid (SA) was also considered.

Results

The results showed that Trichoderma spp. BBP-6 could affect the activities of antioxidant enzymes PAL, POD, CAT, SOD, GR, PPO, and APX to interfere with the stability of the white H. marmoreus antioxidant enzyme system and cause the mushroom severe browning and nutrition loss, as well as general quality deterioration. Bacillus sp. 1-23 could produce chitinase and chitosanase enzymes to inhibit Trichoderma spp. BBP-6 directly. SA reinforced this inhibitory. Bacillus sp. 1-23 alone or combined with SA could help white H. marmoreus from the Trichoderma spp. BBP-6 infection to effectively maintain nutrients, restore and stabilize the antioxidant system, and reduce the production of malondialdehyde, superoxide anion and hydrogen peroxide.

Discussion

Thus, such treatments could be considered potential methods to alleviate damage from disease and extend the shelf life of white H. marmoreus.

A ratiometric fluorescent probe for rapid and specific detection of hypochlorite

Hypochlorite (ClO- ), as a kind of essential reactive oxygen species, plays a crucial role in vitro and in vivo. Here, a ratiometric fluorescent probe (TPAM) was designed and constructed for sensing ClO- based on substituted triphenylamine and malononitrile, which exhibited obvious colour transfer from orange to colourless under daylight accompanied by noticeable fluorescence change from red to green in response to ClO- . TPAM could effectively monitor ClO- with the merits of fast response, excellent selectivity, high sensitivity and a low detection limit of 0.1014 μM. 1 H NMR, mass spectra and theoretical calculations proved that ClO- caused the oxidation of the carbon-carbon double bond in TPAM, resulting in compound 1 and marked changes in colour and fluorescence. In addition, TPAM was utilized for imaging ClO- in living cells successfully with good photostability and biocompatibility.

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.

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.

Study of Mechanical Response of Paper-Based Microfluidic System as a Potential Milk Tester

Milk is considered a complete meal that requires supervision to determine its suitability for human consumption. The development of sustainable devices that evaluate food properties has gained importance due to the necessity of integrating these instruments into the production chain. However, the materials employed to develop it, such as polymers, semiconductors, and glass, lack sustainability and require specialized equipment to fabricate them. Different chemical techniques have been used to miniaturize these detection systems such as microfluidics, which have been used in milk component detection using colorimetry. In this work, a cantilever beam paper-based microfluidic system is proposed to evaluate differences in milk, according to nutritional information, using its electromechanical response. A 20-microliter milk drop is deposited in the system, which induces hygroexpansion and deflection due to liquid transport within the paper. Likewise, a conductive path is added on the beam top surface to supply a constant current that induces heat to evaporate the solution. According to the results obtained, it is possible to point out differences between trademarks with this microfluidic system. The novelty of this system relies on the paper electromechanical response that integrates the hygroexpansion-induced displacement, which can be used for further applications such as milk microtesters instead of colorimetric tests that use paper as a property-evaluation platform in combination with chemical reactions.

Monitoring of Hypochlorite Level in Fruits, Vegetables, and Dairy Products: A BODIPY-Based Fluorescent Probe for the Rapid and Highly Selective Detection of Hypochlorite

Hypochlorite/hypochlorous acid (ClO^-/HOCl), among the diverse reactive oxygen species, plays a vital role in various biological processes. Besides, ClO^- is widely known as a sanitizer for fruits, vegetables, and fresh-cut produce, killing bacteria and pathogens. However, excessive level of ClO^- can lead to the oxidation of biomolecules such as DNA, RNA, and proteins, threatening vital organs. Therefore, reliable and effective methods are of utmost importance to monitor trace amounts of ClO^-. In this work, a novel BODIPY-based fluorescent probe bearing thiophene and a malononitrile moiety (BOD-CN) was designed and constructed to efficiently detect ClO^-, which exhibited distinct features such as excellent selectivity, sensitivity (LOD = 83.3 nM), and rapid response (<30 s). Importantly, the probe successfully detected ClO^- in various spiked water, milk, vegetable, and fruit samples. In all, BOD-CN offers a clearly promising approach to describe the quality of ClO^--added dairy products, water, fresh vegetables, and fruits.

Nutritional quality and adulterants of cow raw milk, pasteurized and cottage cheese collected along value chain from three regions of Ethiopia

Milk is a nutritionally rich food for humans. However, fulfilling the quality of milk is a major concern for milk factories, nutrient requirements, and public health. The objective of this research was to assess the composition of raw and pasteurized milk and cheese, evaluate change in milk and cheese composition along the value chain, and identify adulteration of milk. A total of 160 composite samples were determined using lactoscan and conventional approved methods along value chain. Results indicate that there were significant (p < 0.05) changes of in milk composition along the value chain in the study regions. The range values were; total solid (8.41-11.7%), protein (2.25-3.06%), fat (2.16-3.17%), lactose (3.33-4.76%), ash (0.52-0.73%), P (62.7-84.2 mg/100 g) and Ca (78.2-109 mg/100 g) of liquid milk were obtained in all regions. Liquid milk was found to be adulterated by water along the value chains in all regions (ranged from 0 to 24.8%). Formalin (4 samples) and starch (1 sample) were detected at farmer's and collectors' respectively. In all regions, there was no significant (p > 0.05) difference in cheese nutritional quality between farmers and retailers. The grand mean for moisture, protein, fat, total ash, Ca, P and pH values were 77.1%, 17.1%, 1.42%, 1.18%, 37.8 mg/100 g, 88.2 mg/100 g and 3.7 respectively. Comparison of liquid products with the Compulsory Ethiopian Standard (CES) indicates that 80.2% for fat, protein, and SNF in raw and pasteurized milk were below the CES. In conclusion, liquid milk had poor nutritional composition and varied along the value chain in the study regions. Moreover, there is milk fraud where all dairy value chain add water into milk and milk consumers are consuming lower nutrients and paying for substandard liquid milk. Therefore, training should be provided to all value chain to improve the quality of milk products and quantification of formalin and other adulterants need to be further studied.

Colorimetric and fluorescent dual-signals probes for naked-eye detection of hydrogen peroxide and applications in milk samples and in vivo

Excessive residual hydrogen peroxide (H₂O₂) disinfectant in food is harmful to human health. Therefore, it is necessary to develop efficient detection methods for H₂O₂ detection. In this work, we designed and synthesized five D-A molecules 3a-3e by introducing electron-donor substituents (-OCH₃ and -CH₃) to the electron-acceptor dicyanoisophorone skeleton in order to find out the suitable probes for H₂O₂ detection. Among them, two promising probes, 3a and 3c, are screened out according to structure-property relationships. Based on the principle of intramolecular charge transfer (ICT), 3a and 3c express colorimetric and fluorescent dual-signals towards H₂O₂ with low detection limits (0.20 μM and 0.14 μM) and rapid response (within 20 mins). The reaction mechanism between probes and H₂O₂ is determined by ¹H NMR and HRMS. Density functional theory (DFT) calculations are measured to study the regulation mechanism of structure adjustment on probs performance. Furthermore, a smartphone RGB analysis is utilized as a portable platform for the quantitative detection of H₂O₂ without complicated instruments, indicating a high efficiency and on-site detection method for H₂O₂. In addition, probes are applied to detect H₂O₂ in milk samples, HepG-2 cells and zebrafish, suggesting the promising applications in food samples and physiological systems.

A paired emitter-detector diode-based photometer for the determination of sodium hypochlorite adulteration in milk

This paper reports on developing a low cost but efficient paired emitter-detector diode (PEDD)-based photometer. The photometer consists of a white light-emitting diode (LED) as the emitter diode, an RGB LED as the detector diode, and a multimeter for recoding the signal. The developed PEDD-based photometer was utilized for the determination of liquid bleach adulteration in cow milk samples. N,N-Diethyl-p-phenylenediamine sulfate aqueous solution of pH 6 was used as a probe to monitor the presence of residual active chlorine in milk. The results showed that the developed method could be used to determine sodium hypochlorite in the concentration range of 0.5 to 20.0 ppm Cl₂ with 0.14 and 0.46 ppm Cl₂ limit of detection and limit of quantification, respectively. The intraday and interday precisions of the method at two concentration levels of 5.5 and 13.7 ppm Cl₂ were 1.04% and 0.52%, and 1.81% and 1.02%, respectively. The recoveries of 114.2% and 106.9% were obtained for 5.5 and 13.7 ppm Cl₂ concentrations levels, respectively. Real sample analyzes results showed that "maybe" liquid bleach adulteration in milk is the case for local distributors of raw milk.

A card instead of a lab: A ligand embedded in a bio-composite of starch/gelatin intelligent film for milk quality test followed by colorimetric analysis

Milk adulteration analysis at different stages of the production chain, which profiteers widely use to mask milk quality parameters, is problematic. We prepared selective intelligent thin bio-films and a dedicated app to test milk adulteration in an out-of-lab setting. In this regard, each reagent is immobilized on a piece of starch/gelatin film as color-changeable polymeric support, which is then pasted onto a transparent test card. Incorporating starch and gelatin as the backbone of these composite films can significantly improve the mechanical properties, color, and potential applications in real-time monitoring. After 5 min floating of the card on the surface of the milk, our developed software as a Progressive Web Application can be used to locate and read the QR codes of the samples to estimate qualitative and quantitative information about the presence of some preservatives. These preservatives ranged from 0.1 to 200.0 mg/L, 1.0-100.0 mg/L, 0.001-0.05 mol/L in 10 mL of milk with the LODs of 0.1, 1.0 mg/L, and 0.001 mmol/L, for H₂O₂, Cr(VI), and salicylic acid, respectively. The pH monitoring film also indicates the freshness/spoilage of the milk. The portability, ease of use, and low cost of testing coupled with the app, make it an attractive alternative to lab-based analysis.

Nontargeted detection and recognition of adulterants in milk powder using Raman imaging and neural networks

Raman imaging technology combined with targeted chemometrics can play a vital role in the rapid detection of milk powder adulteration, which threatens the lives of infants and other people. However, these methods always suffer from a narrow detection range. Nontargeted methods show a broader detection range but cannot recognize adulterants. Here, a novel nontargeted chemometric method, named as the adversarial discrimination neural network (ADNN), is proposed to detect and recognize adulterants simultaneously. The method comprises building a tight boundary in the feature space of Raman images to discriminate milk powder samples from the majority of adulterated cases. Then a first-order partial derivative of the ADNN is calculated to recognize different adulterants through a local approximation strategy. A validation set containing samples adulterated with various adulterants at concentrations ranging from 0.3% to 1.5% w/w was provided to challenge the proposed method. The validated detection accuracy of the proposed method for authentic and adulterated samples was 99.9% and 99.7% and the adulterants were recognized correctly. The ADNN-Raman represents a novel nontargeted and end-to-end tool for detecting and recognizing adulterants in milk powder simultaneously, providing new insights into nontargeted chemometric analysis.

Mechanisms and Health Aspects of Food Adulteration: A Comprehensive Review

Food adulteration refers to the alteration of food quality that takes place deliberately. It includes the addition of ingredients to modify different properties of food products for economic advantage. Color, appearance, taste, weight, volume, and shelf life are such food properties. Substitution of food or its nutritional content is also accomplished to spark the apparent quality. Substitution with species, protein content, fat content, or plant ingredients are major forms of food substitution. Origin misrepresentation of food is often practiced to increase the market demand of food. Organic and synthetic compounds are added to ensure a rapid effect on the human body. Adulterated food products are responsible for mild to severe health impacts as well as financial damage. Diarrhea, nausea, allergic reaction, diabetes, cardiovascular disease, etc., are frequently observed illnesses upon consumption of adulterated food. Some adulterants have shown carcinogenic, clastogenic, and genotoxic properties. This review article discusses different forms of food adulteration. The health impacts also have been documented in brief.

How Chemometrics Can Fight Milk Adulteration

Adulteration and fraud are amongst the wrong practices followed nowadays due to the attitude of some people to gain more money or their tendency to mislead consumers. Obviously, the industry follows stringent controls and methodologies in order to protect consumers as well as the origin of the food products, and investment in these technologies is highly critical. In this context, chemometric techniques proved to be very efficient in detecting and even quantifying the number of substances used as adulterants. The extraction of relevant information from different kinds of data is a crucial feature to achieve this aim. However, these techniques are not always used properly. In fact, training is important along with investment in these technologies in order to cope effectively and not only reduce fraud but also advertise the geographical origin of the various food and drink products. The aim of this paper is to present an overview of the different chemometric techniques (from clustering to classification and regression applied to several analytical data) along with spectroscopy, chromatography, electrochemical sensors, and other on-site detection devices in the battle against milk adulteration. Moreover, the steps which should be followed to develop a chemometric model to face adulteration issues are carefully presented with the required critical discussion.

Non-targeted detection of food adulteration using an ensemble machine-learning model

Recurrent incidents of economically motivated adulteration have long-lasting and devastating effects on public health, economy, and society. With the current food authentication methods being target-oriented, the lack of an effective methodology to detect unencountered adulterants can lead to the next melamine-like outbreak. In this study, an ensemble machine-learning model that can help detect unprecedented adulteration without looking for specific substances, that is, in a non-targeted approach, is proposed. Using raw milk as an example, the proposed model achieved an accuracy and F1 score of 0.9924 and 0. 0.9913, respectively, when the same type of adulterants was presented in the training data. Cross-validation with spiked contaminants not routinely tested in the food industry and blinded from the training data provided an F1 score of 0.8657. This is the first study that demonstrates the feasibility of non-targeted detection with no a priori knowledge of the presence of certain adulterants using data from standard industrial testing as input. By uncovering discriminative profiling patterns, the ensemble machine-learning model can monitor and flag suspicious samples; this technique can potentially be extended to other food commodities and thus become an important contributor to public food safety.

3D Paper-based milk adulteration detection device

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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.05\%$$\end{document}0.05% (vol./vol.) to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.2\%$$\end{document}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.

Thermal Denaturation of Milk Whey Proteins: A Comprehensive Review on Rapid Quantification Methods Being Studied, Developed and Implemented

Heat treatment of milk signifies a certain degree of protein denaturation, which modifies the functional properties of dairy products. Traditional methods for detecting and quantifying the denaturation of whey proteins are slow, complex and require sample preparation and qualified staff. The world’s current trend is to develop rapid, real-time analytical methods that do not destroy the sample and can be applied on/in-line during processing. This review presents the rapid methods that are being studied, developed and/or applied to determine and quantify the thermal denaturation of whey proteins, including spectroscopic, electrochemical and miniaturized methods. The selected methods save a significant amount of time and money compared to the traditional ones. In addition, the review emphasizes the methods being applied directly to milk and/or that have potential for on/in/at-line application. There are interesting options to quantify thermal denaturation of whey proteins such as biosensors, nanosensors and microchips, which have fast responses and could be automated. In addition, electrochemical sensors are simple to use and portable, while spectroscopy alternatives are suitable for on/in/at-line process.

CRISPR-Cas-based detection for food safety problems: Current status, challenges, and opportunities

Food safety is one of the biggest public issues occurring around the world. Microbiological, chemical, and physical hazards can lead to food safety issues, which may occur at all stages of the supply chain. In order to tackle food safety issues and safeguard consumer health, rapid, accurate, specific, and field-deployable detection methods meeting diverse requirements are one of the imperative measures for food safety assurance. CRISPR-Cas system, a newly emerging technology, has been successfully repurposed in biosensing and has demonstrated huge potential to establish conceptually novel detection methods with high sensitivity and specificity. This review focuses on CRISPR-Cas-based detection and its current status and huge potential specifically for food safety inspection. We firstly illustrate the pending problems in food safety and summarize the popular detection methods. We then describe the potential applications of CRISPR-Cas-based detection in food safety inspection. Finally, the challenges and futuristic opportunities are proposed and discussed. Generally speaking, the current food safety detection methods are still unsatisfactory in some ways such as being time-consuming, displaying unmet sensitivity and specificity standards, and there is a comparative paucity of multiplexed testing and POCT. Recent studies have shown that CRISPR-Cas-based biosensing is an innovative and fast-expanding technology, which could make up for the shortcomings of the existing methods or even replace them. To sum up, the implementation of CRISPR-Cas and the integration of CRISPR-Cas with other techniques is promising and desirable, which is expected to provide "customized" and "smart" detection methods for food safety inspection in the coming future.

Development of an ultrasensitive molecularly imprinted poly‐(ortho‐phenylenediamine) based sensor for the determination of melamine adulteration in milk and infant formula

A sensitive molecularly imprinted poly‐(ortho‐phenylenediamine) electrochemical sensor was fabricated for selective melamine detection in milk and infant formula. The pencil graphite electrode (PGE) was modified by deposition of Au nanoparticles and reduced graphene oxide (RGO) on its surface. The fabrication of the electrode in various stages was monitored using cyclic voltammetry. The immobilized RGO, MIP, and gold nanoparticles on the PGE surface were morphologically characterized by field‐emission scanning electron microscopy (FESEM). Under the optimized conditions, the linear range and the limit of detection (LOD) were 10^–17–10^–8 M and 2.64 × 10^–16 M (S/N = 3), respectively. The prepared sensor exhibited a good reproducibility and repeatability response. The recovery range of melamine‐spiked milk and infant formula was 92.7%–103.9% and 93.5%–105.8%, respectively. The sensor could apply successfully for melamine determination in milk and infant formula samples.

Rational design of an allosteric G-quadruplex aptamer probe for ultra-sensitive detection of melamine in milk

Efficient and accurate detection of melamine in dairy products remains a crucial yet challenging task. Herein, an allosterically modulated G-quadruplex-integrated aptamer is rationally designed with thymine-rich recognition termini for melamine binding. The detection process is facile by simply introducing the analyte into the mixture consisting of G-quadruplex aptamer probes, exonuclease III, and thioflavin T (ThT). The detection feasibility is confirmed by the polyacrylamide gel electrophoresis and fluorescence measurement results. This exonuclease III-assisted signal amplifiable approach works well in a linear range from 0.1 nM to 0.1 μM. Moreover, a detection limit as low as 83 pM is easily achieved, which is almost five orders of magnitude smaller than the maximum allowable melamine levels (about 8 μM) defined by many countries all over the world. The whole assay time for each test is no longer than 1 h. Additionally, the scheme is highly specific and satisfactory recovery rates (from 91% to 104%) are readily obtained when challenged with melamine-spiked milk samples. Therefore, the label-free, turn-on, low-cost, and time-efficient method can be used for reliable detection of melamine in an easily manipulated and ultra-sensitive manner, which may find its utilization in the field of food safety, biomedical engineering, and clinical diagnosis.

Specifically triggered dissociation based ratiometric electrochemical sensor for H2O2 measurement in food samples

A novel ratiometric strategy based electrochemical sensor was developed to quantitative assay of H₂O₂ in different food samples. 4-aminophenylboronic acid pinacol ester (ABAPE) dissociation was specifically triggered by H₂O₂ to generate electro-active 4-aminophenol (4-AP), which not only can be oxidized to indirectly indicate the concentration of H₂O₂, but also endowed the sensor with high selectivity. Meanwhile, a reference probe of poly(thionine) (TH) was modified with ketjen black (KB) and gold nanoparticles (AuNPs) on electrode surface. KB and AuNPs displayed high electrocatalytic activity to 4-AP. A current ratio between 4-AP and TH (i/i_TH) showed a good linear relationship with the concentration of H₂O₂ in a range of 3.0 × 10^-7 - 1.0 × 10^-4 mol/L (0.010 ppm - 3.40 ppm) with a limit of detection of 2.6 × 10^-7 mol/L (0.009 ppm) (S/N = 3). Moreover, the ratiometric strategy based sensor possessed good accuracy, reliability, and stability, and successfully determined H₂O₂ in food samples with satisfactory results.

Refractometric Detection of Adulterated Milk Based on Multimode Interference Effects

This paper reports on the refractometric detection of water-adulterated milk using an optical fiber sensor whose principle of operation is based on multimode interference (MMI). The device is manufactured in a simple way by splicing a segment of coreless multimode fiber (NC-MMF) between two single-mode fibers (SMFs); neither functionalization nor deposition of a sensing material is required. MMI takes place in the NC-MMF and, when fed with a broadband spectrum, a transmission peak appears at the output of the MMI device due to its inherent filter-like response, whose position depends on the effective refractive index (RI) of the medium surrounding the NC-MMF. Therefore, when the sensor is immersed in different milk−water mixtures, the peak wavelength shifts according to the RI of the mixture. In this way, adulterated milk can be detected from the wavelength shift of the transmission peak. The system was tested with two commercial brands of milk, and adulterations were clearly distinguished in both cases. In the range of interest, from no dilution up to 50% dilution, the sensor exhibits a linear response with a sensitivity of −0.04251 and −0.03291 nm/%, respectively, for the two samples tested. The measurement protocol is repeatable and allows for locating the peak wavelength within <0.34 nm over several repetitions using different samples with the same concentration. A thermal sensitivity of 0.85 nm/°C was obtained, which suggests that the temperature needs to be maintained as fixed during the measurements. The approach presented can be extended to other scenarios as a quality control tool in beverages for human consumption, showing the advantages of simple construction, high sensitivity, and the potential for real-time monitoring.

The creation of selective imprinted cavities on quartz crystal microbalance electrode for the detection of melamine in milk sample

Molecularly imprinted polymer based nanofilms specific to melamine were synthesized on quartz crystal microbalance (QCM) electrode surface. Contact angle measurements, atomic force microscopy, ellipsometry and scanning electron microscopy were used for characterizations process. Some of the findings of the study are as follows: pH 6.0 was found as optimal working pH for melamine detection. Prepared MIP QCM electrode showed a linearity of 99.53% in the concentration range of 50-1000 ng/mL. Langmuir-Freundlich hybrid model was the best fitted isotherm for whole concentration range. The performance of MIP QCM electrode was also confirmed by determining of melamine in melamine spiked milk samples. As a conclusion, the results figured out that not only QCM nanosensor for specific melamine detection but also polymerization strategy were classified as an intriguing alternative for developing new melamine sensing platforms. Limit of detection (LOD) and limit of quantification (LOQ) were calculated as 2.3 ng/mL and 7.8 ng/mL, respectively.

Melamine induced changes in histopathology of the main organs and transcriptional levels of MAPK signaling genes in kidneys of female mice

Melamine is an important chemical raw material used in industries, which has potential health risks to animals and humans. Current research mainly focuses on the toxic effects of high-dose melamine ingestion. However, there are few reports on whether melamine at the current limited standard dose has adverse effects on various tissues and organs, and whether there are sensitive target genes for risk evaluation. For this, 24 female Kunming mice were fed 0, 1.8-, 3.6-, and 7.2- mg/kg/d melamine via drinking water for consecutive 28 days, respectively. The morphological changes of the ovarian, hepatic, and renal tissues were firstly observed. The results demonstrated that the histopathology of ovary, liver, and especially in kidney had been altered by melamine intake in female. And then, the transcriptional levels of MAPK signaling genes including p38, ERK1, ERK2, JNK1, and JNK2 in kidneys were investigated by real-time PCR. The data showed that ERK1 and p38 mRNAs expressions were up-regulated significantly by melamine, suggesting that ERK1 and p38 transcriptional levels in the kidney might to be considered as candidate targets for lower-dose melamine toxicity. This study not only provides potential targets for the diagnosis and prevention of melamine damage, but also helps to assess the health risks of the current minimum allowable levels of melamine in food and environment.

Identification of milk from different animal and plant sources by desorption electrospray ionisation high-resolution mass spectrometry (DESI-MS)

This study used desorption electrospray ionisation mass spectrometry (DESI-MS) to analyse and detect and classify biomarkers in five different animal and plant sources of milk for the first time. A range of differences in terms of features was observed in the spectra of cow milk, goat milk, camel milk, soya milk, and oat milk. Chemometric modelling was then used to classify the mass spectra data, enabling unique or significant markers for each milk source to be identified. The classification of different milk sources was achieved with a cross-validation percentage rate of 100% through linear discriminate analysis (LDA) with high sensitivity to adulteration (0.1-5% v/v). The DESI-MS results from the milk samples analysed show the methodology to have high classification accuracy, and in the absence of complex sample clean-up which is often associated with authenticity testing, to be a rapid and efficient approach for milk fraud control.

A Bayesian Approach to Predict Food Fraud Type and Point of Adulteration

Primary and secondary food processing had been identified as areas vulnerable to fraud. Besides the food processing area, other stages within the food supply chain are also vulnerable to fraud. This study aims to develop a Bayesian network (BN) model to predict food fraud type and point of adulteration i.e., the occurrence of fraudulent activity. The BN model was developed using GeNie Modeler (BayesFusion, LLC) based on 715 notifications (1979-2018) from Food Adulteration Incidents Registry (FAIR) database. Types of food fraud were linked to six explanatory variables such as food categories, year, adulterants (chemicals, ingredients, non-food, microbiological, physical, and others), reporting country, point of adulteration, and point of detection. The BN model was validated using 80 notifications from 2019 to determine the predictive accuracy of food fraud type and point of adulteration. Mislabelling (20.7%), artificial enhancement (17.2%), and substitution (16.4%) were the most commonly reported types of fraud. Beverages (21.4%), dairy (14.3%), and meat (14.0%) received the highest fraud notifications. Adulterants such as chemicals (21.7%) (e.g., formaldehyde, methanol, bleaching agent) and cheaper, expired or rotten ingredients (13.7%) were often used to adulterate food. Manufacturing (63.9%) was identified as the main point of adulteration followed by the retailer (13.4%) and distribution (9.9%).

Handheld Devices for Food Authentication and Their Applications: A Review

This review summarises miniaturised technologies, commercially available devices, and device applications for food authentication or measurement of features that could potentially be used for authentication. We first focus on the handheld technologies and their generic characteristics: (1) technology types available, (2) their design and mode of operation, and (3) data handling and output systems. Subsequently, applications are reviewed according to commodity type for products of animal and plant origin. The 150 applications of commercial, handheld devices involve a large variety of technologies, such as various types of spectroscopy, imaging, and sensor arrays. The majority of applications, ~60%, aim at food products of plant origin. The technologies are not specifically aimed at certain commodities or product features, and no single technology can be applied for authentication of all commodities. Nevertheless, many useful applications have been developed for many food commodities. However, the use of these applications in practice is still in its infancy. This is largely because for each single application, new spectral databases need to be built and maintained. Therefore, apart from developing applications, a focus on sharing and re-use of data and calibration transfers is pivotal to remove this bottleneck and to increase the implementation of these technologies in practice.