Biosensing approaches to detect potential milk contaminants: a comprehensive review

Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools

To enhance the resilience of food systems to food safety risks, it is vitally important for national authorities and international organizations to be able to identify emerging food safety risks and to provide early warning signals in a timely manner. This review provides an overview of existing and experimental applications of artificial intelligence (AI), big data, and internet of things as part of early warning and emerging risk identification tools and methods in the food safety domain. There is an ongoing rapid development of systems fed by numerous, real-time, and diverse data with the aim of early warning and identification of emerging food safety risks. The suitability of big data and AI to support such systems is illustrated by two cases in which climate change drives the emergence of risks, namely, harmful algal blooms affecting seafood and fungal growth and mycotoxin formation in crops. Automation and machine learning are crucial for the development of future real-time food safety risk early warning systems. Although these developments increase the feasibility and effectiveness of prospective early warning and emerging risk identification tools, their implementation may prove challenging, particularly for low- and middle-income countries due to low connectivity and data availability. It is advocated to overcome these challenges by improving the capability and capacity of national authorities, as well as by enhancing their collaboration with the private sector and international organizations.

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.

Studying the concentration of xenobiotics in milk and developing the biosensor method for their rapid determination

In this article the content of toxic xenobiotics (heavy metals and pesticides) in cow milk collected from 5 districts of Eastern Kazakhstan was examined and their cumulative properties were determined. The content of organochlorine pesticides (HCCH, DDT) was not detected in the analyzed milk. The content of mercury and arsenic in milk samples does not exceed the maximum allowable concentration (0.005 and 0.05 mg/kg, respectively). The content of cadmium above the maximum allowable concentration (0.03 mg/kg) was found in milk sampled from Shemonaikha and Katon-Karagai districts. The content of lead and zinc above the maximum allowable concentration (0.1 and 5.0 mg/kg, respectively) was found in milk samples taken from all 5 studied districts. The content of copper above the maximum allowable concentration (1.0 mg/kg) was found in milk samples collected from 4 districts under study (Borodulikha, Beskaragai, Shemonaikha and Katon-Karagai). Based on the analysis of information data the need to develop an accelerated method of determining toxic xenobiotics in milk was substantiated. The basic directions of modernization of the biosensor for determination of cadmium and lead salts in milk and dairy products were selected. A new approach to the process of immobilization of the enzyme on the surface of a substrate for cadmium and lead salts determination in milk has been developed. The efficiency of using a polymeric plate with a graphite conducting layer as a basis for the enzyme biosensor was established.

Research Advances in the Analysis of Estrogenic Endocrine Disrupting Compounds in Milk and Dairy Products

Milk and dairy products are sources of exposure to estrogenic endocrine disrupting compounds (e-EDCs). Estrogenic disruptors can accumulate in organisms through the food chain and may negatively affect ecosystems and organisms even at low concentrations. Therefore, the analysis of e-EDCs in dairy products is of practical significance. Continuous efforts have been made to establish effective methods to detect e-EDCs, using convenient sample pretreatments and simple steps. This review aims to summarize the recently reported pretreatment methods for estrogenic disruptors, such as solid-phase extraction (SPE) and liquid phase microextraction (LPME), determination methods including gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), Raman spectroscopy, and biosensors, to provide a reliable theoretical basis and operational method for e-EDC analysis in the future.

Bulk and Surface Acoustic Wave Biosensors for Milk Analysis

Milk and dairy products are common foods and, therefore, are subject to regular controls. Such controls cover both the identification and quantification of specific components and the determination of physical parameters. Components include the usual milk ingredients, mainly carbohydrates, proteins, and fat, and any impurities that may be present. The latter range from small molecules, such as drug residues, to large molecules, e.g., protein-based toxins, to pathogenic microorganisms. Physical parameters of interest include viscosity as an indicator of milk gelation. Bulk and surface acoustic wave sensors, such as quartz crystal microbalance (QCM) and surface acoustic wave (SAW) devices, can principally be used for both types of analysis, with the actual application mainly depending on the device coating and the test format. This review summarizes the achievements of acoustic sensor devices used for milk analysis applications, including the determination of physical liquid parameters and the detection of low- and high-molecular-weight analytes and microorganisms. It is shown how the various requirements resulting from the respective analytes and the complex sample matrix are addressed, and to what extent the analytical demands, e.g., with regard to legal limits, are met.

Novel Sensor Approaches of Aflatoxins Determination in Food and Beverage Samples

The rapid quantification of toxins in food and beverage products has become a significant issue in overcoming and preventing many life-threatening diseases. Aflatoxin-contaminated food is one of the reasons for primary liver cancer and induces some tumors and cancer types. Advancements in biosensors technology have brought out different analysis methods. Therefore, the sensing performance has been improved for agricultural and beverage industries or food control processes. Nanomaterials are widely used for the enhancement of sensing performance. The enzymes, molecularly imprinted polymers (MIP), antibodies, and aptamers can be used as biorecognition elements. The transducer part of the biosensor can be selected, such as optical, electrochemical, and mass-based. This review explains the classification of major types of aflatoxins, the importance of nanomaterials, electrochemical, optical biosensors, and QCM and their applications for the determination of aflatoxins.