A technique for evaluating and compensating for the drift of eight mass-sensitive sensor array in an open detection cell was developed to take into account the influence of external factors (temperature, changes in the chemical composition of the background) for out-of-laboratory analysis of biosamples when long-term monitoring of health state humans and animals. The effective way to compensate the sensor signal drift when the sorption properties of the sensitive coatings change during their long-term intensive operation is the daily internal standardization of the system. Distilled water is proposed as a standard for the biosamples based on the water matrix (blood, exhaled breath condensate, urine, etc.). Internal standardization is based on daily calculating the specific sensor signals by dividing the sensor signals for biosample on the corresponding averaged values from 3-5 measurements of standard. The stability of the sensor array operation is estimated using the theory of statistical process control (multivariate exponentially weighted moving average control charts) based on the specific signal of the sensor array. The control limits for the statistic quantity of the central tendency for each sensor and the whole array, variations of the sensor signals were determined. The average time to signal and run lengths for statistically substantiated monitoring of the electronic nose stability were calculated. Based on the analysis of tendency and variations in sensor signals for six months of operation, a technique was drawn up to control the stability of the sensor array for the out-of-laboratory analysis of biosamples. This approach is successfully verified by the classification of the results of the analysis of blood and water samples obtained for four months. The proposed technique can be introduced into the software algorithm of the electronic nose, which will increase the correctness of decision-making during long-term monitoring of the health state of humans and animals.

The study's goal was to build a mathematical model capable of classifying biosamples with minor errors into groups corresponding to clinical diagnoses by the original output data of the mass-sensitive sensor array. The nasal secretion from humans and animals was investigated. One hundred forty-four calves were clinically and laboratory examined and divided by the health of respiratory organs into three groups. A sample of nasal secretion was taken from each calf. The gaseous phase over samples was measured using an array of 8 mass-sensitive sensors with solid-state nanostructured coatings in the open detection cell. During the sorption and desorption of volatile substances excreted from the samples, the sensor responses were recorded in software and then processed by un-, semi – and supervised machine learning methods. In total, 50 algorithms for processing sensor data were studied, including t-SNA, self-learning model DBSCAN, Yarovsky algorithm, BOSSVS, SAXVSM, LearningShapelets, MultivariateClassifier. The semisupervised model based on the Yarovsky algorithm had good classification reliability and gave all samples a confidence gap of more than 0.5, and for the majority of samples, the gap was no less than 0.9. Also, the nonlinear transformation of the original sensor data was used in order to obtain the simplest two-dimensional manifold on which all data points will be located separately, such as Locally Linear Embedding, Local Tangent Space Alignment, Hessian Eigenmapping, Modified Locally Linear Embedding, Isomap, Multi-dimensional Scaling, Spectral Embedding, t-distributed Stochastic Neighbor Embedding. The supervised machine learning models using the Dynamic Time Warping metric of similarity between two time series and the k-NN algorithm for classification achieved a correct classification accuracy equal 0.83. Recommendations by application of the different machine learning algorithms depending on the task of diagnostics were formulated.

Milk is one of the most complete foods for humans, containing nutrients including carbohydrates, proteins, fats, minerals, and vitamins [1].

Owing to its rich composition, milk becomes a substrate for the growth of undesirable microorganisms that can easily deteriorate milk. To prevent this from happening, prohibited substances are fraudulently added [2]. One of those substances is hydrogen peroxide (H₂O₂), which is widely used in the dairy industry as an antimicrobial agent, thus helping preserve the raw milk in the absence of refrigeration [3].

Despite its conventional use, when added to milk, H₂O₂ solution can cause a decrease in the nutritional value of the food, due to the destruction of vitamins A and E, which generates reactive and cytotoxic oxygen species, including hydroxyl radicals, that can initiate oxidation and damage nucleic acids, lipids and proteins. By consequence, when ingested, milk can lead to negative effects on the health of the population, especially in individuals immunocompromised persons [2][3].

Here, it is presented a study for the detection and quantification of H₂O₂ using a chemiluminescence technique. A small low-cost hydroxyethyl cellulose sensitive membrane combined with a high-sensitive photodetector is used to measure H₂O₂ concentrations from raw to ultra-pasteurized milk samples.

Localized Surface Plasmon Resonance (LSPR) and Metal-Enhanced Fluorescence (MEF)-based optical biosensors provide unique advantages compared to other sensing technologies to design point-of-care (POC) diagnostic tools. These devices exploit the capability of noble-metal nanoparticles of absorbing light at a well-defined wavelength. The need for wearable, flexible and easy-to-use diagnostic tools has brought to the development of plasmonic nanocomposites, whose performances are strongly dependent on both the optical properties of plasmonic nanoparticles and mechanical properties of the polymeric matrix. An optical platform based on spherical gold nanoparticles (AuNPs) embedded in high molecular weight poly-(ethylene glycol) diacrylate (PEGDA) hydrogel is proposed. As a hydrogel, PEGDA represents a biocompatible, flexible, transparent polymeric network to design wearable, 3D, plasmonic biosensors for the detection of targets with different molecular weights for the early diagnosis of disease. The swelling capability of PEGDA is directly correlated to the plasmonic decoupling of AuNPs embedded within the matrix. A study on the effect of swelling on the optical response of the PEGDA/AuNPs composites was investigated in a model system. Specifically, citrate AuNPs were modified with cysteamine and the interaction biotin-streptavidin is monitored within the 3D hydrogel network. Also, metal-enhanced fluorescence is observed within the PEGDA/AuNPs nanocomposites, which can be exploited to achieve an ultra-low limit of detection. Citrate-stabilized AuNPs (~65 nm) are synthesized via seeded-growth method, embedded in PEGDA 10 kDa pre-polymer solutions, and polymerized by UV light exposure. Citrate-displacement via cysteamine, biotin interaction and Cy®3-Streptavidin conjugation are performed by soaking the PEGDA/AuNPs nanocomposite in the prepared solutions. LSPR signal was monitored via transmission mode customized setup and MEF signal was detected via Fluorescence and Confocal Microscopes.

The microfluidic paper-based analytical devices (μPADs) have witnessed a great extent of innovation over the past decade developing new components and materials assisting the diagnosis of different diseases and sensing of a wide range of biological, chemical, optical, and electrochemical phenomena. The novel paper-based cantilever (PBC) actuator is one the major component that allows autonomous loading and control of multiple fluid reagents required for the accurate operation of paper-based microfluidic devices. This paper provides an extensive overview of numerical and experimental modeling of fluidically controlled PBC actuators for automation of the paper-based assay. The PBC model undergoing hygro-expansion utilize quasi-static 2-D fluid loaded structure governed by the Euler-Bernoulli beam theory for small and moderately large deflections. Solution for the model can avail the response of paper-based actuators for response deflection θ, within 0° to 10° under the assumption of insignificant cross-sectional deformation. The actuation of PBC obtained using a quasi-static theory shows that our results are consistent with quantitative experiments demonstrating the adequacy of models.

Electrochemical analysis based on screen-printed electrodes (SPEs) represents a great alternative to conventional analytical methods such as ICP-MS or LC-MS, due to their portability, sensitivity, selectivity, and cost effectiveness. In addition, functionalization of SPEs with nanomaterials has been reported to provide an enhanced analytical performance [1,2]. In this regard, silver nanoparticles (AgNPs) were synthesized and appropriately characterized, showing spherical silver nanoseeds (Ag-NS) with a diameter of 12.20 ± 0.04 nm. Using spin coating methodology, the synthesized AgNPs were used to modify screen-printed carbon nanofiber electrodes (SPCNFEs). Ag-NS deposition onto the electrode surface was confirmed by scanning electron microscopy (SEM). Furthermore, the analytical response of the modified electrodes (Ag-NS-SPCNFE) was evaluated for the determination of trace lead(II), cadmium(II), and arsenic(V) using differential pulse anodic stripping voltammetry (DPASV), obtaining detection limits of 2.8, 2.1, and 0.6 µg·L^-1, respectively [3,4]. The results validated the hypothesis that Ag-NS modified electrodes presented higher sensitivity and better analytical performance than non-modified SPCNFE. Finally, the modified SPCNFEs were tested towards the determination of As(V) in a spiked tap water sample, showing a good agreement with concentrations determined by inductively coupled plasma-mass spectrometry (ICP-MS).

Metal oxide semiconductor (MOS) gas sensors are widely used for their numerous advantages, including simplicity of use, low cost, fast response and recovery time, high sensitivity and the capability to detect several analytes. Among MOS, doped or undoped tin oxide (SnO₂), was considered as a good candidate to develop a high-performance resistive sensor for the detection of volatile organic compounds (VOCs).

VOCs impact on the profile of flavours, that as known affect our senses of smell and taste, contributing to identify appealing and affordable foods and drinks, characterizing unambiguously the products quality.

As an example, diacetyl is the VOC that confers a butter‐like aroma in many foods and beverages; it is naturally produced in small quantities during the fermentation and the storage processes in many products such as beer, wine, brandy, balsamic vinegar, roasted coffee, honey, butter, yogurt, and several cheeses. However, if diacetyl threshold value is overcome its presence may indicate an issue in the production or storage processes. Therefore, the monitoring of diacetyl concentrations contributes to the quality of the final product.

In this work, SnO₂ was used as sensing layer for diacetyl detection. The metal oxide powder was obtained through a hydrothermal process and characterized by means of complementary investigation techniques. A thin film of SnO₂ was deposited on an alumina planar substrate supplied with interdigitated platinum electrodes and opportunely conditioned to promote its stabilization. The detection of diacetyl vapor, obtained by bubbling air in liquid diacetyl maintained at a controlled temperature, was performed in different working conditions. The effects of temperature, flow rate, humidity and analyte concentration of pure, aqueous and alcoholic diacetyl solutions were investigated.

From these preliminary results, SnO₂ showed promising sensing characteristics toward VOCs detection such as diacetyl. However, further investigations are necessary to improve the developed sensing system.

Allergies to seafood have a relevant impact on public health since the unknown intake of products thereof can cause serious health problems. Tropomyosin (TPM) has been identified as a major shellfish allergen. Crustaceans, especially shrimps, are of particular concern because of the presence of TPM in their muscle tissue. Although attempts have been made to reduce its allergenicity by boiling, this does not guarantee the total absence of TPM [1]. So, the detection of trace levels of TPM in food products can prevent health problems. For this purpose, electrochemical immunosensors are adequate analytical tools since they provide highly selective, sensitive, fast, and cheap analysis and are suitable for in situ applications.

In this work, a simple voltammetric immunosensor for the determination of TPM in commercial food products was developed. A sandwich-type immunoassay was performed on screen-printed carbon electrodes. Monoclonal and polyclonal antibodies were used to capture and detect TPM. To obtain the analytical signal an alkaline phosphatase-labelled secondary antibody and 3-indoxyl phosphate / silver nitrate (enzymatic substrate) were employed; the enzymatically deposited silver was analysed by linear sweep voltammetry. Using a factorial design, several experimental parameters were optimized: capture antibody 20 µg mL^-1; secondary antibody dilution 1:2,000; alkaline phosphatase-labelled detection antibody dilution 1:40,000; and an assay time of ~3 h. A linear concentration range was established between 2.5 and 20 ng mL^-1 (i_p (µA) = 0,787 × [TPM] (ng mL^-1) + 5,45, n = 5, r = 0,990, V_x0 = 8.8 %) and the LOD was 1.7 ng mL^-1. These performance characteristics will allow the quantification of trace levels of the target allergen in food products.

[1] Laly, S.J, Sankar, T.V., Panda, S.K., Effect of pressure cooking alone and in combination with other treatments on shrimp allergic protein, tropomyosin. Journal of Food Science and Technology (2021). https://doi.org/10.1007/s13197-021-05124-2

The adsorption of a gas on the surface of a semiconducting oxide can induce a significant change in the electrical resistance of the material. This effect is at the basis of the development of chemiresistors for gas detection. Due to their high sensitivity, tunable selectivity, easy production, small dimensions and low cost, they are successfully used in a broad range of applications (pollutant monitoring, food quality control, industrial systems control, medical diagnosis) to detect a large number of gaseous compounds. Despite this, an increasing demand of gas sensors with high performances has been documented. Many actions can be made to improve the sensing performances, such as the synthesis of nanostructures with high specific surface area, the loading with noble metals, but the first issue is to understand the sensing mechanism of the materials and its sensing properties. This work is aimed to determine the sensing mechanism for a variety of semiconducting oxides (single, doped or combined) correlating them with the sensing performances of the sensors. The functional materials (SnO₂ MoO3, WO3, NiO, ZnO, TiO₂, W-Sn mixed oxide, etc.) were prepared and characterized by means of spectroscopic techniques (absorbance FT-IR, diffuse reflectance UV-Vis-NIR) to shed light on the electronic properties and defects involved at the roots of the sensing capability. The spectroscopic responses were studied both for the interaction with pure gases and for mixture pollutant/O2 at different concentrations. Furthermore, the functional materials we deposited on alumina substrates to obtain thick films for electrical characterization and gas sensing measurement. Finally, from the cross analysis of the results a description of the specific sensing mechanism is proposed for each material.

The growing human population and the discovery of new diseases and emerging pandemics have increased the need for healthcare treatments and medications with innovative design. The emergence of nanotechnology provides a platform for novel diagnostic and therapeutic in vivo non-invasive detection and treatment of ailments.

It is now the era of IOT (internet of things) and data acquisition and interpretation from various parts of the human body in real time is possible with interconnected sensors and information transfer devices. Miniaturization, low power consumption and price with compatibility to existing network circuits are essential requirements in IOT. Biosensors made from nanostructured materials are the ideal choice due to the unique structural, chemical, and electronic properties of these materials with the advantage of large surface to volume ratio which makes them very successful for use as sensors for detection of diseases, drug carriers, filters, fillers and reaction catalysts in healthcare applications.

In this mini review, we will review the recent progress made in research and applications of biosensors in health and preventive medicine. The focus of the article will be on biosensors made from layered nanomaterials like graphene and its structural analogs molybdenum disulphide (MoS₂) and boron nitride (BN). We will discuss and highlight the present capabilities of the different Nano forms of these materials in the detection and analysis of diseases. Their efficiency in terms of detection limits, sensitivity and adaptability to different environments will also be discussed. In addition, the challenges and future perspectives of using Nano-biosensors to develop efficient diagnostic, therapeutic and cost effective monitoring devices with smart technologies will be explored.