Combining Chemometrics and Sensors: Toward New Applications in Monitoring and Environmental Analysis

AI in analytical chemistry: Advancements, challenges, and future directions

This article explores the influence and applications of Artificial Intelligence (AI) in analytical chemistry, highlighting its potential to revolutionize the analysis of complex data sets and the development of innovative analytical methods. Additionally, it discusses the role of AI in interpreting large-scale data and optimizing experimental processes. AI has been fundamental in managing heterogeneous data and in advanced analysis of complex spectra in areas such as spectroscopy and chromatography. The article also examines the historical development of AI in chemistry, its current challenges, including the interpretation of AI models and the integration of large volumes of data. Finally, it forecasts future trends and the potential impact of AI on analytical chemistry, emphasizing the need for ethical and secure approaches in the use of AI.

Predicting leachate impact on groundwater using electrical conductivity and oxidation-reduction potential measurements: An empirical and theoretical approach

This study developed innovative predictive models of groundwater pollution using in situ electrical conductivity (EC) and oxidation-reduction potential (ORP) measurements at livestock carcass burial sites. Combined electrode analysis (EC and ORP) and machine learning techniques efficiently and accurately distinguished between leachate and background groundwater. Two models-empirical and theoretical-were constructed based on a supervised classification framework. The empirical model constructs a classifier with high accuracy, sensitivity, and specificity, utilizing the comprehensive in situ EC and ORP measurements. The theoretical model with only two end members achieves comparable performance by simulating the leachate-groundwater interactions using a geochemical mixing model. Besides enhancing the early detection capabilities, our approach considerably reduces the reliance on extensive hydrochemical analyses, thus streamlining the monitoring process. Moreover, the use of field parameters was found to proactively identify potential pollution incidents, enhancing the efficiency of groundwater monitoring strategies. Our approach is applicable to various waste disposal sites, indicating its extensive potential for environmental monitoring and management.

Quantitative Analysis of Trace Analytes with Highly Sensitive SERS Tags on Hydrophobic Interface

Surface-enhanced Raman scattering (SERS) presents a promising avenue for trace matter detection by using plasmonic nanostructures. To tackle the challenges of quantitatively analyzing trace substances in SERS, such as poor enrichment efficiency and signal reproducibility, this study proposes a novel approach using Au@internal standard@Au nanospheres (Au@IS@Au NSs) for realizing the high sensitivity and stability in SERS substrates. To verify the feasibility and stability of the SERS performances, the SERS substrates have exhibited exceptional sensitivity for detecting methyl blue molecules in aqueous solutions within the concentration range from 10-4 M to 10-13 M. Additionally, this strategy also provides a feasible way of quantitative detection of antibiotic in the range of 10-4 M to 10-10 M. Trace antibiotic residue on the surface of shrimp in aquaculture waters was successfully conducted, achieving a remarkably low detection limit of 10-9 M. The innovative approach has great potential for the rapid and quantitative detection of trace substances, which marks a noteworthy step forward in environmental detection and analytical methods by SERS.

A Highly Sensitive and Stretchable Core-Shell Fiber Sensor for Gesture Recognition and Surface Pressure Distribution Monitoring

This work reports a highly-strain flexible fiber sensor with a core-shell structure utilizes a unique swelling diffusion technique to infiltrate carbon nanotubes (CNTs) into the surface layer of Ecoflex fibers. Compared with traditional blended Ecoflex/CNTs fibers, this manufacturing process ensures that the sensor maintains the mechanical properties (923% strain) of the Ecoflex fiber while also improving sensitivity (gauge factor is up to 3716). By adjusting the penetration time during fabrication, the sensor can be customized for different uses. As an application demonstration, the fiber sensor is integrated into the glove to develop a wearable gesture language recognition system with high sensitivity and precision. Additionally, the authors successfully monitor the pressure distribution on the curved surface of a soccer ball by winding the fiber sensor along the ball's surface.

Sensors for Emerging Water Contaminants: Overcoming Roadblocks to Innovation

Ensuring water quality and safety requires the effective detection of emerging contaminants, which present significant risks to both human health and the environment. Field deployable low-cost sensors provide solutions to detect contaminants at their source and enable large-scale water quality monitoring and management. Unfortunately, the availability and utilization of such sensors remain limited. This Perspective examines current sensing technologies for detecting emerging contaminants and analyzes critical barriers, such as high costs, lack of reliability, difficulties in implementation in real-world settings, and lack of stakeholder involvement in sensor design. These technical and nontechnical barriers severely hinder progression from proof-of-concepts and negatively impact user experience factors such as ease-of-use and actionability using sensing data, ultimately affecting successful translation and widespread adoption of these technologies. We provide examples of specific sensing systems and explore key strategies to address the remaining scientific challenges that must be overcome to translate these technologies into the field such as improving sensitivity, selectivity, robustness, and performance in real-world water environments. Other critical aspects such as tailoring research to meet end-users' requirements, integrating cost considerations and consumer needs into the early prototype design, establishing standardized evaluation and validation protocols, fostering academia-industry collaborations, maximizing data value by establishing data sharing initiatives, and promoting workforce development are also discussed. The Perspective describes a set of guidelines for the development, translation, and implementation of water quality sensors to swiftly and accurately detect, analyze, track, and manage contamination.

A single recognition unit-based virtual sensor Array: Applying 3D fluorescence spectroscopy to inner filter effect-based sensing

A convenient, fast, low-cost detection and discrimination method is demanded for environmental monitoring but still it remains more technological challenges. Herein, we demonstrate that the inner filter effect (IFE), in combination with three-dimensional fluorescence spectroscopy, can offer a virtual sensor array (VSA) as apropersolution. And with the aid of pattern recognition techniques, it is feasible to recognize compounds with structural similarities economically and effectively. In this study, with the help of visual clustering plots of principal component analysis (PCA), a prediction model based on hierarchical strategy was made using support vector machine (SVM) method for the qualitative profiling of aromatic pollutants. The VSA was constructed by a single metal-organic framework (MOF) recognition unit (MOF-74 (Zn)) with the excitation wavelength as external regulatory factors. Pattern characteristics of four aromatics with very similar structures (phenylamine, chlorobenzene, nitrobenzene, and phenol), both single analyte and binary mixtures, were acquired. The primary constituents of multi-dimensional spectral signals were subsequently extracted and fed into a vector machine to construct a prediction model through 10-fold cross-validation optimization, resulting in a classification accuracy of 100% for single analytes and 96% for mixtures. Quantitative research has shown that, except for chlorobenzene, all three other analytes can be predicted in concentration within an acceptable error range, and the mixture can be predicted proportionally. Moreover, the VSA can be used to distinguish these pollutants in tap and river water also. We propose for the first time a new tack for the construction of VSA in a general manner, namely using three-dimensional full range fluorescence scanning for IFE based sensing to get multiple times of information resulting from different weak interaction between analyte and sensor for decision-making.

Spatio-temporal evaluation of surface water quality of Tawi watershed in the Himalayan region of Jammu (J&K, UT) using algal pollution indices: a geospatial approach

In the present work, an investigation was performed based on the genera and species stated in Palmer pollution index to show the extent of organic pollution in the surface water of the Tawi watershed in the Jammu province of the Union Territory of Jammu and Kashmir using algal pollution indices. Sampling was carried out for two seasons, pre-monsoon (PRM) and post-monsoon (POM), at 16 locations distributed over the entire Tawi watershed. The physico-chemical variables like water temperature, pH, electrical conductivity, TDS, total alkalinity, total hardness, DO, BOD, COD, nitrate, and phosphate were analyzed. The seasonal distribution of the pollution-tolerant algal genera and species was recorded and the algal pollution index for both genus (AGP index) and species (ASP index) was also calculated. The concentration of BOD, COD, and nitrate in the sampled river water was found to be higher during the PRM season as compared to the POM season. The lower stretch of the watershed (Jammu Sub-Watershed) falls in class IV-V as per the polluted river stretch priority ranking based on BOD levels as BOD levels are >3 mg/L in the downstream locations during both seasons. A total of 23 algal taxa belonging to 8 families, Chlorophyceae (4 algal genera), Cyanophyceae (2 algal genera), Bacillariophyceae (7 algal genera), Zygnematophyceae (3 algal genera), Trebouxiophyceae (2 algal genera), Ulvophyceae (1 algal genus), Mediophyceae (1 algal genus), and Euglenophyceae (3 algal genera), have been reported in the Tawi watershed. The results of the Palmer indices showed a lack of organic pollution in the upstream, varying pollution levels in the midstream, and partially high to very high organic pollution levels in the downstream of the watershed. Comparative temporal analysis of the distribution of pollution-tolerant algal genera and species showed more organic pollution during PRM. Navicula and Cymbella were found to be the most abundant genera in almost all the stations, whereas Ulothrix, Cocconeis, Anacystis, and Crucigenia were the least recorded genera in the entire watershed. The results will enhance the understanding of the health status of the watershed, and provide database for watershed vulnerability assessment for sustainability and watershed management with spatio-temporal improvement.

Review of technology advances to assess rice quality traits and consumer perception

The increase in rice consumption and demand for high-quality rice is impacted by the growth of socioeconomic status in developing countries and consumer awareness of the health benefits of rice consumption. The latter aspects drive the need for rapid, low-cost, and reliable quality assessment methods to produce high-quality rice according to consumer preference. This is important to ensure the sustainability of the rice value chain and, therefore, accelerate the rice industry toward digital agriculture. This review article focuses on the measurements of the physicochemical and sensory quality of rice, including new and emerging technology advances, particularly in the development of low-cost, non-destructive, and rapid digital sensing techniques to assess rice quality traits and consumer perceptions. In addition, the prospects for potential applications of emerging technologies (i.e., sensors, computer vision, machine learning, and artificial intelligence) to assess rice quality and consumer preferences are discussed. The integration of these technologies shows promising potential in the forthcoming to be adopted by the rice industry to assess rice quality traits and consumer preferences at a lower cost, shorter time, and more objectively compared to the traditional approaches.

Microbial degradation and transformation of PPCPs in aquatic environment: A review

The Pharmaceuticals and Personal Care Products (PPCPs) presence at harmful levels has been identified in aquatic ecosystems all over the world. Currently, PPCPs are more common in aquatic regions and have been discovered to be extremely harmful to aquatic creatures. Waste-water treatment facilities are the primary cause of PPCPs pollution in aquatic systems due to their limited treatment as well as the following the release of PPCPs. The degree of PPCPs elimination is primarily determined by the method applied for the remediation. It must be addressed in an eco-friendly manner in order to significantly improve the environmental quality or, at the very least, to prevent the spread as well as effects of toxic pollutants. However, when compared to other methods, environmentally friendly strategies (biological methods) are less expensive and require less energy. Most biological methods under aerobic conditions have been shown to degrade PPCPs effectively. Furthermore, the scientific literature indicates that with the exception of a few extremely hydrophobic substances, biological degradation by microbes is the primary process for the majority of PPCPs compounds. Hence, this review discusses about the optimistic role of microbe concerned in the degradation or transformation of PPCPs into non/less toxic form in the polluted environment. Accordingly, more number of microbial strains has been implicated in the biodegradation/transformation of harmful PPCPs through a process termed as bioremediation and their limitations.

Electrospun nanofibers: promising nanomaterials for biomedical applications

With the rapid development of nanotechnology and nanomaterials science, electrospun nanofibers emerged as a new material with great potential for a variety of applications. Electrospinning is a simple and adaptable process for generation of nanofibers from a viscoelastic fluid using electrostatic repulsion between surface charges. Electrospinning has been used to manufacture nanofibers with low diameters from a wide range of materials. Electrospinning may also be used to construct nanofibers with a variety of secondary structures, including those having a porous, hollow, or core–sheath structure. Due to many attributes including their large specific surface area and high porosity, electrospun nanofibers are suitable for biosensing and environmental monitoring. This book chapter discusses the different methods of nanofiber preparations and the challenges involved, recent research progress in electrospun nanofibers, and the ways to commercialize these nanofiber materials.

Self-organizing map algorithm for assessing spatial and temporal patterns of pollutants in environmental compartments: A review

The evaluation of the spatial and temporal distribution of pollutants is a crucial issue to assess the anthropogenic burden on the environment. Numerous chemometric approaches are available for data exploration and they have been applied for environmental health assessment purposes. Among the unsupervised methods, Self-Organizing Map (SOM) is an artificial neural network able to handle non-linear problems that can be used for exploratory data analysis, pattern recognition, and variable relationship assessment. Much more interpretation ability is gained when the SOM-based model is merged with clustering algorithms. This review comprises: (i) a description of the algorithm operation principle with a focus on the key parameters used for the SOM initialization; (ii) a description of the SOM output features and how they can be used for data mining; (iii) a list of available software tools for performing calculations; (iv) an overview of the SOM application for obtaining spatial and temporal pollution patterns in the environmental compartments with focus on model training and result visualization; (v) advice on reporting SOM model details in a paper to attain comparability and reproducibility among published papers as well as advice for extracting valuable information from the model results is presented.

Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review

Spectroscopic methods deliver a valuable non-destructive analytical tool that provides simultaneous qualitative and quantitative characterization of various samples. Apples belong to the world's most consumed crops and with the current challenges of climate change and human impacts on the environment, maintaining high-quality apple production has become critical. This review comprehensively analyzes the application of spectroscopy in near-infrared (NIR) and visible (Vis) regions, which not only show particular potential in evaluating the quality parameters of apples but also in optimizing their production and supply routines. This includes the assessment of the external and internal characteristics such as color, size, shape, surface defects, soluble solids content (SSC), total titratable acidity (TA), firmness, starch pattern index (SPI), total dry matter concentration (DM), and nutritional value. The review also summarizes various techniques and approaches used in Vis/NIR studies of apples, such as authenticity, origin, identification, adulteration, and quality control. Optical sensors and associated methods offer a wide suite of solutions readily addressing the main needs of the industry in practical routines as well, e.g., efficient sorting and grading of apples based on sweetness and other quality parameters, facilitating quality control throughout the production and supply chain. This review also evaluates ongoing development trends in the application of handheld and portable instruments operating in the Vis/NIR and NIR spectral regions for apple quality control. The use of these technologies can enhance apple crop quality, maintain competitiveness, and meet the demands of consumers, making them a crucial topic in the apple industry. The focal point of this review is placed on the literature published in the last five years, with the exceptions of seminal works that have played a critical role in shaping the field or representative studies that highlight the progress made in specific areas.

Detection of Plastic Granules and Their Mixtures

Chemically pure plastic granulate is used as the starting material in the production of plastic parts. Extrusion machines rely on purity, otherwise resources are lost, and waste is produced. To avoid losses, the machines need to analyze the raw material. Spectroscopy in the visible and near-infrared range and machine learning can be used as analyzers. We present an approach using two spectrometers with a spectral range of 400-1700 nm and a fusion model comprising classification, regression, and validation to detect 25 materials and proportions of their binary mixtures. one dimensional convolutional neural network is used for classification and partial least squares regression for the estimation of proportions. The classification is validated by reconstructing the sample spectrum using the component spectra in linear least squares fitting. To save time and effort, the fusion model is trained on semi-empirical spectral data. The component spectra are acquired empirically and the binary mixture spectra are computed as linear combinations. The fusion model achieves very a high accuracy on visible and near-infrared spectral data. Even in a smaller spectral range from 400-1100 nm, the accuracy is high. The visible and near-infrared spectroscopy and the presented fusion model can be used as a concept for building an analyzer. Inexpensive silicon sensor-based spectrometers can be used.

Multiplex Identification of Post-Translational Modifications at Point-of-Care by Deep Learning-Assisted Hydrogel Sensors

Multiplex detection of protein post-translational modifications (PTMs), especially at point-of-care, is of great significance in cancer diagnosis. Herein, we report a machine learning-assisted photonic crystal hydrogel (PCH) sensor for multiplex detection of PTMs. With closely-related PCH sensors microfabricated on a single chip, our design achieved not only rapid screening of PTMs at specific protein sites by using only naked eyes/cellphone, but also the feasibility of real-time monitoring of phosphorylation reactions. By taking advantage of multiplex sensor chips and a neural network algorithm, accurate prediction of PTMs by both their types and concentrations was enabled. This approach was ultimately used to detect and differentiate up/down regulation of different phosphorylation sites within the same protein in live mammalian cells. Our developed method thus holds potential for POC identification of various PTMs in early-stage diagnosis of protein-related diseases.

Using Machine Learning to make nanomaterials sustainable

Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.

Miniaturized NIR Spectroscopy in Food Analysis and Quality Control: Promises, Challenges, and Perspectives

The ongoing miniaturization of spectrometers creates a perfect synergy with the common advantages of near-infrared (NIR) spectroscopy, which together provide particularly significant benefits in the field of food analysis. The combination of portability and direct onsite application with high throughput and a noninvasive way of analysis is a decisive advantage in the food industry, which features a diverse production and supply chain. A miniaturized NIR analytical framework is readily applicable to combat various food safety risks, where compromised quality may result from an accidental or intentional (i.e., food fraud) origin. In this review, the characteristics of miniaturized NIR sensors are discussed in comparison to benchtop laboratory spectrometers regarding their performance, applicability, and optimization of methodology. Miniaturized NIR spectrometers remarkably increase the flexibility of analysis; however, various factors affect the performance of these devices in different analytical scenarios. Currently, it is a focused research direction to perform systematic evaluation studies of the accuracy and reliability of various miniaturized spectrometers that are based on different technologies; e.g., Fourier transform (FT)-NIR, micro-optoelectro-mechanical system (MOEMS)-based Hadamard mask, or linear variable filter (LVF) coupled with an array detector, among others. Progressing technology has been accompanied by innovative data-analysis methods integrated into the package of a micro-NIR analytical framework to improve its accuracy, reliability, and applicability. Advanced calibration methods (e.g., artificial neural networks (ANN) and nonlinear regression) directly improve the performance of miniaturized instruments in challenging analyses, and balance the accuracy of these instruments toward laboratory spectrometers. The quantum-mechanical simulation of NIR spectra reveals the wavenumber regions where the best-correlated spectral information resides and unveils the interactions of the target analyte with the surrounding matrix, ultimately enhancing the information gathered from the NIR spectra. A data-fusion framework offers a combination of spectral information from sensors that operate in different wavelength regions and enables parallelization of spectral pretreatments. This set of methods enables the intelligent design of future NIR analyses using miniaturized instruments, which is critically important for samples with a complex matrix typical of food raw material and shelf products.

Recent Advances and Applications of Rapid Microbial Assessment from a Food Safety Perspective

Unsafe food is estimated to cause 600 million cases of foodborne disease, annually. Thus, the development of methods that could assist in the prevention of foodborne diseases is of high interest. This review summarizes the recent progress toward rapid microbial assessment through (i) spectroscopic techniques, (ii) spectral imaging techniques, (iii) biosensors and (iv) sensors designed to mimic human senses. These methods often produce complex and high-dimensional data that cannot be analyzed with conventional statistical methods. Multivariate statistics and machine learning approaches seemed to be valuable for these methods so as to "translate" measurements to microbial estimations. However, a great proportion of the models reported in the literature misuse these approaches, which may lead to models with low predictive power under generic conditions. Overall, all the methods showed great potential for rapid microbial assessment. Biosensors are closer to wide-scale implementation followed by spectroscopic techniques and then by spectral imaging techniques and sensors designed to mimic human senses.

IR-Based Novel Device for Real-Time Online Acquisition of Fish Heart ECG Signals

We developed an infrared (IR)-based real-time online monitoring device (US Patent No: US 10,571,448 B2) to quantify heart electrocardiogram (ECG) signals to assess the water quality based on physiological changes in fish. The device is compact, allowing us to monitor cardiac function for an extended period (from 7 to 30 days depending on the rechargeable battery capacity) without function injury and disturbance of swimming activity. The electrode samples and the biopotential amplifier and microcontroller process the cardiac-electrical signals. An infrared transceiver transmits denoised electrocardiac signals to complete the signal transmission. The infrared receiver array and biomedical acquisition signal processing system send signals to the computer. The software in the computer processes the data in real time. We quantified ECG indexes (P-wave, Q-wave, R-wave, S-wave, T-wave, PR-interval, QRS-complex, and QT-interval) of carp precisely and incessantly under the different experimental setup (CuSO₄ and deltamethrin). The ECG cue responses were chemical-specific based on CuSO₄ and deltamethrin exposures. This study provides an additional technology for noninvasive water quality surveillance.

Advances in Optical Sensors for Persistent Organic Pollutant Environmental Monitoring

Optical chemical sensors are widely applied in many fields of modern analytical practice, due to their simplicity in preparation and signal acquisition, low costs, and fast response time. Moreover, the construction of most modern optical sensors requires neither wire connections with the detector nor sophisticated and energy-consuming hardware, enabling wireless sensor development for a fast, in-field and online analysis. In this review, the last five years of progress (from 2017 to 2021) in the field of optical chemical sensors development for persistent organic pollutants (POPs) is provided. The operating mechanisms, the transduction principles and the types of sensing materials employed in single selective optical sensors and in multisensory systems are reviewed. The selected examples of optical sensors applications are reported to demonstrate the benefits and drawbacks of optical chemical sensor use for POPs assessment.

Degradation of Sub-Micrometer Sensitive Polymer Layers of Acoustic Sensors Exposed to Chlorpyrifos Water-Solution

The detection of organophosphates, a wide class of pesticides, in water-solution has a huge impact in environmental monitoring. Acoustic transducers are used to design passive wireless sensors for the direct detection of pesticides in water-solution by using tailored polymers as sensitive layers. We demonstrate by combining analytical chemistry tools that organophosphate molecules strongly alter polymer layers widely used in acoustic sensors in the presence of water. This chemical degradation can limit the use of these polymers in detection of organophosphates in water-solution.

Recent Advances in Chemical Sensors for Soil Analysis: A Review

The continuously rising interest in chemical sensors’ applications in environmental monitoring, for soil analysis in particular, is owed to the sufficient sensitivity and selectivity of these analytical devices, their low costs, their simple measurement setups, and the possibility to perform online and in-field analyses with them. In this review the recent advances in chemical sensors for soil analysis are summarized. The working principles of chemical sensors involved in soil analysis; their benefits and drawbacks; and select applications of both the single selective sensors and multisensor systems for assessments of main plant nutrition components, pollutants, and other important soil parameters (pH, moisture content, salinity, exhaled gases, etc.) of the past two decades with a focus on the last 5 years (from 2017 to 2021) are overviewed.

Passive sampling with suspect screening of polar pesticides and multivariate analysis in river catchments: Informing environmental risk assessments and designing future monitoring programmes

Pollution of surface water by polar pesticides is a major environmental risk, particularly in river catchments where potable water supplies are abstracted. In these cases, there is a need to understand pesticide sources, occurrence and fate. Hence, we developed a novel strategy to improve water quality management at the catchment scale using passive sampling coupled to suspect screening and multivariate analysis. Chemcatcher® passive sampling devices were deployed (14 days) over a 12 month period at eight sites (including a water supply works abstraction site) in the Western Rother, a river catchment in South East England. Sample extracts (n = 197) were analysed using high-resolution liquid chromatography-quadrupole-time-of-flight mass spectrometry and compounds identified against a commercially available database. A total of 128 pesticides from different classes were found. Statistical analysis of the qualitative screening data was used to identify clusters of pesticides with similar spatiotemporal pollution patterns. This enabled pesticide sources and fate to be identified. At the water supply works abstraction site, spot sampling and passive sampling were found to be complementary, however, the passive sampling method in conjunction with suspect screening detected 50 pesticides missed by spot sampling combined with targeted analysis. Geospatial data describing pesticide application rates was found to be poorly correlated to their detection frequency using the Chemcatcher®. Our analysis prioritised 61 pesticides for inclusion in a future water quality risk assessment at the abstraction site. It was also possible to design a seasonal monitoring programme to effectively characterise the spatiotemporal pesticide profiles within the catchment. A work flow of how to incorporate passive sampling coupled to suspect screening into existing regulatory monitoring is proposed. Our novel approach will enable water quality managers to target the mitigation (non-engineered actions) of pesticide pollution within the catchment and hence, to better inform drinking water treatment processes and save on operational costs.

Using an SGB Decision Tree Approach to Estimate the Properties of CRM Made by Biomass Pretreated with Ionic Liquids

The use of ionic liquids (ILs) for biomass pretreatment to produce cellulose-rich materials (CRMs) has been well proven. In this research, due to the wide range of applications and ease of using artificial intelligence procedures, on the basis of the algorithm of stochastic gradient boosting (SGB) decision tree, an artificial intelligence approach is proposed to estimate the properties of cellulose-rich materials (CRMs). That being the case, the dataset of the empirical output values was gathered and was randomly broken down into datasets for testing and training. These results show that the best forecasting tool for calculating the properties of CRMs is the developed model. Furthermore, the accuracy of the databank of the biodiesel target values has been examined. In contrast, the influences of model contributed variables on the output have been examined as a new issue. It reveals that the most influencing variable in determining the properties of CRMs is the cellulose enrichment factor. Therefore, this research provides an innovative and accurate tool for predicting the properties of CRMs and sensitivity investigation on effective parameters to help investigators developing the optimized process.

Ten Years of Lateral Flow Immunoassay Technique Applications: Trends, Challenges and Future Perspectives

The Lateral Flow Immunoassay (LFIA) is by far one of the most successful analytical platforms to perform the on-site detection of target substances. LFIA can be considered as a sort of lab-in-a-hand and, together with other point-of-need tests, has represented a paradigm shift from sample-to-lab to lab-to-sample aiming to improve decision making and turnaround time. The features of LFIAs made them a very attractive tool in clinical diagnostic where they can improve patient care by enabling more prompt diagnosis and treatment decisions. The rapidity, simplicity, relative cost-effectiveness, and the possibility to be used by nonskilled personnel contributed to the wide acceptance of LFIAs. As a consequence, from the detection of molecules, organisms, and (bio)markers for clinical purposes, the LFIA application has been rapidly extended to other fields, including food and feed safety, veterinary medicine, environmental control, and many others. This review aims to provide readers with a 10-years overview of applications, outlining the trends for the main application fields and the relative compounded annual growth rates. Moreover, future perspectives and challenges are discussed.

Interactions between mercury and environmental factors: A chemometric assessment in seafood from an eutrophic estuary in southeastern Brazil

Guanabara Bay (GB) is an estuary in Brazil, constantly the target of pollutants, such as mercury (Hg). Thus, our study aimed to evaluate (i) total mercury (THg) content in shrimp and squid species from GB; (ii) associate THg content to contamination in swimming crabs; (iii) explore potential differences between species, and size; (iv) correlate abiotic water data to the determined THg contents; (v) verify if Hg concentrations are below acceptable limits. Swimming crabs showed greater Hg contamination compared to other species. For shrimp only biometric variables are related to Hg, while for squid, only abiotic. Only squids did not show a correlation between Hg and animal size. Finally, the detected Hg values are below the tolerable limits established by legislations. Our results indicate that the dynamics of Hg contamination differs between groups and that further studies are needed to elucidate the mechanisms that affect bioaccumulation in different species.

Analysis of Pathogenic Bacterial and Yeast Biofilms Using the Combination of Synchrotron ATR-FTIR Microspectroscopy and Chemometric Approaches

Biofilms are assemblages of microbial cells, extracellular polymeric substances (EPS), and other components extracted from the environment in which they develop. Within biofilms, the spatial distribution of these components can vary. Here we present a fundamental characterization study to show differences between biofilms formed by Gram-positive methicillin-resistant Staphylococcus aureus (MRSA), Gram-negative Pseudomonas aeruginosa, and the yeast-type Candida albicans using synchrotron macro attenuated total reflectance-Fourier transform infrared (ATR-FTIR) microspectroscopy. We were able to characterise the pathogenic biofilms' heterogeneous distribution, which is challenging to do using traditional techniques. Multivariate analyses revealed that the polysaccharides area (1200-950 cm^-1) accounted for the most significant variance between biofilm samples, and other spectral regions corresponding to amides, lipids, and polysaccharides all contributed to sample variation. In general, this study will advance our understanding of microbial biofilms and serve as a model for future research on how to use synchrotron source ATR-FTIR microspectroscopy to analyse their variations and spatial arrangements.

Mathematical Modelling of Biosensing Platforms Applied for Environmental Monitoring

In recent years, mathematical modelling has known an overwhelming integration in different scientific fields. In general, modelling is used to obtain new insights and achieve more quantitative and qualitative information about systems by programming language, manipulating matrices, creating algorithms and tracing functions and data. Researchers have been inspired by these techniques to explore several methods to solve many problems with high precision. In this direction, simulation and modelling have been employed for the development of sensitive and selective detection tools in different fields including environmental control. Emerging pollutants such as pesticides, heavy metals and pharmaceuticals are contaminating water resources, thus threatening wildlife. As a consequence, various biosensors using modelling have been reported in the literature for efficient environmental monitoring. In this review paper, the recent biosensors inspired by modelling and applied for environmental monitoring will be overviewed. Moreover, the level of success and the analytical performances of each modelling-biosensor will be discussed. Finally, current challenges in this field will be highlighted.

The Multiomics Analyses of Fecal Matrix and Its Significance to Coeliac Disease Gut Profiling

Gastrointestinal (GIT) diseases have risen globally in recent years, and early detection of the host’s gut microbiota, typically through fecal material, has become a crucial component for rapid diagnosis of such diseases. Human fecal material is a complex substance composed of undigested macromolecules and particles, and the processing of such matter is a challenge due to the unstable nature of its products and the complexity of the matrix. The identification of these products can be used as an indication for present and future diseases; however, many researchers focus on one variable or marker looking for specific biomarkers of disease. Therefore, the combination of genomics, transcriptomics, proteomics and metabonomics can give a detailed and complete insight into the gut environment. The proper sample collection, sample preparation and accurate analytical methods play a crucial role in generating precise microbial data and hypotheses in gut microbiome research, as well as multivariate data analysis in determining the gut microbiome functionality in regard to diseases. This review summarizes fecal sample protocols involved in profiling coeliac disease.

A high-throughput and machine learning resistance monitoring system to determine the point of resistance for Escherichia coli with tetracycline: Combining UV-visible spectrophotometry with principal component analysis

UV-visible spectroscopy (UV-Vis) is routinely used in microbiology as a tool to check the optical density (OD) pertaining to the growth stages of microbial cultures at the single wavelength of 600 nm, better known as the OD₆₀₀ . Typically, modern UV-Vis spectrophotometers can scan in the region of approximately 200-1000 nm in the electromagnetic spectrum, where users do not extend the use of the instrument's full capability in a laboratory. In this study, the full potential of UV-Vis spectrophotometry (multiwavelength collection) was used to examine bacterial growth phases when treated with antibiotics showcasing the ability to understand the point of resistance when an antibiotic is introduced into the media and therefore understand the biochemical changes of the infectious pathogens. A multiplate reader demonstrated a high throughput experiment (96 samples) to understand the growth of Escherichia coli when varied concentrations of the antibiotic tetracycline was added into the well plates. Principal component analysis (PCA) and partial least squares discriminant analysis were then used as the data mining techniques to interpret the UV-Vis spectral data and generate machine learning "proof of principle" for the UV-Vis spectrophotometer plate reader. Results from this study showed that the PCA analysis provides an accurate yet simple visual classification and the recognition of E. coli samples belonging to each treatment. These data show significant advantages when compared to the traditional OD₆₀₀ method where we can now understand biochemical changes in the system rather than a mere optical density measurement. Due to the unique experimental setup and procedure that involves indirect use of antibiotics, the same test could be used for obtaining practical information on the type, resistance, and dose of antibiotic necessary to establish the optimum diagnosis, treatment, and decontamination strategies for pathogenic and antibiotic resistant species.

Nano-plastics and their analytical characterisation and fate in the marine environment: From source to sea

Polymer contamination is a major pollutant in all waterways and a significant concern of the 21st Century, gaining extensive research, media, and public attention. The polymer pollution problem is so vast; plastics are now observed in some of the Earth's most remote regions such as the Mariana trench. These polymers enter the waterways, migrate, breakdown; albeit slowly, and then interact with the environment and the surrounding biodiversity. It is these biodiversity and ecosystem interactions that are causing the most nervousness, where health researchers have demonstrated that plastics have entered the human food chain, also showing that plastics are damaging organisms, animals, and plants. Many researchers have focused on reviewing the macro and micro-forms of these polymer contaminants, demonstrating a lack of scientific data and also a lack of investigation regarding nano-sized polymers. It is these nano-polymers that have the greatest potential to cause the most harm to our oceans, waterways, and wildlife. This review has been especially ruthless in discussing nano-sized polymers, their ability to interact with organisms, and the potential for these nano-polymers to cause environmental damage in the marine environment. This review details the breakdown of macro-, micro-, and nano-polymer contamination, examining the sources, the interactions, and the fates of all of these polymer sizes in the environment. The main focus of this review is to perform a comprehensive examination of the literature of the interaction of nanoplastics with organisms, soils, and waters; followed by the discussion of toxicological issues. A significant focus of the review is also on current analytical characterisation techniques for nanoplastics, which will enable researchers to develop protocols for nanopolymer analysis and enhance understanding of nanoplastics in the marine environment.

Verification of Chromatographic Profile of Primary Essential Oil of Pinus sylvestris L. Combined with Chemometric Analysis

Chromatographic profiles of primary essential oils (EO) deliver valuable authentic information about composition and compound pattern. Primary EOs obtained from Pinus sylvestris L. (PS) from different global origins were analyzed using gas chromatography coupled to a flame ionization detector (GC-FID) and identified by GC hyphenated to mass spectrometer (GC-MS). A primary EO of PS was characterized by a distinct sesquiterpene pattern followed by a diterpene profile containing diterpenoids of the labdane, pimarane or abietane type. Based on their sesquiterpene compound patterns, primary EOs of PS were separated into their geographical origin using component analysis. Furthermore, differentiation of closely related pine EOs by partial least square discriminant analysis proved the existence of a primary EO of PS. The developed and validated PLS-DA model is suitable as a screening tool to assess the correct chemotaxonomic identification of a primary pine EOs as it classified all pine EOs correctly.