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Journal of Chromatography. A Aug 2022Analytical derivatization is a technique that alters the structure of an analyte and produces a product more suitable for analysis. While this process can be... (Review)
Review
Analytical derivatization is a technique that alters the structure of an analyte and produces a product more suitable for analysis. While this process can be time-consuming and add reagents to the procedure, it can also facilitate the isolation of the analyte(s), enhance analytes' stability, improve separation and sensitivity, and reduce matrix interferences. Since derivatization is a functional group analysis, it improves selectivity by separating reactive from neutral compounds during sample preparation. This technique introduces detector-orientated tags into analytes that lack suitable physicochemical properties for detection at low concentrations. Notably, many regulatory bodies, especially those in the environmental field, require these characteristics in analytical methods. This review focuses on note-worthy analytical derivatization methods employed in environmental analyses with functional groups, phenol, carboxylic acid, aldehyde, ketone, and thiol in aqueous, soil, and atmospheric sample matrices. Both advantages and disadvantages of analytical derivatization techniques are discussed. In addition, we discuss the future directions of analytical derivatization methods in environmental analysis and the potential challenges.
Topics: Aldehydes; Carboxylic Acids; Indicators and Reagents; Ketones; Phenols
PubMed: 35901668
DOI: 10.1016/j.chroma.2022.463348 -
Advances in Biochemical... 2022What is the impact of cellular heterogeneity on process performance? How do individual cells contribute to averaged process productivity? Single-cell analysis is a key...
What is the impact of cellular heterogeneity on process performance? How do individual cells contribute to averaged process productivity? Single-cell analysis is a key technology for answering such key questions of biotechnology, beyond bulky measurements with populations. The analysis of cellular individuality, its origins, and the dependency of process performance on cellular heterogeneity has tremendous potential for optimizing biotechnological processes in terms of metabolic, reaction, and process engineering. Microfluidics offer unmatched environmental control of the cellular environment and allow massively parallelized cultivation of single cells. However, the analytical accessibility to a cell's physiology is of crucial importance for obtaining the desired information on the single-cell production phenotype. Highly sensitive analytics are required to detect and quantify the minute amounts of target analytes and small physiological changes in a single cell. For their application to biotechnological questions, single-cell analytics must evolve toward the measurement of kinetics and specific rates of the smallest catalytic unit, the single cell. In this chapter, we focus on an introduction to the latest single-cell analytics and their application for obtaining physiological parameters in a biotechnological context from single cells. We present and discuss recent advancements in single-cell analytics that enable the analysis of cell-specific growth, uptake, and production kinetics, as well as the gene expression and regulatory mechanisms at a single-cell level.
Topics: Biotechnology; Cell Proliferation; Microfluidic Analytical Techniques; Microfluidics; Single-Cell Analysis
PubMed: 32737554
DOI: 10.1007/10_2020_134 -
Annual Review of Analytical Chemistry... Jun 2023This review summarizes the current status of development in photoluminescent probes, multidimensional photoluminescence detection, and multivariate data analysis... (Review)
Review
This review summarizes the current status of development in photoluminescent probes, multidimensional photoluminescence detection, and multivariate data analysis methods. It then highlights reports featuring multivariate analysis of multidimensional measurements of photoluminescent probes published between June 2015 and June 2022, emphasizing work in the last 5 years. Important trends include the development of probe arrays, which provide fingerprint responses to the analyte(s) of interest and facilitate the analysis of complex samples; the application of neural networks and deep learning to pattern recognition and feature selection in photoluminescence images; and the application of multiway multivariate analysis to mining matrices, three-way arrays, and higher-order measurements, including hyperspectral intensity and lifetime images. These examples illustrate the increase in information extraction provided by the combination of multidimensional measurements and multivariate analysis.
PubMed: 37127054
DOI: 10.1146/annurev-anchem-091522-033010 -
ACS Omega Dec 2022Despite a large amount of money being spent on both food analyses and control measures, various food-borne illnesses associated with pathogens, toxins, pesticides,... (Review)
Review
Despite a large amount of money being spent on both food analyses and control measures, various food-borne illnesses associated with pathogens, toxins, pesticides, adulterants, colorants, and other contaminants pose a serious threat to human health, and thus food safety draws considerable attention in the modern pace of the world. The presence of various biogenic amines in processed food have been frequently considered as the primary quality parameter in order to check food freshness and spoilage of protein-rich food. Various conventional detection methods for detecting hazardous analytes including microscopy, nucleic acid, and immunoassay-based techniques have been employed; however, recently, array-based sensing strategies are becoming popular for the development of a highly accurate and precise analytical method. Array-based sensing is majorly facilitated by the advancements in multivariate analytical techniques as well as machine learning-based approaches. These techniques allow one to solve the typical problem associated with the interpretation of the complex response patterns generated in array-based strategies. Consequently, the machine learning-based neural networks enable the fast, robust, and accurate detection of analytes using sensor arrays. Thus, for commercial applications, most of the focus has shifted toward the development of analytical methods based on electrical and chemical sensor arrays. Therefore, herein, we briefly highlight and review the recently reported array-based sensor systems supported by machine learning and multivariate analytics to monitor food safety and quality in the field of food forensics.
PubMed: 36591133
DOI: 10.1021/acsomega.2c05632 -
Materials (Basel, Switzerland) Jul 2021The ability to stimulate mammalian cells with light, brought along by optogenetic control, has significantly broadened our understanding of electrically excitable... (Review)
Review
The ability to stimulate mammalian cells with light, brought along by optogenetic control, has significantly broadened our understanding of electrically excitable tissues. Backed by advanced (bio)materials, it has recently paved the way towards novel biosensing concepts supporting bio-analytics applications transversal to the main biomedical stream. The advancements concerning enabling biomaterials and related novel biosensing concepts involving optogenetics are reviewed with particular focus on the use of engineered cells for cell-based sensing platforms and the available toolbox (from mere actuators and reporters to novel multifunctional opto-chemogenetic tools) for optogenetic-enabled real-time cellular diagnostics and biosensor development. The key advantages of these modified cell-based biosensors concern both significantly faster (minutes instead of hours) and higher sensitivity detection of low concentrations of bioactive/toxic analytes (below the threshold concentrations in classical cellular sensors) as well as improved standardization as warranted by unified analytic platforms. These novel multimodal functional electro-optical label-free assays are reviewed among the key elements for optogenetic-based biosensing standardization. This focused review is a potential guide for materials researchers interested in biosensing based on light-responsive biomaterials and related analytic tools.
PubMed: 34361345
DOI: 10.3390/ma14154151 -
Critical Reviews in Food Science and... 2024Neural network (i.e. deep learning, NN)-based data analysis techniques have been listed as a pivotal opportunity to protect the integrity and safety of the global food... (Review)
Review
Neural network (i.e. deep learning, NN)-based data analysis techniques have been listed as a pivotal opportunity to protect the integrity and safety of the global food supply chain and forecast $11.2 billion in agriculture markets. As a general-purpose data analytic tool, NN has been applied in several areas of food science, such as food recognition, food supply chain security and omics analysis, and so on. Therefore, given the rapid emergence of NN applications in food safety, this review aims to provide a comprehensive overview of the NN application in food analysis for the first time, focusing on domain-specific applications in food analysis by introducing fundamental methodology, reviewing recent and notable progress, and discussing challenges and potential pitfalls. NN demonstrated that it has a bright future through effective collaboration between food specialist and the broader community in the food field, for example, superiority in food recognition, sensory evaluation, pattern recognition of spectroscopy and chromatography. However, major challenges impeded NN extension including void in the food scientist-friendly interface software package, incomprehensible model behavior, multi-source heterogeneous data, and so on. The breakthrough from other fields proved NN has the potential to offer a revolution in the immediate future.
Topics: Neural Networks, Computer; Humans; Food Analysis; Food Safety; Food Technology; Food Supply; Deep Learning
PubMed: 36322538
DOI: 10.1080/10408398.2022.2139217 -
Drug Metabolism Reviews Aug 2021A reliable, rapid, and effective bioanalytical method is essential for the determination of the pharmacokinetic, pharmacodynamic, and toxicokinetic parameters that... (Review)
Review
A reliable, rapid, and effective bioanalytical method is essential for the determination of the pharmacokinetic, pharmacodynamic, and toxicokinetic parameters that inform the safety and efficacy profile of investigational drugs. The overall goal of bioanalytical method development is to elucidate the procedure and operating conditions under which a method can sufficiently extract, qualify, and/or quantify the analyte(s) of interest and/or their metabolites for the intended purpose. Given the difference in the physicochemical properties of small and large molecule drugs, different strategies need to be adopted for the development of an effective and efficient bioanalytical method. Herein, we provide an overview of different sample preparation strategies, analytical platforms, as well as procedures for achieving high throughput for bioanalysis of small and large molecule drugs.
Topics: Drug Discovery; Humans; Mass Spectrometry
PubMed: 34310243
DOI: 10.1080/03602532.2021.1959606 -
Talanta May 2020Quantification and qualification of an analyte of interest in pharmaceutical tablets from different manufacturers/companies are a hard task because of the potential...
Quantification and qualification of an analyte of interest in pharmaceutical tablets from different manufacturers/companies are a hard task because of the potential presence of various interfering molecules. Indeed, the composition of the tablets covers a wide range of interferents which can be even unknown. As a consequence, we propose to determine the concentration of an analyte of interest regardless of the interferents using the concept of universal calibration. Universal calibration paves the way to the quantification of a specific chemical entity in samples with various compositions and different interferents. This is possible by the trilinear structure of analyte's signal. In fact, the second-order advantage resulting from the second-order universal calibration models is exploited. However, a new second-order calibration strategy was conducted in this work using Trilinear Factor Extraction (TFE). A simulated data set was exemplified to highlight the ability of the proposed procedure in order to accurate extraction of the analyte's concentration profile. Additionally, two real data sets were also explored in order to test the TFE method. In the first case, Acetaminophen was quantified using fluorescence spectroscopy in tablets with different formulations from 6 companies. In the second experimental data, a peptide (Valine-Tyrosine-Valine) was successfully quantified in different samples using spectrofluorimetric data. Finally, these real data sets were analyzed by Multivariate Curve resolution - Alternating Least-Squares (MCR-ALS) under non-negativity and trilinearity constraints for the sake of comparison. The calculated Root Mean Square Error of Predictions (RMSEP) of Acetaminophen were 0.028 and 0.026 for the MCR-ALS and TFE models, respectively. On the other hand, for the second experimental data set, the RMSEP were 0.216 and 0.165, respectively. Finally, based on a paired t-test, the results of MCR-ALS and TFE were not significantly different.
PubMed: 32113550
DOI: 10.1016/j.talanta.2020.120787 -
Chemical Science Nov 2022Continuous efforts to produce functional nanomaterials and flexible/stretchable devices have promoted cumbersome, laboratorial, detection processes toward wearable and... (Review)
Review
Continuous efforts to produce functional nanomaterials and flexible/stretchable devices have promoted cumbersome, laboratorial, detection processes toward wearable and portable intelligent sensing approaches. Responding to the challenges of the multiple analytes, mixtures, and complex components of practical samples, sensing array and multivariate analysis techniques have a significant advantage in terms of superior analytical capabilities, , they are convenient, rapid, sensitive and have high-throughput for multi-analyte identification in food safety, clinical diagnoses, and environmental monitoring. Besides traditional molecular design and recognition mechanisms, materials with micro/nano structures also contribute to strong signals, sensitive responses, and novel properties. In this review, through a new perspective of signal amplification for responsive discrimination, we summarize progress in developing sensing arrays based on diverse micro/nanomaterials and their integrated devices for multi-analyte discrimination. An overview of strategies for constructing sensing arrays through various micro or nano building blocks, including 0D nanoparticle assembly and modification, 1D nanowires and fibers, 2D graphene and textiles, is schematized. Then, portable and wearable devices integrating colorimetric sensors or flexible electrochemical electrodes with the newest microelectronic units and circuit boards are presented. Meanwhile, the latest artificial intelligence (AI) algorithms are introduced for massive data analysis in complex biological and environmental systems. With future developments in facile and accurate discrimination for multi-analyte research, extended applications will gear up in various fields.
PubMed: 36382296
DOI: 10.1039/d2sc03750e -
Biomedical Chromatography : BMC Jul 2023The understanding of principles that drive the separation in reversed-phase chromatography plays an important role in the prediction of the elution of solutes in... (Review)
Review
The understanding of principles that drive the separation in reversed-phase chromatography plays an important role in the prediction of the elution of solutes in RP-HPLC. The separation in RP-HPLC is based on the principle of adsorption and partition. In addition, the logP value, the pK value of the drug and chromatographic parameters like mobile phase pH, buffer concentration, organic modifier and mobile phase additives also influence the retention and selectivity of the analyte. It was found that hydrophobic, electrostatic, hydrogen bonding and other specific interactions between the stationary phase and the solutes, along with the hydrophobicity of an analyte molecule (logP), modify the retention behaviour of the analytes. This article gives special attention to the influence of ionization and ion interaction on the separation of analytes. The drug molecules with different logP values containing protonated and deprotonated acids, bases and zwitterions are selected as examples and this article addresses various issues related to the method development, relationships between analyte retention and mobile phase pH and the pK value of the analyte. The advances in this regard, with highlights on topics such as mechanisms of retention and various factors that influence the retention behaviour of analytes, are also updated with suitable examples.
Topics: Chromatography, Reverse-Phase; Chromatography, High Pressure Liquid; Hydrogen-Ion Concentration
PubMed: 35962484
DOI: 10.1002/bmc.5482