Field Of Analytical Chemistry Research Articles

Immunological Analytical Techniques for Cosmetics Quality Control and Process Monitoring

Cosmetics analysis represents a rapidly expanding field of analytical chemistry as new cosmetic formulations are increasingly in demand on the market and the ingredients required for their production are constantly evolving. Each country applies strict legislation regarding substances in the final product that must be prohibited or regulated. To verify the compliance of cosmetics with current regulations, official analytical methods are available to reveal and quantitatively determine the analytes of interest. However, since ingredients, and the lists of regulated/prohibited substances, rapidly change, dedicated analytical methods must be developed ad hoc to fulfill the new requirements. Research focuses on finding innovative techniques that allow a rapid, inexpensive, and sensitive detection of the target analytes in cosmetics. Among the different methods proposed, immunological techniques are gaining interest, as they make it possible to carry out low-cost analyses on raw materials and finished products in a relatively short time. Indeed, immunoassays are based on the specific and selective antibody/antigen reaction, and they have been extensively applied for clinical diagnostic, alimentary quality control and environmental security purposes, and even for routine analysis. Since the complexity and variability of the matrices, as well as the great variety of compounds present in cosmetics, are analogous with those from food sources, immunological methods could also be applied successfully in this field. Indeed, this would provide a valid approach for the monitoring of industrial production chains even in developing countries, which are currently the greatest producers of cosmetics and the major exporters of raw materials. This review aims to highlight the immunological techniques proposed for cosmetics analysis, focusing on the detection of prohibited/regulated compounds, bacteria and toxins, and allergenic substances, and the identification of counterfeits.

A compound post-column re-focusing approach in supercritical fluid chromatography

Supercritical-fluid chromatography (SFC) is regaining popularity in various fields of analytical chemistry owning to significant advances in instrumentation made in the past decade. However, due to the CO2 based mobile phase and the high flow rates often employed, detection of trace amounts of analytes and coupling with certain detectors or other chromatography techniques are still difficult under many circumstances. In this study we propose a post-column re-focusing approach for SFC analysis to achieve not only signal enhancement in UV-Vis detection, but also actual concentration enhancement of the analyte. By heart-cutting and transporting a selected fraction from the SFC flow into a trapping column with a flushing solvent, re-focusing of the collected analytes can be achieved by re-mobilization with another solvent once the depressurized CO2 is eliminated. By carefully selecting the trapping stationary phase and the two solvents, signal-enhancement ratios between 2.2 and 6.4 were realized for four representative compounds eluting with very different percentages of SFC modifier (methanol). The actual concentration enhancement was lower (ratios between 1.7 to 2.9), because the UV response of the analytes was found to differ significantly under SFC and LC conditions.

Time-Resolved ATR-FTIR Spectroscopy and Macro ATR-FTIR Spectroscopic Imaging of Inorganic Treatments for Stone Conservation.

In this study, the novel application of ATR–FTIR spectroscopy and macro ATR–FTIR spectroscopic imaging overcame an analytical challenge in conservation science: the time-resolved, chemical, and spatial investigation of the reaction of inorganic treatments for stone conservation (ammonium oxalate, AmOx; ammonium phosphate, DAP) occurring in water-based solutions. The aim was to (1) assess the composition and localization of reaction products and their phase variation during the reaction in real time and directly in an aqueous environment and (2) investigate the reaction of AmOx and DAP with calcite and the transformations induced to the substrate with a time-resolved approach. The new analytical results showed that for both treatments, the formation of new crystalline phases initiated at the early stages of the reaction. Their composition changed during the treatment and led to more stable phases. The reactivity of the stone substrate to the treatments varied as a function of the stone material features, such as the specific surface area. A clear influence of post-treatment rinsing on the final composition of reaction phases was observed. Above all, our research demonstrates the actual feasibility, practicality, and high potential of an advanced ATR–FTIR spectroscopic approach to investigate the behavior of conservation treatments and provided new analytical tools to address the choices of conservation in pilot worksites. Lastly, this study opens novel analytical perspectives based on the new possible applications of ATR–FTIR spectroscopic imaging in the field of conservation science, materials science, and analytical chemistry.

A fluorescent sensor recognized by the FA1 site for highly sensitive detection of HSA

Human serum albumin (HSA), as the most abundant protein in blood plasma, plays a crucial role in many physiological processes. The abnormal HSA level in serum or in urine is often associated with various diseases. Therefore, to achieve highly sensitive and selective quantification of HSA is of great importance for disease diagnosis and preventive medicine. Herein, an HSA-selective light-up fluorescent sensor, DCM-ML, was successfully developed for quantitative detection of HSA. DCM-ML exhibited good (photo-) stability and strong fluorescence enhancement around 630 nm in the presence of HSA in complex samples containing numerous biological analytes. Upon addition of HSA into DCM-ML containing solution, a good linear relationship (R2 > 0.99) between the fluorescence intensity of DCM-ML and HSA concentration from 0 to 0.08 mg/mL was obtained with the detection limit of 0.25 μg/mL. The sensing mechanism of the sensor towards HSA was demonstrated to be via recognition in the fatty acid site 1 (FA1), instead of the most reported binding sites (Sudlow I and II) in HSA, for the first time, by both the displacement experiments and molecular docking simulation. Thus, DCM-ML can also be assumed as a potential FA1 site-binding marker for examining drugs binding to the FA1 site in HSA. At last, the utilization of sensor DCM-ML for quantification and validation of HSA in urine samples and cell culture medium was effectively demonstrated. Therefore, the development of DCM-ML should find great application potentials in the fields of analytical chemistry and clinical medicine as a highly sensitive HSA sensor.

Sequential storage and release of microdroplets

Droplet microfluidic methods have opened up the possibility of studying a plethora of phenomena ranging from biological to physical or chemical processes at ultra low volumes and high throughput. A key component of such approaches is the ability to trap droplets for observation, and many device architectures for achieving this objective have been developed. A challenge with such approaches is, however, recovering the droplets following their confinement for applications involving further analysis. Here, we present a device capable of generating, confining and releasing microdroplets in a sequential manner. Through a combination of experimental and computational simulations, we shed light on the key features required for successful droplet storage and retrieval. Moreover, we explore the effect of the flow rate of the continuous phase on droplet release, determining that a critical rate is needed to ensure complete droplet deformation through constrictions holding the droplets in place prior to release. Finally, we find that once released, droplets can be retrieved and collected off chip. The ability to generate, store and sequentially release droplets renders such a device particularly promising for future applications where reactions may not only be monitored on-chip, but droplets can also be retrieved for further analysis, facilitating new exploratory avenues in the fields of analytical chemistry and biology.

Use of green alternative solvents in dispersive liquid-liquid microextraction: A review.

Dispersive liquid-liquid microextraction is one of the most widely used microextraction techniques currently in the analytical chemistry field, mainly due to its simplicity and rapidity. The operational mode of this approach has been constantly changing since its introduction, adapting to new trends and applications. Most of these changes are related to the nature of the solvent employed for the microextraction. From the classical halogenated solvents (e.g., chloroform or dichloromethane), different alternatives have been proposed in order to obtain safer and non-pollutants microextraction applications. In this sense, low-density solvents, such as alkanols, switchable hydrophobicity solvents, and ionic liquids were the first and most popular replacements for halogenated solvents, which provided similar or better results than these classical dispersive liquid-liquid microextraction solvents. However, despite the good performances obtained with low-density solvents and ionic liquids, researchers have continued investigating in order to obtain even greener solvents for dispersive liquid-liquid microextraction. For that reason, in this review, the evolution over the last five years of the three types of solvents already mentioned and two of the most promising solvent alternatives (i.e., deep eutectic solvents and supramolecular solvents), have been studied in detail with the purpose of discussing which one provides the greenest alternative.

Open-source lab hardware: Low noise adjustable two-stage gain transimpedance amplifier with DC offset for low-light detection

An open-source, low noise, low cost, and tunable transimpedance amplifier is presented. The compact circuit board requires few parts and costs less than $65 USD. The transimpedance amplifier is intended for low-light detection and operation with commercial photomultiplier tubes (PMTs). It provides a much more cost-effective acquisition tool compared to competitive products on the market. The system can easily be assembled and modified to suit specific current sensing applications. The amplifier features two variable gains and an adjustable DC offset to optimize dynamic range and suppress potential bias in the signal. With a target bandwidth of DC to 2 Hz and fourth-order Sallen-Key cutoff, the design is ideally suited for various applications in the field of analytical chemistry and biology, such as laser-induced fluorescence detection or chemiluminescence measurements.

Research progress in pipette tip solid-phase extraction technology and its application

<p indent=0mm>Micro-extraction technology has developed rapidly in the field of analytical chemistry, and has attracted widespread attention because of its advantages of miniaturization, automation, environmentally friendly and easy to combine with analytical instruments. Pipette tip solid-phase extraction (PT-SPE), as a new sample pretreatment technology, integrates advantages of consuming less adsorbent and solvent, small bed volume, easy operation and good versatility. It has been widely used in the field of environment, food and biological samples, <italic>etc</italic>. This review overviews the categories of adsorbents, different types and diversified configurations of PT-SPE, summarizes the technique of PT-SPE devices coupling with different analytical instruments, and prospects further trends and prospects in this field.

Machine Learning with Explainable Artificial Intelligence Vision for Characterization of Solution Conductivity Using Optical Emission Spectroscopy of Plasma in Aqueous Solution

AbstractThis study presents an explainable artificial intelligence (XAI) vision for optical emission spectroscopy (OES) of plasma in aqueous solution. We aim to characterize the plasma and OES with XAI. Trained with 18000 spectra, a multilayer artificial neural network (ANN) model accurately predicted the solution conductivity. Local interpretable model‐agnostics explanations (LIME), an XAI method, interpreted the model through perturbing spectral features and fitting the feature contribution with a linear model. LIME showed that OH, Hγ, and Hβ emission lines were critical to the model, differing from the lines typically selected by humans. The results demonstrated that machine captured the spectral features neglected by humans. We believe using XAI for plasma OES analysis impacts the fields of plasma and analytical chemistry.

The Obtainment Process of Biopolymer Coated NPK Fertilizer and its Comparison with Analog on Berry Plants

The transition to a highly productive and environmentally friendly agricultural sector, the development and implementation of systems for the rational use of mineral fertilizers for obtaining safe and high-quality, including functional, food products is one of the main tasks allocated by the President of the Russian Federation V. V. Putin. Due to this decree, the theory of mineral nutrition has received rapid development in recent decades within scientific and technological progress in the field of analytical chemistry and physics, molecular biology, and genetics. Information on various physiological and biochemical functions of plant nutrients has been obtained, and the regularities of the distribution of elements in various parts of plants and fruits (berries) have been also generalized. The main forms of nutrients in the soil and the factors determining the bioavailability of macro and micronutrients have been identified. The aim of this research paper is to produce a biodegradable fertilizer where Azofoska Universal (N16: P16: K16) was uniformly coated within layer size by water-soluble biopolymer through continuous flow reactor V-star. The obtained samples were further tested and compared to well-known American analog slow-released fertilizer TAGROW on berry plants under greenhouse conditions within, berry root plant volume, stress resistance of plants, yield, the resulting fruits were studied for sugar levels using an RHB-32ATC refractometer. Moreover, infrared spectroscopy data for obtained biopolymer coated Azafoska Universal is illustrated in this research.

Logarithmic Data Processing Can Be Used Justifiably in the Plotting of a Calibration Curve.

The article is a response to a recent opinion piece that log concentration values should not be applied in analytical chemistry. An essential aim in the development of analytical chemistry methods is to obtain more sensitive and accurate detection values. For the application of chemical analysis methods, the obtained experiment data need to fit with the mathematical functions in the first place. As influenced by different detection principles and analytical methods, data can be displayed in a coordinate system with two linear axes for linear function fitting, or the data can first be taken through a logarithmic transformation and then for function fitting. Using raw data or data after logarithmic transformation primarily depends on analytical principles, without special rules of data formats. For example, ultraviolet-visible spectrophotometric data are more suitable for direct linear fitting. However, enzyme-catalyzed reaction or electrochemical data in logarithmic form are more appropriate for function fitting. This transformation of data form will not affect the soundness of fit statistics; rather, it simplifies the complexity of function analysis and calculation, which are the essence of analytical chemistry. In this brief article, we provide justification and legitimacy of the application of logarithmic processing in various fields of quantitative analytical chemistry.

One molecule to light it all: The era of dual-state emission

One molecule to light it all: The era of dual-state emission

Separable surface enhanced Raman spectroscopy sensor platformed by HPTLC for facile screening of malachite green in fish

Facile and effective coupling of SERS sensor to chromatographic tools has been an attractive goal in the field of food analytical chemistry for years. Platformed by HPTLC, a separable SERS sensor was developed in this study and its analytical power was exemplarily evaluated by screening trace malachite green in fish. To enable enough detectability and reproducibility of SERS measurements, a flexible substrate based on the hybrid of AgNP and glass fiber paper was easily fabricated. Meanwhile, the crude extract of fish muscle was separated on the aluminum foil backed HPTLC plate, resulting in spiral isolation of the analyte from co-extracted matrix. Guided by the visible standard in parallel, bands of interest were thereafter transferred to the eluent by a handy puncher. Jointly with the as-prepared flexible substrate, the band eluent was measured by Raman spectroscopy, responding sensitive and characteristic fingerprint signals. Meanwhile, performances of the established SERS sensor were also examined, showing adequate detectability (LOD < 10 μg/kg), precision (RSD%<11.8%) and linearity (R2 = 0.9975 from 0.05 to 10 ng/band) for screening purpose. Apart from that, the analysis robustness for practical application was further validated with fish sample, giving spike-recovery rates from 78.8 to 87.3%. This work demonstrated that HPTLC platformed SERS sensor can reach an ideal balance between efficiency, throughput and reliability, and therefore might be a promising tool especially suitable for screening trace contaminants in sophisticated mixtures.

Highly Sensitive, Naked Eye, and On-Spot Detection of pH Change Using Novel Phenothiazine Based Schiff Base

Background: Visual pH sensors have received significant attention in the fields of environmental monitoring, analytical chemistry, food safety and biomedicine. Therefore, a single organic moiety showing both naked-eye and fluorescence sensing of pH change are rare and in higher demands. Objective: A novel phenothiazine based Schiff base PTz-SB was synthesized via facile organic transformations. This molecule showed promising naked-eye and on-spot pH sensing application, both using UV-visible and fluorescence techniques. Method: The novel phenothiazine based Schiff base was synthesized via facile organic transformation. The Schiff base was applied for both naked eye and fluorescence sensing of pH, using UV-visible and fluorescence techniques, respectively. Result: A Redshifts of 27 nm and 80 nm were observed in the λmax of absorption and emission spectra, respectively, on changing the pH from 12.2 to 1. The naked-eye pH sensing may be attributed to the change in colour from blue (higher pH) to dark green (lower pH), under daylight conditions. Furthermore, the change in fluorescence spectra is more pronounced. The fluorescence colour of the compound changes from dark blue to green and then finally to orange, on changing the pH from 12.2 to 4 to 1.5, respectively. Moreover, the electrochemical studies of the Schiff base were also procured. The bandgap obtained from the cyclic voltammetry studies was found to be 2.04 eV, which is characteristic of green emission. Conclusion: The easily synthesizable novel Schiff base can be utilized for real-life, on-spot practical application of pH sensing, which does not require sophisticated analytical instruments. Moreover, the time and cost of detection of pH using this thermally robust Schiff base are also very promising.

The Role of Analytical Chemistry in Agrofood

The State of Food Security and Nutrition in the World program for sustainable development presents a transformative vision, recognizing that our planet is changing, bringing with it new challenges that must be overcome if we want to live in a world without hunger, food insecurity, and malnutrition, in any of its forms. At the same time, there is a direct relationship between the quality of food and health. The production of high-quality food in high quantities is an emerging concern. However, the challenges are enormous, as demonstrated by the following: i) there is a demand for food production, occurring amid a declining rural labor force; ii) there is an increasing amount of raw material production (e.g., biomass generated) for a market that has been only mildly explored; iii) there is a lack of simpler and cheaper analytical alternatives to apply in undeveloped countries, whose developments is highly dependent of agriculture; and iv) there is the necessity to adopt more efficient and sustainable production methods that are adapted to climate change. On almost all these fronts, the field of analytical chemistry has a lot to contribute.

Sensitivity and generalized analytical sensitivity expressions for quantitative analysis using convolutional neural networks

In recent years, convolutional neural networks and deep neural networks have been used extensively in various fields of analytical chemistry. The use of these models for calibration tasks has been highly effective; however, few reports have been published on their properties and characteristics of analytical figures of merit. Currently, most performance measures for these types of networks only incorporate some function of prediction error. While useful, these measures are incomplete and cannot be used as an objective comparison among different models. In this report, a new method for calculating the sensitivity of any type of neural network is proposed and studied on both simulated and real datasets. Generalized analytical sensitivity is defined and calculated for neural networks as an additional figure of merit. Moreover, the dependence of convolutional neural networks on regularization dataset size is studied and compared with other conventional calibration methods.

A novel small molecular liquid crystal catalytic amplification-nanogold SPR aptamer absorption assay for trace oxytetracycline

Liquid crystals (LCs) have been applied for a long time in the field of analytical chemistry. To date, there are no reports about utilization of LCs as the catalyst to amplification analytical signal. In this article, three small molecules LCs in water aqueous solutions were characterized using molecular spectra and particle size analysis. The characterization indicated that there are nanoparticles in the system. Among the them, 4-heptylbenzoic acid (HPB) exhibits the most sensitive performance in the analytical system based on the reduction of HAuCl4 to gold nanoparticles (AuNPs) by NaH2PO2 by the spectrophotometric slope evaluation procedure. As the concentration of LCs catalyst increases, the AuNPs surface plasmon resonance (SPR) absorption peak at 550 nm increases linearly, that can be utilized to amply the absorption signal. Based on the LCs catalytic amplification reaction and immunoreaction, a new SPR spectrophotometric analysis method was developed for the label-free detection of oxytetracycline, with a detection limit of 0.50 ng/mL. The method was also successfully applied for the detection of oxytetracycline-spiked environmental water samples to demonstrate its practical usefulness.

Data Mining, Machine Learning, Deep Learning, Chemometrics. Definitions, common points and Trends (Spoiler Alert: VALIDATE your models!)

Concepts like Machine Learning, Data Mining or Artificial Intelligence have become part of our daily life. This is mostly due to the incredible advances made in computation (hardware and software), the increasing capabilities of generating and storing all types of data and, especially, the benefits (societal and economical) that generate the analysis of such data. Simultaneously, Chemometrics has played an important role since the late 1970s, analyzing data within natural science (and especially in Analytical Chemistry). Even with the strong parallelisms between all of the abovementioned terms and being popular with most of us, it is still difficult to clearly define or differentiate the meaning of Machine Learning, Data Mining, Artificial Intelligence, Deep Learning and Chemometrics. This manuscript brings some light to the definitions of Machine Learning, Data Mining, Artificial Intelligence and Big Data Analysis, defines their application ranges and seeks an application space within the field of analytical chemistry (a.k.a. Chemometrics). The manuscript is full of personal, sometimes probably subjective, opinions and statements. Therefore, all opinions here are open for constructive discussion with the only purpose of Learning (like the Machines do nowadays).

PhotoATRP Approach to Poly(methyl methacrylate) with Aggregation-Induced Emission

In the past decades, polymeric fluorescent materials with aggregation-induced emission (AIE) properties play important roles in the fields of biomedical and analytical chemistry, and the synthesis of well-defined polymers under mild conditions is in demand. In this report, narrowly distributed poly(methyl methacrylate)s (PMMAs) with AIE properties were prepared in mild conditions using 4-(1,2,2-triphenylvinyl)benzyl 2-bromo-2-phenylacetate (TPE-BPA), a novel initiator with an AIE-based functional group. This approach resulted in PMMAs with a fluorescent tag at the chain end (TPE-PMMA). Specifically, a high efficiency of initiation was observed using a low amount of air-stable copper(II) bromide/tris(2-pyridylmethyl)amine (CuIIBr2/TPMA) as a photocatalyst under the irradiation of LED@405 nm. Moreover, the TPE presented in the system did not affect the polymerization kinetics of MMA and the temporal control. The dispersities of the obtained PMMAs were as low as 1.12. Interestingly, TPE-PMMA with narrow dispersity was also obtained (Mw/Mn < 1.25) in the presence of air. Regarding the fluorescent properties of TPE-PMMAs, it was observed that their AIE intensities increased with the molecular weights. Moreover, they could also be used to probe the increased viscosity of the solution, indicating their potential of synthesizing functional sensors. The approach proposed in this work is convenient to prepare poly(methacrylate)s with well-defined structures in the presence of AIE-based fluorescent tags, and it thus paves a new way for the rational design of AIE-functionalized polymeric materials.

AI in Measurement Science.

Measurement of biological systems containing biomolecules and bioparticles is a key task in the fields of analytical chemistry, biology, and medicine. Driven by the complex nature of biological systems and unprecedented amounts of measurement data, artificial intelligence (AI) in measurement science has rapidly advanced from the use of silicon-based machine learning (ML) for data mining to the development of molecular computing with improved sensitivity and accuracy. This review presents an overview of fundamental ML methodologies and discusses their applications in disease diagnostics, biomarker discovery, and imaging analysis. We next provide the working principles of molecular computing using logic gates and arithmetical devices, which can be employed for in situ detection, computation, and signal transduction for biological systems. This review concludes by summarizing the strengths and limitations of AI-involved biological measurement in fundamental and applied research.