Chemometrics

Phenotypic profiling based on body fluid traces discovered at the scene of crime: Raman spectroscopy of urine stains for race differentiation

Modern criminal investigations heavily rely on trace bodily fluid evidence as a rich source of DNA. DNA profiling of such evidence can result in the identification of an individual if a matching DNA profile is available. Alternatively, phenotypic profiling based on the analysis of body fluid traces can significantly narrow down the pool of suspects in a criminal investigation. Urine stain is a frequently encountered specimen at the scene of crime. Raman spectroscopy offers great potential as a universal confirmatory method for the identification of all main body fluids, including urine. In this proof-of-concept study, Raman spectroscopy combined with advanced statistics was used for race differentiation based on the analysis of urine stains. Specifically, a Random Forest (RF) model was built, which allowed for differentiating Caucasian (CA) and African American (AA) descent donors with 90% accuracy based on Raman spectra of dried urine samples. Raman spectra were collected from samples of 28 donors varying in age and sex. This novel technology offers great potential as a universal forensic tool for phenotypic profiling of a potential suspect immediately at the scene of a crime, providing invaluable information for a criminal investigation.

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.

A Slug Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction Optimization

Flow processing offers many opportunities to optimize reactions in a rapid and automated manner, yet often requires relatively large quantities of input materials. To combat this, the use of a flexible slug flow reactor, equipped with two analytical instruments, for low-volume optimization experiments are reported. A Buchwald-Hartwig amination toward the drug olanzapine, with 6 independent optimizable variables, is optimized using three different automated approaches: self-optimization, design of experiments, and kinetic modeling. These approaches are complementary and provide differing information on the reaction: pareto optimal operating points, response surface models, and mechanistic models, respectively. The results are achieved using <10% of the material that would be required for standard flow operation. Finally, a chemometric model is built utilizing automated data handling and three subsequent validation experiments demonstrate good agreement between the slug flow reactor and a standard (larger scale) flow reactor.

Towards the rapid detection of haze-forming proteins

Protein haze in white wine can be a serious quality defect because consumers perceive hazy wines as "spoiled". Unfortunately, a specific method for the detection, or selective treatment, of such proteins in affected wines does not exist. Herein we investigate on the development of an easy-to-use sensor device that allows detection of haze-forming proteins (HFPs). Such a device is expected to overcome the limitations of the "heat test" currently used to assess the protein content in wine and the amount of bentonite needed to remove such proteins. To this aim, three different approaches were explored. Firstly, an impedimetric immunosensor against chitinases was developed and its performance assessed. Secondly, the exploitation of the dual role of HFPs as biorecognition element and analyte to develop an impedimetric biosensor was evaluated, in what can be considered a very unique strategy, representing a new paradigm in biosensing. Lastly, Fourier transform infrared (FT-IR) spectra were collected for various wine samples and chemometric tools such as discrete wavelet transform (DWT) and artificial neural networks (ANNs) were used to achieve the quantification of HFPs. Detection of HFPs at the μg/L level was achieved with both impedimetric biosensors, whereas the FT-IR-based approach allowed their quantification at the mg/L level in wine samples directly. The sensitivity of the developed methods may enable the rapid assessment of wine protein content.

Quantitative Analysis of Acetone in Transformer Oil Based on ZnO NPs@Ag NWs SERS Substrates Combined with a Stoichiometric Model

Acetone is an essential indicator for determining the aging of transformer insulation. Rapid, sensitive, and accurate quantification of acetone in transformer oil is highly significant in assessing the aging of oil-paper insulation systems. In this study, silver nanowires modified with small zinc oxide nanoparticles (ZnO NPs@Ag NWs) were excellent surface-enhanced Raman scattering (SERS) substrates and efficiently and sensitively detected acetone in transformer oil. Stoichiometric models such as multiple linear regression (MLR) models and partial least square regressions (PLS) were investigated to quantify acetone in transformer oil and compared with commonly used univariate linear regressions (ULR). PLS combined with a preprocessing algorithm provided the best prediction model, with a correlation coefficient of 0.998251 for the calibration set, 0.997678 for the predictive set, a root mean square error in the calibration set (RMSECV = 0.12596 mg/g), and a prediction set (RMSEP = 0.11408 mg/g). For an acetone solution of 0.003 mg/g, the mean absolute percentage error (MAPE) was the lowest among the three quantitative models. For a concentration of 7.29 mg/g, the MAPE was 1.60%. This method achieved limits of quantification and detections of 0.003 mg/g and 1 μg/g, respectively. In general, these results suggested that ZnO NPs@Ag NWs as SERS substrates coupled with PLS simply and accurately quantified trace acetone concentrations in transformer oil.

Mechanistic insight into differential interactions of iron oxide nanoparticles with native, glycated albumin and their effect on erythrocytes parameters

Nanoparticles and protein bioconjugates have been studied for multiple biomedical applications. We sought to investigate the interaction and structural modifications of bovine serum albumin (BSA) with iron oxide nanoparticles (IONPs). The IONPs were green synthesized using E. crassipes aqueous leaf extract following characterization using transmission electron microscopy, energy dispersive X-ray analysis and X-Ray Diffraction. Two different concentrations of native/glycated albumin (0.5 and 1.5 mg/ml) with IONPs were allowed to interact for 1 h at 37 °C. Glycation markers, protein modification markers, cellular antioxidant, and hemolysis studies showed structural modifications and conformational changes in albumin due to the presence of IONPs. UV-Visible absorbance resulted in hyperchromic and bathochromic effects of IONPs-BSA conjugates. Fluorescence measurements of tyrosine, tryptophan, advanced glycated end products, and ANS binding assay were promising and quenching effects proved IONPs-BSA conjugate formation. In FTIR of BSA-IONPs, transmittance was increased in amide A and B bands while decreased in amide I and II bands. In summary, native PAGE, HPLC, and FTIR analysis displayed a differential behaviour of IONPs with native and glycated BSA. These results provided an understanding of the interaction and structural modifications of glycated and native BSA which may provide fundamental repercussions in future studies.

Authentication of Herbal Medicines Based on Modern Analytical Technology Combined with Chemometrics Approach: A Review

Since ancient times, herbal medicines (HMs) have been widely popular with consumers as a "natural" drug for health care and disease treatment. With the emergence of problems, such as increasing demand for HMs and shortage of resources, it often occurs the phenomenon of shoddy exceed and mixing the false with the genuine in the market. There is an urgent need to evaluate the quality of HMs to ensure their important role in health care and disease treatment, and to reduce the possibility of threat to human health. Modern analytical technology is can be analyzed for analyzing chemical components of HMs or their preparations. Reflecting complex chemical components' characteristic curves in the analysis sample, and the comprehensive effect of active ingredients of HMs. In this review, modern analytical technology (chromatography, spectroscopy, mass spectrometry), chemometrics methods (unsupervised, supervised) and their advantages, disadvantages, and applicability were introduced and summarized. In addition, the authentication application of modern analytical technology combined with chemometrics methods in four aspects, including origin, processing methods, cultivation methods, and adulteration of HMs have also been discussed and illustrated by a few typical studies. This article offers a general workflow of analytical methods that have been applied for HMs authentication and explains that the accuracy of authentication in favor of the quality assurance of HMs. It was provided reference value for the development and application of modern HMs.

Fraud Detection of Herbal Medicines Based on Modern Analytical Technologies Combine with Chemometrics Approach: A Review

Fraud in herbal medicines (HMs), commonplace throughout human history, is significantly related to medicinal effects with sometimes lethal consequences. Major HMs fraud events seem to occur with a certain regularity, such as substitution by counterfeits, adulteration by addition of inferior production-own materials, adulteration by chemical compounds, and adulteration by addition of foreign matter. The assessment of HMs fraud is in urgent demand to guarantee consumer protection against the four fraudulent activities. In this review, three analysis platforms (targeted, non-targeted, and the combination of non-targeted and targeted analysis) were introduced and summarized. Furthermore, the integration of analysis technology and chemometrics method (e.g., class-modeling, discrimination, and regression method) have also been discussed. Each integration shows different applicability depending on their advantages, drawbacks, and some factors, such as the explicit objective analysis or the nature of four types of HMs fraud. In an attempt to better solve four typical HMs fraud, appropriate analytical strategies are advised and illustrated with several typical studies. The article provides a general workflow of analysis methods that have been used for detection of HMs fraud. All analysis technologies and chemometrics methods applied can conduce to excellent reference value for further exploration of analysis methods in HMs fraud.

Retrieval of aboveground crop nitrogen content with a hybrid machine learning method

Hyperspectral acquisitions have proven to be the most informative Earth observation data source for the estimation of nitrogen (N) content, which is the main limiting nutrient for plant growth and thus agricultural production. In the past, empirical algorithms have been widely employed to retrieve information on this biochemical plant component from canopy reflectance. However, these approaches do not seek for a cause-effect relationship based on physical laws. Moreover, most studies solely relied on the correlation of chlorophyll content with nitrogen, and thus neglected the fact that most N is bound in proteins. Our study presents a hybrid retrieval method using a physically-based approach combined with machine learning regression to estimate crop N content. Within the workflow, the leaf optical properties model PROSPECT-PRO including the newly calibrated specific absorption coefficients (SAC) of proteins, was coupled with the canopy reflectance model 4SAIL to PROSAIL-PRO. The latter was then employed to generate a training database to be used for advanced probabilistic machine learning methods: a standard homoscedastic Gaussian process (GP) and a heteroscedastic GP regression that accounts for signal-to-noise relations. Both GP models have the property of providing confidence intervals for the estimates, which sets them apart from other machine learners. Moreover, a GP-based sequential backward band removal algorithm was employed to analyze the band-specific information content of PROSAIL-PRO simulated spectra for the estimation of aboveground N. Data from multiple hyperspectral field campaigns, carried out in the framework of the future satellite mission Environmental Mapping and Analysis Program (EnMAP), were exploited for validation. In these campaigns, corn and winter wheat spectra were acquired to simulate spectral EnMAP data. Moreover, destructive N measurements from leaves, stalks and fruits were collected separately to enable plant-organ-specific validation. The results showed that both GP models can provide accurate aboveground N simulations, with slightly better results of the heteroscedastic GP in terms of model testing and against in situ N measurements from leaves plus stalks, with root mean square error (RMSE) of 2.1 g/m2. However, the inclusion of fruit N content for validation deteriorated the results, which can be explained by the inability of the radiation to penetrate the thick tissues of stalks, corn cobs and wheat ears. GP-based band analysis identified optimal spectral settings with ten bands mainly situated in the shortwave infrared (SWIR) spectral region. Use of well-known protein absorption bands from the literature showed comparative results. Finally, the heteroscedastic GP model was successfully applied on airborne hyperspectral data for N mapping. We conclude that GP algorithms, and in particular the heteroscedastic GP, should be implemented for global agricultural monitoring of aboveground N from future imaging spectroscopy data.

Determination of leucine and isoleucine/allo-isoleucine by electrospray ionization-tandem mass spectrometry and partial least square regression: Application to saliva samples

Herein we propose, for the first time, a rapid method based on flow injection analysis, electrospray ionization-tandem mass spectrometry (FIA-ESI-MS/MS) and multivariate calibration for the determination of l-leucine, l-isoleucine and L-allo-isoleucine in saliva. As far as we know, multivariate calibration has never been applied to the data from this non-separative approach. The possibilities of its use were explored and the results obtained were compared with the corresponding ones when using univariate calibration. Partial least square regression (PLS1) multivariate calibration models were built for each analyte by analyzing different saliva samples, and were subsequently applied to the analysis of another set of samples which had not been used in any calibration step. For Leu, the model worked satisfactorily with root mean square errors in the prediction step of 17%. This error can be considered acceptable and is common in methodologies that do not include a separation step. Results were compared with those obtained when univariate calibration was used, using the m/z transition 132.1 → 43.0 as the quantitation variable. In this case, the obtained results were not acceptable, with RMSEP of 236%, due to the fact that saliva samples contained another compound, different to the target analytes, which also shared the same transition. Ile and aIle have the same fragmentation patterns, so quantification of the sum of both compounds was performed, with RMSEP of 14% using a PLS1 model. Similar results were obtained when a univariate calibration model using the m/z transition 132.1 → 69.0 was employed. However, the use of this transition should be carefully examined when other compounds present in the matrix contribute to the analytical signal. The method increases sample throughput more than one order of magnitude compared to the corresponding LC-ESI-MS/MS method and is especially suitable as screening. When abnormally high or low concentrations of the analytes studied are obtained, the use of the method that includes separation is recommended to confirm the results.

Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions

Nitrogen (N) is considered as one of the most important plant macronutrients and proper management of N therefore is a pre-requisite for modern agriculture. Continuous satellite-based monitoring of this key plant trait would help to understand individual crop N use efficiency and thus would enable site-specific N management. Since hyperspectral imaging sensors could provide detailed measurements of spectral signatures corresponding to the optical activity of chemical constituents, they have a theoretical advantage over multi-spectral sensing for the detection of crop N. The current study aims to provide a state-of-the-art overview of crop N retrieval methods from hyperspectral data in the agricultural sector and in the context of future satellite imaging spectroscopy missions. Over 400 studies were reviewed for this purpose, identifying those estimating mass-based N (N concentration, N%) and area-based N (N content, Narea) using hyperspectral remote sensing data. Retrieval methods of the 125 studies selected in this review can be grouped into: (1) parametric regression methods, (2) linear nonparametric regression methods or chemometrics, (3) nonlinear nonparametric regression methods or machine learning regression algorithms, (4) physically-based or radiative transfer models (RTM), (5) use of alternative data sources (sun-induced fluorescence, SIF) and (6) hybrid or combined techniques. Whereas in the last decades methods for estimation of Narea and N% from hyperspectral data have been mainly based on simple parametric regression algorithms, such as narrowband vegetation indices, there is an increasing trend of using machine learning, RTM and hybrid techniques. Within plants, N is invested in proteins and chlorophylls stored in the leaf cells, with the proteins being the major nitrogen-containing biochemical constituent. However, in most studies, the relationship between N and chlorophyll content was used to estimate crop N, focusing on the visible-near infrared (VNIR) spectral domains, and thus neglecting protein-related N and reallocation of nitrogen to non-photosynthetic compartments. Therefore, we recommend exploiting the estimation of nitrogen via the proxy of proteins using hyperspectral data and in particular the short-wave infrared (SWIR) spectral domain. We further strongly encourage a standardization of nitrogen terminology, distinguishing between N% and Narea. Moreover, the exploitation of physically-based approaches is highly recommended combined with machine learning regression algorithms, which represents an interesting perspective for future research in view of new spaceborne imaging spectroscopy sensors.

Application of Identification and Evaluation Techniques for Ethnobotanical Medicinal Plant of Genus Panax: A Review

Genus Panax, as worldwide medicinal plants, has a medical history for thousands of years. Most of the entire genus are traditional ethnobotanical medicine in China, Myanmar, Thailand, Vietnam and Laos, which have given rise to international attention and use. This paper reviewed more than 210 articles and related books on the research of Panax medicinal plants and their Chinese patent medicines published in the last 30 years. The purpose was to review and summarize the species classification, geographical distribution, and ethnic minorities medicinal records of the genus Panax, and further to review the analytical tools and data analysis methods for the authentication and quality assessment of Panax medicinal materials and Chinese patent medicines. Five main technologies applied in the identification and evaluation of Panax have been introduced and summarized. Chromatography was the most widely used one. Further research and development of molecular identification technology had the potential to become a mainstream identification technology. In addition, some novel, controversial, and worthy methods including electronic noses, electronic eyes, and DNA barcoding were also introduced. At the same time, more than 80% of the researches were carried out by a combination of chemometric pattern-recognition technologies and multi-analysis technologies. All the technologies and methods applied can provide strong support and guarantee for the identification and evaluation of genus Panax, and also conduce to excellent reference value for the development and in-depth research of new technologies in Panax.

Bioelectronic tongue: Current status and perspectives

In the course of evolution, nature has endowed humans with systems for the recognition of a wide range of tastes with a sensitivity and selectivity which are indispensable for the evaluation of edibility and flavour attributes. Inspiration by a biological sense of taste has become a basis for the design of instruments, operation principles and parameters enabling to mimic the unique properties of their biological precursors. In response to the demand for fast, sensitive and selective techniques of flavouring analysis, devices belonging to the group of bioelectronic tongues (B-ETs) have been designed. They combine achievements of chemometric analysis employed for many years in electronic tongues (ETs), with unique properties of bio-inspired materials, such as natural taste receptors (TRs) regarding receptor/ligand affinity. Investigations of the efficiency of the prototype devices create new application possibilities and suggest successful implementation in real applications. With advances in the field of biotechnology, microfluidics and nanotechnologies, many exciting developments have been made in the design of B-ETs in the last five years or so. The presented characteristics of the recent design solutions, application possibilities, critical evaluation of potentialities and limitations as well as the outline of further development prospects related to B-ETs should contribute to the systematisation and expansion of our knowledge.

Phenotype Profiling for Forensic Purposes: Determining Donor Sex Based on Fourier Transform Infrared Spectroscopy of Urine Traces

Forensic science is an important field of analytical chemistry where vibrational spectroscopy, in particular Fourier transform infrared spectroscopy and Raman spectroscopy, present advantages as they have a nondestructive nature, high selectivity, and no need for sample preparation. Herein, we demonstrate a method for determination of donor sex, based on attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy of dry urine traces. Trace body fluid evidence is of special importance to the modern criminal investigation as a source of individualizing DNA evidence. However, individual identification of a urine donor is generally difficult because of the small amount of DNA. Therefore, the development of an innovative method to provide phenotype information about the urine donor-including sex-is highly desirable. In this study, we developed a multivariate discriminant model for the ATR FT-IR spectra of dry urine to identify the donor sex. Rigorous selection of significant wavenumbers on the spectrum using a genetic algorithm enabled superb discrimination performance for the model and conclusively indicated a chemical origin for donor sex differences, which was supported by physiological knowledge. Although further investigations need to be conducted, this proof-of-concept study demonstrates the great potential of the developed methodology for phenotype profiling based on the analysis of urine traces.

Discrimination of constructed air samples using multivariate analysis of full scan membrane introduction mass spectrometry (MIMS) data

RATIONALE

Volatile and semi-volatile organic compounds (S/VOCs) are important atmospheric pollutants affecting both human and environmental health. They are directly measured as an unresolved mixture using membrane introduction mass spectrometry (MIMS). We apply chemometric techniques to discriminate, classify, and apportion air samples from a variety of sources.

METHODS

Full scan mass spectra of lab-constructed air samples were obtained using a polydimethylsiloxane membrane interface and an electron ionization ion trap mass spectrometer. Normalized full scan spectra were analyzed using principal component analysis (PCA), cluster analysis, and k-nearest neighbours (kNN) for sample discrimination and classification. Multivariate curve resolution (MCR) was used to extract pure component contributions. Similar techniques were applied to VOC mixtures sampled from different woodsmoke emissions and from the headspace above aqueous hydrocarbon solutions.

RESULTS

PCA successfully discriminated 32 constructed VOC mixtures from nearly 300 air samples, with cluster analysis showing similar results. Further, kNN classification (k = 1) correctly classified all but one test set sample, and MCR successfully identified the pure compounds used to construct the VOC mixtures. Real-world samples resulting from the combustion of different wood species and those associated with water contaminated with different commercial hydrocarbon products were similarly discriminated by PCA.

CONCLUSIONS

Chemometric techniques have been evaluated using full scan MIMS spectra with a series of VOC mixtures of known composition containing known compounds, and successfully applied to samples with known sources, but unknown molecular composition. These techniques have application to source identification and apportionment in real-world environmental samples impacted by atmospheric pollutants.

Rapid and Simultaneous Prediction of Eight Diesel Quality Parameters through ATR-FTIR Analysis

Quality assessment of diesel fuel is highly necessary for society, but the costs and time spent are very high while using standard methods. Therefore, this study aimed to develop an analytical method capable of simultaneously determining eight diesel quality parameters (density; flash point; total sulfur content; distillation temperatures at 10% (T10), 50% (T50), and 85% (T85) recovery; cetane index; and biodiesel content) through attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and the multivariate regression method, partial least square (PLS). For this purpose, the quality parameters of 409 samples were determined using standard methods, and their spectra were acquired in ranges of 4000-650 cm^-1. The use of the multivariate filters, generalized least squares weighting (GLSW) and orthogonal signal correction (OSC), was evaluated to improve the signal-to-noise ratio of the models. Likewise, four variable selection approaches were tested: manual exclusion, forward interval PLS (FiPLS), backward interval PLS (BiPLS), and genetic algorithm (GA). The multivariate filters and variables selection algorithms generated more fitted and accurate PLS models. According to the validation, the FTIR/PLS models presented accuracy comparable to the reference methods and, therefore, the proposed method can be applied in the diesel routine monitoring to significantly reduce costs and analysis time.

Multivariate Curve Resolution for Signal Isolation from Fast-Scan Cyclic Voltammetric Data

The use of multivariate analysis techniques, such as principal component analysis–inverse least-squares (PCA–ILS), has become standard for signal isolation from in vivo fast-scan cyclic voltammetric (FSCV) data due to its superior noise removal and interferent-detection capabilities. However, the requirement of collecting separate training data for PCA–ILS model construction increases experimental complexity and, as such, has been the source of recent controversy. Here, we explore an alternative method, multivariate curve resolution–alternating least-squares (MCR–ALS), to circumvent this issue while retaining the advantages of multivariate analysis. As compared to PCA–ILS, which relies on explicit user definition of component number and profiles, MCR–ALS relies on the unique temporal signatures of individual chemical components for analyte-profile determination. However, due to increased model freedom, proper deployment of MCR–ALS requires careful consideration of the model parameters and the imposition of constraints on possible model solutions. As such, approaches to achieve meaningful MCR–ALS models are characterized. It is shown, through use of previously reported techniques, that MCR–ALS can produce similar results to PCA–ILS and may serve as a useful supplement or replacement to PCA–ILS for signal isolation from FSCV data.

Interaction between 8-methoxypsoralen and trypsin: Monitoring by spectroscopic, chemometrics and molecular docking approaches

8-Methoxypsoralen (8-MOP) is a naturally occurring furanocoumarin with various biological activities. However, there is little information on the binding mechanism of 8-MOP with trypsin. Here, the interaction between 8-MOP and trypsin in vitro was determined by multi-spectroscopic methods combined with the multivariate curve resolution-alternating least squares (MCR-ALS) chemometrics approach. An expanded UV-vis spectral data matrix was analysed by MCR-ALS, the concentration profiles and pure spectra for the three reaction species (trypsin, 8-MOP and 8-MOP-trypsin) were obtained to monitor the interaction between 8-MOP and trypsin. The fluorescence data suggested that a static type of quenching mechanism occurred in the binding of 8-MOP to trypsin. Hydrophobic interaction dominated the formation of the 8-MOP-trypsin complex on account of the positive enthalpy and entropy changes, and trypsin had one high affinity binding site for 8-MOP with a binding constant of 3.81×10⁴Lmol^-1 at 298K. Analysis of three dimensional fluorescence, UV-vis absorption and circular dichroism spectra indicated that the addition of 8-MOP induced the rearrangement of the polypeptides carbonyl hydrogen-bonding network and the conformational changes in trypsin. The molecular docking predicted that 8-MOP interacted with the catalytic residues His57, Asp102 and Ser195 in trypsin. The binding patterns and trypsin conformational changes may result in the inhibition of trypsin activity. This study has provided insights into the binding mechanism of 8-MOP with trypsin.

Hitchhiker's Guide to Voltammetry: Acute and Chronic Electrodes for in Vivo Fast-Scan Cyclic Voltammetry

Fast-scan cyclic voltammetry (FSCV) has been used for over 20 years to study rapid neurotransmission in awake and behaving animals. These experiments were first carried out with carbon-fiber microelectrodes (CFMs) encased in borosilicate glass, which can be inserted into the brain through micromanipulators and guide cannulas. More recently, chronically implantable CFMs constructed with small diameter fused-silica have been introduced. These electrodes can be affixed in the brain with minimal tissue response, which permits longitudinal measurements of neurotransmission in single recording locations during behavior. Both electrode designs have been used to make novel discoveries in the fields of neurobiology, behavioral neuroscience, and psychopharmacology. The purpose of this Review is to address important considerations for the use of FSCV to study neurotransmitters in awake and behaving animals, with a focus on measurements of striatal dopamine. Common issues concerning experimental design, data collection, and calibration are addressed. When necessary, differences between the two methodologies (acute vs chronic recordings) are discussed. The topics raised in this Review are particularly important as the field moves beyond dopamine toward new neurochemicals and brain regions.

Failure of Standard Training Sets in the Analysis of Fast-Scan Cyclic Voltammetry Data

The use of principal component regression, a multivariate calibration method, in the analysis of in vivo fast-scan cyclic voltammetry data allows for separation of overlapping signal contributions, permitting evaluation of the temporal dynamics of multiple neurotransmitters simultaneously. To accomplish this, the technique relies on information about current-concentration relationships across the scan-potential window gained from analysis of training sets. The ability of the constructed models to resolve analytes depends critically on the quality of these data. Recently, the use of standard training sets obtained under conditions other than those of the experimental data collection (e.g., with different electrodes, animals, or equipment) has been reported. This study evaluates the analyte resolution capabilities of models constructed using this approach from both a theoretical and experimental viewpoint. A detailed discussion of the theory of principal component regression is provided to inform this discussion. The findings demonstrate that the use of standard training sets leads to misassignment of the current-concentration relationships across the scan-potential window. This directly results in poor analyte resolution and, consequently, inaccurate quantitation, which may lead to erroneous conclusions being drawn from experimental data. Thus, it is strongly advocated that training sets be obtained under the experimental conditions to allow for accurate data analysis.

Spectral normalisation by error minimisation for prediction of conversion in solvent-free catalytic chain transfer polymerisations

This work provides a robust method to determine spectral normalization points in reactions with no known constant responses.

Bioelectronic tongues: New trends and applications in water and food analysis

Over the last years, there has been an increasing demand for fast, highly sensitive and selective methods of analysis to meet new challenges in environmental monitoring, food safety and public health. In response to this demand, biosensors have arisen as a promising tool, which offers accurate chemical data in a timely and cost-effective manner. However, the difficulty to obtain sensors with appropriate selectivity and sensitivity for a given analyte, and to solve analytical problems which do not require the quantification of a certain analyte, but an overall effect on a biological system (e.g. toxicity, quality indices, provenance, freshness, etc.), led to the concept of electronic tongues as a new strategy to tackle these problems. In this direction, to improve the performance of electronic tongues, and thus to spawn new application fields, biosensors have recently been incorporated to electronic tongue arrays, leading to what is known as bioelectronic tongues. Bioelectronic tongues provide superior performance by combining the capabilities of electronic tongues to derive meaning from complex or imprecise data, and the high selectivity and specificity of biosensors. The result is postulated as a tool that exploits chemometrics to solve biosensors' interference problems, and biosensors to solve electronic tongues' selectivity problems. The review presented herein aims to illustrate the capabilities of bioelectronic tongues as analytical tools, especially suited for screening analysis, with particular emphasis in water analysis and the characterization of food and beverages. After briefly reviewing the key concepts related to the design and principles of electronic tongues, we provide an overview of significant contributions to the field of bioelectronic tongues and their future perspectives.

High-throughput fabrication and screening improves gold nanoparticle chemiresistor sensor performance

Chemiresistor sensor arrays are a promising technology to replace current laboratory-based analysis instrumentation, with the advantage of facile integration into portable, low-cost devices for in-field use. To increase the performance of chemiresistor sensor arrays a high-throughput fabrication and screening methodology was developed to assess different organothiol-functionalized gold nanoparticle chemiresistors. This high-throughput fabrication and testing methodology was implemented to screen a library consisting of 132 different organothiol compounds as capping agents for functionalized gold nanoparticle chemiresistor sensors. The methodology utilized an automated liquid handling workstation for the in situ functionalization of gold nanoparticle films and subsequent automated analyte testing of sensor arrays using a flow-injection analysis system. To test the methodology we focused on the discrimination and quantitation of benzene, toluene, ethylbenzene, p-xylene, and naphthalene (BTEXN) mixtures in water at low microgram per liter concentration levels. The high-throughput methodology identified a sensor array configuration consisting of a subset of organothiol-functionalized chemiresistors which in combination with random forests analysis was able to predict individual analyte concentrations with overall root-mean-square errors ranging between 8-17 μg/L for mixtures of BTEXN in water at the 100 μg/L concentration. The ability to use a simple sensor array system to quantitate BTEXN mixtures in water at the low μg/L concentration range has direct and significant implications to future environmental monitoring and reporting strategies. In addition, these results demonstrate the advantages of high-throughput screening to improve the performance of gold nanoparticle based chemiresistors for both new and existing applications.

Second order multivariate curve resolution of Fourier transform infrared spectroscopic data of the photo-induced crosslinking of thymine functionalized polymers

A meaningful characterization of the photo-induced curing process of materials based on styrene monomers functionalized with thymine and charged ionic groups was accomplished using FT-IR spectroscopy in combination with second-order multivariate calibration algorithms. The polymer composition as well as the irradiation dose effects on the photo-crosslinking of copolymer films was experimentally determined. Each FT-IR absorption spectra was decomposed into the contribution of individual species by means of chemometric algorithms. A second-order strategy involving a three-way array for each sample and analyzing all arrays simultaneously was used. Temperature and solvent frequently have a strong influence on the FT-IR peak producing shifts and trilinearity lost. A new methodology to properly pre-align the spectroscopic matrix data is used based on the decomposition of a three-way array via a suitably initialized and constrained PARAFAC model. The chemical reaction mechanism describing the underlying process in terms of identifiable steps was determined. Associated key parameters and equilibrium rate constants that characterize the interconversion and stability of diverse species were predicted. Additionally, it was possible to quantify all the species even in the presence of a non-calibrated compound.

Trials and tribulations of 'omics data analysis: assessing quality of SIMCA-based multivariate models using examples from pulmonary medicine

Respiratory diseases are multifactorial heterogeneous diseases that have proved recalcitrant to understanding using focused molecular techniques. This trend has led to the rise of 'omics approaches (e.g., transcriptomics, proteomics) and subsequent acquisition of large-scale datasets consisting of multiple variables. In 'omics technology-based investigations, discrepancies between the number of variables analyzed (e.g., mRNA, proteins, metabolites) and the number of study subjects constitutes a major statistical challenge. The application of traditional univariate statistical methods (e.g., t-test) to these "short-and-wide" datasets may result in high numbers of false positives, while the predominant approach of p-value correction to account for these high false positive rates (e.g., FDR, Bonferroni) are associated with significant losses in statistical power. In other words, the benefit in decreased false positives must be counterbalanced with a concomitant loss in true positives. As an alternative, multivariate statistical analysis (MVA) is increasingly being employed to cope with 'omics-based data structures. When properly applied, MVA approaches can be powerful tools for integration and interpretation of complex 'omics-based datasets towards the goal of identifying biomarkers and/or subphenotypes. However, MVA methods are also prone to over-interpretation and misuse. A common software used in biomedical research to perform MVA-based analyses is the SIMCA package, which includes multiple MVA methods. In this opinion piece, we propose guidelines for minimum reporting standards for a SIMCA-based workflow, in terms of data preprocessing (e.g., normalization, scaling) and model statistics (number of components, R2, Q2, and CV-ANOVA p-value). Examples of these applications in recent COPD and asthma studies are provided. It is expected that readers will gain an increased understanding of the power and utility of MVA methods for applications in biomedical research.

Experimental and theoretical methods for the analyses of dynamic combinatorial libraries

Progresses in spatial and temporal analytical tools open new avenues for the study and control of increasingly complex chemical systems.

A Review on the Structural Studies of Batteries and Host Materials by X-Ray Absorption Spectroscopy

This review highlights the use of the X-ray absorption spectroscopy (XAS) as a local structural tool for selected atoms in several host materials. The main characteristics of XAS to be element-sensitive and its applicability to all states of matter, including crystalline solids and amorphous and liquid states, permit an in-depth study of the structural properties of a large variety of materials. This includes intercalation materials where a host structure can accommodate guest species. Host guest equilibria are at the basis of a large variety of technological applications; in particular they have been used for energy storage, ion-exchange membranes, electrochromism, and analytical sensing. A selection of XAS experiments conducted in the field of batteries, mainly on cathodes, and applications in the field of metal hexacyanoferrates and double layered hydroxides are outlined.