Chemometrics

Compositional analysis of copper and iron-based alloys using LIBS coupled with chemometric method

The present manuscript deals with the utility of the calibration-free LIBS and calibration curve methods for the compositional study of different alloys using laser-induced breakdown spectroscopy (LIBS). In the process of alloying in the smelting industry, metal concentration in different alloys affects the physical and chemical properties of the final products. Therefore, LIBS can be used as an efficient quantitative analysis tool for online monitoring of the quality of the products. This is because LIBS can be performed online, in situ, without any pre-processing, and need no sample preparation for the compositional analysis of any type of materials present in any phase (solid, liquid, gas or even molten alloys in the industries). In the present study, four alloys (three copper and one iron-based alloy) consisting of Cu, Al, Zn, Ni, Fe, Cr and Mn as major and Sn and Si as minor elements were selected for the study using calibration-free laser-induced breakdown spectroscopy (CF-LIBS) and calibration curve method i.e. partial least square regression (PLSR). For the CF-LIBS method, the temporal delay has been optimized in order to satisfy the optically thin and local thermal equilibrium (LTE) condition of the plasma. For the PLSR method, different regions of the strongest emission lines of constituents have been selected for quantitative analysis. The study of time-resolved LIBS spectra and the variation of plasma parameters with respect to the delay time is also discussed. The utility of the combined technique of CF-LIBS with the PLSR method for rapid monitoring and quality assessment of desired material/products without any sample pretreatment, thus reducing the cost of the analysis, is presented in this paper.

Sex Determination of Human Nails Based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy in Forensic Context

This study reports on the successful use of a machine learning approach using attenuated total reflectance Fourier transform infrared (ATR FT-IR) spectroscopy for the classification and prediction of a donor's sex from the fingernails of 63 individuals. A significant advantage of ATR FT-IR is its ability to provide a specific spectral signature for different samples based on their biochemical composition. The infrared spectrum reveals unique vibrational features of a sample based on the different absorption frequencies of the individual functional groups. This technique is fast, simple, non-destructive, and requires only small quantities of measured material with minimal-to-no sample preparation. However, advanced multivariate techniques are needed to elucidate multiplex spectral information and the small differences caused by donor characteristics. We developed an analytical method using ATR FT-IR spectroscopy advanced with machine learning (ML) based on 63 donors' fingernails (37 males, 26 females). The PLS-DA and ANN models were established, and their generalization abilities were compared. Here, the PLS scores from the PLS-DA model were used for an artificial neural network (ANN) to create a classification model. The proposed ANN model showed a greater potential for predictions, and it was validated against an independent dataset, which resulted in 92% correctly classified spectra. The results of the study are quite impressive, with 100% accuracy achieved in correctly classifying donors as either male or female at the donor level. Here, we underscore the potential of ML algorithms to leverage the selectivity of ATR FT-IR spectroscopy and produce predictions along with information about the level of certainty in a scientifically defensible manner. This proof-of-concept study demonstrates the value of ATR FT-IR spectroscopy as a forensic tool to discriminate between male and female donors, which is significant for forensic applications.

Fire releases micro- and nanoplastics: Raman imaging on burned disposable gloves

Raman imaging can effectively characterise microplastics and nanoplastics, which is validated here to capture the items released from the plastic gloves when subjected to a mimicked fire. During the COVID-19 pandemic, large quantities of personal protective equipment (PPE) units have been used, such as the disposable gloves. If discarded and poorly managed, plastics gloves might break down to release secondary contaminants. The breakdown process can be accelerated by burning in a bushfire or at the incineration plants. During the burning process, the functional groups on the surface can be burned differently due to their different thermal stabilities. The different degrees of burning can be distinguished and visualised via Raman imaging. In the meantime, at the bottom of the burned plastics, microplastics and nanoplastics can be generated at a significant amount. The possible false Raman imaging on microplastics and nanoplastics is also discussed, by effectively extracting and distinguishing the weak signal from the background or noise. Overall, these findings confirm the importance of effectively working waste incineration plants and litter prevention, and suggest that Raman imaging is a suitable approach to characterise microplastics and nanoplastics.

Application and Progress of Chemometrics in Voltammetric Biosensing

The voltammetric electrochemical sensing method combined with biosensors and multi-sensor systems can quickly, accurately, and reliably analyze the concentration of the main analyte and the overall characteristics of complex samples. Simultaneously, the high-dimensional voltammogram contains the rich electrochemical features of the detected substances. Chemometric methods are important tools for mining valuable information from voltammetric data. Chemometrics can aid voltammetric biosensor calibration and multi-element detection in complex matrix conditions. This review introduces the voltammetric analysis techniques commonly used in the research of voltammetric biosensor and electronic tongues. Then, the research on optimizing voltammetric biosensor results using classical chemometrics is summarized. At the same time, the incorporation of machine learning and deep learning has brought new opportunities to further improve the detection performance of biosensors in complex samples. Finally, smartphones connected with miniaturized voltammetric biosensors and chemometric methods provide a high-quality portable analysis platform that shows great potential in point-of-care testing.

Nature inspired computation and ensemble neural network to build a robust model for spectral data

UV spectrophotometry was introduced for simultaneous determination of Itraconazole (ITZ) and Secnidazole (SEZ) in their mixture without any prior separation. In this study, fourteen nature-inspired algorithms combined with partial least squares (PLS) regression were used as baseline algorithms. Then, an ensemble neural networks model was introduced. The performance of the models was evaluated using parameters like the root average squared error (RASE), coefficient of determination (R²), and The average absolute error (AAE). RASE, R² and AAE values of (0.1131, 0.9995, and 0.0819) and (0.1798, 0.9954, and 0.1365) were obtained for calibration and test sets of ITZ, respectively. RASE, R² and AAE values of (0.5812, 0.9962, and 0.4360) and (0.4903, 0.9957 and 0.3917) were obtained for calibration and test sets of SEZ, respectively. The models in this study can be useful for the researchers who are interested to work on the simultaneous determination of active ingredients in pharmaceutical dosage forms using UV spectroscopy. The proposed method was applied to the pharmaceutical dosage form.

Cocoa bean fingerprinting via correlation networks

Cocoa products have a remarkable chemical and sensory complexity. However, in contrast to other fermentation processes in the food industry, cocoa bean fermentation is left essentially uncontrolled and is devoid of standardization. Questions of food authenticity and food quality are hence particularly challenging for cocoa. Here we provide an illustration how network science can support food fingerprinting and food authenticity research. Using a large dataset of 140 cocoa samples comprising three cocoa fermentation/processing stages and eight countries, we obtain correlation networks between the cocoa samples by computing measures of pairwise correlation from their liquid chromatography-mass spectrometry (LC-MS) profiles. We find that the topology of correlation networks derived from untargeted LC-MS profiles is indicative of the fermentation and processing stage as well as the origin country of cocoa samples. Progressively increasing the correlation threshold firstly reveals network clusters based on processing stage and later country-based clusters. We present both, qualitative and quantitative evidence through network visualization, network statistics and concepts from machine learning. In our view, this network-based approach for classifying mass spectrometry data has broad applicability beyond cocoa.

Trends in artificial aroma sensing by means of electronic nose technologies to advance dairy production - a review

Controversies surrounding the name and how the electronics nose (e-nose) works have been at the center stage since the advent of the technology. Notwithstanding the controversies, the technology has gained popularity in the sensory analysis of dairy foods, because of its rapid results delivery on product aroma profile or pattern, which can be used to assess quality. This review critically evaluated the advances made in the application of the e-nose or artificial sensory system in the dairy industry, focusing on the evaluation of milk, yoghurt and cheese properties, and the trends and prospects of the technology. Most of the e-nose devices applied in the available scientific publications used sensors such as metal oxide semiconductor sensors (MOS), metal-oxide-semiconductor field-effect transistor (MOSFET), conducting polymers composites and quartz microbalance (QMB), and flame ionization detector FID, in a recent study. Though known for aroma sensing, the technology has been applied to evaluate the shelf life or microbial spoilage and to discriminate dairy products based on the volatile profile composition, as determined by the sensors. In most cases, the limitation of the technology is the inability of it to provide information on the nature of constituting compounds, except in gas chromatography and mass spectrometry-based e-nose systems.

Multivariate Analysis Reveals Different Responses of Antioxidant Defense in Wheat Plants Exposed to Arsenic (As) and Cadmium (Cd)

Background:

Multivariate analysis is a chemometric tool that has been little explored to determine physiological status under heavy metal stress. Nevertheless, PCA has an unexplored potential to determine the plant physiologic status and its modification under stress factors like heavy metals.

Objectives:

This work aims to assess the physiological and biochemical effects and responses of wheat plants under the different exposition of As and Cd using multivariate models.

Materials and Methods:

Wheat plants growing in a greenhouse were exposed to 0, 10 and 50 mg kg-1 soil of As and 0, 10 and 33 50 mg kg-1 soil of Cd until growth stage 5. After 56 days, wheat leaves and roots were collected to determine dry weight, lipid peroxidation and the activity of three enzymes: catalase, ascorbate peroxidase and guaiacol peroxidase. These measures were considered as the variables of three performed multivariate models to determine physiological status.

Results:

Through the interpretation of score plot and loading plot in combination, it was possible to determine that both As and Cd affect chlorophyll content and antioxidant response. However, a chlorophyll decrease and a lipid peroxidation increase were observed together with an inhibition of antioxidant response more accentuated in wheat plants exposed to As than those exposed to Cd.

Conclusions:

Multivariate analysis allows us to determine the differences between the physiological behavior of both stressors, which turn this chemometric tools useful for the characterization of a physiological response.

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.

NIR spectroscopy-multivariate analysis for discrimination and bioactive compounds prediction of different Citrus species peels

Near Infrared (NIR) method combined with chemometrics was utilized to achieve the target of deeper insight into the chemical diversity and to discriminate the different species and chemovarieties of Citrus peels. Unsupervised principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used for comparing of samples. A clear separation among the eight investigated species and cultivars was revealed, except for the red and white C. paradisi peels samples. Furthermore, fingerprint-bioflavonoids content relationship was modeled by partial least squares regression. A practical approach based on reflectance NIR measurements and partial least squares regression (PLSR) was demonstrated for quantitative determination of the bioflavonoids hesperidin and diosmin and compared to other reported methods. The regression coefficients (R²) between predicted values and pre-determined hesperidin and diosmin content were >0.98, indicating the possibility to simultaneously quantify hesperidin and diosmin in Citrus samples directly from NIR measurements using an adequate PLS regression. Citrus sinensis followed by Citrus reticulata samples were found the most enriched in the bioflavonoids hesperidin and diosmin. NIR-multivariate analysis can therefore be used for discrimination of different varieties and selection of citrus species with desired amounts of specific bioflavonoids which could successfully be analyzed in such complex plant matrices which can prove useful for further pharmaceutical implementation.

Drift Subtraction for Fast-Scan Cyclic Voltammetry Using Double-Waveform Partial-Least-Squares Regression

Background-subtracted fast-scan cyclic voltammetry (FSCV) provides a method for detecting molecular fluctuations with high spatiotemporal resolution in the brain of awake and behaving animals. The rapid scan rates generate large background currents that are subtracted to reveal changes in analyte concentration. Although these background currents are relatively stable, small changes do occur over time. These changes, referred to as electrochemical drift, result in background-subtraction artifacts that constrain the utility of FSCV, particularly when quantifying chemical changes that gradually occur over long measurement times (minutes). The voltammetric features of electrochemical drift are varied and can span the entire potential window, potentially obscuring the signal from any targeted analyte. We present a straightforward method for extending the duration of a single FSCV recording window. First, we have implemented voltammetric waveforms in pairs that consist of a smaller triangular sweep followed by a conventional voltammetric scan. The initial, abbreviated waveform is used to capture drift information that can serve as a predictor for the contribution of electrochemical drift to the subsequent full voltammetric scan using partial-least-squares regression (PLSR). This double-waveform partial-least-squares regression (DW-PLSR) paradigm permits reliable subtraction of the drift component to the voltammetric data. Here, DW-PLSR is used to improve quantification of adenosine, dopamine, and hydrogen peroxide fluctuations occurring >10 min from the initial background position, both in vitro and in vivo. The results demonstrate that DW-PLSR is a powerful tool for evaluating and interpreting both rapid (seconds) and gradual (minutes) chemical changes captured in FSCV recordings over extended durations.

Screening of Combinatorial Quality Markers for Natural Products by Metabolomics Coupled With Chemometrics. A Case Study on Pollen Typhae

Natural products, especially for traditional Chinese medicines (TCMs), are of great importance to cure diseases. Yet it was hard to screen the influential quality markers for monitoring the quality. A simple and comprehensive strategy was developed and validated to screen for the combinatorial quality markers for precise quality evaluation and discrimination of natural products. In this study, Pollen Typhae (PT) and it's processed products carbonized PT were selected as the representative case. Firstly, metabolomics data of 49 batches crude PT and carbonized PT was obtained by ultra high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS). Then, metabolomics approaches were performed to screen for the potential markers that lead to the quality difference. Finally, chemometric methods were used to validate the accuracy of combinatorial quality markers. Thus, 42 compounds were identified from PT, 5 markers (isorhamnetin-3-O-(2G-α-L-rhamnosyl)-rutinoside, isorhamnetin-3-O-neohesperidoside, astragalin, kaempferol and umbelliferone) were successfully screened, identified, quantified and regarded as combinatorial quality markers for precise quality evaluation of crude and carbonized PT. It was demonstrated that the established comprehensively strategy provide an efficient tool for precise quality evaluation of natural products from the whole.

A Review of Calibration Transfer Practices and Instrument Differences in Spectroscopy

Calibration transfer for use with spectroscopic instruments, particularly for near-infrared, infrared, and Raman analysis, has been the subject of multiple articles, research papers, book chapters, and technical reviews. There has been a myriad of approaches published and claims made for resolving the problems associated with transferring calibrations; however, the capability of attaining identical results over time from two or more instruments using an identical calibration still eludes technologists. Calibration transfer, in a precise definition, refers to a series of analytical approaches or chemometric techniques used to attempt to apply a single spectral database, and the calibration model developed using that database, for two or more instruments, with statistically retained accuracy and precision. Ideally, one would develop a single calibration for any particular application, and move it indiscriminately across instruments and achieve identical analysis or prediction results. There are many technical aspects involved in such precision calibration transfer, related to the measuring instrument reproducibility and repeatability, the reference chemical values used for the calibration, the multivariate mathematics used for calibration, and sample presentation repeatability and reproducibility. Ideally, a multivariate model developed on a single instrument would provide a statistically identical analysis when used on other instruments following transfer. This paper reviews common calibration transfer techniques, mostly related to instrument differences, and the mathematics of the uncertainty between instruments when making spectroscopic measurements of identical samples. It does not specifically address calibration maintenance or reference laboratory differences.

Electrochemical Selectivity Achieved Using a Double Voltammetric Waveform and Partial Least Squares Regression: Differentiating Endogenous Hydrogen Peroxide Fluctuations from Shifts in pH

Hydrogen peroxide (H₂O₂) is a reactive oxygen species that serves as an important signaling molecule in normal brain function. At the same time, excessive H₂O₂ concentrations contribute to myriad pathological consequences resulting from oxidative stress. Studies to elucidate the diverse roles that H₂O₂ plays in complex biological environments have been hindered by the lack of robust methods for probing dynamic H₂O₂ fluctuations in living systems with molecular specificity. Background-subtracted fast-scan cyclic voltammetry at carbon-fiber microelectrodes provides a method of detecting rapid H₂O₂ fluctuations with high temporal and spatial resolution in brain tissue. However, H₂O₂ fluctuations can be masked by local changes in pH (ΔpH), because the voltammograms for these species can have significant peak overlap, hindering quantification. We present a method for removing ΔpH-related contributions from complex voltammetric data. By employing two distinct potential waveforms per scan, one in which H₂O₂ is electrochemically silent and a second in which both ΔpH and H₂O₂ are redox active, a clear distinction between H₂O₂ and ΔpH signals is established. A partial least-squares regression (PLSR) model is used to predict the ΔpH signal and subtract it from the voltammetric data. The model has been validated both in vitro and in vivo using k-fold cross-validation. The data demonstrate that the double waveform PLSR model is a powerful tool that can be used to disambiguate and evaluate naturally occurring H₂O₂ fluctuations in vivo.

Simultaneous determination of multiple components in explosives using ultraviolet spectrophotometry and a partial least squares method

We used UV spectrophotometry and a chemometric method to develop a novel method for the simultaneous determination of multiple components in explosives.

Raman spectroscopy enables noninvasive biochemical characterization and identification of the stage of healing of a wound

Accurate and rapid assessment of the healing status of a wound in a simple and noninvasive manner would enable clinicians to diagnose wounds in real time and promptly adjust treatments to hasten the resolution of nonhealing wounds. Histologic and biochemical characterization of biopsied wound tissue, which is currently the only reliable method for wound assessment, is invasive, complex to interpret, and slow. Here we demonstrate the use of Raman microspectroscopy coupled with multivariate spectral analysis as a simple, noninvasive method to biochemically characterize healing wounds in mice and to accurately identify different phases of healing of wounds at different time-points. Raman spectra were collected from "splinted" full thickness dermal wounds in mice at 4 time-points (0, 1, 5, and 7 days) corresponding to different phases of wound healing, as verified by histopathology. Spectra were deconvolved using multivariate factor analysis (MFA) into 3 "factor score spectra" (that act as spectral signatures for different stages of healing) that were successfully correlated with spectra of prominent pure wound bed constituents (i.e., collagen, lipids, fibrin, fibronectin, etc.) using non-negative least squares (NNLS) fitting. We show that the factor loadings (weights) of spectra that belonged to wounds at different time-points provide a quantitative measure of wound healing progress in terms of key parameters such as inflammation and granulation. Wounds at similar stages of healing were characterized by clusters of loading values and slowly healing wounds among them were successfully identified as "outliers". Overall, our results demonstrate that Raman spectroscopy can be used as a noninvasive technique to provide insight into the status of normally healing and slow-to-heal wounds and that it may find use as a complementary tool for real-time, in situ biochemical characterization in wound healing studies and clinical diagnosis.

Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability

Despite the intrinsic elemental analysis capability and lack of sample preparation requirements, laser-induced breakdown spectroscopy (LIBS) has not been extensively used for real-world applications, e.g., quality assurance and process monitoring. Specifically, variability in sample, system, and experimental parameters in LIBS studies present a substantive hurdle for robust classification, even when standard multivariate chemometric techniques are used for analysis. Considering pharmaceutical sample investigation as an example, we propose the use of support vector machines (SVM) as a nonlinear classification method over conventional linear techniques such as soft independent modeling of class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA) for discrimination based on LIBS measurements. Using over-the-counter pharmaceutical samples, we demonstrate that the application of SVM enables statistically significant improvements in prospective classification accuracy (sensitivity), because of its ability to address variability in LIBS sample ablation and plasma self-absorption behavior. Furthermore, our results reveal that SVM provides nearly 10% improvement in correct allocation rate and a concomitant reduction in misclassification rates of 75% (cf. PLS-DA) and 80% (cf. SIMCA)-when measurements from samples not included in the training set are incorporated in the test data-highlighting its robustness. While further studies on a wider matrix of sample types performed using different LIBS systems is needed to fully characterize the capability of SVM to provide superior predictions, we anticipate that the improved sensitivity and robustness observed here will facilitate application of the proposed LIBS-SVM toolbox for screening drugs and detecting counterfeit samples, as well as in related areas of forensic and biological sample analysis.

Metabolomics techniques in nanotoxicology studies

The rapid growth in the development of nanoparticles for uses in a variety of applications including targeted drug delivery, cancer therapy, imaging, and as biological sensors has led to questions about potential toxicity of such particles to humans. High-throughput methods are necessary to evaluate the potential toxicity of nanoparticles. The omics technologies are particularly well suited to evaluate toxicity in both in vitro and in vivo systems. Metabolomics, specifically, can rapidly screen for biomarkers related to predefined pathways or processes in biofluids and tissues. Specifically, oxidative stress has been implicated as a potential mechanism of toxicity in nanoparticles and is generally difficult to measure by conventional methods. Furthermore, metabolomics can provide mechanistic insight into nanotoxicity. This chapter focuses on the application of both LC/MS and NMR-based metabolomics approaches to study the potential toxicity of nanoparticles.

Modelling multi-pulse population dynamics from ultrafast spectroscopy

Current advanced laser, optics and electronics technology allows sensitive recording of molecular dynamics, from single resonance to multi-colour and multi-pulse experiments. Extracting the occurring (bio-) physical relevant pathways via global analysis of experimental data requires a systematic investigation of connectivity schemes. Here we present a Matlab-based toolbox for this purpose. The toolbox has a graphical user interface which facilitates the application of different reaction models to the data to generate the coupled differential equations. Any time-dependent dataset can be analysed to extract time-independent correlations of the observables by using gradient or direct search methods. Specific capabilities (i.e. chirp and instrument response function) for the analysis of ultrafast pump-probe spectroscopic data are included. The inclusion of an extra pulse that interacts with a transient phase can help to disentangle complex interdependent pathways. The modelling of pathways is therefore extended by new theory (which is included in the toolbox) that describes the finite bleach (orientation) effect of single and multiple intense polarised femtosecond pulses on an ensemble of randomly oriented particles in the presence of population decay. For instance, the generally assumed flat-top multimode beam profile is adapted to a more realistic Gaussian shape, exposing the need for several corrections for accurate anisotropy measurements. In addition, the (selective) excitation (photoselection) and anisotropy of populations that interact with single or multiple intense polarised laser pulses is demonstrated as function of power density and beam profile. Using example values of real world experiments it is calculated to what extent this effectively orients the ensemble of particles. Finally, the implementation includes the interaction with multiple pulses in addition to depth averaging in optically dense samples. In summary, we show that mathematical modelling is essential to model and resolve the details of physical behaviour of populations in ultrafast spectroscopy such as pump-probe, pump-dump-probe and pump-repump-probe experiments.

Demon voltammetry and analysis software: analysis of cocaine-induced alterations in dopamine signaling using multiple kinetic measures

The fast sampling rates of fast scan cyclic voltammetry make it a favorable method for measuring changes in brain monoamine release and uptake kinetics in slice, anesthetized, and freely moving preparations. The most common analysis technique for evaluating changes in dopamine signaling uses well-established Michaelis-Menten kinetic methods that can accurately model dopamine release and uptake parameters across multiple experimental conditions. Nevertheless, over the years, many researchers have turned to other measures to estimate changes in dopamine release and uptake, yet to our knowledge no systematic comparison amongst these measures has been conducted. To address this lack of uniformity in kinetic analyses, we have created the Demon Voltammetry and Analysis software suite, which is freely available to academic and non-profit institutions. Here we present an explanation of the Demon Voltammetry acquisition and analysis features, and demonstrate its utility for acquiring voltammetric data under in vitro, in vivo anesthetized, and freely moving conditions. Additionally, the software was used to compare the sensitivity of multiple kinetic measures of release and uptake to cocaine-induced changes in electrically evoked dopamine efflux in nucleus accumbens core slices. Specifically, we examined and compared tau, full width at half height, half-life, T₂₀, T₈₀, slope, peak height, calibrated peak dopamine concentration, and area under the curve to the well-characterized Michaelis-Menten parameters, dopamine per pulse, maximal uptake rate, and apparent affinity. Based on observed results we recommend tau for measuring dopamine uptake and calibrated peak dopamine concentration for measuring dopamine release.

Rank estimation and the multivariate analysis of in vivo fast-scan cyclic voltammetric data

Principal component regression has been used in the past to separate current contributions from different neuromodulators measured with in vivo fast-scan cyclic voltammetry. Traditionally, a percent cumulative variance approach has been used to determine the rank of the training set voltammetric matrix during model development; however, this approach suffers from several disadvantages including the use of arbitrary percentages and the requirement of extreme precision of training sets. Here, we propose that Malinowski's F-test, a method based on a statistical analysis of the variance contained within the training set, can be used to improve factor selection for the analysis of in vivo fast-scan cyclic voltammetric data. These two methods of rank estimation were compared at all steps in the calibration protocol including the number of principal components retained, overall noise levels, model validation as determined using a residual analysis procedure, and predicted concentration information. By analyzing 119 training sets from two different laboratories amassed over several years, we were able to gain insight into the heterogeneity of in vivo fast-scan cyclic voltammetric data and study how differences in factor selection propagate throughout the entire principal component regression analysis procedure. Visualizing cyclic voltammetric representations of the data contained in the retained and discarded principal components showed that using Malinowski's F-test for rank estimation of in vivo training sets allowed for noise to be more accurately removed. Malinowski's F-test also improved the robustness of our criterion for judging multivariate model validity, even though signal-to-noise ratios of the data varied. In addition, pH change was the majority noise carrier of in vivo training sets while dopamine prediction was more sensitive to noise.

Multivariate concentration determination using principal component regression with residual analysis

Data analysis is an essential tenet of analytical chemistry, extending the possible information obtained from the measurement of chemical phenomena. Chemometric methods have grown considerably in recent years, but their wide use is hindered because some still consider them too complicated. The purpose of this review is to describe a multivariate chemometric method, principal component regression, in a simple manner from the point of view of an analytical chemist, to demonstrate the need for proper quality-control (QC) measures in multivariate analysis and to advocate the use of residuals as a proper QC method.