Figures of merit of plasmon lattice resonance sensors: shape and material matters.

The figure of merit (FOM) of plasmon lattice resonance (PLR) sensors based on the array of metal/Si/SiO2nanoparticles has been investigated. We demonstrate the shape and material of metal nanoparticles have remarkable effects on the PLR and FOM. FOM is governed by full-widths at half maximum (FWHM) and sensitivity of the PLR. Three different types of PLR can be generated by changing Ag nanoparticles' shapes (pillars, cubes, spheres). One (named PLR1) is mainly originated from the coupling between Mie resonance of individual Si nanopillars and diffraction waves. PLR1of Ag/Si/SiO2nanoparticle arrays is limited in sensing applications due to lower intensity (for Ag pillars and Ag cubes), or smaller FOM (for Ag spheres). The other two are named PLR2. PLR2of Ag/Si/SiO2nanoparticle array with Ag pillars (or Ag cubes) is mainly originated from the coupling between the quadrupole resonance of individual Ag nanopillars (or Ag cubes) and diffraction waves. While PLR2of Ag/Si/SiO2nanoparticle array with Ag spheres is mainly originated from the coupling between dipole resonance of individual Ag nanospheres and diffraction waves. The optimal Ag nanoparticles' shape in FOM is pillar due to the smallest FWHM of PLR2of Ag/Si/SiO2nanoparticle array with Ag pillars. Meanwhile, a comparison of FOM between Au, Ag and Al nanopillars of fixed size is made. The optimal material of metal nanopillars to obtain a high FOM is Ag due to higher sensitivity and narrower FWHM.

Chromatographic Applications in the Multi-Way Calibration Field.

In this review, recent advances and applications using multi-way calibration protocols based on the processing of multi-dimensional chromatographic data are discussed. We first describe the various modes in which multi-way chromatographic data sets can be generated, including some important characteristics that should be taken into account for the selection of an adequate data processing model. We then discuss the different manners in which the collected instrumental data can be arranged, and the most usually applied models and algorithms for the decomposition of the data arrays. The latter activity leads to the estimation of surrogate variables (scores), useful for analyte quantitation in the presence of uncalibrated interferences, achieving the second-order advantage. Recent experimental reports based on multi-way liquid and gas chromatographic data are then reviewed. Finally, analytical figures of merit that should always accompany quantitative calibration reports are described.

Matrix-Matched Calibration Curves for Assessing Analytical Figures of Merit in Quantitative Proteomics.

Mass spectrometry is a powerful tool for quantifying protein abundance in complex samples. Advances in sample preparation and the development of data-independent acquisition (DIA) mass spectrometry approaches have increased the number of peptides and proteins measured per sample. Here, we present a series of experiments demonstrating how to assess whether a peptide measurement is quantitative by mass spectrometry. Our results demonstrate that increasing the number of detected peptides in a proteomics experiment does not necessarily result in increased numbers of peptides that can be measured quantitatively.

Scanning electrochemical microscopy: an analytical perspective.

Scanning electrochemical microscopy (SECM) has evolved from an electrochemical specialist tool to a broadly used electroanalytical surface technique, which has experienced exciting developments for nanoscale electrochemical studies in recent years. Several companies now offer commercial instruments, and SECM has been used in a broad range of applications. SECM research is frequently interdisciplinary, bridging areas ranging from electrochemistry, nanotechnology, and materials science to biomedical research. Although SECM is considered a modern electroanalytical technique, it appears that less attention is paid to so-called analytical figures of merit, which are essential also in electroanalytical chemistry. Besides instrumental developments, this review focuses on aspects such as reliability, repeatability, and reproducibility of SECM data. The review is intended to spark discussion within the community on this topic, but also to raise awareness of the challenges faced during the evaluation of quantitative SECM data.

Area of Feasible Figures of Merit (AF-FOMs) for second-order multivariate calibrations in Multivariate Curve Resolution (MCR).

BACKGROUND: The significance and necessity of using powerful multivariate curve resolution (MCR) techniques in the study and investigation of chemical systems are clear and obvious. It has long been recognized the importance of using second-order data to extract both quantitative and qualitative information in analytical chemistry through multivariate calibration instead of univariate calibration. Although the calculation of analytical figures of merit (AFOMs) in multivariate calibrations seems to be complicated, in recent years these parameters have been reported for each developed analytical method based on multivariate calibrations. RESULTS: It is well-known that using MCR to analyze second-order data may not produce a unique solution, a phenomenon associated with rotational ambiguity, which leads to the existence of a region or area of feasible solutions (AFS). This fact led us to argue that, instead of having uniquely defined AFOMs (sensitivity, selectivity, limit of detection, limit of quantitation, etc.), there should be an AFOM for every possible solution in the AFS. Following this argument, we report for the first time the generation of the Area of Feasible FOMs (AF-FOMs). The existence of a range of different FOMs in the AFS can be fully interpreted. It can also be predicted which AFOMs will have maximum or minimum values in each feasible band, and what kind of incremental or decremental changes will occur. Herein, the systematic grid search method was used to compute all feasible solutions and to calculate the AFOMs inside the feasible band. SIGNIFICANCE: The claims were supported by analyzing artificially generated two-component data sets. The data sets include a single calibrated analyte and a single uncalibrated interferent, which was only present in the test samples. In addition, real experimental data aimed at the determination of therapeutic drugs in both water and human urine samples were analyzed. Finally, the arguments were generalized to a three-component simulated system, having a single analyte and two uncalibrated interferents.

Validation and estimation of uncertainty of an LC-MS/MS method for the simultaneous determination of 34 mycotoxins in cocoa beans.

The aim of this manuscript was to validate and apply an analytical methodology for the simultaneous determination of 34 mycotoxins in cocoa. The extraction method used in the tests was a liquid-liquid partition by NaCl addition with a freezing step followed by quantification using LC-MS/MS. The results were discussed based on national and international directives for food contaminants. The recoveries and precision were adequate, except for the mycotoxins ionized with the ammonium adduct (NH4+), E-cristinine and ß-ZOL. This result directly influenced the measurement uncertainty of these mycotoxins, because the precision and the correction factor of the recovery were the factors with the greatest impact on the uncertainty of the method. The evaluation of the matrix effect showed considerable signal suppression for 53 % of the evaluated mycotoxins. Nevertheless, the mycotoxins exhibited relatively low quantification limits, with values between 1 and 75 µg kg-1. The validated methodology was applied to 15 cocoa samples collected in warehouses in Brazil. Positive results were found for all the evaluated samples, in which nine toxins were detected out of the 34 investigated.

General Figures of Merit ZQ for Thermoelectric Generators Under Constant Heat-In Flux Boundary.

The thermoelectric figure of merit ZT bridges the efficiency and material parameters for a thermoelectric device operating under constant temperature of the hot- and cold-source thermal boundary (Type-I TB). However, many application scenarios fall under the constant heat-in flux (qh ) and constant cold-source temperature (Tc ) thermal boundary (Type-II TB), for which a figure of merit is absent for more than half a century. This study aims to fill this gap and propose a figure of merit ZQD for the thermoelectric devices under the Type-II TB condition, defined as Z Q D = ( Z T c Z T c + 1 ) ( h κ ) ( q h T c ) $Z{Q}_{\mathrm{D}} = ( {\frac{{Z{T}_{\mathrm{c}}}}{{Z{T}_{\mathrm{c}} + 1}}} )( {\frac{h}{\kappa }} )( {\frac{{{q}_{\mathrm{h}}}}{{{T}_{\mathrm{c}}}}} )$ , where Z, h, and κ are the traditional figure of merit, leg height, and thermal conductivity, respectively. The effectiveness of ZQD is verified through both numerical calculations and experiments, which are more accurate and practical than ZT. Furthermore, a system-level figure of merit ZQS is suggested after considering the external thermal resistance. Finally, optimization strategies for thermoelectric systems based on ZQS are proposed, showing a 30% enhancement in the efficiency. ZQD and ZQS are expected to be widely used in the thermoelectric field.

Microfluidic Blood Separation: Key Technologies and Critical Figures of Merit.

Blood is a complex sample comprised mostly of plasma, red blood cells (RBCs), and other cells whose concentrations correlate to physiological or pathological health conditions. There are also many blood-circulating biomarkers, such as circulating tumor cells (CTCs) and various pathogens, that can be used as measurands to diagnose certain diseases. Microfluidic devices are attractive analytical tools for separating blood components in point-of-care (POC) applications. These platforms have the potential advantage of, among other features, being compact and portable. These features can eventually be exploited in clinics and rapid tests performed in households and low-income scenarios. Microfluidic systems have the added benefit of only needing small volumes of blood drawn from patients (from nanoliters to milliliters) while integrating (within the devices) the steps required before detecting analytes. Hence, these systems will reduce the associated costs of purifying blood components of interest (e.g., specific groups of cells or blood biomarkers) for studying and quantifying collected blood fractions. The microfluidic blood separation field has grown since the 2000s, and important advances have been reported in the last few years. Nonetheless, real POC microfluidic blood separation platforms are still elusive. A widespread consensus on what key figures of merit should be reported to assess the quality and yield of these platforms has not been achieved. Knowing what parameters should be reported for microfluidic blood separations will help achieve that consensus and establish a clear road map to promote further commercialization of these devices and attain real POC applications. This review provides an overview of the separation techniques currently used to separate blood components for higher throughput separations (number of cells or particles per minute). We present a summary of the critical parameters that should be considered when designing such devices and the figures of merit that should be explicitly reported when presenting a device's separation capabilities. Ultimately, reporting the relevant figures of merit will benefit this growing community and help pave the road toward commercialization of these microfluidic systems.

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.

Optimizing Electronic Quality Factor toward High-Performance Ge1- x - y Tax Sby Te Thermoelectrics: The Role of Transition Metal Doping.

Owing to high intrinsic figure-of-merit implemented by multi-band valleytronics, GeTe-based thermoelectric materials are promising for medium-temperature applications. Transition metals are widely used as dopants for developing high-performance GeTe thermoelectric materials. Herein, relevant work is critically reviewed to establish a correlation among transition metal doping, electronic quality factor, and figure-of-merit of GeTe. From first-principle calculations, it is found that Ta, as an undiscovered dopant in GeTe, can effectively converge energy offset between light and heavy conduction band extrema to enhance effective mass at high temperature. Such manipulation is verified by the increased Seebeck coefficient of synthesized Ge1- x - y Tax Sby Te samples from 160 to 180 µV K-1 at 775 K upon doping Ta, then to 220 µV K-1 with further alloying Sb. Characterization using electron microscopy also reveals the unique herringbone structure associated with multi-scale lattice defects induced by Ta doping, which greatly hinder phonon propagation to decrease thermal conductivity. As a result, a figure-of-merit of ≈2.0 is attained in the Ge0.88 Ta0.02 Sb0.10 Te sample, reflecting a maximum heat-to-electricity efficiency up to 17.7% under a temperature gradient of 400 K. The rationalized beneficial effects stemming from Ta doping is an important observation that will stimulate new exploration toward high-performance GeTe-based thermoelectric materials.

Evaluating selection criteria for optimized excitation coils in magnetorelaxometry imaging.

Objective.Magnetorelaxometry imaging (MRXI) is an experimental imaging technique applicable for noninvasive, qualitative and quantitative imaging of magnetic nanoparticles (MNPs). Accurate reconstructions of nanoparticle distributions are crucial for several novel treatment methods employing MNPs such as magnetic drug targeting or magnetic hyperthermia therapy. Hence, it is desirable to design MRXI setups such that the reconstruction accuracy is maximized for a given set of design parameters. Several attempts exist in literature that focus on the improvement of MRXI and other related linear inverse problems with respect to various figures of merit. However, to date it remains unclear, which approach leads to the largest benefit for the reconstruction accuracy. Thus, the aim of this study is to compare the different figures of merit, thereby determining the most reliable and effective optimization approach for magnetorelaxometry setups.Approach.In the present simulation study, we translate these figures of merit to various cost functions, allowing us to optimize the electromagnetic coil positions and radii of two distinct MRXI setups with an adapted tabu search algorithm. Multiple artificial MNP phantoms are reconstructed employing the optimized setups and the resulting imaging qualities are subsequently compared.Main results.The extensive amount of generated synthetic data unprecedented in previous MRXI studies identifies the condition number as the most reliable indicator for good imaging results. This is the case for both the qualitative as well as the quantitative reconstruction accuracies.Significance.The results of this study show that optimized coil configurations increase the reconstruction quality compared to the state-of-the-art. The insights obtained here can also be extended to other design parameters of MRXI setups, thus enabling more reliable reconstructions of MNP ensembles which will ultimately render the aforementioned treatment methods safer and more efficient.

Analytical Figures of Merit for Multisensor Arrays.

Multisensor arrays employing various sensing principles are a rapidly developing field of research as they allow simple and inexpensive quantification of various parameters in complex samples. Quantitative analysis with such systems is based on multivariate regression techniques, and deriving of traditional analytical figures of merit (e.g., sensitivity, selectivity, limit of detection, and limit of quantitation) for such systems is not obvious and straightforward. Nevertheless, it is absolutely needed for further development of the multisensor research field and for introducing these instruments into the general context of analytical chemistry. Here, we report on the protocol for calculation of sensitivity, selectivity, and detection limits for multisensor arrays. The results are provided and discussed in detail for several real-world data sets.

Establishing Meaningful Limits of Detection for Ion-Selective Electrodes and Other Nonlinear Sensors.

Although IUPAC has recommended a probabilistic approach to determining limit of detection (LOD) based on false-positive and false-negative rates for more than 20 years, the LOD definition for ion-selective electrodes (ISEs) long predates these recommendations and conflicts substantively with them. Although it is well known that the ISE LOD definition does not follow best practice, it continues to be used due to simplicity and a lack of available methods for estimating LOD for nonlinear sensors. Here, we use ISEs as a model system for estimation of LOD for nonlinear sensors that is consistent with broad IUPAC recommendations and justified using statistical theory. Using freely available software, the new approach and updated definition is demonstrated through theory, simulation, and an environmental application. The results show that the current LOD definition for ISEs performs substantially worse than the proposed definition when assessed against IUPAC recommendations, including ignoring sensor noise and LOD uncertainty, leading to bias of an order of magnitude or more. Further, the environmental application shows that the new definition, which includes estimates of LOD uncertainty, allows more objective assessment of sensor response and fitness for purpose. The growing demand for ultrasensitive sensors that operate in complex matrices has pushed the boundaries of traditional calibration approaches. These sensors often operate near their limit of detection (LOD), with additional challenges created if their response is nonlinear. These challenges are amplified when assessing new sensors, since they may be less reproducible and noisier than benchmark techniques.

Uncertainty estimation and misclassification probability for classification models based on discriminant analysis and support vector machines.

Uncertainty estimation provides a quantitative value of the predictive performance of a classification model based on its misclassification probability. Low misclassification probabilities are associated with a low degree of uncertainty, indicating high trustworthiness; while high misclassification probabilities are associated with a high degree of uncertainty, indicating a high susceptibility to generate incorrect classification. Herein, misclassification probability estimations based on uncertainty estimation by bootstrap were developed for classification models using discriminant analysis [linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA)] and support vector machines (SVM). Principal component analysis (PCA) was used as variable reduction technique prior classification. Four spectral datasets were tested (1 simulated and 3 real applications) for binary and ternary classifications. Models with lower misclassification probabilities were more stable when the spectra were perturbed with white Gaussian noise, indicating better robustness. Thus, misclassification probability can be used as an additional figure of merit to assess model robustness, providing a reliable metric to evaluate the predictive performance of a classifier.

The effect of constraints on the analytical figures of merit achieved by extended multivariate curve resolution-alternating least-squares.

Multivariate curve resolution-alternating least-squares (MCR-ALS) is the model of choice when dealing with some non-trilinear arrays, specifically when the data are of chromatographic origin. To drive the iterative procedure to chemically interpretable solutions, the use of constraints becomes essential. In this work, both simulated and experimental data have been analyzed by MCR-ALS, applying chemically reasonable constraints, and investigating the relationship between selectivity, analytical sensitivity (γ) and root mean square error of prediction (RMSEP). As the selectivity in the instrumental modes decreases, the estimated values for γ did not fully represent the predictive model capabilities, judged from the obtained RMSEP values. Since the available sensitivity expressions have been developed by error propagation theory in unconstrained systems, there is a need of developing new expressions or analytical indicators. They should not only consider the specific profiles retrieved by MCR-ALS, but also the constraints under which the latter ones have been obtained.

A systematic study on the effect of noise and shift on multivariate figures of merit of second-order calibration algorithms.

In the present study, multivariate analytical figures of merit (AFOM) for three well-known second-order calibration algorithms, parallel factor analysis (PARAFAC), PARAFAC2 and multivariate curve resolution-alternating least squares (MCR-ALS), were investigated in simulated hyphenated chromatographic systems including different artifacts (e.g., noise and peak shifts). Different two- and three-component systems with interferences were simulated. Resolved profiles from the target components were used to build calibration curves and to calculate the multivariate AFOMs, sensitivity (SEN), analytical sensitivity (γ), selectivity (SEL) and limit of detection (LOD). The obtained AFOMs for different simulated data sets using different algorithms were used to compare the performance of the algorithms and their calibration ability. Furthermore, phenanthrene and anthracene were analyzed by GC-MS in a mixture of polycyclic aromatic hydrocarbons (PAHs) to confirm the applicability of multivariate AFOMs in real samples. It is concluded that the MCR-ALS method provided the best resolution performance among the tested methods and that more reliable AFOMs were obtained with this method for the studied chromatographic systems with various levels of noise, elution time shifts and presence of unknown interferences.

Determination of trace elements in calcium rich carbonate rocks by Wavelength Dispersive X-ray Fluorescence Spectrometry for environmental and geological studies.

A simple, rapid and non destructive Wavelength Dispersive X-ray Fluorescence Spectrometry (WDXRFS) was developed for the determination of trace elements such as V, Cr, Co, Ni, Cu, Zn, Pb, Ba, La, Ce, Nd, Rb, Sr, Y, Zr, and Nb in carbonate rocks with high calcium content. Samples of marble, limestone, fluorite ore and carbonatite-like rocks were chosen as objects under investigation. These samples have wide ranges of major and trace element contents, and high concentration of calcite (70-98%) in calcium rich carbonates. The sample mass required for infinite thickness was calculated for each element. In order to determine V, Cr, Co, Ni, Cu, Zn, Ba, La, Nd, Ce, sample weighting 1g was pressed with a pressure of 100kN. For the determination of Rb, Sr, Y, Zr, Nb, Pb, the sample mass was increased up to 5g. The calibration curves were constructed by employing the International Certified reference materials (ICRMs) and in-house standard reference materials (HSRMs) of various types of rocks and sediments, and the matrix effects were taken into account using the influence coefficients (α-correction equations). Analytical figures of merit have also been assessed. The calculated values of the instrumental limit of the detection were within the interval from 0.5 to 4.0mgkg-1. The repeatability and reproducibility were found to be satisfactory with the relative standard deviations lower than 5%. The accuracy was evaluated by the analysis of two reference materials and the comparison with the ICP-MS results. A good agreement was achieved between the reference and measured values with recoveries ranging from 85% to 115%. The relative disagreements between the XRF and ICP-MS results were less than 10%.

At-line determination of pharmaceuticals small molecule's blending end point using chemometric modeling combined with Fourier transform near infrared spectroscopy.

This article summarizes the development and validation of a Fourier transform near infrared spectroscopy (FT-NIR) method for the rapid at-line prediction of active pharmaceutical ingredient (API) in a powder blend to optimize small molecule formulations. The method was used to determine the blend uniformity end-point for a pharmaceutical solid dosage formulation containing a range of API concentrations. A set of calibration spectra from samples with concentrations ranging from 1% to 15% of API (w/w) were collected at-line from 4000 to 12,500cm-1. The ability of the FT-NIR method to predict API concentration in the blend samples was validated against a reference high performance liquid chromatography (HPLC) method. The prediction efficiency of four different types of multivariate data modeling methods such as partial least-squares 1 (PLS1), partial least-squares 2 (PLS2), principal component regression (PCR) and artificial neural network (ANN), were compared using relevant multivariate figures of merit. The prediction ability of the regression models were cross validated against results generated with the reference HPLC method. PLS1 and ANN showed excellent and superior prediction abilities when compared to PLS2 and PCR. Based upon these results and because of its decreased complexity compared to ANN, PLS1 was selected as the best chemometric method to predict blend uniformity at-line. The FT-NIR measurement and the associated chemometric analysis were implemented in the production environment for rapid at-line determination of the end-point of the small molecule blending operation.

A new and consistent parameter for measuring the quality of multivariate analytical methods: Generalized analytical sensitivity.

Generalized analytical sensitivity (γ) is proposed as a new figure of merit, which can be estimated from a multivariate calibration data set. It can be confidently applied to compare different calibration methodologies, and helps to solve literature inconsistencies on the relationship between classical sensitivity and prediction error. In contrast to the classical plain sensitivity, γ incorporates the noise properties in its definition, and its inverse is well correlated with root mean square errors of prediction in the presence of general noise structures. The proposal is supported by studying simulated and experimental first-order multivariate calibration systems with various models, namely multiple linear regression, principal component regression (PCR) and maximum likelihood PCR (MLPCR). The simulations included instrumental noise of different types: independently and identically distributed (iid), correlated (pink) and proportional noise, while the experimental data carried noise which is clearly non-iid.

Multivariate analytical figures of merit as a metric for evaluation of quantitative measurements using comprehensive two-dimensional gas chromatography-mass spectrometry.

The present contribution is devoted to develop multivariate analytical figures of merit (AFOMs) as a new metric for evaluation of quantitative measurements using comprehensive two-dimensional gas chromatography-mass spectrometry (GC×GC-MS). In this regard, new definition of sensitivity (SEN) is extended to GC×GC-MS data and then, other multivariate AFOMs including analytical SEN (γ), selectivity (SEL) and limit of detection (LOD) are calculated. Also, two frequently used second- and third-order calibration algorithms of multivariate curve resolution-alternating least squares (MCR-ALS) as representative of multi-set methods and parallel factor analysis (PARAFAC) as representative of multi-way methods are discussed to exploit pure component profiles and to calculate multivariate AFOMs. Different GC×GC-MS data sets with different number of components along with various levels of artifacts are simulated and analyzed. Noise, elution time shifts in both chromatographic dimensions, peak overlap and interferences are considered as the main artifacts in this work. Additionally, a new strategy is developed to estimate the noise level using variance-covariance matrix of residuals which is very important to calculate multivariate AFOMs. Finally, determination of polycyclic aromatic hydrocarbons (PAHs) in aromatic fraction of heavy fuel oil (HFO) analyzed by GC×GC-MS is considered as real case to confirm applicability of the proposed metric in real samples. It should be pointed out that the proposed strategy in this work can be used for other types of comprehensive two-dimensional chromatographic (CTDC) techniques like comprehensive two dimensional liquid chromatography (LC×LC).