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In this tutorial review, we provide a guiding reference on the good practice in building calibration and correlation experiments, and we explain how the results should be evaluated and interpreted. The review centers on calibration experiments where the relationship between response and concentration is expected to be linear, although certain of the described principles of good practice can be applied to non-linear systems, as well. Furthermore, it gives prominence to the meaning and correct interpretation of some of the statistical terms commonly associated with calibration and regression. To reach a mutual understanding in this significant field, we present, through a practical example, a step-by-step procedure, which deals with typical challenges related to linearity and outlier assessment, calculation of the associated error of the predicted concentration, and limits of detection. The utilization of regression lines to compare analytical methods is also elaborated. The results of regression and correlation data are acquired by implementing the Excel spreadsheet of Microsoft, being perhaps one of the most widely used user-friendly software in education and research. [ABSTRACT FROM AUTHOR]

Simple Summary: Digestibility is a crucial factor for assessing feed costs for dairy cattle. Rapid analysis of fecal composition is necessary to obtain accurate data on digestive efficiency. Traditional wet chemical analysis methods are time-consuming; therefore, the potential of near-infrared reflectance spectroscopy technique was explored for assessing fecal chemical components in dairy cattle. The technique has been widely used to predict the nutritional contents of raw materials and complete feeds. In the present study, near-infrared reflectance spectroscopy could successfully predict the chemical composition of dairy cattle feces, thereby reducing analysis time and workload. Traditional methods for determining the chemical composition of cattle feces are uneconomical. In contrast, near-infrared reflectance spectroscopy (NIRS) has emerged as a successful technique for assessing chemical compositions. Therefore, in this study, the feasibility of NIRS in terms of predicting fecal chemical composition was explored. Cattle fecal samples were subjected to chemical analysis using conventional wet chemistry techniques and a NIRS spectrometer. The resulting fecal spectra were used to construct predictive equations to estimate the chemical composition of the feces in both cows and calves. The coefficients of determination for calibration (RSQ) were employed to evaluate the calibration of the predictive equations. Calibration results for cows (dry matter [DM], RSQ = 0.98; crude protein [CP], RSQ = 0.93; ether extract [EE], RSQ = 0.91; neutral detergent fiber [NDF], RSQ = 0.82; acid detergent fiber [ADF], RSQ = 0.89; ash, RSQ = 0.84) and calves (DM, RSQ = 0.92; CP, RSQ = 0.89; EE, RSQ = 0.77; NDF, RSQ = 0.76; ADF, RSQ = 0.92; ash, RSQ = 0.97) demonstrated that NIRS is a cost-effective and efficient alternative for assessing the chemical composition of dairy cattle feces. This provides a new method for rapidly predicting fecal chemical content in cows and calves. [ABSTRACT FROM AUTHOR]

Assessment of the adequacy of a proposed linear calibration curve is necessarily subjective in chemical analysis. If the outlier points in calibration are not identified and discarded, the constructed model will not have much validity and does not warrant the accuracy and precision of prediction step. Recognizing of influential points, outlier data, and discarding them is one of the steps in data processing that has been considered in various sciences. The outlier points can arise from (I) bad design of calibration set and (II) gross error in doing experiments. Therefore, we aimed to extract a map that recognizes the following issues: (A) the existence of data with high regression coverage that is far from the rest and will strongly affect the accuracy of the calibration equation, high leverage points; (B) large error in the experimental process: The recorded signal does not match the desired concentration; and (C) points with a concentration lower than the limit of quantification, which is calculated by considering the standard error of regression instead of the standard deviation of blank. The efficiency of the proposed roadmap will be reviewed, and this will give a new perspective on the calibration equation to avoid common mistakes in analytical chemistry. To achieve the above goal, visual and statistically significant tests will be used and all tests will be performed in a simple Microsoft Excel environment. [ABSTRACT FROM AUTHOR]

Calibration transfer is an essential activity in analytical chemistry in order to avoid a complete recalibration. Currently, the most popular calibration transfer methods, such as piecewise direct standardization and dynamic orthogonal projection, require a certain amount of standard or reference samples to guarantee their effectiveness. To achieve higher efficiency, it is desirable to perform the transfer with as few reference samples as possible. To this end, we propose a new calibration transfer method by using a calibration database from a master instrument (source domain) and only one spectrum with known properties from a slave instrument (target domain). We first generate a counterpart of this spectrum in the source domain by a multivariate Gaussian kernel. Then, we train a filter to make the response function of the slave instrument equivalent to that of the master instrument. To avoid the need for labels from the target domain, we also propose an unsupervised way to implement our method. Compared with several state-of-the-art methods, the results on one simulated dataset and two real-world datasets demonstrate the effectiveness of our method. Traditionally, the demand for certain amounts of reference samples during calibration transfer is cumbersome. Our approach, which requires only one reference sample, makes the transfer process simple and fast. In addition, we provide an alternative for performing unsupervised calibration transfer. As such, the proposed method is a promising tool for calibration transfer. [Display omitted] • A new calibration transfer method is introduced, which requires only one spectrum from target domain. • The calibration transfer method can be conducted in an unsupervised way. • A multivariate Gaussian kernel is introduced to generate a virtual sample in source domain. • Results from simulated and real-world datasets demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

The coefficient of correlation (r) and the coefficient of determination (R 2 or r 2) have long been used in analytical chemistry, bioanalysis and forensic toxicology as figures demonstrating linearity of the calibration data in method validation. We clarify here what these two figures are and why they should not be used for this purpose in the context of model fitting for prediction. R 2 evaluates whether the data are better explained by the regression model used than by no model at all (i.e. a flat line of slope = 0 and intercept |$\bar y$|⁠), and to what degree. Hopefully, in the context of calibration curves, the fact that a linear regression better explains the data than no model at all should not be a point of contention. Upon closer examination, a series of restrictions appear in the interpretation of these coefficients. They cannot indicate whether the dataset at hand is linear or not, because they assume that the regression model used is an adequate model for the data. For the same reason, they cannot disprove the existence of another functional relationship in the data. By definition, they are influenced by the variability of the data. The slope of the calibration curve will also change their value. Finally, when heteroscedastic data are analyzed, the coefficients will be influenced by calibration levels spacing within the dynamic range, unless a weighted version of the equations is used. With these considerations in mind, we suggest to stop using r and R 2 as figures of merit to demonstrate linearity of calibration curves in method validations. Of course, this does not preclude their use in other contexts. Alternative paths for evaluation of linearity and calibration model validity are summarily presented. [ABSTRACT FROM AUTHOR]

The article offers information on production method of Molnupiravir. It mentions It is freely soluble in methanol R, soluble in dehydrated ethanol R, sparingly soluble in acetonitrile R and water R; and also mentions Molnupiravir should be kept in tightly closed containers, protected from moisture, as it is slightly hygroscopic and exhibits polymorphism.

This work uses digital images, near-infrared (NIR) spectra, data fusion, and multivariate calibration to develop fast, non-laborious, and cheap methods for determining moisture and total protein and phosphorus contents in powdered chicken egg samples. These analyzed samples were obtained after breaking, homogenizing, lyophilizing, and pulverizing whole chicken eggs. Digital images were captured employing a scanner, while NIR spectra were recorded using benchtop and portable spectrometers. The proposed multivariate calibration methods were better in terms of correlation coefficient (r) and ratio performance deviation of prediction (RPD) for predicting total protein content using digital images (r = 0.995 and RPD = 9.55) than those obtained by benchtop and portable NIR spectra. On the other hand, in predicting moisture (r = 0.974 and RPD = 4.35) and total phosphorus content (r = 0.98 and RPD = 4.3), the benchtop NIR produced better results. The analytical performance of data fusion methods was slightly poorer than the best methods using preprocessed NIR spectra or digital images separately. Therefore, these proposed methods proved to be a good alternative tool for destructive chemical analysis, given that powdered chicken egg samples could be analyzed without using chemicals and without generating waste harmful to health and the environment, following the basic principles of Green Chemistry. [Display omitted] • Development of digital images and NIR chemometrics methods for chicken egg analysis. • Determination of the quality parameters in lyophilized chicken eggs. • Successful determination of moisture, and total protein and phosphorus contents. • Valuable alternative to standard reference methods for routine analyses. [ABSTRACT FROM AUTHOR]

• The risk of bisphenols (BPA, BPF, and BPS) in thermal papers and canned food samples was assessed. • A method for determining bisphenols based on HPLC-DAD was developed and validated. • An integrated calibration method was modified and supported by the H-point standard addition method to compensate interference effects. • BPA concentration in canned food products was exceeded. • Risk factors connected to the receipts (BPF and BPS) proved harmful to cashiers due to long exposure time. The most crucial purpose of the measurement is to obtain a reliable result that reflects the actual qualitative and/or quantitative features of the tested material. The overriding goal of analytical chemistry is to obtain accurate results after compensating of various interference effects as well as non-linear calibration dependence. A new approach based on an integrated calibration method (ICM) supported by H-point standard addition method (HPSAM) has been used to improve the quality of analytical results. The proposed methodological approach was extended using the step-by-step dilution procedure, and five measurement conditions were used to eliminate multiplicative, additive, and non-linear interferences. On this basis, a set of estimations is obtained to improve the quality of the analytical results. The analytical usefulness of the proposed approach was tested on the example of the determination of three compounds from the group of bisphenols (BPs) using the chromatographic technique - HPLC-DAD (high-performance liquid chromatography with diode array detection). Compared to the reference method - fluorescence spectroscopy - the obtained results were characterized by excellent accuracy (RE=3 % in most cases). The developed methodology allowed to carry out a risk assessment on BPA, BPF, and BPS present in samples of shop receipts and canned food. Store clerks have been shown to be particularly vulnerable to PBF and BPS in receipts due to skin permeation (exposure factors were equal to 308.97 µg/g for BPF and 181.89 µg/g for BPS). Consumers should also pay close attention to the BPA found in canned food samples (the average concentration was equal to 20.61 µg/mL, and the tolerable daily intake was exceeded over 165.000 times). The analytical method and the methodological approach were evaluated using the RGB model and the AGREE approach - it was shown that the method can be successfully used for other analytical purposes (the method is White) and is environmentally friendly (Significance=0.63). [ABSTRACT FROM AUTHOR]

Recommendations are given for metrological terminology in analytical chemistry. Analytical chemistry is defined, and concepts related to laboratory practice are termed and defined. Recommendations are given concerning the terminology of quality assurance in analytical chemistry. Terms draw on the extensive quality literature, particularly from ISO. [ABSTRACT FROM AUTHOR]

[Display omitted] • PFCE2: an updated parameter-free calibration enhancement (PFCE) for NIR data. • PFCE2 improves robustness with two novel constraints on model coefficients. • PFCE2 enhances efficiency and versatility with a multitask learning method. • PFCE2 tackles scenarios of calibration transfer, maintenance, and enhancement. Near-infrared (NIR) spectroscopy is a widely used technique for chemical analysis, but it has faced challenges of calibration transfer, maintenance, and enhancement among different instruments and conditions. The parameter-free calibration enhancement (PFCE) framework was developed to address these challenges with non-supervised (NS), semi-supervised (SS), and full-supervised (FS) methods. This study presented PFCE2, an updated version of the PFCE framework that incorporates two new constraints and a new method to improve the robustness and efficiency of calibration enhancement. First, normalized L2 and L1 constraints were introduced to replace the correlation coefficient (Corr) constraint used in the original PFCE. These constraints preserve the parameter-free feature of PFCE and impose smoothness or sparsity on the model coefficients. Second, multitask PFCE (MT-PFCE) was proposed within the framework to address the calibration enhancement among multiple instruments, enabling the framework to be versatile for all possible calibration transfer situations. Demonstrations conducted on three NIR datasets of tablets, plant leaves, and corn showed that the PFCE methods with the new L2 and L1 constraints can result in more accurate and robust predictions than the Corr constraint, especially when the standard sample size is small. Moreover, MT-PFCE could refine all models in the involved scenarios at once, leading to significant enhancement in model performance, compared to the original PFCE method with the same data requirements. Finally, the applicable situations of the PFCE framework and other analogous calibration transfer methods were summarized, facilitating users to choose suitable methods for their application. The source codes written in both MATLAB and Python are available at https://github.com/JinZhangLab/PFCE and https://pypi.org/project/pynir/ , respectively. [ABSTRACT FROM AUTHOR]

Quantitative nuclear magnetic resonance (qNMR) is an analytical technique that offers numerous advantages in pharmaceutical applications including minimum sample preparation and rapid data collection times with no need for response factor corrections, being a powerful tool for assaying drug content in both drug discovery and early drug development. In the present work, we have applied qNMR, using both the internal standard and the electronic reference to access in vivo concentrations 2 calibration methods, to assess the purity of RI76, a novel antifungal drug candidate. NMR acquisition and processing parameters were optimized in order to obtain spectra with intense, well‐resolved signals of completely relaxed nuclei. The analytical method was validated following current guidelines, demonstrating selectivity, linearity, accuracy, precision, and robustness. The calibration approaches were statistically compared, and no significant difference was observed when comparing the obtained results and their dispersion in terms of relative standard deviation. The proposed qNMR method may, therefore, be used for both qualitative and quantitative assessments of RI76 in early drug development and for characterization of this compound. [ABSTRACT FROM AUTHOR]

A review is presented of recent multi-way calibration protocols involving three-, four- and five-way data. A brief description of the various data structures and processing models is shown, with emphasis on model selection based on the matching between the data structure and the physicochemical model assumptions. Calibration research works are then discussed, classified according to the type of measured instrumental data and model/algorithm selection. Image 1 • Recent multi-way calibration protocols are reviewed. • The relationship between data structures and chemometric models is detailed. • Second-, third- and fourth-order data generation is discussed. • Implications to green analytical chemistry are underlined. [ABSTRACT FROM AUTHOR]

Multivariate calibration models are one of the most predominant tools in analytical chemistry for the non-destructive quantification of constituents. While pipelines, methods, and software are widely available for training calibration models, the monitoring strategies to track the prediction performance in time are still limited and are one of the emerging research topics in chemometrics and machine learning. Moreover, for the sake of efficiency, the development of modern monitoring strategies requires tools that do not require new reference values at each time point. The current study addresses the problem of monitoring calibration models for regression. We present a monitoring paradigm and propose two methods to monitor the performance of the model. The two methods focus on bias drift and spectral variation drift. We present the derivation of the methods and their application to two real case studies from the agrofood industry involving milk composition and apple harvesting. Conclusions and recommendations are presented for practitioners, as well as views for future work in this domain of research. • Views and concepts about multivariate calibration monitoring are presented. • Two unsupervised metrics to monitor calibration models in regression were derived. • Focus on monitoring models for potential bias and spectral variation drift. • Two real cases from the agrofood industry were monitored. [ABSTRACT FROM AUTHOR]

• Age estimation by aspartic acid racemization requires a wide age range of teeth. • Standard samples could be prepared by the racemization rate increased via heating. • The error ranges between actual and estimated ages were within ±5 years. • The stability of racemization rates in prepared powdered samples was high. • This technique would be useful when it is difficult to collect age-known teeth. Age estimation in adults based on aspartic acid racemization (AAR) provides fewer errors and higher precision than that based on bone morphology for the identification of cadavers. The technique has been established in some labs as a routine method. However, as the essential requisites for the technique, a wide age range of teeth of the same type as the target tooth must be collected for calibration for each examination. We investigated whether dentin standard samples could be prepared by increasing the AAR rate via heat. Powdered dentin was prepared from a maxillary first premolar (13 years) and heated for 0–72 h at 110 °C. The extent of AAR increased significantly with heating time and the correlation was strong (r = 0.913; p < 0.01). Similar results were found for a mandibular canine (24 years, r = 0.948; p < 0.01) and a maxillary third molar (20 years, r = 0.944; p < 0.01). We attempted to estimate the age of four maxillary first premolars of persons aged 25–58 years by using the heated samples (18 years, 12 h to 7 days). The differences between the actual and estimated ages were within ±5 years. The stability of the AAR rates in the powdered dentin during storage at 22–25 °C, 4 °C, and −30 °C was examined after 1 year and no significant changes had occurred. We were able to prepare dentin standard samples and created a calibration curve. This is a pilot study that needs to be validated before it can be used in forensic practice. [ABSTRACT FROM AUTHOR]

One of the most interesting and important techniques in applied mathematics is interpolation and approximation. It can be also shown that basic theory of fuzzy sets and operations with fuzzy numbers can be used, and result very convenient, for solving many analytical chemistry problems: as depth profile comparison or calibration with errors in signals and/or concentrations, for spectra interpretation, or even for automatic qualitative analysis or expert systems with X-ray spectroscopy. In this special context, we introduce in this paper a new fuzzy interpolation method of fuzzy data or functions, specially indicated in all these chemical applications. We will use bicubic splines (cubic splines of two variables) of class C 2 as linear combinations of a basis constructed by a tensor product of univariate B-splines in each of these variables. First we establish a bicubic interpolation spline method of a given fuzzy data set or a given fuzzy bivariate function. We proof the existence of a unique solution of this problem and we show a convergence result. Finally, we test the effectiveness of this method by some numerical and graphical examples. [ABSTRACT FROM AUTHOR]

Multivariate curve resolution (MCR) methods are powerful chemometric approaches that have been significantly involved to study the complex chemical systems. The accuracy of MCR results is directly related to the applied constraints which determine the properties of the resolved profiles. Constraints have been, and still are, an active field of research in MCR studies. Different constraints have different impacts on the range of feasible solutions, so it is important to study the effect of each constraint, to examine its compliance with physico-chemical principles, and to find sufficient conditions to ensure unique solutions. In this study, we focus on known-value constraint and the requirements for reducing the range of feasible solutions under this constraint. In addition, several theoretical rules are presented to determine the minimum number of required known-values in order to get a unique solution. The theory of known-values was accessed using several simulated and experimental datasets. As shown previously, known-value information can be applied in quantitative analysis of first-order data. The prediction performance of MCR-ALS method under known-value constraint was compared to the results of the well stablished partial least squares (PLS) method in the presence of minimum number of required calibration samples. The comparison shows the advantage of using MCR-ALS method under known-value constraint over the PLS results in such experiments. [ABSTRACT FROM AUTHOR]

Linear models for calibration curves are overwhelmingly created based on minimization of least squares error, with their goodness-of-fit (GOF) quantified using the square of the correlation coefficient (R 2 ). Yet, R 2 has well-known disadvantages when used to quantify GOF of calibration curves stemming from its calculation based on the absolute error of the signal (i.e., calculated vs. experimental). These disadvantages are exacerbated when using a geometric series of concentrations for calibration standards (e.g., 1, 2, 5, 10, etc.) and when calibration curves span 2–3 orders of magnitude, which is typical for modern analytical techniques. While there are multiple alternative GOF measures, R 2 overwhelmingly persists in the field of Analytical Chemistry as the most reported measure of GOF. We evaluated R 2 , alternative GOF measures, and multiple quantitative bias parameters, along with residual analysis, for over 60 experimental calibration curves. R 2 did a poor job of consistently and accurately quantifying the GOF over the entire calibration curve. This was especially true for situations where the low concentration calibrators were not accurately described by the calibration equation. While other GOF parameters, including the sum of the absolute percent error, mean absolute percent error, and quality coefficient, did a better job of describing GOF of calibration curves, each had significant theoretical and/or practical disadvantages. Therefore, we introduce a descriptive GOF parameter called Percent Residual Accuracy (%RA or PRA) which equally weights the accuracy of all calibrators into a single value, generally falling between 0% and 100%, with 100% representing a perfect fit and a “good” fit for calibration data producing a %RA of 90–100%. The %RA much more effectively described the GOF for the entire calibration range than R 2 , and it similarly quantified GOF as compared to the other GOF parameters tested. With the performance and practical advantages of %RA, we conclude that it is the most advantageous GOF parameter and that it should be reported as a standard GOF measure for calibration curves. [ABSTRACT FROM AUTHOR]

The contribution of chemometrics to important stages throughout the entire analytical process such as experimental design, sampling, and explorative data analysis, including data pretreatment and fusion, was described in the first part of the tutorial “Chemometrics in analytical chemistry.” This is the second part of a tutorial article on chemometrics which is devoted to the supervised modeling of multivariate chemical data, i.e., to the building of calibration and discrimination models, their quantitative validation, and their successful applications in different scientific fields. This tutorial provides an overview of the popularity of chemometrics in analytical chemistry. [ABSTRACT FROM AUTHOR]

A simple protocol was developed to measure seawater calcium carbonate saturation states (Ω spec ) spectrophotometrically. Saturation states are typically derived from the separate measurement of two other carbon system parameters, with each requiring unique instrumentation and often complex measurement protocols. Using the new protocol, the only required equipment is a thermostatted laboratory spectrophotometer. For each seawater sample, spectrophotometric measurements of pH (visible absorbance) are made in paired optical cells, one with and one without added nitric acid. Ultraviolet absorbance is measured to determine the amount of added acid based on the direct proportionality between nitrate concentration and UV absorbance. Coupled measurements of pH and the alkalinity change that accompanies the nitric acid addition allow calculation of a seawater sample's original carbonate ion concentration and saturation state. These paired absorbance measurements yield Ω spec (and other carbonate system parameters), with each sample requiring about 12 min processing time. Initially, an instrument-specific nitrate molar absorptivity coefficient must be determined (due to small but significant discrepancies in instrumental wavelength calibrations), but thereafter no further calibration is needed. In this work, the 1σ precision of replicate measurements of aragonite saturation state was found to be 0.020, and the average difference between Ω spec and Ω calculated conventionally from measured total alkalinity and pH (Ω calc ) was −0.11% ± 0.96% (a level of accuracy comparable to that obtained from spectrophotometric measurements of carbonate ion concentration). Over the entire range of experimental conditions, 0.97 < Ω < 3.17 ( n  = 125), all measurements attained the Global Ocean Acidification Observing Network's “weather level” goal for accuracy and 90% attained the more stringent “climate level” goal. When Ω spec was calculated from averages of duplicate samples ( n  = 56), the precision improved to 0.014 and the average difference between Ω spec and Ω calc improved to −0.11% ± 0.73%. Additionally, 97% of the duplicate-based Ω spec measurements attained the “climate level” accuracy goal. These results indicate that the simple measurement protocol developed in this work should be widely applicable for monitoring fundamental seawater changes associated with ocean acidification. [ABSTRACT FROM AUTHOR]

Nondestructive determination of soluble solids content (SSC) has been used in the fruit industry by using near infrared (NIR) spectroscopy. The robustness of prediction models, which is of great importance in practical application, remains a challenge because of the variability of fruit samples associated with different maturity stages and storage status. Local calibration was investigated in this study as means of improving prediction robustness. As robustness is often reduced by extrapolation, we assessed the robustness by the accuracy of predicting extrapolation samples (samples outside the range of the calibration set). Local calibration was effective in improving the robustness of models compared with global calibration. It is proposed that local calibration optimizes the composition of calibration subset by selecting the samples of same level of starch fractions for each sample to be predicted, and thus provides better robustness due to the homogeneity. [ABSTRACT FROM AUTHOR]

Identification and quantification of an unknown illicit drug (cocaine) from both simulated drug paraphernalia and simulated urine can be achieved via direct analysis in real time (DART) triple quadrupole linear ion trap (QTRAP) mass spectrometry (MS). We describe a novel, hands-on laboratory activity which utilizes the recent DART technology interfaced with a tandem mass spectrometer for the purpose of integrating recent technology into a quantitative analysis course, reinforcing chemical concepts, improving critical thinking and written communication skills, and providing a context for real-life analyses. The hands-on laboratory activity requires students to use good laboratory practice, perform common laboratory calculations, and think critically to perform both qualitative and quantitative analyses and answer real-life forensic questions. Assessments include prelaboratory activity questions, observations during the activity, postlaboratory activity written report, and anonymous summative activity. [ABSTRACT FROM AUTHOR]

Current laboratory methods for comprehensive thrombophilia status require large blood volumes and long turn-around times. We demonstrate the feasibility of performing thrombophilia panel testing of enzymatic functional assays on a microfluidic cartridge using low sample volume. Functional assays for Antithrombin III, Protein C, Factor VIII, and plasminogen were adapted on the digital microfluidic platform by developing novel fluorogenic substrates and establishing on-cartridge fluorescence (360/460 nm) detection. Cartridge vs. microtiter plate results were compared using samples obtained from pediatric patients. Linear regression and Bland-Altman plots were used to establish correlations. Results were not significantly different when performed on-cartridge compared to microtiter plates. Importantly, the sample volume required is significantly lower for all on-cartridge compared to microtiter plate assays (25 µL vs. 2 ml). This study demonstrates the feasibility of thrombophilia panel testing with high-fidelity using small plasma volume. The efficacy of this near-patient technology in clinical settings needs further investigation. [ABSTRACT FROM AUTHOR]

A near-infrared (NIR) calibration was developed using an efficient offline approach to enable a quantitative partial least-squares (PLS) chemometric model to measure and monitor the concentration of active pharmaceutical ingredients (API) in powder blends in the feed frame (FF) of a tablet press. The approach leveraged an offline "feed frame table," which was designed to mimic the full process from a NIR measurement perspective, thereby facilitating a more robust model by allowing more sources of variability to be included in the calibration by minimizing the consumption of API and other raw materials. The design of experiment (DOE) for the calibration was established by an initial risk assessment and included anticipated variability from factors related to formulation, process, environment, and instrumentation. A test set collected on the feed frame table was used to refine the PLS model. Additional fully independent test sets collected from the continuous drug product manufacturing process not only demonstrated the accuracy and precision of the model but also illustrated its robustness to material variability and process variability including mass flow rate and feed frame paddle speed. Further, it demonstrated that a calibration can be generated on the offline feed frame table and then successfully implemented on the full process equipment in a robust manner. Additional benefits of using the feed frame table include streamline model monitoring and maintenance activities in a manufacturing setting. The real-time monitoring enabled by this offline calibration approach can be useful as a key component of the control strategy for continuous manufacturing processes for drug products, including detecting special cause variations such as transient disturbances and enabling product collection/rejection based upon predetermined concentration limits, and may play an important role in enabling real-time release testing (RTRt) for manufactured pharmaceutical products. [ABSTRACT FROM AUTHOR]

Mass transfer coefficients of the water boundary layer ( kw) were measured using alabaster dissolution kinetics in a diffusion cell that was operated at stirring rates between 90 min−1 and 600 min−1, aiming to provide a more robust characterization of the effect of hydrodynamics on the uptake of polar compounds by passive samplers, as compared with characterizations in terms of stirring rates and water flow velocities. The measured kw helped to quantitatively understand calcium sulfate transport through a poly(ethersulfone) membrane and 2 water boundary layers (at both sides of the membrane). Alabaster-based kw value were used to understand atrazine transport in the diffusion cell, allowing the conclusion that atrazine transport in the membrane is via the pore space, rather than via the polymer matrix. The merits of measuring alabaster dissolution rates for passive sampler calibration and application in the field are discussed. The authors propose that passive sampler calibrations be carried out under controlled kw conditions, rather than under controlled stirring rates or flow velocities. This would facilitate the interpretation of passive sampler calibration studies and the translation of laboratory-based water sampling rates to flow conditions that apply in the field. Environ Toxicol Chem 2017;36:1517-1524. © 2016 SETAC [ABSTRACT FROM AUTHOR]

Multivariate calibration is an important procedure for analytical chemistry. Automated or self-configuring methods can be used by scientists who lack expertise, may be embedded into data processing pipelines, and are less prone to user bias; however, the development of such algorithms is often neglected by the chemometrics community. Support vector regression (SVR) is a powerful method for accommodating megavariate data. SVR offers the advantage of fast calibration and flexibility in a variety of loss functions (ie, minimization of the residual error). By embedding bootstrapped Latin partitions (BLPs) into the calibration, the key parameter, the cost C, can be optimized to furnish an automated method. The methods are termed super SVR (sSVR). The BLP predictions of the calibration set accurately model the external prediction error of the entire calibration set. Prediction rates for super partial least squares (sPLS) are compared with sSVRs using three loss functions, Gauss, Laplace, and Huber. For linear data with uniformly distributed noise, sPLS is faster and gave better predictions. However, for data with outliers or a real data set of single-beam near infrared spectra of bovine plasma, gasoline, and wheat, the sSVRs performed better than sPLS, and generally, the Huber loss function gave the best results. [ABSTRACT FROM AUTHOR]

This study describes the application of laser-induced breakdown spectroscopy (LIBS) for the direct determination of Ca, K and Mg in powdered milk and solid dietary supplements. The following two calibration strategies were applied: (i) use of the samples to calculate calibration models (milk) and (ii) use of sample mixtures (supplements) to obtain a calibration curve. In both cases, reference values obtained from inductively coupled plasma optical emission spectroscopy (ICP OES) after acid digestion were used. The emission line selection from LIBS spectra was accomplished by analysing the regression coefficients of partial least squares (PLS) regression models, and wavelengths of 534.947, 766.490 and 285.213 nm were chosen for Ca, K and Mg, respectively. In the case of the determination of Ca in supplements, it was necessary to perform a dilution (10-fold) of the standards and samples to minimize matrix interference. The average accuracy for powdered milk ranged from 60% to 168% for Ca, 77% to 152% for K and 76% to 131% for Mg. In the case of dietary supplements, standard error of prediction (SEP) varied from 295 (Mg) to 3782 mg kg − 1 (Ca). The proposed method presented an analytical frequency of around 60 samples per hour and the step of sample manipulation was drastically reduced, with no generation of toxic chemical residues. [ABSTRACT FROM AUTHOR]

Several flaws of the conventional standard-addition method (SAM) have been unravelled. By revisiting the theory, it has been shown that the SAM cannot be used to correct completely for interferences in analytical chemistry. Therefore, a new theory of SAM was developed, which takes into account the uncertainty of measurement and relates this information to differences that might stem from interferences. The SAM may be used to correct for rotational effects but it cannot be used to correct for translational effects, which confirms earlier findings. A full set of equations were supplied for undertaking a full method validation including a new set of parameters of performance. The equations were tested on determination by UV–VIS spectrophotometry of l -ascorbic acid. Previous results are reviewed and discussed in view of these new findings and recommendations are given about the importance of carrying out full method validations as a prerequisite to the SAM. [ABSTRACT FROM AUTHOR]

The generalized calibration strategy (GCS), developed and previously applied to chemical analysis, has been adapted to two-component (2C) analysis. According to the 2C-GCS procedure, a set of 10 calibration solutions containing a sample and standards of two analytes in well-defined composition was diluted. The measurements performed at a given dilution stage allow the concentration of both analytes in a sample to be evaluated with six apparent concentrations calculated with various mathematical approaches. As a result, the method allows the detection, examination, and elimination of nonlinear and interference effects with multiplicative and additive characteristics. To perform 2C-GCS automatically and effectively, a dedicated flow sequential injection system was designed to be fully controlled by a computer. Caffeine and paracetamol were determined in synthetic and pharmaceutical samples using this calibration approach. The analytes were determined with good precision and accuracy with low consumption of sample and standard solutions. On the basis of this experimental model, the influence of effects and tendencies in the examined analytical system was detected and evaluated. [ABSTRACT FROM AUTHOR]

According to the guide Vocabulary in Metrology (VIM3) (JCGM, 2008), the definition of the concepts of trueness and accuracy has been revised, which has an important impact on analytical chemistry. Additionally, Eurachem/CITAC has published a new edition of the guide to Quantifying Uncertainty in Analytical Measurement (QUAM) (CITAC and Eurachem, 2012). These two documents, together, form a new basis for the evaluation of data. Results of prominent technologies of inductively coupled plasma mass spectrometry (ICP-MS), for determination of chloride-isotope ratios (35Cl/37Cl) and inductively coupled plasma optical emission spectrometry (ICP-OES) for determination of sodium, were evaluated in terms of the true level of uncertainty and revealed a genuine problem for science that was not addressed in VIM3 and QUAM. Comparison of theory and experimentation definitely requires statistical tools, but in contemporary science two approaches to the implementation of statistics in decision making are used: 1. Short-term precision and 2. long-term precision. Both approaches are valid and both are described using the same methods of statistics. However, they lead to completely different conclusions and decisions. Despite good intentions and new concepts, as well as practices and procedures for quality assurance, it is shown by these two examples that these efforts may be inadequate or mislead scientists into making major mistakes in the decision-making process. A set of equations is supplied, which are based on the propagation of uncertainty, and the implication of results and conclusions for other fields of science is discussed. [ABSTRACT FROM AUTHOR]

An original focus on univariate calibration as an experimental process of quantitative analysis is presented. A novel classification system is introduced against the background of the present situation concerning nomenclature of calibration methods. Namely, it has been revealed that four methods well-known in analytical chemistry: the conventional method, the internal standard method, the indirect method and the dilution method, can be split into those carried out in both the interpolative and the extrapolative mode. It is then shown that the basic procedures of all these methods can be modified including different approaches, such as matrix-matched technique, spiking the sample with a reactant, bracketing calibration, and others. For the first time (as compared to monographies dealing with univariate calibration) it is reviewed how the methods are mixed and integrated with one another thereby creating new calibration strategies of extended capabilities in terms of enhanced resistance to the interference and non-linear effects – as the main sources of systematic calibration errors. As additional novelty, rationally possible combinations of the calibration methods – not met hitherto in the literature – have been predicted. Finally, some general rules relating to calibration are formulated and the main calibration problems that still need to be solved are displayed. [ABSTRACT FROM AUTHOR]

Mass spectrometry (MS) is a mainstream chemical analysis technique in the twenty-first century. It has contributed to numerous discoveries in chemistry, physics and biochemistry. Hundreds of research laboratories scattered all over the world use MS every day to investigate fundamental phenomena on the molecular level. MS is also widely used by industry--especially in drug discovery, quality control and food safety protocols. In some cases, mass spectrometers are indispensable and irreplaceable by any other metrological tools. The uniqueness of MS is due to the fact that it enables direct identification of molecules based on the mass-to charge ratios as well as fragmentation patterns. Thus, for several decades now, MS has been used in qualitative chemical analysis. To address the pressing need for quantitative molecular measurements, a number of laboratories focused on technological and methodological improvements that could render MS a fully quantitative metrological platform. In this theme issue, the experts working for some of those laboratories share their knowledge and enthusiasm about quantitative MS. I hope this theme issue will benefit readers, and foster fundamental and applied research based on quantitative MS measurements. [ABSTRACT FROM AUTHOR]

Concepts from data science, machine learning, deep learning and artificial neural networks are spreading in many disciplines. The general idea is to exploit the power of statistical tools to interpret complex and, in many cases, non-linear data. Specifically in analytical chemistry, many chemometrics tools are being developed. However, they tend to get more complex without necessarily improving the prediction ability, which conspires against parsimony. In this report, we show how non-linear analytical data sets can be solved with equal or better efficiency by easily interpretable modified linear models, based on the concept of local sample selection before model building. The latter activity is conducted by choosing a sub-set of samples located in the neighborhood of each unknown sample in the space spanned by the latent variables. Two experimental examples related to the use of near infrared spectroscopy for the analysis of target properties in food samples are examined. The comparison with seemingly more complex chemometric models reveals that local regression is able to achieve similar analytical performance, with considerably less computational burden. [Display omitted] • Non-linear multivariate data are studied with a local linear model. • Calibration samples are selected which are close to the test sample. • The results are comparable with those from deep learning techniques. • Experimental data sets are analyzed and compared. [ABSTRACT FROM AUTHOR]

[Display omitted] • Nine coumarins in Cnidii Fructus were simultaneously determinated. • Satisfactory results were acquired in the presence of complex matrix interferences. • The univariate calibration method based on HPLC-UV detection was compared. • The proposed analytical method is simple, high-efficiency and low-cost. High performance liquid chromatography-diode array detection (HPLC-DAD) assisted with second-order calibration model based on alternating trilinear decomposition-assisted multivariate curve resolution (ATLD-MCR) model was proposed to quantify nine coumarins in traditional Chinese medicine (TCM) samples of Cnidii Fructus. This advanced analytical strategy shows good linearity (r > 0.9991), rapidity (t < 10.50 min) and accuracy, in low chromatographic resolution data even in the presence of potential interfering species. Satisfactory qualitative and quantitative results were achieved in the presence of complex matrix interferences from the TCM samples and the second-order advantage was fully exploited. The average spiked recoveries for most target analytes are in the range of 84.1%–108.0% with the standard deviations less than 6.9%. Furthermore, the quantitative results were further compared with a classical univariate calibration applied to a series of peak area data acquired from HPLC-UV detection. Statistical parameters and analytical figures of merit were calculated according to modern analytical protocols to validate the accuracy of the proposed analytical method. The results show that the proposed analytical method had the advantages of high-efficiency, low-cost and met the requirements of green analytical chemistry. Therefore, it promises to be a potential and universal way for qualitative and quantitative analysis of coumarins in pharmaceutical and food fields. [ABSTRACT FROM AUTHOR]

This commentary discusses differences between the so-called "mass sensitivity" and "concentration sensitivity". These terms are freely used in analytical chemistry literature to characterize operation of analytical techniques and methods. The type of sensitivity in an analytical method delimits the method's applications (e.g., in analysis of volume-limited and concentrated, or large-volume but dilute samples). It is helpful to instruct students how to distinguish between mass-sensitive and concentration-sensitive methods. Introduction of mass and concentration sensitivity can be included in graduate courses related to instrumental analysis, and, if time is available, also in the upper-level undergraduate courses. [ABSTRACT FROM AUTHOR]

Most calibration models in the literature assume that the errors are normally distributed. Unfortunately, when there are outliers in the data, these models often have very poor performance. In this work, we address this problem and propose a linear calibration model for replicate measurement by assuming that the error model follows a family of symmetric distributions, which includes lighter and heavier tails distributions than the normal, such as the Student-t, power exponential and logistic of type II distributions. In presence of outliers, these distributions are more stable than the normal distribution. Likelihood-based methodology is used to estimate the model parameters and the Fisher information matrix is used to construct confidence intervals for the unknown value of interest. Furthermore, a simulation study is carried out to verify the asymptotic properties of the proposed model main parameter. Finally, we present an application of the proposed model using a real data set from chemical analysis. [ABSTRACT FROM AUTHOR]

Visible absorbance spectroscopy is a widely used tool in chemical, biochemical, and medical laboratories. The theory and methods of absorbance spectroscopy are typically introduced in upper division undergraduate chemistry courses, but could be introduced earlier with the right curriculum and instrumentation. A major challenge in teaching spectroscopy is gaining access to laboratory equipment, which can be expensive. Even common educational spectrophotometers still carry a substantial cost and have the disadvantage of being inherently closed designs. We report on a 3D-printable smartphone spectrophotometer that is very inexpensive to build, yet retains the functionality and analytical accuracy necessary to teach concepts like the Beer-Lambert Law. The optical components are arranged in an intuitive, accessible way so that students can see each relevant part and experiment with the parameters. Here, we describe the device and provide exercises to teach different concepts in analytical spectrophotometry. [ABSTRACT FROM AUTHOR]

The GEOTRACES Intermediate Data Product 2014 (IDP2014) is the first publicly available data product of the international GEOTRACES programme, and contains data measured and quality controlled before the end of 2013. It consists of two parts: (1) a compilation of digital data for more than 200 trace elements and isotopes (TEIs) as well as classical hydrographic parameters, and (2) the eGEOTRACES Electronic Atlas providing a strongly inter-linked on-line atlas including more than 300 section plots and 90 animated 3D scenes. The IDP2014 covers the Atlantic, Arctic, and Indian oceans, exhibiting highest data density in the Atlantic. The TEI data in the IDP2014 are quality controlled by careful assessment of intercalibration results and multi-laboratory data comparisons at cross-over stations. The digital data are provided in several formats, including ASCII spreadsheet, Excel spreadsheet, netCDF, and Ocean Data View collection. In addition to the actual data values the IDP2014 also contains data quality flags and 1-σ data error values where available. Quality flags and error values are useful for data filtering. Metadata about data originators, analytical methods and original publications related to the data are linked to the data in an easily accessible way. The eGEOTRACES Electronic Atlas is the visual representation of the IDP2014 data providing section plots and a new kind of animated 3D scenes. The basin-wide 3D scenes allow for viewing of data from many cruises at the same time, thereby providing quick overviews of large-scale tracer distributions. In addition, the 3D scenes provide geographical and bathymetric context that is crucial for the interpretation and assessment of observed tracer plumes, as well as for making inferences about controlling processes. [ABSTRACT FROM AUTHOR]

Near infrared (NIR) hyperspectral imaging (HSI) has established itself as a powerful non-destructive tool for the chemical analysis of heterogeneous samples. However, one of the main disadvantages of NIR HSI is that the technique suffers from instrumentation-related problems, which in turn affect the acquired images. In general, focal plane array (FPA) based hyperspectral systems are affected by spatial and spectral non-uniform response, the presence of defective sensors (e.g. dead or saturated sensors), and temporal and spatial (e.g. dark current) noise. Another issue is each new camera system needs to be calibrated to assess its specific responses to light. To correct for these issues, we used known standards to measure the response of the sensors and capture the location of the field of view and defective sensors using linear and quadratic models. The parameters of these models were then used as input features for classification of sensor responses using a k -means algorithm. The results conclude that linear models are insufficiently precise for calibration but estimate sufficiently accurately the system’s response and functionality. Specifically, it was shown that the classification method discriminates non-responsive regions effectively. [ABSTRACT FROM AUTHOR]

The present review – Part II of a comprehensive review on solid-phase microextraction (SPME) applied to complex matrices – aims to describe recent developments in SPME technology applied in the fields of food analysis. We briefly introduce a perspective on the most commonly performed types of analysis within food studies, and place particular attention on the more recent SPME developments of new extraction phases, as this is recognized as the driving force behind most of the advances in the technique in this area. We address quantitation in this review with a concise, yet assertive, discussion on calibration strategies of SPME methods in complex matrices. [ABSTRACT FROM AUTHOR]

Background Ethylene glycol poisoning remains a rare but important presentation to acute toxicology units. Guidelines recommended that ethylene glycol should be available as an 'urgent' test within 4 h, but these are difficult to deliver in practice. This study assessed a semi-quantitative enzymatic spectrophotometric assay for ethylene glycol compatible with automated platforms. Methods The ethylene glycol method was assessed in 21 samples from patients with an increased anion gap and metabolic acidosis not due to ethylene glycol ingestion, and seven samples known to contain ethylene glycol. All samples were analysed in random order in a blinded manner to their origin on a laboratory spectrophotometer. Results In this study, seven samples were known to contain ethylene glycol at concentrations >100 mg/L. The method correctly identified all seven samples as containing ethylene glycol. No false-positives were observed. Thirteen samples gave clear negative results. Ethylene glycol was present at <20 mg/L in one sample, but this sample remained within the limits of the negative control. Passing-Bablock correlation of estimates of ethylene glycol concentration against results obtained when the samples had been analysed using the quantitative method on an automated analyser showed a good correlation (R = 0.84) but with an apparent under-recovery. Conclusions A semi-quantitative assay for ethylene glycol was able to discriminate well between samples containing ethylene glycol and those with other causes of acidosis. It is a practical small-scale assay for rapid identification of cases of ethylene glycol poisoning. [ABSTRACT FROM AUTHOR]

The method of least-squares (LS) has a built-in procedure for estimating the standard errors (SEs) of the adjustable parameters in the fit model: They are the square roots of the diagonal elements of the covariance matrix. This means that one can use least-squares to obtain numerical values of propagated errors by defining the target quantities as adjustable parameters in an appropriate LS fit model. Often this will be an exact, weighted, nonlinear fit, requiring special precautions to circumvent program idiosyncrasies and extract the desired a priori SEs. These procedures are reviewed for several commercial programs and illustrated specifically for the KaleidaGraph program. Examples include the estimation of ΔHº, ΔSº, ΔGº, and Kº(T) and their SEs from Kº (equilibrium constant) values at two temperatures, with and without uncertainty in T, which is included using the effective variance method, a general-purpose LS procedure for including uncertainty in independent variables. In some cases, the target quantities can be obtained from the original data analysis, by redefining the fit model to include the quantity of interest as an adjustable parameter, automatically handling correlation problems. Examples include the uncertainty in the fit function itself, line areas from spectral line profile data, and the analysis of spectrophotometric data for complex formation. [ABSTRACT FROM AUTHOR]

Aerosols formed during shooting events were studied with various techniques including the wide range size resolving sampling system Nano-ID® Select, followed by inductively coupled plasma mass spec-trometry chemical analysis, scanning electron microscopy, and fast mobility particle sizing. The total lead mass aerosol concentration ranged from 2.2 to 72 µg m-3. It was shown that the mass concentration of the most toxic compound lead is much lower than the total mass concentration. The deposition fraction in various compartments of the respiratory system was calculated using the ICRP lung deposition model. It was found that the deposition fraction in the alveolar range varies by a factor >3 for the various aerosols collected, depending on the aerosol size distribution and total aerosol concentration, demonstrating the importance of size resolved sampling in health risk evaluation. The proportion of the total mass of airborne particles deposited in the respiratory tract varies from 34 to 70%, with a median of 55.9%, suggesting the health risk based upon total mass significantly overestimates the accumulated dose and therefore the health risk. A comparison between conventional and so called 'green' ammunition confirmed significant lowering of concentrations of lead and other toxic metals like antimony in the atmosphere of indoor shooting ranges using 'green' ammunition, although higher concentrations of manganese and boron were measured. These metals are likely to be the constituents of new types of primers. They occur predominantly in the size fraction <250nm of aerosols. [ABSTRACT FROM AUTHOR]

For solid sample quantitative analyses by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), the main analytical problem is the calibration step: evaluation of the function (equation) that correlates the signal with the concentration of analytes in the sample. Except for basic standards preparation, problems result from non-stoichiometric effects during vaporization, transport of ablated aerosols, atomization, and ionization in the plasma. These effects, called elemental fractionation, are mainly sample matrix dependent and thus suggest that standards used for calibration should accurately match the sample matrix. Preparation of such standards is a difficult and time-consuming process, so since the beginning of LA-ICP-MS applications for quantification of solid sample composition, different approaches have been developed to solve the problem, primarily complete matrix matching. Because the calibration of LA-ICP-MS is a key factor for its quantification capabilities, this review summarizes recent calibration approaches and related standards preparation techniques for the analysis of various solid materials by LA-ICP-MS. Selected papers concern the application of reference glasses, solution based standards, synthetic standards based on the main sample matrix component or powdered matrix certified reference materials, matrix-matched standards based on spiked sample materials, and non-matrix-matched standards, for calibration. Isotope dilution methods and signal normalization protocols used in order to improve precision are also considered. [ABSTRACT FROM AUTHOR]

The oxidation and reduction behavior of cefdinir (CEF) was studied by experimental methods and computational calculations at B3LYP/6-31+G (d)//AM1. Voltammetric studies were carried out based on two irreversible reduction peaks at approximately -0.5 and -1.2 V on a hanging mercury drop electrode (HMDE) and on one irreversible oxidation peak at approximately 1.0 V on a glassy carbon electrode (GCE) versus Ag/AgCl, KCl (3.0M) in Britton-Robinson (BR) buffer at pH 4.2 and 5.0, respectively. Differential pulse adsorptive stripping voltammetric methods have been developed and validated for determination of CEF in different samples. The linear range was established as 0.25-40.0 µM for HMDE and 0.40-10.0 µM for GCE. Limit of quantification was calculated to be 0.20 and 0.26 µM for HMDE and GCE, respectively. These methods were successfully applied to assay the drug in tablets and human serum with good recoveries between 92.7% and 107.3% having relative standard deviation less than 10%. [ABSTRACT FROM AUTHOR]

Rational functions of the Padé type are used for the calibration curve (CCM), and standard addition (SAM) methods purposes. In this paper, the related functions were applied to results obtained from the analyses of (a) nickel with use of FAAS method, (b) potassium according to FAES method, and © salicylic acid according to HPLC-MS/MS method. A uniform, integral criterion of nonlinearity of the curves, obtained according to CCM and SAM, is suggested. This uniformity is based on normalization of the approximating functions within the frames of a unit area. [ABSTRACT FROM AUTHOR]

Advances in the chemical, crystallographic and isotopic characterisation of geological and environmental materials can often be ascribed to technological improvements in analytical hardware or to innovative approaches to data acquisition and/or its interpretation. This biennial review addresses key laboratory methods that form much of the foundation for analytical geochemistry; again, this contribution is presented as a compendium of laboratory techniques. We highlight advances that have appeared since January 2012 and that are of particular significance for the chemical and isotopic characterisation of geomaterials. Prominent scientists from the selected analytical fields present publications they judge to be particular noteworthy, providing background information about the method and assessing where further opportunities might be anticipated. In addition to the well-established technologies such as thermal ionisation mass spectrometry and plasma emission spectroscopy, this publication also presents new or rapidly growing methods such as electron backscattered diffraction analysis and atom probe tomography - a very sensitive method providing atomic scale information. [ABSTRACT FROM AUTHOR]

This work demonstrates a novel method for rapid determination of total solid content in viscous liquid (polymer-enriched) samples. The method is based multiple headspace extraction gas chromatography (MHE-GC) on a headspace vial at a temperature above boiling point of water. Thus, the trend of water loss from the tested liquid due to evaporation can be followed. With the limited MHE-GC testing (e.g., 5 extractions) and a one-point calibration procedure (i.e., recording the weight difference before and after analysis), the total amount of water in the sample can be determined, from which the total solid contents in the liquid can be calculated. A number of black liquors were analyzed by the new method which yielded results that closely matched those of the reference method; i.e., the results of these two methods differed by no more than 2.3%. Compared with the reference method, the MHE-GC method is much simpler and more practical. Therefore, it is suitable for the rapid determination of the solid content in many polymer-containing liquid samples. [ABSTRACT FROM AUTHOR]

Abstract: There has been a considerable research on the process analytical technology (PAT) and real-time monitoring based on NIR, but the model development is still an important issue and persons in charge have difficulty in building good models. In this study, to realize efficient NIR-based real-time monitoring of ingredient concentration and establish a model development method, we investigated the effect of a calibration set, spectral preprocessing, wavelengths, and other factors on the prediction error through pilot and commercial scale blending experiments. The results confirmed that the small prediction error was realized by a calibration set, including dynamic measurement spectra acquired with the target blender. In addition, the results demonstrated that locally weighted partial least squares (LW-PLS) achieved the smaller prediction error than conventional PLS. The present study has also clarified that spectral preprocessing methods and wavelengths selected to build a model affect the prediction error of ingredient concentration interactively. A wide wavelength range should be selected when the spectral preprocessing does not lessen the effect of baseline variation, while a narrow wavelength range should be selected when it strongly decreases the effect. [Copyright &y& Elsevier]

Purpose: Mammographic density has been shown to be an indicator of breast cancer risk and also reduces the sensitivity of screening mammography. Currently, there is no accepted standard for measuring breast density. Dual energy mammography has been proposed as a technique for accurate measurement of breast density. The purpose of this study is to validate its accuracy in postmortem breasts and compare it with other existing techniques. Methods: Forty postmortem breasts were imaged using a dual energy mammography system. Glandular and adipose equivalent phantoms of uniform thickness were used to calibrate a dual energy basis decomposition algorithm. Dual energy decomposition was applied after scatter correction to calculate breast density. Breast density was also estimated using radiologist reader assessment, standard histogram thresholding and a fuzzy C-mean algorithm. Chemical analysis was used as the reference standard to assess the accuracy of different techniques to measure breast composition. Results: Breast density measurements using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm, and dual energy were in good agreement with the measured fibroglandular volume fraction using chemical analysis. The standard error estimates using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean, and dual energy were 9.9%, 8.6%, 7.2%, and 4.7%, respectively. Conclusions: The results indicate that dual energy mammography can be used to accurately measure breast density. The variability in breast density estimation using dual energy mammography was lower than reader assessment rankings, standard histogram thresholding, and fuzzy C-mean algorithm. Improved quantification of breast density is expected to further enhance its utility as a risk factor for breast cancer. [ABSTRACT FROM AUTHOR]