Quantification of an Active Substance in a Tablet by NIR and Raman Spectroscopy

1998 ◽  
Vol 6 (1) ◽  
pp. 279-289 ◽  
Author(s):  
Ingela Jedvert ◽  
Mats Josefson ◽  
Frans Langkilde
Keyword(s):  
Raman Spectroscopy ◽  
Active Substance ◽  
Reference Method ◽  
Spectroscopic Technique ◽  
Calibration Model ◽  
Baseline Correction ◽  
Spectroscopic Techniques ◽  
Long Term Stability ◽  
Calibration Models

Spectroscopic techniques in combination with chemometrics give opportunities to analyse tablets without time-consuming sample preparation. The aim of the present study was to develop a method to quantify the active substance, isosorbide-5-mononitrate, in Imdur® 120 mg tablets either by NIR diffuse reflectance or Raman spectroscopy. The calibration set was selected to simulate, with the available samples, as closely as possible a full factorial design with three factors. The reference method was liquid chromatography (LC). Calibration models with different baseline correction methods, different parts of wavelength range and different measures of weights have been evaluated. The calibration model found for each spectroscopic technique is discussed. The accuracy for the spectroscopic techniques were equal in merit to the LC method. Both the NIR and the Raman calibrations also showed a good long-term stability. With the baseline correction methods used for the spectra, it was possible to analyse tablets after 1.5 years. In conclusion it is possible to quantify Imdur® 120 mg with either NIR or Raman spectroscopy.

scholarly journals Development of a general calibration model and long-term performance evaluation of low-cost sensors for air pollutant gas monitoring

2019 ◽  
Vol 12 (2) ◽  
pp. 903-920 ◽  
Author(s):  
Carl Malings ◽  
Rebecca Tanzer ◽  
Aliaksei Hauryliuk ◽  
Sriniwasa P. N. Kumar ◽  
Naomi Zimmerman ◽  
...  
Keyword(s):  
Electrochemical Sensors ◽  
Low Cost ◽  
Air Pollutant ◽  
Calibration Model ◽  
Linear Regression Models ◽  
Quadratic Regression ◽  
Neural Network Models ◽  
Calibration Models ◽  
New Models

Abstract. Assessing the intracity spatial distribution and temporal variability in air quality can be facilitated by a dense network of monitoring stations. However, the cost of implementing such a network can be prohibitive if traditional high-quality, expensive monitoring systems are used. To this end, the Real-time Affordable Multi-Pollutant (RAMP) monitor has been developed, which can measure up to five gases including the criteria pollutant gases carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3), along with temperature and relative humidity. This study compares various algorithms to calibrate the RAMP measurements including linear and quadratic regression, clustering, neural networks, Gaussian processes, and hybrid random forest–linear regression models. Using data collected by almost 70 RAMP monitors over periods ranging up to 18 months, we recommend the use of limited quadratic regression calibration models for CO, neural network models for NO, and hybrid models for NO2 and O3 for any low-cost monitor using electrochemical sensors similar to those of the RAMP. Furthermore, generalized calibration models may be used instead of individual models with only a small reduction in overall performance. Generalized models also transfer better when the RAMP is deployed to other locations. For long-term deployments, it is recommended that model performance be re-evaluated and new models developed periodically, due to the noticeable change in performance over periods of a year or more. This makes generalized calibration models even more useful since only a subset of deployed monitors are needed to build these new models. These results will help guide future efforts in the calibration and use of low-cost sensor systems worldwide.

scholarly journals Monitoring the Caustic Dissolution of Aluminum Alloy in a Radiochemical Hot Cell Using Raman Spectroscopy

2020 ◽  
Vol 74 (10) ◽  
pp. 1252-1262
Author(s):  
Luke R. Sadergaski ◽  
David W. DePaoli ◽  
Kristian G. Myhre
Keyword(s):  
Raman Spectroscopy ◽  
Analytical Data ◽  
Practical Method ◽  
Chemical Processing ◽  
Spectroscopic Techniques ◽  
Least Squares Regression ◽  
Radioactive Materials ◽  
Calibration Models ◽  
Dissolved Aluminum ◽  
Analytical Approaches

Chemical processing of highly radioactive materials commonly takes place in heavily shielded hot cells. The remote, real-time monitoring of chemical processing streams via optical spectroscopic techniques in hot cells may be particularly useful. Here, we describe the implementation of Raman spectroscopy and chemometric analysis to monitor the dissolution of aluminum-clad targets containing irradiated aluminum–neptunium oxide cermet pellets in caustic solutions in a hot cell environment. Partial least squares regression analysis was used to generate calibration models to quantify the concentration of dissolved aluminum, nitrate, and hydroxide in solutions within the radiochemical hot cell. This work explored a systematic approach to optimize a matrix of calibration standards using a D-optimal experimental design. The Design of Experiments-based regression model, in comparison to more traditional analytical approaches, was found to be the more practical method for building calibration models, with fewer samples, to obtain informative analytical data from Raman spectra.

Accuracy of the FT-NIR Method in Evaluating the Fat Content of Milk Using Calibration Models Developed for the Reference Methods According to Röse-Gottlieb and Gerber

2016 ◽  
Vol 99 (5) ◽  
pp. 1305-1309 ◽  
Author(s):  
Jiri Mlcek ◽  
Lukas Dvorak ◽  
Kvetoslava Sustova ◽  
Katarzyna Szwedziak
Keyword(s):  
Reference Method ◽  
Nir Spectroscopy ◽  
Fat Content ◽  
Cow’S Milk ◽  
Calibration Model ◽  
Cow's Milk ◽  
Near Ir ◽  
Calibration Models ◽  
The Rose ◽  
Raw Cow’S Milk

Abstract The study examined the effect of the choice of reference method on the functionality and reliability of calibrations in near-IR (NIR) spectroscopy intended for measuring the fat content in raw cow's milk. The fat content in the milk samples was evaluated using methods according to either Röse-Gottlieb or Gerber. The same samples were then subjected to analysis on an Antaris FT-NIR spectrometer. Using a partial least-squares algorithm, calibration models were created for both methods from the values measured. The calibration models show very good values of standard error of calibration: 0.133 for the Gerber method and 0.095 for the Röse-Gottlieb method. These calibrations were subsequently used to analyze 30 new samples of cow's milk of undefined fat content, and the differences in the values were evaluated using statistical paired t-test to a median value at a probability level of α = 0.05. No statistically significant differences were found. It was revealed that the reference method used for calibrating the device evaluating the fat content in raw cow's milk has no effect on the functionality and reliability of the calibration model.

scholarly journals Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring

2018 ◽  
Author(s):  
Carl Malings ◽  
Rebecca Tanzer ◽  
Aliaksei Hauryliuk ◽  
Sriniwasa P. N. Kumar ◽  
Naomi Zimmerman ◽  
...  
Keyword(s):  
Linear Models ◽  
Low Cost ◽  
Air Pollutant ◽  
Superior Performance ◽  
Calibration Model ◽  
Quadratic Regression ◽  
Calibration Models ◽  
Using Data ◽  
One Year

Abstract. Assessing the intra-city spatial distribution and temporal variability of air quality can be facilitated by a dense network of monitoring stations. However, the cost of implementing such a network can be prohibitive if traditional high-quality, expensive monitoring systems are used. To this end, the Real-time Affordable Multi-Pollutant (RAMP) monitor has been developed, which can measure up to five gases including the criteria pollutant gases carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3), along with temperature and relative humidity. This study compares various algorithms to calibrate the RAMP measurements including linear and quadratic regression, clustering, neural networks, Gaussian processes, and random forests. Using data collected by more than sixty RAMP monitors over periods ranging up to eighteen months, it was found that quadratic regression models or a hybrid of random forest and linear models tend to be the most effective calibration models overall. In specific cases, other types of models can have comparable or even superior performance. Furthermore, generalized calibration models may be used instead of individual models with only a small reduction in overall performance. For long-term deployments, it is recommended that new models be developed each year, due to the noticeable change in performance when models for one year were used for processing data collected in the subsequent year. This makes annually-developed generalized calibration models even more useful since only a subset of deployed monitors are needed to build these models. These results will help guide future efforts in the calibration and use of low-cost sensor systems worldwide.

scholarly journals Non-Targeted Detection of Adulterants in Almond Powder Using Spectroscopic Techniques Combined with Chemometrics

Foods ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 876
Author(s):  
Mohammad Akbar Faqeerzada ◽  
Santosh Lohumi ◽  
Rahul Joshi ◽  
Moon S. Kim ◽  
Insuck Baek ◽  
...  
Keyword(s):  
High Throughput Screening ◽  
Spectroscopic Technique ◽  
Detection Accuracy ◽  
Calibration Model ◽  
Spectroscopic Techniques ◽  
Food Authenticity ◽  
Highly Sensitive ◽  
Powder Samples ◽  
Ft Ir ◽  
Targeted Detection

Methods that combine targeted techniques and chemometrics for analyzing food authenticity can only facilitate the detection of predefined or known adulterants, while unknown adulterants cannot be detected using such methods. Therefore, the non-targeted detection of adulterants in food products is currently in great demand. In this study, FT-IR and FT-NIR spectroscopic techniques were used in combination with non-targeted chemometric approaches, such as one-class partial least squares (OCPLS) and data-driven soft independent modeling of class analogy (DD-SIMCA), to detect adulterants in almond powder adulterated with apricot and peanut powders. The reflectance spectra of 100 pure almond powder samples from two different varieties (50 each) were collected to develop a calibration model based on each spectroscopic technique; each model was then evaluated for four independent sets of two varieties of almond powder samples adulterated with different concentrations of apricot and peanut powders. Classification using both techniques was highly sensitive, the OCPLS approach yielded 90–100% accuracy in different varieties of samples with both spectroscopic techniques, and the DD-SIMCA approach achieved the highest accuracy of 100% when used in combination with FT-IR in all validation sets. Moreover, DD-SIMCA, combined with FT-NIR, achieved a detection accuracy between 91% and 100% for the different validation sets and the misclassified samples belong to the 5% and 7% adulteration sets. These results suggest that spectroscopic techniques, combined with one-class classifiers, can be used effectively in the high-throughput screening of potential adulterants in almond powder.

scholarly journals Provisional standardization of hepcidin assays: creating a traceability chain with a primary reference material, candidate reference method and a commutable secondary reference material

2019 ◽  
Vol 57 (6) ◽  
pp. 864-872 ◽  
Author(s):  
Laura E. Diepeveen ◽  
Coby M.M. Laarakkers ◽  
Gustavo Martos ◽  
Marta E. Pawlak ◽  
Fatih F. Uğuz ◽  
...  
Keyword(s):  
Reference Material ◽  
Clinical Laboratory ◽  
Isotope Dilution Mass Spectrometry ◽  
Reference Method ◽  
Serum Samples ◽  
Traceability Chain ◽  
Long Term Stability ◽  
Purity Analysis ◽  
Primary Reference

Abstract Background Hepcidin concentrations measured by various methods differ considerably, complicating interpretation. Here, a previously identified plasma-based candidate secondary reference material (csRM) was modified into a serum-based two-leveled sRM. We validated its functionality to increase the equivalence between methods for international standardization. Methods We applied technical procedures developed by the International Consortium for Harmonization of Clinical Laboratory Results. The sRM, consisting of lyophilized serum with cryolyoprotectant, appeared commutable among nine different measurement procedures using 16 native human serum samples in a first round robin (RR1). Harmonization potential of the sRM was simulated in RR1 and evaluated in practice in RR2 among 11 measurement procedures using three native human plasma samples. Comprehensive purity analysis of a candidate primary RM (cpRM) was performed by state of the art procedures. The sRM was value assigned with an isotope dilution mass spectrometry-based candidate reference method calibrated using the certified pRM. Results The inter-assay CV without harmonization was 42.1% and 52.8% in RR1 and RR2, respectively. In RR1, simulation of harmonization with sRM resulted in an inter-assay CV of 11.0%, whereas in RR2 calibration with the material resulted in an inter-assay CV of 19.1%. Both the sRM and pRM passed international homogeneity criteria and showed long-term stability. We assigned values to the low (0.95±0.11 nmol/L) and middle concentration (3.75±0.17 nmol/L) calibrators of the sRM. Conclusions Standardization of hepcidin is possible with our sRM, which value is assigned by a pRM. We propose the implementation of this material as an international calibrator for hepcidin.

Monitoring of Emulsion Polymerization Reactors by Raman Spectroscopy: Calibration Model Maintenance

2005 ◽  
Vol 59 (10) ◽  
pp. 1280-1285 ◽  
Author(s):  
Oihana Elizalde ◽  
José M. Asua ◽  
Jose R. Leiza
Keyword(s):  
Raman Spectroscopy ◽  
Fourier Transform ◽  
Partial Least Squares Regression ◽  
Copolymer Composition ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Model Mismatch ◽  
Calibration Models ◽  
Solids Content ◽  
Ft Raman

Fourier transform (FT)-Raman combined with partial least squares regression (PLS-R) calibration models allows the accurate monitoring of solids content, copolymer composition, and free amounts of monomers in starved semi-batch emulsion copolymerizations. The calibration models remain valid as long as the spectrometer and the measuring conditions are unchanged. Unfortunately, maintenance and/or repairing of the spectrometer result in changes in the relative intensities of the peaks of the Raman spectrum, reducing the performance of the calibration models. Therefore, a strategy for the up-date of the PLS-R calibration models is needed. Strategies for calibration model maintenance were assessed, and we found that the best strategy was to build a new model composed of the old PLS-R model plus a PLS-R model able to account for the model mismatch of the old model.

A multivariate calibration procedure for UV/VIS spectrometric quantification of organic matter and nitrate in wastewater

2003 ◽  
Vol 47 (2) ◽  
pp. 63-71 ◽  
Author(s):  
G. Langergraber ◽  
N. Fleischmann ◽  
F. Hofstädter
Keyword(s):  
Municipal Wastewater ◽  
Multivariate Calibration ◽  
Correlation Coefficients ◽  
Calibration Procedure ◽  
Calibration Model ◽  
Long Term Stability ◽  
Global Calibration ◽  
Outlier Tests ◽  
Single Instrument

A submersible UV/VIS spectrometer for in-situ real-time measurements is presented. It utilises the UV/VIS range (200-750 nm) for simultaneous measurement of COD, filtered COD, TSS and nitrate with just a single instrument. A global calibration is provided that is valid for typical municipal wastewater compositions. Usually high correlation coefficients can be achieved using this standard setting. By running a local calibration improvements concerning trueness, precision and long term stability of the results can be achieved. The calibration model is built by means of PLS, various validation procedures and outlier tests to reach both high correlation quality and robustness. This paper describes the UV/VIS spectrometer and the calibration procedure.

Stability and Change in Emotional Intelligence: Exploring the Transition to Young Adulthood

2005 ◽  
Vol 26 (2) ◽  
pp. 100-106 ◽  
Author(s):  
James D.A. Parker ◽  
Donald H. Saklofske ◽  
Laura M. Wood ◽  
Jennifer M. Eastabrook ◽  
Robyn N. Taylor
Keyword(s):  
High School ◽  
Emotional Intelligence ◽  
Postsecondary Education ◽  
Life Transition ◽  
Full Time ◽  
Major Life ◽  
Long Term Stability ◽  
Time Period ◽  
Transition From High School

Abstract. The concept of emotional intelligence (EI) has attracted growing interest from researchers working in various fields. The present study examined the long-term stability (32 months) of EI-related abilities over the course of a major life transition (the transition from high school to university). During the first week of full-time study, a large group of undergraduates completed the EQ-i:Short; 32 months later a random subset of these students (N = 238), who had started their postsecondary education within 24 months of graduating from high school, completed the measures for a second time. The study found EI scores to be relatively stable over the 32-month time period. EI scores were also found to be significantly higher at Time 2; the overall pattern of change in EI-levels was more than can be attributed to the increased age of the participants.

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