scholarly journals Multivariate Method Based on Raman Spectroscopy for Quantification of Dipyrone in Oral Solutions

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Luciana Lopes Guimarães ◽  
Letícia Parada Moreira ◽  
Bárbara Faria Lourenço ◽  
Walber Toma ◽  
Renato Amaro Zângaro ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Quality Control ◽  
Principal Component Regression ◽  
Principal Component ◽  
Quantitative Model ◽  
Calibration Model ◽  
Analytical Methodology ◽  
Figures Of Merit ◽  
Model Based ◽  
Raman Probe

This work employed a quantitative model based on Raman spectroscopy and principal component regression (RS/PCR) to quantify the active ingredient dipyrone (metamizole) in commercially available formulations as an analytical methodology for quality control in the pharmaceutical industry. Raman spectra were collected using a dispersive Raman spectrometer (830 nm, 250 mW excitation, and 20 s exposure time) coupled to a Raman probe. Solutions of dipyrone diluted in water in the range of 80 to 120% of the concentration of commercial formulations (500 mg/mL) were used to develop a calibration model based on PCR to obtain the figures of merit for class I validation from the Brazilian Sanitary Surveillance Agency (ANVISA, RE no. 899/2003). This spectral model was then used to predict the concentration of dipyrone in commercial formulations from distinct brands with 500 mg/mL. A prediction error of 6.5 mg/mL (1.3%) was found for this PCR model using the diluted samples. Commercial formulations had predicted concentrations with a difference below 5.0% compared to the label concentration, indicating the applicability of Raman spectroscopy for quality control in the final product.

scholarly journals Quantitative Evaluation of Acetaminophen in Oral Solutions by Dispersive Raman Spectroscopy for Quality Control

2012 ◽  
Vol 27 ◽  
pp. 215-228 ◽  
Author(s):  
Viviane G. Borio ◽  
Rubens Vinha ◽  
Renata A. Nicolau ◽  
Hueder Paulo M. de Oliveira ◽  
Carlos J. de Lima ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Quality Control ◽  
Pharmaceutical Industry ◽  
Raman Spectra ◽  
Near Infrared ◽  
Calibration Model ◽  
Fiber Optic Probe ◽  
Analytical Methodology ◽  
Gold Standard Method ◽  
Optic Probe

This work used dispersive Raman spectroscopy to evaluate acetaminophen in commercially available formulations as an analytical methodology for quality control in the pharmaceutical industry. Raman spectra were collected using a near-infrared dispersive Raman spectrometer (830 nm, 50 mW, 20 s exposure time) coupled to a fiber optic probe. Solutions of acetaminophen diluted in excipient (70 to 120% of the commercial concentration of 200 mg/mL) were used to develop a calibration model based on partial least squares (PLSs) applied to Raman spectra of solutions and, subsequently, obtain linearity, accuracy, precision (repeatability), and sensitivity of the method using the near-infrared spectroscopy (NIRS) as a gold standard method. This model was used to predict the acetaminophen concentration in commercial samples from different lots of acetaminophen formulations (200 mg/mL) with a PLS-prediction error of about 0.6%. Commercial medicines had PLS predicted concentrations errors below 2.5%, whereas NIRS had an error of about 3.7% compared to the label concentration. It has been demonstrated the applicability of Raman spectroscopy with fiber probe for quality control in pharmaceutical industry of commercial formulations.

scholarly journals Constrained regularization for noninvasive glucose sensing using Raman spectroscopy

2015 ◽  
Vol 08 (04) ◽  
pp. 1550022 ◽  
Author(s):  
Wei-Chuan Shih
Keyword(s):  
Raman Spectroscopy ◽  
Multivariate Calibration ◽  
Experimental Studies ◽  
Principal Component Regression ◽  
Principal Component ◽  
Glucose Sensing ◽  
Model Complexity ◽  
Calibration Model ◽  
Noise Rejection ◽  
Implicit Model

Multivariate calibration is an important tool for spectroscopic measurement of analyte concentrations. We present a detailed study of a hybrid multivariate calibration technique, constrained regularization (CR), and demonstrate its utility in noninvasive glucose sensing using Raman spectroscopy. Similar to partial least squares (PLS) and principal component regression (PCR), CR builds an implicit model and requires knowledge only of the concentrations of the analyte of interest. Calibration is treated as an inverse problem in which an optimal balance between model complexity and noise rejection is achieved. Prior information is included in the form of a spectroscopic constraint that can be obtained conveniently. When used with an appropriate constraint, CR provides a better calibration model compared to PLS in both numerical and experimental studies.

Monitoring of Chemical Speciation of DEA – CO2 – Water System by Raman Spectroscopy

2015 ◽  
Vol 1113 ◽  
pp. 358-363 ◽  
Author(s):  
Muhammad Zubair Shahid ◽  
Humbul Suleman ◽  
Adulhalim Shah Maulud ◽  
Mohammad Azmi Bustam Khalil ◽  
Zakaria Man
Keyword(s):  
Raman Spectroscopy ◽  
Water System ◽  
Chemical Species ◽  
Principal Component Regression ◽  
Principal Component ◽  
Carbon Dioxide Separation ◽  
Key Factor ◽  
Kinetics Of ◽  
Respective Characteristic ◽  
Good Agreement

Carbon dioxide separation has gained immense importance since its detrimental effects towards our environment has been realized. Commercially, CO2has been captured by absorption in alkanolamines such as diethanolamine (DEA), since many years. The thermodynamics and kinetics of the process is a key factor towards its efficiency and significantly depends on its qualitative and quantitative speciation. In this work, the analysis of speciation for CO2loaded aqueous DEA has been performed by Raman spectroscopy. Experimentally determined CO2loading data and modified Kent Eisenberg equation was used to quantify the chemical species present. The speciation results were fitted with the respective characteristic Raman peaks of (CO3-, HCO3-, DEACOO-, DEA, DEA+, CO2) by Principal Component Regression (PCR). The fitted results showed good agreement with thermodynamically predicted chemical species.

scholarly journals Preliminary Assessment of Parmigiano Reggiano Authenticity by Handheld Raman Spectroscopy

Foods ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1563
Author(s):  
Mario Li Vigni ◽  
Caterina Durante ◽  
Sara Michelini ◽  
Marco Nocetti ◽  
Marina Cocchi
Keyword(s):  
Raman Spectroscopy ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Calibration Model ◽  
Preliminary Assessment ◽  
Model Based ◽  
Intact Sample ◽  
Non Destructive ◽  
Maximum Content ◽  
Parmigiano Reggiano

Raman spectroscopy, and handheld spectrometers in particular, are gaining increasing attention in food quality control as a fast, portable, non-destructive technique. Furthermore, this technology also allows for measuring the intact sample through the packaging and, with respect to near infrared spectroscopy, it is not affected by the water content of the samples. In this work, we evaluate the potential of the methodology to model, by multivariate data analysis, the authenticity of Parmigiano Reggiano cheese, which is one of the most well-known and appreciated hard cheeses worldwide, with protected denomination of origin (PDO). On the other hand, it is also highly subject to counterfeiting. In particular, it is critical to assess the authenticity of grated cheese, to which, under strictly specified conditions, the PDO is extended. To this aim, it would be highly valuable to develop an authenticity model based on a fast, non-destructive technique. In this work, we present preliminary results obtained by a handheld Raman spectrometer and class-modeling (Soft Independent Modeling of Class Analogy, SIMCA), which are extremely promising, showing sensitivity and specificity of 100% for the test set. Moreover, another salient issue, namely the percentage of rind in grated cheese, was addressed by developing a multivariate calibration model based on Raman spectra. It was possible to obtain a prediction error around 5%, with 18% being the maximum content allowed by the production protocol.

Determination of Chemical Species in MDEA – Carbon Dioxide – Water System by Raman Spectroscopy

2015 ◽  
Vol 1113 ◽  
pp. 261-266 ◽  
Author(s):  
Humbul Suleman ◽  
Muhammad Zubair Shahid ◽  
Abdulhalim Shah Maulud ◽  
Zakaria Man ◽  
Mohammad Azmi Bustam Khalil
Keyword(s):  
Carbon Dioxide ◽  
Raman Spectroscopy ◽  
Chemical Speciation ◽  
Water System ◽  
Chemical Species ◽  
Principal Component Regression ◽  
Principal Component ◽  
Carbon Dioxide Absorption

Alkanolamines based carbon dioxide absorption from flue gases remains the most industrially implemented technique. The effective design of absorbers and associated equipment requires robust thermodynamic and kinetic models thus, instigating research efforts in chemical speciation and characterization of CO2loaded alkanolamine solutions. In this study, the potential of Raman spectroscopy has been investigated to determine the in situ chemical speciation in MDEA – CO2– Water system. The Raman spectra have been fitted to thermodynamic values using principal component regression. Results are in good agreement for carbonate, bicarbonate, MDEA and protonated MDEA chemical species.

Depth-Resolved Near-Infrared Spectroscopy

1996 ◽  
Vol 50 (2) ◽  
pp. 285-291 ◽  
Author(s):  
Nadhamuni G. Nerella ◽  
James K. Drennen
Keyword(s):  
Near Infrared ◽  
Principal Component Regression ◽  
Principal Component ◽  
Depth Resolution ◽  
New Drugs ◽  
Penetration Enhancers ◽  
Calibration Model ◽  
Near Ir ◽  
Hydrogel Matrix

While there is substantial evidence proving the success of transdermal drug delivery, there have been few accomplishments in the area of depth-resolved prediction of drug concentration during diffusion through a matrix. Such a method for noninvasive quantification of a diffusing species could assist in the development of new drugs, dosage forms, and penetration enhancers. Near-infrared depth-resolved measurements were accomplished by strategically controlling the amount of reflected light reaching the detectors using a combination of diaphragms with different-diameter apertures. Near-IR spectra were collected from a set of cellulose and Silastic® membranes to prove the possibility of depth-resolved near-IR measurements. Principal component regression was used to estimate the depth resolution of this method, yielding an average resolution of 31 μm. Further, to demonstrate depth-resolved near-IR spectroscopy in a practical in vitro system, we determined concentrations of salicylic acid (SA) in a hydrogel matrix during diffusion experiments carried out for up to three hours. An artificial-neural-network-based calibration model was developed which predicted SA concentrations accurately ( R2 = 0.993, SEP = 123 μg/mL).

scholarly journals Identification and Quantification of Adulterants in Coffee (Coffea arabica L.) Using FT-MIR Spectroscopy Coupled with Chemometrics

Foods ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 851
Author(s):  
Mauricio Flores-Valdez ◽  
Ofelia Gabriela Meza-Márquez ◽  
Guillermo Osorio-Revilla ◽  
Tzayhri Gallardo-Velázquez
Keyword(s):  
Standard Error ◽  
Coffea Arabica ◽  
Principal Component Regression ◽  
Principal Component ◽  
Quantitative Model ◽  
Economic Benefits ◽  
Production Volume ◽  
Mir Spectroscopy ◽  
Multiple Variables ◽  
Coffea Arabica L

Food adulteration is an illegal practice performed to elicit economic benefits. In the context of roasted and ground coffee, legumes, cereals, nuts and other vegetables are often used to augment the production volume; however, these adulterants lack the most important coffee compound, caffeine, which has health benefits. In this study, the mid-infrared Fourier transform spectroscopy (FT-MIR) technique coupled with chemometrics was used to identify and quantify adulterants in coffee (Coffea arabica L.). Coffee samples were adulterated with corn, barley, soy, oat, rice and coffee husks, in proportions ranging from 1–30%. A discrimination model was developed using the soft independent modeling of class analogy (SIMCA) framework, and quantitative models were developed using such algorithms as the partial least squares algorithms with one variable (PLS1) and multiple variables (PLS2) and principal component regression (PCR). The SIMCA model exhibited an accuracy of 100% and could discriminate among all the classes. The quantitative model with the highest performance corresponded to the PLS1 algorithm. The model exhibited an R2c: ≥ 0.99, standard error of calibration (SEC) of 0.39–0.82, and standard error of prediction (SEP) of 0.45–0.94. The developed models could identify and quantify the coffee adulterants, and it was considered that the proposed methodology can be applied to identify and quantify the adulterants used in the coffee industry.

Calibration Methods for NIRS Instruments: A Theoretical Evaluation and Comparisons by Data Splitting and Simulations

1994 ◽  
Vol 48 (3) ◽  
pp. 327-332 ◽  
Author(s):  
Trygve Almøy ◽  
Espen Haugland
Keyword(s):  
Prediction Error ◽  
Principal Component Regression ◽  
Principal Component ◽  
Calibration Method ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Theoretical Evaluation ◽  
Number Of Components ◽  
Calibration Methods ◽  
Small Eigenvalues

The properties of the recently proposed calibration method called restricted principal component regression (RPCR) were evaluated and compared with partial least-squares regression (PLSR) and two types of principal component regression (PCR1, selected according to the size of the eigenvalues, and PCR2, selected according to the t-value). RPCR can be considered a compromise between PCR and PLSR, since the first component of RPCR is equivalent to the first component of PLSR, while the rest can be regarded as principal components on a space orthogonal to the first. The methods showed almost the same properties when the irrelevant components had small eigenvalues. The prediction error of RPCR selected according to the size of the eigenvalues was intermediate to those of PCR1 and PLSR when the number of components was low, while RPCR and PCR1 nearly coincided when the number of components exceeded the number of relevant ones. The prediction error minimum was about the same for RPCR, PCR1, and PLSR, but the minimum of PLSR was obtained when a lower number of components were included in the calibration model.

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