Near-infrared spectroscopic identification and quantification of active pharmaceutical ingredients in closed capsules: a feasibility study for pediatric doses

2019 ◽  
Vol 11 (40) ◽  
pp. 5185-5194
Author(s):  
David L. Mainka ◽  
Andreas Link
Keyword(s):  
Feasibility Study ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Active Pharmaceutical Ingredients ◽  
Pharmaceutical Ingredients ◽  
Infrared Spectroscopic ◽  
Quality Checks ◽  
3D Printed ◽  
Spectroscopic Identification ◽  
Identification And Quantification

3D printed NIR spectroscopy sample holders were evaluated as means for improvement of quality checks of pharmacy compounded capsules.

Feasibility study for rapid near-infrared spectroscopic identification of different gallbladder diseases by direct analysis of bile juice

The Analyst ◽  
2019 ◽  
Vol 144 (24) ◽  
pp. 7236-7241
Author(s):  
Eunjin Jang ◽  
Tung Duy Vu ◽  
Dongho Choi ◽  
Yun Kyung Jung ◽  
Kyeong Geun Lee ◽  
...  
Keyword(s):  
Feasibility Study ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Direct Analysis ◽  
Gallbladder Diseases ◽  
Infrared Spectroscopic ◽  
Spectroscopic Identification

A whole-sample-covering near-infrared (NIR) spectroscopy scheme has been adopted for the simple drop-and-dry measurement of raw bile juice for the identification of gallbladder (GB) diseases of stone, polyp, and cancer.

scholarly journals Authentication of Antibiotics Using Portable Near-Infrared Spectroscopy and Multivariate Data Analysis

2020 ◽  
pp. 000370282095808
Author(s):  
Sulaf Assi ◽  
Basel Arafat ◽  
Kathryn Lawson-Wood ◽  
Ian Robertson
Keyword(s):  
Data Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Active Pharmaceutical Ingredients ◽  
Global Public Health ◽  
Generic Medicines ◽  
Pharmaceutical Ingredients ◽  
Amoxicillin Trihydrate ◽  
Characteristic Features

Counterfeit medicines represent a global public health threat warranting the development of accurate, rapid, and nondestructive methods for their identification. Portable near-infrared (NIR) spectroscopy offers this advantage. This work sheds light on the potential of combining NIR spectroscopy with principal component analysis (PCA) and soft independent modelling of class analogy (SIMCA) for authenticating branded and generic antibiotics. A total of 23 antibiotics were measured “nondestructively” using a portable NIR spectrometer. The antibiotics corresponded to six different active pharmaceutical ingredients being: amoxicillin trihydrate and clavulanic acid, azithromycin dihydrate, ciprofloxacin hydrochloride, doxycycline hydrochloride, and ofloxacin. NIR spectra were exported into Matlab R2018b where data analysis was applied. The results showed that the NIR spectra of the medicines showed characteristic features that corresponded to the main excipient(s). When combined with PCA, NIR spectroscopy could distinguish between branded and generic medicines and could classify medicines according to their manufacturing sources. The PCA scores showed the distinct clusters corresponding to each group of antibiotics, whereas the loadings indicated which spectral features were significant. SIMCA provided more accurate classification over PCA for all antibiotics except ciprofloxacin which products shared many overlapping excipients. In summary, the findings of the study demonstrated the feasibility of portable NIR as an initial method for screening antibiotics.

scholarly journals Optimal Regression Method for Near-Infrared Spectroscopic Evaluation of Articular Cartilage

2017 ◽  
Vol 71 (10) ◽  
pp. 2253-2262 ◽  
Author(s):  
Mithilesh Prakash ◽  
Jaakko K. Sarin ◽  
Lassi Rieppo ◽  
Isaac O. Afara ◽  
Juha Töyräs
Keyword(s):  
Articular Cartilage ◽  
Variable Selection ◽  
Least Squares ◽  
Multivariate Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Regression Technique ◽  
Selection Methods ◽  
Tissue Properties ◽  
Infrared Spectroscopic

Near-infrared (NIR) spectroscopy has been successful in nondestructive assessment of biological tissue properties, such as stiffness of articular cartilage, and is proposed to be used in clinical arthroscopies. Near-infrared spectroscopic data include absorbance values from a broad wavelength region resulting in a large number of contributing factors. This broad spectrum includes information from potentially noisy variables, which may contribute to errors during regression analysis. We hypothesized that partial least squares regression (PLSR) is an optimal multivariate regression technique and requires application of variable selection methods to further improve the performance of NIR spectroscopy-based prediction of cartilage tissue properties, including instantaneous, equilibrium, and dynamic moduli and cartilage thickness. To test this hypothesis, we conducted for the first time a comparative analysis of multivariate regression techniques, which included principal component regression (PCR), PLSR, ridge regression, least absolute shrinkage and selection operator (Lasso), and least squares version of support vector machines (LS-SVM) on NIR spectral data of equine articular cartilage. Additionally, we evaluated the effect of variable selection methods, including Monte Carlo uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA), backward interval PLS (BiPLS), genetic algorithm (GA), and jackknife, on the performance of the optimal regression technique. The PLSR technique was found as an optimal regression tool (R2Tissue thickness = 75.6%, R2Dynamic modulus = 64.9%) for cartilage NIR data; variable selection methods simplified the prediction models enabling the use of lesser number of regression components. However, the improvements in model performance with variable selection methods were found to be statistically insignificant. Thus, the PLSR technique is recommended as the regression tool for multivariate analysis for prediction of articular cartilage properties from its NIR spectra.

scholarly journals Feasibility study of cyclodextrins as active pharmaceutical ingredients for the treatment of GM1-gangliosidosis

2016 ◽  
Vol 11 (1) ◽  
pp. 183-184 ◽  
Author(s):  
Yuki Maeda ◽  
Keiichi Motoyama ◽  
Taishi Higashi ◽  
Yuka Horikoshi ◽  
Toru Takeo ◽  
...  
Keyword(s):  
Feasibility Study ◽  
Active Pharmaceutical Ingredients ◽  
Gm1 Gangliosidosis ◽  
Pharmaceutical Ingredients

Near Infrared Spectroscopic Identification of Alkyl Aromatic Esters and Phenyl Ketones

2018 ◽  
Vol 85 (1) ◽  
pp. 27-31
Author(s):  
D. V. Nelyubov ◽  
D. A. Vazhenin ◽  
A. A. Kudriavtsev ◽  
A. Yu. Buzolina
Keyword(s):  
Near Infrared ◽  
Aromatic Esters ◽  
Infrared Spectroscopic ◽  
Spectroscopic Identification ◽  
Phenyl Ketones

3D Printed Reactors for Synthesis of Active Pharmaceutical Ingredients in Continuous Flow

2020 ◽  
Vol 24 (10) ◽  
pp. 2197-2207 ◽  
Author(s):  
Manuel C. Maier ◽  
Alessia Valotta ◽  
Katharina Hiebler ◽  
Sebastian Soritz ◽  
Kristian Gavric ◽  
...  
Keyword(s):  
Continuous Flow ◽  
Active Pharmaceutical Ingredients ◽  
Pharmaceutical Ingredients ◽  
3D Printed

scholarly journals Simultaneous Quantification of Paracetamol and Caffeine in Powder Blends for Tableting by NIR-Chemometry

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Dana Maria Muntean ◽  
Cristian Alecu ◽  
Ioan Tomuta
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Method Development ◽  
Near Infrared ◽  
Reference Method ◽  
Active Pharmaceutical Ingredients ◽  
Pharmaceutical Ingredients ◽  
Drug Content ◽  
Powder Blends

Near-infrared spectroscopy (NIRS) is a technique widely used for rapid and nondestructive analysis of solid samples. A method for simultaneous analysis of the two components of paracetamol and caffeine from powder blends has been developed by using chemometry with near-infrared spectroscopy (NIRS). The method development was performed on samples containing 80, 90, 100, 110, and 120% active pharmaceutical ingredients, and near-infrared spectroscopy (NIRS) spectra of prepared powder blends were recorded and analyzed in order to develop models for the prediction of drug content. Many calibration models were applied in order to perform quantitative determination of drug content in powder, and choosing the appropriate number of factors (principal components) proved to be of highly importance for a PLS chemometric calibration. Once the methods were developed, they were validated in terms of trueness, precision, and accuracy. The results obtained by NIRS methods were compared with those obtained by HPLC reference method, and no significant differences were found. Therefore, the NIR chemometry methods were proved to be a suitable tool for predicting chemical properties of powder blends and for simultaneous determination of active pharmaceutical ingredients.

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