Score-based quantitative principal component analysis with application to the study of active pharmaceutical ingredients based on attenuated total reflection fourier-transform-infrared spectra

2016 ◽  
Vol 31 (2) ◽  
pp. e2863 ◽  
Author(s):  
Grażyna Balcerowska-Czerniak ◽  
Bogumiła Kupcewicz
Keyword(s):  
Principal Component Analysis ◽  
Fourier Transform ◽  
Infrared Spectra ◽  
Principal Component ◽  
Fourier Transform Infrared ◽  
Component Analysis ◽  
Total Reflection ◽  
Attenuated Total Reflection ◽  
Active Pharmaceutical Ingredients ◽  
Pharmaceutical Ingredients

Chemometric Approach to a Rapid Attenuated Total Reflection Fourier Transform Infrared Analysis of Complex Heroin-Based Mixtures

2020 ◽  
pp. 000370282096971
Author(s):  
Nataša Radosavljević Stevanović ◽  
Milena Jovanović ◽  
Federico Marini ◽  
Slavica Ražić
Keyword(s):  
Principal Component Analysis ◽  
Fourier Transform ◽  
Principal Component ◽  
Fourier Transform Infrared ◽  
Total Reflection ◽  
Attenuated Total Reflection ◽  
Illegal Market ◽  
Ft Ir

Heroin is one of the most frequently seized drugs in Southeastern Europe. Due to the position in the Balkan route, the Republic of Serbia keeps important role in suppression of the trafficking of heroin for domestic and foreign illegal market. This research is aimed to provide a good scientific approach in the field of seized heroin analysis. Two different forms of heroin are present in the illegal market, mostly in mixtures with typical “cutting” agents: caffeine, paracetamol, and sugars. It was observed that the quantity of pure heroin in seized samples slightly increases from year to year. The aim of this study was to produce a reliable and fast procedure for classification of illicit heroin samples and determination of the concentration range of heroin in the samples. For that purpose, the attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR) technique was used and combined with such chemometric methods as principal component analysis, cluster analysis, and partial least squares. Principal component analysis (PCA) as an unsupervised model was used for exploratory purposes to identify trends, similarities, and differences between samples by reducing the dimensionality of the data. The cluster classification of examined samples turned out to be extremely useful to evaluate the possibilities of the ATR FT-IR technique to classify the samples appropriately into the patterns, the constituted clusters. Additionally, partial least square was the suitable method for the purpose of determination of the heroin hydrochloride concentration range in examined samples. It is proved that the joined application of spectroscopy and chemometrics can be extremely convenient and useful for forensic and drugs control laboratories.

PHARMACEUTICAL FORMULATION MANUFACTURER DISCRIMINATION BY INFRARED SPECTROSCOPY AND PRINCIPAL COMPONENT ANALYSIS

2019 ◽  
pp. 7-15
Author(s):  
E.M. Basova ◽  
Yu.N. Litvinenko ◽  
N.А. Polotnyanko
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Ir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Active Pharmaceutical Ingredients ◽  
Pharmaceutical Ingredients ◽  
The Third ◽  
Chemometric Technique ◽  
Tablet Formulations

In the present work Fournier transform infrared (IR) spectroscopy in association with chemometric technique was employed to identify kind of tablet formulations containing paracetamol and/or caffeine as active pharmaceutical ingredients. 13 samples of 5 commercially available brand tablets of different manufacturers and batches were bayed in local pharmacies. IR spectra of samples were recorded in the range 600—4000 cm-1 and subjected to and principal component analysis (PCA) which allowed to clearly identify 5 clusters in the scores plot using the third and the second principal components, corresponding to the brands of tablets. For Paracetamol and Caffeine-sodium benzoate tablets the combination of IR spectroscopy and PCA was able to recognize the manufacturer on the basis of distance between samples in clusters in the PCA scores plot.

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