Near-infrared spectroscopy in the pharmaceutical industry

Blanco, M.; Coello, J.; Iturriaga, H.; Maspoch, S.; Pezuela, C. de la

doi:10.1039/a802531b

Cited by 229 publications

(138 citation statements)

References 142 publications

(140 reference statements)

Supporting

Mentioning

126

Contrasting

Unclassified

Order By: Relevance

“…It is well known that the first and second derivative transformations remove the baseline shifts and bias included in FT-NIR spectra [1,18]. So, the first and second derivatives of the spectra represented by SNV were calculated by Savitzky-Golay method (Step 2) [19], The kth derivative coefficient of is denoted by…”

Section: Discussionmentioning

confidence: 99%

“…Mathematical transformations are required to use the FT-NIR data for qualitative analysis. Firstly, in data preprocessing (Step 1 in Fig.1), absorbances (log 1/R) of FT-NIR were transformed by Standard normal variates (SNV) which is reported to have effect of decreasing the fluctuation of absorbance (log 1/R) [1,17,18]. Transformation of absorbance to standard normal variate is represented by Eq.…”

Section: Discussionmentioning

confidence: 99%

“…NIR spectroscopy has important advantages: it allows the nondestructive analysis of solid samples, requires very little or no sample preparation, and enables extremely fast analysis [2]. Furthermore, NIR spectroscopy has already demonstrated its ability to determine different types or species of herbal drugs to screen for their geographical origins or to quantify marker substances.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting Rank of Japanese Green Teas by Derivative Profiles of Spectra Obtained from Fourier Transform Near-Infrared Reflectance Spectroscopy

Ikeda

Altaf‐Ul‐Amin

Parvin

et al. 2008

J. Comput. Aided Chem.

View full text Add to dashboard Cite

A rapid and easy method for extracting features from spectra obtained from Fourier transform near-infrared (FT-NIR) reflectance spectroscopy was examined by using the 1 st and 2 nd derivatives and Spearman's rank correlation. This method can select features from the overall wavelength. Therefore, this method can be considered suitable for the quality estimation of foods. Practically, a set of ranked green tea samples from a Japanese commercial tea contest were analyzed by FT-NIR in order to create a reliable quality-prediction model. The 2 nd derivative was determined for reducing noise and amplifying the fundamental features. Feature selection from the amplified data was performed using relations between the tea ranks and the derivative coefficients. Finally, a reliable quality-prediction model of green tea was formulated by using single linear and PLS regressions. Furthermore, we discuss possibility of the derivative coefficients as feature representation in FT-NIR.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Rank of Japanese Green Teas by Derivative Profiles of Spectra Obtained from Fourier Transform Near-Infrared Reflectance Spectroscopy

Ikeda

Altaf‐Ul‐Amin

Parvin

et al. 2008

J. Comput. Aided Chem.

View full text Add to dashboard Cite

show abstract

“…Utiliza-se, então, o teste F onde um valor crítico, F crit , é escolhido empiricamente ou a partir de uma tabela do teste F. Se o F cal for menor do que o valor crítico adotado, a amostra desconhecida pode ser classificada como pertencente à classe considerada. 23,[35][36][37] Em geral, esta manipulação matemática já se encontra inserida nos pacotes quimiométricos e os resultados das classificações são apresentados em tabelas. 37 Nessas tabelas as amostras classificadas como pertencentes ao respectivo modelo SIMCA são indicadas pela presença de asteriscos (*); a ausência do asterisco indica que as amostras não estão inseridas naquele respectivo modelo (classe).…”

Section: Introductionunclassified

Análise screening de vinhos empregando um analisador fluxo-batelada, espectroscopia UV-VIS e quimiometria

et al. 2010

View full text Add to dashboard Cite

Recebido em 12/3/09; aceito em 13/8/09; publicado na web em 8/1/10 SCREENING ANALYSIS OF WINES USING FLOW-BATCH ANALYZER, UV-VIS SPECTROSCOPY AND CHEMOMETRICS. A simple, robust, versatile, high analytical frequency method was proposed to check if a sample of wine is within the range of standards set by the manufacturer, using the UV-VIS spectroscopy, multivariate analysis and a flow-batch analyzer. Two hundred and fifty-two samples of wines were analyzed. The results from the application of Hierachical Cluster Analysis (HCA) to the matrix of the data involving all samples show the formation of fifteen types of wine. A Soft Independent Modelling of Class Analogy (SIMCA) model was constructed and used to classify the samples of the overall forecast. As a result, it is observed that the prediction was performed with a success rate of 99.2% for a confidence level of 95%. This shows that the proposed methodology can be used as an effective tool for classifying of samples of wines.Keywords: quality control of wines; flow-batch analyzer; chemometrics. INTRODUÇÃOO vinho é uma bebida simples, resultante da conversão gradual dos açúcares presentes no mosto de uvas frescas em álcool, CO 2 e energia por intermédio do levedo.1 Desta forma, vinhos de má qualidade são facilmente elaborados, bem como bastante propícios a falsificações e/ou adulterações. As falsificações e/ou adulterações de alimentos constituem crime contra a saúde pública, na medida em que são comercializados produtos ilegais, sem controle e sem fiscalização, que podem prejudicar a saúde da população, além de lesar o consumidor que paga caro por um produto de qualidade e de sonegar impostos aos órgãos governamentais. Muitos são os relatos de casos de falsificação e/ou adulterações de vinhos e, preocupadas com este problema, as indústrias produtoras tentam, a todo custo, tomar medidas que amenizem esta situação.Existe uma vasta variedade de vinhos e a forma mais comum para classificá-los corresponde ao seu aspecto visual e pigmentação característica. Porém, existem outras classificações que têm a ver com o processo de vinificação ou, ainda, quanto ao teor de açúcar. A classificação e a caracterização de vinhos vêm sendo realizadas através da utilização de alguns parâmetros como teor alcoólico, pH, acidez fixa e volátil, teor de cinzas etc.2 Para isso têm sido utilizados métodos espectroscópicos de massa, 3 de emissão óptica, 4 de espectroscopia NIR 5 e de absorção atômica em chama. 6 No entanto, a técnica analítica padrão de referência utilizada para a verificação da autenticidade de vinhos no Brasil é a cromatografia.7 Tal técnica apresenta um custo elevado de seus instrumentos e de manutenção constante, bem como requer a preparação de reagentes e soluções padrão com produção de resíduos prejudiciais à saúde e/ou ao meio ambiente, além de apresentar uma baixa frequência analítica. Uma boa alternativa para superar estes inconvenientes é o desenvolvimento de métodos analíticos que combinam o uso da espectrometria de absorção molecular UV-VIS, analisadores automá...

show abstract

“…The application of NIR in solid dosage form processes is becoming increasingly widespread. For reviews on the use of NIR spectroscopy in the pharmaceutical industry, refer to Morisseau & Rhodes (1995) and Blanco et al (1998b).…”

Section: Process Monitoringmentioning

confidence: 99%

Predictive and correlative techniques for the design, optimisation and manufacture of solid dosage forms

Hardy

Cook

2003

Journal of Pharmacy and Pharmacology

View full text Add to dashboard Cite

There is much interest in predicting the properties of pharmaceutical dosage forms from the properties of the raw materials they contain. Achieving this with reasonable accuracy would aid the faster development and manufacture of dosage forms. A variety of approaches to prediction or correlation of properties are reviewed. These approaches have variable accuracy, with no single technique yet able to provide an accurate prediction of the overall properties of the dosage form. However, there have been some successes in predicting trends within a formulation series based on the physicochemical and mechanical properties of raw materials, predicting process scale-up through mechanical characterisation of materials and predicting product characteristics by process monitoring. Advances in information technology have increased predictive capability and accuracy by facilitating the analysis of complex multivariate data, mapping formulation characteristics and capturing past knowledge and experience.

show abstract

Near-infrared spectroscopy in the pharmaceutical industry

Cited by 229 publications

References 142 publications

Predicting Rank of Japanese Green Teas by Derivative Profiles of Spectra Obtained from Fourier Transform Near-Infrared Reflectance Spectroscopy

Predicting Rank of Japanese Green Teas by Derivative Profiles of Spectra Obtained from Fourier Transform Near-Infrared Reflectance Spectroscopy

Análise screening de vinhos empregando um analisador fluxo-batelada, espectroscopia UV-VIS e quimiometria

Predictive and correlative techniques for the design, optimisation and manufacture of solid dosage forms

Contact Info

Product

Resources

About