Comparison of different chemometric and analytical methods for the prediction of particle size distribution in pharmaceutical powders

Sarraguça, Mafalda C.; Cruz, Ana Rita; Amaral, Maria Helena; Costa, Paulo; Lopes, João A.

doi:10.1007/s00216-010-4230-6

Cited by 20 publications

(19 citation statements)

References 31 publications

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“…As examples, hydrochlorothiazide was determined in powdered pharmaceutical samples (77); chondroitin, glucosamine, and methyl sulfonyl methane in tablets (78); amoxicillin in powdered drugs (79) and oral suspensions (80); ranitidine during the manufacturing process (in granulates and cores) and in the final step (in coated tablets) (81); azithromycin in tablets (82); and particle size distribution in paracetamol powders (83), all using the successful combination NIR spectroscopy/PLS. Glucose, fructose, and maltose were measured in depilatory formulations without any sample pretreatment, based on MIR spectroscopy/PLS, with results similar to HPLC with online IR detection (84).…”

Section: Pharmaceutical and Biomedical Analysesmentioning

confidence: 99%

Recent Applications of First- and Second-Order Multivariate Calibration to Analytical Chemistry

Arancibia

Damiani

Ibáñez

et al. 2014

Journal of AOAC INTERNATIONAL

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This report reviews recent literature on the application of multivariate calibration techniques to both first- and second-order data, aimed at the analytical determination of analytes of interest or sample properties in a variety of industrial, pharmaceutical, food, and environmental samples, including examples of process control. The most used data processing tools are briefly described, with emphasis on the advantages that can be obtained by applying specific combinations of multivariate data and algorithms. The main focus is on works devoted to first-order data (i.e., spectra, chromatograms, etc.) combined with partial least-squares regression, which has become the standard for this type of analytical research. A brief discussion on recent work on second-order data and algorithms is also included, as this field is rapidly growing, although at present it does not show, the general applicability of the first-order counterparts.

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Section: Pharmaceutical and Biomedical Analysesmentioning

confidence: 99%

Recent Applications of First- and Second-Order Multivariate Calibration to Analytical Chemistry

Arancibia

Damiani

Ibáñez

et al. 2014

Journal of AOAC INTERNATIONAL

View full text Add to dashboard Cite

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“…In powder analysis, some qualitative work with NIR has been reported, such as rapid identification of the production area [ 19 ] and brand traceability [ 20 ]. And some work about quantitative analysis has also been done in relating the NIR spectra to different quality attributes of powders, such as content of ingredients [ 6 ], flow ability [ 21 ], content of moisture [ 22 ], and particle size [ 23 ]. And subsequent unit operations of solid dosage preparations could benefit from the quality control of powders.…”

Section: Introductionmentioning

confidence: 99%

Rapid Characterization of Tanshinone Extract Powder by Near Infrared Spectroscopy

Luo

Shi

et al. 2015

International Journal of Analytical Chemistry

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Chemical and physical quality attributes of herbal extract powders play an important role in the research and development of Chinese medicine preparations. The active pharmaceutical ingredients have a direct impact on the herbal extract's efficacy, while the physical properties of raw material affect the pharmaceutical manufacturing process and the final products' quality. In this study, tanshinone extract powders from Salvia miltiorrhiza which are widely used for the treatment of cardiovascular diseases in the clinic are taken as the research object. Both the chemical information and physical information of tanshinone extract powders are analyzed by near infrared (NIR) spectroscopy. The partial least squares (PLS) and least square support vector machine (LS-SVM) models are investigated to build the relationship between NIR spectra and reference values. PLS models performed well for the content of crytotanshinone, tanshinone IIA, the moisture, and average median particle size, while, for specific surface area and tapped density, the LS-SVM models performed better than the PLS models. Results demonstrated NIR to be a valid and fast process analytical technology tool to simultaneously determine multiple quality attributes of herbal extract powders and indicated that there existed some nonlinear relationship between NIR spectra and physical quality attributes.

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“…In addition, Sarraguça et al compared the estimation of the particle size distribution of a pharmaceutical powder using NIR. The estimations were made by considering the former data blocks separately and together using a multiblock approach [18]. Furthermore, particle size determination of amoxicillin trihydrate particles was developed by Bittner.…”

Section: Introductionmentioning

confidence: 99%

Robust PLS Prediction Model for Saikosaponin A inBupleurum chinenseDC. Coupled with Granularity-Hybrid Calibration Set

Du²,

Shi

et al. 2015

Journal of Analytical Methods in Chemistry

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This study demonstrated particle size effect on the measurement of saikosaponin A in Bupleurum chinense DC. by near infrared reflectance (NIR) spectroscopy. Four types of granularity were prepared including powder samples passed through 40-mesh, 65-mesh, 80-mesh, and 100-mesh sieve. Effects of granularity on NIR spectra were investigated, which showed to be wavelength dependent. NIR intensity was proportional to particle size in the first combination-overtone and combination region. Local partial least squares model was constructed separately for every kind of samples, and data-preprocessing techniques were performed to optimize calibration model. The 65-mesh model exhibited the best prediction ability with root mean of square error of prediction (RMSEP) = 0.492 mg·g−1, correlation coefficient (R P) = 0.9221, and relative predictive determinant (RPD) = 2.58. Furthermore, a granularity-hybrid calibration model was developed by incorporating granularity variation. Granularity-hybrid model showed better performance than local model. The model performance with 65-mesh samples was still the most accurate with RMSEP = 0.481 mg·g−1, R P = 0.9279, and RPD = 2.64. All the results presented the guidance for construction of a robust model coupled with granularity-hybrid calibration set.

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Comparison of different chemometric and analytical methods for the prediction of particle size distribution in pharmaceutical powders

Cited by 20 publications

References 31 publications

Recent Applications of First- and Second-Order Multivariate Calibration to Analytical Chemistry

Recent Applications of First- and Second-Order Multivariate Calibration to Analytical Chemistry

Rapid Characterization of Tanshinone Extract Powder by Near Infrared Spectroscopy

Robust PLS Prediction Model for Saikosaponin A inBupleurum chinenseDC. Coupled with Granularity-Hybrid Calibration Set

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