Journal
JOURNAL OF ANALYTICAL METHODS IN CHEMISTRY
Volume 2015, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2015/583841
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Funding
- National Natural Science Foundation of China [81303218]
- Ministry of Education of China [20130013120006]
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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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