Quality-by-Design: Multivariate Model for Multicomponent Quantification in Refining Process of Honey

Xiaoying Li; Zhisheng Wu; Xin Feng; Shanshan Liu; Xiaojie Yu; Qun Ma; Yanjiang Qiao

doi:10.4103/0973-1296.196310

Quality-by-Design: Multivariate Model for Multicomponent Quantification in Refining Process of Honey

Pharmacogn Mag. 2017 Jan-Mar;13(49):193-198. doi: 10.4103/0973-1296.196310.

Authors

Xiaoying Li¹, Zhisheng Wu¹, Xin Feng¹, Shanshan Liu¹, Xiaojie Yu¹, Qun Ma¹, Yanjiang Qiao¹

Affiliation

¹ Department of Science and Technology Development of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, P.R. China.

Abstract

Objective: A method for rapid analysis of the refining process of honey was developed based on near-infrared (NIR) spectroscopy.

Methods: Partial least square calibration models were built for the four components after the selection of the optimal spectral pretreatment method and latent factors.

Results: The models covered the samples of different temperatures and time points, therefore the models were robust and universal.

Conclusions: These results highlighted that the NIR technology could extract the information of critical process and provide essential process knowledge of the honey refining process.

Summary: A method for rapid analysis of the refining process of honey was developed based on near-infrared (NIR) spectroscopy. Abbreviation used: NIR: Near-infrared; 5-HMF: 5-hydroxymethylfurfural; RMSEP: Root mean square error of prediction; R: correlation coefficients; PRESS: prediction residual error-sum squares; TCM: Traditional Chinese medicine; HPLC: High-performance liquid chromatography; HPLC-DAD: HPLC-diode array detector; PLS: Partial least square; MSC: multiplicative scatter correction; RMSECV: Root mean square error of cross validation; RPD: Residual predictive deviation; 1D: 1^st order derivative; SG: Savitzky-Golay smooth; 2D: 2^nd order derivative.

Keywords: Honey; near-infrared; partial least; rapid analysis; refining process; squares.