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Wu S, Jia C, Wang L, Ye C, Li Z, Li W. Rapid characterization of physical properties for the pharmaceutical pellet cores based on NIR spectroscopy and ensemble learning. Eur J Pharm Biopharm 2024; 197:114214. [PMID: 38364874 DOI: 10.1016/j.ejpb.2024.114214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 01/19/2024] [Accepted: 02/09/2024] [Indexed: 02/18/2024]
Abstract
During the development of sustained-release pellets, the physical characteristics of the pellet cores can affect drug release in the preparation. The method based on near-infrared (NIR) spectroscopy and ensemble learning was proposed to swiftly assess the physical properties of the pellet cores. In the research, the potential of three algorithms, direct standardization (DS), partial least squares regression (PLSR) and generalized regression neural network (GRNN), was investigated and compared. The performance of the DS, PLSR and GRNN models were improved after applying bootstrap aggregating (Bagging) ensemble learning. And the Bagging-GRNN model showed the best predictive capacity. Except for inter-particle porosity, the mean absolute deviations of other 11 physical parameters were less than 1.0. Furthermore, the cosine coefficient values between the actual and predicted physical fingerprints was higher than 0.98 for 15 out of the 16 validation samples when using the Bagging-GRNN model. To reduce the model complexity, the 60 variables significantly correlated with angle of repose, particle size (D50) and roundness were utilized to develop the simplified Bagging-GRNN model. And the simplified model showed satisfactory predictive capacity. In summary, the developed ensemble modelling strategy based NIR spectra is a promising approach to rapidly characterize the physical properties of the pellet cores.
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Affiliation(s)
- Sijun Wu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Chaoliang Jia
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Li Wang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Cheng Ye
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Wenlong Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China.
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Wu S, Wang L, Zhou G, Liu C, Ji Z, Li Z, Li W. Strategies for the content determination of capsaicin and the identification of adulterated pepper powder using a hand-held near-infrared spectrometer. Food Res Int 2023; 163:112192. [PMID: 36596130 DOI: 10.1016/j.foodres.2022.112192] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 11/11/2022] [Accepted: 11/15/2022] [Indexed: 11/27/2022]
Abstract
To achieve the goals of rapid content determination of capsaicin and adulteration detection of pepper powder. The method based on the hand-held near-infrared spectrometer combined with ensemble preprocessing was proposed. DoE-based ensemble preprocessing technique was utilized to develop the partial least squares regression models of red pepper [Capsicum annuum L. var. conoides (Mill.) Irish] powders. The performance of final models was evaluated using coefficient of determination (R2), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD). Model development using selective ensemble preprocessing gave the best prediction of capsaicin in Yanjiao pepper powder (R2 = 0.9800, RPD = 7.090, RMSEP = 0.00689) and Tianying pepper powder (R2 = 0.8935, RPD = 3.017, RMSEP = 0.06154). Moreover, the potential of grey wolf optimizer-support vector machine (GWO-SVM) to detect adulterated pepper powder was investigated. The samples were composed of two authentic products and three different adulterants with different adulteration levels. The results showed that the classification accuracy of GWO-SVM model for Yanjiao peppers was over 90 %, which realized the adulteration detection of Yanjiao pepper. And GWO-SVM showed better performance in detecting adulterated Tianying pepper compared to hierarchical cluster analysis, orthogonal partial least squares discriminant analysis and random forest. In summary, the quality control strategy established in this paper can provide a solution for the adulteration detection and quality evaluation of pepper powder in a rapid and on-site way.
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Affiliation(s)
- Sijun Wu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Long Wang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Guoming Zhou
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Chao Liu
- Shandong wisdom instrument Co., Ltd., Jinan 250000, China
| | - Zhongrui Ji
- Shandong wisdom instrument Co., Ltd., Jinan 250000, China
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Wenlong Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; State key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China.
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