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Awotunde O, Cai J, Azar CGE, Medina D, Eyolfson SI, Hayes K, Waffo C, Djang'eing'a RM, Ziemons EM, Sacré PY, Lieberman M. Field assessment of active ingredient quantity in pharmaceutical tablets with limited calibration of near infrared spectra: An application to ciprofloxacin tablets. J Pharm Biomed Anal 2024; 246:116189. [PMID: 38733763 DOI: 10.1016/j.jpba.2024.116189] [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: 12/19/2023] [Revised: 04/12/2024] [Accepted: 04/26/2024] [Indexed: 05/13/2024]
Abstract
Portable near-infrared (NIR) spectrophotometers have emerged as valuable tools for identifying substandard and falsified pharmaceuticals (SFPs). Integration of these devices with chemometric and machine learning models enhances their ability to provide quantitative chemical insights. However, different NIR spectrophotometer models vary in resolution, sensitivity, and responses to environmental factors such as temperature and humidity, necessitating instrument-specific libraries that hinder the wider adoption of NIR technology. This study addresses these challenges and seeks to establish a robust approach to promote the use of NIR technology in post-market pharmaceutical analysis. We developed support vector machine and partial least squares regression models based on binary mixtures of lab-made ciprofloxacin and microcrystalline cellulose, then applied the models to ciprofloxacin dosage forms that were assayed with high performance liquid chromatography (HPLC). A receiver operating characteristic (ROC) analysis was performed to set spectrophotometer independent NIR metrics to evaluate ciprofloxacin dosage forms as "meets standard," "needs HPLC assay," or "fails standard." Over 200 ciprofloxacin tablets representing 50 different brands were evaluated using spectra acquired from three types of NIR spectrophotometer with 85% of the prediction agreeing with HPLC testing. This study shows that non-brand-specific predictive models can be applied across multiple spectrophotometers for rapid screening of the conformity of pharmaceutical active ingredients to regulatory standard.
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Affiliation(s)
- Olatunde Awotunde
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Jin Cai
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA
| | | | - Diane Medina
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Samantha I Eyolfson
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Kathleen Hayes
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Christelle Waffo
- University of Liege (ULiege), CIRM, Laboratory of Pharmaceutical Analytical Chemistry, Department of Pharmacy, Liege, Belgium
| | - Roland Marini Djang'eing'a
- University of Liege (ULiege), CIRM, Laboratory of Pharmaceutical Analytical Chemistry, Department of Pharmacy, Liege, Belgium
| | - Eric M Ziemons
- University of Liege (ULiege), CIRM, Laboratory of Pharmaceutical Analytical Chemistry, Department of Pharmacy, Liege, Belgium
| | - Pierre-Yves Sacré
- University of Liege (ULiege), CIRM, Research Support Unit in Chemometrics, Department of Pharmacy, Liege, Belgium
| | - Marya Lieberman
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA.
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Ciza P, Sacre PY, Waffo C, Kimbeni T, Masereel B, Hubert P, Ziemons E, Marini R. " Comparison of several strategies for the deployment of a multivariate regression model on several handheld NIR instruments. Application to the Quality Control of Medicines. ". J Pharm Biomed Anal 2022; 215:114755. [DOI: 10.1016/j.jpba.2022.114755] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 03/22/2022] [Accepted: 04/03/2022] [Indexed: 11/26/2022]
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Zhang Z, Li Y, Li C, Wang Z, Chen Y. Algorithm of Stability-Analysis-Based Feature Selection for NIR Calibration Transfer. SENSORS 2022; 22:s22041659. [PMID: 35214562 PMCID: PMC8880237 DOI: 10.3390/s22041659] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/11/2022] [Accepted: 02/18/2022] [Indexed: 12/03/2022]
Abstract
For conventional near-infrared spectroscopy (NIR) technology, even within the same sample, the NIR spectral signal can vary significantly with variation of spectrometers and the spectral collection environment. In order to improve the applicability and application of NIR prediction models, effective calibration transfer is essential. In this study, a stability-analysis-based feature selection algorithm (SAFS) for NIR calibration transfer is proposed, which is used to extract effective spectral band information with high stability between the master and slave instruments during the calibration transfer process. The stability of the spectrum bands shared between the master and slave instruments is used as the evaluation index, and the genetic algorithm was used to select suitable thresholds to filter out the spectral feature information suitable for calibration transfer. The proposed SAFS algorithm was applied to two near-infrared datasets of corn oil content and larch wood density. Simultaneously, its calibration transfer performances were compared with two classical feature selection methods. The effects of different preprocessing algorithms and calibration transfer algorithms were also assessed. The model with the feature variables selected by the SAFS obtained the best prediction. The SAFS algorithm can simplify the spectral data to be transferred and improve the transfer efficiency, and the universality of the SAFS allows it to be used to optimize calibration transfer in various situations. By combining different preprocessing and classic feature selection methods with this, the sensitivity of the correlation between spectral data and component information are improved significantly, as well as the effect of calibration transfer, which will be deeply developed.
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Shan P, Li Z, Wang Q, He Z, Wang S, Zhao Y, Wu Z, Peng S. Self-organizing maps-based generalized feature set selection for model adaption without reference data for batch process. Anal Chim Acta 2021; 1188:339205. [PMID: 34794558 DOI: 10.1016/j.aca.2021.339205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 10/19/2021] [Accepted: 10/20/2021] [Indexed: 12/01/2022]
Abstract
When fourier transform infrared spectroscopy (FTIR) techniques combined with multivariate calibration are used to measure the key process features or analyte concentrations during batch process, model adaption is indispensable for maintaining the predictability of a primary calibration model in new secondary batches. Many model adaption methods conforming to the actual application scenario of batch process have been proposed. Here we report on a novel standard-free model adaption method without reference measurement called variable selection strategy with self-organizing maps (VSSOM). It uses self-organizing maps (SOM) to classify the whole spectral variables into multiple classes according to the spectra from primary batch and secondary batch, respectively; and the corresponding primary feature subsets and secondary feature subsets are formed firstly. Secondly, candidate feature subsets without empty elements are generated by operating intersection between any primary feature subsets and any secondary feature subsets. Thirdly, the candidate feature subset with minimum root mean square error of cross-validation (RMSECV) for the primary calibration set is selected as the optimal feature subset. In this manner, the optimal feature subset can be identified from the candidate feature subsets. In other words, VSSOM aims to create a stable and consistent feature subset across different batches provided that it selects better features within the intersection sets between primary feature subsets and any secondary feature subsets. Two batch process datasets (γ-polyglutamic acid fermentation and paeoniflorin extraction) are presented for comparing the VSSOM method with No transfer partial least squares (PLS), boxcar signal transfer (BST), successive projection algorithm (SPA), transfer component analysis (TCA) and domain-invariant iterative partial least squares (DIPALS). Experimental results show that VSSOM has superior performance and comparable prediction performance in all the scenarios.
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Affiliation(s)
- Peng Shan
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China.
| | - Zhigang Li
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China
| | - Qiaoyun Wang
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China
| | - Zhonghai He
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China
| | - Shuyu Wang
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China
| | - Yuhui Zhao
- School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China
| | - Zhui Wu
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China
| | - Silong Peng
- Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
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Mishra P, Nikzad-Langerodi R, Marini F, Roger JM, Biancolillo A, Rutledge DN, Lohumi S. Are standard sample measurements still needed to transfer multivariate calibration models between near-infrared spectrometers? The answer is not always. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116331] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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