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Desai PM, Truong T, Marathe S. Detailed accounts of segregation mechanisms and the evolution of pharmaceutical blend segregation analysis: A review. Int J Pharm 2024; 665:124739. [PMID: 39321901 DOI: 10.1016/j.ijpharm.2024.124739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 09/01/2024] [Accepted: 09/20/2024] [Indexed: 09/27/2024]
Abstract
Segregation refers to the separation of components in a powder mixture, resulting in potential issues related to concentration inhomogeneity. Any well-mixed blend that undergoes secondary processing is inherently susceptible to segregation which, if unmitigated, will lead to active compound concentration variance and poorer product quality. The consequences range from adverse financial impact to manufacturers with product failures to the detrimental health effects to product users. Hence, the topic of segregation is of paramount importance to the industry, requiring it to be dissected and scrutinized intensively by scientists worldwide. This review provides a well-crafted theoretical framework designed to understand the common segregation mechanisms that manufacturing facilities face, followed by the efforts to gauge the degree of segregation. To minimize segregation in blends, various approaches - mathematical modeling, empirical experiments, and empirical methods with modeling consideration - have been utilized in segregation research and are covered in this review. The past segregation studies from many fields are discussed, with particular emphasis on pharmaceuticals. The review also discusses the evolution and advances in mixing technology and storage systems implemented by the pharmaceutical industry to prevent segregation. In the conclusion, the authors articulated their perspectives on potential mitigation measures, including suggestions for improvements and future studies.
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Affiliation(s)
- Parind M Desai
- Drug Product Development, Medicine Development & Supply, GSK R&D, Collegeville, PA, USA.
| | - Triet Truong
- Drug Product Development, Medicine Development & Supply, GSK R&D, Collegeville, PA, USA
| | - Sushrut Marathe
- Drug Product Development, Medicine Development & Supply, GSK R&D, Collegeville, PA, USA
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2
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Khanolkar A, Thorat V, Patil B, Samanta G. Towards a real-time release of blends and tablets using NIR and Raman spectroscopy at commercial scales. Pharm Dev Technol 2023; 28:265-276. [PMID: 36847606 DOI: 10.1080/10837450.2023.2185256] [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: 12/15/2022] [Revised: 01/31/2023] [Accepted: 02/20/2023] [Indexed: 03/01/2023]
Abstract
Near Infrared and Raman spectroscopy-based Process Analytical Technology tools were used for monitoring blend uniformity (BU) and content uniformity (CU) for solid oral formulations. A quantitative Partial Least Square model was developed to monitor BU as real-time release testing at a commercial scale. The model having the R2, and root mean square error of 0.9724 and 2.2047, respectively can predict the target concentration of 100% with a 95% confidence interval of 101.85-102.68% even after one year. The tablets from the same blends were investigated for CU using NIR and Raman techniques both in reflection and transmission mode. Raman reflection technique was found to be the best and the PLS model was developed using tablets compressed at different concentrations, hardness, and speed. The model with R2 and RMSE of 0.9766 and 1.9259, respectively was used for the quantification of CU. Both the BU and CU models were validated for accuracy, precision, specificity, linearity, and robustness. The accuracy was proved against the HPLC method with a relative standard deviation of less than 3%. The equivalency for BU by NIR and CU by Raman was evaluated using Schuirmann's Two One-sided tests and found equivalent to HPLC within a 2% acceptable limit.
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Affiliation(s)
- Aruna Khanolkar
- QbD Department, Integrated Product Development, Cipla Ltd, Mumbai, Maharashtra, India
| | - Viraj Thorat
- QbD Department, Integrated Product Development, Cipla Ltd, Mumbai, Maharashtra, India
| | - Bhaskar Patil
- QbD Department, Integrated Product Development, Cipla Ltd, Mumbai, Maharashtra, India
| | - Gautam Samanta
- QbD Department, Integrated Product Development, Cipla Ltd, Mumbai, Maharashtra, India
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Liang Y, Zhao L, Guo J, Wang H, Liu S, Wang L, Chen L, Chen M, Zhang N, Liu H, Nie C. Just-in-Time Learning-Integrated Partial Least-Squares Strategy for Accurately Predicting 71 Chemical Constituents in Chinese Tobacco by Near-Infrared Spectroscopy. ACS OMEGA 2022; 7:38650-38659. [PMID: 36340111 PMCID: PMC9631892 DOI: 10.1021/acsomega.2c04139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 10/06/2022] [Indexed: 06/16/2023]
Abstract
Near-infrared spectroscopy has been widely used to characterize the chemical composition of tobacco because it is fast, economical, and nondestructive. However, few predictive models perform ideally when applied to large spectral libraries of tobacco and its various chemical indicators. In this study, the just-in-time learning-integrated partial least-squares (JIT-PLS) modeling strategy was applied for the first time to quantitatively analyze 71 chemical components in Chinese tobacco. Approximately 18000 tobacco samples from China were analyzed to find appropriately similar measurements and propose suitable and flexible similar subsets from the calibration for each test sample. In total, 879 representative aged tobacco leaf samples and 816 cigarette samples were used as external instances to evaluate the practical predicting ability of the proposed method. The most suitable similar subsets for each test sample could be selected by limiting the Euclidean distance and number of similar subsets to 0-3.0 × 10-9 and 10-300, respectively. The majority of the JIT-PLS models performed significantly better than traditional PLS models. Specifically, using JIT-PLS instead of traditional PLS models increased the R 2 values from 0.347-0.984 to 0.763-0.996, and from 0.179-0.981 to 0.506-0.989 for the prediction of 67 and 71 components in aged tobacco leaf and cigarette samples, respectively. Good prediction ability was demonstrated for routine chemical components, polyphenolic compounds, organic acids, and other compounds, with the mean ratios of prediction to deviation (RPDmean) being 7.74, 4.39, 4.05, and 5.48, respectively). The proposed methodology could simultaneously determine 67 major components in large and complicated tobacco spectral libraries with high precision and accuracy, which will assist tobacco and cigarette quality control in collecting as well as processing stages.
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Affiliation(s)
- Youyan Liang
- Zhengzhou
Tobacco Research Institute of CNTC, Zhengzhou, Henan450001, China
| | - Le Zhao
- Zhengzhou
Tobacco Research Institute of CNTC, Zhengzhou, Henan450001, China
| | - Junwei Guo
- Zhengzhou
Tobacco Research Institute of CNTC, Zhengzhou, Henan450001, China
| | - Hongbo Wang
- Zhengzhou
Tobacco Research Institute of CNTC, Zhengzhou, Henan450001, China
| | - Shaofeng Liu
- Zhengzhou
Tobacco Research Institute of CNTC, Zhengzhou, Henan450001, China
| | - Luoping Wang
- Technology
Center of China Tobacco Yunnan Industrial Co. Ltd., Kunming650231, China
| | - Li Chen
- Zhengzhou
Tobacco Research Institute of CNTC, Zhengzhou, Henan450001, China
| | - Mantang Chen
- Zhengzhou
Tobacco Research Institute of CNTC, Zhengzhou, Henan450001, China
| | - Nuohan Zhang
- Zhengzhou
Tobacco Research Institute of CNTC, Zhengzhou, Henan450001, China
| | - Huimin Liu
- Zhengzhou
Tobacco Research Institute of CNTC, Zhengzhou, Henan450001, China
| | - Cong Nie
- Zhengzhou
Tobacco Research Institute of CNTC, Zhengzhou, Henan450001, China
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Khanolkar A, Patil B, Thorat V, Samanta G. Development of Inline Near-Infrared Spectroscopy Method for Real-Time Monitoring of Blend Uniformity of Direct Compression and Granulation-Based Products at Commercial Scales. AAPS PharmSciTech 2022; 23:235. [PMID: 36002672 DOI: 10.1208/s12249-022-02392-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/08/2022] [Indexed: 11/30/2022] Open
Abstract
Blending is a critical intermediate unit operation for all solid oral formulations. For blend uniformity testing, API content in the blend must be quantified precisely. A detailed study was conducted to demonstrate the suitability of inline NIR (near-infrared) spectroscopy for blend uniformity testing of two solid oral formulations: existing direct compression (DC) product with a multistep blending process and granulation-based product with API granules. Both qualitative and quantitative methods were developed at a laboratory scale using statistical moving block standard deviation (MBSD) and multivariate data analysis such as principal component analysis (PCA) and partial least squares (PLS) regression. The qualitative MBSD method demonstrated that there was no need for multiple steps for the existing DC product. Hence, a simplified single-step process was developed for blending. Quantitative PLS models for blending processes of both the products were developed, validated, and successfully implemented at a commercial scale for the real-time release of blends. Results obtained from the validated model were in good agreement with the current method of sampling and chromatography.
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Affiliation(s)
- Aruna Khanolkar
- QbD Department, Integrated Product Development, Cipla Ltd., Maharashtra, Mumbai, India
| | - Bhaskar Patil
- QbD Department, Integrated Product Development, Cipla Ltd., Maharashtra, Mumbai, India
| | - Viraj Thorat
- QbD Department, Integrated Product Development, Cipla Ltd., Maharashtra, Mumbai, India
| | - Gautam Samanta
- QbD Department, Integrated Product Development, Cipla Ltd., Maharashtra, Mumbai, India.
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Wang K, He K, Du W, Long J. Novel adaptive sample space expansion approach of NIR model for in-situ measurement of gasoline octane number in online gasoline blending processes. Chem Eng Sci 2021. [DOI: 10.1016/j.ces.2021.116672] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Yaginuma K, Tanabe S, Sugiyama H, Kano M. Prediction Performance and Economic Efficiency of Soft Sensors for in-Line Water Content Monitoring in Fluidized Bed Granulation: PP-Based Model vs. NIRS-Based Model. Chem Pharm Bull (Tokyo) 2021; 69:548-556. [PMID: 34078801 DOI: 10.1248/cpb.c20-01016] [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] [Indexed: 11/22/2022]
Abstract
Soft sensors play a crucial role as process analytical technology (PAT) tools. They are classified into physical models, statistical models, and their hybrid models. In general, statistical models are better estimators than physical models. In this study, two types of standard statistical models using process parameters (PPs) and near-infrared spectroscopy (NIRS) were investigated in terms of prediction accuracy and development cost. Locally weighted partial least squares regression (LW-PLSR), a type of nonlinear regression method, was utilized. Development cost was defined as the cost of goods required to construct an accurate model of commercial-scale equipment. Eleven granulation lots consisting of three laboratory-scale, two pilot-scale, and six commercial-scale lots were prepared. Three commercial-scale granulation lots were selected as a validation dataset, and the remaining eight granulation lots were utilized as calibration datasets. The results demonstrated that the PP-based and NIRS-based LW-PLSR models achieved high prediction accuracy without using the commercial-scale data in the calibration dataset. This practical case study clarified that the construction of accurate LW-PLSR models requires the calibration samples with the following two features: 1) located near the validation samples on the subspace spanned by principal components (PCs), and 2) having a wide range of variations in PC scores. In addition, it was confirmed that the reduction in cost and mass fraction of active pharmaceutical ingredient (API) made the PP-based models more cost-effective than the NIRS-based models. The present work supports to build accurate models efficiently and save the development cost of PAT.
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Affiliation(s)
- Keita Yaginuma
- Formulation Technology Research Laboratories, Pharmaceutical Technology Division, Daiichi Sankyo Co., Ltd.,Department of Systems Science, Kyoto University
| | - Shuichi Tanabe
- Formulation Technology Research Laboratories, Pharmaceutical Technology Division, Daiichi Sankyo Co., Ltd
| | | | - Manabu Kano
- Department of Systems Science, Kyoto University
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Kim EJ, Kim JH, Kim MS, Jeong SH, Choi DH. Process Analytical Technology Tools for Monitoring Pharmaceutical Unit Operations: A Control Strategy for Continuous Process Verification. Pharmaceutics 2021; 13:919. [PMID: 34205797 PMCID: PMC8234957 DOI: 10.3390/pharmaceutics13060919] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 05/31/2021] [Accepted: 06/16/2021] [Indexed: 11/16/2022] Open
Abstract
Various frameworks and methods, such as quality by design (QbD), real time release test (RTRT), and continuous process verification (CPV), have been introduced to improve drug product quality in the pharmaceutical industry. The methods recognize that an appropriate combination of process controls and predefined material attributes and intermediate quality attributes (IQAs) during processing may provide greater assurance of product quality than end-product testing. The efficient analysis method to monitor the relationship between process and quality should be used. Process analytical technology (PAT) was introduced to analyze IQAs during the process of establishing regulatory specifications and facilitating continuous manufacturing improvement. Although PAT was introduced in the pharmaceutical industry in the early 21st century, new PAT tools have been introduced during the last 20 years. In this review, we present the recent pharmaceutical PAT tools and their application in pharmaceutical unit operations. Based on unit operations, the significant IQAs monitored by PAT are presented to establish a control strategy for CPV and real time release testing (RTRT). In addition, the equipment type used in unit operation, PAT tools, multivariate statistical tools, and mathematical preprocessing are introduced, along with relevant literature. This review suggests that various PAT tools are rapidly advancing, and various IQAs are efficiently and precisely monitored in the pharmaceutical industry. Therefore, PAT could be a fundamental tool for the present QbD and CPV to improve drug product quality.
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Affiliation(s)
- Eun Ji Kim
- Department of Pharmaceutical Engineering, Inje University, Gimhae-si, Gyeongnam 621-749, Korea; (E.J.K.); (J.H.K.)
| | - Ji Hyeon Kim
- Department of Pharmaceutical Engineering, Inje University, Gimhae-si, Gyeongnam 621-749, Korea; (E.J.K.); (J.H.K.)
| | - Min-Soo Kim
- College of Pharmacy, Pusan National University, Busandaehak-ro 63 heon-gil, Geumjeong-gu, Busan 46241, Korea;
| | - Seong Hoon Jeong
- College of Pharmacy, Dongguk University-Seoul, Dongguk-ro-32, Ilsan-Donggu, Goyang 10326, Korea;
| | - Du Hyung Choi
- Department of Pharmaceutical Engineering, Inje University, Gimhae-si, Gyeongnam 621-749, Korea; (E.J.K.); (J.H.K.)
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Yaginuma K, Tanabe S, Miyano T, Nakagawa H, Suzuki S, Ando S, Kano M. Scale-Free Soft Sensor for Monitoring of Water Content in Fluid Bed Granulation Process. Chem Pharm Bull (Tokyo) 2021; 68:855-863. [PMID: 32879226 DOI: 10.1248/cpb.c20-00315] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
In-line monitoring of granule water content during fluid bed granulation is important to control drug product qualities. In this study, a practical scale-free soft sensor to predict water content was proposed to cope with the manufacturing scale changes in drug product development. The proposed method exploits two key ideas to construct a scale-free soft sensor. First, to accommodate the changes in the manufacturing scale, the process parameters (PPs) that are critical to water content at different manufacturing scales were selected as input variables. Second, to construct an accurate statistical model, locally weighted partial least squares regression (LW-PLSR), which can cope with collinearity and nonlinearity, was utilized. The soft sensor was developed using both laboratory (approx. 4 kg) data and pilot (approx. 25 kg) scale data, and the prediction accuracy in the commercial (approx. 100 kg) scale was evaluated based on the assumption that the process was scaled-up from the pilot scale to the commercial scale. The developed soft sensor exhibited a high prediction accuracy, which was equivalent to the commonly used near-infrared (NIR) spectra-based method. The proposed method requires only standard instruments; therefore, it is expected to be a cost-effective alternative to the NIR spectra-based method.
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Affiliation(s)
- Keita Yaginuma
- Formulation Technology Research Laboratories, Pharmaceutical Technology Division, Daiichi Sankyo Co., Ltd.,Department of Systems Science, Kyoto University
| | - Shuichi Tanabe
- Formulation Technology Research Laboratories, Pharmaceutical Technology Division, Daiichi Sankyo Co., Ltd
| | - Takuya Miyano
- Formulation Technology Research Laboratories, Pharmaceutical Technology Division, Daiichi Sankyo Co., Ltd
| | - Hiroshi Nakagawa
- Formulation Technology Research Laboratories, Pharmaceutical Technology Division, Daiichi Sankyo Co., Ltd
| | - Satoshi Suzuki
- Formulation Technology Research Laboratories, Pharmaceutical Technology Division, Daiichi Sankyo Co., Ltd
| | - Shuichi Ando
- Formulation Technology Research Laboratories, Pharmaceutical Technology Division, Daiichi Sankyo Co., Ltd
| | - Manabu Kano
- Department of Systems Science, Kyoto University
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Zhang X, Wei C, Song Z. Fast Locally Weighted PLS Modeling for Large-Scale Industrial Processes. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c03932] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Xinmin Zhang
- State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Chihang Wei
- State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Zhihuan Song
- State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
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Zhong L, Gao L, Li L, Zang H. Trends-process analytical technology in solid oral dosage manufacturing. Eur J Pharm Biopharm 2020; 153:187-199. [DOI: 10.1016/j.ejpb.2020.06.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 06/11/2020] [Accepted: 06/14/2020] [Indexed: 10/24/2022]
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Shi Z, Hermiller J, Muñoz SG. Estimation of mass-based composition in powder mixtures using Extended Iterative Optimization Technology (EIOT). AIChE J 2018. [DOI: 10.1002/aic.16417] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Zhenqi Shi
- Small Molecule Design & Development; Lilly Research Laboratories; Indianapolis IN, 46285
| | - James Hermiller
- Small Molecule Design & Development; Lilly Research Laboratories; Indianapolis IN, 46285
| | - Salvador García Muñoz
- Small Molecule Design & Development; Lilly Research Laboratories; Indianapolis IN, 46285
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Lopes LC, Brandão IV, Sánchez OC, Franceschi E, Borges G, Dariva C, Fricks AT. Horseradish peroxidase biocatalytic reaction monitoring using Near-Infrared (NIR) Spectroscopy. Process Biochem 2018. [DOI: 10.1016/j.procbio.2018.05.024] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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13
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Yeo WS, Saptoro A, Kumar P. Development of Adaptive Soft Sensor Using Locally Weighted Kernel Partial Least Square Model. CHEMICAL PRODUCT AND PROCESS MODELING 2017. [DOI: 10.1515/cppm-2017-0022] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
AbstractLocally weighted partial least square (LW-PLS) model has been commonly used to develop adaptive soft sensors and process monitoring for numerous industries which include pharmaceutical, petrochemical, semiconductor, wastewater treatment system and biochemical. The advantages of LW-PLS model are its ability to deal with a large number of input variables, collinearity among the variables and outliers. Nevertheless, since most industrial processes are highly nonlinear, a traditional LW-PLS which is based on a linear model becomes incapable of handling nonlinear processes. Hence, an improved LW-PLS model is required to enhance the adaptive soft sensors in dealing with data nonlinearity. In this work, Kernel function which has nonlinear features was incorporated into LW-PLS model and this proposed model is named locally weighted kernel partial least square (LW-KPLS). Comparisons between LW-PLS and LW-KPLS models in terms of predictive performance and their computational loads were carried out by evaluating both models using data generated from a simulated plant. From the results, it is apparent that in terms of predictive performance LW-KPLS is superior compared to LW-PLS. However, it is found that computational load of LW-KPLS is higher than LW-PLS. After adapting ensemble method with LW-KPLS, computational loads of both models were found to be comparable. These indicate that LW-KPLS performs better than LW-PLS in nonlinear process applications. In addition, evaluation on localization parameter in both LW-PLS and LW-KPLS is also carried out.
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Inagaki K, Yamashita Y. Real-Time Monitoring of Moisture Content in a Fluidized Bed Drying Process. KAGAKU KOGAKU RONBUN 2016. [DOI: 10.1252/kakoronbunshu.42.179] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Koji Inagaki
- Engineering Research Laboratory, Morinaga Milk Industry Co., Ltd
| | - Yoshiyuki Yamashita
- Department of Chemical Engineering, Tokyo University of Agriculture and Technology
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Sparse Sample Regression Based Just-In-Time Modeling (SSR-JIT): Beyond Locally Weighted Approach**This study was supported by JSPS KAKENHI 15K06554. ACTA ACUST UNITED AC 2016. [DOI: 10.1016/j.ifacol.2016.07.392] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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16
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Evaluation of conversion during the synthesis of aluminum (III) methacrylate-based copolymers using Raman spectroscopy and multivariate curve resolution. Microchem J 2015. [DOI: 10.1016/j.microc.2015.05.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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17
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Bakri B, Weimer M, Hauck G, Reich G. Assessment of powder blend uniformity: Comparison of real-time NIR blend monitoring with stratified sampling in combination with HPLC and at-line NIR Chemical Imaging. Eur J Pharm Biopharm 2015; 97:78-89. [DOI: 10.1016/j.ejpb.2015.10.002] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2015] [Revised: 09/17/2015] [Accepted: 10/02/2015] [Indexed: 10/22/2022]
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Miyano T, Nakagawa H, Watanabe T, Minami H, Sugiyama H. Operationalizing Maintenance of Calibration Models Based on Near-Infrared Spectroscopy by Knowledge Integration. J Pharm Innov 2015. [DOI: 10.1007/s12247-015-9226-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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