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Dong X, Yan X, Wan Y, Gao D, Jiao J, Wang H, Qu H. Enhancing real-time cell culture monitoring: Automated Raman model optimization with Taguchi method. Biotechnol Bioeng 2024; 121:1831-1845. [PMID: 38454569 DOI: 10.1002/bit.28688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 11/18/2023] [Accepted: 02/20/2024] [Indexed: 03/09/2024]
Abstract
Raman spectroscopy has found widespread usage in monitoring cell culture processes both in research and practical applications. However, commonly, preprocessing methods, spectral regions, and modeling parameters have been chosen based on experience or trial-and-error strategies. These choices can significantly impact the performance of the models. There is an urgent need for a simple, effective, and automated approach to determine a suitable procedure for constructing accurate models. This paper introduces the adoption of a design of experiment (DoE) method to optimize partial least squares models for measuring the concentration of different components in cell culture bioreactors. The experimental implementation utilized the orthogonal test table L25(56). Within this framework, five factors were identified as control variables for the DoE method: the window width of Savitzky-Golay smoothing, the baseline correction method, the order of preprocessing steps, spectral regions, and the number of latent variables. The evaluation method for the model was considered as a factor subject to noise. The optimal combination of levels was determined through the signal-to-noise ratio response table employing Taguchi analysis. The effectiveness of this approach was validated through two cases, involving different cultivation scales, different Raman spectrometers, and different analytical components. The results consistently demonstrated that the proposed approach closely approximated the global optimum, regardless of data set size, predictive components, or the brand of Raman spectrometer. The performance of models recommended by the DoE strategy consistently surpassed those built using raw data, underscoring the reliability of models generated through this approach. When compared to exhaustive all-combination experiments, the DoE approach significantly reduces calculation times, making it highly practical for the implementation of Raman spectroscopy in bioprocess monitoring.
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Affiliation(s)
- Xiaoxiao Dong
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Xu Yan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Hisun Biopharmaceutical Co. Ltd., Hangzhou, China
| | - Yuxiang Wan
- Hisun Biopharmaceutical Co. Ltd., Hangzhou, China
| | - Dong Gao
- Hisun Biopharmaceutical Co. Ltd., Hangzhou, China
| | - Jingyu Jiao
- Hisun Biopharmaceutical Co. Ltd., Hangzhou, China
| | - Haibin Wang
- Hisun Biopharmaceutical Co. Ltd., Hangzhou, China
| | - Haibin Qu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
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2
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Schulze HG, Rangan S, Vardaki MZ, Blades MW, Turner RFB, Piret JM. Rapid Vector-Based Peak Fitting and Resolution Enhancement for Correlation Analyses of Raman Hyperspectra. APPLIED SPECTROSCOPY 2023; 77:957-969. [PMID: 37254554 PMCID: PMC10543951 DOI: 10.1177/00037028231176805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 04/25/2023] [Indexed: 06/01/2023]
Abstract
Spectroscopic peak parameters are important since they provide information about the analyte under study. Besides obtaining these parameters, peak fitting also resolves overlapped peaks. Thus, the obtained parameters should permit the construction of a higher-resolution version of the original spectrum. However, peak fitting is not an easy task due to computational reasons and because the true nature of the analyte is often unknown. These difficulties are major impediments when large hyperspectral data sets need to be processed rapidly, such as for manufacturing process control. We have developed a novel and relatively fast two-part algorithm to perform peak fitting and resolution enhancement on such data sets. In the first part of the algorithm, estimates of the total number of bands and their parameters were obtained from a representative spectrum in the data set, using a combination of techniques. Starting with these parameter estimates, all the spectra were then iteratively and rapidly fitted with Gaussian bands, exploiting intrinsic features of the Gaussian distribution with vector operations. The best fits for each spectrum were retained. By reducing the obtained bandwidths and commensurately increasing their amplitudes, high-resolution spectra were constructed that greatly improved correlation-based analyses. We tested the performance of the algorithm on synthetic spectra to confirm that this method could recover the ground truth correlations between highly overlapped peaks. To assess effective peak resolution, the method was applied to low-resolution spectra of glucose and compared to results from high-resolution spectra. We then processed a larger spectral data set from mammalian cells, fixed with methanol or air drying, to demonstrate the resolution enhancement of the algorithm on complex spectra and the effects of resolution-enhanced spectra on two-dimensional correlation spectroscopy and principal component analyses. The results indicated that the algorithm would allow users to obtain high-resolution spectra relatively fast and permit the recovery of important aspects of the data's intrinsic correlation structure.
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Affiliation(s)
| | - Shreyas Rangan
- Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, Canada
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada
| | - Martha Z. Vardaki
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece
| | - Michael W. Blades
- Department of Chemistry, The University of British Columbia, Vancouver, BC, Canada
| | - Robin F. B. Turner
- Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, Canada
- Department of Chemistry, The University of British Columbia, Vancouver, BC, Canada
- Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada
| | - James M. Piret
- Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, Canada
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada
- Department of Chemical and Biological Engineering, The University of British Columbia, Vancouver, BC, Canada
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3
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Yao J, Su H, Yao Z. Blind source separation of coexisting background in Raman spectra. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 238:118417. [PMID: 32438289 DOI: 10.1016/j.saa.2020.118417] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 04/19/2020] [Accepted: 04/25/2020] [Indexed: 06/11/2023]
Abstract
Due to the Raman signal coexists with other scatter spectra which leads to the low ratio of the wanted signal and high background, the appropriate method should be applied to enhance this ratio. The nature of raw spectra is a multi-source system, so its determinacy must be ensured by multi-input. Besides, the faithfulness of output should be provided. Then, the huge fall within the frequencies of Raman and background almost satisfies separating demand for independent component analysis (ICA), and this analysis can give help to the achievement of the two type signals classing and estimate the optimal number of source and match ICA output signals to Raman or background. Thus, based on ICA and the mixing-entropy criteria, the background and Raman adapting calibration kit (BRACK) method is proposed, which is a kind of multiple raw spectral inputs and multiple output (MIMO) method. This method firstly divides the raw data into two parts of Raman and background by ICA, identifies Raman signal by entropy criterion, then restores the part of Raman signal. BRACK method obtains several advantages, for instance, well-adapted, no need for any additional option or extra-intervention, high fidelity, and no unwanted external information. In principle, the correction of background and Raman signals can be expected to be completed by BRACK method.
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Affiliation(s)
- Ju Yao
- School of Chemical Engineering, The University of Queensland, St Lucia, QLD 4072, Australia.
| | - Hui Su
- Guangxi Key Laboratory of Green Processing of Sugar Resources, College of Biological and Chemical Engineering, Guangxi University of Science and Technology, Liuzhou 545006, Guangxi, PR China
| | - Zhixiang Yao
- Guangxi Key Laboratory of Green Processing of Sugar Resources, College of Biological and Chemical Engineering, Guangxi University of Science and Technology, Liuzhou 545006, Guangxi, PR China.
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Rangan S, Schulze HG, Vardaki MZ, Blades MW, Piret JM, Turner RFB. Applications of Raman spectroscopy in the development of cell therapies: state of the art and future perspectives. Analyst 2020; 145:2070-2105. [DOI: 10.1039/c9an01811e] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
This comprehensive review article discusses current and future perspectives of Raman spectroscopy-based analyses of cell therapy processes and products.
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Affiliation(s)
- Shreyas Rangan
- Michael Smith Laboratories
- The University of British Columbia
- Vancouver
- Canada
- School of Biomedical Engineering
| | - H. Georg Schulze
- Michael Smith Laboratories
- The University of British Columbia
- Vancouver
- Canada
| | - Martha Z. Vardaki
- Michael Smith Laboratories
- The University of British Columbia
- Vancouver
- Canada
| | - Michael W. Blades
- Department of Chemistry
- The University of British Columbia
- Vancouver
- Canada
| | - James M. Piret
- Michael Smith Laboratories
- The University of British Columbia
- Vancouver
- Canada
- School of Biomedical Engineering
| | - Robin F. B. Turner
- Michael Smith Laboratories
- The University of British Columbia
- Vancouver
- Canada
- Department of Chemistry
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5
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Schulze HG, Rangan S, Piret JM, Blades MW, Turner RFB. Developing Fully Automated Quality Control Methods for Preprocessing Raman Spectra of Biomedical and Biological Samples. APPLIED SPECTROSCOPY 2018; 72:1322-1340. [PMID: 29855196 DOI: 10.1177/0003702818778031] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Spectral preprocessing is frequently required to render Raman spectra useful for further processing and analyses. The various preprocessing steps, individually and sequentially, are increasingly being automated to cope with large volumes of data from, for example, hyperspectral imaging studies. Full automation of preprocessing is especially desirable when it produces consistent results and requires minimal user input. It is therefore essential to evaluate the "quality" of such preprocessed spectra. However, relatively few methods exist to evaluate preprocessing quality, and fully automated methods for doing so are virtually non-existent. Here we provide a brief overview of fully automated spectral preprocessing and fully automated quality assessment of preprocessed spectra. We follow this with the introduction of fully automated methods to establish figures-of-merit that encapsulate preprocessing quality. By way of illustration, these quantitative methods are applied to simulated and real Raman spectra. Quality factor and quality parameter figures-of-merit resulting from individual preprocessing step quality tests, as well as overall figures-of-merit, were found to be consistent with the quality of preprocessed spectra.
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Affiliation(s)
- H Georg Schulze
- 1 Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, Canada
| | - Shreyas Rangan
- 1 Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, Canada
| | - James M Piret
- 1 Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, Canada
- 2 Department of Chemical and Biological Engineering, The University of British Columbia, Vancouver, BC, Canada
| | - Michael W Blades
- 3 Department of Chemistry, The University of British Columbia, Vancouver, BC, Canada
| | - Robin F B Turner
- 1 Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, Canada
- 3 Department of Chemistry, The University of British Columbia, Vancouver, BC, Canada
- 4 Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada
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Neal SL. Multivariate Analysis of Mixed Lipid Aggregate Phase Transitions Monitored Using Raman Spectroscopy. APPLIED SPECTROSCOPY 2018; 72:102-113. [PMID: 28805070 DOI: 10.1177/0003702817729347] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
The phase behavior of aqueous 1,2-dimyristoyl-sn-glycero-3-phosphorylcholine (DMPC)/1,2-dihexanoyl-sn-glycero-3-phosphocholine (DHPC) mixtures between 8.0 ℃ and 41.0 ℃ were monitored using Raman spectroscopy. Temperature-dependent Raman matrices were assembled from series of spectra and subjected to multivariate analysis. The consensus of pseudo-rank estimation results is that seven to eight components account for the temperature-dependent changes observed in the spectra. The spectra and temperature response profiles of the mixture components were resolved by applying a variant of the non-negative matrix factorization (NMF) algorithm described by Lee and Seung (1999). The rotational ambiguity of the data matrix was reduced by augmenting the original temperature-dependent spectral matrix with its cumulative counterpart, i.e., the matrix formed by successive integration of the spectra across the temperature index (columns). Successive rounds of constrained NMF were used to isolate component spectra from a significant fluorescence background. Five major components exhibiting varying degrees of gel and liquid crystalline lipid character were resolved. Hydrogen-bonded water networks exhibiting varying degrees of organization are associated with the lipid components. Spectral parameters were computed to compare the chain conformation, packing, and hydration indicated by the resolved spectra. Based on spectral features and relative amounts of the components observed, four components reflect long chain lipid response. The fifth component could reflect the response of the short chain lipid, DHPC, but there were no definitive spectral features confirming this assignment. A minor component of uncertain assignment that exhibits a striking response to the DMPC pre-transition and chain melting transition also was recovered. While none of the spectra resolved exhibit features unequivocally attributable to a specific aggregate morphology or step in the gelation process, the results are consistent with the evolution of mixed phase bicelles (nanodisks) and small amounts of worm-like DMPC/DHPC aggregates, and perhaps DHPC micelles, at low temperature to suspensions of branched and entangled worm-like aggregates above the DMPC gel phase transition and perforated multi-lamellar aggregates at high temperature.
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Affiliation(s)
- Sharon L Neal
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, USA
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7
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Georg Schulze H, Konorov SO, Piret JM, Blades MW, Turner RFB. Empirical Factors Affecting the Quality of Non-Negative Matrix Factorization of Mammalian Cell Raman Spectra. APPLIED SPECTROSCOPY 2017; 71:2681-2691. [PMID: 28937262 DOI: 10.1177/0003702817732117] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Mammalian cells contain various macromolecules that can be investigated non-invasively with Raman spectroscopy. The particular mixture of major macromolecules present in a cell being probed are reflected in the measured Raman spectra. Determining macromolecular identities and estimating their concentrations from these mixture Raman spectra can distinguish cell types and otherwise enable biological research. However, the application of canonical multivariate methods, such as principal component analysis (PCA), to perform spectral unmixing yields mathematical solutions that can be difficult to interpret. Non-negative matrix factorization (NNMF) improves the interpretability of unmixed macromolecular components, but can be difficult to apply because ambiguities produced by overlapping Raman bands permit multiple solutions. Furthermore, theoretically sound methods can be difficult to implement in practice. Here we examined the effects of a number of empirical approaches on the quality of NNMF results. These approaches were evaluated on simulated mammalian cell Raman hyperspectra and the results were used to develop an enhanced procedure for implementing NNMF. We demonstrated the utility of this procedure using a Raman hyperspectral data set measured from human islet cells to recover the spectra of insulin and glucagon. This was compared to the relatively inferior PCA of these data.
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Affiliation(s)
- H Georg Schulze
- 1 Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, Canada
| | - Stanislav O Konorov
- 1 Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, Canada
- 2 Department of Chemistry, The University of British Columbia, Vancouver, BC, Canada
| | - James M Piret
- 1 Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, Canada
- 3 Department of Chemical and Biological Engineering, The University of British Columbia, Vancouver, BC, Canada
| | - Michael W Blades
- 2 Department of Chemistry, The University of British Columbia, Vancouver, BC, Canada
| | - Robin F B Turner
- 1 Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, Canada
- 2 Department of Chemistry, The University of British Columbia, Vancouver, BC, Canada
- 4 Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada
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8
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Rangan S, Kamal S, Konorov SO, Schulze HG, Blades MW, Turner RFB, Piret JM. Types of cell death and apoptotic stages in Chinese Hamster Ovary cells distinguished by Raman spectroscopy. Biotechnol Bioeng 2017; 115:401-412. [DOI: 10.1002/bit.26476] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Revised: 10/01/2017] [Accepted: 10/08/2017] [Indexed: 01/22/2023]
Affiliation(s)
- Shreyas Rangan
- Genome Science & Technology Program; Vancouver Canada
- Michael Smith Laboratories; Vancouver Canada
| | - Sepehr Kamal
- Genome Science & Technology Program; Vancouver Canada
- Michael Smith Laboratories; Vancouver Canada
| | - Stanislav O. Konorov
- Michael Smith Laboratories; Vancouver Canada
- Department of Electrical and Computer Engineering; Vancouver Canada
| | - Hans Georg Schulze
- Michael Smith Laboratories; Vancouver Canada
- Department of Electrical and Computer Engineering; Vancouver Canada
| | | | - Robin F. B. Turner
- Michael Smith Laboratories; Vancouver Canada
- Department of Electrical and Computer Engineering; Vancouver Canada
| | - James M. Piret
- Genome Science & Technology Program; Vancouver Canada
- Michael Smith Laboratories; Vancouver Canada
- Chemical and Biological Engineering; University of British Columbia; Vancouver Canada
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9
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Bocklitz TW, Guo S, Ryabchykov O, Vogler N, Popp J. Raman Based Molecular Imaging and Analytics: A Magic Bullet for Biomedical Applications!? Anal Chem 2015; 88:133-51. [DOI: 10.1021/acs.analchem.5b04665] [Citation(s) in RCA: 72] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Affiliation(s)
- Thomas W. Bocklitz
- Institute
of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
- Leibniz Institute of Photonic Technology (IPHT), Albert-Einstein-Strasse 9, 07745 Jena, Germany
| | - Shuxia Guo
- Institute
of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
- Leibniz Institute of Photonic Technology (IPHT), Albert-Einstein-Strasse 9, 07745 Jena, Germany
- InfectoGnostics
Forschungscampus Jena e.V., Zentrum für Angewandte Forschung, Philosophenweg 7, 07743 Jena, Germany
| | - Oleg Ryabchykov
- Institute
of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
- Leibniz Institute of Photonic Technology (IPHT), Albert-Einstein-Strasse 9, 07745 Jena, Germany
- InfectoGnostics
Forschungscampus Jena e.V., Zentrum für Angewandte Forschung, Philosophenweg 7, 07743 Jena, Germany
| | - Nadine Vogler
- Institute
of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
- Leibniz Institute of Photonic Technology (IPHT), Albert-Einstein-Strasse 9, 07745 Jena, Germany
- InfectoGnostics
Forschungscampus Jena e.V., Zentrum für Angewandte Forschung, Philosophenweg 7, 07743 Jena, Germany
| | - Jürgen Popp
- Institute
of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
- Leibniz Institute of Photonic Technology (IPHT), Albert-Einstein-Strasse 9, 07745 Jena, Germany
- InfectoGnostics
Forschungscampus Jena e.V., Zentrum für Angewandte Forschung, Philosophenweg 7, 07743 Jena, Germany
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