Combined Raman and Turbidity Probe for Real-Time Analysis of Variable Turbidity Streams

Lines, Amanda M.; Bello, Job M.; Gasbarro, Christina; Bryan, Samuel A.

doi:10.1021/acs.analchem.1c05228

Cited by 5 publications

(3 citation statements)

References 37 publications

(64 reference statements)

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“…This band change is unlikely to originate from the cell’s own Raman signal, but rather from turbidity of the culture media that increases with increasing cells and their corpses. 43 , 44 The effect of turbidity should also extend to the peak intensity of components other than water. However, in the in-line Raman spectra in Figure 2b , the baseline in the low wavenumber region increased rather than attenuated.…”

Section: Resultsmentioning

confidence: 99%

Development of Raman Calibration Model Without Culture Data for In-Line Analysis of Metabolites in Cell Culture Media

et al. 2023

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In this study, we developed a method to build Raman calibration models without culture data for cell culture monitoring. First, Raman spectra were collected and then analyzed for the signals of all the mentioned analytes: glucose, lactate, glutamine, glutamate, ammonia, antibody, viable cells, media, and feed agent. Using these spectral data, the specific peak positions and intensities for each factor were detected. Next, according to the design of experiment method, samples were prepared by mixing the above-mentioned factors. Raman spectra of these samples were collected and were used to build calibration models. Several combinations of spectral pretreatments and wavenumber regions were compared to optimize the calibration model for cell culture monitoring without culture data. The accuracy of the developed calibration model was evaluated by performing actual cell culture and fitting the in-line measured spectra to the developed calibration model. As a result, the calibration model achieved sufficiently good accuracy for the three components, glucose, lactate, and antibody (root mean square errors of prediction (RMSEP) = 0.23, 0.29, and 0.20 g/L, respectively). This study has presented innovative results in developing a culture monitoring method without using culture data, while using a basic conventional method of investigating the Raman spectra of each component in the culture media and then utilizing a design of experiment approach.

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Section: Resultsmentioning

confidence: 99%

Development of Raman Calibration Model Without Culture Data for In-Line Analysis of Metabolites in Cell Culture Media

et al. 2023

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“…Frequently, based on learning from this modeling exercise and the treatment of the student’s own data, students participate during or after the expiration of the internship term in the adaptation of their modeled lab results to the inclusion of that material in peer reviewed publications. ,− The learning outcomes and corresponding methods of assessment, which were developed for this 10-week internship format, are shown in Table .…”

Section: Student Experience With Chemometric Analysismentioning

confidence: 99%

Practical Guide to Chemometric Analysis of Optical Spectroscopic Data

et al. 2023

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The methodology and mathematical treatment of several classic multivariate methods for the analysis of spectroscopic data is demonstrated in a straightforward way that can be used as a basis for teaching an undergraduate introductory course on chemometric analysis. The multivariate techniques of classical least-squares (CLS), principal component regression (PCR), and partial least-squares (PLS), as well as the univariate Beer’s law method have been described and compared, building students’ understanding by starting with the univariate method and progressing step by step into the multivariate methods. Equations for the production of regression vectors from training set spectral data are described and their use demonstrated for the prediction of constituent concentrations on a separate validation set of spectra. Extreme care is taken to ensure consistency in variable formatting of data matrices. This provides a key foundation to understand how spectral data are manipulated using these different mathematical approaches for building quantitative regression models. Each method is applied to a real-world data set, and the results are discussed to show students the types of information that can be gleaned from each method. A training set comprising 20 infrared absorbance spectra containing 3 constituents (benzene, polystyrene, and gasoline) of known composition are used to demonstrate the matrix operations for each regression method. A separate set of 12 real-world napalm samples (containing benzene, polystyrene, and gasoline) are used as a validation set to demonstrate the ability to utilize the regression models on an unknown data set. A toolbox (PNNL Chemometric Toolbox) written in MATLAB language is supplied in the Supporting Information and can be used as a companion for understanding the development and deployment of the chemometric algorithms described in this paper. The data sets of the infrared spectra are also supplied, allowing users to build and inspect the chemometric models on their own. Finally, the Toolbox includes scripts to assist users in loading their own data sets into MATLAB and performing CLS, PCR, and PLS on their data.

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“…Given the complexity of optical data collected online, chemometric modeling approaches are ideal for accurately analyzing signals and computing component ratios. The applications of these techniques have been described elsewhere, − and when applied to the current work, they offer key opportunities to improve gas-phase analysis.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Hydrogen Isotopes Utilizing Raman Spectroscopy Paired with Chemometric Analysis for Application across Multiple Systems

Felmy,

Cox,

Espley

et al. 2024

Anal. Chem.

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Online and real-time analysis of a chemical process is a major analytical challenge that can drastically change the way the chemical industry or chemical research operates. With in situ analyses, a new and powerful understanding of chemistry can be gained; however, building robust tools for long-term monitoring faces many challenges, including compensating for instrument drift, instrument replacement, and sensor or probe replacement. Accounting for these changes by recollecting calibration data and rebuilding quantification models can be costly and time-consuming. Here, methods to overcome these challenges are demonstrated with an application of Raman spectroscopy to monitoring hydrogen isotopes with varied speciation within dynamic gas streams. Specifically, chemical data science tools such as chemometric modeling are leveraged along with several examples of calibration transfer approaches. Furthermore, the optimization of instrument and sensor cell parameters for targeted gas-phase analyses is discussed. While the particular focus on hydrogen is highly beneficial within the nuclear energy sector, mechanisms built and demonstrated here are widely applicable to optical spectroscopy monitoring in numerous other chemical systems that can be leveraged in other processes.

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Combined Raman and Turbidity Probe for Real-Time Analysis of Variable Turbidity Streams

Cited by 5 publications

References 37 publications

Development of Raman Calibration Model Without Culture Data for In-Line Analysis of Metabolites in Cell Culture Media

Development of Raman Calibration Model Without Culture Data for In-Line Analysis of Metabolites in Cell Culture Media

Practical Guide to Chemometric Analysis of Optical Spectroscopic Data

Quantification of Hydrogen Isotopes Utilizing Raman Spectroscopy Paired with Chemometric Analysis for Application across Multiple Systems

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