In this work, an innovative approach using K-means and multivariate curve resolution-purity based algorithm (MCR-Purity) for the evaluation and quantification of carboxymyoglobin (Mb-CO) formation from Deoxy-Myoglobin (Deoxy-Mb) was presented. Through a multilevel multifactor experimental design, samples with different concentrations of Mb-CO were created. The UV–Vis spectra of these samples were submitted to K-means analysis, finding 3 clusters. The mean spectra of the clusters were extracted and it was possible to detect 2 totally differentiable groups through peaks 423 and 434 nm, which are wavelengths related to the Mb-CO and Deoxy-Mb components, respectively. The spectral data were subjected to MCR-Purity analysis. The MCR-Purity result successfully described the analyzed reaction, explaining more than 99.9% of the variance (R2) with a LOF of 1.43%. Then, a predictive model of MbCO was created through the linear relationship between MCR-Purity contributions and known concentrations of MbCO. The performance parameters of the created predictive model were R2CV = 0.98, RMSECV = 0.58 and RPDcv = 7.8 for the training set, and R2P = 0.98, RMSEP = 0.7 and RPDp = 6.8 for the test set. Thus, the predictive model presented an excellent performance considering that the Mb-CO variation is comprised between 0 and 21 µM. Therefore, these results demonstrate that the application of the proposed strategy to the analysis of spectral data presenting overlapping bands is feasible and robust.

An innovative spectroscopic approach for qualitative and quantitative evaluation of Mb-CO from myoglobin carbonylation reaction through chemometrics methods

Berrino E.;
2021-01-01

Abstract

In this work, an innovative approach using K-means and multivariate curve resolution-purity based algorithm (MCR-Purity) for the evaluation and quantification of carboxymyoglobin (Mb-CO) formation from Deoxy-Myoglobin (Deoxy-Mb) was presented. Through a multilevel multifactor experimental design, samples with different concentrations of Mb-CO were created. The UV–Vis spectra of these samples were submitted to K-means analysis, finding 3 clusters. The mean spectra of the clusters were extracted and it was possible to detect 2 totally differentiable groups through peaks 423 and 434 nm, which are wavelengths related to the Mb-CO and Deoxy-Mb components, respectively. The spectral data were subjected to MCR-Purity analysis. The MCR-Purity result successfully described the analyzed reaction, explaining more than 99.9% of the variance (R2) with a LOF of 1.43%. Then, a predictive model of MbCO was created through the linear relationship between MCR-Purity contributions and known concentrations of MbCO. The performance parameters of the created predictive model were R2CV = 0.98, RMSECV = 0.58 and RPDcv = 7.8 for the training set, and R2P = 0.98, RMSEP = 0.7 and RPDp = 6.8 for the test set. Thus, the predictive model presented an excellent performance considering that the Mb-CO variation is comprised between 0 and 21 µM. Therefore, these results demonstrate that the application of the proposed strategy to the analysis of spectral data presenting overlapping bands is feasible and robust.
2021
Carboxy-myoglobin
K-means
Multivariate curve resolution
UV–Vis spectroscopy
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14085/21929
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