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Hemodialysis monitoring using mid‐ and near‐infrared spectroscopy with partial least squares regression
Author(s) -
Henn Raphael,
Kirchler Christian G.,
Schirmeister Zora L.,
Roth Andreas,
Mäntele Werner,
Huck Christian W.
Publication year - 2018
Publication title -
journal of biophotonics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.877
H-Index - 66
eISSN - 1864-0648
pISSN - 1864-063X
DOI - 10.1002/jbio.201700365
Subject(s) - partial least squares regression , chemistry , analyte , creatinine , urea , dialysis , attenuated total reflection , infrared spectroscopy , near infrared spectroscopy , analytical chemistry (journal) , spectroscopy , linear regression , test set , biological system , chromatography , artificial intelligence , computer science , machine learning , optics , surgery , biochemistry , organic chemistry , quantum mechanics , biology , medicine , physics
Blood constituents such as urea, glucose, lactate, phosphate and creatinine are of high relevance in monitoring the process of detoxification in ambulant dialysis treatment. In the present work, 2 different vibrational spectroscopic techniques are used to determine those molecules quantitatively in artificial dialysate solutions. The goal of the study is to compare the performance of near‐infrared (NIR) and mid‐infrared (MIR) spectroscopy in hyphenation with partial least squares regression (PLSR) directly by using the same sample set. The results show that MIR spectroscopy is better suited to analyze the analytes of interest. Multilevel multifactor design is used to cover the relevant concentration variations during dialysis. MIR spectroscopy coupled to a multi reflection attenuated total reflection (ATR) cell enables reliable prediction of all target analytes. In contrast, the NIR spectroscopic method does not give access to all 5 components but only to urea and glucose. For both methods, coefficients of determination greater or equal to 0.86 can be achieved in the test‐set validation process for urea and glucose. Lactate, phosphate and creatinine perform well in the MIR with R 2 ≥ 0.95 using test‐set validation.