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Original study on mathematical models for analysis of cellulose water content from absorbance/wavenumber shifts in ATR FT-IR spectrum.

ABSTRACT: The aim of this research was to evaluate the applicability of the attenuated total reflectance Fourier-transform infrared (ATR FT-IR) spectroscopy in the quantitative analysis of the moisture content in cellulose (from 0.5 to 11.0 wt.%). Innovatively, this work describes the variations in both absorbance and wavenumber of 16 absorption bands plotted as a function of cellulose water amount measured with Karl-Fischer titration. Different regression models were investigated (simple linear, semilogarithmic, power) and the adjusted coefficient of determination (R²) was given for each calculation. While model exhibited R² > 90%, the standard error of calibration (SEC) was presented and an external validation has been performed. Regarding the absorbance-water content relationship, data recorded for sixteen peaks was successfully fitted with linear functions exhibiting R² > 90%. The highest value of R² = 98.7% and standard error of prediction SEP = 0.3wt.% have been assigned to the maximum from 3339 to 3327 cm^-1 (-OH), proving ATR FT-IR usefulness in quantitative analysis.

SUBMITTER: Cichosz S

PROVIDER: S-EPMC9672360 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Original study on mathematical models for analysis of cellulose water content from absorbance/wavenumber shifts in ATR FT-IR spectrum.

Cichosz Stefan S Masek Anna A Dems-Rudnicka Katarzyna K

Scientific reports 20221117 1

The aim of this research was to evaluate the applicability of the attenuated total reflectance Fourier-transform infrared (ATR FT-IR) spectroscopy in the quantitative analysis of the moisture content in cellulose (from 0.5 to 11.0 wt.%). Innovatively, this work describes the variations in both absorbance and wavenumber of 16 absorption bands plotted as a function of cellulose water amount measured with Karl-Fischer titration. Different regression models were investigated (simple linear, semiloga ...[more]

PMID: 36396855

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Project description:The reliability of Fourier-transform infrared (FT-IR) spectroscopy for Klebsiella pneumoniae typing and outbreak control has been previously assessed, but issues remain in standardization and reproducibility. We developed and validated a reproducible FT-IR with attenuated total reflectance (ATR) workflow for the identification of K. pneumoniae lineages. We used 293 isolates representing multidrug-resistant K. pneumoniae lineages causing outbreaks worldwide (2002-2021) to train a random forest classification (RF) model based on capsular (KL)-type discrimination. This model was validated with 280 contemporaneous isolates (2021-2022), using wzi sequencing and whole-genome sequencing as references. Repeatability and reproducibility were tested in different culture media and instruments throughout time. Our RF model allowed the classification of 33 capsular (KL)-types and up to 36 clinically relevant K. pneumoniae lineages based on the discrimination of specific KL- and O-type combinations. We obtained high rates of accuracy (89%), sensitivity (88%), and specificity (92%), including from cultures obtained directly from the clinical sample, allowing to obtain typing information the same day bacteria are identified. The workflow was reproducible in different instruments throughout time (>98% correct predictions). Direct colony application, spectral acquisition, and automated KL prediction through Clover MS Data analysis software allow a short time-to-result (5 min/isolate). We demonstrated that FT-IR ATR spectroscopy provides meaningful, reproducible, and accurate information at a very early stage (as soon as bacterial identification) to support infection control and public health surveillance. The high robustness together with automated and flexible workflows for data analysis provide opportunities to consolidate real-time applications at a global level. IMPORTANCE We created and validated an automated and simple workflow for the identification of clinically relevant Klebsiella pneumoniae lineages by FT-IR spectroscopy and machine-learning, a method that can be extremely useful to provide quick and reliable typing information to support real-time decisions of outbreak management and infection control. This method and workflow is of interest to support clinical microbiology diagnostics and to aid public health surveillance.

Dataset Information

Original study on mathematical models for analysis of cellulose water content from absorbance/wavenumber shifts in ATR FT-IR spectrum.

Publications

Original study on mathematical models for analysis of cellulose water content from absorbance/wavenumber shifts in ATR FT-IR spectrum.

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