Application of Fourier transform-mid infrared reflectance spectroscopy for monitoring Korean traditional rice wine ‘Makgeolli’ fermentation

2016 ◽  
Vol 230 ◽  
pp. 753-760 ◽  
Author(s):  
Dae-Yong Kim ◽  
Byoung-Kwan Cho ◽  
Seung Hyun Lee ◽  
Kyungdo Kwon ◽  
Eun Soo Park ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Reflectance Spectroscopy ◽  
Rice Wine ◽  
Infrared Reflectance ◽  
Mid Infrared ◽  
Infrared Reflectance Spectroscopy ◽  
Traditional Rice

Using absorbance peak of carbonate to select suitable regression model before predicting soil inorganic carbon concentration by mid-infrared reflectance spectroscopy

2021 ◽  
Author(s):  
Cécile Gomez ◽  
Tiphaine Chevallier ◽  
Patricia Moulin ◽  
Bernard G. Barthès
Keyword(s):  
Prediction Model ◽  
Regression Models ◽  
Inorganic Carbon ◽  
Reflectance Spectroscopy ◽  
Soil Samples ◽  
Infrared Reflectance ◽  
Mid Infrared ◽  
Soil Inorganic Carbon ◽  
Infrared Reflectance Spectroscopy ◽  
Absorbance Peak

<p><span>Mid-Infrared reflectance spectroscopy (MIRS, 4000 – 400 cm<sup>-1</sup>) is being considered to provide accurate estimations of soil inorganic carbon (SIC) contents. Usually, the prediction performances by MIRS are analyzed using figures of merit based on entire test datasets characterized by large SIC ranges, without paying attention to performances at sub-range scales. This work aims to <em>1)</em> evaluate the performances of MIR regression models for SIC prediction, for a large range of SIC test data (0-100 g/kg) and for several regular sub-ranges of SIC values (0-5, 5-10, 10-15 g/kg, etc.) and <em>2)</em> adapt the prediction model depending on sub-ranges of test samples, using the absorbance peak at 2510 cm<sup>-1</sup> for separating SIC-poor and SIC-rich test samples. This study used a Tunisian MIRS topsoil dataset including 96 soil samples, mostly rich in SIC, to calibrate and validate SIC prediction models; and a French MIRS topsoil dataset including 2178 soil samples, mostly poor in SIC, to test them. Two following regression models were used: a partial least squares regression (PLSR) using the entire spectra and a simple linear regression (SLR) using the height of the carbonate absorbance peak at 2150 cm<sup>-1</sup>.</span></p><p><span>First, our results showed that PLSR provided <em>1) </em>better performances than SLR on the Validation Tunisian dataset (R<sup>2</sup><sub>test</sub> of 0.99 vs. 0.86, respectively), but <em>2) </em>lower performances than SLR on the Test French dataset (R<sup>2</sup><sub>test</sub> of 0.70 vs. 0.91, respectively). Secondly, our results showed that on the Test French dataset, predicted SIC values were more accurate for SIC-poor samples (< 15 g/kg) with SLR (RMSE<sub>test</sub> from 1.5 to 7.1 g/kg, depending on the sub-range) than with PLSR prediction model (RMSE<sub>test </sub>from 7.3 to 14.8 g/kg, depending on the sub-range). Conversely, predicted SIC values were more accurate for carbonated samples (> 15 g/kg) with PLSR (RMSE<sub>test</sub> from 4.4 to 10.1 g/kg, depending on the sub-range) than with SLR prediction model (RMSE<sub>test</sub> from 6.8 to 14 g/kg, depending on the sub-range). Finally, our results showed that the absorbance peak at 2150 cm<sup>-1</sup> could be used before prediction to separate SIC-poor and SIC-rich test samples (452 and 1726 samples, respectevely). The SLR and PLSR regression methods applied to these SIC-poor and SIC-rich test samples, respectively, provided better prediction performances (<em>R²</em><sub><em>test </em></sub>of 0.95 and <em>RMSE</em><sub><em>test</em></sub> of 3.7 g/kg<sup></sup>). </span></p><p><span>Finally, this study demonstrated that the use of the spectral absorbance peak at 2150 cm<sup>-1</sup> provided useful information on Test samples and helped the selection of the optimal prediction model depending on SIC level, when using calibration and test sample sets with very different SIC distributions.</span></p>

Prediction of soil organic and inorganic carbon concentrations in Tunisian samples by mid-infrared reflectance spectroscopy using a French national library

2020 ◽  
Author(s):  
Tiphaine Chevallier ◽  
Cécile Gomez ◽  
Patricia Moulin ◽  
Imane Bouferra ◽  
Kaouther Hmaidi ◽  
...  
Keyword(s):  
Inorganic Carbon ◽  
Prediction Models ◽  
Reflectance Spectroscopy ◽  
Soil Samples ◽  
Infrared Reflectance ◽  
Least Squares Regression ◽  
Mid Infrared ◽  
Climate Conditions ◽  
Infrared Reflectance Spectroscopy ◽  
Soil Database

<p>Mid-Infrared Reflectance Spectroscopy (MIRS, 4000–400 cm<sup>-1</sup>) is being considered to provide accurate estimations of soil properties, including soil organic carbon (SOC) and soil inorganic carbon (SIC) contents. This has mainly been demonstrated when datasets used to build, validate and test the prediction model originate from the same area A, with similar geopedological conditions. The objective of this study was to analyze how MIRS performed when used to predict SOC and SIC contents, from a calibration database collected over a region A, to predict over a region B, where A and B have no common area and different soil and climate conditions. This study used a French MIRS soil dataset including 2178 soil samples to calibrate SIC and SOC prediction models with partial least squares regression (PLSR), and a Tunisian MIRS soil dataset including 96 soil samples to test them. Our results showed that using the French MIRS soil database i) SOC and SIC of French samples were successfully predicted, ii) SIC of Tunisian samples was also predicted successfully, iii) local calibration significantly improved SOC prediction of Tunisian samples and iv) prediction models seemed more robust for SIC than for SOC. So in future, MIRS might replace, or at least be considered as, a conventional physico-chemical analysis technique, especially when as exhaustive as possible calibration database will become available.</p>

scholarly journals Micro-Fourier-Transform Infrared Reflectance Spectroscopy as Tool for Probing IgG Glycosylation in COVID-19 Patients

2021 ◽  
Author(s):  
Carla Bandeira ◽  
Karen Madureira ◽  
Meire Rossi ◽  
Juliana Gallo ◽  
Ana Paula da Silva ◽  
...  
Keyword(s):  
Immune Response ◽  
Fourier Transform ◽  
Blood Serum ◽  
Fourier Transform Infrared ◽  
Reflectance Spectroscopy ◽  
Receiver Operating Curve ◽  
Infrared Reflectance ◽  
Serum Samples ◽  
Infrared Reflectance Spectroscopy ◽  
Igg Glycosylation

Abstract It has been reported that patients diagnosed with COVID-19 become critically ill primarily around the time of activation of the adaptive immune response. However the role of antibodies in the worsening of disease is not obvious. Higher titers of anti-spike immunoglobulin IgG1 associated to low fucosylation of the antibody Fc tail have been associated to excessive inflammatory response. In contrast it has been also reported in literature that NP-, S-, RBD- specific IgA, IgG, IgM are not associated with SARS-CoV-2 viral load, indicating that there is no obvious correlation between antibody response and viral antigen detection. In the present work the micro-Fourier-Transform Infrared reflectance spectroscopy (micro-FTIR) was employed to investigate blood serum samples of healthy and COVID-19 (mild or oligosynthomatic) individuals (82 healthcare workers volunteers in “Instituto de Infectologia Emilio Ribas”, São Paulo, Brazil). The molecular-level-sensitive, multiplexing quantitative and qualitative FTIR data probed on 1 mL of dryed biofluidwas compared to Signal-to-Cutoff index of chemiluminescent immunoassays CLIA and ELISA (IgG antibodies against SARS-CoV-2). Our main result indicated that 1702-1785 cm-1 spectral window (carbonyl C=O vibration) appeared to be a spectral marker of the degree of IgG glycosylation, allowing to probe distinctive subpopulations of COVID-19 patients, depending on their degree of severity. The specificity was 87.5 % while the detection rate of true positive was 100%. The computed area under the receiver operating curve was equivalent to CLIA, ELISA and other ATR-FTIR methods (> 0.85). In summary, overall discrimination of healthy and COVID-19 individuals and severity prediction as well could also be potentially implemented using micro-FTIR reflectance spectroscopy on blood serum samples. Considering the minimal and reagent-free sample preparation procedures combined to fast (few minutes) outcome of FTIR we can state that this technology is very suitable for fast screening of immune response of individuals with COVID-19. It would be an important tool in prospective studies, helping investigate the physiology of the asymptomatic, oligosymptomatic, or severe individuals and measure the extension of infection dissemination in patients.

Mid-infrared reflectance spectroscopy of carbonaceous chondrites and Calcium–Aluminum-rich inclusions

2020 ◽  
Vol 193 ◽  
pp. 105078 ◽  
Author(s):  
Andreas Morlok ◽  
Benjamin Schiller ◽  
Iris Weber ◽  
Mohit Melwani Daswani ◽  
Aleksandra N. Stojic ◽  
...  
Keyword(s):  
Reflectance Spectroscopy ◽  
Carbonaceous Chondrites ◽  
Infrared Reflectance ◽  
Mid Infrared ◽  
Infrared Reflectance Spectroscopy
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