scholarly journals Quantification of glycated hemoglobin indicator HbA1c through near-infrared spectroscopy

2014 ◽  
Vol 07 (04) ◽  
pp. 1350060 ◽  
Author(s):  
Tao Pan ◽  
Minmiao Li ◽  
Jiemei Chen ◽  
Haiyan Xue
Keyword(s):  
Near Infrared ◽  
Glycated Hemoglobin ◽  
Correlation Coefficients ◽  
Partial Least Square ◽  
Least Square ◽  
Spectral Determination ◽  
Spectroscopic Analyses ◽  
Biochemical Indicator ◽  
New Strategy

A new strategy for quantitative analysis of a major clinical biochemical indicator called glycated hemoglobin (HbA1c) was proposed. The technique was based on the simultaneous near-infrared (NIR) spectral determination of hemoglobin (Hb) and absolute HbA1c content (Hb • HbA1c) in human hemolysate samples. Wavelength selections were accomplished using the improved moving window partial least square (MWPLS) method for stability. Each model was established using an approach based on randomness, similarity, and stability to obtain objective, stable, and practical models. The optimal wavebands obtained using MWPLS were 958 to 1036 nm for Hb and 1492 to 1858 nm for Hb • HbA1c, which were within the NIR overtone region. The validation root mean square error and validation correlation coefficients of prediction (V-SEP, V-R P ) were 3.4 g L-1 and 0.967 for Hb, respectively, whereas the corresponding values for Hb • HbA1c were 0.63 g L-1 and 0.913. The corresponding V-SEP and V-R P were 0.40% and 0.829 for the relative percentage of HbA1c. The experimental results confirm the feasibility for the quantification of HbA1c based on simultaneous NIR spectroscopic analyses of Hb and Hb • HbA1c.

scholarly journals Monitoring of CO2 Absorption Solvent in Natural Gas Process Using Fourier Transform Near-Infrared Spectrometry

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Mohd Yusop Nurida ◽  
Dolmat Norfadilah ◽  
Mohd Rozaiddin Siti Aishah ◽  
Chan Zhe Phak ◽  
Syafiqa M. Saleh
Keyword(s):  
Fourier Transform ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Infrared Spectrometry ◽  
Calibration Model ◽  
Co2 Absorption ◽  
Processing Unit

The analytical methods for the determination of the amine solvent properties do not provide input data for real-time process control and optimization and are labor-intensive, time-consuming, and impractical for studies of dynamic changes in a process. In this study, the potential of nondestructive determination of amine concentration, CO2 loading, and water content in CO2 absorption solvent in the gas processing unit was investigated through Fourier transform near-infrared (FT-NIR) spectroscopy that has the ability to readily carry out multicomponent analysis in association with multivariate analysis methods. The FT-NIR spectra for the solvent were captured and interpreted by using suitable spectra wavenumber regions through multivariate statistical techniques such as partial least square (PLS). The calibration model developed for amine determination had the highest coefficient of determination (R2) of 0.9955 and RMSECV of 0.75%. CO2 calibration model achieved R2 of 0.9902 with RMSECV of 0.25% whereas the water calibration model had R2 of 0.9915 with RMSECV of 1.02%. The statistical evaluation of the validation samples also confirmed that the difference between the actual value and the predicted value from the calibration model was not significantly different and acceptable. Therefore, the amine, CO2, and water models have given a satisfactory result for the concentration determination using the FT-NIR technique. The results of this study indicated that FT-NIR spectroscopy with chemometrics and multivariate technique can be used for the CO2 solvent monitoring to replace the time-consuming and labor-intensive conventional methods.

scholarly journals Development of a Multivariate Predictive Dissolution Model for Tablets Coated with Cellulose Ester Blends

2020 ◽  
Vol 13 (10) ◽  
pp. 311
Author(s):  
Eman M. Mohamed ◽  
Tahir Khuroo ◽  
Hamideh Afrooz ◽  
Sathish Dharani ◽  
Khaldia Sediri ◽  
...  
Keyword(s):  
Cellulose Acetate ◽  
Near Infrared ◽  
Correlation Coefficients ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Cellulose Ester ◽  
Cellulose Esters ◽  
Dissolution Model ◽  
Predicted Values

The focus of the present investigation was to develop a predictive dissolution model for tablets coated with blends of cellulose acetate butyrate (CAB) 171-15 and cellulose acetate phthalate (C-A-P) using the design of experiment and chemometric approaches. Diclofenac sodium was used as a model drug. Coating weight gain (X1, 5, 7.5 and 10%) and CAB 171-15 percentage (X2, 33.3, 50 and 66.7%) in the coating composition relative to C-A-P and were selected as independent variables by full factorial experimental design. The responses monitored were dissolution at 1 (Y1), 8 (Y2), and 24 (Y3) h. Statistically significant (p < 0.05) effects of X1 on Y1 and X2 on Y1, Y2, and Y3 were observed. The models showed a good correlation between actual and predicted values as indicated by the correlation coefficients of 0.964, 0.914, and 0.932 for Y1, Y2, and Y3, respectively. For the chemometric model development, the near infrared spectra of the coated tablets were collected, and partial least square regression (PLSR) was performed. PLSR also showed a good correlation between actual and model predicted values as indicated by correlation coefficients of 0.916, 0.964, and 0.974 for Y1, Y2, and Y3, respectively. Y1, Y2, and Y3 predicted values of the independent sample by both approaches were close to the actual values. In conclusion, it is possible to predict the dissolution of tablets coated with blends of cellulose esters by both approaches.

Determination of Proteins in Human Serum by Near-Infrared Spectroscopy with Partial Least Square Analysis

2016 ◽  
Vol 49 (18) ◽  
pp. 2964-2976 ◽  
Author(s):  
Huixian Guo ◽  
Furong Huang ◽  
Yuanpeng Li ◽  
Tao Fang ◽  
Siqi Zhu ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Human Serum ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Analysis

scholarly journals   Near infrared spectroscopy for deoxynivalenol content estimation in intact wheat grain

2012 ◽  
Vol 58 (No. 4) ◽  
pp. 196-203 ◽  
Author(s):  
V. Dvořáček ◽  
A. Prohasková ◽  
J. Chrpová ◽  
L. Štočková
Keyword(s):  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Resistance Breeding ◽  
Partial Least Square ◽  
Least Square ◽  
Prediction Errors ◽  
Wheat Grain ◽  
Wide Range

Non-invasive determination of deoxynivalenol (DON) still presents a challenging problem. Therefore, the present study was aimed at a rapid determination of DON in whole wheat grain by means of FT-NIR spectroscopy, with a wide range of concentrations for potential applications in breeding programs and common systems of quality management using partial least square calibration (PLS) and discriminant analysis technique (DA). Using a set of artificially infected wheat samples with a known content of DON, four PLS models with different concentration range were created. The broadest model predicting DON in the concentration range of 0&ndash;90 mg/kg possessed the highest correlation coefficients of calibration and cross validation (0.94 and 0.88); but also possessed the highest prediction errors (SEP = 6.23 mg/kg). Thus the subsequent combination of DA as the wide range predictive model and the low-range PLS model was used. This technique gave more precise results in the samples with lower DON concentrations &ndash; below 30 mg/kg (SEP = 2.35 mg/kg), when compared to the most wide-range PLS model (SEP = 5.95 mg/kg).<br />Such technique enables to estimate DON content in collections of artificially infected wheat plants in Fusarium resistance breeding experiments. &nbsp;

Near-Infrared Spectroscopic Measurement of Organic Matter in Soil

2012 ◽  
Vol 499 ◽  
pp. 414-418
Author(s):  
Tao Pan ◽  
Zhen Tao Wu ◽  
Jie Mei Chen
Keyword(s):  
Organic Matter ◽  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Modeling Prediction ◽  
Validation Set ◽  
Selection Of

Near-infrared (NIR) spectroscopy was successfully applied to chemical free and rapid determination of the organic matter in soil, and moving window partial least square (MWPLS) combining with Savitzky-Golay (SG) smoothing was used to the selection of NIR waveband. Thirty-five samples were randomly selected from all 97 collected soil samples as the validation set. The remaining 62 samples were divided into similar modeling calibration set (37 samples) and modeling prediction set (25 samples) based on partial least square cross-validation predictive bias (PLSPB). The selected waveband was 1896 nm to 2138 nm; the SG smoothing parameters and PLS factor OD, DP, NSP and F were 2, 6, 71 and 15, respectively; the modeling effect M-SEP and M-RPwere 0.219% and 0.944, respectively; the validating effect V-SEP and V-RPwere 0.243% and 0.878, respectively. The result provided a reliable NIR model and valuable references for designing specialized NIR instruments.

scholarly journals Rapid Determination of Methyl Salicylate and Menthol in Activating Collaterals Oil by Near Infrared Spectroscopy

2021 ◽  
Vol 292 ◽  
pp. 01040
Author(s):  
Yao Wanqing ◽  
Peng Mengxia ◽  
Chen Ziyun ◽  
Wang Yanxia ◽  
Lin Tao
Keyword(s):  
Relative Error ◽  
Methyl Salicylate ◽  
Near Infrared ◽  
Accurate Determination ◽  
Absolute Error ◽  
Partial Least Square ◽  
Least Square ◽  
Content Model ◽  
The Absolute

The near infrared spectra of 10 kinds of commercial collaterals oil samples were collected by liquid transmission analysis module, and the contents of methyl salicylate and menthol in collaterals oil were determined by gas chromatography-mass spectrometry (GC-MS). The quantitative analysis model of methyl salicylate content (model 1) and menthol content (model 2) was established by correlating spectral information with measured values by partial least square method (PLS) in chemometrics. Model 1 was used to detect the content of methyl salicylate in activating oil. The predicted results showed that the absolute error was in the range of -0.098~0.082%, and the relative error was in the range of -9.986~8.195%. Model 2 was used to detect menthol in activating collaterals oil. The predicted results showed that the absolute error was in the range of -0.173~0.194%, and the relative error was in the range of -7.25~9.69%. A new method for rapid and accurate determination of methyl salicylate and menthol in activating collaterals oil was established.

Application of near Infrared Spectroscopy for the Quantification of Quality Parameters in Selected Vegetables and Essential Oil Plants

1998 ◽  
Vol 6 (A) ◽  
pp. A125-A130 ◽  
Author(s):  
H. Schulz ◽  
H.-H. Drews ◽  
R. Quilitzsch ◽  
H. Krüger
Keyword(s):  
Essential Oil ◽  
Oil Content ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Quality Parameters ◽  
Partial Least Square ◽  
Least Square ◽  
Total Carotenoids ◽  
Good Calibration

The use of near-infrared (NIR) spectroscopy for the prediction of the essential oil content and composition in various umbelliferae genotypes was investigated. Furthermore an NIR method was developed for the quantification of total carotenoids and sugars present in different carrot varieties. Applying partial least square algorithm very good calibration statistics ( SECV, R2) were obtained for the prediction of the essential oil content in fennel (0.47, 0.83), caraway (0.29, 0.93), dill (0.30, 0.96) and coriander (0.29, 0.93). Satisfactory calibration results were received for the NIR determination of total carotenoids (1.54, 0.80) and of saccharose(0.74, 0.76) in carrots. The performed study demonstrates that NIR can be used to rapidly and accurately predict secondary metabolites such as carotenoids, anethole, fenchone, estragole, limonene and carvone occurring in vegetables and in fruits of various essential oil plants.

Reagentless near-Infrared Determination of Cholesterol in Undiluted Human Serum Using Interval Partial Least Square

2011 ◽  
Vol 345 ◽  
pp. 128-133
Author(s):  
Hong Zhi Gao ◽  
Qi Peng Lu ◽  
Fu Rong Huang
Keyword(s):  
Human Serum ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Prediction Ability ◽  
Sample Set ◽  
Cholesterol Measurement

In order to determination of cholesterol in human serum with no reagent using near-infrared (NIR) spectroscopy. Interval partial least square (iPLS) was proposed as an effective variable selection approach for multivariate calibration. For this purpose, an independent sample set was employed to evaluate the prediction ability of the resulting model. The spectrum was split into different interval. Then, the informative region of cholesterol (1688-1760nm), in which the PLS model has a low RMSEP with 0.241mmol/L and a high R with 0.975, is selected with 23 intervals. The results indicate that, the informative region of cholesterol can be obtained by iPLS and applied to design the simpler reagentless NIR instruments for inexpensive cholesterol measurement in future.

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