Near-infrared spectra of aqueous solutions of some tetra-n-alkylammonium bromides

1967 ◽  
Vol 71 (5) ◽  
pp. 1358-1363 ◽  
Author(s):  
Kurt W. Bunzl
Keyword(s):  
Aqueous Solutions ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Near Infrared Spectra

Near Infrared Spectra of Water in Aqueous Solutions of Organic Salts. A Solvation Study of Bu4NBr, , and

1971 ◽  
Vol 49 (12) ◽  
pp. 2008-2013 ◽  
Author(s):  
C. Jolicoeur ◽  
Nguyen Dinh The ◽  
A. Cabana
Keyword(s):  
Infrared Spectrum ◽  
Aqueous Solutions ◽  
Infrared Spectra ◽  
Near Infrared ◽  
The Other ◽  
Simple Analysis ◽  
Organic Salts ◽  
Solvent Interactions ◽  
Near Infrared Spectra ◽  
Qualitative Similarity

The near-infrared spectrum of water at various temperatures and in aqueous solutions of Bu4NBr, [Formula: see text], and [Formula: see text] is studied in the region 0.8–1.2 µ. The spectra are recorded differentially with respect to water by varying the cell lengths as to take into account density changes or the volume occupied by the solutes. A simple analysis of these spectra illustrates the qualitative similarity between the effects of the Bu4N+ ion and that of a temperature decrement on the spectrum of water. On the other hand striking differences are observed in the differential spectra obtained with Bu4NBr and the phenyl substituted: salts. Further differences are exhibited in the relative solute-solvent interactions of [Formula: see text] and [Formula: see text].

Water can be a probe for sensing glucose in aqueous solutions by temperature dependent near infrared spectra

2017 ◽  
Vol 957 ◽  
pp. 47-54 ◽  
Author(s):  
Xiaoyu Cui ◽  
Xiuwei Liu ◽  
Xiaoming Yu ◽  
Wensheng Cai ◽  
Xueguang Shao
Keyword(s):  
Aqueous Solutions ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Temperature Dependent ◽  
Near Infrared Spectra

Near infrared spectra of water and aqueous solutions

1964 ◽  
Vol 14 (1-4) ◽  
pp. 268-278 ◽  
Author(s):  
Hideo Yamatera ◽  
Brian Fitzpatrick ◽  
Gilbert Gordon
Keyword(s):  
Aqueous Solutions ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Near Infrared Spectra

Glucose induced variation of water structure from temperature dependent near infrared spectra

RSC Advances ◽  
2016 ◽  
Vol 6 (107) ◽  
pp. 105729-105736 ◽  
Author(s):  
Xiaoyu Cui ◽  
Wensheng Cai ◽  
Xueguang Shao
Keyword(s):  
Aqueous Solutions ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Water Structure ◽  
Temperature Dependent ◽  
Near Infrared Spectra ◽  
Induced Variation ◽  
Water Species

The different effects of glucose on water species provide evidence to explain the bioprotective function of carbohydrates in aqueous solutions.

Short-Wavelength Near-Infrared Spectra of Sucrose, Glucose, and Fructose with Respect to Sugar Concentration and Temperature

2003 ◽  
Vol 57 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Mirta Golic ◽  
Kerry Walsh ◽  
Peter Lawson
Keyword(s):  
Aqueous Solutions ◽  
Least Squares ◽  
Infrared Spectra ◽  
Short Wavelength ◽  
Near Infrared ◽  
Deuterium Oxide ◽  
Sugar Concentration ◽  
The Third ◽  
Near Infrared Spectra ◽  
Stretching Vibrations

Short-wavelength near-infrared (SW-NIR) (700–1100 nm) spectra of aqueous solutions of sucrose, d-glucose, and d-fructose were monitored with respect to change in temperature and sugar concentration. Sugar OH and CH related vibrations were identified by analysis of the spectra of sugar solutions in deuterium oxide (D2O), and of sucrose- d8 solutions in D2O. Absorption spectra were explained in terms of the second and third overtones of OH stretching vibrations and the third overtone of CH2 and CH stretchings. In deuterated solutions, CH and CH2 higher overtone vibration bands became apparent. The major spectral effect of decreased temperature or increased sugar concentration was a decrease in absorbance at 960 nm and an increase in absorbance at 984 nm, interpreted as an increase in the degree of H bonding. Partial least-squares (PLS) calibrations on sugar concentrations (with spectra collected at several sample temperatures) relied strongly on the 910 nm sugar CH related bands, whereas calibrations on temperature depended equally on all OH associated vibrations (750, 840, 960, and 985 nm).

Rapid Determining Contents of the Rhubarb Anthraquinones Compounds by Support Vector Machines Modeling based on Near Infrared Spectra

2020 ◽  
Vol 16 ◽  
Author(s):  
Linqi Liu ◽  
JInhua Luo ◽  
Chenxi Zhao ◽  
Bingxue Zhang ◽  
Wei Fan ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Rapid Determination ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Near Infrared Spectra ◽  
Aloe Emodin ◽  
Relative Differences

BACKGROUND: Measuring medicinal compounds to evaluate their quality and efficacy has been recognized as a useful approach in treatment. Rhubarb anthraquinones compounds (mainly including aloe-emodin, rhein, emodin, chrysophanol and physcion) are its main effective components as purgating drug. In the current Chinese Pharmacopoeia, the total anthraquinones content is designated as its quantitative quality and control index while the content of each compound has not been specified. METHODS: On the basis of forty rhubarb samples, the correlation models between the near infrared spectra and UPLC analysis data were constructed using support vector machine (SVM) and partial least square (PLS) methods according to Kennard and Stone algorithm for dividing the calibration/prediction datasets. Good models mean they have high correlation coefficients (R2) and low root mean squared error of prediction (RMSEP) values. RESULTS: The models constructed by SVM have much better performance than those by PLS methods. The SVM models have high R2 of 0.8951, 0.9738, 0.9849, 0.9779, 0.9411 and 0.9862 that correspond to aloe-emodin, rhein, emodin, chrysophanol, physcion and total anthraquinones contents, respectively. The corresponding RMSEPs are 0.3592, 0.4182, 0.4508, 0.7121, 0.8365 and 1.7910, respectively. 75% of the predicted results have relative differences being lower than 10%. As for rhein and total anthraquinones, all of the predicted results have relative differences being lower than 10%. CONCLUSION: The nonlinear models constructed by SVM showed good performances with predicted values close to the experimental values. This can perform the rapid determination of the main medicinal ingredients in rhubarb medicinal materials.

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