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ATR-FT-IR
Applied Sciences-Basel · 2019 ·Vol. 9 ·期 24 ·5336 · IF 2.217

Analysis of Methanol Gasoline by ATR-FT-IR Spectroscopy

Qi XIA,Lei-ming YUAN,Xiaojing CHEN,Liuwei MENG,Guangzao HUANG

Featured ApplicationThe quality of methanol gasoline is important to internal combustion engines. Infrared spectroscopy technology shows its feasibility to qualitatively and quantitatively measure the quality of gasoline. Combined with the chemometrics, this technology can be used to determinate the adulteration category or fineness, providing the qualified gasoline for the market. Spectra analytical technology is a promising method in applying in the gasoline industry and speeding up the industrial application.AbstractMethanol gasoline blends are a more economical, and environmentally friendly fuels than gasoline alone, and are widely used in the transportation industry. The content of methanol in methanol gasoline plays an important role in ensuring the quality of gasoline. In some solutions, due to the shortage of energy and illegal profits, the problem of gasoline adulteration and its fineness, has received more and more attention, which would seriously affect the operating condition and service life of internal combustion engines. Therefore, it is very important to identify the correct level of gasoline. However, the traditional detection method is complex and time-consuming. To this end, the feasibility of using attenuated total reflectance Fourier transform infrared (ATR-FTIR) methods coupled with chemometrics methods were investigated to quantitatively and qualitatively analyze methanol gasoline. The qualitative analysis result of partial least squares discriminant analysis (PLS-DA) obtained 100% and 98.66% accuracy in the calibration set and the prediction set, respectively. As for quantitative analysis; two regression algorithms of partial least squares regression (PLSR) and the least square support vector machine (LS-SVM), as well as two variables selection methods of the successive projections algorithm (UVE) competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE) were combined to establish the quantitative model. By comparing the performance of the optimal models; the UVE-PLSR model performed best with a residual predictive deviation (RPD) value of 6.420. The qualitative and quantitative analysis results demonstrate the feasibility of using ATR-FTIR spectra to detect the methanol in methanol gasoline. It is believed that the promising IR spectra will be widely used in gasoline energy quality control in the further.Keywords:methanol gasoline;infrared spectroscopy;partial least square discriminant analysis (PLS-DA);multivariate regression;variable selection

CAS / MDL: 67-56-1
研究领域: 化学计量学 · 能源科学
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INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES · 2024 · IF 7.7

Multiple fingerprint and pattern recognition analysis on polysaccharides of four edible mushrooms

Shuangyu Shao,Xiali Si,Yingtao Zhang,Jun Li,Pengfei Tu,Qingying Zhang

Quality analysis of edible mushrooms based on polysaccharides is generally difficult due to their complicated structures and hard separation. Here, multiple fingerprint analysis of polysaccharides based on chromatographic and spectrometric techniques were developed, and then applied in comparative analysis of Auricularia heimuer (AH), Auricularia cornea (AC), Auricularia cornea ʻYu Muerʼ (ACY) and Tremella fuciformis (TF). Firstly, polysaccharides were obtained with the molecular weights between 1.783 × 10 6 and 6.774 × 10 6  Da. Then, complete hydrolysis by TFA and enzyme digestion by cellulase were employed and subsequently analyzed by HPLC-UV, GC–MS, HILIC-HPLC-ELSD and HILIC-HPLC-ESI − -HCD-MS/MS, and ATR-FT-IR were used to characterize the functional groups of intact polysaccharides. By chemometric analysis, differential markers of d -xyl, l -fuc, l -arb, d -glc, disaccharide and hexasaccharide were selected, and AC and ACY were proved to be same species from the viewpoint of polysaccharides firstly. Furthermore, the structures of oligomers with DPs of 2–8 and →4)- β - d -Glc p -(1→ unit with different contents were inferred by combinatory analysis of ESI − -MS/MS, glycosidic linkage, monosaccharide compositions and functional groups. In conclusion, the combinatory method of multiple fingerprint and pattern recognition is powerful not only for structural elucidation of polysaccharides, but also for quality analysis and species differentiation of edible mushrooms from the perspective of biological polysaccharides.

CAS / MDL: 89-25-8
研究领域: 食品科学 · 分析化学 · 真菌学 · 生物化学
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