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等级
analytical standard
质量水平
方案
≥98% (HPLC)
旋光性
[α]/D 34±1°, c = 10 in H2O
保质期
limited shelf life, expiry date on the label
分析物化学类别
oligosaccharides
mp
239 °C (dec.) (lit.)
应用
food and beverages
包装形式
neat
SMILES字符串
OC[C@@H](O)[C@@H](O[C@@H]1O[C@H](CO)[C@@H](O)[C@H](O)[C@H]1O)[C@H](O)[C@@H](O)C=O
InChI
1S/C12H22O11/c13-1-4(16)7(18)11(5(17)2-14)23-12-10(21)9(20)8(19)6(3-15)22-12/h1,4-12,14-21H,2-3H2/t4-,5+,6+,7+,8+,9-,10+,11+,12-/m0/s1
InChI key
DKXNBNKWCZZMJT-WELRSGGNSA-N
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一般描述
Cellobiose is a disaccharide, commonly classified as a reducing sugar. It is mostly produced as an intermediate in the hydrolysis of the polysaccharide cellulose.
应用
D-(+)-Cellobiose may be used as an analytical standard in the following:
D-(+)-Cellobiose may be used as an analytical reference standard for the quantification of the analyte in caramel samples using gas–liquid chromatography coupled to mass spectrometry (GLC–MS).
- Training, testing, and external validation of the gradient retention model developed in ion chromatography using the artificial intelligence-quantitative structure retention relationship (QSRR) model approach.
- Amperometric detection of the analyte in anion-exchange chromatography using copper/cupric oxide nanostructured electrode.
D-(+)-Cellobiose may be used as an analytical reference standard for the quantification of the analyte in caramel samples using gas–liquid chromatography coupled to mass spectrometry (GLC–MS).
储存分类代码
11 - Combustible Solids
WGK
WGK 3
闪点(°F)
Not applicable
闪点(°C)
Not applicable
法规信息
监管及禁止进口产品
General, Organic, and Biological Chemistry, 6 (2012)
Nanostructured cupric oxide electrode: An alternative to amperometric detection of carbohydrates in anion-exchange chromatography
Analytica Chimica Acta, 906, 89-97 (2016)
Qualitative and quantitative evaluation of mono- and disaccharides in d-fructose, d-glucose and sucrose caramels by gas?liquid chromatography?mass spectrometry: Di-d-fructose dianhydrides as tracers of caramel authenticity
Journal of Chromatography A, 844(1-2), 283-293 (1999)
Development of Gradient Retention Model in Ion Chromatography. Part II: Artificial Intelligence QSRR Approach
Chromatographia, 77, 997-1007 (2014)
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