Fast saccharide mapping method for quality consistency evaluation of commercial xylooligosaccharides collected in China / 药物分析学报

Due to the extensive use of xylooligosaccharides (XOS) as functional food ingredients,many inferior goods and even adulterants are generally found in the market,which may pose a health hazard to certain populations.Chromatography method such as high-performance liquid chromatography (HPLC) and high-performance thin-layer chromatography (HPTLC) is traditionally applied for the quality analysis of XOS.However,it is time consuming due to the prolonged separation and pre-or post-derivatization procedure.In this study,a fast saccharide mapping method based on matrix-assisted laser desorption/time-of-flight mass spectrometry (MALDI-TOF-MS) was developed for the quality consistency analysis of 22 batches of XOS collected from different manufacturers in China.The time needed for saccharides analysis using MALDI-MS was less than 30 min for one plate,at least 6 times faster than that by the traditional HPTLC chromatography method.In addition,MALDI-MS possessed higher resolution for XOS with DP4-DP7 based on the difference of m/z,which is hardly separated using HPTLC.The results showed that XOS were present only in samples XY01-XY11,samples XY12-XY14 only consisted of hex oligo-saccharides,and samples XY15-XY22 were free of oligosaccharides.These indicate that the quality consistency of XOS products in the China market was poor,which should be carefully investigated.

Rapid identification of three varieties of Chrysanthemum with near infrared spectroscopy

A total of 139 batches of Chrysanthemum samples were randomly divided into calibration set (92 batches) and prediction set (47 batches). The near infrared diffuses reflectance spectra of Chrysanthemum varieties were preprocessed by a first order derivative (D1) and autoscaling, and a modelwas built using partial least squares analysis. In this study, three Chrysanthemum varieties were identified, the accuracy rates in calibration sets of Dabaiju, Huju, and Xiaobaiju are 97.60, 96.65, and 94.70%, respectively; And 95.16, 86.11, and 93.46% accuracy rate in prediction sets was obtained. The research results demonstrate that the qualitative analysis can be conducted by machine learning combined with Near-Infrared Spectroscopy, which provides a new method for rapid and non-invasive identification of Chrysanthemum varieties.