Assembling an adapted smoothing method and a classifier of wavelet transform combined support vector machine ( SVM) , a Raman spectrum recognition approach was built for low signal noise ratio situation. Firstly, spectra data were denoised by the adapted smoothing method. The smoothing window was adapted to the signal noise ratio, which would effectively remove noise with the intensity of the signal well remained. Secondly, the wavelet transform was used for dimension reduction of the data. The decomposition level of wavelet transform was optimized according to the best classification result of the training set. Lastly, SVM was used for classification. Cross Validation ( CV ) was applied to obtain the optimized parameters of SVM. Conditions for the effective parameters were searched considering the relation between the cross_validation result and the classification accuracy. Combined with the surface enhanced Raman scattering ( SERS ) technology , the developed spectrum recognition approach was used for qualitative analysis of methamphetamine ( MAMP ) and 3, 4_methylenedioxymethamphetamine ( MDMA ) in peopleˊs urine, where the detecting accuracy is above 95. 0%. The uniform Au nanorods (NRs) SERS substrate synthetized by the Hefei Institute of Intelligent Machines of Chinese Academy of Sciences was used for the experiment. Raman spectra were acquired using an Inspector Raman ( DeltaNu) spectrometer, with the excitation wavelength of 785 nm and the integrate time of 5 seconds.

Objective:To analyze the biological characteristics, phylogenic features and clinical significance of SQ219 and SQ220 isolated from clinical sputum and midstream urine specimens.Methods:The culture and biochemical characteristics of the two strains were observed. VITEK2 System, drug sensitivity testing and MALDI-TOF mass spectrometry were used for bacterial identification. Phylogenetic analysis based on 16S rRNA and core genome was performed. The average nucleotide identity (ANI) based on whole genome sequences was calculated.Results:SQ219 and SQ220 were Gram-stain-negative, aerobic, catalase- and oxidase-positive, and non-motile bacteria. Their optimum growth was observed in NaCl-free medium at 30℃ and pH7. Flexirubin-type pigments were produced by SQ220 on Colombia blood agar, but not by SQ219. Both SQ219 and SQ220 were resistant to aztreonam, amikacin, tobramycin and colistin, which was consistent with the drug resistance phenotype of genus Chryseobacterium. The genome sequences of SQ219 and SQ220 were 5.08 Mb and 4.80 Mb in length, and the G+ C contents were 36.72% and 36.36%, respectively. Both strains carried β-lactam resistance gene ( blaCGA). 16S rRNA phylogenetic analysis showed that SQ219 and SQ220 were closely related to Chryseobacterium gambrini DSM18014 T with the similarities of 98.93% and 98.36%, respectively. Core genome phylogenetic analysis revealed that SQ219 and SQ220 were highly homologous to Chryseobacterium gambrini DSM18014 T. However, the ANI values between the two strains and Chryseobacterium gambrini DSM18014 T were 92.49% and 93.27%, respectively, below the threshold for prokaryotic species identification. Conclusions:Based on the phenotypic and phylogenetic data, SQ219 and SQ220 represent a novel species of the genus Chryseobacterium. This study would help promote the understanding of the evolution of Chrysobacterium and provide reference for the identification of new species of Chrysobacterium.