Identification of Colorants in Food by Surface-enhanced Raman Spectroscopy and Wavelet-based Reverse Search

使用金纳米粒子为增强因子的表面增强拉曼光谱技术,通过连续小波变换将拉曼光谱信号转化到小波空间(墨西哥帽小波作为小波基)。该步骤能够减轻信号中基线; 变化及随机噪音的影响并找到峰位置和最佳小波尺度系数。依据小波空间中的信息,对混合物光谱及标准谱光谱进行反向搜索得到反向搜索匹配系数(Revere; match quality,; RMQ),作为判断混合物中目标成分是否存在的依据。该算法可对混合物中的目标物质进行准确定性,并已成功应用于多种食品中色素鉴定。食品中色素的检出率; 达到99%,且结果稳健,其效果明显优于传统的命中质量系数法(Hit quality index,; HQI)。这证实了小波空间反向搜索方法是一种快速而准确的拉曼光谱定性算法。In this study, a gold nanocrystal colloid was used as the enhancement; factor for surfaceenhanced Raman scattering (SERS). Raman spectra were; transformed by continuous wavelet transform (CWT),and Mexican hat; wavelet were chosen as the wavelet basis. This procedure could be used; to alleviate the influence of baseline variations and random noise,and; find peak positions and the best scale wavelet coefficients of signal.; Reverse search method was proposed to compare the spectrum of an unknown; sample with a spectrum of standard using the information in wavelet; space. Reverse match quality (RMQ) could be obtained automatically to; determine whether a substance is present. It was used to identify; colorants in a variety of food successfully. The colorants could be; identified with 99 percent accuracy. It shows a better performance; compared with traditional hit quality index (HQI). The study confirmed; that the wavelet-based reverse search is feasible and accurate in; qualitative analysis.国家自然科学基金资助项目; 国家重大科学仪器设备开发专

铜和镉复合污染对红树林区微生物群落结构和沉积物酶活性的影响

采用野外样地研究与室内根箱栽培相结合的方法,对漳江口红树林保护区铜、镉污染生境沉积物微生物结构与土壤酶活性进行研究,旨在探讨其对重金属的响应.微生物磷脂脂肪酸数据表明漳江口红树林湿地3种红树植物秋茄(Kandelia obovata)、白骨壤(Avicennia marina)、桐花树(Aegiceras corniculatum)根际土壤微生物群落的多样性与林外光滩区域比较差异有统计学意义(P<0.01).根际沉积物中荧光素水解酶(Fluorescein diacetate,FDA酶)、脲酶、蔗糖酶活性与光滩差异有统计学意义(P<0.05).基于野外研究,室内开展不同浓度Cu和Cd(Cu 60mg/kg,Cd 2mg/kg和Cu 60mg/kg,Cd 4 mg/kg)处理的根箱栽培实验.结果表明,重金属污染对根际土壤微生物生物量和微生物多样性的抑制效应有统计学意义(P<0.05).随着重沉积物中重金属浓度增加,土壤中的3种酶的活性差异有统计学意义(P<0.05).微生物生物量,磷脂脂肪酸含量与土壤酶活性的相关性有统计学意义(P<0.05).土壤酶活性与微生物群落结构可作为指示红树林重金属污染的敏感指标.国家自然科学基金(31570503);;国家重点研发计划(2017YFC0506102

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel

JUNO sensitivity on proton decay p → ν K + searches*

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the $p\to \bar{\nu} K^+$ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar{\nu} K^+$ is 36.9% ± 4.9% with a background level of $0.2\pm 0.05({\rm syst})\pm$$0.2({\rm stat})$ events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is $9.6 \times 10^{33}$ years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies