基于显微共聚焦拉曼光谱技术快速检测3种梭菌中肉毒梭菌

提出了一种基于显微共聚焦拉曼光谱技术的肉毒梭菌快速鉴别方法.利用共聚焦显微拉曼光谱技术(CRM)采集了肉毒梭菌、艰难梭菌和产气荚膜梭菌的拉曼光谱,比较了3种梭菌的平均拉曼光谱,采用基线校正、标准正态变换、Savitzky-Golay 5点平滑和最大最小值归一化预处理后,借助主成分分析(PCA)降维并提取特征变量,对样本...     

常见食源性致病菌的光谱快速识别

利用拉曼光谱技术建立了4种常见食源性致病菌的分类鉴别方法。在600～1800 cm-1光谱范围内，对蜡样芽胞杆菌、副溶血性弧菌、小肠结肠炎耶尔森菌与金黄色葡萄球菌共186个单细菌进行光谱信息的采集。使用Savitzky-Golay卷积平滑算法与一阶导数对原始光谱数据进行预处理，采用主成分分析方法（PCA）并结合多层感知器（MLP）神经网络构建对4种致病菌的分类识别模型，探究了不同光谱预处理方法下的PCA-MLP模型预测效果，经平滑算法与一阶导数处理过的光谱数据训练集准确率为98.5%，测试集准确率为99.2%。本文建立的光谱技术结合PCA-MLP模型可实现对食品中常见致病菌的快速鉴别。     

表面增强拉曼光谱技术在快速、灵敏检测食源性致病菌中的应用

食源性致病菌的快速、灵敏检测是食品和药品安全领域关注的重点.表面增强拉曼光谱(SERS)技术凭借其检测速度快、信息丰富、灵敏度高、无损等优势在食源性致病菌的快速、灵敏检测中受到广泛关注.本文从SERS检测基底材料入手,综述了液相基底、刚性固相基底、柔性固相基底等材料的特点和性能,并对其在致病菌快速、灵敏检测中的应用进行...     

食源性致病菌的表面增强拉曼光谱检测技术研究进展

食源性致病菌引起的疾病的快速管控与预防是当前各国面临的食品安全监管难题之一,受到社会各界的广泛关注.目前常用的食源性致病菌检测方法存在步骤复杂、耗时、灵敏度低或选择性差等局限,发展快速、可靠的食源性致病菌检测方法仍是食品安全和公众健康的热点研究领域.表面增强拉曼光谱(SERS)作为一种新型的光谱快检技术,具有灵敏度高、...     

基于显微拉曼检测蛋白核小球藻鉴别丁草胺及草甘膦

以蛋白核小球藻(Chlorella pyrenoidosa)作为鉴别载体,利用共聚焦显微拉曼光谱仪分别获取生长在除草剂草甘膦、丁草胺污染水体以及正常水体的蛋白核小球藻β-胡萝卜素的拉曼光谱信息,对2种除草剂进行了鉴别.利用预处理后的光谱信号,建立偏最小二乘回归(PLS)预测模型及线性判别分析(LDA)分类模型.当阈值为±0.3时,全波段建立的PLS模型对草甘膦和丁草胺的预测正确率高达83.33%,特征峰建立的LDA分类模型对2种除草剂的分类正确率均达到了100%.结果表明,利用蛋白核小球藻为载体对丁草胺和草甘膦2种除草剂进行鉴别是可行的,且LDA分类模型更适合除草剂的分类研究.     

显微共聚焦拉曼光谱结合群分析的黑色直液笔墨迹鉴别研究

黑色直液笔是一种新型书写工具,目前对该种笔墨迹的相关研究较少。为给文件检验工作中墨迹的分析提供新的参考依据,本实验使用显微共聚焦拉曼光谱技术,采集了30支不同品牌、型号的黑色直液笔墨迹光谱数据,进行Savitzky-Golay卷积平滑处理后,依据光谱图的拉曼位移及拉曼谱峰差异对墨迹进行初步分析。设置聚类方法为组间联接,区间距离测量方式为平方欧式距离,对采集的光谱数据进行群分析,将30支黑色直液笔墨迹样本分成了3类,并与品牌建立了相关联系;同时通过主成分分析验证了群分析的可靠性和准确性。研究表明,显微共聚焦拉曼光谱技术结合群分析方法可实现对黑色直液笔墨迹的无损分析及有效鉴别,该方法操作简便、结果准确,适用于法庭科学文件检验。     

用于电化学界面研究的共焦显微拉曼光谱技术(英文)

系统地介绍了将共焦显微拉曼光谱系统用于电化学界面研究的方法 ,包括铂电极的粗糙和电化学拉曼电解池的设计 .进行了铂上氢、氧和氯共吸附的拉曼光谱研究 .通过对甲醇氧化过程的现场跟踪 ,提出检测界面区溶液浓度变化和计算溶液 pH值的方法 .实验表明拉曼光谱技术可作为研究实际应用体系的重要工具 .     

共焦显微拉曼光谱监测聚合物载体支载的化学反应

在固相载体上进行的有机合成[1-2]以及使用固相载体支载的试剂作为反应物或清除剂[3-4]的有机合成具有产物分离纯化简单、可以使用过量试剂促使反应完全、固相可以重复使用和容易实现自动化合成等诸多优点而受到重视,是组合化学[5-6]及高通量筛选[7]技术中建立分子多样性化合物     

激光共聚焦显微拉曼快速测定食用调和油脂肪酸

目的:采用激光共聚焦显微拉曼光谱技术快速测定食用调和油饱和脂肪酸(Saturated fatty acids,SFA)、单不饱和脂肪酸(Monounsaturated fatty acids,MUFA)和多不饱和脂肪酸(Polyunsaturated fatty acids,PUFA)含量及比例。方法:通过导数预处理净化拉曼光谱信息,采用偏最小二乘法建立优化后的SFA、MUFA、PUFA的拉曼定量预测模型,为计算脂肪酸比例提供准确的数据基础。结果:SFA、MUFA和PUFA定量分析模型的决定系数R2均大于0.99,相对分析误差RPD均大于3,表明模型具有较高的稳定性和良好的预测能力。结论:激光拉曼光谱法结合化学计量学方法可以快速、准确地测定食用调和油SFA、MUFA、PUFA含量及比例,为快速检测食用调和油品质提供切实可行的检测手段。     

基于主成分分析-Fisher判别分析的食品类塑料瓶物证差分拉曼光谱分类

食品类塑料瓶物证携带许多潜在证据信息,目前针对此类物证的检验研究尚处于探索阶段。利用差分拉曼光谱对46个食品类塑料瓶样品进行检验,依据样品材质及光谱特征峰可将样品分为三类。利用主成分分析（Principal component analysis, PCA）-Fisher判别分析,绘制主成分得分图,构建判别函数,建立分类模型。结果表明,食品类塑料瓶样品具有明显的聚类关系,原始分类与交叉验证分类准确率达到100 %。差分拉曼光谱结合PCA-Fisher判别分析,检验鉴别食品类塑料瓶物证具有一定的科学性。     

激光共聚焦拉曼光谱法检测潜在指纹上的太安

采用激光共聚焦拉曼光谱法检测潜在指纹上太安(PETN)颗粒。用手指蘸取PETN炸药粉末后在铝箔胶带表面按压,对指纹表面进行喷碳处理后,在激光共聚焦拉曼光谱仪上在白光条件下以10倍物镜采集指纹,再在激光条件下以50倍长焦物镜对指纹上4.0mm×4.0mm的区域进行成像,根据PETN的特征拉曼散射峰得到PETN颗粒在指纹上的分布信息。通过液相色谱-串联质谱法(LC-MS/MS)对试验结果的准确性进行了确认。     

Rapid Authentication of Olive Oil by Raman Spectroscopy Using Principal Component Analysis

A method of principal component analysis was employed to authenticate genuine olive oil based on Raman spectroscopy, which can reliably distinguish olive oil from other types of oils and can also accurately identify the level of adulteration in a set of olive oil samples contaminated with 5% or more of other types of oils, such as soybean oil, rapeseed oil, sunflower seed oil, and corn oil. The method is very easy, effective, time-saving, and requires minimal sample preparation. Therefore, the method is a promising technique for the rapid authentication application of olive oil.

[Supplementary materials are available for this article. Go to the publisher's online edition of Analytical Letters for the following free supplemental resource(s): Additional text and table]     

Visualization of Ferroelectric Domains in Thin Films of Molecular Materials Using Confocal Micro-Raman Spectroscopy

Ferroelectrics are an important class of functional materials. Among all their unique properties, the study of their ferroelectric domains and domain walls is of great interest due to their importance in ferroelectric applications. There are many methods to characterize ferroelectric domains, namely, scanning probe microscopy, optical microscopy, electron microscopy, etc. Currently, newly emerged molecular ferroelectrics are attracting much attention from chemists, physicists and researchers in material sciences due to their structural flexibility, light mass, simple fabrication, etc. However, for the characterization of molecular ferroelectric domains, most conventional methods require either a complicated preparation process or direct contact between physical probes and material surfaces, limiting the development of molecular ferroelectric materials. In this report, we have demonstrated that confocal micro-Raman spectroscopy, as a nondestructive and noncontact in-situ method, is very suitable for studying the ferroelectric polarization and structures of domains in molecular ferroelectrics. Taking recently reported molecular ferroelectric trimethylchloromethyl ammonium trichlorocadmium(II) (TMCM-CdCl₃) as an example, the non-180° domains have been characterized and visualized at different temperatures. Such a simple and extendable method requires minimum sample preparation, which would further benefit the research of molecular ferroelectric domain engineering and promote the miniaturization and integration of molecular ferroelectric films.     

高分辨激光诱导击穿光谱技术用于碳质页岩光谱特征分析

碳质页岩是一种重要的非常规油气源岩,包含丰富的页岩气储存信息。在地质油气勘探领域,识别物性相近的碳质页岩是地质学的难题之一。为快速准确判别碳质页岩特性,本研究利用激光诱导击穿光谱( LIBS)技术对碳质页岩的光谱进行特征分析。以1064 nm纳秒脉冲激光作为激发光源,采用高分辨中阶梯光栅光谱仪配合ICCD搭建高分辨光学系统,利用LIBS装置分析四川盆地某井2396~3428 m不同深度处的5个典型碳质页岩样品,共获取碳质页岩中主量元素Si,Al,Fe,Ca,Mg,K和微量元素Cu,Cr,Ni,Sr,Mn,Ti,Rb等22种元素的350条发射谱线。基于高分辨LIBS系统采集的丰富谱线信息,采用主成分分析( PCA)法提取光谱贡献率最大的主成分得分二维图,识别不同类别碳质页岩样品,实现定性分析。结果表明,高分辨LIBS实验装置不仅可用于碳质页岩主量元素Si,Al,Fe,Ca,Mg,K等谱线分析,同时对微量元素如Ni,Cr,Mn,Sr等也具有很好的灵敏性。此外,LIBS技术与主成分分析( PCA)法结合可以更好地用于碳质页岩定性分析领域,提供科学的碳质页岩岩性判断数据和快速的分类手段,为页岩气开采和评估提供强有力的工具。     

Development of Near Infrared Spectroscopy for Rapid Quality Assessment of Red Ginseng

Near infrared spectroscopy(NIRS) was developed as a rapid analysis method for the qualitative and quantitative assessment of the quality of red ginseng. Discriminant analysis(DA) based on principal component analysis and Mahalanobis distance was used to distinguish red ginseng from counterfeits non-destructively. The result shows that the proposed method could distinguish red ginseng from counterfeits correctly and no misclassified sample was found in both training and test sets. The partial least squares(P...     

近红外光谱技术结合主成分分析法用于子宫内膜癌的诊断

应用近红外光谱技术结合化学计量学方法研究了子宫内膜癌组织近红外光谱特征提取和早期诊断的可行性. 测定了154 例子宫内膜组织切片的近红外光谱, 选取适宜的波段和光谱预处理方法进行主成分分析, 很好地区分了癌变、增生和正常子宫内膜组织切片, 并且分辨出处于不同分化期的组织切片, 为子宫内膜癌的早期诊断提供了可靠依据. 该法快速、简便, 有望发展成为一种新型的肿瘤无创诊断方法.     

基于多参数融合的红外光谱对混合物的组分识别研究

为解决混合物组分的识别问题,该文以7种标准品以及由7种标准品配制的26种混合物为研究对象,以准确率与误判率为混合组分识别效果的评价指标,在研究谱峰匹配算法、非负最小二乘匹配算法与相关系数匹配系数算法的基础上,采用一种基于多特征融合的BP神经网络模型红外光谱技术对混合物组分进行识别,并与逻辑回归模型进行了对比。结果表明,3种单一匹配算法的识别准确率均低于76.31%,多特征融合的逻辑回归模型与多特征融合的BP神经网络模型预测集的识别准确率分别为83.33%和98.18%,误判率分别为4.76%和1.82%。研究结果表明,中红外光谱技术结合BP神经网络模型可以更好地进行混合物的组分识别。为了进一步探究模型对混合组分最低浓度的检测能力,采用香兰素和乙醇两组分溶液进行检出限的研究。结果表明混合物中香兰素的质量浓度为0.03 g/mL时准确率为100.00%,误判率为14.29%;香兰素质量浓度低于0.03 g/mL时,准确率下降20.00%,误判率上升19.04%。故可认为该研究能识别出的香兰素乙醇溶液中香兰素的最低质量浓度为0.03 g/mL。     

应用傅里叶变换红外光谱区分鉴定四种食源性致病菌的研究

利用傅里叶变换红外光谱技术(FT-IR)对大肠杆菌O157: H7、肠炎沙门氏菌、单核增生性李斯特菌、金黄色葡萄球菌这四种典型食源性致病菌进行了研究. 对其红外谱图进行基线校正和归一化处理, 对其谱带归属进行判别分析, 选取具有菌株特性的1500～800 cm^－1的谱带, 进行一阶导数运算, 结合化学计量学方法主成分分析(PCA)和聚类分析(CA), 并进行相关系数的统计学分析, 得出以下结果: 四种食源性致病菌最大相关系数为0.937, 最小相关系数为0.058, 而且四种不同的细菌在PCA和CA分析图上也达到了很好的区分效果. 研究表明, FT-IR有望成为一种快速检测食源性致病菌的新方法.     

Characterization of Automobile Plastics by Principal Component Analysis and Near-Infrared Spectroscopy

Based on near-infrared diffuse reflection spectroscopy, multivariate calibration models for discarded automobile plastic were constructed using principal component analysis and clustering analysis to rapidly characterize four widely employed materials: polypropylene, polyethylene, acrylonitrile butadiene styrene, and polymethylmethacrylate with an accuracy rate of 97%. The method was shown to rapidly discriminate waste automobile plastic.     

A new regression method based on independent component analysis

Based on independent component analysis (ICA), a new regression method, independent component regression (ICR), was developed to build the model of NIR spectra and the routine components of plant samples. It is found that ICR and principal component regression (PCR) are completely equivalent when they are applied in quantitative prediction. However, independent components (ICs) can give more chemical explanation than principal components (PCs) because independence is a high-order statistic that is a much stronger condition than orthogonality. Three ICs are obtained by ICA from the NIR spectra of plant samples; it is found that they are strongly correlated to the NIR spectra of water, hydrocarbons and organonitrogen compounds, respectively. Therefore, ICA may be a promising tool to retrieve both quantitative and qualitative information from complex chemical data sets.