Improvement of Tunable Diode Laser Absorption System with a Novel Multipass Cell and Wavelet Transform

Combination of Tunable Diode Laser Absorption Spectroscopy(TDLAS)technique and multipass cell is an attractive approach for ultrahigh sensitive detection of trace gases.Theoretically,based on Beer-Lambert law,the longer optical path length and the larger gas absorption,the lower concentration gas could be detected.However,lower radiation intensity and inevitable etalon fringe resulted from multiple reflections would greatly weaken the Signal-to-Noise Ratio(SNR)and thus an expected ultrahigh sensitive detection system is difficult to achieve.In order to fully make use of the advantages of TDLAS and multipass cell,the base length and the total optical path length of the multipass cell are needed to be carefully balanced.Furthermore,the harmonic signals contaminated by various noises are processed with wavelet transform method.As a demonstration of this method,few low concentrations of gas CO in N2 are measured employing TDLAS technique and a novel sealed multipass cell with total optical length of 114 m.The detection limit is about 5×10-6(volume ratio),which is one order of magnitude better than earlier noise reduction.

Nondestructive determination of GABA in germinated brown rice with near infrared spectroscopy based on wavelet transform denoising

The objective of this study was to analyze the content ofγ-aminobutyric acid(GABA)in germinated brown rice(GBR)by using near-infrared spectroscopy(NIRS)and the pretreatment method of wavelet de-noising(WD).The prediction accuracy of the NIRS model established by the Daubechies5 wavelet basis function at 3 level denoising treatment is the highest,the correlation coefficient for calibration(rc)is 0.931,the root mean square error of calibration(RMSEC)is 0.4038 mg/100 g,the Bias of calibration is 0.006,the correlation coefficient for prediction(rp)is 0.916,the root mean square error of prediction(RMSEP)is 0.4329 mg/100 g,the Bias of prediction is 0.010,and the ratio of performance to deviation(RPD)is 4.911.Results showed that the predicted and actual values had high correlation.Therefore,these results indicate that the NIRS model treated by WD is feasible to detect GABA content in GBR rapidly and nondestructively.

Quantitative Analysis of Berberine in Processed Coptis by Near-Infrared Diffuse Reflectance Spectroscopy

The near-infrared（NIR） diffuse reflectance spectroscopy was used to study the content of Berberine in the processed Coptis. The allocated proportions of Coptis to ginger, yellow liquor or Evodia rutaecarpa changed according to the results of orthogonal design as well as the temperature. For as withdrawing the full and effective information from the spectral data as possible, the spectral data was preprocessed through first derivative and multiplicative scatter correetion（MSC） according to the optimization results of different preprocessing methods. Firstly, the model was established by partial least squares（PLS）; the coefficient of determination（R2） of the prediction was 0.839, the root mean squared error of prediction（RMSEP） was 0.1422, and the mean relative error（RME） was 0.0276. Secondly, for reducing the dimension and removing noise, the spectral variables were highly effectively compressed via the wavelet transformation（WT） technology and the Haar wavelet was selected to decompose the spectral signals. After the wavelet coefficients from WT were input into the artificial neural network（ANN） instead of the spectra signal, the quantitative analysis model of Berberine in processed Coptis was established. The R^2 of the model was 0.9153, the RMSEP was 0.0444, and the RME was 0.0091. The values of appraisal index, namely R^2, RMSECV, and RME, indicate that the generalization ability and prediction precision of ANN are superior to those of PLS. The overall results show that NIR spectroscopy combined with ANN can be efficiently utilized for the rapid and accurate analysis of routine chemical compositions in Coptis. Accordingly, the result can provide technical support for the further analysis of Berberine and other components in processed Coptis. Simultaneously, the research can also offer the foundation of quantitative analysis of other NIR application.

DETECTING BILATERAL FUNCTIONAL CONNECTIVITY IN THE PREFRONTAL CORTEX DURING A STROOP TASK BY NEAR-INFRARED SPECTROSCOPY

Near infrared spectroscopy(NIRS)is generally accepted as a functional brain imaging technology for brain activation study.With multichannel highly sensitive NIRS instruments,it has become possible to assess functional connectivity of different brain negions by NIRS.However,the feasibility needs to be validated in complex cognitive activities.In this study,we recorded the hemodynamic activity of the bilateral prefrontal cortex(PFC)during a color-word matching Stroop task.Wavelet transform coberence(WTC)analysis was applied to ascss the functional conectivity of all homologous channel pairs within the left/right PFC.Both the behavioral and brain activ ation results showed signifcant Stroop effects.The results of WTC analysis revealed that,bilateral functional connectivity was significantly stronger during both the incongruent stimuli and neutral stimuli compared to that of the rest period.It also showed significant Stroop effect.Our findings demonstrate that,NIRS bcomes a valuable tool to elucidate the functional conectivity of brain cortex in complex cognitive activities.

Online quantitative analysis of soluble solids content in navel oranges using visible-nearinfrared spectroscopy and variable selection methods

Variable selection is applied widely for visible-near infrared(Vis-NIR)spectroscopy analysis of internal quality in fruits.Different spectral variable selection methods were compared for online quantitative analysis of soluble solids content(SSC)in navel oranges.Moving window partial least squares(MW-PLS),Monte Carlo uninformative variables elimination(MC-UVE)and wavelet transform(WT)combined with the MC-UVE method were used to select the spectral variables and develop the calibration models of online analysis of SSC in navel oranges.The performances of these methods were compared for modeling the Vis NIR data sets of navel orange samples.Results show that the WT-MC-UVE methods gave better calibration models with the higher correlation cofficient(r)of 0.89 and lower root mean square error of prediction(RMSEP)of 0.54 at 5 fruits per second.It concluded that Vis NIR spectroscopy coupled with WT-MC-UVE may be a fast and efective tool for online quantitative analysis of SSC in navel oranges.

基于近红外光谱特征的冷冻小龙虾鲜度快速检测方法

为建立快速检测冷冻小龙虾鲜度的近红外光谱模型,采集解冻的小龙虾虾尾、虾仁及虾糜的近红外光谱,分别利用一阶导数、多元散射校正、小波变换(wavelet transform,WT)和标准正态变换进行预处理,并利用偏最小二乘(partial least squares,PLS)与卷积神经网络(convolutional neural network,CNN)算法将预处理前后的光谱数据分别与总挥发性盐基氮(total volatile basic nitrogen,TVB-N)含量关联,构建定量预测模型并比较建模效果,选取较佳模型,探究模型预测准确度和适用性。结果显示,预处理方法明显影响了建立模型的精度,光谱经预处理建立的CNN模型与PLS模型相比,具备更好地预测小龙虾TVB-N含量的能力。其中,虾仁光谱经WT预处理建立的CNN模型对验证集的预测准确度最高,校正集与验证集的相关系数分别为0.97、0.96,校正集与验证集的均方根误差分别为1.26、0.93mg/100g。近红外光谱的准确度、精密度与灵敏度均在合理范围内,方法学验证结果良好。综合考虑实际应用中快速、准确、低损伤等需求,确定WT-CNN-虾仁模型为预测冷冻小龙虾中TVB-N含量的最优模型。这些结果表明,WT-CNN-虾仁模型在预测冷冻小龙虾TVB-N含量、快速评价新鲜度方面具有巨大潜力。

心血管外科围手术期的神经监测

心血管外科手术神经系统并发症风险较高,可加重疾病负担,围手术期实时监测神经功能可辅助临床医师尽早发现神经系统异常,及时干预并降低神经系统并发症风险,其主要监测指标包括脑氧饱和度、脑电信号、脑血流量等。本文综述心血管外科围手术期常用的神经监测技术及其研究进展,以为减少神经系统并发症、提高患者预后提供理论依据。

Terahertz Spectroscopic Characterization and Thickness Evaluation of Internal Delamination Defects in GFRP Composites

The use of terahertz time-domain spectroscopy(THz-TDS)for the nondestructive testing and evaluation(NDT&E)of materials and structural systems has attracted significant attention over the past two decades due to its superior spatial resolution and capabilities of detecting and characterizing defects and structural damage in non-conducting materials.In this study,the THz-TDS system is used to detect,localize and evaluate hidden multi-delamination defects(i.e.,a three-level multi-delamination system)in multilayered GFRP composite laminates.To obtain accurate results,a wavelet shrinkage de-noising algorithm is used to remove the noise from the measured time-of-flight(TOF)signals.The thickness and location of each delamination defect in the z-direction(i.e.,through-the-thickness direction)are calculated from the de-noised TOF signals considering the interaction between the pulsed THz waves and the different interfaces in the GFRP composite laminates.A comparison between the actual and the measured thickness values of the delamination defects before and after the wavelet shrinkage denoising process indicates that the latter provides better results with less than 3.712%relative error,while the relative error of the non-de-noised signals reaches 16.388%.Also,the power and absorbance levels of the THz waves at every interface with different refractive indices in the GFRP composite laminates are evaluated based on analytical and experimental approaches.The present study provides an adequate theoretical analysis that could help NDT&E specialists to estimate the maximum thickness of GFRP composite materials and/or structures with different interfaces that can be evaluated by the THz-TDS.Also,the accuracy of the obtained results highlights the capabilities of the THz-TDS for the NDT&E of multilayered GFRP composite laminates.

基于迁移学习的新精神活性物质拉曼光谱识别研究

随着精神活性物质(NPS)对社会的潜在危害越来越严重,提高新型毒品的快速检测、识别能力是当前打击毒品犯罪的重中之重。本研究采集了196份新精神活性物质缴获样本拉曼光谱,结合连续小波变换和卷积神经网络迁移学习模型,对五种检测仪器获得的拉曼光谱数据进行分析和识别。对VGG16、InceptionV3和ResNet50三种深度学习模型分类效能的比较结果显示,单仪器新精神活性物质识别能力为80.2%~100%不等,多仪器综合识别能力为88.6%。通过合理的光谱预处理方法,能够将不同仪器的NPS光谱检测数据,统一格式并进行批量的分析和识别,精准提高了拉曼光谱的数据提取和利用效率。

基于高斯混合模型扣除毛发SERS信号中增强基底的背景峰

在利用表面增强拉曼光谱(SERS)对毛发中痕量物质进行分析时,该SERS信号中毛发特征峰与增强基底背景峰会相互耦合。在耦合情况下,背景峰会被误识别为毛发特征峰,导致待测物的识别错误,此外具有高峰强特性的背景峰对毛发中微弱特征峰产生掩盖干扰。因此,背景峰的扣除是解决上述问题的重要途径,但常规的扣峰方法会导致周围邻峰的严重失真。针对上述问题提出了高斯混合模型,该模型在表征SERS信号的同时又使得各特征峰相互独立,在扣峰过程中对周围邻峰不产生干扰,既实现干扰峰的扣除又保证了邻峰的微失真。高斯混合模型的核心问题在于模型参数的求解,文中提出了小波变换与共轭梯度法,分别解决模型的初始参数问题及最优解问题。小波变换通过映射放大SERS信号的细节信息,充分提取该信号的细微特征信息,将该特征信息作为模型的初始参数。其中共轭梯度法是迭代优化方法,将模型参数进行循环迭代优化,最终收敛结果即为模型参数的最优解。综上两种方法可准确建立高斯混合模型,模型中单高斯函数为SERS信号的特征峰,且两者的峰形保持一致。在扣除SERS信号的背景峰时应遵循以下过程,包括有效数据的提取、模型建立和峰的扣除。其中有效数据的提取是对空白与滴样的增强基底进行同位置检测,由此得到一组SERS信号。模型建立是通过高斯混合模型对滴样SERS信号进行表征,该信号可由多个高斯函数表现。最后利用空白增强基底的特征峰对滴样的SERS信号进行指认,其中峰形相似且峰位相同的特征峰可扣除。实验结果表明,方差值比最小时,高斯混合模型的峰位、峰宽、峰强等特征与毛发SERS信号基本相同,此时高斯混合模型可准确表征毛发SERS信号的特征信息。在对7组毛发进行扣峰实验时,毛发SERS信号中背景峰扣除率达到50%~100%,同时毛发的特征峰也得到有效提取。在对真实毛发样本进行快速分析时,该模型识别出了毒品曲马多。

高湿度环境下爆炸物太赫兹光谱的特征提取与精准识别

材料太赫兹吸收谱的指纹特性已被广泛应用于物质识别,但实际大气环境下,水蒸气对太赫兹波的强烈吸收会导致光谱严重振荡,假峰、弱峰、混叠峰相继增多,严重影响寻峰比对的精度及物质识别的能力。针对上述问题,提取相对湿度为2%,15%,35%,45%和60%环境下爆炸物的太赫兹吸收光谱信息数据,利用连续小波变换将光谱在频域上展开得到具有特征唯一性的频域尺度图;再基于深度学习方法,以ResNet-50网络模型为基本网络结构,对上述5种不同湿度环境下得到的爆炸物频域尺度图进行网络分类训练,其测试集分类准确率达96.6%。为验证该技术在未经训练湿度样本下的有效性,将相对湿度为50%,55%和67%时爆炸物的时域信号送入该识别系统,分类准确率可达96.2%。实验结果表明,基于小波变换与ResNet-50网络分类训练的太赫兹物质识别方法相比于传统寻峰方法大幅提升了爆炸物在高湿度环境下的识别准确率,规避了降噪、平滑等一系列复杂预处理操作,极大拓展了太赫兹光谱探测技术的工程适应性,为山地、森林、洼地等高湿度、极复杂的作战环境下精确探测、识别地雷等爆炸物提供了技术支持。

基于近红外光谱的小波变换结合正交匹配追踪建立秦艽中龙胆苦苷的定量分析模型

针对近红外光谱高维小样本数据以及信号具有稀疏先验的特点,提出了小波变换(WT)结合正交匹配追踪(OMP)建立秦艽中龙胆苦苷定量分析模型的方法。将204个秦艽样品按照Kennard-Stone法以3∶1的比例进行划分,得到153个校正集样品和51个测试集样品,利用傅里叶变换近红外光谱仪采集样品的近红外光谱,采用WT对原始近红外光谱数据进行预处理,选择小波基函数为Daubechies(db5),小波分解层数为5,小波阈值为0.1,采用OMP建立龙胆苦苷的定量分析模型。结果表明:该模型预测性能较好,校正集对应的决定系数(R_(C)^(2))为0.9940,校正均方根误差(RMSEC)为0.0819,测试集对应的决定系数(R_(P)^(2))为0.9854,预测均方根误差(RMSEP)为0.1124;利用所建模型分析204个秦艽样品,龙胆苦苷预测值与参考值基本一致。

隐匿危险品高准确度太赫兹光谱识别方法

爆炸物等危险品的分子振动和转动能级在太赫兹频谱段具有独特的指纹谱特性,且太赫兹波对非极性物质及介电材料有较强的透过性及低能性,因此利用太赫兹光谱可以实现障碍物隐匿复杂环境下的危险品无损探测。目前各种相关材料的太赫兹吸收光谱标准库并不完善,且市面上各类太赫兹光谱仪硬件参数不同、检测标准不统一,导致单纯依赖特征吸收峰的识别方法并不可靠。针对上述问题,提出一种不依赖于吸收峰准确性的物质识别技术路线:提取物质在不同频率分辨率、不同障碍物隐匿情况下的太赫兹吸收谱,利用Marr小波变换在频域上展开得到具有特征唯一性的小波频域尺度图,建立样本集;其次,结合迁移学习方法,利用Xception网络对样本集进行训练识别。实验结果表明,此方法可以很好地对不同障碍物隐匿环境中的危险品进行分类识别,识别准确率可达94%。说明此方法的识别准确性不受系统频率分辨率即吸收谱精确度等系统因素影响,为邮件及快递包裹等障碍物隐匿危险品无损检测、定性识别提供了一种新的技术思路。

傅里叶变换衰减全反射红外光谱法的应用与进展

随着傅里叶变换红外光谱仪的应用及化学计量学的发展 ,傅里叶变换衰减全反射红外光谱法(ATR FTIR)成为用传统透过法制样效果不理想 (或制样复杂 )的样品及表层结构分析的有利工具和手段。ATR FTIR技术已应用到纺织、质检、公安等各个领域。目前 ,人们正在针对ATR FTIR的特性 ,开展应用研究。为此 ,文章主要介绍了国内外应用ATR FTIR技术进行深度剖析的研究 ,对材料表面的定性分析 ,组分的定量分析 ,光学纤维与ATR附件的联用技术 ,以及在其他领域 (如运用ATR FTIR技术考察中空纤维的结构及取向变化 ,研究皮肤促进剂的作用机理 )

小波变换在近红外光谱分析中的应用进展

小波变换(WT)具有很好的时频分离特征,信息处理能力强,已广泛用于分析化学领域;本文就小波变换在近红外光谱领域的应用进行简述。小波变换用于近红外预处理,提取有用信息,消除背景干扰,可以提高近红外的分析精度和模型稳健性;用于数据压缩可以减少数据库存储空间,提高建模速度;小波系数用于模型传递,具有传递速度快,稳健性强,所需标样少等特点;小波变换可以与神经网络、遗传算法等结合,在近红外分析领域呈现出良好的发展前景。

基于小波变换的番茄总糖近红外无损检测

分别采用小波消噪、常数偏移消除等11种光谱预处理方法,对番茄总糖含量(质量分数)的近红外光谱进行预处理,通过偏最小二乘法定量校正模型预测值比较得出,小波消噪是适合番茄近红外光谱的最佳预处理方法,小波消噪的总糖质量分数近红外光谱优选区域为11998.9～6097.8cm-1和4601.3～4246.5cm-1,在此光谱区内建立的番茄总糖质量分数偏最小二乘法模型预测值与实测值的相关系数为0.930,内部交叉验证均方差为0.466%,校正标准差为0.469%,预测标准差为0.260%。试验结果表明:小波消噪后建立的近红外光谱模型能准确地对番茄总糖含量进行快速无损检测。

阿达玛变换光谱和成像技术的应用和研究进展

阿达玛变换(HT)作为一种多通道光谱调制技术,具有多通道同时检测能力、多通道成像能力以及适用于数据处理等优点。综述了近十年来HT光谱和成像技术在分析科学中的应用和研究进展。主要从HT模板编码技术和HT激发序列应用技术等方面讨论了其最新发展和存在的问题,并展望了其发展前景。

小波变换及其在化学中的应用

简介了小波变换的原理,综述了从1992年到1998年9月小波变换在化学中的应用.引参考文献65篇.

近红外光谱技术用于复杂植物样品中无机离子测定的新方法

植物样品中的无机离子以一定形式与具有近红外吸收的有机基团结合 ,因而可以借助近红外光谱技术测定其含量。对烟草样品近红外光谱数据及其小波变换处理后的小波系数 ,采用偏最小二乘法预测其中钾离子的含量 ,建立了小波变换与近红外光谱技术结合用于复杂植物样品中无机离子测定的新方法。结果表明 :近红外光谱数据压缩为原始大小的 3 3% ,基本上没有光谱信息的丢失 ;基于小波系数的模型优于传统的全谱模型 ,对于无机离子的测定可以取得较为准确的预测结果。

基于小波变换的木材近红外光谱去噪研究

木材近红外光谱常常被一系列噪声所污染,影响光谱分析结果。为了提高近红外光谱分析精度,需要对光谱数据进行预处理。光谱导数可以消除光谱背景干扰和基线漂移等因素影响,提高光谱分辨率,但导数光谱在增强信号的同时,也使信号噪声得到增强。应用小波变换对杉木木材近红外一阶导数光谱进行去噪研究,分别采用9点平滑法、25点平滑法、非线性小波硬阈值和软阈值法、9点平滑+小波变换法和25点平滑+小波变换法对光谱数据进行去噪研究。结果显示,小波变换能够有效去除导数光谱中的噪声信号,保留光谱中的有效信息,提高光谱信噪比,提高光谱的分析能力,在木材近红外光谱分析中具有很好的应用前景。