Using pXRF and vis-NIR spectra for predicting properties of soils developed in loess

Visible near-infrared (vis-NIR) and portable X-ray fluorescence (pXRF) spectrometers have been increasingly utilized for predicting soil properties worldwide. However, only a few studies have focused on splitting the predictive models by horizons to evaluate prediction performance and systematically compare prediction performance for A, B, and combined A+B horizons. Therefore, we investigated the performance of pXRF and vis-NIR spectra, as individual or combined, for predicting the clay, silt, sand, total carbon (TC), and pH of soils developed in loess, and compared their prediction performance for A, B, and A+B horizons. Soil samples (176 in A horizon and 172 in B horizon) were taken from Mollisols and Alfisols in 136 pedons in Wisconsin, USA and analyzed for clay, silt, sand, pH, and TC. The pXRF and vis-NIR spectrometers were used to measure the pXRF and vis-NIR soil spectra. Data were separated into calibration (n = 244, 70%) and validation (n = 104, 30%) datasets. The Savitzky-Golay filter was applied to preprocess the pXRF and vis-NIR spectra, and the first 10 principal components (PCs) were selected through principal component analysis (PCA). Five types of predictor, i.e., PCs from vis-NIR spectra, pXRF of beams at 0–40 and 0–10 keV (XRF40 and XRF10, respectively) spectra, combined XRF40 and XRF10 (XRF40+XRF10) spectra, and combined XRF40, XRF10, and vis-NIR (XRF40+XRF10+vis-NIR) spectra, were compared for predicting soil properties using a machine learning algorithm (Cubist model). A multiple linear regression (MLR) model was applied to predict clay, silt, sand, pH, and TC using pXRF elements. The results suggested that pXRF spectra had better prediction performance for clay, silt, and sand, whereas vis-NIR spectra produced better TC and pH predictions. The best prediction performance for sand (R2= 0.97), silt (R2= 0.95), and clay (R2= 0.84) was achieved using vis-NIR+XRF40+XRF10 spectra in B horizon, whereas the best prediction performance for TC (R2= 0.93) and pH (R2= 0.79) was achieved using vis-NIR+XRF40+XRF10 spectra in A+B horizon. For all soil properties, the best MLR model had a lower prediction accuracy than the Cubist model. It was concluded that pXRF and vis-NIR spectra can be successfully applied for predicting clay, silt, sand, pH, and TC with high accuracy for soils developed in loess, and that spectral models should be developed for different horizons to achieve high prediction accuracy.

Deep learning for determining pure isotropic proton spectra from solidstate spectra

Recently,an article on ^(1)H solid-state NMR spectra was published,in which the authors proposed a deep learning approach to infer the pure isotropic proton NMR spectra obtained at an infinite magic angle spinning(MAS)rate.This approach even allowed to obtain,by far,the best resolved ^(1)H spectra of molecular solids[1](https://doi.org/10.1002/anie.202216607).Deep learning based artificial intelligence is developing rapidly,and its application is deepening.Currently,there are many applications of deep learning in the field of magnetic resonance,such as the reconstruction of the under-sampled multidimensional spectra[2-4],the deconvolution of two-dimensional NMR spectra[5]and noise suppression and weak peak retrial[6],etc.

Tunable spectral continuous shift of high-order harmonic generation in atoms by a plasmon-assisted shaping pulse

We delve into the phenomenon of high-order harmonic generation within a helium atom under the influence of a plasmon-assisted shaping pulse.Our findings reveal an intriguing manipulation of the frequency peak position in the harmonic emission by adjusting the absolute phase parameter within the frequency domain of the shaping pulse.This phenomenon holds potential significance for experimental setups necessitating precisely tuned single harmonics.Notably,we observe a modulated shift in the created harmonic photon energy,spanning an impressive range of 1.2 eV.This frequency peak shift is rooted in the asymmetry exhibited by the rising and falling edges of the laser pulse,directly influencing the position of the peak frequency emission.Our study quantifies the dependence of this tuning range and the asymmetry of the laser pulse,offering valuable insights into the underlying mechanisms driving this phenomenon.Furthermore,our investigation uncovers the emergence of semi-integer order harmonics as the phase parameter is altered.We attribute this discovery to the intricate interference between harmonics generated by the primary and secondary return cores.This observation introduces an innovative approach for generating semi-integer order harmonics,thus expanding our understanding of high-order harmonic generation.Ultimately,our work contributes to the broader comprehension of complex phenomena in laser-matter interactions and provides a foundation for harnessing these effects in various applications,particularly those involving precise spectral control and the generation of unique harmonic patterns.

Wavelength calibration and spectral analysis of vacuum ultraviolet spectroscopy in EAST

A vacuum ultraviolet(VUV)spectroscopy with a focal length of 1 m has been engineered specifically for observing edge impurity emissions in Experimental Advanced Superconducting Tokamak(EAST).In this study,wavelength calibration for the VUV spectroscopy is achieved utilizing a zinc lamp.The grating angle and charge-coupled device(CCD)position are carefully calibrated for different wavelength positions.The wavelength calibration of the VUV spectroscopy is crucial for improving the accuracy of impurity spectral data,and is required to identify more impurity spectral lines for impurity transport research.Impurity spectra of EAST plasmas have also been obtained in the wavelength range of 50–300 nm with relatively high spectral resolution.It is found that the impurity emissions in the edge region are still dominated by low-Z impurities,such as carbon,oxygen,and nitrogen,albeit with the application of fulltungsten divertors on the EAST tokamak.

Core level excitation spectra of La and Mn ions in LaMnO3

Manganese-based perovskite is popular for research on ferromagnetic materials,and its spectroscopic studies are essential for understanding its electronic structure,dielectric,electrical,and magnetic properties.In this paper,the M-edge spectra of La ions and the M-edge,L-edge,and K-edge spectra of Mn ions in LaMnO3 are calculated by considering both the free-ion multiplet calculation and the crystal field effects.We analyze spectral shapes,identify peak origins,and estimate the oxidation states of La and Mn ions in LaMnO3 theoretically.It is concluded that La ions in LaMnO3 predominantly exist in the trivalent state,while Mn ions exist primarily in the trivalent state with a minor presence of tetravalent ions.Furthermore,the calculated spectra are in better conformity with the experimental spectra when the proportion of Mn3+is 90%and Mn4+is 10%.This article enhances our comprehension of the oxidation states of La and Mn within the crystal and also provides a valuable guidance for spectroscopic investigations of other manganates.

Discrimination of pressed sesame oil:A comparison study of non-targeted UV spectral fingerprints combined with different chemometric methods

This study explores the utilization of various chemometric analytical methods for determining the quality of pressed sesame oil with different adulteration levels of refined sesame oil using UV spectral fingerprints.The goal of this study was to provide a reliable tool for assessing the quality of sesame oil.The UV spectra of 51 samples of pressed sesame oil and 420 adulterated samples with refined sesame oil were measured in the range of 200-330 nm.Various classification and prediction methods,including linear discrimination analysis(LDA),support vector machines(SVM),soft independent modeling of class analogy(SIMCA),partial least squares regression(PLSR),support vector machine regression(SVR),and back-propagation neural network(BPNN),were employed to analyze the UV spectral data of pressed sesame oil and adulterated sesame oil.The results indicated that SVM outperformed the other classification methods in qualitatively identifying adulterated sesame oil,achieving an accuracy of 96.15%,a sensitivity of 97.87%,and a specificity of 80%.For quantitative analysis,BPNN yielded the best prediction results,with an R^(2) value of 0.99,RMSEP of 2.34%,and RPD value of 10.60(LOD of 8.60%and LOQ of 28.67%).Overall,the developed models exhibited significant potential for rapidly identifying and predicting the quality of sesame oil.

Vis-NIR光谱模式识别结合SG平滑用于转基因甘蔗育种筛查

以Savitzky-Golay(SG)平滑筛选,主成分分析(PCA)分别结合有监督的线性判别分析(LDA)、无监督的系统聚类分析(HCA),应用于转基因甘蔗育种筛查的可见-近红外(Vis-NIR)无损检测。提出兼顾随机性、稳定性的定标、预测、检验框架;取田间种植处于伸长期甘蔗叶样品456个,具有Bt基因和Bar基因的转基因样品(阳)306个,非转基因样品(阴)150个;随机选取156个为检验集(阴性50、阳性106),余下为建模集(阴性100、阳性200,共300),建模集再随机划分为定标集(阴性50、阳性100,共150)、预测集(阴性50、阳性100,共150)共50次;扩充SG平滑点数,同时删除绝对值偏小的高阶导数模式,共264个平滑模式用于模型筛选;采用前3个主成分两两组合,再根据模型效果选出最优主成分组合;基于所有定标、预测集划分和SG平滑模式,建立SG-PCA-LDA和SG-PCA-HCA模型,根据平均预测效果优选参数,使模型具有稳定性;最后用检验集进行模型检验。经SG平滑后,PCA-LDA和PCA-HCA的建模精度、稳定性均显著改善;最优SG-PCA-LDA模型阳性、阴性样品检验识别率分别达到94.3%和96.0%;最优SG-PCA-HCA模型阳性、阴性样品检验识别率分别达到92.5%和98.0%。结果表明:Vis-NIR光谱模式识别结合SG平滑可用于转基因甘蔗叶的准确识别,提供了一种简便的转基因甘蔗育种筛查方法。

利用OSC算法消除土壤含水量变化对Vis-NIR光谱估算有机质的影响

【目的】快速、准确地监测土壤有机质对于精准农业的发展具有重要意义。可见光-近红外(visible and near-infrared,Vis-NIR)光谱技术在土壤属性估算、数字化土壤制图等方面应用较为广泛,然而,在田间进行光谱测量,易受土壤含水量(soil moisture,SM)、温度、土壤表面状况等因素的影响,导致光谱信息中包含大量干扰信息,其中,SM变化是影响光谱观测结果最为显著的因素之一。此研究的目的是探讨OSC算法消除其影响,提升Vis-NIR光谱定量估算土壤有机质(soil organic matter,SOM)的精度。【方法】以江汉平原公安县和潜江市为研究区域,采集217份耕层(0—20 cm)土壤样本,进行风干、研磨、过筛等处理,采用重铬酸钾-外加热法测定SOM;将总体样本划分为3个互不重叠的样本集:建模集S^0(122个样本)、训练集S^1(60个样本)、验证集S^2(35个样本);设计SM梯度试验(梯度间隔为4%),在实验室内获取S^1和S^2样本集的9个梯度SM(0%—32%)的土壤光谱数据;分析SM对土壤Vis-NIR光谱反射率的影响,采用外部参数正交化算法(external parameter orthogonalization,EPO)、正交信号校正算法(orthogonal signal correction,OSC)消除SM对土壤光谱的干扰;利用主成分分析(principal component analysis,PCA)的前两个主成分得分和光谱相关系数两种方法检验消除SM干扰前、后的效果;基于偏最小二乘回归(partial least squares regression,PLSR)方法建立EPO和OSC处理前、后的SOM估算模型,利用决定系数(coefficient of determination,R^2)、均方根误差(root mean square error,RMSE)和RPD(the ratio of prediction to deviation)3个指标比较PLSR、EPO-PLSR、OSC-PLSR模型的性能。【结果】土壤Vis-NIR光谱受SM的影响十分明显,随着SM的增加,土壤光谱反射率呈非线性降低趋势。OSC处理前的湿土光谱数据主成分得分散点相对分散,与干土光谱数据主成分得分空间的位置不重叠,不同SM梯度之间的光谱相关系数变化较大;OSC处理后的湿土光谱数据主成分得分空间的位置基本与干土光谱数据相重合,各样本光谱数据之间相似性很高,不同SM梯度之间的光谱相关系数变化较小。9个SM梯度的EPO-PLSR模型的验证平均R^2_(pre)、RPD分别为0.69、1.7。9个SM梯度的OSC-PLSR模型的验证平均R^2_(pre)、RPD分别为0.72、1.89,校正后的OSC-PLSR模型受SM的较小,有效提升SOM估算模型的精度和鲁棒性。【结论】OSC能够消除SM变化对土壤Vis-NIR光谱的影响,可为将来田间原位实时监测SOM信息提供一定的理论支撑。

Cobe Spectra与Fenwal CS 3000 Plus血细胞分离器外周血造血干/祖细胞的采集效能比较

为了评价Cobe Spectra(Version 6.1)和Fenwal CS 3000 Plus两种血细胞分离机在造血干／祖细胞采集中的 采集效能,对36人64次外周血造血干／祖细胞的采集进行了回顾性分析。20人42次使用Cobe Spectra采集,16人 22次使用Fenwal CS 3000 Plus采集。对CD34+细胞采集量、采集效率以及红细胞与血小板的采集量进行比较。结 果表明:Cobe Spectra和Fenwal CS 3000 Plus在CD34+细胞采集量和采集效率上无显著差异。CD34+细胞采集量 与供者白细胞、单核细胞、造血祖细胞、CD34+细胞的动员情况呈正相关,在多因素stepwise线性回归模型中,采集 前外周血干／祖细胞浓度是唯一有意义的影响因素。Fenwal CS 3000 Plus的采集效率与外周血单核细胞量呈负相 关。Fenwal CS 3000 Plus对红细胞的收集量显著高于Cobe Spectra,并且采集后供者外周血血小板降低程度较Cobe Spectra更严重。结论:Cobe Spectra(Version 6.1)和Fenwal CS 3000 Plus在CD34+细胞的采集能力上无显著差别。 当外周血单核细胞数量高,或是血型不合移植的及血小板减少的供者,推荐使用Cobe Spectra血细胞分离机。

基于野外Vis-NIR光谱的土壤有机质预测与制图

利用野外实时快速获取的土壤光谱进行土壤有机质(SOM)预测与制图是精确农业与土壤遥感制图的必然需要,利用ASD FieldSpec Pro FR野外型光谱仪实时快速获取的光谱数据,去除噪声较大的边缘波段后,进行倒数的对数转换(Log(1/R))为吸收光谱。在分析吸收光谱和光谱指数与SOM关系的基础上,采用偏最小二乘回归法进行SOM的建模预测并借助地统计学方法进行SOM空间变异制图研究。结果表明,建模效果好的指标分别为特征波段(R2=0.91,RPD=3.28),归一化光谱指数(R2=0.90,RPD=3.08),特征波段与3个光谱指数组合(R2=0.87,RPD=2.67),全波段(R2=0.95,RPD=4.36)。光谱指标的克里格制图与实测SOM制图表现出相同的空间变异趋势,不同的指标均达到了较好的预测效果。

基于ArcGIS和Vis-NIR脐橙园土壤养分含量分布图预测研究

以赣南区某脐橙园土壤为研究对象,针对不用层土壤的全氮、全磷及有机质养分信息,采用傅里叶型近红外光谱仪和可见/近红外光谱检测装置不同参数配比下组合,采集土壤样品光谱。用化学方法测定养分真值,结合光谱预处理方法和化学计量学算法建立定量检测数学模型,选择出最优的仪器参数、最合适的预处理方法和算法,确定最终的数学模型。实验采样点的数量是有限的,要想得到整个研究区的养分分布数据就需要采用克里金插值法(Kriging)对这些采样点各养分进行插值,运用ArcGIS软件中的地统计分析模块功能,对采样点的土壤全氮、全磷及有机质的真实化学值和最优模型预测值进行克里金插值,得出采样区的土壤养分空间分布图。

土壤含水量对采用Vis-NIR光谱分析土壤质地的影响

Vis-NIR光谱应用于野外土壤质地分析时,其分析精度将受到土壤含水量的影响。该文旨在定量研究基于Vis-NIR光谱对土壤质地分析时,土壤含水量对精度的影响。在实验室条件下分别测定8种不同含水量状态下土壤样品（78个）的Vis-NIR光谱反射率,运用偏最小二乘回归（PLSR）分别建立不同含水量状态下土壤质地分析模型,每一模型分别分析8种不同含水量状态下验证集的土壤质地。结果表明,当土壤处于同一湿度状态时,各含水量状态下均可获得较好的结果,粘粒含量和砂粒含量最佳分析模型对应的含水量分别为150-200g/kg和200-250g/kg。当土壤含水量差异较大时,分析精度随着验证样本与建模样本水分含量的差异增大而急剧降低。研究认为,当土壤处于同一湿度状态时,可直接应用Vis-NIR光谱分析湿土的土壤质地,在土壤水分状态差异较大时,宜根据含水量建立分组分析模型。

基于正交信号校正的Vis-NIR光谱土壤质地预测

为提高基于VIS-NIR光谱的土壤质地预测精度,引入了正交信号校正(OSC)光谱预处理算法。分别用原始光谱、微分处理、OSC处理光谱,建立偏最小二乘回归(PLSR)模型。结果表明,OSC-PLSR模型验证精度高于其他两种方法所建模型,砂粒含量OSC-PLSR模型的RMSEp为5.94,粘粒含量OSC-PLSR模型RMSEp为1.25,相比PLSR模型,分别降低22.22%和9.42%。OSC算法在土壤质地的VIS-NIR反演中能有效消除不相关因素的影响,提高模型预测精度。

基于Vis-NIR光谱的土壤质地BP神经网络预测

为快速、准确地获取土壤质地信息,提出了应用Vis-NIR光谱结合BP神经网络的建模方法。以河南封丘县的86个土壤样本为研究对象,以原始光谱和微分光谱主成分为输入变量,建立土壤粘粒和砂粒的BP神经网络预测模型,并将其预测结果与多元线性逐步回归模型进行比较。结果表明:基于原始光谱主成分的BP人工神经网络预测结果最好,优于多元逐步回归模型,预测粘粒和砂粒的RMSE分别为1.62和6.52。BP神经网络所建模型训练时间短、准确度也较高,能实现对土壤质地的高效预测。

MEBO治疗Spectra双脉冲激光祛斑术后创面的临床疗效

目的观察Spectra双脉冲激光祛斑术后创面应用湿润烧伤膏（Moist Exposed Burn Ointment,MEBO）治疗的临床效果。方法将200例采用Spectra双脉冲激光祛斑术祛斑的患者随机分为治疗组和对照组,每组100例。治疗组治疗结束后用冰袋敷面0.5~1.0 h,然后将MEBO均匀涂抹于创面,厚约1mm,每6 h换药1次,3 d后停用,并每日以冷水洁面;对照组治疗结束后仅用冰袋冰敷0.5~1.0 h,不做其他处理,保持创面清洁干燥,3 d后每日以冷水洁面,对比观察两组患者的疼痛情况、创面愈合时间。结果治疗组的疼痛情况（无痛81例,疼痛19例）与对照组（无痛25例,疼痛75例）相比,P〈0.01,差异具有统计学意义;治疗组的创面平均愈合时间（9.53 d）与对照组（10.65 d）相比,P〈0.01,差异具有统计学意义。结论湿润烧伤膏（MEBO）治疗Spectra双脉冲激光祛斑术后创面,疗效显著,具有止痛效果好、创面愈合时间短等作用。

Bi-normalized response spectra and scalar periods for developing uniform seismic design spectra

Aiming at the uniform features of acceleration response spectra, two scalar periods-the response spectral predominant period Tp and the smoothed spectral predominant period To are employed to normalize the abscissa of the normalized response spectra （NRS） of ground motions, respectively. Engineering characteristics of 5% -damped NRS, and the bi-normalized response spectra （BNRS） are investigated accounting for the effects of soil condition and fault distance. Nearly 600 horizontal ground motion components during the Chi-Chi earthquake are included in the analysis. It shows that the NRS strongly depends on soil condition and fault distance. However, soil condition and distance have only a slight influence on two kinds of BNRS. Dispersion analysis indicates that such normalization can reduce scatter in the derivation of response spectral shapes. Finally, a parametric analysis of the scalar periods （Tp, To） is performed and then compared with those of previous studies. These special and particular aspects of earthquake response spectra and scalar periods need to be considered in developing earthquake-resistant design criteria.

基于灰色关联度和Vis-NIR的不同贮藏方式下番茄光谱特性分析

[目的]确定不同贮藏方式下,影响番茄样本定性判别的主要品质指标。[方法]本文采用可见近红外光谱技术分析了不同贮藏方式下番茄的光谱特性;引入主成分分析和灰色关联度分析方法对不同贮藏方式的番茄样本进行定性判别和贡献指标确定。[结果]不同贮藏方式下番茄的光谱特性有所不同,可用主成分分析提取的3个敏感波段1 927、1 401、1 222 nm(累积贡献率为98.92%)对其进行区分;与1 927 nm吸光度值关联度最大的为可滴定酸,与1 401 nm吸光度值关联度最大的为可溶性固形物,与1 222 nm吸光度值关联度最大的为可滴定酸。[结论]可滴定酸和可溶性固形物是影响其主成分分析的主要品质指标,也是影响上述不同贮藏方式下番茄样本分类的指标。该研究可为后续基于光谱技术的不同贮藏方式下番茄品质快速检测提供依据。

Effects of Doubled_CO_2 Concentration on Ultrastructure, Supramolecular Architecture and Spectral Characteristics of Chloroplasts from Wheat

Wheat ( Triticum aestivum L.) plants were grown under ambient and doubled_CO 2(plus 350 μL/L) concentration in cylindrical open_top chamber to examine their effects on the ultrastructure, supramolecular architecture, absorption spectrum and low temperature (77 K) fluorescence emission spectrum of the chloroplasts from wheat leaves. The results were briefly summarized as follows: (1) The wheat leaves possessed normally developed chloroplasts with intact grana and stroma thylakoid membranes; The grana intertwined with stroma thylakoid membranes and increased slightly in stacking degree and the width of granum, in spite of more accumulated starch grains within the chloroplasts than those in control; (2) The particle density in the stacked region of the endoplasmic fracture face (EFs) and protoplasmic fracture face (PFs) and in the unstacked region the endoplasmic fracture face (EFu) and the protoplasmic fracture face (PFu) was significantly higher than that of control. Furthermore, in some cases many more particles on EFs faces of thylakoid membranes appeared as a paracrystalline particle array; (3) The variations in the structure of chloroplasts were consistent with the absorption spectra and the low temperature (77 K) fluorescence emission spectra of the chloroplasts developed under the doubled_CO 2 concentration. Results indicate that the capability of light energy absorption of chloroplasts and regulative capability of excitation energy distribution between PSⅡ and PSⅠ were raised by doubled_CO 2 concentration. This is very favorable for final productivity of wheat.

Quantitative Relationship between Leaf Area Index and Canopy Reflectance Spectra of Rice under Different Nitrogen Levels

Monitoring rice growth by spectral remote sensing technology can provide scientific basis for the high yield and efficient production of rice. Field experiments with different nitrogen application amounts using Tianyouhuazhan rice as test sam- ples were set up to study the relationship between rice leaf area index （LAI） and canopy reflectance spectral. The results showed that： the LAI increased with the amount of applied nitrogen; the canopy reflectance spectral showed significant re- sponse characteristics to groups with different nitrogen application levels; the corre- lation coefficient of LAI and canopy spectral reflectance reached the maximum at 720 nm red edge region. The mathematical model was constructed to predict the LAI according to the canopy reflectance spectra of rice.

Spectra双脉冲激光对黄褐斑抗氧化功能、甲皱微循环的影响

目的:探讨Spectra双脉冲激光祛斑术对黄褐斑患者抗氧化功能、甲皱微循环的影响。方法:105例黄褐斑患者采用随机数字表法分为观察组(n=53)和对照组(n=52),对照组接受常规治疗,观察组在常规治疗的基础上接受Spectra双脉冲激光祛斑术治疗。比较两组疗效,治疗后血清氧化应激指标、甲皱微循环积分、生活质量改善情况及患者美观满意度。结果:观察组总有效率为92.45%,显著高于对照组的76.92%(P<0.05);治疗后,两组超氧化物歧化酶(Super oxide dismutase,SOD)水平明显升高,而丙二醛(Malondialdehyde,MDA)、一氧化氮(Nitric oxide,NO)水平、甲皱微循环积分(血流速度、管袢形态、袢周状态及总积分)、黄褐斑生活质量评分(Melasma quality of life scale,MelasQOL)及皮肤病生活质量指数(Dermatology life quality index,DLQI)明显降低(P<0.05),且观察组SOD水平显著高于对照组,MDA、NO水平、甲皱微循环积分、MelasQOL评分、DLQI指数均显著低于对照组(P<0.05);观察组美观满意度为90.57%,优于对照组的71.15%(P<0.05)。结论:采用Spectra双脉冲激光祛斑术治疗黄褐斑,能通过调节患者SOD、MDA、NO水平,增强其抗氧化功能,促进甲皱微循环改善,继而提高治疗效果,改善患者生活质量,有较高的临床应用价值。