Accurate method based on data filtering for quantitative multi-element analysis of soils using CF-LIBS

To obtain more stable spectral data for accurate quantitative analysis of multi-element,especially for the large-area in-situ elements detection of soils, we propose a method for a multielement quantitative analysis of soils using calibration-free laser-induced breakdown spectroscopy(CF-LIBS) based on data filtering. In this study, we analyze a standard soil sample doped with two heavy metal elements, Cu and Cd, with a specific focus on the line of Cu I324.75 nm for filtering the experimental data of multiple sample sets. Pre-and post-data filtering,the relative standard deviation for Cu decreased from 30% to 10%, The limits of detection(LOD)values for Cu and Cd decreased by 5% and 4%, respectively. Through CF-LIBS, a quantitative analysis was conducted to determine the relative content of elements in soils. Using Cu as a reference, the concentration of Cd was accurately calculated. The results show that post-data filtering, the average relative error of the Cd decreases from 11% to 5%, indicating the effectiveness of data filtering in improving the accuracy of quantitative analysis. Moreover, the content of Si, Fe and other elements can be accurately calculated using this method. To further correct the calculation, the results for Cd was used to provide a more precise calculation. This approach is of great importance for the large-area in-situ heavy metals and trace elements detection in soil, as well as for rapid and accurate quantitative analysis.

Research on batch multielement rapid quantitative analysis based on the standard curve-assisted calibration-free laserinduced breakdown spectroscopy method

This study proposes a batch rapid quantitative analysis method for multiple elements by combining the advantages of standard curve(SC)and calibration-free laser-induced breakdown spectroscopy(CF-LIBS)technology to achieve synchronous,rapid,and accurate measurement of elements in a large number of samples,namely,SC-assisted CF-LIBS.Al alloy standard samples,divided into calibration and test samples,were applied to validate the proposed method.SC was built based on the characteristic line of Pb and Cr in the calibration sample,and the contents of Pb and Cr in the test sample were calculated with relative errors of 6%and 4%,respectively.SC built using Cr with multiple characteristic lines yielded better calculation results.The relative contents of ten elements in the test sample were calculated using CF-LIBS.Subsequently,the SC-assisted CF-LIBS was executed,with the majority of the calculation relative errors falling within the range of 2%-5%.Finally,the Al and Na contents of the Al alloy were predicted.The results demonstrate that it effectively enables the rapid and accurate quantitative analysis of multiple elements after a single-element SC analysis of the tested samples.Furthermore,this quantitative analysis method was successfully applied to soil and Astragalus samples,realizing an accurate calculation of the contents of multiple elements.Thus,it is important to advance the LIBS quantitative analysis and its related applications.

Simplified quantitative analysis method and its application in the insitu synthesized copper-based azide chips

Copper-based azide(Cu(N_(3))2 or CuN_(3),CA)chips synthesized by in-situ azide reaction and utilized in miniaturized explosive systems has become a hot research topic in recent years.However,the advantages of in-situ synthesis method,including small size and low dosage,bring about difficulties in quantitative analysis and differences in ignition capabilities of CA chips.The aim of present work is to develop a simplified quantitative analysis method for accurate and safe analysis of components in CA chips to evaluate and investigate the corresponding ignition ability.In this work,Cu(N_(3))2 and CuN_(3)components in CA chips were separated through dissolution and distillation by utilizing the difference in solubility and corresponding content was obtained by measuring N_(3)-concentration through spectrophotometry.The spectrophotometry method was optimized by studying influencing factors and the recovery rate of different separation methods was studied,ensuring the accuracy and reproducibility of test results.The optimized method is linear in range from 1.0-25.0 mg/L,with a correlation coefficient R^(2)=0.9998,which meets the requirements of CA chips with a milligram-level content test.Compared with the existing ICP method,component analysis results of CA chips obtained by spectrophotometry are closer to real component content in samples and have satisfactory accuracy.Moreover,as its application in miniaturized explosive systems,the ignition ability of CA chips with different component contents for direct ink writing CL-20 and the corresponding mechanism was studied.This study provided a basis and idea for the design and performance evaluation of CA chips in miniaturized explosive systems.

Quantitative analysis and prediction of the sound field convergence zone in mesoscale eddy environment based on data mining methods

The mesoscale eddy(ME)has a significant influence on the convergence effect in deep-sea acoustic propagation.This paper use statistical approaches to express quantitative relationships between the ME conditions and convergence zone(CZ)characteristics.Based on the Gaussian vortex model,we construct various sound propagation scenarios under different eddy conditions,and carry out sound propagation experiments to obtain simulation samples.With a large number of samples,we first adopt the unified regression to set up analytic relationships between eddy conditions and CZ parameters.The sensitivity of eddy indicators to the CZ is quantitatively analyzed.Then,we adopt the machine learning(ML)algorithms to establish prediction models of CZ parameters by exploring the nonlinear relationships between multiple ME indicators and CZ parameters.Through the research,we can express the influence of ME on the CZ quantitatively,and achieve the rapid prediction of CZ parameters in ocean eddies.The prediction accuracy(R)of the CZ distance(mean R:0.9815)is obviously better than that of the CZ width(mean R:0.8728).Among the three ML algorithms,Gradient Boosting Decision Tree has the best prediction ability(root mean square error(RMSE):0.136),followed by Random Forest(RMSE:0.441)and Extreme Learning Machine(RMSE:0.518).

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

In Deep UV Quantitative Analysis of Multi-Element Low Alloy Steel by Laser-Induced Breakdown Spectroscopy

The multi-element components of low alloy steel were quantified by using laser-induced breakdown spectroscopy (LIBS) in deep UV. The Nd:YAG pulsed laser was used to produce plasma. The spectrum was simultaneously obtained by deep UV spectrometer. This paper studied the influence of experiment parameters on LIBS spectral intensity, such as delay, energy of laser, and the distance between the focusing lens and the surface of the sample. With the optimal expe- riment parameters, the characteristic lines of C, Ni, Si, Cr and Cu contained in low alloy steel were selected for quantit- ative analysis and the calibration curves of these elements were obtained. The linear correlation coefficient was good. Using the calibration curves to quantitative analysis for the sample 05-d, and the relative error of analytical results is less than 10% for most elements.

Spectra-assisted laser focusing in quantitative analysis of laser-induced breakdown spectroscopy for copper alloys

Laser-induced breakdown spectroscopy(LIBS)is a capable technique for elementary analysis,while LIBS quantitation is still under development.In quantitation,precise laser focusing plays an important role because it ensures the distance between the laser and samples.In the present work,we employed spectral intensity as a direct way to assist laser focusing in LIBS quantitation for copper alloys.It is found that both the air emission and the copper line could be used to determine the position of the sample surface by referencing the intensity maximum.Nevertheless,the fine quantitation was only realized at the position where the air emission(e.g.O(I)777.4 nm)reached intensity maximum,and also in this way,a repeatable quantitation was successfully achieved even after 120 days.The results suggested that the LIBS quantitation was highly dependent on the focusing position of the laser,and spectra-assisted focusing could be a simple way to find the identical condition for different samples’detection.In the future,this method might be applicable in field measurements for LIBS analysis of solids.

DecodeSTORM:A user-friendly ImageJ plug-in for quantitative data analysis in single-molecule localization microscopy

Quantitative data analysis in single-molecule localization microscopy(SMLM)is crucial for studying cellular functions at the biomolecular level.In the past decade,several quantitative methods were developed for analyzing SMLM data;however,imaging artifacts in SMLM experiments reduce the accuracy of these methods,and these methods were seldom designed as user-friendly tools.Researchers are now trying to overcome these di±culties by developing easyto-use SMLM data analysis software for certain image analysis tasks.But,this kind of software did not pay su±cient attention to the impact of imaging artifacts on the analysis accuracy,and usually contained only one type of analysis task.Therefore,users are still facing di±culties when they want to have the combined use of different types of analysis methods according to the characteristics of their data and their own needs.In this paper,we report an ImageJ plug-in called DecodeSTORM,which not only has a simple GUI for human–computer interaction,but also combines artifact correction with several quantitative analysis methods.DecodeSTORM includes format conversion,channel registration,artifact correction(drift correction and localization¯ltering),quantitative analysis(segmentation and clustering,spatial distribution statistics and colocalization)and visualization.Importantly,these data analysis methods can be combined freely,thus improving the accuracy of quantitative analysis and allowing users to have an optimal combination of methods.We believe DecodeSTORM is a user-friendly and powerful ImageJ plug-in,which provides an easy and accurate data analysis tool for adventurous biologists who are looking for new imaging tools for studying important questions in cell biology.

Quantitative morphometric analysis of a deep-water channel in the Taranaki Basin, New Zealand

The morphological changes of deep-water channels have an important influence on the distributions of channel sand reservoirs,so it is important to explore the morphological change process of deep-water channel for the exploration and development of deep-water oil and gas.Based on a typical sinuous Quaternary channel(Channel I)in the Taranaki Basin,New Zealand,a variety of seismic interpretation techniques were applied to quantitatively characterize the morphological characteristics of the Channel I,and the relationships between the quantitative parameters and the morphological changes of the Channel I,as well as the controlling factors affecting those morphological changes,were discussed.The results are as follows:(1)in the quantitative analysis,six parameters were selected:the channel depth,width,sinuosity,and aspect ratio(width/depth),the channel swing amplitude(λ)and the channel bend frequency(ω);(2)according to the quantitative morphological parameters of the channel(mainly including three parameters such as channel sinuosity,ωandλ),the Channel I was divided into three types:the low-sinuous channel(LSC),the high-sinuous channel(HSC),the moderate-sinuous channel(MSC).U-shaped channel cross-sections developed in the LSC,V-shaped channel cross-sections developed in the HSC,including inclined-V and symmetric-V cross-sections,and dish-shaped channel cross-sections developed in the MSC;(3)the morphological characteristics of the LSC and MSC were related to their widths and depths,while the morphology of the HSC was greatly affected by the channel width,a change in depth did not affect the HSC morphology;(4)the morphological changes of the Channel I were controlled mainly by the slope gradient,the restricted capacity of the channel and the differential in fluid properties.

Energy-dispersive X-ray Spectroscopy for the Quantitative Analysis of Pyrite Thin Specimens

To explore ways to improve the accuracy of quantitative analysis of samples in the micrometer to nanometer range of magnitudes,we adopted analytical transmission electron microscopy(AEM/EDS)for qualitative and quantitative analysis of pyrite materials.Additionally,the k factor of pyrite is calculated experimentally.To develop an appropriate non-standard quantitative analysis model for pyrite materials,the experimentally calculated k factor is compared with that estimated from the non-standard quantitative analytical model of the instrument software.The experimental findings demonstrate that the EDS attached to a TEM can be employed for precise quantitative analysis of micro-and nanoscale regions of pyrite materials.Furthermore,it serves as a reference for improving the results of the EDS quantitative analysis of other sulfides.

Consequences of Insufficient Selectivity in Quantitative and Qualitative Chemical Analysis

A problem in chemical analysis in connection with measurements of a substance normally occurring in a sample, or identification of a substance which should not exist in a sample, is insufficient selectivity. In this article, we analyze this problem and propose remedies. We use a real doping case to illustrate how chemical noise causes a serious selectivity problem, probably causing a false positive outcome.

Geographical origin identification of winter jujube(Ziziphus jujuba Dongzao')by using multi-element fingerprinting with chemometrics

Winter jujube(Ziziphus jujuba'Dongzao')is greatly appreciated by consumers for its excellent quality,but brand infringement frequently occurs in the market.Here,we first determined a total of 38 elements in 167 winter jujube samples from the main winter jujube producing areas of China by inductively coupled plasma mass spectrometer(ICP-MS).As a result,16 elements(Mg,K,Mn,Cu,Zn,Mo,Ba,Be,As,Se,Cd,Sb,Ce,Er,Tl,and Pb)exhibited significant differences in samples from different producing areas.Supervised linear discriminant analysis(LDA)and orthogonal projection to latent structures discriminant analysis(OPLS-DA)showed better performance in identifying the origin of samples than unsupervised principal component analysis(PCA).LDA and OPLS-DA had a mean identification accuracy of 87.84 and 94.64%in the testing set,respectively.By using the multilayer perceptron(MLP)and C5.0,the prediction accuracy of the models could reach 96.36 and 91.06%,respectively.Based on the above four chemometric methods,Cd,Tl,Mo and Se were selected as the main variables and principal markers for the origin identification of winter jujube.Overall,this study demonstrates that it is practical and precise to identify the origin of winter jujube through multi-element fingerprint analysis with chemometrics,and may also provide reference for establishing the origin traceability system of other fruits.

Quantitative Analysis of Land Use Landscape Pattern in Shihezi Desert Oasis

Based on the land use status map of Shihezi City,the land use landscape map of Shihezi City was made with the support of GIS then many landscape indices were selected to quantitatively analyze the landscape pattern characteristics of Shihezi Oasis from three aspects of landscape diversity index,landscape space configuration index and plaque characteristics index. The results showed that the main plaque types of the landscape in Shihezi reclamation area are arable land and water areas,which totally account for 58% of the total area; farmland,gardens,woodland,urban sites,industrial and mining land use were embedded in this oasis landscape in the form of mosaics,and their fractal index was high due to the strong impact of human activities; arable land and mining land distribution was scattered,but marshes and other uncultivated land were relatively concentrated. The dominance degree of the landscape was low because there was no advantageous type in this landscape and all patches distributed equably which was easily to be disturbed so that the whole landscape was unstable. Comprehensively speaking,the landscape in Shihezi region was rather complicate,and its diversity index,fragmentation degree and heterogeneity were high.

Qualitative and Quantitative Analysis of Alkaloids in Cortex Phellodendri by HPLC-ESI-MS/MS and HPLC-DAD

A combined method of high performance liquid chromatograph-elecrtrospray-ionization mass spectrometer（HPLC-ESI-MS/MS） coupled with a photodiode array detector（HPLC-DAD） and principal component analysis（PCA） was applied to the qualitative and quantitative analyses of alkaloids in Cortex Phellodendri（CP） samples, and to the differentiation of two species of CP, Cortex Phellodendri Chinensis（CPC） and Cortex Phellodendri Amurensis（CPA）. Twenty-two peaks appeared in the HPLC-MS base peak chromatogram of CP detected by the HPLC-ESI-MS/MS analysis, and the alkaloids were identified according to the MSn data, the known MS fragmentation rules and the literature data. Five alkaloids including berberine, palmatine, jatrorrhizine, phellodendrine and magnoflorine were simultaneously determinated by the HPLC-DAD. Berberine was the primary component in all CP samples, and the contents of berberine and palmatine were exploited to be two critical parameters for effective discrimination between the two species of CP. The average content of berberine in CPC（58.75 mg/g） was higher than that in CPA（9.16 mg/g）, while the content of palmatine was less, only 0.25 mg/g in CPC and 4.19 mg/g in CPA. With the use of PCA, samples datasets were separated successfully into two different clusters corresponding to the two species, and berberine, pahnatine, phellodendrine and magnoflorine contribute most to the above mentioned calssifying . The proposed method oroved to be a useful tool in the aualitv control of Chinese herbal medicines.

Quantitative analysis of γ-oryzanol content in cold pressed rice bran oil by TLC-image analysis method

Objective:To develop and validate an image analysis method for quantitative analysis ofγ-oryzanol in cold pressed rice bran oil.Methods:TLC-densitometric and TLC-image analysis methods were developed,validated,and used for quantitative analysis of γ-oryzanol in cold pressed rice bran oil.The results obtained by these two different quantification methods were compared by paired t-test.Results:Both assays provided good linearity,accuracy,reproducibility and selectivity for determination of γ-oryzanol.Conclusions:The TLC-densitomelric and TLC-image analysis methods providett a similar reproducibility,accuracy and selectivity for the quantitative determination of γ-oryzanol in cold pressed rice bran oil.A statistical comparison of the quantitative determinations of γ-oryzanol in samples did not show any statistically significant difference between TLC-densitometric and TLC-image analysis methods.As both methods were found to be equal,they therefore can be used for the determination of γ-oryzanol in cold pressed rice bran oil.

Quantitative traits correlative analysis and growth comparison among different populations of bay scallop,Argopecten irradians

The shell traits and weight traits are measured in cultured populations of bay scallop, Argopecten irradians. The results of regression analysis show that the regression relationships for all the traits are significant (P<0.01). The correlative coefficients between body weight, as well as tissue weight with shell length, shell height and shell width are significant (P<0.05). But the correlative coefficients between the anterior and posterior auricle length with body weight as well as tissue weight are not significant (P>0.05). The multiple regression equation is obtained to estimate live body weight and tissue weight. The above traits except anterior and posterior auricle length are used for the growth and production comparison among three cultured populations, Duncan's new multiple range procedure analysis shows that all the traits in the Lingshuiqiao (LSQ) population are much more significant than those of the other two populations (P<0.01), and there is no significant difference between the Qipanmo (QPM) and Dalijia (DLJ) populations in all traits (P>0.05). The results indicate that the LSQ population has a higher growth rate and is expected to be more productive than the other two populations.

Nondestructive Quantitative Analysis of Cofrel Medicines by Double ANN-NIR Spectroscopy

In this paper, a double artificial neural network （DANN） algorithm was used to parse near infrared （NIR） reflectance spectrum of Cofrel medicines. The contents of benproperine phosphate, which is the effective ingredient in Cofrel medicines, were accurately nondestructive quantitatively predicted. Compared the results with those of HPLC, the relative errors （RE %） were less than 0.18%. The analytical results could be applied to qualitative control of Cofrel medicines.

Quantitative analysis of sialic acids in Chinese conventional foods by HPLC-FLD

Background: Sialic acids are a family of ninecarbon sugar compounds with carboxylic acyl derivatives. It exists in bacteria, fish, mammals and other living organisms, participates in and regulates many important life events, such as cell recognition, membrane flow, endocytosis and so on. Sialic acid is usually located in the outermost layer of the sugar part of the cell membrane and the key positions of secreted glycoconjugates (glycolipids, glycoprotein and lipopolysaccharide). Sialic acid (Sia) is an important material foundation for variety of the structure and founction of glycoconjugates. Sia has been known as nearly 50 members, including N-acetylneuraminic acid (Neu5Ac), N-glycoulylneuraminic acid (Neu5Gc) and deaminoneuraminic acid (KDN) as its core monomer. The rest of the sialic acids are derived from these three monomers. The contents of Sia in Chinese food products are unknown. Objective: To determine the contents of Sia in raw and cooked red meat, seafood, poultry and so on. Design: The following food products were purchased from a Chinese supermarket: pork, beef, lamb, salmon, cod, tuna, cow milk, cheese, butter, duck, chicken and chicken eggs. Human milk was collected from Xiamen Maternity and Child Health Care Hospital (Xiamen, China). All tissues were homogenized and hydrolyzed with 0.05 M, 0.1 M and 0.2 M TFA for 150 min at 80°C in dark, respectively. The concentrations of Neu5Ac, Neu5Gc and KDN were determined by using HPLC with fluorescence detector. Results: The contents of total Sia (μg/g tissue or μg/mL liquid sample) in Chinese raw meat were highest in lamb (269.60), followed by pork (254.88), duck (200.63), chicken (162.86) and beef (88.03). The percentages of Neu5Gc were 36.08%, 26.48%, 0%, 0% and 28.40%, respectively. Cod contained higher levels of Sia (171.63) than salmon (104.43) and tuna (77.98). Only Neu5Ac was 50 found in detected aquatic product. Egg yolk contained the highest level of Sia (682.04), and a higher level of Sia (390.67) was found in the egg white. Also our result showed that human milk contained extremely high level of Sia (602.55). Neu5Ac was the predominant form of Sia in all the deteced samples. KDN was found in cow milk only among the samples, the content was 1.14 μg/g. Conclusion: The highest content of Sia in examined Chinese foods was found in 56 eggs, followed by lamb, pork, duck, cod, chicken, salmon, beef and tuna. Knowledge of the Sia content in conventional foods may help us to better understand possible medical disorders involving the uptake of the “non-human” Neu5Gc from our diet.

Quantitative analysis of the dynamic change and spatial differences of the ecological security: a case study of Loess Plateau in northern Shaanxi Province

Using the theory and method of the ecological footprint, and combining the changes of regional land use, resource environment, population, society and economy, this paper calculated the ecological footprint, ecological carrying capacity and ecological surplus/loss in 1986-2002 on the Loess Plateau in northern Shaanxi Province. What is more, this paper has put forward the concept of ecological pressure index, set up ecological pressure index models, and ecological security grading systems, and the prediction models of different ecological footprints, ecological carrying capacity, ecological surplus and ecological safety change, and also has assessed the ecological footprint demands of 10,000 yuan GDE The results of this study are as follows： （1） the ecological carrying capacity in northern Shaanxi shows a decreasing trend, the difference of reducing range is the fastest; （2） the ecological footprint appears an increasing trend; （3） ecological pressure index rose to 0.91 from 0.44 during 1986-2002 on the Loess Plateau of northern Shaanxi with an increase of 47%; and （4） the ecological security in the study area is in a critical state, and the ecological oressure index has been increasing rapidlv.

Quantitative Analysis of Moisture Effect on Black Soil Reflectance

Several studies have demonstrated that soil reflectance decreases with increasing soil moisture content, or increases when the soil moisture reaches a certain content; however, there are few analyses on the quantitative relationship between soil reflectance and its moisture, especially in the case of black soils in northeast China. A new moisture adjusting method was developed to obtain soil reflectance with a smaller moisture interval to describe the quantitative relationship between soil reflectance and moisture. For the soil samples with moisture contents ranging from air-dry to saturated, the changes in soil reflectance with soil moisture can be depicted using a cubic equation. Both moisture threshold (MT) and moisture inflexion (MI) of soil reflectance can also be determined by the equation. When the moisture range was smaller than MT, soil reflectance can be simulated with a linear model. However, for samples with different soil organic matter (OM), the parameters of the linear model varied regularly with the OM content. Based on their relationship, the soil moisture can be estimated from soil reflectance in the black soil region.