Toward Circumventing Collinearity Effect in Nonlinear Spectral Mixture Analysis by Using a Spectral Shape Measure

Nonlinear spectral mixture analysis (NSMA) is a widely used unmixing algorithm. It can fit the mixed spectra adequately, but collinearity effect among true and virtual endmembers will decrease the retrieval accuracies of endmember fractions. Use of linear spectral mixture analysis (LSMA) can effectively reduce the degree of collinearity in the NSMA. However, the inadequate modeling of mixed spectra in the LSMA will also yield retrieval errors, especially for the cases where the multiple scattering is not ignorable. In this study, a generalized spectral unmixing scheme based on a spectral shape measure, i.e. spectral information divergence (SID), was applied to overcome the limitations of the conventional NSMA and LSMA. Two simulation experiments were undertaken to test the performances of the SID, LSMA and NSMA in the mixture cases of treesoil, tree-concrete and tree-grass. Results demonstrated that the SID yielded higher accuracies than the LSMA for almost all the mixture cases in this study. On the other hand, performances of the SID method were comparable with the NSMA for the tree-soil and tree-grass mixture cases, but significantly better than the NSMA for the tree-concrete mixture case. All the results indicate that the SID method is fairly effective to circumvent collinearity effect within the NSMA, and compensate the inadequate modeling of mixed spectra within the LSMA.

Artificial Neural Networks Applied to Gas Mixture Analysis

An array composed of sixteen gas sensors was constructed to analyze gas mixtures quantitatively. The data of responses from the sensor array to ethane, propane and propylene were treated by three-layer ANN with BP algorithms and PLS. The analytical results indicated that the concentration predicted with ANN is better than that with PLS. The average prediction errors for ethane, propane and propylene were 5.11%, 8.28%, 2.64%, respectively.

NMR diffusion analysis of catalytic conversion mixtures from lignocellulose biomass using PSYCHE-iDOSY

The component analysis and structure characterization of complex mixtures of biomass conversion remain a challenging work.Hence,developing effective and easy to use techniques is necessary.Diffusion-ordered NMR spectroscopy(DOSY)is a non-selective and non-invasive method capable of achieving pseudo-separation and structure assignments of individual compounds from biomass mixtures by providing diffusion coefficients(D)of the components.However,the conventional 1H DOSY NMR is limited by crowded resonances when analyzing complex mixtures containing similar chemical structure resulting in similar coefficient.Herein we describe the application of an advanced diffusion NMR method,Pure Shift Yielded by CHirp Excitation DOSY(PSYCHE-iDOSY),which can record high-resolution signal diffusion spectra efficiently separating compounds in model and genuine mixture samples from cellulose,hemicellulose and lignin.Complicated sets of isomers(D-glucose/D-fructose/D-mannose and 1,2-/1,5-pentadiol),homologous compounds(ethylene glycol and 1,2-propylene glycol),model compounds of lignin,and a genuine reaction system(furfuryl alcohol hydrogenolysis with ring opening)were successfully separated in the diffusion dimension.The results show that the ultrahigh-resolution DOSY technique is capable of detecting and pseudo-separating the mixture components of C_(5)/C_(6) sugar conversion products and its derivative hydrogenation/hydrogenolysis from lignocellulose biomass.

Mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution

Impervious surface mapping is essential for urban environmental studies.Spectral Mixture Analysis(SMA)and its extensions are widely employed in impervious surface estimation from medium-resolution images.For SMA,inappropriate endmember combinations and inadequate endmember classes have been recognized as the primary reasons for estimation errors.Meanwhile,the spectral-only SMA,without considering urban spatial distribution,fails to consider spectral variability in an adequate manner.The lack of endmember class diversity and their spatial variations lead to over/underestimation.To mitigate these issues,this study integrates a hierarchical strategy and spatially varied endmember spectra to map impervious surface abundance,taking Wuhan and Wuzhou as two study areas.Specifically,the piecewise convex multiple-model endmember detection algorithm is applied to automatically hierarch-ize images into three regions,and distinct endmember combinations are independently developed in each region.Then,spatially varied endmember spectra are synthesized through neighboring spectra using the distance-based weight.Comparative analysis indicates that the proposed method achieves better performance than Hierarchical SMA and Fixed Four-endmembers SMA in terms of MAE,SE,and RMSE.Further analysis suggests that the hierarch-ical strategy can expand endmember class types and considerably improve the performance for the study areas in general,specifically in less developed areas.Moreover,we find that spatially varied endmember spectra facilitate the reduction of heterogeneous surface material variations and achieve the improved performance in developed areas.

SPECTROPHOTOMETRIC ANALYSIS OF MIXTURES OF TRACE METAL BY DOUBLE-SYSTEM AND MULTIVARIATE CALIRRATION

In this paper,a new method for simultaneous spectrophotometric analysis of multi- component with double-system was developed.The multivariate calibration method,principal component analysis-partial least squares(PCA-PLS),was described and applied to the processing of measurement data.A demonstration,simultaneous determination of cobalt,nickel,copper,zinc and iron with double-system(5-Br-PADAP and PAR as chromogenic chelate reagents,respectively) was given.The results showed that the method with douhle-system gave better precision than those with single system and MLR(in this paper,AKC method was selected)did not give satis- fied precision in any situation.

PARTIAL LEAST-SQUARES(PLS)REGRESSION AND SPECTROPHOTOMETRY AS APPLIED TO THE ANALYSIS OF MULTICOMPONENT MIXTURES

The UV absorption spectra of o-naphthol,α-naphthylamine,2,7-dihydroxy naphthalene,2,4-dimethoxy ben- zaldehyde and methyl salicylate,overlap severely;therefore it is impossible to determine them in mixtures by traditional spectrophotometric methods.In this paper,the partial least-squares(PLS)regression is applied to the simultaneous determination of these compounds in mixtures by UV spectrophtometry without any pretreatment of the samples.Ten synthetic mixture samples are analyzed by the proposed method.The mean recoveries are 99.4%,996%,100.2%,99.3% and 99.1%,and the relative standard deviations(RSD) are 1.87%,1.98%,1.94%,0.960% and 0.672%,respectively.

Detecting soil salinity with arid fraction integrated index and salinity index in feature space using Landsat TM imagery

Modeling soil salinity in an arid salt-affected ecosystem is a difficult task when using remote sensing data because of the complicated soil context （vegetation cover, moisture, surface roughness, and organic matter） and the weak spectral features of salinized soil. Therefore, an index such as the salinity index （SI） that only uses soil spectra may not detect soil salinity effectively and quantitatively. The use of vegetation reflectance as an indirect indicator can avoid limitations associated with the direct use of soil reflectance. The normalized difference vegetation index （NDVI）, as the most common vegetation index, was found to be responsive to salinity but may not be available for retrieving sparse vegetation due to its sensitivity to background soil in arid areas. Therefore, the arid fraction integrated index （AFⅡ） was created as supported by the spectral mixture analysis （SMA）, which is more appropriate for analyzing variations in vegetation cover （particularly halophytes） than NDVI in the study area. Using soil and vegetation separately for detecting salinity perhaps is not feasible. Then, we developed a new and operational model, the soil salinity detecting model （SDM） that combines AFⅡ and SI to quantitatively estimate the salt content in the surface soil. SDMs, including SDM1 and SDM2, were constructed through analyzing the spatial characteristics of soils with different salinization degree by integrating AFⅡ and SI using a scatterplot. The SDMs were then compared to the combined spectral response index （COSRI） from field measurements with respect to the soil salt content. The results indicate that the SDM values are highly correlated with soil salinity, in contrast to the performance of COSRI. Strong exponential relationships were observed between soil salinity and SDMs （R2〉0.86, RMSE〈6.86） compared to COSRI （R2=0.71, RMSE=16.21）. These results suggest that the feature space related to biophysical properties combined with AFII and SI can effectively provide information on soil salinity.

Carbon uptake and change in net primary productivity of oasis-desert ecosystem in arid western China with remote sensing technique

Arid and semi-arid ecosystems exhibit a spatially complex biogeophysical structure. According to arid western special climate-vegetation characters, the fractional cover of photosynthetic vegetation （PV）, non-photosynthetic vegetation （NPV）, bare soil and water are unmixed, using the remote sensing spectral mixture analysis. We try the method to unmix the canopy funation structure of arid land cover in order to avoid the differentiation of regional vegetation system and the disturbance of environmental background. We developed a modified production efficiency model NPP-PEM appropriate for the arid area at regional scale based on the concept of radiation use efficiency. This model refer to the GLO-PEM and CASA model was driven with remotely sensed observations, and calculates not just the conversion efficiency of absorbed photosynthetically active radiation but also the carbon fluxes that determine net primary productivity （NPP）. We apply and validate the model in the Kaxger and Yarkant river basins in arid western China. The NPP of the study area in 1992 and 1998 was estimated based on the NPP-PEM model. The results show that the improved PEM model, considering the photosynthetical activation of heterogeneous functional vegetation, is in good agreement with field measurements and the existing literature. An accurate agreement （R2= 0.85, P〈0.001） between the estimates and the ground-based measurement was obtained. The spatial distribution of mountain-oasis-desert ecosystem shows an obvious heterogeneous carbon uptake. The results are applicable to arid ecosystem studies ranging from characterizing carbon cycle, carbon flux over arid areas to monitoring change in mountain-oasis-desert productivity, stress and management.

Optimization of Protein Content and Dietary Fibre in a Composite Flour Blend Containing Rice (<i>Oryza sativa</i>), Sorghum [<i>Sorghum bicolor</i>(L.) Moench] and Bamboo (<i>Yushania alpine</i>) Shoots

Initiatives on tackling food insecurity among global emerging economies are being focused on enriching native staple foods with locally available nutritious underutilized crops. The objective of this study was to optimize protein content and dietary fibre in rice (Oryza sativa) flour using Sorghum (Sorghum bicolor L.) and Bamboo shoots (Yushania alpine). An extreme vertices design of mixture approach with 11 runs was employed in the study using MINITAB® software. The 11 blends from 11 generated runs and individual ingredient samples were analyzed for nutritional composition. Energy value and energy-to-protein ratio for the samples was calculated. Bamboo shoots flour (BSF) had the highest content for all proximate components except total carbohydrates on dry weight basis. Rice had the highest content of total carbohydrates at 77.71% and energy to protein ratio of 53.72 kcal/g. Sorghum had the highest mean total phenolic and condensed tannins of 45.512 (mg GAE/kg) and 2.512 (mg CE/g) while rice the least with 0.042 (mg GAE/kg) and 0.102 (mg CE/g), respectively. Fresh bamboo shoots had the highest level content of HCN of 117.81 mg/kg. Other dried ingredients had a mean HCN content of 2.313, 1.584 and 0.066 mg/kg for dried BSF, sorghum and rice respectively. Increasing the quantity of BSF and sorghum flour in the blends consequentially increased the protein content, dietary fibre and total minerals. Optimum blend was established to be 50:27:23 for rice, sorghum and BSF, respectively. This blend had 13.4% protein, 6.2% dietary fibre and 3.9% total minerals. Regression analysis showed that apart from dry matter, all other constituents were significantly predictable during optimization with R2 > 0.7530. Cluster analysis showed that the nutritional components analyzed are in four main clusters. Cluster 1: Dry matter and protein digestibility, cluster 2: Carbohydrates, energy value and energy ratio, cluster 3: Protein, fibre and ash while cluster 4: Crude fat only. These findings of the optimum composite ratio and other blends could contribute in addressing the food insecurity for low income countries.

Definition of water exchange zone between the Bohai Sea and Yellow Sea and the effect of winter gale on it

The marine dynamic environment of the Bohai Sea and the Yellow Sea in the winter of 2006 is simulated by the Regional Ocean Modelling System（ROMS） marine numerical model. Using the simulated temperature and salinity, the water exchange zone between the Bohai Sea and Yellow Sea is defined through the Spectral Mixture Model（SMM）. The influence of winter gales on the water exchange is also discussed. It is found that the Yellow Sea water masses in winter are distributed in a ＂tongue＂ shape in the Bohai Strait region, the water exchange zone presents a zonal distribution along the margin of the ＂tongue＂, with a tendency of running from northwest to southeast, and the water exchange is intensified at the tip of the ＂tongue＂. Besides, the coastal area in the northernmost Yellow Sea does not participate in the water exchange between the Bohai Sea and Yellow Sea. The result shows that the winter gale events play a role in enhancing the water exchange. It is specifically shown by the facts： the Yellow Sea warm current is enhanced to intrude the Bohai Sea by the gale process; the water exchange zone extends into the Bohai Sea; the water exchange belt in the southern part becomes wider; the mixture zone of river runoff with the Bohai Sea water upon its entry is enlarged and shifts northwards. Within two days after the gale process, the exchange zone retreats toward the Yellow Sea and the exchange zone resulted from the Huanghe River（Yellow River） runoff also shrinks back shoreward.

Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised fault classification

As an indispensable part of process monitoring, the performance of fault classification relies heavily on the sufficiency of process knowledge. However, data labels are always difficult to acquire because of the limited sampling condition or expensive laboratory analysis, which may lead to deterioration of classification performance.To handle this dilemma, a new semi-supervised fault classification strategy is performed in which enhanced active learning is employed to evaluate the value of each unlabeled sample with respect to a specific labeled dataset.Unlabeled samples with large values will serve as supplementary information for the training dataset. In addition,we introduce several reasonable indexes and criteria, and thus human labeling interference is greatly reduced. Finally,the fault classification effectiveness of the proposed method is evaluated using a numerical example and the Tennessee Eastman process.

Performance Enhancement in Dye-Sensitized Solar Cells with Composite Mixtures of TiO_2 Nanoparticles and TiO_2 Nanotubes

In the present investigation,a new composite nanostructured photoanodes were prepared using TiO_2 nanotubes（TNTs） with TiO_2 nanoparticles（TNPs）.TNPs were synthesized by sol-gel method,and TNTs were prepared through alkali hydrothermal method.Dye-sensitized solar cells（DSSCs） were fabricated with different photoanodes comprising of various ratios of TNTs ＋ TNPs,synthetic indigo dye as photosensitizer,PMII（l-propyl-3-methylimidazolium iodide） as ionic liquid electrolyte and cobalt sulfide as counter electrode.The structures and morphologies of TNPs and TNTs were analyzed through X-ray diffractometer,transmission electron microscope and scanning electron microscopes.The results of the investigation showed that the DSSC-4 made with composite photoanode structure（TNTs/TNPs）（90% of TNPs ＋ 10% of TNTs） had improved photocurrent efficiency（2.11%） than pure TNPs（1.00%） and TNT film（0.78%）.Electrochemical impedance spectra revealed that the composite TNTs/TNPs film-based DSSCs possessed the lowest charge-transfer resistances and longest electron lifetime.Hence,it could be concluded that the composite TNTs/TNPs photoanode facilitates the charge transport rate and enhances the efficiencies of DSSCs.

Extracting subpixel vegetation NDVI time series for evaluating the mixed pixel effect on GPP estimation in urban areas

The normalized difference vegetation index(NDVI)is the most widely used vegetation index for monitoring vegetation vigor and cover.As NDVI time series are usually derived at coarse or medium spatial resolutions,pixel size often represents a mixture of vegetated and non-vegetated surfaces.In heterogeneous urban areas,mixed pixels impede the accurate estimation of gross primary productivity(GPP).To address the mixed pixel effect on'NDVI time series and GPP estimation,we proposed a framework to extract subpixel vegetation NDVI(NDVI_(vege))from Landsat OLI images in urban areas,using endmember fractions,mixed NDVI(NDVI_(mix)),and NDVI.of non-vegetation,endmembers.Results demonstrated that the NDVI_(vege) extracted by this framework agreed well with the true NDVI_(vege) cross seasons and vegetation fractions,with R^(2) ranging from 0.74 to 0.82 and RMSE ranging from 0.03 to 0.04.The NDVI_(vege) time series was applied to evaluate vegetation GP in Wuhan,China.The total annual GPp estimated with NDVI_(vege) was 28-35%higher than the total annual GPP estimated with NDVI_(mix) implying uncertainty in the GPP estimations caused by mixed pixels.This study showed the potential of the proposed framework to resolve NDVI_(vege) for characterizing vegetation dynamics in heterogeneous areas.

Assessing environmental impacts of urban growth using remote sensing

This paper provides a study of the changes in land use in urban environments in two cities,Wuhan,China and western Sydney in Australia.Since mixed pixels are a characteristic of medium resolution images such as Landsat,when used for the classification of urban areas,due to changes in urban ground cover within a pixel,Multiple Endmember Spectral Mixture Analysis(MESMA)together with Super-Resolution Mapping(SRM)are employed to derive class fractions to generate classification maps at a higher spatial resolution using an Artificial Neural Network(ANN)predicted Wavelet method.Landsat images over the two cities for a 30-year period,are classified in terms of vegetation,buildings,soil and water.The classifications are then processed using Indifrag software to assess the levels of fragmentation caused by changes in the areas of buildings,vegetation,water and soil over the 30 years.The extents of fragmentation of vegetation,buildings,water and soil for the two cities are compared,while the percentages of vegetation are compared with recommended percentages of green space for urban areas for the benefit of health and well-being of inhabitants.Changes in Ecosystem Service Values(ESVs)resulting from the urbanization have been assessed for Wuhan and Sydney.The UN Sustainable Development Goals(SDG)for urban areas are being assessed by researchers to better understand how to achieve the sustainability of cities.

Improved resolution of mixed STR profiles using a fully automated differential cell lysis/DNA extraction method

Sexual assault evidence often contains sperm cells,which are typically separated from nonsperm cells using manual differential lysis procedures.The goal of this study was to evaluate the automated QIAGEN QIAcube for this purpose and to compare it to manual QIAGEN and manual organic differential methods using DNA yields and STR profile data for assessment.DNA yields were determined by qPCR,followed by multiplex STR amplification,CE analysis,and mixture interpretation.The automated method was capable of effective cell separation,producing DNA yields sufficient for STR amplification.Further,sperm fraction human:male DNA ratios from the QIAcube samples were consistently closer to the desired 1:1 and STR profiles were less likely to result in mixtures,with 6–8fewer female alleles detected(median 1.5 alleles).Ultimately,using the QIAcube for automated differential processing of semen-containing mixtures reduces the need for downstream mixture interpretation and improves STR profile quality with substantially less hands-on time.

Characteristics of dry deposited mineral particles associated with weather conditions in the adjacent sea areas of East China during a cruise in spring 2011

Dry deposited particles, larger than 1.3 μm, were collected under clear, cloudy, and foggy conditions during a cruise, traversing the Yellow Sea and the East China Sea from 23 March to 8 April 2011. In these areas, air masses are influenced by pollution outflows from the Asian continent. The size and elemental composition of dry deposited particles were investigated using a scanning electron microscope. Number-size distributions of these particles were approximately lognormal. Under clear conditions, the mode size was about 5.0 μm, with a mean diameter of 6.9 μm. Under cloudy and foggy conditions, the mean diameters were 5.7 and 6.0 μm, respectively, but the mode sizes were vague. Non-mixed mineral particles, sea salt, and mixed mineral-sea salt particles were the major particle types. Correspondingly, Al and Si were the most frequently detected elements. Frequencies of K-, Ca-, and S-containing particles were highest under foggy conditions, while the frequency of Na-containing particles was lowest. These results indicate that fog favored sulfate production on the particles and led to the deposited mineral particles more abundant in secondary salt, suggesting the importance to consider the dependence of the comoosition of deoosited mineral narticles on weather as well as narticle size.