Development of a national VNIR soil-spectral library for soil classification and prediction of organic matter concentrations

Soil visible-near infrared diffuse reflectance spectroscopy(vis-NIR DRS)has become an important area of research in the fields of remote and proximal soil sensing.The technique is considered to be particularly useful for acquiring data for soil digital mapping,precision agriculture and soil survey.In this study,1581 soil samples were collected from 14 provinces in China,including Tibet,Xinjiang,Heilongjiang,and Hainan.The samples represent 16 soil groups of the Genetic Soil Classification of China.After air-drying and sieving,the diffuse reflectance spectra of the samples were measured under laboratory conditions in the range between 350 and 2500 nm using a portable vis-NIR spectrometer.All the soil spectra were smoothed using the Savitzky-Golay method with first derivatives before performing multivariate data analyses.The spectra were compressed using principal components analysis and the fuzzy k-means method was used to calculate the optimal soil spectral classification.The scores of the principal component analyses were classified into five clusters that describe the mineral and organic composition of the soils.The results on the classification of the spectra are comparable to the results of other similar research.Spectroscopic predictions of soil organic matter concentrations used a combination of the soil spectral classification with multivariate calibration using partial least squares regression(PLSR).This combination significantly improved the predictions of soil organic matter(R2=0.899;RPD=3.158)compared with using PLSR alone(R2=0.697;RPD=1.817).

Lithological mapping with multispectral data–setup and application of a spectral database for rocks in the Balakot area, Northern Pakistan

In the frame of landslide susceptibility assessment, a spectral library was created to support the identification of materials confined to a particular region using remote sensing images. This library, called Pakistan spectral library(pklib) version 0.1, contains the analysis data of sixty rock samples taken in the Balakot region in Northern Pakistan.The spectral library is implemented as SQLite database. Structure and naming are inspired by the convention system of the ASTER Spectral Library. Usability, application and benefit of the pklib were evaluated and depicted taking two approaches, the multivariate and the spectral based. The spectral information were used to create indices. The indices were applied to Landsat and ASTER data tosupportthespatial delineation of outcropping rock sequences instratigraphic formations. The application of the indices introduced in this paper helps to identify spots where specific lithological characteristics occur. Especially in areas with sparse or missing detailed geological mapping, the spectral discrimination via remote sensing data can speed up the survey. The library can be used not only to support the improvement of factor maps for landslide susceptibility analysis, but also to provide a geoscientific basisto further analyze the lithological spotin numerous regions in the Hindu Kush.

Data organization and management of mine typical object spectral libraries

With the development of mining industry,people have obtained profits from it,but they are facing environmental damages.In order to monitor these environmental changes,a spectral library is set up for the spectrum data organization and management of mine typical objects.Most of the spectrum data come from the long-term field measuring in mining area and other spectral libraries.For the data quality control and error detection in the measuring data,an inner precision calculation method is presented and a series of interactive graphical controls are developed for the spectrum visualization and analysis.Through extracting and saving spectrum characters for the mine typical objects,realizs spectrum matching and classification for new measured spectrum samples are realized by using Euclidean distance,Aitchison distance,Pearson correlation coefficient and vector angular cosine methods.Based on the matching result,this work is able to gather dynamically physicochemical environment parameters from the library and gives an early warning for the mine environmental changes.

Spectral separability of tropical forest tree species using airborne hyperspectral imager

In Malaysia, airborne hyperspectral remote sensing is a relatively new technique used for research and commercial value in forest inventory and mapping. An advantage of airborne remote sensing, compared to satellite remote sensing, is its capability of offering a very high spatial resolution images. Thus, UPM-TropAIR AISA＇s airborne hyperspectral imagery that has been used in this study provides great quantity, better quality and also lower cost in identifying, quantifying and mapping of the Malaysian tropical timber forest resources. For the first stage in this study, the development of spectral library is deemed required in order for the Spectral Angle Mapper （SAM） classification be used to separate and map individual tree species in a tropical mixed mountain forest of Gunong Stong Forest Reserve. Pre-processing, enhancement and interpretation of image were conducted using ENVI Version 4.0 software. Results indicated that a total of eight commercial timber tree species was identified and mapped in a study plot of 5 ha using the TropAIR airborne hyperspectral imager with the aid of ground truthings.

Constructing a Raman and surface-enhanced Raman scattering spectral reference library for fine-particle analysis

Fine particles associated with haze pollution threaten the health of over 400 million people in China. Owing to excellent non-destructive fingerprint recognition characteristics, Raman and surface-enhanced Raman scattering(SERS) are often used to analyze the composition of fine particles to determine their physical and chemical properties as well as reaction mechanisms. However, there is no comprehensive Raman spectral library of fine particles. Furthermore, various studies that used SERS for fine-particle composition analysis showed that the uniqueness of the SERS substrates and different excitation wavelengths can produce a different spectrum for the same fine-particle component. To overcome this limitation, we conducted SERS experiments with a portable Raman spectrometer using two common SERS substrates(silver(Ag) foil and gold nanoparticles(Au NPs)) and a 785 nm laser. Herein, we introduced three main particle component types(sulfate-nitrate-ammonium(SNA), organic material, and soot) with a total of 39 chemical substances. We scanned the solid Raman, liquid Raman, and SERS spectra of these substances and constructed a fine-particle reference library containing 105 spectra. Spectral results indicated that for soot and SNA, the differences in characteristic peaks mainly originated from the solid-liquid phase transition;Ag foil had little effect on this difference, while the Au NPs caused a significant red shift in the peak positions of polycyclic aromatic hydrocarbons. Moreover, with various characteristic peak positions in the three types of spectra, we could quickly and correctly distinguish substances. We hope that this spectral library will aid in the future identification of fine particles.

DPHL:A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery

To address the increasing need for detecting and validating protein biomarkers in clinical specimens,mass spectrometry(MS)-based targeted proteomic techniques,including the selected reaction monitoring(SRM),parallel reaction monitoring(PRM),and massively parallel dataindependent acquisition(DIA),have been developed.For optimal performance,they require the fragment ion spectra of targeted peptides as prior knowledge.In this report,we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples.To build the spectral resource,we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker.We then applied the workflow to generate DPHL,a comprehensive DIA pan-human library,from 1096 data-dependent acquisition(DDA)MS raw files for 16 types of cancer samples.This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer(PCa)patients.Thereafter,PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated.As a second application,the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma(DLBCL)patients and 18 healthy control subjects.Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM.These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery.DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.

Spectral signatures of hydrilla from a tank and field setting

The invasion of hydrilla in many waterways has caused significant problems resulting in high main- tenance costs for eradicating this invasive aquatic weed. Present identification methods employed for detecting hydrilla invasions such as aerial photography and videos are difficult, costly, and time consuming. Remote sensing has been used for assessing wetlands and other aquatic vegetation, but very little information is available for detecting hydrilla invasions in coastal estuaries and other water bodies. The objective of this study is to construct a library of spectral signatures for identifying and classifying hydrilla invasions. Spectral signatures of hydrilla were collected from an experimental tank and field locations in a coastal estuary in the upper Chesapeake Bay. These measurements collected from the experimental tank, resulted in spectral signatures with an average peak surface reflectance in the near-infrared （NIR） region of 16% at a wavelength of 818 nm. However, the spectral measure- ments, collected in the estuary, resulted in a very different spectral signature with two surface reflectance peaks of 6% at wavelengths of 725 nm and 818 nm. The difference in spectral signatures between sites are a result of the components in the water column in the estuary because of increased turbidity （e.g., nutrients, dissolved matter and suspended matter）, and canopy being lower （submerged） in the water column. Spectral signatures of hydrilla observed in the tank and the field had similar characteristics with low reflectance in visible region of the spectrum from 400 to 700 nm, but high in the NIR region from 700 to 900 nm.

HyperDB：A Hyperspectral Land Class Database Designed for an Image Processing System

Hyperspectral remote sensing is becoming more and more important amongst remote sensing techniques. In this paper, we present a hyperspectral database（Hyper DB） designed to cooperate with an embedded hyperspectral image processing system developed by the authors. Hyperspectral data are recognized and categorized by their land coverage class and band information, and can be imported from various sources such as airborne and spaceborne sensors carried by airplanes or satellites, as well as handhold instruments based on in situ ground observations. Spectral library files can be easily stored, indexed, viewed, and exported. Since Hyper DB follows standard design principles—independence, data safety, and compatibility—it satisfies the practical demand for managing categorized hyperspectral data, and can be readily expanded to other peripheral applications.

YPED: An Integrated Bioinformatics Suite and Database for Mass Spectrometry-based Proteomics Research

We report a significantly-enhanced bioinformatics suite and database for proteomics research called Yale Protein Expression Database（YPED） that is used by investigators at more than 300 institutions worldwide. YPED meets the data management, archival, and analysis needs of a high-throughput mass spectrometry-based proteomics research ranging from a singlelaboratory, group of laboratories within and beyond an institution, to the entire proteomics community. The current version is a significant improvement over the first version in that it contains new modules for liquid chromatography–tandem mass spectrometry（LC–MS/MS） database search results, label and label-free quantitative proteomic analysis, and several scoring outputs for phosphopeptide site localization. In addition, we have added both peptide and protein comparative analysis tools to enable pairwise analysis of distinct peptides/proteins in each sample and of overlapping peptides/proteins between all samples in multiple datasets. We have also implemented a targeted proteomics module for automated multiple reaction monitoring（MRM）/selective reaction monitoring（SRM） assay development. We have linked YPED＇s database search results and both label-based and label-free fold-change analysis to the Skyline Panorama repository for online spectra visualization. In addition, we have built enhanced functionality to curate peptide identifications into an MS/MS peptide spectral library for all of our protein database search identification results.