Nothing Special   »   [go: up one dir, main page]

Safanelli et al., 2023 - Google Patents

An interlaboratory comparison of mid-infrared spectra acquisition: Instruments and procedures matter

Safanelli et al., 2023

View HTML
Document ID
15905337115854875583
Author
Safanelli J
Sanderman J
Bloom D
Todd-Brown K
Parente L
Hengl T
Adam S
Albinet F
Ben-Dor E
Boot C
Bridson J
Chabrillat S
Deiss L
Demattê J
Demyan M
Dercon G
Doetterl S
van Egmond F
Ferguson R
Garrett L
Haddix M
Haefele S
Heiling M
Hernandez-Allica J
Huang J
Jastrow J
Karyotis K
Machmuller M
Khesuoe M
Margenot A
Matamala R
Miesel J
Mouazen A
Nagel P
Patel S
Qaswar M
Ramakhanna S
Resch C
Robertson J
Roudier P
Sabetizade M
Shabtai I
Sherif F
Sinha N
Six J
Summerauer L
Thomas C
Toloza A
Tomczyk-Wójtowicz B
Tsakiridis N
van Wesemael B
Woodings F
Zalidis G
Żelazny W
Publication year
Publication venue
Geoderma

External Links

Snippet

Diffuse reflectance spectroscopy has been extensively employed to deliver timely and cost- effective predictions of a number of soil properties. However, although several soil spectral laboratories have been established worldwide, the distinct characteristics of instruments and …
Continue reading at www.sciencedirect.com (HTML) (other versions)

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30386—Retrieval requests
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30587—Details of specialised database models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/28—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for programming tools or database systems, e.g. ontologies, heterogeneous data integration, data warehousing or computing architectures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/70—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds
    • G06F19/708—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for data visualisation, e.g. molecular structure representations, graphics generation, display of maps or networks or other visual representations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/06—Investment, e.g. financial instruments, portfolio management or fund management
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run

Similar Documents

Publication Publication Date Title
Dangal et al. Accurate and precise prediction of soil properties from a large mid-infrared spectral library
Demattê et al. Soil analytical quality control by traditional and spectroscopy techniques: Constructing the future of a hybrid laboratory for low environmental impact
Shi et al. Prediction of soil organic matter using a spatially constrained local partial least squares regression and the C hinese vis–NIR spectral library
Bowring et al. Engineering cyber infrastructure for U‐Pb geochronology: Tripoli and U‐Pb_Redux
Jiang et al. Estimating soil organic carbon of cropland soil at different levels of soil moisture using VIS-NIR spectroscopy
Zhai et al. Estimation of nitrogen, phosphorus, and potassium contents in the leaves of different plants using laboratory-based visible and near-infrared reflectance spectroscopy: comparison of partial least-square regression and support vector machine regression methods
Guo et al. Evaluating calibration and spectral variable selection methods for predicting three soil nutrients using Vis-NIR spectroscopy
Sila et al. Evaluating the utility of mid-infrared spectral subspaces for predicting soil properties
Brown et al. Chemometrics
Zhang et al. Detection of canopy chlorophyll content of corn based on continuous wavelet transform analysis
Will et al. Comparative analysis of Middle Stone Age artifacts in Africa (CoMSAfrica).
Peng et al. Estimation of soil nutrient content using hyperspectral data
Yu et al. Prediction of soil properties based on characteristic wavelengths with optimal spectral resolution by using Vis-NIR spectroscopy
Cohen et al. Reflectance spectroscopy for routine agronomic soil analyses
Lin et al. Ordered homogeneity pursuit lasso for group variable selection with applications to spectroscopic data
Zhao et al. Calibration transfer based on affine invariance for NIR without transfer standards
Xuemei et al. Using short wave visible–near infrared reflectance spectroscopy to predict soil properties and content
Li et al. Modeling and prediction of soil organic matter content based on visible-near-Infrared spectroscopy
Li et al. Combining variable selection and multiple linear regression for soil organic matter and Total nitrogen estimation by DRIFT-MIR spectroscopy
Zhao et al. Simultaneous quantification and visualization of photosynthetic pigments in Lycopersicon esculentum mill. Under different levels of nitrogen application with visible-near infrared hyperspectral imaging technology
Guo et al. A Review of Research on Forest Ecosystem Quality Assessment and Prediction Methods
Konkol et al. Porchlight: An Accessible and Interactive Aid in Preprocessing of Spectral Data
Wei et al. Dimensionality reduction statistical models for soil attribute prediction based on raw spectral data
Yu et al. Comparing two different development methods of external parameter orthogonalization for estimating organic carbon from field-moist intact soils by reflectance spectroscopy
Safanelli et al. An interlaboratory comparison of mid-infrared spectra acquisition: Instruments and procedures matter