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This Special Issue, focusing on the value of mineralogical monitoring for the mining and minerals industry, should include detailed investigations and characterizations of minerals and ores of the following fields for ore and process control: Lithium ores—determination of lithium contents by XRD methods; Copper ores and their different mineralogy; Nickel lateritic ores; Iron ores and sinter; Bauxite and bauxite overburden; Heavy mineral sands. The value of quantitative mineralogical analysis, mainly by XRD methods, combined with other techniques for the evaluation of typical metal ores and other important minerals, will be shown and demonstrated for different minerals. The different steps of mineral processing and metal contents bound to different minerals will be included. Additionally, some processing steps, mineral enrichments, and optimization of mineral determinations using XRD will be demonstrated. Statistical methods for the treatment of a large set of XRD patterns of ores and mineral concentrates, as well as their value for the characterization of mineral concentrates and ores, will be demonstrated. Determinations of metal concentrations in minerals by different methods will be included, as well as the direct prediction of process parameters from raw XRD data.

Technology: general issues --- History of engineering & technology --- Mining technology & engineering --- barite --- mineralogy --- industrial application --- beneficiation --- specific gravity --- bauxite overburden --- Belterra Clay --- mineralogical quantification --- Rietveld analysis --- machine learning --- artificial intelligence --- mining --- mineralogical analysis --- bauxite --- available alumina --- reactive silica --- XRD --- PLSR --- lithium --- quantification --- clustering --- Rietveld --- cluster analysis --- spodumene --- petalite --- lepidolite --- triphylite --- zinnwaldite --- amblygonite --- chalcopyrite --- ore blending --- copper flotation --- nickel laterite --- ore sorting --- framboidal pyrite --- sulfide minerals --- flotation --- process mineralogy --- heavy minerals --- ilmenite --- titania slag --- rietveld --- Magneli phases --- barite --- mineralogy --- industrial application --- beneficiation --- specific gravity --- bauxite overburden --- Belterra Clay --- mineralogical quantification --- Rietveld analysis --- machine learning --- artificial intelligence --- mining --- mineralogical analysis --- bauxite --- available alumina --- reactive silica --- XRD --- PLSR --- lithium --- quantification --- clustering --- Rietveld --- cluster analysis --- spodumene --- petalite --- lepidolite --- triphylite --- zinnwaldite --- amblygonite --- chalcopyrite --- ore blending --- copper flotation --- nickel laterite --- ore sorting --- framboidal pyrite --- sulfide minerals --- flotation --- process mineralogy --- heavy minerals --- ilmenite --- titania slag --- rietveld --- Magneli phases

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• Reference Manager
• EndNote
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This Special Issue, focusing on the value of mineralogical monitoring for the mining and minerals industry, should include detailed investigations and characterizations of minerals and ores of the following fields for ore and process control: Lithium ores—determination of lithium contents by XRD methods; Copper ores and their different mineralogy; Nickel lateritic ores; Iron ores and sinter; Bauxite and bauxite overburden; Heavy mineral sands. The value of quantitative mineralogical analysis, mainly by XRD methods, combined with other techniques for the evaluation of typical metal ores and other important minerals, will be shown and demonstrated for different minerals. The different steps of mineral processing and metal contents bound to different minerals will be included. Additionally, some processing steps, mineral enrichments, and optimization of mineral determinations using XRD will be demonstrated. Statistical methods for the treatment of a large set of XRD patterns of ores and mineral concentrates, as well as their value for the characterization of mineral concentrates and ores, will be demonstrated. Determinations of metal concentrations in minerals by different methods will be included, as well as the direct prediction of process parameters from raw XRD data.

barite --- mineralogy --- industrial application --- beneficiation --- specific gravity --- bauxite overburden --- Belterra Clay --- mineralogical quantification --- Rietveld analysis --- machine learning --- artificial intelligence --- mining --- mineralogical analysis --- bauxite --- available alumina --- reactive silica --- XRD --- PLSR --- lithium --- quantification --- clustering --- Rietveld --- cluster analysis --- spodumene --- petalite --- lepidolite --- triphylite --- zinnwaldite --- amblygonite --- chalcopyrite --- ore blending --- copper flotation --- nickel laterite --- ore sorting --- framboidal pyrite --- sulfide minerals --- flotation --- process mineralogy --- heavy minerals --- ilmenite --- titania slag --- rietveld --- Magneli phases --- n/a

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This book shows recent and innovative applications of the use of hyperspectral technology for optimal quantification of crop, vegetation, and soil biophysical variables at various spatial scales, which can be an important aspect in agricultural management practices and monitoring. The articles collected inside the book are intended to help researchers and farmers involved in precision agriculture techniques and practices, as well as in plant nutrient prediction, to a higher comprehension of strengths and limitations of the application of hyperspectral imaging to agriculture and vegetation. Hyperspectral remote sensing for studying agriculture and natural vegetation is a challenging research topic that will remain of great interest for different sciences communities in decades.

Research & information: general --- Environmental economics --- hyperspectral LiDAR --- Red Edge --- AOTF --- vegetation parameters --- leaf chlorophyll content --- DLARI --- MDATT --- adaxial --- abaxial --- spectral reflectance --- peanut --- field spectroscopy --- hyperspectral --- heavy metals --- grapevine --- PLS --- SVM --- MLR --- multi-angle observation --- hyperspectral remote sensing --- BRDF --- vegetation classification --- object-oriented segmentation --- spectroscopy --- artificial intelligence --- proximal sensing data --- precision agriculture --- spectra --- vegetation --- plant --- classification --- discrimination --- feature selection --- waveband selection --- support vector machine --- random forest --- Natura 2000 --- invasive species --- expansive species --- biodiversity --- proximal sensor --- macronutrient --- micronutrient --- remote sensing --- hyperspectral imaging --- platforms and sensors --- analytical methods --- crop properties --- soil characteristics --- classification of agricultural features --- canopy spectra --- chlorophyll content --- continuous wavelet transform (CWT) --- correlation coefficient --- partial least square regression (PLSR) --- reproducibility --- replicability --- partial least squares --- Ethiopia --- Eragrostis tef --- hyperspectral remote sensing for soil and crops in agriculture --- hyperspectral imaging for vegetation --- plant traits --- high-resolution spectroscopy for agricultural soils and vegetation --- hyperspectral databases for agricultural soils and vegetation --- hyperspectral data as input for modelling soil, crop, and vegetation --- product validation --- new hyperspectral technologies --- future hyperspectral missions --- hyperspectral LiDAR --- Red Edge --- AOTF --- vegetation parameters --- leaf chlorophyll content --- DLARI --- MDATT --- adaxial --- abaxial --- spectral reflectance --- peanut --- field spectroscopy --- hyperspectral --- heavy metals --- grapevine --- PLS --- SVM --- MLR --- multi-angle observation --- hyperspectral remote sensing --- BRDF --- vegetation classification --- object-oriented segmentation --- spectroscopy --- artificial intelligence --- proximal sensing data --- precision agriculture --- spectra --- vegetation --- plant --- classification --- discrimination --- feature selection --- waveband selection --- support vector machine --- random forest --- Natura 2000 --- invasive species --- expansive species --- biodiversity --- proximal sensor --- macronutrient --- micronutrient --- remote sensing --- hyperspectral imaging --- platforms and sensors --- analytical methods --- crop properties --- soil characteristics --- classification of agricultural features --- canopy spectra --- chlorophyll content --- continuous wavelet transform (CWT) --- correlation coefficient --- partial least square regression (PLSR) --- reproducibility --- replicability --- partial least squares --- Ethiopia --- Eragrostis tef --- hyperspectral remote sensing for soil and crops in agriculture --- hyperspectral imaging for vegetation --- plant traits --- high-resolution spectroscopy for agricultural soils and vegetation --- hyperspectral databases for agricultural soils and vegetation --- hyperspectral data as input for modelling soil, crop, and vegetation --- product validation --- new hyperspectral technologies --- future hyperspectral missions

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In the last few decades, near-infrared (NIR) spectroscopy has distinguished itself as one of the most rapidly advancing spectroscopic techniques. Mainly known as an analytical tool useful for sample characterization and content quantification, NIR spectroscopy is essential in various other fields, e.g. NIR imaging techniques in biophotonics, medical applications or used for characterization of food products. Its contribution in basic science and physical chemistry should be noted as well, e.g. in exploration of the nature of molecular vibrations or intermolecular interactions. One of the current development trends involves the miniaturization and simplification of instrumentation, creating prospects for the spread of NIR spectrometers at a consumer level in the form of smartphone attachments—a breakthrough not yet accomplished by any other analytical technique. A growing diversity in the related methods and applications has led to a dispersion of these contributions among disparate scientific communities. The aim of this Special Issue was to bring together the communities that may perceive NIR spectroscopy from different perspectives. It resulted in 30 contributions presenting the latest advances in the methodologies essential in near-infrared spectroscopy in a variety of applications.

n/a --- pocket-sized spectrometer --- standard germination tests --- total hydroxycinnamic derivatives --- hyperspectral image --- quantitative analysis modeling --- tissue --- chemotherapy --- FTIR spectroscopy --- cheese --- biomeasurements --- chemometrics --- affine invariance --- rapid identification --- biodiagnosis --- bioanalytical applications --- fat --- NIRS --- pixel-wise --- paraffin-embedded --- late preterm --- maize kernel --- photonics --- hyperspectral image processing --- image processing --- colorectal cancer --- test set validation --- deep convolutional neural network --- near-infrared fluorescence --- classification --- variety discrimination --- near-infrared hyperspectral imaging --- ensemble learning --- light --- origin traceability --- Paris polyphylla var. yunnanensis --- Fourier transform mid-infrared spectroscopy --- dry matter --- Fourier transform infrared spectroscopy --- hyperspectral imaging --- FT-NIR spectroscopy --- proximal sensing --- perfusion measurements --- near-infrared spectroscopy --- stained --- carotenoids --- cellular imaging --- perturbation --- direct model transferability --- clinical classifications --- counterfeit and substandard pharmaceuticals --- hyperspectral imaging technology --- spectral imaging --- SVM --- nutritional parameters --- extra virgin olive oil --- ethanol --- osteopathy --- living cells --- object-wise --- water-mirror approach --- Chrysanthemum --- bootstrapping soft shrinkage --- FTIR --- PLS-R --- multivariate data analysis --- combination bands --- binary dragonfly algorithm --- geographical origin --- Vitis vinifera L. --- glucose --- detection --- di-(2-picolyl)amine --- non-destructive sensor --- splanchnic --- adulteration --- animal origin --- melamine --- artemether --- MicroNIR™ --- brain --- fluorescent probes --- Folin–Ciocalteu --- SCiO --- support vector machine --- anharmonic quantum mechanical calculations --- PLSR --- Zn(II) --- RMSEP --- overtones --- blackberries --- pasta/sauce blends --- FT-IR --- partial least squares calibration --- partial least squares (PLS) --- auxiliary diagnosis --- handheld near-infrared spectroscopy --- precision viticulture --- partial least squares --- seeds vitality --- freeze-damaged --- near infrared --- discriminant analysis --- corn seed --- quantum chemical calculation --- anharmonic calculation --- Trichosanthis Fructus --- moisture --- analytical spectroscopy --- Raman spectroscopy --- NIR spectroscopy --- calibration transfer --- imaging --- water --- lumefantrine --- BRAF V600E mutation --- wavelength selection --- bone cancer --- imaging visualization --- near infrared spectroscopy --- raisins --- chemometric techniques --- data fusion --- prepared slices --- Ewing sarcoma --- biomonitoring --- Rubus fructicosus --- VIS/NIR hyperspectral imaging --- combinations bands --- quantitative analysis model --- partial least square regression --- DFT calculations --- TreeBagger --- antimalarial tablets --- accelerated aging --- agriculture --- crude drugs --- spectroscopy --- rice seeds --- PLS --- isotopic substitution --- multivariate calibration --- phytoextraction --- Fourier-transform near-infrared spectroscopy --- phenolics --- deparaffinized --- near-infrared (NIR) spectroscopy --- SIMCA --- counter propagation artificial neural network --- fructose --- PLS-DA --- ultra-high performance liquid chromatography --- aquaphotomics --- support vector machine-discriminant analysis --- hier-SVM --- DNA --- NIR --- support vector machine model --- API --- principal component analysis --- Folin-Ciocalteu