Up-to-date information on soil properties and the ability to track changes in soil properties over time are critical for improving multiple decisions on soil security at various scales, ranging from global climate change modeling and policy to national level environmental and development planning, to farm and field level resource management. Diffuse reflectance infrared spectroscopy has become an indispensable laboratory tool for the rapid estimation of numerous soil properties to support various soil mapping, soil monitoring, and soil testing applications. Recent advances in hardware technology have enabled the development of handheld sensors with similar performance specifications as laboratory-grade near-infrared (NIR) spectrometers.

Here, we've compiled a hand-held NIR spectral library (1350-2550 nm) using the NeoSpectra Handheld NIR Analyzer developed by Si-Ware. Each scanner is fitted with Fourier-Transform technology based on the semiconductor Micro Electromechanical Systems (MEMS) manufacturing technique, promising accuracy, and consistency between devices.

This library includes 2,106 distinct mineral soil samples scanned across 9 of these portable low-cost NIR spectrometers (indicated by serial no). 2,016 of these soil samples were selected to represent the diversity of mineral soils found in the United States, and 90 samples were selected across Ghana, Kenya, and Nigeria. 519 of the US samples were selected and scanned by Woodwell Climate Research Center. These samples were queried from the USDA NRCS NSSC-KSSL Soil Archives as having a complete set of eight measured properties (TC, OC, TN, CEC, pH, clay, sand, and silt). They were stratified based on the major horizon and taxonomic order, omitting the categories with less than 500 samples. Three percent of each stratum (i.e., a combination of major horizon and taxonomic order) was then randomly selected as the final subset retrieved from KSSL's physical soil archive as 2-mm sieved samples. The remaining 1,604 US samples were queried from the USDA NRCS NSSC-KSSL Soil Archives by the University of Nebraska - Lincoln to meet the following criteria: Lower depth <= 30 cm, pH range 4.0 to 9.5, Organic carbon <10%, Greater than lower detection limits, Actual physical samples available in the archive, Samples collected and analyzed from 2001 onwards, Samples having complete analyses for high-priority properties (Sand, Silt, Clay, CEC, Exchangeable Ca, Exchangeable Mg, Exchangeable K, Exchangeable Na, CaCO3, OC, TN), & MIR scanned.

All samples were scanned dry 2mm sieved. ~20g of sample was added to a plastic weighing boat where the NeoSpectra scanner would be placed down to make direct contact with the soil surface. The scanner was gently moved across the surface of the sample as 6 replicate scans were taken. These replicates were then averaged so that there is one spectra per sample per scanner in the resulting database.

A subset of 1,976 US topsoil samples was used to create Cubist models for 8 soil properties including bulk density (BD, <2mm fraction, 1/3 Bar, units in grams per cubic centimeter), calcium carbonate (CaCO3, <2mm fraction, units in weight percent), clay content (percent), buffered ammonium-acetate exchangeable potassium (Ex. K, units in centimoles of charge per kilogram of soil), pH, sand content (percent), silt content (percent), and estimated organic carbon (SOC, estimated after inorganic carbon removal, units in weight percent). Two strategies were evaluated for handling scanner-to-scanner variability: averaging scans per sample (avg) versus retaining replicate scans across all scanners (reps) during model building. Cubist avg models and cubist reps models are provided here for the 8 soil properties outlined in “.qs” file format and can be opened and worked with in the R programming language. The subset of 1,976 samples has also been provided here for reproducibility (1976_NSlibrary_withmetadata.csv).

The repository contains:

Neospectra_database_column_names.csv: describes the variables (columns) of site and soil data, and the range of near-infrared (NIR, 1350-2550 nm) and mid-infrared (MIR, 600-4000 cm-1) spectra. The CSV is composed of the file name, column name, type, example, and description with measurement unit.

Neospectra_WoodwellKSSL_MIR.csv: the equivalent MIR spectra of neospectra samples fetched from the KSSL database and formatted to the OSSL specifications.

Neospectra_WoodwellKSSL_soil+site+NIR.csv: soil, site, and Neospectra's NIR. Each row contains one replicated spectra of a given scanner (6 repeats per scanner per soil sample). Soil and site info is filled within the same soil sample.

1976_NSlibrary_withmetadata.csv: data matrix for reproducible model calibration.

Models:

log..bd_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for log(1+BD).

log..caco3_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for log(1+CaCO3).

clay_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for clay.

log..k.ex_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for log(1+Ex. K).

ph.h2o_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for pH.

sand_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for sand.

silt_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for silt.

log..soc_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for log(1+SOC).

log..bd_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for log(1+BD).

log..caco3_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for log(1+CaCO3).

clay_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for clay.

log..k.ex_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for log(1+Ex. K).

ph.h2o_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for pH.

sand_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for sand.

silt_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for silt.

log..soc_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for log(1+SOC).

108 Global import shipment records of Nir Analyser with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The dataset is composed of chemical analysis results and the NIR spectra of 490 solid animal organic waste products that were collected in 2 campaigns conducted in 2018 and 2019. The sampling was designed to capture the large diversity of animal species (mainly cattle, pigs and poultry), type of farming and storage modes). Compositional parameters (dry matter, organic matter, total and ammonium nitrogen, phosphorus, potassium, calcium and magnesium contents) were analyzed according to French AFNOR standards. Samples were scanned using a Q-interline AgriQuant B8 equipped with a patented spiral sampler, which aggregates the heterogeneity of the sample. This dataset covers a wide range of variability in the composition of solid animal manure, and is of great interest to chemometricians and agronomists in search of references on the fertilizing value of these products.

This dataset consists of reflectance spectra of small lunar areas measured in 120 spectral channels from 0.62 to 2.6 micrometers using the McCord circular-variable-filter (CVF) near-infrared photometer. A small aperture allows light from the selected lunar area to pass through the CVF and onto the detector. Each spectral channel is obtained sequentially as the CVF is rotated. Data were acquired using the 2.2 m telescope on Mauna Kea, Hawaii. All spectra have been scaled to a given wavelength. Spectral resolution varies somewhat from run to run. The first half of the CVF produces data at approximately twice the spectral resolution as the second half joined are unreliable. All data were acquired as relative reflectance spectra and were calibrated to scaled reflectance data using a directional-hemispheric (diffuse) spectrum of Apollo 16 soil acquired by J. B. Adams.

These datasets contain Near-infrared (NIR) absorbance spectra of the wavelength range 309-149 nm of mango mesocarp with corresponding Dry Matter Content (DMC) values.

The file "MangoDMC_NIR_Data_v3.csv" contains data as used in the publication "Achieving robustness across season, location and cultivar for a NIRS model for intact mango fruit dry matter content" (Postharvest Biology and Technology, 2020, 168:111202; https://www.sciencedirect.com/science/article/pii/S0925521420301629), with addition of data from an additional harvest season, as used in the publication "Evaluation of 1D Convolutional Neural Network in Estimation of Mango Dry Matter Content" (Spectrochimica Acta Part A 2024 311: 124003; https://www.sciencedirect.com/science/article/pii/S1386142524001690). This file is as presented in version 3 of this data repository.

The current version (4) has an additional file ".csv". This file augments the data of version 3 with data from additional instruments and seasons as used in the submitted thesis of Jeremy Walsh, 2024, Central Queensland University, "Deep Learning in Estimation of Fruit Attributes Using Near Infrared Spectroscopy".

Global Near Infrared (NIR) Spectrometers Industry's Compound Annual Growth Rate will be 6.8% from 2023 to 2030. What is driving the Near Infrared (NIR) Spectrometers Market?

Increasing demand in various industries drives of the Global Near-Infrared (NIR) Spectrometers market.

NIR spectrometers are widely used in the pharmaceutical industry for various applications such as raw material identification, quality control of finished products, and monitoring drug formulation processes. NIR spectroscopy allows rapid and non-destructive analysis, making it a valuable tool for pharmaceutical companies to ensure the quality and consistency of their products. The food and agriculture sector extensively uses NIR spectrometers for quality assessment, process monitoring, and authenticity testing. NIR spectroscopy helps in analyzing food composition, determining nutritional values, detecting contaminants or adulterants, and assessing the ripeness or freshness of agricultural products. It enables quick and accurate analysis, contributing to improved food safety and quality control. NIR spectrometers find applications in the chemical industry for process monitoring, reaction analysis, and quality control. They are used to measure chemical composition, identify chemical reactions, monitor reaction kinetics, and ensure product consistency. The ability of NIR spectrometers to provide real-time analysis facilitates process optimization and enhances production efficiency. NIR spectroscopy plays a crucial role in environmental analysis and monitoring. It is utilized to assess water quality, identify pollutants, analyze soil composition, and monitor air quality. NIR spectrometers enable rapid analysis of environmental samples, facilitating timely decision-making and effective environmental management. NIR spectrometers are employed in the petrochemical and energy sectors for various applications such as quality control of fuels and lubricants, monitoring of refinery processes, and analysis of oil and gas samples. NIR spectroscopy helps in ensuring the compliance of petroleum products with regulatory standards, optimizing refinery operations, and analyzing the composition and properties of hydrocarbon samples. Introduction of Near Infrared (NIR) Spectrometers

Near-infrared (NIR) spectrometers are instruments used to analyze the interaction between light in the near-infrared range and a sample. They are widely used in various fields, including chemistry, pharmaceuticals, food and agriculture, materials science, and biomedical research. NIR spectrometers provide valuable information about the chemical composition, structure, and physical properties of substances. NIR spectrometers operate based on the principle of absorption spectroscopy. When NIR light (wavelengths typically ranging from 700 to 2500 nm) passes through a sample, it interacts with the molecular bonds within the sample. Different chemical compounds absorb light at specific wavelengths, resulting in characteristic absorption patterns or spectra. NIR spectrometers consist of a light source, a sample holder or cuvette, a detector, and a data acquisition system. The light source emits NIR radiation, which is directed onto the sample. The sample absorbs certain wavelengths of light, and the transmitted or reflected light is detected by the detector. The detector converts the light signal into an electrical signal that is then processed and analyzed by the data acquisition system. NIR spectrometers can analyze a wide range of samples, including liquids, solids, and gases. They are often used for quantitative analysis, such as determining the concentration of a particular compound in a sample. NIR spectroscopy can also provide qualitative information, identifying the presence or absence of specific compounds or functional groups. NIR spectroscopy is a non-destructive technique that allows analysis without sample preparation or chemical reagents. NIR spectrometers provide real-time or near-real-time analysis, allowing for quick measurements and high sample throughput. It can be applied to a broad range of materials, including organic and inorganic compounds, liquids, solids, and gases. It can be used for qualitative and quantitative analysis, as well as for process monitoring and quality control. In many cases, only small amounts of samples are required for analysis, making it suitable for precious or limited samples.

This data set is:

X_scale.npu - preprocessed spectrum intensity data (NIR, NIR).

Wavelength from 400 nm to 2500 nm in 1 nm increments It turns out 2100 values And 21780 soul samples, each sample was taken twice, it turns out 43560 values

Pre-processing includes: 1. Savitsky-Goley smoothing filter + 1 order derivative (43560, 2100) 2. Discrete wavelet transform (DWT) (Array of approximation coefficients (average values)) (43560, 2100) 3. Discrete Wavelet transform (DWT) (Array of detail coefficients (high frequencies)) (43560, 2100) The data has dimension (43560, 2100x3)=(43560, 6300).

y_data.csv - soil chemical composition data for all sample values, consisting of pH, P, K, N values

Multispectral-Spoof contains face images and printed spoofing attacks recorded in Visible (VIS) and Near-Infrared (NIR) spectra for 21 identities.

Multispectral-Spoof is a dataset for multi-spectral face recognition and presentation attack detection (anti-spoofing). The dataset contains images of Bona Fide VIS and NIR images as well as VIS and NIR printed presentation attacks (spoofing attacks) to VIS and NIR systems. The number of clients in the dataset is 21. The recordings are done using a uEye camera with resolution 1280x1024. When recording the images in NIR, a NIR filter of 800nm has been mounted to the camera.

Recording the real accesses

The set of real accesses contains recordings of VIS and NIR images for the identities. For each identity, a total of 5 images in VIS and 5 images in NIR are recorded for each of 7 environment conditions. 1 of these conditions is set up in an hallway space, while the rest 6 conditions are set up in an office. So, in total, the database contains 7*5=35 images per client in VIS and 7*5=35 images per client in NIR spectrum. Thus, the total number of real accesses per client is 70.

Recording the spoofing attacks

For each client in the database, 3 images in VIS and 3 images in NIR are selected from the original database. The chosen images are from the ones with the best quality in terms of recording conditions. These images are then printed on a paper using black & white printed with resolution of 600dpi. During the recording of the spoofing attacks, the printed images are attached on a fixed support. For each of the printed images, we recorded 4 spoofing attacks in 3 lighting conditions, both in VIS and NIR spectra. Thus, the total number of spoofing attacks per client is 6 * 2 * 3 * 4 = 144.

Database protocol

The recorded images are divided into train, development and test set, and the clients in each of the sets do not overlap. There are 9 clients in the train subset, 6 in the development and 6 in the test subset. The subsets do not overlap, meaning that a client in one subset can not appear in any other subset.

• Client IDs in the world subset: 2,4,7,9,10,12,16,17,21
• Client IDs in the dev subset: 1,3,5,6,8,11
• Client IDs in the test subset: 13,15,18,19,20,22

Out of the 75 real access images per client, 10 are taken into the enrollment set: 5 from VIS and 5 from NIR spectra.

Reference paper

I. Chingovska, N. Erdogmus, A. Anjos. S. Marcel, “Face Recognition Systems Under Spoofing Attacks”, in Springer “Face Recognition Across the Imaging Spectrum” (Editor Thirimachos Bourlai), 2016.
10.1007/978-3-319-28501-6_8
https://publications.idiap.ch/index.php/publications/show/3539

Near InfraRed red-edge imagery for Northern Ireland from 2009 to 2011 was acquired by the Landmap project from RapidEye. The imagery has a spatial resolution of 6.5metres and contains 5 spectral bands. The Joint Information Systems Committee (JISC) funded Landmap service which ran from 2001 to July 2014 collected and hosted a large amount of earth observation data for the majority of the UK. After removal of JISC funding in 2013, the Landmap service is no longer operational, with the data now held at the NEODC.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Abstract:

NEW VERSION V.002 (Some Lat Long Coordinates added).

Soil spectroscopy has emerged as a solution to the limitations associated with traditional soil surveying and analysis methods, addressing the challenges of time and financial resources. Analyzing the soil's spectral reflectance enables to observe the soil composition and simultaneously evaluate several attributes because the matter, when exposed to electromagnetic energy, leaves a "spectral signature" that makes such evaluations possible. The Soil Spectral Library (SSL) consolidates soil spectral patterns from a specific location, facilitating accurate modeling and reducing time, cost, chemical products, and waste in surveying and mapping processes. Therefore, an open access SSL benefits society by providing a fine collection of free data for multiple applications for both research and commercial use.

BSSL Description and Usefulness

The Brazilian Soil Spectral Library (BSSL), available at https://bibliotecaespectral.wixsite.com/english, is a comprehensive repository of soil spectral data. Coordinated by JAM Demattê and managed by the GeoCiS research group, the BSSL was initiated in 1995 and published by Demattê and collaborators in 2019. This initiative stands out due to its coverage of diverse soil types, given Brazil's significance in the agricultural and environmental domains and its status as the fifth largest territory in the world (IBGE, 2023). In addition, a Middle Infrared (MIR) dataset has been published (Mendes et al., 2022), part of which is included in this repository. The database covers 16,084 sites and includes harmonized physicochemical and spectral (Vis-NIR-SWIR and MIR range) soil data from various sources at 0-20 cm depth. All soil samples have Vis-NIR-SWIR data, but not all have MIR data.

The BSSL provides open and free access to curated data for the scientific community and interested individuals. Unrestricted access to the BSSL supports researchers in validating their results by comparing measured data with predicted values. This initiative also facilitates the development of new models and the improvement of existing ones. Moreover, users can employ the library to test new models and extract information about previously unknown soil properties. With its extensive coverage of tropical soil classes, the BSSL is considered one of the most significant soil spectral libraries worldwide, with 42 institutions and 61 researchers participating. However, 47 collaborators from 29 institutions have authorized the data opening. Other researchers can also provide their data upon request through the coordinator of this initiative.

The data from the BSSL project can also help wet labs to improve their analytical capabilities, contributing to developing hybrid wet soil laboratory techniques and digital soil maps while informing decision-makers in formulating conservation and land use policies. The soil's capacity for different land uses promotes soil health and sustainability.

Coverage

The BSSL data covers all regions of Brazil, including 26 states and the Federal District. It is in a .xlsx format and has a total size of 305 Mb. The table is structured in sheets with rows for observations, and columns, representing various soil attributes in the surface layer, from 0 to 20 cm depth. The database includes environmental and physicochemical properties (22 columns and 16,084 rows), Vis-NIR-SWIR spectral bands (2151 columns and 16,084 rows), and MIR channels (681 columns and 1783 rows). An ID unique column can merge the sheet for each attribute or spectral range.

Accessing original data source

Using these data requires their reference in any situation under copyright infringement penalty. Three mechanisms are available for users to reach the original and complete data contributors:

a) Refer to sheet two for name and code-based searches;

b) Visit the website https://bibliotecaespectral.wixsite.com/english/lista-de-cedentes or locate the contributors' list by Brazilian state;

c) Visit the website of the Brazilian Soil Spectral Service – Braspecs http://www.besbbr.com.br/, an online platform for soil analysis that uses part of the current SSL (Demattê et al., 2022) - It was developed and managed by GeoCiS. There, owners from all over the country can be found.

Proceeding to data analysis

We registered and organized the samples at the ESALQ/USP Soil Laboratory. Some samples arrived without preliminary data analyses, so we analyzed them for soil organic matter (SOM), granulometry, cation exchange capacity (CEC), pH in water, and the presence of Ca, Mg, and Na, following the recommendations of Donagemma et al. (2011).

The GeoCiS research group performed spectral analyses following the procedures described by Bellinaso et al. (2010). Demattê et al. (2019) provide detailed methods for sampling, preparation, and soil analyses, including reflectance spectroscopy. Latitude and longitude data can be requested directly from the data owner. In summary, the following steps are involved in data acquisition.

a) We subjected the soil samples to a preliminary treatment, which involved drying them in an oven at 45°C for 48 hours, grinding them, and sieving them through a 2mm mesh;

b) We placed the samples in Petri dishes with a diameter of 9 cm and a height of 1.5 cm;

c) We homogenized and flattened the surface of the samples to reduce the shading caused by larger particles or foreign bodies, making them ready for spectral readings;

d) The spectral analyses took place in a darkened room to avoid interference from natural light. We used a computer to record the electromagnetic pulses through an optical fiber connected to the sensor, capturing the spectral response of the soil sample;

e) We obtained reflectance data in the Visible-Near Infrared-Shortwave Infrared (Vis-NIR-SWIR) range using a FieldSpec 3 spectroradiometer (Analytical Spectral Devices, ASD, Boulder, CO), which operates in the spectral range from 350 to 2500 nm;

f) The sensor had a spectral resolution of 3 nm from 350-700 nm and 10 nm from 700-2500 nm, automatically interpolated to 1 nm spectral resolution in the output data, resulting in 2151 channels (or bands); and

g) We positioned the lamps at 90° from each other and 35 cm away from the sample, with a zenith angle of 30°.

The sensor captured the light reflected through the fiber optic cable, which was positioned 8 cm from the sample's surface.

We used two 50W halogen lamps as the power source for the artificial light. It's important to note that we took three readings for each sample at different positions by rotating the Petri dish by 90°.

Each reading represents the average of 100 scans taken by the sensor. From these three readings, we calculated the final spectrum of the samples. Notably, the laboratory's equipment and procedures for soil sample spectral analyses followed the ASD's recommendations, particularly about sensor calibration using a white spectralon plate as a 100% reflectance standard.

For the analysis in the Middle Infrared (MIR) spectral region, we followed the procedures outlined by Mendes et al. (2022). We milled the soil fraction smaller than 2 mm, sieved it to 0.149 mm, and scanned it using a Fourier Transform Infrared (FT-IR) alpha spectroradiometer (Bruker Optics Corporation, Billerica, MA 01821, USA) equipped with a DRIFT accessory.

The spectroradiometer measured the diffuse reflectance using Fourier transformation in the spectral range from 4000 cm^-1 to 600 cm^-1, with a resolution of 2 cm^-1. We conducted these measurements in the Geotechnology Laboratory of the Department of Soil Science at Esalq-USP. We took the average of 32 successive readings to obtain a soil spectrum. Sensor calibration took place before each spectral acquisition of the sample set by standardizing it against the maximum reflectance of a gold plate.

Dataset characterization

The database, named BSSL_DB_Key_Soils, has five sheets containing the key soil attributes, Vis-NIR-SWIR and MIR datasets, descriptions of the contributors and the proximal sensing methods used for spectral soil analysis. The sheets can be linked by "ID_Unique" columns, which bring the corresponding rows according to the data type. Some cells are empty because collaborators have already provided data in this way. However, we have decided to keep them in the database because they have other soil key attributes. Every Column in the data sheets is described as follows:

Sheet 1. BSSL_Soil_Attributes_Dataset

Column 1. ID_unique: Sequential code assigned to every record;

Column 2. Owner code: Acronym assigned to each contributor who allowed access to their proprietary data;

Column 3. Vis_NIR_SWIR_availability: availability of spectral data in visible, near-infrared, and shortwave infrared ranges;

Column 4. MIR_availability: availability of spectral data in the middle infrared range;

Column 5. Sampling: type of soil sampling;

Column 6. Depth_cm: soil surface layer depth in centimeters;

Column 7. Lat: Latitude;

Column 8. Lat: Longitude;

Column 9. Region: Brazilian geographical region of samples' source;

Column 10.

AusPlots is the core program delivered by TERN Surveillance. AusPlots is a plot-based surveillance monitoring program, undertaking baseline assessments of ecosystems across the country. The aim of AusPlots is to establish and maintain a national network of plots that enables consistent ecological assessment and ongoing monitoring. The AusPlots network collects a range of field data for integration with other existing data sources and current knowledge. More information about TERN Surveillance can be found at their website - https://www.tern.org.au/tern-observatory/tern-ecosystem-surveillance/

As of 2020 there are about 690 active AusPlot sites distributed across Australia.

During 2018 to 2019, most of the soil specimens collected at the AusPlots sites were scanned with an ASD portable vis-NIR spectrometer (PANalytical Inc., Boulder, CO, USA). As there is a specific field data collection protocol carried out at each site i.e., numerous spatial distributed samples collected in a grid-design of specified dimensions, this meant a substantial number of specimens to scan. In total there were 19,380 vis-nir spectra collected. The scanning was done at CSIRO Waite Campus with a CSIRO owned vis-NIR spectrometer.

Accompanying the soil vis-NIR library is a dataset of analytical soil measurements for 367 soil specimens selected the Ausplots soil specimen archive.

Lineage: The condition of the soil for vis-NIR data collection was: Air-dried and ground to <2mm

Soil vis-NIR spectra were collected using a Labspec ASD portable vis-NIR spectrometer (PANalytical Inc., Boulder, CO, USA) Serial # 4103.

Measurement units of the soil vis-NIR spectra is reflectance and is output to 1nm spectral resolution. The data ranges from 350-2500nm.

In total 367 specimens were selected and characterized in the laboratory. 21 of these were randomly selected and analysed as blind duplicates. The soil variables included: • Electrical conductivity (3A1; dS/m) • pH (4A1; 1:5 soil:water) • pH (4B4; 0.01M CaCl2) • Total soil carbon (6B2b; %) • Soil organic carbon (6B3b; %) • Total Nitrogen (7A8; %) • Calcium Carbonate (19B2; %) • Cation Exchange Capacity (15D2; Ammonium Acetate cmol(+/-)/kg) • Soil texture, clay silt and sand (P10; %) • Soil digestions (17A3; mg/kg): Ca, K, Mg, Na, S, Al, As, B, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, P, Pb, Sb, Se, Zn.

Lab methods associated to the codes above are described in Rayment and Lyons (2010) except for nitrogen (7AH) where Matejovic (1997) is the method reference.

References:

Rayment, G, Lyons, DJ (2010) 'Soil Chemical Methods - Australasia.' (CSIRO Publishing). Matejovic, I., 1997. Determination of carbon and nitrogen in samples of various soils by the dry combustion. Communications in Soil Science and Plant Analysis 28(17-18), 1499-1511.

This database contains 542 NIR spectra of cassava puree acquired in CIAT (Colombia), by using FOSS DS2500 NIR spectrometer. Cassava harvested at CIAT (Colombia) from various fields and years: 2022: 1 field "progeny" in January and February 2022. 2 harvests (trials) Field 202108CQQU2_ciat (M RTB): 22-02 and 22-05 on 12 Jan. and 8 Feb. 2022, repectively (10 and 11 MAP) This database contains also laboratory data measured on the same cassava roots samples: water absorption at 20 minutes of cooking (WA20) and water absorption at 30 minutes of cooking (WA30) the Dry matter values (DM) correspond to predicted values using a specific claibration developped by CIAT. Spectra included in this database have been acquired using: BELALCAZAR, J., TRAN, T., MEGHAR, K., & DAVRIEUX, F. (2021). NIRS Measurement on Fresh Ground Cassava. High-Throughput Phenotyping Protocols (HTPP), WP3. Cali, Colombia: RTBfoods Laboratory Standard Operating Procedure, 9 p.

the spectroscopic data obtained from a homemade NIR spectrometer developed for agricultural quality analysis, along with the calibration and validation of a model database for predicting agricultural soil properties. We collected NIR spectral data from 190 soil samples taken at a depth of 0-20 cm from agricultural areas in northern Thailand, including vegetable farms, orchards, and field crops. The acquisition process started by air-drying the soil and sieving it through 2.0 mm and 0.5 mm mesh. Six preprocessing techniques, including Savitzky-Golay smoothing, multiplicative scatter correction (MSC), standard normal variate (SNV), first derivative, second derivative, and mean centering, were used with partial least squares (PLS) regression to create the prediction model for soil organic matter and total carbon. Seventy percent of the sample was divided into calibration and the remaining thirty percent was validation. Our results demonstrate the effectiveness of these models. The NIR dataset spanning 900-1,700 nm proved to be an ideal wavelength range for developing a portable/handheld NIR spectrometer, with potential for further accuracy improvements through model refinement.

116 Global import shipment records of Nir with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This dataset includes average near-infrared (NIR) reflectance spectra for 68 main-belt asteroids that were observed at the NASA Infrared Telescope Facility (IRTF), Mauna Kea, Hawaii, from April 2001 to January 2015. Raw NIR spectral data were obtained under mostly uniform instrumental conditions and include observations of the asteroids, extinction stars, and solar analog stars that were necessary for data reduction and production of the final average asteroid NIR reflectance spectra. SpecPR and Spextool were used during data reduction to produce the final spectra and both programs utilize similar functions that include sky background subtraction, telluric corrections, channel shifting, and averaging routines. The set of asteroids observed include a wide variety of taxonomic types and include V-, S-, M-, X-types that correspond to a wide variety of surface mineralogies, rock types, and potential meteorite analogs.

With tunable red to near-infrared (NIR) emissions, Mn4+- and Cr3+-activated phosphors have been extensively investigated in recent years and are emerging as an alternative to commercial rare earth ions-activated phosphors in various applications. This work aims to build a comprehensive database to collect and analyze the reported data on Mn4+- and Cr3+-activated phosphors to date. We recalculate and modify the spectral levels, Racah parameters, and crystal field parameters uniformly after the correction of the related empirical formulas. The systematical and reliable phosphor database paves the way to achieving the rational design of d3 ion-activated phosphors.

This dataset presents near infrared spectra of soil samples from the experimental INRAE stations of the CAREX network including Auzeville, Epoisses, Crouel, Theix, Lusignan, Lusignan_Oasys and Ploudaniel sites (n=1040). Spectra data were acquired using a near infrared spectrometer BUCHI at Laboratoire d'Analyses des sols (LAS), Arras. The granulometric fractions and chemical properties measurements are available with their uncertainties. The tables of NIR spectra and chemical analysis and granulometry of soils from Isère (n=28) and from Plaine_de_Versailles (n=99) locations were added. The details of the transformed NIR spectra table of Plaine_de_Versailles are available at https://doi.org/10.15454/LXKFAS.

The High-Quality Wide Multi-Channel Attack database (HQ-WMCA) database consists of 2904 short multi-modal video recordings of both bona-fide and presentation attacks. There are 555 bonafide presentations from 51 participants and the remaining 2349 are presentation attacks. The data is recorded from several channels including color, depth, thermal, infrared (spectra), and short-wave infrared (spectra).

Reference paper:

@ARTICLE{Heusch_TBIOM_2020,
      author = {Heusch, Guillaume and George, Anjith and Geissb{\"u}hler, David and Mostaani, Zohreh and Marcel, S{\'{e}}bastien},
      title = {Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks},
     journal = {IEEE Transactions on Biometrics, Behavior, and Identity Science},
       year = {2020},
    publisher = {IEEE},
       url = {http://publications.idiap.ch/downloads/papers/2020/Heusch_TBIOM_2020.pdf}
}

Preprocessed images for some of the channels are also provided for the data used in the reference publication. The HQ-WMCA database is produced at Idiap within the framework of "IARPA BATL" project and it is intended for research, development, and testing in biometrics and biomedical analysis.

More details about the data can be found in the following research report.

@TECHREPORT{Mostaani_Idiap-RR-22-2020,
      author = {Mostaani, Zohreh and George, Anjith and Heusch, Guillaume and Geissenbuhler, David and Marcel, S{\'{e}}bastien},
     projects = {Idiap, ODIN/BATL},
      month = {9},
      title = {The High-Quality Wide Multi-Channel Attack (HQ-WMCA) database},
       type = {Idiap-RR},
      number = {Idiap-RR-22-2020},
       year = {2020},
   institution = {Idiap},
       pdf = {https://publidiap.idiap.ch/downloads//reports/2020/Mostaani_Idiap-RR-22-2020.pdf}
}

142 views (3 recent) & 2. The raw NIR spectra were collected by means of MicroNIR spectrometer (Viavi) on wooden boards used for the preparation of delamination samples. The reference data were collected with available alternative to NIR methods. Unavailable. All technical details are described in the manuscript "Feasibility of portable NIR spectrometer for quality assurance in glue-laminated timber production" The raw NIR spectra were collected by means of MicroNIR spectrometer (Viavi) on wooden boards used for the preparation of delamination samples. The reference data were collected with available alternative to NIR methods.