Влияние литологии на перераспределение метана в угленосной толще

Розглянутий вплив літології на перерасподіл метану у вугільних пластах на шахтах Красноармійського району Донбасу.The influence of litology on methane re-distribution in coal beds on the mines of Krasnoarmeysk region of Donbas were considered

Optimization criteria in sample selection step of local regression for quantitative analysis of large soil NIRS database

International audienceLarge soil spectral libraries compiling thousands of NIR (Near Infrared) reflectance spectra have been created encompassing a wide diversity and heterogeneity of spectra. Among the many chemometric approaches to the calibration of chemical and physical properties from these large libraries, local calibrations have the advantage of being able to select the most similar spectra to the spectrum of a target sample. This is particularly relevant when dealing with highly heterogeneous media such as soils, where the mineral matrix has a strong influence on spectral features. A crucial step in the implementation of local calibration procedures is the construction of local neighbourhoods. In this study, we investigate the influence of index computation and neighbour selection on calibration results using local PLSR models on a large soil spectral database. Our indices combine two spectral compression methods (Principal Component Analysis or Fast Fourier Transform) with two distinct distance metrics (Mahalanobis distance or correlation coefficient). Based on a large collection of soil samples provided by the French National Soil Quality Monitoring programme, we constructed calibration models to estimate two chemical (organic carbon and cationic exchange capacity) and two physical (clay and sand content) factors. After neighbour selection, local Partial Least Squares regressions were applied to the selected spectra. Our results highlight the utility of the Fourier transformation of the spectra compared to the classical PCA compression method in achieving a more appropriate neighbourhood selection. We propose an index based on the coefficient correlation with FFT compression that led to a neighbourhood selection giving the best prediction results for the four considered soil constituent

Exploring the links between Vis NIR spectra and bacterial communities structure of soils by PLS2 regression models

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A dataset of the chemical composition and near-infrared spectroscopy measurements of raw cattle, poultry and pig manure

International audienceOrganic waste products (OWPs) from livestock have a high fertilizer value (N, P, K), but can also lead to environmental problems when applied in excessive quantities. Because their composition varies greatly, it is important to develop fast, reliable and inexpensive methods for determining their chemical contents. Near-infrared spectroscopy (NIRS) offers the possibility of rapid analysis of samples and requires little sample preparation, and previous studies have demonstrated that NIRS could be able to determine the most important compositional parameters of solid animal manure. The recent development of low-cost miniaturized spectrometers even enables manure-spreading equipment to be equipped with sensors to measure the composition in real time, and some applications are already being commercialized for the spreading of liquid OWPs. In-situ analysis of these very heterogeneous products (roughness, humidity) is a challenge for such applications, because spectral acquisition must be performed on raw samples with no preparation. To evaluate the accuracy with which NIRS estimates dry matter content, organic matter, total and ammonium nitrogen, phosphorus, potassium, calcium and magnesium contents, we created a large calibration database representative of raw solid animal manures encountered in Brittany. A total of 490 samples of solid OWPs from livestock farms were collected in the early spring from 270 farms in Brittany (western France), 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 were analyzed according to analysis methods certified by the French standards organization (AFNOR). Samples were scanned using a Q-interline AgriQuant B8 equipped with a patented spiral sampler, which aggregates the heterogeneity of the sample. NIRS measurements were made in triplicate. Because the dataset covers a wide range of variability in the composition of solid animal manure, these data are of great interest to chemometrics experts and agronomists in search of references on the fertilizing value of products

Dataset of chemical and near-infrared spectroscopy measurements of fresh and dried poultry and cattle manure

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