Dataset Information

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

ABSTRACT: Combined with multivariate calibration methods, near-infrared (NIR) spectroscopy is a non-destructive, rapid, precise and inexpensive analytical method to predict chemical contents of organic products. Nevertheless, one practical limitation of this approach is that performance of the calibration model may decrease when the data are acquired with different spectrometers. To overcome this limitation, standardization methods exist, such as the piecewise direct standardization (PDS) algorithm. The dataset presented in this article consists of 332 manure samples from poultry and cattle, sampled from farms located in major regions of livestock production in mainland France and Reunion Island. The samples were analysed for seven chemical properties following conventional laboratory methods. NIR spectra were acquired with three spectrometers from fresh homogenized and dried ground samples and then standardized using the PDS algorithm. This important dataset can be used to train and test chemometric models and is of particular interest to NIR spectroscopists and agronomists who assess the agronomic value of animal waste.

SUBMITTER: Goge F

PROVIDER: S-EPMC7749372 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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

Gogé Fabien F Thuriès Laurent L Fouad Youssef Y Damay Nathalie N Davrieux Fabrice F Moussard Géraud G Roux Caroline Le CL Trupin-Maudemain Séverine S Valé Matthieu M Morvan Thierry T

Data in brief 20201213

Combined with multivariate calibration methods, near-infrared (NIR) spectroscopy is a non-destructive, rapid, precise and inexpensive analytical method to predict chemical contents of organic products. Nevertheless, one practical limitation of this approach is that performance of the calibration model may decrease when the data are acquired with different spectrometers. To overcome this limitation, standardization methods exist, such as the piecewise direct standardization (PDS) algorithm. The d ...[more]

PMID: 33365375

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Project description:Organic 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.

Project description:Organic wastes originating from livestock, agro-industry or urban activities may represent true resources when recycled for new uses, for example, as soil improvers, organic fertilizers or bioenergy sources. The compositional characteristics of these organic resources (ORs) can vary considerably depending on origin, nature, processing, stage, and state. Despite being of potential interest to different stakeholders in a circular economy, the variability in OR characteristics and the difficulty of accessing reliable, fast and inexpensive analysis methods may curb the recycling of OR in the agriculture or bioenergy sectors. As is the case in other low-income countries, scarcity of data on OR characteristics and the difficulty in assessing these data (due to cost and the sparsity of laboratories) is particularly acute in Madagascar, thus impairing the rational utilization of OR in the agricultural or bioenergy sectors. Visible-near infrared spectroscopy (VIS-NIR) has proven to be suitable for the fast, reliable and low-cost determination of the composition of different ORs, usually through the development of calibration models based on one type of OR by single research or lab groups. It is challenging to develop VIS-NIR models based on several types of ORs encompassing a wide range of target characteristics. Another challenging issue is the extension of databases containing spectra acquired on different spectrometers to increase model genericity. In both cases, standardization can be performed to resolve the problem of developing models for diverse ORs whose spectra originate from different laboratories. To assess the ability to develop VIS-NIR models with as much genericity as possible, we built a large database containing a wide diversity of ORs produced in Madagascar. The data presented in this paper were obtained by chemical and spectral analyses of 1,000 ORs collected from five districts in Madagascar. The data are accompanied by fine-grained metadata defined by 32 descriptors of ORs, including origin (animal, agro-industrial, and urban); nature (manure, agro-industrial waste, and compost); farm type (smallholder and agricultural factory); exploitation type (smallholder farm, factory farm, on-farm compost facility, and town compost facility); diversity of animal feed, litter, sex, and age; and diversity of bedding material. The chemical properties (including the organic nitrogen, organic carbon, organic matter, inorganic matter, phosphorus, potassium, calcium, magnesium, zinc, copper, nickel, chromium, cadmium, and lead and soluble, hemicellulose, cellulose, lignin and cutin fractions) were analyzed following laboratory standards. The number of analyses performed ranged from 39 to 180 depending on the chemical property. VIS-NIR spectra were acquired using a Labspec spectrometer. To facilitate the merging of spectral data or the development of VIS-NIR models based on broad datasets, the spectra were presented in raw form and after standardization. The dataset is original in terms of sources and width. This dataset should be of particular interest to chemometricians, biogeochemists, agronomists, energy planners, hygienists and other professionals involved in recycling ORs for various new purposes in low-income countries and elsewhere.

Project description:Experiencing frustration while driving can harm cognitive processing, result in aggressive behavior and hence negatively influence driving performance and traffic safety. Being able to automatically detect frustration would allow adaptive driver assistance and automation systems to adequately react to a driver's frustration and mitigate potential negative consequences. To identify reliable and valid indicators of driver's frustration, we conducted two driving simulator experiments. In the first experiment, we aimed to reveal facial expressions that indicate frustration in continuous video recordings of the driver's face taken while driving highly realistic simulator scenarios in which frustrated or non-frustrated emotional states were experienced. An automated analysis of facial expressions combined with multivariate logistic regression classification revealed that frustrated time intervals can be discriminated from non-frustrated ones with accuracy of 62.0% (mean over 30 participants). A further analysis of the facial expressions revealed that frustrated drivers tend to activate muscles in the mouth region (chin raiser, lip pucker, lip pressor). In the second experiment, we measured cortical activation with almost whole-head functional near-infrared spectroscopy (fNIRS) while participants experienced frustrating and non-frustrating driving simulator scenarios. Multivariate logistic regression applied to the fNIRS measurements allowed us to discriminate between frustrated and non-frustrated driving intervals with higher accuracy of 78.1% (mean over 12 participants). Frustrated driving intervals were indicated by increased activation in the inferior frontal, putative premotor and occipito-temporal cortices. Our results show that facial and cortical markers of frustration can be informative for time resolved driver state identification in complex realistic driving situations. The markers derived here can potentially be used as an input for future adaptive driver assistance and automation systems that detect driver frustration and adaptively react to mitigate it.

Dataset Information

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

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

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