Dataset Information

Dataset of organic sample near infrared spectra acquired on different spectrometers.

ABSTRACT: This dataset presents 127 raw near infrared spectra of different organic samples acquired on three different spectrometers in three different labs. An example of data processing is shown to create six spectra transfer models between the three spectrometers (two by two). In order to build and validate these transfer models, the dataset was split into two sets of spectra: a first set was used to compute six spectra transfer models thanks to the Piecewise Direct standardisation function (PDS). A second set of spectra, independent of the first one was used to validate transfer models. Spectrum treatments and models were created on ChemFlow (https://vm-chemflow-francegrille.eu/), a free online chemometric software that includes all the necessary functions.

SUBMITTER: Chauvergne C

PROVIDER: S-EPMC7494442 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Dataset of organic sample near infrared spectra acquired on different spectrometers.

Chauvergne Céline C Bonnal Laurent L Bastianelli Denis D Carrère Hélène H Griveau Yves Y Jacquemot Marie-Pierre MP Reymond Matthieu M Méchin Valérie V Rossard Virginie V Latrille Éric É

Data in brief 20200902

This dataset presents 127 raw near infrared spectra of different organic samples acquired on three different spectrometers in three different labs. An example of data processing is shown to create six spectra transfer models between the three spectrometers (two by two). In order to build and validate these transfer models, the dataset was split into two sets of spectra: a first set was used to compute six spectra transfer models thanks to the Piecewise Direct standardisation function (PDS). A se ...[more]

PMID: 32984461

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Project description:The prediction accuracy of soil properties by proximal soil sensing has made their application more practical. However, in order to gain sufficient accuracy, samples are typically air-dried and milled before spectral measurements are made. Calibration of the spectra is usually achieved by making wet chemistry measurements on a subset of the field samples and local regression models fitted to aid subsequent prediction. Both sample handling and wet chemistry can be labour and resource intensive. This study aims to quantify the uncertainty associated with soil property estimates from different methods to reduce effort of field-scale calibrations of soil spectra. We consider two approaches to reduce these expenses for predictions made from visible-near-infrared ((V)NIR), mid-infrared (MIR) spectra and their combination. First, we considered reducing the level of processing of the samples by comparing the effect of different sample conditions (in-situ, unprocessed, air-dried and milled). Second, we explored the use of existing spectral libraries to inform calibrations (based on milled samples from the UK National Soil Inventory) with and without 'spiking' the spectral libraries with a small subset of samples from the study fields. Prediction accuracy of soil organic carbon, pH, clay, available P and K for each of these approaches was evaluated on samples from agricultural fields in the UK. Available P and K could only be moderately predicted with the field-scale dataset where samples were milled. Therefore this study found no evidence to suggest that there is scope to reduce costs associated with sample processing or field-scale calibration for available P and K. However, the results showed that there is potential to reduce time and cost implications of using (V)NIR and MIR spectra to predict soil organic carbon, clay and pH. Compared to field-scale calibrations from milled samples, we found that reduced sample processing lowered the ratio of performance to inter-quartile range (RPIQ) between 0% and 76%. The use of spectral libraries reduced the RPIQ of predictions relative to field-scale calibrations from milled samples between 54% and 82% and the RPIQ was reduced between 29% and 70% for predictions when spectral libraries were spiked. The increase in uncertainty was specific to the combination of soil property and sensor analysed. We conclude that there is always a trade-off between prediction accuracy and the costs associated with soil sampling, sample processing and wet chemical analysis. Therefore the relative merits of each approach will depend on the specific case in question.

Dataset Information

Dataset of organic sample near infrared spectra acquired on different spectrometers.

Publications

Dataset of organic sample near infrared spectra acquired on different spectrometers.

Similar Datasets

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