Abstract
Near-Infrared Spectroscopy is a cost-effective and environmentally friendly technique that could represent an alternative to conventional soil analysis methods, including total organic carbon (TOC). Soil fertility and quality are usually measured by traditional methods that involve the use of hazardous and strong chemicals. The effects of physical soil characteristics, such as moisture content and particle size, on spectral signals could be of great interest in order to understand and optimize prediction capability and set up a robust and reliable calibration model, with the future perspective of being applied in the field. Spectra of 46 soil samples were collected. Soil samples were divided into three data sets: unprocessed, only dried and dried, ground and sieved, in order to evaluate the effects of moisture and particle size on spectral signals. Both separate and combined normalization methods including standard normal variate (SNV), multiplicative scatter correction (MSC) and normalization by closure (NCL), as well as smoothing using first and second derivatives (DV1 and DV2), were applied to a total of seven cases. Pretreatments for model optimization were designed and compared for each data set. The best combination of pretreatments was achieved by applying SNV and DV2 on partial least squares (PLS) modelling. There were no significant differences between the predictions using the three different data sets (p < 0.05). Finally, a unique database including all three data sets was built to include all the sources of sample variability that were tested and used for final prediction. External validation of TOC was carried out on 16 unknown soil samples to evaluate the predictive ability of the final combined calibration model. Hence, we demonstrate that sample preprocessing has minor influence on the quality of near infrared spectroscopy (NIR) predictions, laying the ground for a direct and fast in situ application of the method. Data can be acquired outside the laboratory since the method is simple and does not need more than a simple band ratio of the spectra.
Highlights
Soil is an essential pillar of agriculture and any form of human intervention influences its activity and the equilibrium of the entire ecosystem [1]
Near infrared (NIR) spectroscopy has well-known characteristics [11], and represents a radical break from conventional analytical assays, because a sample is characterized in terms of its whole light absorption properties rather than being treated with various chemicals to isolate specific components [12]
Several studies have been published on NIR spectroscopy being applied to predict various soil properties in different configurations [13,14]
Summary
Soil is an essential pillar of agriculture and any form of human intervention influences its activity and the equilibrium of the entire ecosystem [1]. Soil total organic carbon (TOC) is usually defined as the total amount of the organic carbon-containing part in the soil, including residual components of original organic tissues, their degradation products and the products synthetized by soil fauna [6]. It represents a useful indicator of soil fertility as well as soil quality. Several soil properties have been predicted with acceptable accuracy, including for example soil organic matter [15], nitrogen [16], pH [17] and water content [18] Both NIR and medium infrared (MIR) regions of the spectra have been investigated for soil analysis
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