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While among the biochar from UOW blends, 650Ms has the highest recalcitrance despite lower graphitization compared to 650P as seen in its FTIR spectrum. 650Ms has more silica inherited from Ms (sec 3.1), which increases the recalcitrance through Si encapsulation of carbon [113]. In addition, the higher Fe content in 650Ms (sec 3.2.2) also promotes graphitization [114] and higher recalcitrance [115]. Thus, more than aromaticity, these factors make 650Ms most recalcitrant as a carbon sink in soil. The water extractable organic carbon from 650Ms will also be lowest among the biochar from blends [115]. 650Mp has the lowest TORi despite signals of good aromaticity in its IR spectra. This is due to the catalytic activity of AAEM species during its oxidation reaction [116] and the comparatively lower amount of Si and Fe in it.
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Spectroscopy has been developed considerably over the years ( Guidetti, Beghi & Giovenzana, 2012; Nicolaï et al., 2007) reaching a mature stage. Their applicability for the evaluation of agro-food products has widely been proven. Visible and near infrared (vis/NIR) spectroscopy fulfill all the requirements for continuous monitoring of compounds that can be related to the taste and nutritional value of the products. Nevertheless, these technologies are currently adopted mainly by research centers or big companies equipped with laboratories and trained personnel, due to their cost and complexity of use. A simplification of these technologies is necessary to make them accessible also to operative staff in small and medium enterprises. The sector is therefore interested in new simplified systems and low-cost compact tools in view of their application at different levels of the supply chain, with a real possibility of wide diffusion of these technologies from the manufacturing process up to the point of sale and to the consumer. For a simplification and a greater diffusion of these non-destructive techniques, in recent years, interest has shifted towards the development of portable systems usable in pre- and post-harvest ( Temma, Hanamatsu & Shinoki, 2002; Walsh, Guthrie & Burney, 2000; Zude et al., 2006). Chemometrics can be applied for the selection of a small number of relevant variables representing the most useful information contained in the full spectra ( Sun, 2010; Xiaobo et al., 2010).
The large number of spectral variables in most data sets often results in an unreliable prediction of the dependent variable. Chemometrics can be used for the selection of a small number of relevant variables, which represent the most useful information contained in the full spectra. In order to extract this useful information from the NIRs spectra, different variable selection methods could be performed ( Chong & Jun, 2005; Xiaobo et al., 2010; Mehmood et al., 2012). In this way the spectral noise and the variables containing redundant information can be eliminated, and the sampling time of each spectrum could be reduced. However, variables selection may not always lead to better predictive results. For instance, a variable that is completely useless by itself can provide a significant improvement in performance when taken in combination with others ( Xiaobo et al., 2010).
Spectral Python (SPy) is a pure Python module for processing hyperspectral imagedata. It has functions for reading, displaying, manipulating, and classifyinghyperspectral imagery. It can be used interactively from the Python commandprompt or via Python scripts. SPy is free, Open Source software distributedunder the MIT License.To see some examples of how SPy can be used, you may want to jump straight tothe documentation sections on Displaying Data or Spectral Algorithms. Acategorized listing of the main classes and functions are in theClass/Function Glossary. You can download SPy fromGitHubor the Python Package Index (PyPI).See the Installing SPy section section of the documentation for details. 041b061a72