Abstract
Olive husk (OH) quality, in respect of constituting particles characteristics (olive stones and pulp residues as result after pressing), represents an important issue. OH particles size class distribution and composition play, in fact, an important role for OH utilization as: organic amendment, bio-mass, food ingredient, plastic filler, abrasive, raw material in the cosmetic sector, dietary animal supplementation, etc. . OH is characterised by a strong variability according to olive characteristics and olive oil production process. Actually it does not exist any strategy able to quantify OH chemical-physical attributes versus its correct utilisation adopting simple, efficient and low costs analytical tools. Furthermore the possibility to perform its continuous monitoring, without any samples collection and analysis at laboratory scale, could strongly enhance OH utilization, with a great economic and environmental benefits. In this paper an analytical approach, based on HyperSpectral Imaging (HSI) is presented. HSI allows to perform, also on-line, a full quantification of OH characteristics in order to qualify this product for its further re-use, with particular reference as bio-mass. HSI was applied to different samples of OH, characterized by different moisture, different residual pulp content and different size class distributions. Results are presented and critically evaluated.
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Keywords
- Partial Little Square
- Hyperspectral Image
- Partial Little Square Model
- Principal Component Regression
- Carbon Foam
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Bonifazi, G., Serranti, S. (2011). Hyperspectral Sensing Techniques Applied to Bio-masses Characterization: The Olive Husk Case. In: Li, D., Liu, Y., Chen, Y. (eds) Computer and Computing Technologies in Agriculture IV. CCTA 2010. IFIP Advances in Information and Communication Technology, vol 347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18369-0_89
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DOI: https://doi.org/10.1007/978-3-642-18369-0_89
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