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30 May 2022 Data optimization to predict quality parameters in oranges analysed using hyperspectral imaging
Irina Torres, Marina Cocchi, María Teresa Sánchez, Ana Garrido Varo, Dolores Pérez Marín
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Proceedings Volume PC12120, Sensing for Agriculture and Food Quality and Safety XIV; PC121200B (2022) https://doi.org/10.1117/12.2619837
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States
Abstract
Hyperspectral images are typically acquired at high spatial and spectral resolutions, being essential the reduction of data for the implementation of this technology at industrial level. The aim of this work was the optimization and development of algorithms for the selection of the region of interest in oranges hyperspectral data. PLS and its multilinear version, NPLS, were used to model the internal quality of oranges. The results obtained in external validation enabled to carry out a screening of the product according to its flavour, confirming that the use of multilinear models could reduce the noise and data redundancy.
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Irina Torres, Marina Cocchi, María Teresa Sánchez, Ana Garrido Varo, and Dolores Pérez Marín "Data optimization to predict quality parameters in oranges analysed using hyperspectral imaging", Proc. SPIE PC12120, Sensing for Agriculture and Food Quality and Safety XIV, PC121200B (30 May 2022); https://doi.org/10.1117/12.2619837
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KEYWORDS
Hyperspectral imaging
Algorithm development
Data modeling
Nanolithography
Cameras
Data acquisition
Modeling
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