Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins

By Juan Gómez-Sanchís, Luis Gómez-Chova, Nuria Aleixos, Gustau Camps-Valls, C. Montesinos-Herrero, E. Moltó, José Blasco. 2008

151 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Industrial and Manufacturing Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/f2e6fdb3963cc0a7b35fb94a5af073e5c622c060

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In Analytical Chemistry

In Industrial and Manufacturing Engineering

In Plant Science

Citations in the corpus

Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry (2014, background): “Gómez-Sanchis et al. (2008) detected rottenness caused by Penicillium genus fungi in citrus fruits usingmachine learning techniques.”
Potential of hyperspectral imaging and multivariate analysis for rapid and non-invasive detection of gelatin adulteration in prawn (2013, unknown): “, 2008), citrus (Gomez-Sanchis et al., 2008), tomato (Polder et al.”
Colour Measurement and Analysis in Fresh and Processed Foods: A Review (2012, methodology): “Machine vision system is used to detect rotten citrus fruit that is rotten due to Penicillium digitatum (Gómez-Sanchis et al. 2008).”
Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review (2014, background): “Citrus Rottenness detection H-CVS ANN, DT 98% Gomez-Sanchis et al. (2012)”
NIR Spectroscopy Applications for Internal and External Quality Analysis of Citrus Fruit—A Review (2012, background): “Hyperspectral images are made up of hundreds of contiguous wavebands for each spatial position of a sample studied and each pixel in an image contains the spectrum for that specific position (Gómez-Sanchis et al. 2008).”
Recent Developments in Hyperspectral Imaging for Assessment of Food Quality and Safety (2014, methodology): “CCD Mandarin 320–1,100 Qualitative GFC LDA, Classification and regression trees (CART) [80]”
Current and future applications of statistical machine learning algorithms for agricultural machine vision systems (2019, background): “DA-based techniques are among the most commonly used for fruit grading and sorting based on external defects, shape and size (Blasco et al., 2007; Gómez-Sanchis et al., 2008; Blasco et al., 2009a; Blasco et al., 2009b).”
Hyperspectral and multispectral imaging for evaluating food safety and quality (2013, unknown): “Citrus Rottenness detection Area-scan reflectance 460–1020 Gómez-Sanchis et al. (2008)”
Computer Vision System Applied to Classification of “Manila” Mangoes During Ripening Process (2014, methodology): “This setup consisted of a lighting system with a circular fluorescent lamp (TL-E 32 W/54 RS Cool daylight, Philips, Mexico) placed inside a hemispherical diffuser in order to provide homogeneous lighting, similar to the setup described by Gómez-Sanchis et al. (2008).”
Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence (2009, background): “, 2006), rottenness on citrus (Gómez-Sanchis et al., 2008), pits in tart cherries (Qin and Lu, 2005), and cracks in shell eggs (Lawrence et al.”
Hyperspectral Imaging Applications in Agriculture and Agro-Food Product Quality and Safety Control: A Review (2013, methodology): “The same technique, but with LDA and classification and regression tree models (CART), was used for mandarin fruit and the classification accuracy of rotten fruit was greater than 91% (72).”