Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment

By D. Lorente, Nuria Aleixos, Juan Gómez-Sanchís, Sergio Cubero, O. García-Navarrete, José Blasco. Food and Bioprocess Technology, 2012

524 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Biophysics

View Paper: https://www.semanticscholar.org/paper/2d8982289d290b7bf4c9728f038997b024a3c221

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Da‐Wen Sun
2. Jun‐Hu Cheng
3. Hongbin Pu
4. Shuxiang Fan
5. Wenqian Huang
6. Qingyi Wei
7. Di Wu
8. Sergio Cubero

In Biomedical Engineering

1. Da‐Wen Sun
2. Jun‐Hu Cheng
3. Hongbin Pu
4. Shuxiang Fan
5. Wenqian Huang
6. Qingyi Wei
7. Di Wu
8. Lu Wang

In Biophysics

1. Da‐Wen Sun
2. Hongbin Pu
3. Jun‐Hu Cheng
4. Lu Wang
5. Di Wu
6. Z. Xiong
7. Dan Liu
8. José Manuel Amigo

Citations in the corpus

• Hyperspectral near-infrared imaging for the detection of physical damages of pear (2014, background): “Recently, a study has reviewed hyperspectral imaging technique in evaluating fruit and vegetable quality (Lorente et al., 2012).”
• Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging (2017, unknown): “201 Hyperspectral systems capture a huge amount of information that is redundant and 202 correlated, especially between contiguous wavelengths (Lorente et al., 2012).”
• Use of Hyperspectral Imaging to Discriminate the Variety and Quality of Rice (2015, methodology): “Multivariate Classifications PCA is an unsupervised pattern recognition method and can be performed on the full spectra of rice samples in an effort to visualize data trends in a dimensional space (Lin et al. 2011; Lorente et al. 2012).”
• Use of hyperspectral transmittance imaging to evaluate the internal quality of nectarines (2019, background): “It is performed in order to sharpen the separation between groups of observations by maximising the covariance between the wavelengths and the classes, such that a maximum separation among these classes is obtained (Lorente et al., 2012).”
• Computer Vision System Applied to Classification of “Manila” Mangoes During Ripening Process (2014, background): “As concluded by Lorente et al. (2012) in their review, there is a need to correlate the information provided by CVS and the physiochemical and biological changes during mango ripening.”