Prediction of water and protein contents and quality classification of Spanish cooked ham using NIR hyperspectral imaging

By P. Talens, L. Mora, N. Morsy, D. Barbin, Gamal ElMasry, Da‐Wen Sun. 2013

100 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Animal Science and Zoology, Biomedical Engineering

View Paper: https://www.semanticscholar.org/paper/4c0c1bed95220fcceffc1810c14d533da73e168c

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. Hongbin Pu
3. Di Wu
4. Zhiwei Zhu
5. Lu Wang
6. Xin‐an Zeng
7. Feifei Tao
8. Dan Liu

In Animal Science and Zoology

1. Da‐Wen Sun
2. Hongbin Pu
3. Zhiwei Zhu
4. Di Wu
5. Xin‐an Zeng
6. Dan Liu
7. Lu Wang
8. Feifei Tao

In Biomedical Engineering

1. Da‐Wen Sun
2. Hongbin Pu
3. Zhiwei Zhu
4. Di Wu
5. Lu Wang
6. Xin‐an Zeng
7. Feifei Tao
8. Yankun Peng

Citations in the corpus

• A Nondestructive Method for Prediction of Total Viable Count in Pork Meat by Hyperspectral Scattering Imaging (2014, background): “…2002; Park et al. 2006; Yoon et al. 2011), to evaluate meat quality (ElMasry et al. 2011; Kamruzzaman et al. 2012; Liu et al. 2010; Qiao et al. 2007; Talens et al. 2013; Wu et al. 2012), and to detect the bacterial contamination of meat and vegetables (Barbin et al. 2013; Feng and Sun 2013; Feng…”
• Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry (2014, background): “2012a) and Spanish cooked ham (Talens et al. 2013).”
• Application of Hyperspectral Imaging to Discriminate the Variety of Maize Seeds (2015, methodology): “2012), partial least square discriminant analysis (PLS-DA) (Kamruzzaman et al. 2012; Talens et al. 2013), support vector machines (SVM) (Wang et al.”
• Assessment of internal quality of blueberries using hyperspectral transmittance and reflectance images with whole spectra or selected wavelengths (2014, unknown): “, Innovative Food Science and Emerging one-out cross validation (LOOCV) was used to determine the number of latent variables for the calibration models (Barbin, ElMasry, Sun, & Allen, 2013; Talens et al., 2013).”
• Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review — Part II: Applications (2013, methodology): “In addition, NIR hyperspectral imaging in tandem with PLSR modeling also showed reasonable good prediction performance for protein in lamb (Kamruzzaman et al., 2012b), pork (Barbin et al., 2012b), beef (ElMasry et al., 2013), and Spanish cooked ham (Talens et al., 2013).”