Visible/near-infrared hyperspectral imaging for beef tenderness prediction

By G. K. Naganathan, L. M. Grimes, J. Subbiah, C. Calkins, A. Samal, George E. Meyer. 2008

198 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/e4d196ca428d8496637e8d4ebb831b71a04388e1

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. M. Kamruzzaman
3. Xin‐an Zeng
4. Hui Huang
5. Li Liu
6. Lu Wang
7. Michael O. Ngadi
8. Paul Allen

In Animal Science and Zoology

1. Da‐Wen Sun
2. Xin‐an Zeng
3. M. Kamruzzaman
4. Lu Wang
5. Z. Xiong
6. Hui Huang
7. Li Liu
8. Paul Allen

In Biomedical Engineering

1. Da‐Wen Sun
2. M. Kamruzzaman
3. Lu Wang
4. Xin‐an Zeng
5. Z. Xiong
6. Michael O. Ngadi
7. Hui Huang
8. Li Liu

Citations in the corpus

• Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-Food Products: A Review (2012, methodology): “…al., 2008; Peng and Wu, 2008), discriminant analysis (DA) (Nagata et al., 2006; Peng et al., 2009), PLS (Naganathan et al., 2008b; Lawrence et al., 2004), PCA (Naganathan et al., 2008a; and Qiao et al., 2007), independent component analysis (ICA) (Shao et al., 2009), and ANN (ElMasry et al., 2009).”
• Hyperspectral Imaging Applications in Agriculture and Agro-Food Product Quality and Safety Control: A Review (2013, background): “(70) classified beef tenderness into categories (tender, intermediate, and tough) with a classification accuracy of 96.”
• Application of Visible Hyperspectral Imaging for Prediction of Springiness of Fresh Chicken Meat (2015, background): “Consumers often use some of these characteristics such as color, texture features, and flavor as indicators for freshness before making a buying decision (Antonucci et al. 2011; ElMasry et al. 2012; Kamruzzaman et al. 2012a; Kamruzzaman et al. 2013; Naganathan et al. 2008; Wu and Sun 2014).”
• VIS/NIR imaging application for honey floral origin determination (2017, background): “characterization including: meat and fish (beef tenderness prediction [33], pork quality and marbling level assessment [34], fat, color and moisture distribution in salmon fillets [35-37]); fruits and vegetables (citrus canker detection [38], internal quality of blueberries [39], tomato ripeness mea”
• Hyperspectral and multispectral imaging for evaluating food safety and quality (2013, background): “Meat Beef Tenderness evaluation Line-scan reflectance 400–1000 Naganathan et al. (2008)”
• Recent Developments in Hyperspectral Imaging for Assessment of Food Quality and Safety (2014, methodology): “PCA Canonical discriminant [44]”
• Combination of spectra and texture data of hyperspectral imaging for differentiating between free-range and broiler chicken meats (2015, unknown): “If these spatial and spectral information are properly analyzed, not only physical/chemical characteristics of tested objects but also their spatial distributions can be determined (Naganathan et al., 2008; Wu et al., 2012; Wu et al., 2013; Xiong, Sun, Dai, Han, Zeng, & Wang, 2014; Zhu, Zhang, Shao,”