Pork quality and marbling level assessment using a hyperspectral imaging system

By J. Qiao, Michael O. Ngadi, Ning-ning Wang, C. Gariépy, Shiv O. Prasher. 2007

223 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/69174d35264224f09df62c56afe3f98baacbf3c7

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. Di Wu
3. M. Kamruzzaman
4. Hui Huang
5. Li Liu
6. Michael O. Ngadi
7. Paul Allen
8. Feifei Tao

In Animal Science and Zoology

1. Da‐Wen Sun
2. Di Wu
3. M. Kamruzzaman
4. Feifei Tao
5. Yankun Peng
6. Hui Huang
7. Li Liu
8. Gamal ElMasry

In Biomedical Engineering

1. Da‐Wen Sun
2. Di Wu
3. M. Kamruzzaman
4. Michael O. Ngadi
5. Feifei Tao
6. Yankun Peng
7. Hui Huang
8. Li Liu

Citations in the corpus

• Artificial neural network analysis of ultrasound image for the estimation of intramuscular fat content in lamb muscle (2011, unknown): “3.06 1.09 1.02 6.16 neural networks (Brethour, 1994; Shiranita et al., 2000; Chandraratne et al., 2007; Qiao et al., 2007).”
• Recent Developments in Hyperspectral Imaging for Assessment of Food Quality and Safety (2014, methodology): “Applications of hyperspectral imaging in food quality and safety include detection of contaminations [19,20], identification of defects [21,22] and quantification of constituents [23].”
• Discrimination between abiotic and biotic drought stress in tomatoes using hyperspectral imaging (2018, background): “The other spectral ranges (in NIR and SWIR) are linked to physical and chemical characteristics, such as cellulose and lignin [57], as well as carbohydrates, proteins, and water content [58].”
• Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review — Part II: Applications (2013, background): “In addition, long-wave near infrared spectral region had a better ability in predicting drip loss of pork (r2cv = 0.83 vs. r = 0.77) (Barbin et al., 2012c; Qiao et al., 2007).”
• A Nondestructive Method for Prediction of Total Viable Count in Pork Meat by Hyperspectral Scattering Imaging (2014, background): “…2006; Park et al. 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…”
• Hyperspectral and multispectral imaging for evaluating food safety and quality (2013, unknown): “Pork Quality evaluation Line-scan reflectance 430–1000 Qiao et al. (2007)”
• Modern Trends in Hyperspectral Image Analysis: A Review (2018, background): “Hyperspectral imagery contains rich amount of spectral as well as spatial information which makes HSI based methods well suited for assessment of food quality and safety [37].”
• Application of Visible Hyperspectral Imaging for Prediction of Springiness of Fresh Chicken Meat (2015, unknown): “2004), pork (Qiao et al. 2007), lamb (Kamruzzaman et al.”