Classification and quantification of minced mutton adulteration with pork using thermal imaging and convolutional neural network

作者: Rongguang Zhu , Minchong Zheng , Zongxiu Bai , Yaoxin Zhang , Jianfeng Gu

DOI: 10.1016/J.FOODCONT.2021.108044

关键词:

摘要: … , thermal imaging combined with CNN has achieved good results in qualitative classification of different samples and quantitative prediction … thermography, can converts infrared thermal …

参考文章(35)
Ana M. Tocci, Rodolfo H. Mascheroni, Characteristics of Differential Scanning Calorimetry Determination of Thermophysical Properties of Meats Lwt - Food Science and Technology. ,vol. 31, pp. 418- 426 ,(1998) , 10.1006/FSTL.1998.0319
Nanni Costa, C. Stelletta, C. Cannizzo, M. Gianesella, Pietro Lo Fiego, M. Morgante, The use of thermography on the slaughter-line for the assessment of pork and raw ham quality Italian Journal of Animal Science. ,vol. 6, pp. 704- 706 ,(2007) , 10.4081/IJAS.2007.1S.704
Matthias Schmutzler, Anel Beganovic, Gerhard Böhler, Christian W. Huck, Methods for detection of pork adulteration in veal product based on FT-NIR spectroscopy for laboratory, industrial and on-site analysis Food Control. ,vol. 57, pp. 258- 267 ,(2015) , 10.1016/J.FOODCONT.2015.04.019
Ana M. Tocci, Ethel S.E. Flores, Rodolfo H. Mascheroni, Enthalpy, heat capacity and thermal conductivity of boneless mutton between -40 and + 40 °C Lwt - Food Science and Technology. ,vol. 30, pp. 184- 191 ,(1997) , 10.1006/FSTL.1996.0169
Cristina Alamprese, Monica Casale, Nicoletta Sinelli, Silvia Lanteri, Ernestina Casiraghi, Detection of minced beef adulteration with turkey meat by UV-vis, NIR and MIR spectroscopy Lwt - Food Science and Technology. ,vol. 53, pp. 225- 232 ,(2013) , 10.1016/J.LWT.2013.01.027
Qi Qian, Rong Jin, Jinfeng Yi, Lijun Zhang, Shenghuo Zhu, Efficient distance metric learning by adaptive sampling and mini-batch stochastic gradient descent (SGD) Machine Learning. ,vol. 99, pp. 353- 372 ,(2015) , 10.1007/S10994-014-5456-X
Ilya Sutskever, Geoffrey Hinton, Alex Krizhevsky, Ruslan Salakhutdinov, Nitish Srivastava, Dropout: a simple way to prevent neural networks from overfitting Journal of Machine Learning Research. ,vol. 15, pp. 1929- 1958 ,(2014)