TY - GEN
T1 - Detection of Honey Adulteration using Hyperspectral Imaging
AU - Shafiee, Sahameh
AU - Polder, Gerrit
AU - Minaei, Saeid
AU - Moghadam-charkari, Nasrolah
AU - Van Ruth, Saskia
AU - Kuś, Piotr M.
PY - 2016/10/25
Y1 - 2016/10/25
N2 - This study investigates the application of hyperspectral imaging system and data mining based classifiers for honey adulteration detection. Hyperspectral images from pure and adulterated samples were captured in using a VIS-NIR hyperspectral camera (400 – 1000 nm). After preprocessing the images, five different data mining based techniques, including artificial neural network (ANN), support vector machine (SVM), Linear discriminant analysis (LDA), Fisher and Parzen classifiers were applied for supervised image classification. Classifier test results show the highest classification accuracy of 95% for ANN classifier. Other classifiers including SVM with radial basis kernel function (92%), LDA (90%), Fisher (89 %), and Parzen with 84% correct classification rate also showed acceptable results. This research shows the capability of hyperspectral imaging for honey authentication.
AB - This study investigates the application of hyperspectral imaging system and data mining based classifiers for honey adulteration detection. Hyperspectral images from pure and adulterated samples were captured in using a VIS-NIR hyperspectral camera (400 – 1000 nm). After preprocessing the images, five different data mining based techniques, including artificial neural network (ANN), support vector machine (SVM), Linear discriminant analysis (LDA), Fisher and Parzen classifiers were applied for supervised image classification. Classifier test results show the highest classification accuracy of 95% for ANN classifier. Other classifiers including SVM with radial basis kernel function (92%), LDA (90%), Fisher (89 %), and Parzen with 84% correct classification rate also showed acceptable results. This research shows the capability of hyperspectral imaging for honey authentication.
KW - Artificial neural network
KW - Honey adulteration
KW - Hyperspectral imaging
KW - Linear discriminant classifier
KW - Support vector machine
U2 - 10.1016/j.ifacol.2016.10.057
DO - 10.1016/j.ifacol.2016.10.057
M3 - Conference paper
VL - 49
T3 - IFAC-PapersOnLine
SP - 311
EP - 314
BT - 5th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture AGRICONTROL 2016
A2 - Tang, L.
PB - IFAC
T2 - 5th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture AGRICONTROL 2016
Y2 - 14 August 2016 through 17 August 2016
ER -