Classification of Chinese honeys according to their floral origin by near infrared spectroscopy

• Published in: Food chemistry 2012-11, Vol.135 (2), p.338-342
• Main Authors: Chen, Lanzhen, Wang, Jiahua, Ye, Zhihua, Zhao, Jing, Xue, Xiaofeng, Heyden, Yvan Vander, Sun, Qian
• Format: Article
• Language: English
• Subjects: Acacia; Back propagation artificial neural network; Biological and medical sciences; China; Classification techniques; Confectionery products and chocolate industries, honey; discriminant analysis; flora; Flowers - classification; Food industries; Fundamental and applied biological sciences. Psychology; Honey; Honey - analysis; Honey - classification; Mahalanobis-distance discriminant analysis; multivariate analysis; Near infrared spectroscopy; neural networks; Neural Networks (Computer); Quality Control; Spectroscopy, Near-Infrared - methods; Vitex; Ziziphus
• ISSN: 0308-8146; 1873-7072
• DOI: 10.1016/j.foodchem.2012.02.156

► The NIR spectroscopy was feasible and convenient for classification Chinese honey. ► The performance of classification models were both suitable for honey prediction. ► The BP-ANN method is better for honey classification than MD-DA method. ► There are some non-linear relation between honey varieties and their NIR-spectra. The feasibility of near infrared (NIR) spectroscopy and multivariate analysis as tools to classify Chinese honey samples according to their different floral origins was explored. Five kinds of honey, namely, acacia, linden, rape, vitex and jujube, were analysed using a NIR spectrophotometer with a fibre optic probe. Classification models based on the NIR spectra were developed using Mahalanobis-distance discriminant analysis (MD-DA) and a back propagation artificial neural network (BP-ANN). By the MD-DA model, total correct classification rates of 87.4% and 85.3% were observed for the calibration and validation samples, respectively, while the ANN model resulted in total correct classification rates of 90.9% and 89.3% for the calibration and validation sets, respectively. By ANN, the respective correct classification rates of linden, acacia, vitex, rape and jujube were 97.1%, 94.3%, 80.0%, 97.1%, and 85.7% in calibration, and 100%, 93.3%, 80.0%, 100%, and 73.3% in validation. The results indicated that NIR combined with a classification technique could be a suitable technology for the classification of Chinese honeys from different botanical origins.