Pattern Recognition of Near-Infrared Spectroscopy for Non-Destructive Discrimination of Oranges Based on Taste Index
Journal Title: Journal of Agricultural Machinery - Year 2015, Vol 5, Issue 1
Abstract
In recent years, application of near-infrared spectroscopy (NIR) as a non-destructive technique combined with chemometric methods has been widely noticed for quality assessment of food and agricultural products. In chemometric methods, quality analyses are important issues which could be related to pattern recognition. In this research, the feasibility of pattern recognition methods combined with reflectance NIR spectroscopy for non-destructive discrimination of oranges based on their tastes was investigated. To this end, both unsupervised and supervised pattern recognition techniques, hierarchical cluster analysis (HCA) and soft independent modeling of class analogies (SIMCA) were used for assessing the feasibility of variety discrimination and classification (according to their taste), respectively, based on the spectral information of 930-1650nm range. Qualitative analyses indicated that NIR spectra of orange varieties were correctly clustered using unsupervised pattern recognition of HCA. It was also concluded that supervised pattern recognition of SIMCA for NIR spectra of oranges provided excellent results of variety classification based on BrimA index at 5% significance level (classification accuracy of 98.57%). Moreover, wavelengths of 1047.5nm, 1502nm, and 1475nm contributed more than other wavelengths in discriminating two classes. Samples having the same BrimA index were also correctly classified with the high classification accuracy (95.45%) at 5% significance level. The discrimination power of wavelengths of 1475nm, 1583nm, and 1436.75nm were more than those for other wavelengths to achieve this classification. Therefore, reflectance NIR spectroscopy combined with pattern recognition methods can be utilized for determination of other attributes related to taste.
Authors and Affiliations
B. Jamshidi,S. Minaei,E. Mohajerani,H. Ghassemian,
Keywords
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