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Spectroscopy-based food classification with extreme learning machine
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DOI：10.1016/j.chemolab.2014.09.015.png)
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摘要
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The combination of spectroscopy and a novel classification algorithm called extreme learning machine (ELM) was developed for food classification. The performance influence of different input dimensionalities was also investigated. The classification accuracy and speed of five chemometrics techniques, including k-nearest neighbor (KNN), partial least-squares discriminant analysis (PLS-DA), back propagation artificial neural network (BP-ANN), least-squares support vector machine (LS-SVM), and ELM were compared. It was presented that the accuracy of ELM was better than its competitors in most cases. Moreover, on the classification stage, ELM performed much faster than KNN, LS-SVM, and BP-ANN, which indicated that ELM may be a promising method for real-time food classification with a comparable accuracy based on near or mid-infrared spectroscopy. (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Spectroscopy
Food
Classification
Extreme learning machine
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分享收藏Theory and application of near infrared reflectance spectroscopy in determination of food quality
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分享收藏分享收藏分享收藏Comparison of combination and first overtone spectral regions for near-infrared calibration models for glucose and other biomolecules in aqueous solutions.png)
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