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Top2. Machine Learning-Based Nir Spectroscopy Orange Origin Identification Framework
In this paper, a universal framework for rapid and non-destructive identification of the origin of orange is established by the spectral analysis technology based on machine learning. The specific process is shown in Figure 1. First, the preprocessing algorithm is used to shape the spectrum to reduce noise, thereby reducing the interference of the noise in the original data to the classifier; Secondly, the PCA method is used to extract the features of the denoised NIR spectrum, so as to reduce the dimension of the high-dimensional data to an appropriate dimension; Then, use the feature selection algorithm to perform proper feature selection on the reduced-dimensional spectral data to facilitate faster and more accurate learning of the classifier; Finally, choose different classifiers, and select the best classifier to build a spectral recognition model under a unified training framework and performance evaluation index (Ren, Wang, Ning, Xu, Wang, Xing, Wan & Zhang, 2013; Zhao, Guo, Wei & Bo, 2013; Shen, Zou, Shi, Li, Huang & Xu, 2015; Wang, Yan & Yang, 2015; Asir, Appavu & Jebamalar, 2016; El-Bendary, El, Hassanien & Badr, 2015).
Figure 1. Frame diagram of NIR spectrum origin identification and identification based on machine learning