Abstract
In this paper, the combination of Near-Infrared (NIR) spectroscopy and a novel forecasting algorithm called XGBoost was proposed for food internal quality evaluation. First, the original NIR spectral data was preprocessed by Savitzky-Golay smoothing method to reduce the influence of noises. Secondly, the preprocessed spectra was submitted to PCA to extract essential information. Finally, the model was established by using the XGBoost algorithm. The performance of the proposed model was examined by comparing with different models including back propagation neural network (BPNN) and support vector regression (SVR). The results showed that the new proposed model outperformed other two models and this XGBoost-based tool was suitable for food internal quality control.
This study is supported by the CERNET Innovation Project, China (Grant No. NGII20150603), the Fundamental Research Funds for the Central Universities (Grant No. 2022016zrbr12), Gansu Provincial Science & Technology Department (Grant No. 1506RJZA107), the Natural Science Foundation of PR of China (Grant No. 61300230), the Fundamental Research Funds for the Central Universities (Grant No. lzujbky-2016-br03), the Fundamental Research Funds for the Key Research Program of Chongqing Science & Technology Commission (Grant No. cstc2017rgzn-zdyf0064), the Chongqing Provincial Human Resource and Social Security Department (Grant No. cx2017092), and the Central Universities in China (Grant No. 2018CDXYRJ0030, CQU0225001104447).
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Li, L. et al. (2018). Spectroscopy-Based Food Internal Quality Evaluation with XGBoost Algorithm. In: U, L., Xie, H. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 11268. Springer, Cham. https://doi.org/10.1007/978-3-030-01298-4_6
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