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).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zheng, W., Fu, X., Ying, Y.: Spectroscopy-based food classification with extreme learning machine. Chemom. Intell. Lab. Syst. 139, 42–47 (2014)
Porep, J.U., Kammerer, D.R., Carle, R.: On-line application of near infrared (NIR) spectroscopy in food production. Trends Food Sci. Technol. 46(2), 211–230 (2015)
Shen, Y., Wu, Y., Li, L., Li, L.: Nondestructive Detection for forecasting the level of acidity and sweetness of apple based on NIR spectroscopy. In: Proceedings of the 2nd IEEE International Conference on Advanced Information Technology, Electronic and Automation Control, pp. 1250–1257. Chongqing (2017)
Li, L., Wu, Y., Li, L., Huang, B.: Rapid detecting SSC and TAC of peaches based on NIR spectroscopy. In: Proceedings of the 2nd IEEE International Conference on Computational Intelligence and Applications, pp. 312–317. Beijing (2017)
Wu, Y., Li, L., Liu, L., Liu, Y.: Nondestructive measurement of internal quality attributes of apple fruit by using NIR spectroscopy. Multimed. Tools Appl., pp. 1–17 (2017)
Shen, Y., Tian, J., Li, L., Wu, Y., Li, L.: Feasibility of non-destructive internal quality analysis of pears by using near-infrared diffuse reflectance spectroscopy. In: Proceedings of the 9th IEEE International Conference on Modelling, Identification and Control, pp. 31–36. Kunming (2017)
Han, Q.J., Wu, H.L., Cai, C.B., Xu, L., Yu, R.Q.: An ensemble of Monte Carlo uninformative variable elimination for wavelength selection. Anal. Chim. Acta 612(2), 121–125 (2008)
Pearson, K.: LIII. On lines and planes of closest fit to systems of points in space. Lond. Edinb. Dublin Philos. Mag. J. Sci. 2(11), 559–572 (1901)
Liu, Y., Sun, X., Ouyang, A.: Nondestructive measurement of soluble solid content of navel orange fruit by visible CNIR spectrometric technique with PLSR and PCA-BPNN. LWT-Food Sci. Technol. 43(4), 602–607 (2010)
Guo, Y., Ni, Y., Kokot, S.: Evaluation of chemical components and properties of the jujube fruit using near infrared spectroscopy and chemometrics. SpectrochimicaActa Part A: Mol. Biomol. Spectrosc. 153, 79–86 (2016)
Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. San Francisco (2016)
Svetnik, V., Liaw, A., Tong, C., Culberson, J.C., Sheridan, R.P., Feuston, B.P.: Random forest: a classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 43(6), 1947–1958 (2003)
Luckner, M., Topolski, B., Mazurek, M.: Application of XGBoost algorithm in fingerprinting localisation task. In: Saeed, K., Homenda, W., Chaki, R. (eds.) CISIM 2017. LNCS, vol. 10244, pp. 661–671. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59105-6_57
Ghosh, R., Purkayastha, P.: Forecasting profitability in equity trades using random forest, support vector machine and Xgboost. In: Proceedings of the 10th International Conference on Recent Trends in Engineering Science and Management, pp. 473–486. Kuala Lumpur (2017)
Stout, F., Kalivas, J.H., Héberger, K.: Wavelength selection for multivariate calibration using Tikhonov regularization. Appl. Spectrosc. 61(1), 85–95 (2007)
Urbano-Cuadrado, M., De Castro, M.L., Pérez-Juan, P.M., GarcÃa-Olmo, J., Gómez-Nieto, M.A.: Near infrared reflectance spectroscopy and multivariate analysis in enology: determination or screening of fifteen parameters in different types of wines. Anal. Chim. Acta 527(1), 81–88 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-01298-4_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01297-7
Online ISBN: 978-3-030-01298-4
eBook Packages: Computer ScienceComputer Science (R0)