Abstract
133 camelina samples were used to build the Fourier transform infrared (FT-IR) prediction model. Several methods have been used for the establishment of the predicting model, but support vector machine was rarely used in FT-IR area to build the prediction model. The aim of this study was to develop a new model for predicting protein with higher accuracy. In the spectra region 690–1700 cm\(^{-1}\), the SVM method was better than that of PLS and PCR. In the development of SVM, the \(\hbox {R}_{\mathrm{RMSEC}}^{2}\) and \(\hbox {R}_{\mathrm{RMSEP}}^{2}\) of the model were 0.83963 and 0.96578 respectively, and the RPD was 5.5016. The RPD was greater than that of PLS and PCR. The FT-IR was effective in predicting the content of camelina protein and SVM was a better method to build prediction model.
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Kagale, S., Chushin, K., Nixon, J., et al.: The emerging biofuel crop Camelina sativa retains a highly undifferentiated hexaploid genome structure. Nat. Commun. 5(4), 3706 (2011)
Zubr, J.: Oil-seed crop: Camelina sativa. Ind. Crops Prod. 6(2), 113–119 (1997)
Li, Y., Sun, X.S.: Camelina oil derivatives and adhesion properties. Ind. Crops Prod. 73, 73–80 (2015)
Ryhanen, E.L., Perttila, S., Tupasela, T., et al.: Effect of Camelina sativa expeller cake on performance and meat quality of broilers. J. Sci. Food Agric. 87(8), 1489–1494 (2010)
Rokka, T., Alen, K., Valaja, J., et al.: The effect of a Camelina sativa enriched diet on the composition and sensory quality of hen eggs. Food Res. Int. 35(2–3), 253–256 (2002)
Li, N., Qi, G., Sun, X.S., et al.: Adhesion properties of camelina protein fractions isolated with different methods. Ind. Crops Prod. 69, 263–272 (2015)
Zhang, K., Tan, Z., Chen, C., Sun, X.S., et al.: Rapid prediction of camlina seed oil content using near-infrared spectroscopy. Energy Fuels 31(5), 5629–5634 (2017)
Xu, F., Yu, J., Tesso, T., et al.: Qualitative and quantitative analysis of lignocellulosic biomass using infrared techniques: a mini-review. Appl. Energy 104(2), 801–809 (2013)
Benesch, M.G., Lewis, R.N., Mannock, D.A., et al.: A DSC and FTIR spectroscopic study of the effects of the epimeric cholestan-3-ols and cholestan-3-one on the thermotropic phase behavior and organization of dipalmitoylphosphatidylcholine bilayer membranes: comparison with their 5-cholesten analogs. Chem. Phys. Lipids 188, 10–26 (2015)
Wu, Z., Zhao, Y., Zhang, J., et al.: Quality assessment of gentiana rigescens from different geographical origins using FT-IR spectroscopy combined with HPLC. Molecules 22(7), 1238 (2017)
Porras, M.A., Cubitto, M.A., Villar, M.A.: A new way of quantifying the production of poly(hydroxyalkanoate)s using FTIR. J. Chem. Technol. Biotechnol. 91(5), 1240–1249 (2016)
Wu, Z., Xu, E., Long, J., et al.: Use of attenuated total reflectance mid-infrared spectroscopy for rapid prediction of amino acids in Chinese rice wine. J. Food Sci. 80(8), C1670 (2015)
Seung Yeob, S., Young Koung, L., In-Jung, K.: Sugar and acid content of Citrus prediction modeling using FT-IR fingerprinting in combination with multivariate statistical analysis. Food Chem. 190, 1027–1032 (2016)
Kumar, M., Raghava, G.P.: Prediction of nuclear proteins using SVM and HMM models. BMC Bioinf. 10(1), 22–22 (2009)
Liu Jun, Wu, Mengting, Tan Zhenglin, et al.: Overview of data analysis methods in near-infrared spectroscopy nondestructive testing. J. Wuhan Inst. Technol 39(05), 496–502 (2017)
Cherkassky, V., Mulier, F.: Statistical learning theory. Encycl. Sci. Learn. 41(4), 3185–3185 (1998)
Shao, W., Li, Y., Diao, S., et al.: Rapid classification of Chinese quince (Chaenomeles speciosa, Nakai) fruit provenance by near-infrared spectroscopy and multivariate calibration. Anal. Bioanal. Chem. 409(1), 115–120 (2017)
Ulrichs, T., Drotleff, A.M., Ternes, W.: Determination of heat-induced changes in the protein secondary structure of reconstituted livetins (water-soluble proteins from hen’s egg yolk) by FTIR. Food Chem. 172, 909 (2015)
Kyomugasho, C., Christiaens, S., Shpigelman, A., et al.: FT-IR spectroscopy, a reliable method for routine analysis of the degree of methylesterification of pectin in different fruit- and vegetable-based matrices. Food Chem. 176, 82–90 (2015)
Acknowledgements
This work was supported by the Hubei Provincial Department Education Science Technology Research Program—Outstanding Youth Talent Project (HPSFY#Q20111504), the ninth Graduate Innovation Fund of Wuhan Institute of Technology and the Foundation of Hubei Provincial Key Laboratory of Intelligent Robot (HBIR 201608).
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Liu, J., Wu, M., Wang, M. et al. Predicting the content of camelina protein using FT-IR spectroscopy coupled with SVM model. Cluster Comput 22 (Suppl 4), 8401–8406 (2019). https://doi.org/10.1007/s10586-018-1838-3
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DOI: https://doi.org/10.1007/s10586-018-1838-3