| 光谱学与光谱分析 |
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| Study on Quality Identification of Olive Oil Based on Near Infrared Spectra |
| LIU Guo-hai, HAN Wei-qiang, JIANG Hui |
| School of Electrical and Information Engineering，Jiangsu University，Zhenjiang 212013，China |
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Abstract Currently on the market, the sale of olive oil is mainly divided into extra virgin olive oil and common virgin olive oil. In order to identify the qualities of two different olive oils, a new method to identify the quality of olive oil with siPLS-IRIV-PCA algorithm is developed. Based on the near infrared spectral data of olive oil, the efficient spectral subintervals are selected with a synergy interval partial least squares (siPLS). The performance of the model is evaluated by using the root mean square error of cross-validation (RMSECV). The characteristic wavelengths are selected from the efficient spectral subintervals by iteratively retains informative variables (IRIV) algorithm. Principal component analysis (PCA) model is constructed based on the selected characteristic wavelengths. The samples of 90 groups of extra virgin olive oil and 90 groups of common olive oil are identified. PCA uses 1 427 wavelength variables as input variables and the contribution rates of the first two principal components are 51.891 8% and 26.473 2% respectively. siPLS-PCA uses 408 wavelength variables as input variables and the contribution rates of the first two principal components are 56.039 1% and 36.2355%. siPLS-IRIV-PCA uses 6 wavelength variables as input variables and the contribution rates of the first two principal components are 66.347 6% and 32.3043%. The result shows that, compared with PCA and siPLS-PCA, siPLS-IRIV-PCA has the best identification performance. The method is simple and convenient and has a high identification degree which offers a new approach to identify the quality of olive oil.
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Received: 2015-06-19
Accepted: 2015-11-03
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Corresponding Authors:
LIU Guo-hai
E-mail: ghliu@ujs.edu.cn
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[1] Sun Xiaodan, Li Xinhui, Shi Weimin, et al. Journal of Pingdingshan University, 2015, (5): 57.
[2] Sinelli N, Casale M, Di E V, et al. Food Research International, 2010, 43: 2126.
[3] Hui Jiang, Guohai Liu, Congli Mei, et al. Anal. Bioanal. Chem., 2012, 404: 603.
[4] Jiang Hui, Liu Guohai, Mei Congli, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2012, 97: 277.
[5] GUO Zhi-ming, ZHAO Jie-wen, CHEN Quan-sheng, et al(郭志明, 赵杰文, 陈全胜, 等). Optics and Precision Engineering(光学精密工程), 2009, 17(8): 1839.
[6] LIN Fen-fang, CHEN Zhu-lu, WANG Ke, et al(林芬芳, 陈祝炉, 王 珂, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2009, 28(4): 277.
[7] Yong-Huan Yun, Wei-Ting Wang, Min-Li Tan, et al. Analytica Chimica Acta, 2014, 807: 36.
[8] Nrgaard L, Saudland A, Wagner J, et al. Appl. Spectrosc., 2000, 54: 413.
[9] Leardi R, Nrgaard L. J. Chemometric, 2004, 18: 486. |
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