Classification of persimmon fruit origin by near infrared spectrometry and least squares-support vector machines

被引：33

作者：

Khanmohammadi, M. ^{[1
]}

Karami, F. ^{[1
]}

Mir-Marques, A. ^{[2
]}

Bagheri Garmarudi, A. ^{[1
]}

Garrigues, S. ^{[2
]}

de la Guardia, M. ^{[2
]}

机构：

[1] IKIU, Fac Sci, Dept Chem, Qazvin, Iran

[2] Univ Valencia, Dept Analyt Chem, Jeronimo Munoz Bldg,50th Dr Moliner St, E-46100 Valencia, Spain

来源：

JOURNAL OF FOOD ENGINEERING | 2014年 / 142卷

关键词：

Persimmon; Fourier transform near infrared spectrometry; Least squares-support vector machines; Chemometrics; Geographical origin; Protected designation of origin; NIR SPECTROSCOPY; QUALITY; TRANSMITTANCE;

D O I：

10.1016/j.jfoodeng.2014.06.003

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

The main objective of this work has been the authentication by Fourier transform near infrared (FT-NIR) spectrometry of the origin of persimmon fruits cultivated in different regions of Spain. In order to achieve this goal, 166 persimmon samples from 7 different regions of Spain were analyzed by FT-NIR spectrometry. By splitting the spectral data in training and independent test sets, a classification model was built using least squares support vector machines chemometric technique. Orthogonal signal correction and principal component analysis were performed prior to conduct the classification strategy. The verified model was applied for the prediction of the origin of 50 samples from the independent test set being the prediction error 2%. (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：17 / 22

页数：6

共 31 条

[1] Improving hierarchical cluster analysis: A new method with outlier detection and automatic clustering [J].