Artificial neural network for quantitative determination of total protein in yogurt by infrared spectrometry

被引：38

作者：

Khanmohammadi, Mohammadreza ^{[1
]}

Garmarudi, Amir Bagheri ^{[1
,2
,3
]}

Ghasemi, Keyvan ^{[1
]}

Garrigues, Salvador ^{[4
]}

de la Guardia, Miguel ^{[4
]}

机构：

[1] Imam Khomeini Int Univ, Fac Sci, Dept Chem, Qazvin, Iran

[2] Inst Engn Res, Dept Chem, Tehran, Iran

[3] Inst Engn Res, Polymer Labs, Tehran, Iran

[4] Univ Valencia, Dept Analyt Chem, E-46100 Valencia, Spain

来源：

MICROCHEMICAL JOURNAL | 2009年 / 91卷 / 01期

关键词：

Yogurt; Protein content; ATR-FTIR; Artificial neural network; Back propagation; Successive Projection Algorithm; WAVELENGTH SELECTION; MILK; CASEIN; FTIR; PLS;

D O I：

10.1016/j.microc.2008.07.003

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

A method has been introduced for quantitative determination of protein content in yogurt samples based on the characteristic absorbance of protein in 1800-1500 cm(-1) spectral region by mid-FTIR spectroscopy and chemometrics. Successive Projection Algorithm (SPA) wavelength selection procedure, coupled with feed forward Back-Propagation Artificial Neural Network (BP-ANN) model was the benefited chemometric technique. Relative Error of Prediction (REP) in BP-ANN and SPA-BP-ANN methods for training set was 7.25 and 3.70 respectively. Considering the complexity of the sample, the ANN model was found to be reliable, while the proposed method is rapid and simple, without any sample preparation step. (C) 2008 Elsevier B.V. All rights reserved.

引用

页码：47 / 52

页数：6

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