A Hybrid Model for Nondestructive Measurement of Internal Quality of Yogurt

A nondestructive optical method for determining the sugar and acidity contents of yogurt was investigated. Two types of preprocessing were used before the data were analyzed with multivariate calibration methods of principal component artificial neural network (PC-ANN) and partial least square (PLS)...

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Bibliographic Details
Published in2006 International Conference on Computational Intelligence and Security Vol. 1; pp. 836 - 839
Main Authors Yongni Shao, Yong He, Lihong Tan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2006
Subjects
Artificial neural networks
Calibration
Chemicals
Dairy products
Helium
Infrared spectra
Least squares methods
Solids
Spectroscopy
Temperature
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Summary:A nondestructive optical method for determining the sugar and acidity contents of yogurt was investigated. Two types of preprocessing were used before the data were analyzed with multivariate calibration methods of principal component artificial neural network (PC-ANN) and partial least square (PLS). In PC-ANN models, the scores of the principal components were chosen as the input nodes for the input layer of ANN. After adjusting the number of input nodes (principal components), hidden nodes, at the same time learning rate and momentum of the network, a model with a correlation coefficient of 0.89/0.91, a root mean square error of prediction (RMSEP) of 0.41/0.04 showed an excellent prediction performance to sugar/acidity. At the same time, the sensitive wavelengths corresponding to the sugar content and acidity of yogurts were proposed on the basis of regression coefficients by PLS
ISBN:1424406048
9781424406043
DOI:10.1109/ICCIAS.2006.294254