Journal
JOURNAL OF FOOD ENGINEERING
Volume 84, Issue 1, Pages 124-131Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jfoodeng.2007.04.031
Keywords
near/mid-infrared spectroscopy; protein; milk powder; least-squares support vector machines
Categories
Ask authors/readers for more resources
Protein is an important component of milk powder. The fast and non-destructive detection of protein content in milk powder is important. Infrared spectroscopy technique was applied to achieve this purpose. Least-squares support vector machine (LS-SVM) was applied to building the protein prediction model based on spectral transmission rate. The determination coefficient for prediction (R-p(2)) was 0.981 and root mean square error for prediction (RMSEP) was 0.4115. It is concluded that infrared spectroscopy technique can quantify protein content in milk powder fast and non-destructively. The process is simple and easy to operate, and the prediction ability of LS-SVM is better than that of partial least square. Moreover, the comparison of prediction results showed that the performance of model with mid-infrared spectra data was better than that with near infrared spectra data. (C) 2007 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available