Multivariate image analysis and regression for prediction of coating content and distribution in the production of snack foods

• Published in: Chemometrics and intelligent laboratory systems Vol. 67; no. 2; pp. 125 - 144
• Main Authors: Yu, Honglu, MacGregor, John F.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 28.08.2003
• Subjects: Coating prediction; Multivariate image analysis; Partial least squares; Principal component analysis; Snack food process; Snack food process; Partial least squares; Multivariate image analysis; Coating prediction; Principal component analysis
• ISSN: 0169-7439; 1873-3239
• DOI: 10.1016/S0169-7439(03)00065-0

An important problem in the snack food industry is to control the amount of coating applied to the base food product and the distribution of the coating among the individual product pieces. Multivariate image analysis and regression approaches based on Principal Component Analysis (PCA) and Partial Least Squares (PLS) are presented for the extraction of features from RGB (red–green–blue) color images and for their use in predicting the average coating concentration and the coating distribution. Data collected using both on-line and off-line imaging from several different snack food product lines are used to develop and evaluate the approaches. The methods are now being used in the snack food industry for the on-line monitoring and feedback control of product quality. This paper reports on the development of the methods.