Results from the implementation of an online color imaging system on industrial snack food
production lines are presented. Feature information is extracted from images using multivariate
image analysis based on principal component analysis and is used to develop models to predict
the coating content and the coating distribution on the products. The imaging system is used to
monitor these product quality variables and to detect and diagnose operational problems in the
plants. It is also used to directly implement closed-loop feedback control over the coating
concentration.
It is common in the food industry to produce solid-phase products that are not amenable to ready characterization on the production line via traditional on-line instrumentation. This motivates research into novel sensor technologies. In this work, an on-line vibrational sensor is used to develop a soft sensor for real-time prediction of product texture in a commercial snack food process. This in turn enables real-time multivariate statistical process control (MSPC) and indicates the opportunity for automated feedback control. An accelerometer is used to record the vibrational signature generated by a snack food product falling onto a metal surface. The frequency distribution of the acoustic signature is obtained via the discrete Fourier transform. This provides a predictor space from which the textural properties are modeled using partial least-squares (PLS). Excellent results are obtained, with R 2 upward of 96% on an independent test set for two properties that fully span the texture space of this product.
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