An All Optical Method for Assessing Food Quality During Cooking in Large Scale Industrial Ovens Based on Intelligent Monitoring of Colour and Temperature

摘要

A sensor system utilising optical fibre sensing techniques is reported, which has been applied to the food industry in order to control the cooking process in a large-scale industrial oven. By monitoring a wide variety of products as they cook in the oven it has been possible to classify their cooking stage. The Colour Signal is sampled on the surface and within the core using an optical fibre probe in each case. This paper examines the application of Principal Component Analysis, using Karhunen Loeve Decomposition, to the spectral data from the sensor prior to application of pattern recognition through the use of an Artificial Neural Network. Additionally, the core temperature of the product is measured using an optical fibre probe based on fluorescence decay time measurements. Investigations have been carried out to ascertain trends in various products in order to design a general colour scale, which can classify several products using a single neural network. Measurements of product temperature are also included which have been acquired simultaneously to the above colour measurements. The food types discussed in this paper include Steamed Skinless Chicken Fillets, Roast Whole Chickens, Marinated Chicken Wings, Sausages, Pastry, Breadcrumb Coating and Char-grilled chicken fillets.