In-line monitoring of colour and composition during extrusion cooking.
Published: 2004
Language: eng
Number of pages: 168
Description: For colour monitoring a model have been developed to predict the kinetic of colour changes from the operating conditions and the residence time distribution (RTD) assuming that the flow inside the extruder could be approximated by the segregated flow model (SFM).A fiber optic equipped spectrometer capable of scanning both the visible and the near infrared (NIR) wavelengths was successfully interfaced to a twin screw extruder through a specially designed interface inserted between the screws and the die plate in order to monitor the hot melt in-line.Several non-linear models were also implemented to predict the final product characteristics from in-line spectra preprocessed with multiplicative scattering correction (MSC) and second derivative (d2).Among the data preprocessing methods the implementation of Orthogonal Signal Correction (OSC) and wavelet data compression prior of PLS modeling showed good prediction results with a small number of latent variables. The continuum regression (CR) approach also used a small number of parameters and it helped to understand the properties of the various linear regression models: Partial Least Squares (PLS), Principal Component Regression (PCR) and Multiple Linear Regression (MLR) that are more suitable to model the data.While colour of the hot melt in the extruder was determined in real time from the visible spectra the degree of gelatinization (DG), WAI and WSI in the final product had to be experimentally determined off-line and correlated to the NIR spectra.With this system both visible and near infrared spectra were collected in-line and were correlated through multivariate calibration models to some final product characteristics: colour, degree of gelatinization (DG) and two functional properties in the cooked product: Water Absorption Index (WAI) and Water Solubility Index (WSI).The residence time distribution was measured in-line and off-line by stimulus-response technique and both approaches gave comparable results.For NIR monitoring different linear multivariate calibration models for predicting the degree of starch gelatinization, WAI and WSI in real-time were assessed and compared. It was shown that data preprocessing was fundamental for removing undesired effects such as additive and multiplicative light scattering.
Notes: Adviser:  L. L. Diosady.

Thesis (Ph.D.)--University of Toronto, 2004.

Electronic version licensed for access by U. of T. users.

Source: Dissertation Abstracts International, Volume: 65-05, Section: B, page: 2501.