Optimizing twin-screw food extrusion processing through regression modeling and genetic algorithms

Polysaccharide determination and habitat classification for fresh Dendrobiums with hyperspectral imagery and modified RBFNN

This research aimed to study the visual and nondestructive detection of mannose (MN) and Dendrobium polysaccharides (DP) in Dendrobiums by using hyperspectral imaging technology. In order to determine the MN and DP concentrations nondestructively, we built radial basis function neural network (RBFNN) models based on NIR spectra (874-1734 nm) with a novel chemometric method to calculate the radial bases. And excellent results with the R _P ² coefficients of 0.906 and 0.913 were obtained by the MN and DP detection models, respectively. In order to simplify the detection models based on full-range spectra, we designed an innovative genetic algorithm-successive projections algorithm (GA-SPA) strategy to extract the feature bands efficiently in two stages. Based on the feature bands selected by GA-SPA, we established the simplified detection models with the same high performance as those based on full-range spectra. By importing the feature bands of every pixel in the hyperspectral image into the simplified detection models, we successfully generated the distribution maps of MN and DP. Moreover, we also built an RBFNN classifier to categorize the habitats of Dendrobium. And the total classification accuracy reached 0.887. This research makes progress in Dendrobium quality evaluation and spectral detection technology.

Modelling Volume Change and Deformation in Food Products/Processes: An Overview

Volume change and large deformation occur in different solid and semi-solid foods during processing, e.g., shrinkage of fruits and vegetables during drying and of meat during cooking, swelling of grains during hydration, and expansion of dough during baking and of snacks during extrusion and puffing. In addition, food is broken down during oral processing. Such phenomena are the result of complex and dynamic relationships between composition and structure of foods, and driving forces established by processes and operating conditions. In particular, water plays a key role as plasticizer, strongly influencing the state of amorphous materials via the glass transition and, thus, their mechanical properties. Therefore, it is important to improve the understanding about these complex phenomena and to develop useful prediction tools. For this aim, different modelling approaches have been applied in the food engineering field. The objective of this article is to provide a general (non-systematic) review of recent (2005-2021) and relevant works regarding the modelling and simulation of volume change and large deformation in various food products/processes. Empirical- and physics-based models are considered, as well as different driving forces for deformation, in order to identify common bottlenecks and challenges in food engineering applications.