Monitoring the osmotic dehydration process of quince by the novel fusion modular neural networks - fuzzy logic (Fmnn-Fl)

This paper presents a novel approach to monitor food process based on Modular Neural Networks (MNNs) and fuzzy inference system. The proposed MNN consists of three separate modules, each using different image features as input including: edge detection, wavelet transform, and Hough transform. The sugeno fuzzy inference system was used to combine the outputs from each of these modules to classify the images of quince during osmotic dehydration process. To test the method, for classification, database was made of ۱۰۸ quince samples’ images (۱۲ classes). In experiments, the developed architecture achieved ۹۱.۶% recognition accuracy. Next step, solid gain, water loss and moisture content of quince samples were considered as MNNs outputs, whereas osmotic dehydration time and classified images were MNNs inputs. The minimum %MRE (۱۸.۱۵۳) with ۸۹% prediction ability for water loss (WL) was obtained when applying two hidden layers with ۶ neurons per each two layers. The lowest %MRE (۳۵.۵۳۳۵) with ۹۳% prediction ability for solid gain (SG) was obtained when using ۶ and ۸ neurons per first and second layer, respectively. And finally %MRE was at least (۷.۴۷۵۹) with ۹۶% prediction ability for moisture content (MC) by ۶ and ۵ neurons per first and second layer, respectively. The results show that this model could be commendably implemented for quantitative modeling and monitoring of food quality changes during osmotic dehydration process.

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