外文摘要：This study was aimed at assessing the effect of microwave drying at 100, 200, or 300 W on the quality of Cavendish banana slices without pretreatment and with pretreatment using 5% ascorbic acid solution, 5% citric acid solution, 5% gum arabic solution, and ultrasound. Banana slices were imaged using a digital single-lens reflex (SLR) camera. The acquired images were processed to extract texture parameters. The classification models were developed based on image texture parameters selected from a big dataset of 2172 textures of images in different color channels using artificial neural networks. Wide Neural Network, Bilayered Neural Network, Medium Neural Network and three classifiers from the group of function, such as RBF (Radial basis function) Network, Multilayer Perceptron, and WiSARD were applied. Banana slices belonging to 15 classes with different combinations of pretreatment and microwave drying were distinguished with an average accuracy of up to 97.2% for a model built using Multilayer Perceptron. For most models, banana samples microwave-dried at 200 W without pretreatment were classified with the highest correctness. The performed study revealed that the objective, non-destructive, correct, and robust quality assessment of pretreated and microwave-dried banana slices may be performed using image processing and artificial intelligence.
外文关键词：image processing;neural networks;Pretreatment;Microwave drying;Banana;Texture parameters;Classification models
作者：Ropelewska, Ewa;Cetin, Necati;Noutfia, Younes;Gunaydin, Seda
作者单位：Ankara Univ;Natl Inst Hort Res;Univ Erciyes
期刊名称：FOOD CONTROL
期刊影响因子：0.0
出版年份：2024
出版刊次：163
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台