Laboratory of industrial engineering

Hirri A., De Luca M., Ioele G., Balouki A., Bassbasi M., Kzaiber F., Oussama A., Ragno G. (2015): Chemometric classification of citrus juices of Moroccan cultivars by infrared spectroscopy. Czech J. Food Sci., 33: 137–142. Fourier transform – infrared (FTIR) spectroscopy in connection with chemometric methodologies was used as a fast and direct analytical approach to classify citrus cultivars by the measurements on their juice. Modern multivariate analysis responds to the current needs for economic, simple, and fast methods able to classify new unknown samples with great accuracy. A set of 135 samples of citrus juice, representative of three cultivars (Hamlin, Muska, and Valencia), all picked in the same geographical area of Morocco, were analysed. Chemometric discrimination of the juice samples was achieved by principal component analysis (PCA) performed on the FTIR spectral data from the juice samples, showing an explained variance of 97.84% by considering only 2 PCs. A fully corr...

A major issue for fabric quality inspection is in the detection of defaults, it has become an extremely challenging goal for the textile industry to minimize costs in both production and quality inspection. The quality inspection is currently done manually by professionals; hence the need for the implementation of a fast, powerful, robust, and intelligent machine vision system in order to achieve high global quality, uniformity, and consistency of fabrics and to increase productivity. Consequently, the automatic inspection control process can improve productivity and enhance product quality. This article describes the approach used in developing a convolutional neural network for identifying fabric defects from input images of fabric surfaces. The proposed neural network is a pre-trained convolutional model ‘DetectNet’, it was adapted to be more efficient to the fabric image feature extraction. The developed model is capable of successfully distinguishing between defective fabric an...

The internal inspection of fabrics is one of the most important phases of production in order to achieve high quality standard in the textile industry. Therefore, developing efficient automatic internal control mechanism has been an extremely major area of research. In this paper, the famous architecture Googlenet was fine-tuned into two configurations for texture defect classification that was trained on a textile texture database (TILDA). The experimental result, for both configurations, achieved a significant overall accuracy score of 97% for motif and a non-motif-based images and 89% for mixed images. In the results obtained, it was observed that the second model, which updates the last six layers, was more successful than the first one; which updates the last two layers.