Determination of visual quality of tomato paste using computerized inspection system and artificial neural networks

• Published in: Computers and electronics in agriculture 2011-07, Vol.77 (2), p.147-154
• Main Authors: VELIOGLU, Hasan Murat, BOYACI, Ismail Hakki, KURULTAY, Sefik
• Format: Article
• Language: English
• Subjects: Agronomy. Soil science and plant productions; Artificial neural networks; Biological and medical sciences; Color; Computerized inspection system; Dark speck; Fundamental and applied biological sciences. Psychology; Lycopersicon esculentum; Tomato paste
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2011.04.007

► Visual quality of tomato paste. ► Dark speck determination, counting and measuring in tomato paste. ► Color evaluation in Lab format using image processing and artificial neural network. ► Graphical user interface (GUI) design for quick visual analysis of tomato paste. An artificial neural network (ANN) integrated computerized inspection system (CIS) was developed to determine tomato paste color in CIE L ∗, a ∗, and b ∗ color format and the number and size of dark specks which exist in the product. The usability of CIS in the determination of the number and the size of dark specks in tomato paste were investigated by comparing the results of CIS and human inspectors. While the inspectors had difficulties not only in determination of the specks having a diameter less than 0.2 mm but also in correct diameter measurement for all specks, the CIS had good determination and measurement capability. In 99 tomato paste samples, the number of the specks having diameter more than 0.2 mm were found by human inspectors and CIS as 233 and 235, respectively. However, the manual inspection gave inaccurate results for the diameter measurement of the specks. In the color evaluation of the tomato paste, strong correlations ( R) were found between the results estimated from ANN-integrated CIS and those obtained from colorimeter (0.889, 0.958, 0.907 and 0.987 for L ∗ , a ∗ , b ∗ and a ∗ /b ∗, respectively). The whole system is adapted to a graphical user interface (GUI) for use by a non-skilled person working in the tomato paste sector. While manual methods need approximately 5 min, GUI needs 20–25 s to determine, count and classify the dark specks and to measure the product color.