Classification of Quality Characteristics of Surimi Gels from Different Species Using Images and Convolutional Neural Network

• Published in: Processes
• Main Authors: Won Byong Yoon, Timilehin Martins Oyinloye, Jinho Kim
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2023
• Subjects: surimi; gel; color; auto machine learning; Convolutional Neural Network; image classification; Chemical technology; TP1-1185; Chemistry; QD1-999; alaska pollock; Alaska
• Online Access: https://doi.org/10.3390/pr11102864; https://doaj.org/article/1066dd21b34445618eef697e1ec33a49

In the aspect of food quality measurement, the application of image analysis has emerged as a powerful and versatile tool, enabling a highly accurate and efficient automated recognition and the quality classification of visual data. This study examines the feasibility of employing an AI algorithm on labeled images as a non-destructive method to classify surimi gels. Gels were made with different moisture (76–82%) and corn starch (5–16%) levels from Alaska pollock and Threadfin breams. In surimi gelation, interactions among surimi, starch, and moisture caused color and quality shifts. Color changes are indicative of structural and quality variations in surimi. Traditional color measuring techniques using colorimeter showed insignificant differences ( p < 0.05) in color values and whiteness among treatments. This complexity hindered effective grading, especially in intricate formulations. Despite insignificant color differences, they signify structural changes. The Convolutional Neural Network (CNN) predicts the visual impact of moisture and starch on gel attributes prepared with different surimi species. Automated machine learning assesses AI algorithms; and CNN’s 70:30 training/validation ratio involves 400–700 images per category. CNN’s architecture, including input, convolutional, normalization, Rectified Linear Unit (ReLU) activation, and max-pooling layers, detects subtle structural changes in treated images. Model test accuracies exceed 95%, validating CNN’s precision in species and moisture classification. It excels in starch concentrations, yielding > 90% accuracy. Average precision (>0.9395), recall (>0.8738), and F1-score (>0.8731) highlight CNN’s high performance. This study demonstrates CNN’s value in non-destructively classifying surimi gels with varying moisture and starch contents across species, and it provides a solid foundation for advancing our understanding of surimi production processes and their optimization in the pursuit of high-quality surimi products.