Developing a Quality Control System in a Continuous Hot Air Heating Process in Surimi Seafood Processing Using Image Analysis and Artificial Intelligence

Abstract

In this study, the feasibility of classifying surimi gels during a continuous heating process using an artificial intelligence (AI) algorithm on labeled images was investigated. Surimi paste with varying corn starch concentrations (0%, 5%, and 10%) and moisture content levels (78% and 80%) from Alaska pollock were analyzed for the subtle physical changes. Rheological characterization and K-means clustering analysis performed on entire images captured from different batches of heated surimi gel indicated a four-stage transformation from its initial state to gel formation with the temperature ranges spanning 25 to <40 °C, 40 to <50 °C, 50 to <55 °C, and 55 to 80 °C. Subsequently, a Convolutional Neural Network (CNN) model based on the temperature classification was designed to interpret and classify these images. A total of 1000 to 1200 images were used for the training, testing, and validation purposes in the ratio 7:1:2. The CNN architecture incorporated essential elements including an input layer, convolutional layers, rectified linear unit (ReLU) activation functions, normalization layers, and max-pooling layers. The CNN model achieved validation accuracy >92.67% for individual mixture composition, 94.53% for classifying surimi samples based on moisture content, and gelation level, and 89.73% for complex classifications involving moisture content, starch concentration, and gelation stages. Additionally, it exhibited high average precision, recall, and F1 scores (>0.92), indicating precision and sensitivity in identifying relevant instances. The success of CNN in non-destructively classifying surimi gels with different moisture and starch contents is demonstrated in this work.