Prediction of fish quality level with machine learning

Abstract

In this study, sea bream, sea bass, anchovy and trout were captured and recorded using a digital camera during refrigerated storage for 7 days. In addition, their total viable counts (TVC) were determined on a daily basis. Based on the TVC, each fish was classified as 'fresh' when it was <5 log cfu per g, and as 'not fresh' when it was >7 log cfu per g. They were uploaded on a web-based machine learning software called Teachable Machine (TM), which was trained about the pupils and heads of the fish. In addition, images of each species from different angles were uploaded to the software in order to ensure the recognition of fish species by TM. The data of the study indicated that the TM was able to distinguish fish species with high accuracy rates and achieved over 86% success in estimating the freshness of the fish species tested.

Seafood Quality Detection Using Electronic Nose and Machine Learning Algorithms With Hyperparameter Optimization

“…This experiment proved that Random Forest is good to classify meat and predict the microbial population of the meat because it got favorable result [30]. In other research, VOLUME 11, 2023 machine learning is used to determine the freshness level of sea bream, sea bass, anchovies, and trout [31]. This research uses machine learning to measure fish freshness using images recorded by a digital camera for 7 days in refrigerated storage.…”

Towards the development of foods 3D printer: Trends and technologies for foods printing

“…Egg freshness is monitored using the Haugh unit with Machine Learning (ML) models and Near Infrared (NIR) spectroscopy, reaching an accuracy of 87.0 % [ 155 ]. For fish, AI models like Support Vector Machine (SVM) and CNN predict quality using Total Viable Counts (TVC) from images, achieving over 86 % accuracy [ 156 ]. Additionally, Partial Least Squares Regression (PLSR) and Feed-forward Neural Networks (FNN) determine fish freshness by analyzing TVB-N and TBA with visible and NIR images [ 157 ].…”

Recent development in the preservation effect of lactic acid bacteria and essential oils on chicken and seafood products

“…Due to their highly perishable nature, the majority of the seafood is transported in ice across various parts of the country. Higher moisture content and neutral pH of seafood also make them more prone to microbial spoilage (Yavuzer and Köse, 2022). Microbial growth generally leads to the production of alkaline compounds in fish, including ammonia, which results in a higher pH value (9.2) of seafood, thereby indicating incipient spoilage conditions.…”