Classification of Histamine Content in Fish Using Near-Infrared Spectroscopy and Machine Learning Techniques

• Published in: Information (Basel) 2024-09, Vol.15 (9), p.528
• Main Authors: Ninh, Duy Khanh, Phan, Kha Duy, Vo, Cong Tuan, Dang, Minh Nhat, Le Thanh, Nhan
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Biosensors; Chemical analysis; Classification; convolutional neural network; Datasets; Enzymes; Feature extraction; Fish; fish quality assessment; Food; Food quality; Histamine; histamine content; Infrared analysis; Infrared spectra; Infrared spectroscopy; Machine learning; Mackerel; Methods; Near infrared radiation; near-infrared spectroscopy; Neural networks; nondestructive analysis; Quality control; Radiation; Seafood
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info15090528

Near-infrared (NIR) spectroscopy has emerged as a popular technique for assessing food quality due to its advantages over complex chemical analysis methods. However, the application of NIR spectroscopy for evaluating fish quality based on histamine content has not been extensively explored. This study investigates the use of NIR spectroscopy in combination with machine learning (ML) techniques to classify fish samples into two safety classes, Safe and Unsafe, based on their histamine content. A comprehensive NIR dataset comprising 11,360 spectra collected at eight distinct positions within the fish body was obtained from 284 fish samples of mackerel, tuna, and pompano species. ML experiments were conducted to classify fish samples based on whether their histamine content exceeded the permissible limit of 100 ppm. To address class imbalance and optimize ML models, various data pre-processing and feature extraction techniques as well as ML algorithms were explored. The results demonstrated that utilizing NIR data specifically obtained from the tail’s flesh, a specific location within the fish, yielded superior models for fish safety classification. A feature extraction method employing pre-processed NIR spectra and their second derivatives, combined with an optimized convolutional neural network architecture, outperformed traditional ML classifiers with an accuracy of approximately 93%.