Machine Learning Approach for Frozen Tuna Freshness Inspection Using Low-Frequency A-Mode Ultrasound

Despite the ubiquity of ultrasonography in nondestructive inspection, its application to high-attenuation materials is challenging. At frequencies less than 1 MHz, ultrasound can inspect high-attenuation materials owing to its high penetration ability. Such ultrasound data are acquired using a single-element transducer that generates single-channel signals (A-mode). However, low-frequency A-mode ultrasound signals have low-resolution caused by long wavelengths, and less information than B-mode images generated by multi-channel transducers. Discriminating low-resolution data is made possible by recent advances in machine learning technology. This study employs machine learning to develop an inspection method for high-attenuation frozen materials. This study focuses on the inspection of the freshness of frozen tuna, which has a large market but uses a destructive inspection method. We applied eight typical machine learning algorithms to A-mode signal data (43 samples, 3168 signals) of frozen tuna to calculate freshness scores; we used fast Fourier transform in the feature extraction process. Our experiments show that all algorithms could classify the freshness of frozen tuna with statistical significance (
${p}$
< 0.05, one-tailed
${t}$
-test). Furthermore, we investigated the performance improvement in the mean (standard deviation) of the area under the receiver operating characteristic curves by taking the mean of the freshness scores on 24 signals. We observed that the best performance (quadratic discriminant analysis) increased from 0.619 (0.041) using a single signal to 0.724 (0.080) using 24 signals with statistical significance (
${p}$
< 0.05, paired one-tailed
${t}$
-test). This is the first study that inspects frozen tuna using ultrasound and machine learning technology.