Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer

Eui Jung Moon, Youngsik Kim, Yu Xu, Yeul Na, Amato J. Giaccia, Jae Hyung Lee
2020 Sensors  
There has been strong demand for the development of an accurate but simple method to assess the freshness of food. In this study, we demonstrated a system to determine food freshness by analyzing the spectral response from a portable visible/near-infrared (VIS/NIR) spectrometer using the Convolutional Neural Network (CNN)-based machine learning algorithm. Spectral response data from salmon, tuna, and beef incubated at 25 °C were obtained every minute for 30 h and then categorized into three
more » ... es of "fresh", "likely spoiled", and "spoiled" based on time and pH. Using the obtained spectral data, a CNN-based machine learning algorithm was built to evaluate the freshness of experimental objects. In addition, a CNN-based machine learning algorithm with a shift-invariant feature can minimize the effect of the variation caused using multiple devices in a real environment. The accuracy of the obtained machine learning model based on the spectral data in predicting the freshness was approximately 85% for salmon, 88% for tuna, and 92% for beef. Therefore, our study demonstrates the practicality of a portable spectrometer in food freshness assessment.
doi:10.3390/s20154299 pmid:32752216 fatcat:ksalcyyyebg2hkzu54heuubixe