Jae Hyung Lee
Research Subjects
Research Interests
Jae Hyung Lee has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Amato J. Giaccia
Department of Oncology, University of Oxford, Oxford, UK
1 shared publication - Eui Jung Moon
Division of Radiation and Cancer Biology, Department of Radiation Oncology, Stanford University Medical Center, Stanford, California, USA
1 shared publication - Yeul Na
Stratio Inc., Palo Alto, CA 94303, USA
1 shared publication - Youngsik Kim
Stratio, Inc., Seongnam-si 13449, Republic of Korea
1 shared publication - Yu Xu
Department of Radiation Oncology, Stanford University, Stanford, CA, USA; Department of Bioengineering, Stanford University, Stanford, CA, USA
1 shared publication
Latest Publications
- Open Access
Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer
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 states 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.
Open AccessEvaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer
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 states 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.