Development and assessment of a lysophospholipid-based deep learning model to discriminate geographical origins of white rice.
Nguyen Phuoc Long, Dong Kyu Lim, Changyeun Mo, Giyoung Kim, Sung Won Kwon
Author Information
Nguyen Phuoc Long: Research Institute of Pharmaceutical Sciences and College of Pharmacy, Seoul National University, Seoul, 08826, Republic of Korea.
Dong Kyu Lim: Research Institute of Pharmaceutical Sciences and College of Pharmacy, Seoul National University, Seoul, 08826, Republic of Korea. ORCID
Changyeun Mo: National Institute of Agricultural Sciences, Rural Development Administration, Jeonju, 54875, Republic of Korea.
Giyoung Kim: National Institute of Agricultural Sciences, Rural Development Administration, Jeonju, 54875, Republic of Korea.
Sung Won Kwon: Research Institute of Pharmaceutical Sciences and College of Pharmacy, Seoul National University, Seoul, 08826, Republic of Korea. swkwon@snu.ac.kr.
Geographical origin determination of white rice has become the major issue of food industry. However, there is still lack of a high-throughput method for rapidly and reproducibly differentiating the geographical origins of commercial white rice. In this study, we developed a method that employed lipidomics and deep learning to discriminate white rice from Korea to China. A total of 126 white rice of 30 cultivars from different regions were utilized for the method development and validation. By using direct infusion-mass spectrometry-based targeted lipidomics, 17 lysoglycerophospholipids were simultaneously characterized within minutes per sample. Unsupervised data exploration showed a noticeable overlap of white rice between two countries. In addition, lysophosphatidylcholines (lysoPCs) were prominent in white rice from Korea while lysophosphatidylethanolamines (lysoPEs) were enriched in white rice from China. A deep learning prediction model was built using 2014 white rice and validated using two different batches of 2015 white rice. The model accurately discriminated white rice from two countries. Among 10 selected predictors, lysoPC(18:2), lysoPC(14:0), and lysoPE(16:0) were the three most important features. Random forest and gradient boosting machine models also worked well in this circumstance. In conclusion, this study provides an architecture for high-throughput classification of white rice from different geographical origins.
