Non-destructive prediction of protein contents of soybean seeds using near-infrared hyperspectral imaging

• Main Authors: Aulia, Rizkiana, Kim, Yena, Zuhrotul Amanah, Hanim, Muhammad Akbar Andi, Arief, Kim, Haeun, Kim, Hangi, Lee, Wang-Hee, Kim, Kyung-Hwan, Baek, Jeong-Ho, Cho, Byoung-Kwan
• Format: Article PeerReviewed
• Language: English
• Published: Elsevier B.V. 2022
• Subjects: Food technology
• Online Access: https://repository.ugm.ac.id/282850/1/56_Non%20destructive%20prediction.pdf; https://repository.ugm.ac.id/282850/
• Institution: Universitas Gadjah Mada

Protein content is one of the most crucial factors in soybean quality. However, the breeding procedure necessitates the time-consuming and costly selection of elite genotypes from many experimental lines in a destructive manner. The present work aims to predict protein content in single soybean seed...