Proteins and lipids content estimation in soybeans using Raman hyperspectral imaging

Authors: AULIA, RIZKIANA - Chungnam National University; AMANAH, HANIM ZUHROTUL - Gadjah Mada University; LEE, HONGSEOK - Rural Development Administration - Korea; Kim, Moon; Baek, Insuck; Qin, Jianwei - Tony Qin; CHO, BYOUNG-KWAN - Chungnam National University

Submitted to: Frontiers in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/20/2023
Publication Date: 7/22/2023
Citation: Aulia, R., Amanah, H., Lee, H., Kim, M.S., Baek, I., Qin, J., Cho, B. 2023. Proteins and lipids content estimation in soybeans using Raman hyperspectral imaging. Frontiers in Plant Science. 14. Article e1167139. https://doi.org/10.3389/fpls.2023.1167139.
DOI: https://doi.org/10.3389/fpls.2023.1167139

Interpretive Summary: Soybean is an important crop with a rich source of proteins and lipids, both of which are essential for human nutrition and key parameters affecting its quality and market value. Traditional chemical analysis approaches (e.g., Soxhlet and Kjeldahl methods) for determining protein and lipid content of soybeans are destructive, time-consuming, and labor-intensive. This study developed a rapid and non-destructive method to evaluate soybean protein and lipid content based on macro-scale Raman hyperspectral imaging technique. A line-scan Raman hyperspectral imaging system was used to collect images from soybean seeds. Partial least squares regression method was used to develop prediction models to correlate the Raman spectral data with the protein and lipid content of the soybean seeds. Chemical images were created to show the distribution and amount of the protein and lipid on single soybean seeds. The method developed in this study can be used for accurate and efficient estimation of the protein and lipid content, which would benefit the soybean industry for quality control and breeding programs.

Technical Abstract: Unlike standard chemical analysis methods involving time-consuming, labor-intensive, and invasive pretreatment procedures, Raman hyperspectral imaging (HSI) can rapidly and non-destructively detect components without professional supervision. Generally, the Kjeldahl methods and Soxhlet extraction are used to chemically determine the protein and lipid content of soybeans. This study is aimed at developing a high-performance model for estimating soybean protein and lipid content using a non-destructive Raman HSI. Partial least squares regression (PLSR) techniques were used to develop the model using a calibration model based on 70% spectral data, and the remaining 30% of the data were used for validation. The results indicate that the Raman HSI, combined with PLSR, resulted in a protein and lipid model Rp2 of 0.90 and 0.82 with Root Mean Squared Error Prediction (RMSEP) 1.27 and 0.79, respectively. Additionally, this study successfully used the Raman HSI approach to create a prediction image showing the distribution of the targeted components, and could predict protein and lipid based on a single seeds.