Protein and lipid content estimation in soybeans using Raman hyperspectral imaging

Rizkiana Aulia; Hanim Z Amanah; Hongseok Lee; Moon S Kim; Insuck Baek; Jianwei Qin; Byoung-Kwan Cho

doi:10.3389/fpls.2023.1167139

Protein and lipid content estimation in soybeans using Raman hyperspectral imaging

Front Plant Sci. 2023 Aug 4:14:1167139. doi: 10.3389/fpls.2023.1167139. eCollection 2023.

Authors

Rizkiana Aulia¹, Hanim Z Amanah², Hongseok Lee³, Moon S Kim⁴, Insuck Baek⁴, Jianwei Qin⁴, Byoung-Kwan Cho^{1

5}

Affiliations

¹ Department of Smart Agricultural System, Chungnam National University, Daejeon, Republic of Korea.
² Department of Agricultural and Biosystem Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia.
³ National Institute of Crop Science, Rural Development Administration, Miryang, Republic of Korea.
⁴ Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States.
⁵ Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea.

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 R_p² 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.

Keywords: hyperspectral Raman imaging; lipid; non-destructive measurement; protein; soybean; spectral analysis.

Grants and funding

This research was funded by the National Institute of Crop Science (Project No.: PJ015689) of the Rural Development Administration, Republic of Korea.