Protein and lipid content estimation in soybeans using Raman hyperspectral imaging

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.

Modeling of flaxseed protein, oil content, linoleic acid, and lignan content prediction based on hyperspectral imaging

“…Therefore, it is necessary to pre-process the raw spectra to eliminate the noise as much as possible or reduce the influence of other environmental factors on the spectral information. The study employed various preprocessing techniques (Savitzky-Golay (SG) smoothing, normalization, baseline, standard normal variable correction (SNV), moving average (MA), multiple scattering correction (MSC), and first-order derivative (1st Der)) on the raw flaxseed spectra ( Aulia et al., 2023 ). SG is mainly used to achieve the effect of smoothing curves and reducing noise by fitting local polynomials to the original spectra using a sliding window; Normalize can normalizes the spectral data to the same scale, which usually scales the value of each wavelength to a value between 0 and 1.…”

Rapid and Nondestructive Determination of Oil Content and Distribution of Potato Chips Using Hyperspectral Imaging and Chemometrics

“…Soxhlet extraction and gas chromatography are traditional methods to determine the total lipid content of food products, but those analytical techniques can only be used on a laboratory scale and are time-consuming, destructive, and involve toxic and expensive chemical solvents, which are not feasible for real-time monitoring of oil content in production lines . To date, nondestructive and real-time monitoring technologies have been focused on near-infrared (NIR) spectroscopy, Raman spectroscopy (RS), Fourier-transform infrared (FTIR) spectroscopy, and hyperspectral imaging (HSI). , Among these methods, NIR, RS, and FTIR probe spectral information from a single point, and they are incapable of analyzing spatial component distribution and heterogeneity to detect regional quality and safety issues such as molding, hygroscopicity, and oxidation .…”