Detection of sugar content in sugar beets using hyperspectral imaging

Mahdiani, Mohsen; Khojastehpour, Mehdi; Golzarian, Mahmood Reza

doi:10.22034/jelsa.2024.473159.1081

Detection of sugar content in sugar beets using hyperspectral imaging

Document Type : Original research article

Authors

Mohsen Mahdiani ¹

Mehdi Khojastehpour ²

Mahmood Reza Golzarian ³

¹ Ph.D. Student, Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

² Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

³ School of Engineering and IT, Murdoch University, WA 6150, Australia

10.22034/jelsa.2024.473159.1081

Abstract

Measuring the sugar content in sugar beet is challenging because of the labor, expenses, and chemicals required. Hence, there is a necessity for a more efficient method to measure this content. This study aims to establish an efficient, non-invasive method for detecting sugar content in sugar beets using hyperspectral imaging, which could revolutionize quality control in the sugar beet industry. This study used hyperspectral imaging to analyze 400-950 nm sugar beet paste. Pre-processing techniques such as SNV (Standard Normal Variate) and SG (Savitzky-Golay), along with wavelength selection methods such as SPA (Successive Projection Algorithm) and CARS (competitive adaptive reweighted sampling), were applied. Furthermore, various regression models including MLR (multiple linear regression), PLS (Partial Least Squares regression (, and SVR (Support Vector Regression) were employed for prediction. Evaluating these models based on R² and RMSE criteria, the PLS regression model with SNV pre-processing and SPA wavelength selection stood out, achieving an R² value of 0.91% and RMSE of 0.24. These findings suggest the potential of hyperspectral imaging as a rapid and accurate means of determining sugar content in sugar beet across the VIS-NIR spectrum.

Highlights

The analysis of hyperspectral images was investigated to predict the sugar content.
SG and SNV algorithms were used for preprocessing.
CARS and SPA algorithms were used to select the effective wavelengths.
Models for prediction have been created by MLR, PLS and SVR algorithms.
The best model for prediction of sugar content were selected.

Keywords

Hyperspectral imaging

Rapid detection

Regression analysis

Sugar beet

Sugar content

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