Hyperspectral imaging with machine learning for non-destructive classification of Astragalus membranaceus var. mongholicus, Astragalus membranaceus, and similar seeds

Abstract

The roots of Astragalus membranaceus var. mongholicus (AMM) and A. membranaceus (AM) are widely used in traditional Chinese medicine. Although AMM has higher yields and accounts for a larger market share, its cultivation is fraught with challenges, including mixed germplasm resources and widespread adulteration of commercial seeds. Current methods for distinguishing Astragalus seeds from similar (SM) seeds are time-consuming, laborious, and destructive. To establish a non-destructive method, AMM, AM, and SM seeds were collected from various production areas. Machine vision and hyperspectral imaging (HSI) were used to collect morphological data and spectral data of each seed batch, which was used to establish discriminant models through various algorithms. Several preprocessing methods based on hyperspectral data were compared, including multiplicative scatter correction (MSC), standard normal variable (SNV), and first derivative (FD). Then selection methods for identifying informative features in the above data were compared, including successive projections algorithm (SPA), uninformative variable elimination (UVE), and competitive adaptive reweighted sampling (CARS). The results showed that support vector machine (SVM) modeling of machine vision data could distinguish Astragalus seeds from SM with >99% accuracy, but could not satisfactorily distinguish AMM seeds from AM. The FD-UVE-SVM model based on hyperspectral data reached 100.0% accuracy in the validation set. Another 90 seeds were tested, and the recognition accuracy was 100.0%, supporting the stability of the model. In summary, HSI data can be applied to discriminate among the seeds of AMM, AM, and SM non-destructively and with high accuracy, which can drive standardization in the Astragalus production industry.

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

“…Further studies revealed that the average spectral profile of flaxseed has a significant reflectance peak at 420 nm, which is mainly caused by carotenoids ( Yang et al., 2021 ). In addition, the spectral profile shows a clear upward trend in the range of 600-750 nm, which is attributed to the fact that this wavelength corresponds to the vibration of the N-H chemical bond of amino acids in the seeds ( Xu et al., 2022 ). The absorption peak near 980 nm originates from the O-H stretching vibration, which is related to the structure of water molecules ( Yu et al., 2014 ).…”

“…In addition, CARS can more fully consider the correlation between wavelengths, thus better reflecting the characteristics of the data. In hyperspectral data, the CARS algorithm helps select representative characteristic wavelengths more comprehensively, considering that there may be complex relationships between wavelengths ( Xu et al., 2022 ).…”

Optimal-Band Analysis for Chlorophyll Quantification in Rice Leaves Using a Custom Hyperspectral Imaging System

“…With applications ranging from crop analysis to real-time plant health and yield monitoring, HSI has proven invaluable in the agriculture field [5,10,23,[28][29][30][31]. Groundbased HSI systems are appropriate for smaller-scale investigations, while airborne setups are suitable for surveying large areas of land.…”