Soybean Saponin Content Detection Based on Spectral and Image Information Combination

IF 1.7 4区 化学 Q4 BIOCHEMICAL RESEARCH METHODS
Hongmin Sun, Xifan Meng, Yingpeng Han, Xiao Li, Xiaoming Li, Yongguang Li
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引用次数: 0

Abstract

Soybean saponin is a natural antioxidant and is anti-inflammatory. Hyperspectral analysis technology was applied to detect soybean saponin content rapidly and nondestructively in this paper. Firstly, spectral preprocessing methods were studied, and standard normal variable (SNV) was used to remove noise information. Secondly, a two-step hybrid variable selection approach based on synergy interval partial least squares (SiPLS) and iteratively retains informative variables (IRIV) was proposed to extract characteristic variables. Then, the ensemble learning model was constructed by back propagation neural network (BPNN), deep forest (DF), partial least squares regression (PLSR), and extreme gradient boosting (EXG). Finally, image information was combined into spectral data to improve model accuracy. The prediction coefficient of determination () of the final model reached 0.9216. It can provide rapid, nondestructive, and accurate detection technology of soybean saponin content. A combination of spectral and image information will provide a new idea for application of hyperspectral.
基于光谱和图像信息组合的大豆皂素含量检测
大豆皂素是一种天然抗氧化剂,具有消炎作用。本文应用高光谱分析技术快速、无损地检测大豆皂苷的含量。首先,研究了光谱预处理方法,采用标准正态变量(SNV)去除噪声信息。其次,提出了基于协同区间偏最小二乘法(SiPLS)和迭代保留信息变量(IRIV)的两步混合变量选择方法,以提取特征变量。然后,通过反向传播神经网络(BPNN)、深度森林(DF)、偏最小二乘回归(PLSR)和极梯度提升(EXG)构建了集合学习模型。最后,将图像信息与光谱数据相结合,以提高模型的准确性。最终模型的预测决定系数()达到 0.9216。它可以提供快速、无损、准确的大豆皂苷含量检测技术。光谱信息与图像信息的结合将为高光谱的应用提供新的思路。
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来源期刊
Journal of Spectroscopy
Journal of Spectroscopy BIOCHEMICAL RESEARCH METHODS-SPECTROSCOPY
CiteScore
3.00
自引率
0.00%
发文量
37
审稿时长
15 weeks
期刊介绍: Journal of Spectroscopy (formerly titled Spectroscopy: An International Journal) is a peer-reviewed, open access journal that publishes original research articles as well as review articles in all areas of spectroscopy.
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