Wheat Flour Discrimination Using Two-Dimensional Correlation Spectroscopy and Deep Learning.

IF 2.2 3区 化学 Q2 INSTRUMENTS & INSTRUMENTATION
Tianrui Zhang, Yifan Wang, Jiansong Sun, Jing Liang, Bin Wang, Xiaoxuan Xu, Jing Xu, Lei Liu
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引用次数: 0

Abstract

The continuous evolution of deep learning has garnered significant attention in spectroscopy. This study focuses on identifying wheat flour, presenting a more efficient and accurate method by combining two-dimensional correlation spectroscopy (2D-COS) and deep learning techniques. A data set of 316 near-infrared (NIR) spectral samples of four types of wheat flour was collected. By applying three disparate 2D-COS techniques, i.e., synchronous, asynchronous, and integrated, we crafted 948 2D-COS images. These images, obtained by transforming the original one-dimensional spectra into 2D representations, offer richer information for deep learning analysis. The study introduced an 18-layer residual network incorporating a convolutional attention mechanism, specifically tailored for the 2D-COS analysis of wheat flour, aimed at enhancing the model's discriminative capabilities by refining the residual neural network's structure. Achieving an unprecedented recognition accuracy of 100% through methodical optimization and rigorous training on the synchronous 2D-COS data set of wheat flour, the proposed model is a testament to the efficacy of deep learning in spectroscopic analysis. To further exhibit the confluence of 2D-COS with deep learning, t-distributed stochastic neighbor embedding was employed to visualize the distinctive 2D-COS features within the deep learning architecture. Additionally, the model's performance was juxtaposed with prevailing NIR spectral recognition methods, including random forest, gradient boosting decision tree, and artificial neural network. This comparison cemented the proposed approach's superiority in wheat flour categorization. The findings of this study not only introduce a novel and efficient solution for wheat flour quality analysis but also underscore the significant potential of deep learning techniques in spectroscopy applications.

利用二维相关光谱学和深度学习识别小麦粉。
深度学习的不断发展在光谱学领域引起了极大关注。本研究以识别小麦粉为重点,结合二维相关光谱(2D-COS)和深度学习技术,提出了一种更高效、更准确的方法。研究收集了四种小麦粉的 316 个近红外光谱样本数据集。通过应用三种不同的 2D-COS 技术,即同步、异步和集成技术,我们制作了 948 幅 2D-COS 图像。这些图像是通过将原始的一维光谱转化为二维表示而获得的,为深度学习分析提供了更丰富的信息。该研究引入了一个包含卷积注意力机制的 18 层残差网络,专门用于小麦粉的二维-COS 分析,旨在通过完善残差神经网络的结构来增强模型的辨别能力。通过对小麦粉同步 2D-COS 数据集进行有条不紊的优化和严格训练,所提出的模型达到了前所未有的 100% 识别准确率,证明了深度学习在光谱分析中的功效。为了进一步展示 2D-COS 与深度学习的融合,我们采用了 t 分布随机邻域嵌入技术,以可视化深度学习架构中独特的 2D-COS 特征。此外,还将该模型的性能与主流的近红外光谱识别方法(包括随机森林、梯度提升决策树和人工神经网络)进行了对比。这一比较巩固了所提出的方法在小麦粉分类中的优越性。这项研究的结果不仅为小麦粉质量分析引入了一种新颖、高效的解决方案,还凸显了深度学习技术在光谱学应用中的巨大潜力。
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来源期刊
Applied Spectroscopy
Applied Spectroscopy 工程技术-光谱学
CiteScore
6.60
自引率
5.70%
发文量
139
审稿时长
3.5 months
期刊介绍: Applied Spectroscopy is one of the world''s leading spectroscopy journals, publishing high-quality peer-reviewed articles, both fundamental and applied, covering all aspects of spectroscopy. Established in 1951, the journal is owned by the Society for Applied Spectroscopy and is published monthly. The journal is dedicated to fulfilling the mission of the Society to “…advance and disseminate knowledge and information concerning the art and science of spectroscopy and other allied sciences.”
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