利用太赫兹光谱与集合学习相结合识别小米产地

IF 3.1 3区 物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION
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引用次数: 0

摘要

由于不同产地的小米在价格和口感上存在很大差异,因此准确追踪小米的原产地对生产者和消费者来说都至关重要。传统的小米产地鉴别方法费时、费力、复杂且具有破坏性。在本研究中,通过将太赫兹时域光谱与集合学习相结合,开发了一种快速、非破坏性区分小米产地的新方法。首先使用支持向量机(SVM)、随机森林(RF)和核极端学习机(KELM)三种机器学习算法建立不同的判别模型,然后比较六种不同的预处理方法对模型分类性能的影响。结果发现,采用萨维茨基-戈莱预处理的模型在准确判断小米的地理来源方面表现出明显的优势。在这些发现的基础上,研究引入了一种创新的集合学习策略,利用拓扑和堆叠技术,发挥三种算法的集体优势。这种方法的结果表明,它能在无需对任何参数进行微调的情况下区分来自五个不同地区的黍子。预测集的准确率、F1 分数和 Kappa 均为 100%,明显优于单一模型、传统投票法和堆叠法。本研究的最终结果表明,太赫兹时域光谱与 TOPSIS-Stacking 集合学习的集成是一种很有前途的方法,可快速、非侵入性地精确判别小米的地理产地。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of millet origin using terahertz spectroscopy combined with ensemble learning

It’s crucial for both producers and consumers to accurately trace the origin of millet, given the significant differences in price and taste that exist between millets from various origins. The traditional method of identifying the origin of millet is time-consuming, laborious, complex, and destructive. In this study, a new method for fast and non-destructive differentiation of millet origins is developed by combining terahertz time domain spectroscopy with ensemble learning. Firstly, three machine learning algorithms, namely support vector machine (SVM), random forest (RF), and kernel extreme learning machine (KELM), were used to build different discriminative models, and then the impact of six different preprocessing methods on the models’ classification performance was compared. It was observed that models employing Savitzky-Golay preprocessing exhibited pronounced superiority in accurately determining the millet’s geographical origins. Building upon these findings, the research introduces an innovative ensemble learning strategy, leveraging both topsis and stacking techniques, to harness the collective strengths of the three algorithms. The outcomes of this approach reveal its remarkable capacity to distinguish millets originating from five distinct locations without the necessity for any parameter fine-tuning. The accuracy, F1 score, and Kappa on the prediction set are all 100 %, which significantly outperforms the single model, traditional voting method, and stacking method. The culmination of this study suggests that the integration of terahertz time-domain spectroscopy and TOPSIS-Stacking ensemble learning emerges as a promising method for the swift and non-intrusive discrimination of millet geographical origins with remarkable precision.

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来源期刊
CiteScore
5.70
自引率
12.10%
发文量
400
审稿时长
67 days
期刊介绍: The Journal covers the entire field of infrared physics and technology: theory, experiment, application, devices and instrumentation. Infrared'' is defined as covering the near, mid and far infrared (terahertz) regions from 0.75um (750nm) to 1mm (300GHz.) Submissions in the 300GHz to 100GHz region may be accepted at the editors discretion if their content is relevant to shorter wavelengths. Submissions must be primarily concerned with and directly relevant to this spectral region. Its core topics can be summarized as the generation, propagation and detection, of infrared radiation; the associated optics, materials and devices; and its use in all fields of science, industry, engineering and medicine. Infrared techniques occur in many different fields, notably spectroscopy and interferometry; material characterization and processing; atmospheric physics, astronomy and space research. Scientific aspects include lasers, quantum optics, quantum electronics, image processing and semiconductor physics. Some important applications are medical diagnostics and treatment, industrial inspection and environmental monitoring.
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