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激光诱导击穿光谱结合实验设计和机器学习用于鉴别种子活力。

Laser-Induced Breakdown Spectroscopy Associated with the Design of Experiments and Machine Learning for Discrimination of Seed Vigor.

机构信息

SISFOTON-UFMS-Laboratório de Óptica e Fotônica, UFMS-Universidade Federal de Mato Grosso do Sul, Campo Grande 79070-900, MS, Brazil.

Embrapa Instrumentation, São Carlos 13560-970, SP, Brazil.

出版信息

Sensors (Basel). 2022 Jul 6;22(14):5067. doi: 10.3390/s22145067.

DOI:10.3390/s22145067
PMID:35890747
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9316187/
Abstract

Laser-induced breakdown spectroscopy (LIBS) associated with machine learning algorithms (ML) was used to evaluate the seed physiological quality by discriminating the high and low vigor seeds. A 2 factorial design was used to optimize the LIBS experimental parameters for spectral analysis. A total of 120 samples from two distinct cultivars of seeds exhibiting high vigor (HV) and low vigor (LV) in standard tests were studied. The raw LIBS spectra were normalized and submitted to outlier verification, previously to the reduction data dimensionality from principal component analysis. Supervised machine learning algorithm parameters were chosen by leave-one-out cross-validation in the test samples, and it was tested by external validation using a new set of data. The overall accuracy in external validation achieved 100% for HV and LV discrimination, regardless of the cultivar or the classification algorithm.

摘要

激光诱导击穿光谱（LIBS）结合机器学习算法（ML）用于通过区分高活力和低活力种子来评估种子的生理质量。采用 2 因子设计来优化用于光谱分析的 LIBS 实验参数。研究了来自两个不同品种的共 120 个种子样本，这些种子在标准测试中表现出高活力（HV）和低活力（LV）。原始 LIBS 光谱经过归一化并进行异常值验证，然后通过主成分分析进行降维处理。通过在测试样本中进行留一法交叉验证选择有监督机器学习算法参数，并使用新数据集进行外部验证测试。在外部验证中，无论品种或分类算法如何，HV 和 LV 区分的总体准确率均达到 100%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94ac/9316187/6a40b6aed01e/sensors-22-05067-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94ac/9316187/423cc575621b/sensors-22-05067-g001a.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94ac/9316187/423cc575621b/sensors-22-05067-g001a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94ac/9316187/97af5b4c7b84/sensors-22-05067-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94ac/9316187/c8568b811370/sensors-22-05067-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94ac/9316187/5387828fd74f/sensors-22-05067-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94ac/9316187/46075b066fb5/sensors-22-05067-g005a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94ac/9316187/6a40b6aed01e/sensors-22-05067-g006.jpg

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