Uptake and Presence Evaluation of Nanoparticles in Cicer arietinum L. by Infrared Spectroscopy and Machine Learning Techniques.

Abstract

The aim of this work was to study the applicability of infrared spectroscopy combined with machine learning techniques to evaluate the uptake and distribution of gold nanoparticles (AuNPs) and single-walled carbon nanotubes (CNTs) in Cicer arietinum L. (chickpea). Obtained spectral data revealed that the uptake of AuNPs and CNTs by the C. arietinum seedlings' root resulted in the accumulation of AuNPs and CNTs at stem and leaf parts, which consequently led to the heterogeneous distribution of nanoparticles. principal component analysis and support vector machine classification were applied to assess its usefulness for evaluating the results obtained using the attenuated total reflectance-Fourier transform infrared spectroscopy method of C. arietinum plant grown at different conditions. Specific wavenumbers that could classify the different nanoparticle constituents of C. arietinum plant extracts according to their ATR-FTIR spectra were identified within three specific regions: 450-503 cm -1 , 750-870 cm -1 , and 1022-1218 cm -1 , based on larger PCA loadings of C. arietinum ATR-FTIR spectra with distinct spectral differences between samples of interest. The current work paves a path to the future fabrication strategies for AuNPs and single-walled CNTs via plant-based routes and highlights the diversity of the applications of these materials in bio-nanotechnology. These results indicate the importance of family-plant selection, choice of methods, and pathways for the efficient biomolecule delivery, drug cargo, and optimal conditions in the wide spectrum of bioapplications.

Plant phenotyping relevance

赤外分光法と機械学習を用いて、植物体内のナノ粒子の取り込み・分布という植物状態を評価する方法の適用性と分類性能を検討しており、測定・解析手法が中心である。

Code and data availability

The paper's ATR-FTIR spectral data and PCA/SVM analysis results are not publicly deposited; the Data Availability Statement states they are available only upon request from the authors. No public code, dataset, or image repository is provided. The carbonsolution.com URL is merely a commercial supplier of CNTs, not a论文-

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