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.

Classification, uptake, translocation, and detection methods of nanoparticles in crop plants: a review

“…For example, Candan et al used attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) combined with support vector machine (SVM) and principal component analysis (PCA) to detect the uptake and distribution of AuNPs and CNTs in crops, which were shown to be effective. 137 Mehmet et al propose for the first time to detect AgNPs in different tissues of crops by surface-enhanced Raman spectroscopy (SERS) method. 93 They are confident that in the future this new approach will allow them to monitor not only NPs in plants, but also their fate, chemical transformations, and their creative applications such as delivery systems and imaging agents.…”

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

“…Thus, ML is valuable for the ecological risk assessment of NMs and can also extract new knowledge from the algorithm decision-making process. Recently, an SVM model trained on infrared spectroscopy data to predict NP composition in Cicer arietinum samples 403 was shown to be useful for monitoring of the accumulation and distribution of NPs in environmental systems, such as soil and plants.…”

Estimation Chickpea Species and Productivity per Decare with Synthetic Data Generation Methods

“…Kirtis et al [ 17 ] aimed to present a computer vision system for automatic classification of chickpea varieties. The aim of Candan et al [ 18 ] was to study the applicability of infrared spectroscopy combined with ML techniques to evaluate the uptake and distribution of gold nanoparticles and single-walled carbon nanotubes in chickpea.…”