Identification of Copper in Stems and Roots of Jatropha curcas L. by Hyperspectral Imaging

Abstract

The in situ determination of metals in plants used for phytoremediation is still a challenge that must be overcome to control the plant stress over time due to metals uptake as well as to quantify the concentration of these metals in the biomass for further potential applications. In this exploratory study, we acquired hyperspectral images in the visible/near infrared regions of dried and ground stems and roots of Jatropha curcas L. to which different amounts of copper (Cu) were added. The spectral information was extracted from the images to build classification models based on the concentration of Cu. Optimum wavelengths were selected from the peaks and valleys showed in the loadings plots resulting from principal component analysis, thus reducing the number of spectral variables. Linear discriminant analysis was subsequently performed using these optimum wavelengths. It was possible to differentiate samples without addition of copper from samples with low (0.5–1% wt.) and high (5% wt.) amounts of copper (83.93% accuracy, >0.70 sensitivity and specificity). This technique could be used after enhancing prediction models with a higher amount of samples and after determining the potential interference of other compounds present in plants.

Monitoring of the copper persistence on plant leaves using pulsed thermography

“…However, as well known, these methods are expensive, are time-consuming, require highly specialized staff, are destructive, and do not allow in-field analysis. Other alternative strategies for the copper residue detection in plants based on the use of imaging techniques have been proposed in literature in few exploratory studies (García-Martín et al, 2020;Goswami & Das, 2017;Mijovilovich et al, 2020;Zeng et al, 2019). However, compared to the imaging approach that we implement in this work, these methods require analyses both in spectral ranges and with operational protocols more complicated and expensive and which is difficult to implement a prototype monitoring system that works in field.…”

End-to-End Fusion of Hyperspectral and Chlorophyll Fluorescence Imaging to Identify Rice Stresses

“…In general, leaves were more likely to be used for plant phenotyping. Fewer studies have studied the high-throughput phenotyping of stems [66][67][68][69][70][71][72][73]. The overall results showed that stems can be an efficient alternative for plant phenotyping in addition to leaves.…”

Just-in-Time Learning-Integrated Partial Least-Squares Strategy for Accurately Predicting 71 Chemical Constituents in Chinese Tobacco by Near-Infrared Spectroscopy

“…Owing to its rapidity, high efficiency, and nondestructivity, near-infrared (NIR) spectroscopy has been used widely in biological, , petrochemical, pharmaceutical, agricultural, − and food-processing , applications. NIR spectroscopy is also effective in the quantitative analysis of tobacco components. ,, Chemical models for components having high contents, such as total sugar (TS), reducing sugar (RS), total nitrogen (TN), nicotine (NIC), and chlorine (Cl), are relatively robust. ,− However, the performance of trace-component models may be subideal. , Furthermore, models of organic acids, amino acids, and Amadori compounds have rarely been reported, although these compounds critically influence the unique style of tobacco.…”

“…Owing to its rapidity, high efficiency, and nondestructivity, near-infrared (NIR) spectroscopy has been used widely in biological, 8 , 9 petrochemical, 10 pharmaceutical, 11 agricultural, 12 − 14 and food-processing 15 , 16 applications. NIR spectroscopy is also effective in the quantitative analysis of tobacco components.…”