*3.5. Summarizing Heavy Metal Modelling*

Grapevine leaves are a suitable option for the study of the absorption and accumulation of heavy metals [21]. Therefore, the monitoring of heavy metal concentration can ensure food security as well as the reduction of health and ecological risks [16]. In this study, a stress–stroke method was employed to ensure the appearance of heavy metals in grapevine foliage. This method was also used in similar studies [34,38]. It is important to note that expanding heavy metal masses in plant foliage leads to an increase in the number of reactive oxygen species [70]. Reactive oxygen species are produced in the course of electron transfer activities—mainly in chloroplasts and mitochondria. They also have an important role in consequences such as plant growth retardation, chlorophyll content reduction, inhibition of enzymatic activity, damage to biological molecules (such as lipids, proteins, and nucleic acids, especially DNA), cell membrane peroxidation, and damage to important cellular organelles such as chloroplasts and mitochondria [71,72]. Heavy metal stress, like other non-biotic stresses, leads to changes in the pathways of synthesis of secondary plant metabolites and increases or decreases these compounds [73,74]. It was also observed that heavy metal stress leads to changes in the cuticle position of the leaves and the openings of leaves' stomas [73]. Considering the effect of heavy metals on the physico-chemical changes in the plant, the spectral pattern of the plant can change, which leads to the spectral pattern differentiation of stressed leaves from healthy leaves. These differentiations can be determined by field-based spectrometry.

According to our results, SVM and MLR prediction methods performed similarly in estimating Cu contents, but in relation to Zn, Pb, Cr, and Cd, the SVM models outperformed the MLR models (Tables 3 and 4). Therefore, the SVM regression method tends to be preferred. Although, in related studies, MLR was the most-used model due to its clarity and structure simplicity [30,41,45], the results of this study recommend SVM for future investigations. The most important reason for the superiority of SVM as opposed to MLR can be attributed to the nature of the relationships between independent and dependent variables. SVM regression was able to perform more accurately in estimating heavy metals due to its high flexibility in training by using both linear and nonlinear functions in the kernel equation [75]. Similarly, a comparison between MLR and artificial neural network (ANN) methods was performed to estimate heavy metals in rice leaves [38]; the results also showed the superior performance of ANN as opposed to MLR.

A comparison between the results obtained for the testing set and the optimal spectral indices and wavelengths in estimating heavy metal contents in various studies was conducted and is shown in Table 5. Based on the R<sup>2</sup> rate of the test set, the performance order of the presented models was Cd > Zn > Cr > Pb > Cu (Table 5). Therefore, the predictive accuracies for Cd, Zn, and Cu were 86, 85, and 56%, respectively. Li (2011) listed a prediction order accuracy of heavy metals in vegetation as Cr > Pb > Cu > Zn. Furthermore, Zhuang [41] ranked the prediction accuracy of heavy metals in rice as Cu > Pb > Zn, which is different from the findings of the present study (Table 5). The rate of prediction accuracy of Pb in this study is close to the findings of Li [44] and Zhuang [41]. The accuracy of Cr prediction content is also comparable to the results of Li et al. [39]. According to Li [44], Zhuang [41] and Ping et al. [30], Cu predictions were, respectively, 60, 76, and 69%, higher than the present study's result (56%) (Table 5). However, the present study was able to estimate Zn contents with a higher accuracy compared with the results of Li [44], Zhuang [41], and Kooistra et al. [45], as well as Cd contents as compared to the findings of Ping et al. [30] and Liu et al. [37].

As a final remark, in many studies, RDE and VIS regions were reported to be sensitive to the stress caused by heavy metals [36,38,41]. The comparison of the optimal spectral indices and wavelengths selected for the heavy metal rate predicted in the present study and other related studies show discrepancies (Table 5). The number of spectral samples, spectroscopy acquisition level, spectral range, calculated spectral indices, as well as statistical analyses for data reduction and relationship modelling can all play a role in explaining these differences. Finally, it should also be pointed out that each heavy metal has a special effect, leading to distinct responses depending on the plant species (including leaf colour changes, chlorosis, necrosis, dwarfism, giant, leaf and root spreading, etc.), which can justify this finding [76].


**Table 5.** Comparison results of the best models presented in this study and other similar studies in relation to the estimation of heavy metal contents in plant species using field-based spectrometry.

NI11: (R700–R690)/(R700+R690), NI15: (R760–850–R350–400)/(R760–850+R350–400), NI17:(R1220–R510)/ (R1220+R510), R*n*: Reflections at a certain wavelength.

## **4. Conclusions**

In this study, we examined the suitability of in-field hyperspectral data (wavelengths from 350 to 2500 nm and 32 spectral indices) in the estimation of heavy metal contents (Cu, Zn, Pb, Cr, and Cd) in vine leaves. Our most important findings are listed as follows:

(i) The grapevine's foliar spectral signatures (reflectance characteristics) altered when applying heavy metal stress due to their effects on the biochemical components and the leaves' structure. Considerable changes are observed in the VIS, RDE, NIR, and MIR regions of the electromagnetic spectrum.


Altogether, the scenario of joining spectral indices with SVM regression is suggested as the most appropriate method for predicting heavy metal contents in the grapevine leaves. At the same time, this conclusion underpins the usage of in-field spectroscopy data and multivariate statistical analysis for the rapid and eco-friendly monitoring of heavy metals in food-producing ecosystems. This study further revealed that the spectral responses of foliar grapevine and other agriculture/horticulture species to heavy metal stress need to be better understood. Similar studies are required to investigate heavy metal spectral signatures in other plant species. Eventually, the ultimate goal of this research line is to integrate field data with spectral data from overpassing aerial and satellite sensors to up-scale and automate the monitoring strategy to the field scale.

**Author Contributions:** M.M. performed the calculus, designed and conducted the experiments and wrote the paper, J.V. edited and supervised the form of the paper, S.M. managed and supported the heavy metal measurements, M.A. and H.A. observed the scientific content of the paper.

**Funding:** This work was supported by the European Research Council (ERC) under the ERC-2017-STG SENTIFLEX project (grant agreement 755617) and Jochem Verrelst was supported by a Ramón y Cajal Contract (Spanish Ministry of Science, Innovation and Universities). Mohsen Mirzaei was also supported by the Research Institute for Grapes and Raisin (RIGR), Malayer University.

**Acknowledgments:** We thank the scientific editor and the reviewers for their valuable suggestions.

**Conflicts of Interest:** The authors declare no conflict of interest.
