Original research articles

Ozone treatments to induce systemic-acquired resistance in leaves of potted vines: molecular responses and NIR evaluation for identifying effective dose and exposition duration


The European Community has recently imposed considerable restrictions on the use of pesticides, with the establishment of a regulatory framework for the sustainable use of agro-chemicals. However, in the viticulture sector, the intensive use of chemical pesticides, as well as sulfur and copper, is often required. Recently, ozone has been proposed as a possible environmentally friendly tool for controlling the development of pests on vines. However, little is known about the parameters linked to the practical application of ozone for controlling grapevine pests and how it triggers plant defence mechanisms. The main aim of this preliminary study was to determine the concentration of ozone and exposure duration in a treatment for stimulating the expression of systemic acquired resistance (SAR)-related genes, without inducing toxic effects and affecting vine health. In the first trial, three different combinations of ozone concentration and duration of treatment were tested on potted grapevines: i) gaseous ozone at 300 ppb for 12 hours, ii) gaseous ozone at 100 ppb for 6 hours, and iii) gaseous ozone at 100 ppb for 3 hours. Based on the results of the first trial, the potted vines were treated with just 100 ppb for 3 hours in a second trial. Leaves at different developmental stages were sampled. The expression level of systemic acquired resistance-related genes was analysed 12 hours and 7 days after each treatment. Furthermore, physiological parameters and NIR spectra were analysed. Ozone induced a transient up-regulation (limited to 12 hours after the treatments) of chitinases, β-1,3-glucanase and glutathione-S-transferase. On the other hand, pathogen-related (PR) genes showed a more persistent over-expression. The ozone treatment selectively affected the stomatal conductance depending on the different ozone concentrations. Detected NIR spectra revealed significant structural changes in ozone-treated plants, especially in leaves exposed to higher concentrations of ozone. These results suggest that ozone is able to transiently stimulate the expression of some resistance-related genes even at low and non-toxic doses for the vine leaves.


In Europe, although the viticulture sector occupies only 3.3 % of the total cultivated area, the use of chemicals for pest control in wine production accounts for the 65 % of the total chemicals employed in the whole of the agricultural sector (Eurostat, 2007; Blanco-Ward et al., 2021). In recent years, both environmental concerns and the regulatory framework based on integrated pest management (IPM) imposed by the European Community have compelled a search for new strategies and approaches for the protection of vine from pests (Bhadra, 2015; Modesti et al., 2019). The main goal of the IPM is to replace conventional chemical pesticides with more environmentally friendly products. In this context, some studies have suggested the use of ozone at different grapevine phenological stages (Bhadra, 2015; Modesti et al., 2019; Campayo et al., 2019; Romeo-Oliván et al., 2021). Ozone (O3) is a strong oxidative gas which is already used in different steps of wine production (e.g., clean-in-place programmes, disinfection of post-harvest grapes and sulphur dioxide-free vinification) (Carbone and Mencarelli, 2015; Bellincontro et al., 2017; Mencarelli and Bellincontro, 2018). It has also been reported that applying O3 to harvested grapes under controlled conditions may have positive effects on grapes and wine quality, with an increase in different phenolic fractions and extractability, antioxidant enzyme activity and volatile terpenoids (Desanctis et al., 2015; Carbone and Mencarelli, 2015; Bellincontro et al., 2017; Modesti et al., 2018).

However, there is still a lack of knowledge regarding the overall effects of O3 applied to vines. High concentrations of O3 can be deleterious to plant physiology and can often lead to different types of damage, such as a decrease in photosynthetic activity, premature leaves senescence, chlorophyll degradation, metabolic disorders, visible injuries and a decrease in plant productivity. In grape leaves, the damage caused by high O3 levels is described as oxidative stipple. The first symptoms are generally small, brown and dot-like lesions on the upper surface of the leaf (Musselman, 1985). Most of these primary lesions become necrotic while retaining the original stipple appearance (Richards et al., 1959). All these effects are known to be a result of oxidative-related processes (Feng et al., 2008; Heath, 2008; Fuhrer, 2009; Arneth et al., 2010; Ainsworth et al., 2020). On the other hand, when applied at an adequate and controlled concentration, O3 can have germicidal effects on plant pathogens. Indeed, O3 is able to oxidise important pathogen cellular components and thereby reduce their growth (Achen and Yousef, 2001; Tzortzakis et al., 2007). Additionally, it has been reported that induced oxidative stress leads to a pathogen-attack-like response, which includes the activation of systemic acquired resistance (SAR) (Grulke and Heath, 2020; Conklin and Barth, 2004; Langebartels et al., 2002). Once O3 penetrates the leaves through the stomata, reactive oxygen species (ROS) are produced within the cell (Heath, 2007; Heath, 2008; Grulke and Heath, 2020). The production of ROS triggers a pathogen-like response: local programmed cell death (PCD) to avoid the spread of the infection, hypersensitive response (HR) and the subsequent activation of pathogen-related proteins (PR) and other SAR-related genes, such as glutathione S-transferase (GST), chitinases (Chit) and β-1,3 glucanase. All these mechanisms are able to gradually shift the local defence response to a more systemic resistance (Conklin and Barth, 2004; Heath, 2007; Heath, 2008; Grulke and Heath, 2020). This ROS-mediated mechanism is activated by O3 exposition as well. Considering all the above-mentioned issues and the scarce knowledge about the possible use of O3 (as ozonated water) for IPM in viticulture, the hypothesis for the present work was that treating potted vines (Vitis vinifera cv Sangiovese) with appropriate O3 concentrations and duration of expositions, can be an effective tool for stimulating SAR without inducing any physiological damage to the plant. Laboratory trials were therefore carried out to study the physiological and molecular responses of grapevine leaves to O3 applied under controlled conditions (potted plants in a greenhouse).

Materials and methods

1. Ozone treatments

Three-year-old grafted vines (Vitis vinifera cv Sangiovese grafted onto 1103 Paulsen) in pots (2.4 L; 13 x 13 x 18 cm) containing a mixture of soil, peat and fine sand (1:1:1, v/v/v) under controlled irrigation (1 L per plant daily) were used for the study. Each year, the plants were of uniform size and at the same physiological development stage. The first trial was performed in 2019. O3 treatments (three in total repeated on the same plants) were applied once a month (corresponding with BBCH 55, 69 and 77). Twenty plants were placed in a 9 m3 lit (4000 K white LED, 10 W) room at 10 (± 1) °C with an RH of 70 (± 5) % and treated with: i) gaseous O3 at 300 ppb for 12 h ii) gaseous O3 at 100 ppb for 6 h and iii) gaseous O3 at 100 ppb for 3 h (hereafter referred to as first, second and third treatment), using the O3 generator A series, which was equipped with an O3 probe (PC Engineering, Uggiate Trevano, Como, Italy) placed inside the treatment room to maintain a stable O3 concentration throughout the duration of the treatments. Twenty vines did not receive any O3 treatment and were kept in a 9 m3 lit (4000 K white LED, 10 W) room at 10 (± 1) °C with an RH of 70 (± 5) % as the control. The concentrations and duration of ozonation were determined according to the conditions employed in previous studies involving ozonation of wine grapes (Carbone and Mencarelli, 2015). The ozone treatments were performed at 10 °C, taking into account that environmental temperature strongly influences O3 effectiveness and stability, with more pronounced effects at low temperature (Thanomsub et al., 2002; Steenstrup and Floros, 2004; Fan et al., 2007). Based on the results of the 2019 trial (absence of visual damage on the leaves), the concentration of 100 ppb for 3 h of exposition was used in 2020. A total of three treatments were again applied, repeated on the same plants once a month (BBHC 55, 69 and 77) as described above. In both years, sampling for the molecular analyses was carried out 12 h and then 7 d after each treatment. Physiological parameters and NIR spectra were monitored 12 h after each treatment.

2. SAR gene expression analysis by RT-qPCR

To study the possible activation of SAR, the expression of resistance-related genes was determined. Two pathogen-related (PR1 and PR6), two chitinases (Chit B, Chit IV), glutathione S-transferase (GST) and β-1,3 glucanase were selected as SAR marker genes (Heath, 2007). Three leaves (one basal, one median and one apical) were collected from one representative shoot of each vine (20 plants per treatment) 12 h and then 7 d after each treatment. Leaves were randomly split between three different tubes, representing three biological replicates. The sample leaves were immediately frozen in liquid nitrogen and stored at -80 °C for the successive analyses. The frozen leaves were ground to powder using a ceramic mortar and pestle pre-cooled with liquid nitrogen. 100 mg of ground tissue were used for total RNA extraction, using Spectrum™ Plant Total RNA Kit (Sigma-Aldrich, Italy), including DNA digestion with On-Column DNase I Digestion Set (Sigma-Aldrich, Italy). RNA concentration and purity were determined with Nanodrop 2000 spectrophotometer (Thermo Scientific, Italy), verifying the absorbance ratio of 260/280 nm between 1.8 and 2, and the ratio of 260/230 nm between 1.3 and 2. The integrity of the extracted RNA was checked on a 1 % agarose gel. Reverse transcription of the RNA templates to cDNA was carried out using a 50 ng RNA template and 4 µL of ReadyScript™ cDNA Synthesis Mix (Sigma-Aldrich,Italy). DDW (Sigma-Aldrich, Italy) was used to reach a final volume of 20 µL. The PCR conditions were set according to the manufacturer’s protocol. Gene-specific primers were designed with the NCBI primer designing tool, based on the mRNA sequences of the target genes from the Vitis vinifera genome present in GenBank. The primer couples were run on the NBCI Basic Local Alignment Search Tool (BLAST) in order to verify a specific amplification. The primers were synthesised by Sigma-Aldrich (Italy). Before sample analyses, the amplification efficiency of each couple of primers was determined with a standard curve generated using a serial dilution of representative cDNA mixture. Subsequently, each cDNA dilution was used as a template in real-time qPCR reaction and its Ct was determined. The reaction conditions were set as described below. The Ct values were then plotted against the logarithm of the sample quantity and determined by the dilution performed, excluding Ct values above 35. The efficiency was recovered as percentage. The optimal cDNA dilution for a range of acceptable efficiencies (90-110 %) was found to be 1:5 (Livak and Schmittgen, 2001). The forward and reverse sequences, GenBank Accession, as well as the primer efficiencies, are given in supplementary Table S.1. Sample analyses was performed using the SYBR Green PCR Master Mix (Life Technologies™), with a final reaction volume of 10 μl, running on the CFX Connect RT-qPCR System (BioRad©). The RT-qPCR cycle was set as follows: initial denaturation at 95 °C for 2 min, followed by 40 cycles of amplification with denaturation at 95 °C for 15 s and annealing and elongation at 60 °C for 1 min. After the 40 cycles, a melt cycle was performed at 95 °C for 15 s, 60 °C for 1 min, 95 °C for 15 s and 60 °C for 15 s, to detect possible primer dimers or nonspecific amplification in cDNA samples. PCR reactions were run on all biological replicates and a negative control of the PCR mix was performed in addition to the primers in all qPCR runs. For data analysis, the comparative Ct method described in Livak and Schmittgen, (2001) was used. Expression levels were normalised using the ubiquitin (VvUBC) housekeeping gene. The relative quantification of each gene tested was calculated using the 2-ΔΔCt method.

3. Physiological parameters

Twelve h after each treatment, the physiological parameters described below were analysed on three leaves still attached to five randomly selected plants. Ten readings were taken for each leaf. Chlorophyll content was measured using a chlorophyll concentration meter (MC-100, Apogee instruments, inc.). The obtained Chlorophyll Content Index (CCI) was then converted into the concentration of chlorophyll expressed as µmoles per m2 of leaf surface using a linear equation specific to Vitis Vinifera leaves (Padilla et al., 2018). Stomatal conductance was analysed using a leaf porometer (Decagon Devices Leaf Porometer, Decagon Devices, Inc.) equipped with an SC-1 sensor (Decagon Devices, Inc) and expressed in µmol H₂O/m⁻²/s. For the photosynthetic activity measurements, two different parameters, FT and QY, were obtained using a fluorometer (FluorPen FP 110, Photon Systems Instruments, spol. Sr.o.). The leaf surface was first covered with aluminum foil and incubated in the dark for 20 min, then the reading was taken for each leaf. FT indicates the steady-state chlorophyll fluorescence in leaves adapted to darkness and is expressed as relative units. QY is the effective quantum yield of photosystem II and is calculated using the ratio maximal fluorescence intensity to maximal variable fluorescence (Fv/Fm).

4. NIR spectra acquisition

Twelve h after each treatment, five healthy-looking leaves (two basal, two median and one apical), including the three attached ones used for the physiological parameters analyses, from five randomly selected plants, were analszed by detecting NIR (Near Infrared) spectra using a Luminar 5030 Miniature Hand-held NIR Analyzer (Brimrose, Baltimore, MD), based on the NIR-AOTF (Acousto- Optically Tunable Filter) principle (Barnaba et al., 2014). Measurements were performed using the diffuse reflectance detection method and raw spectra were detected in transmittance. Detection was conducted in the 1100–2300 nm range, with 2 nm wavelength increments. Raw spectra were manipulated for absorbance (log 1/T) transformation using SNAP! 2.03 software (Brimrose). Ten spectra were taken for each leaf and then averaged to one spectrum per leaf. The obtained 5 spectra per plant were averaged again to one spectrum per plant. The resulting measurements were used for the subsequent chemometric calculations.

5. Statistical analysis

All the aforementioned data (except NIR spectra) were statistically analysed through Shapiro-Wilk and Bartlett's test to test normality and homogeneity of variances. Once these pre-requisites had been established, the data were compared by unpaired T-Test and Tukey's HSD post-hoc test at < 0.05 using Graphpad Prism 7.01 (GraphPad Software, La Jolla, CA, USA) separately for each sampling time and treatment. Both pattern recognition and regression modelling were performed as chemometric approaches on NIR spectra, which had been detected and manipulated as described in Materials and Methods 4. An agglomerative Hierarchical Cluster Analysis (HCA), based on the Ward's method and a principal component analysis (PCA) calculation, were operated on auto-scaled raw spectra, and the resulting clusterisations were graphically reported as a dendrogram. Moreover, Partial Least Square Discriminant Analysis (PLSDA) regressions were carried out with absorbance spectra (log 1/T) as independent variables (X-block), as well as classes of grouped samples as dependent variables (Y-block) (Fordellone et al., 2018). The regressive results were reported as score plots, with latent variables (LVs) representing the graph axes; confusion matrices and tables were also calculated for statistical indexes of performance, and robustness for both calibration and prediction models. In both PCA and PLSDA computation, the data were cross-validated using the leave-one-out method (Fearn, 2002; Westerhuis et al., 2008). All the computations were performed by Matlab R2013a (MathWorks®, Natick, MA, USA) and PLS Toolbox (Eigenvector Research, Inc., Manson, WA, USA).


To investigate the most effective combination of O3 concentration x duration of exposition, three different treatments were tested in 2019. The first treatment lasting for 12 h with an O3 concentration of 300 ppb induced serious visible damage to the leaf surfaces (Figure 1A). The second one performed maintaining the vines at 100 ppb of O3 for 6 h again showed visual foliar damage (Figure 1B). The damage caused by these two treatments (i.e., 300 ppb for 12 h and 100 ppb for 6 h) were already visible by the end of the treatments. The first treatment resulted in burn damage over the entire surface of the leaves; 80-90 % of the total leaves were damaged. Meanwhile, the second treatment induced oxidative stipple disease (Musselman, 1985), which is characterised by brown and dot-like lesions on the upper side of the leaf (Picture 1.B). These stipple burns were visible on half of the leaves of the treated plants. The third treatment of 100 ppb for 3 h did not cause any visible damage (Figure 1C), and therefore this combination was selected for the trial performed in 2020.

Figure 1. Leaf surface of 2019 O3-treated potted vines (cv Sangiovese) at the end of the following treatments: A) 300 ppb of O3 for 12 h, B) 100 ppb of O3 for 6 h, C) 100 ppb of O3 for 3 h.

1. Gene expression

The gene expression data from the 2019 trials are reported in Figure 2. Chit IV (Figure 2A) showed significant higher expression in the leaves of O3-exposed vines 12 h after the first treatment of 300 ppb for 12 h, while the other treatments did not alter the expression of this gene. On the other hand, Chit B (Figure 2B) and GST (Figure 2C) were statistically up-regulated in O3-treated leaves 12 h after the first and the second treatments (300 ppb for 12 h and 100 ppb for 6 h respectively). Finally, β-1,3 glucanase (Figure 2D), PR1 (Figure 2E) and PR6 (Figure 2F) showed a significantly higher expression in the O3-treated leaves even with the less impacting third treatment, the only treatment not inducing visible damage. In particular, PR1 and PR6 showed a more persistence up-regulation compared to the other genes. Indeed, higher expression levels in the O3-treated leaves were reached not only 12 h post treatments, but also 7 d after the second and the third treatments in terms of PR1, and 7 d after the first and the second treatments in terms of PR6.

Figure 2. Relative expression level of A) Chit IV, B) Chit B, C) GST, D) β-1,3 Glucanase, E) PR1, and F) PR6, analysed by RT-qPCR in leaves collected from 2019 ozone-treated vines at 10 °C (Ozone) and untreated vines at 10 °C (Control), 12 h and 7 d after the following treatments: 300 ppb of O3 for 12 h (First treatment), 100 ppb of O3 for 6 h (Second treatment) and 100 ppb of O3 for 3 h (Third treatment). The average value of three biological replicates is reported with bars representing SD. Stars indicate differences between sample value (**** p < 0.0001; *** p < 0.009; ** p < 0.03; * p < 0.01) based on unpaired T-test performed separately for each sampling time and treatment.

Based on the observations carried out in 2019, 100 ppb of O3 for 3 h was the only treatment applied in 2020. As observed in the previous year, the plants treated in 2020 did not show any visible damage (i.e., none of the leaves were damaged). Regarding the expression of SAR-related genes, most of them were up-regulated in treated leaves compared to the control (Figure 3). In detail, Chit IV, which in the previous year was not over-expressed with 100 ppb of O3, was strongly up-regulated after the second and the third treatments in the 2020 season (Figure 3A). On the other hand, Chit B (Figure 3B) showed significant over-expression in O3-treated leaves for 12 h after the first and the third O3 treatments. GST (Figure 3C) showed very low expression in leaves after the first O3 exposition, was significantly down-regulated in O3-treated vines after the second treatment and was significantly up-regulated after the third treatment, which is similar to the observations for other genes. In accordance with the results of the 2019 trials, the treatment with 100 ppb of O3 for 3 h was effective in stimulating the activity of β-1,3 glucanase, PR1 and PR6 (Figures 3D, 3E and 3F respectively). PR1 and PR6, which in the previous year showed a more persistent induction, were up-regulated only 12 h after the O3 expositions.

Figure 3. Relative expression level of A) Chit IV, B) Chit B, C) GST, D) β-1,3 Glucanase, E) PR1 and F) PR6 analysed by RT-qPCR in leaves collected from 2020 ozone-treated vines at 10 °C (Ozone) and untreated vines kept at 10 °C (Control), 12 h and 7 d after the first, second and third treatment with 100 ppb of O3 for 3 h. The average value of the three biological replicates is reported with bars representing SD. Stars indicate differences between sample value (**** p < 0.0001; *** p < 0.009; ** p < 0.03; * p < 0.01) based on unpaired T-test performed separately for each sampling time and treatment.

2. Physiological parameters

Photosynthetic activity did not show any differences between the O3-treated and control plants. The trials performed in 2019 revealed a slight reduction of chlorophyll content in O3-treated leaves 12 h after the third treatment (i.e., 8.063 ± 0.2 µmoles/m2 in O3-treated vines vs 6.917 ± 0.24 µmoles/m2 in control vines) (data not shown). In 2020, just the FT value was affected exclusively 12 h after the second treatment (FT = 4576 ± 410 in O3-treated vines vs FT = 6068 ± 314 in control vines) (data not shown). Differences were also observed in both years in terms of stomatal conductance. In particular, stomatal conductance was differently affected in the different years and treatments. Indeed, in 2019 (Figure 4, left), O3-treated leaves showed a marked decrease in stomatal conductance compared to the control after the first and second treatments. In contrast, after the third treatment the leaves of ozonated plants showed a completely opposite trend. In 2020 (Figure 4, right), O3-treated leaves showed a significantly lower level of stomatal conductance just 12 h after the second treatment.

Figure 4. Left panel: stomatal conductance expressed in H₂O/m² s in ozone treated vines 12 hours after the following treatments: 300 ppb of O3 for 12 hours (first treatment), 100 ppb of O3 for 6 hours (second treatment) and 100 ppb of O3 for 3 hours (third treatment). All the ozone treatment were performed at 10 °C. Control refers to vines kept at 10 °C in normal atmosphere. The average value of fifteen biological replicates is reported with bars representing SD. Stars indicate differences between sample value (*** p < 0.0001; ** p = 0.008; * p = 0.02) based on unpaired T-test performed separately for each sampling time and treatment. Right panel: stomatal conductance expressed in H₂O/m² s in plants 12 hours after each treatment with 100 ppb of O3 for 3 hours at 10 °C (Ozone) and untreated vines kept at 10 °C (Control). The average value of fifteen biological replicates is reported with bars representing SD. Stars indicate differences between sample value (* p < 0.01) based on unpaired T-test performed separately between each treatment.

3. NIR

PLSDA performed on NIR data from the 2019 trails revealed that the first two latent variables (LVs) explained more than 99 % of the residual variance: 98.04 % (LV1) and 1.48 % (LV2) (Figure 5). The different treatments (T1, T2 and T3) can be seen to be well- segregated in different quadrants. A closer look at the different treatments reveals that a good discrimination is also reached in term of samples (control versus O3), especially in the third treatment (i.e., T3). However, in the PLSDA model performed on NIR data from the second year (2020), such clear clustering of samples and treatments was not observed (Figure 6), even though about 97 % of the variability was explained through the combination of the first two LVs. As a confirmation of this, the results of the confusion matrix for the samples classification by PLSDA in 2019 revealed an average error of 0.02 in calibration, and 0.05 in Cross-Validation (CV) with high correlation (P = precision = total positive/total positive + false positive) ranging between 0.90 and 0.94 (Supplementary Table S.2). On the other hand, in 2020, the performance of the model in assigning samples to the right class of pertinence was worse. In fact, an average error of 0.11 and 0.23 in calibration and cross validation respectively and a very low correlation, especially in CV (P = 0.26), was observed (Supplementary Table S.3). The identified variable importance in projection (VIP) with a score higher than 1 (VIPs) for the 2019 PLSDA model (Figure 7) were associated with spectral wavelengths of around 1450 and 1920 nm. These spectral regions are commonly associated with water content (Seeling et al., 2008; Zhang et al., 2012).

Figure 5. PLSDA performed on NIR spectra detected on leaves collected in 2019 12 h after O3 treatment at 10 °C with 300 ppb for 12 h (T1_O3), 100 ppb for 6 h (T2_O3) and 100 ppb for 3 h (T3_O3) or on control leaves (CK) kept at 10 °C for 12 (T1_CK), 6 (T2_CK) or 3 (T3_CK) h. The graph represents the plotting of LV1 versus LV2.

Figure 6. PLSDA performed on NIR spectra detected on leaves collected in 2020 trial 12 h after each O3 treatment at 10 °C with 100 ppb for 3 h (T1_O3, T2_O3 and T3_O3), or on control leaves (CK) kept at 10 °C for 3 h (T1_CK, T2_CK, T3_CK). The graph represents the plotting of LV1 versus LV2.

Figure 7. variable importance for projection values (VIP) by wavelength for PLSDA-model of 2019 NIR spectra (Figure 6) detected on leaves collected 12 h after O3 treatment at 10 °C with 300 ppb for 12 h (T1_O3), 100 ppb for 6 h (T2_O3) and 100 ppb for 3 h (T3_O3) or on control leaves (CK) kept at 10 °C for 12 (T1_CK), 6 (T2_CK) or 3 (T3_CK) hours.


1. Visible injuries and SAR-related responses

Visible injuries on leaves of vascular plants are indicators of both acute and chronic O3 exposition (Brace et al., 1999). It is well known that relatively high concentrations of O3 in vascular plants such as Acer circinatum, Fagus sylvatica and Vitis vinifera cause rapid mesophyll cell death, even after a few hours of exposition, and leads to the most common visible foliar symptoms, such as small red or purple dots, burns, bleaching (loss of green colour for chlorophyll degradation and loss of photosynthesis efficiency) and necrosis (Richards et al., 1959; Brace et al., 1999; Kadinov et al., 2017). In the present study, visible damage to leaves caused by the two most extreme (in terms of concentration or exposition duration) O3 applications (i.e., 300 ppb for 12 h and 100 ppb for 6 hours) were already present at the end of the treatments. The only treatment which did not induce any visible injury was that performed at 100 ppb of concentration for just 3 h of exposition. 100 ppb can be considered a relatively low amount, and similar concentrations of O3 can be easily reached in the atmosphere. For example, daily typical O3 concentrations in central Italy range from 80 ppb between 8:00 and 13:00 to 40 ppb from 13:00 onwards in an ordinary day, and can easily increase under particular environmental conditions (Blanco-Ward et al., 2021). However, when exposed to O3 in a controlled experimental environment, plant growth conditions are optimised and the plants often have higher stomatal uptake. This means that the same O3 level can be more deleterious for plants grown in laboratory conditions due to a higher O3 uptake, compared to plants growing under more complex and variegated conditions, such as in the field (Grulke and Heath, 2020). In addition, Sangiovese cultivar has been classified as one of the most O3-sensitive Italian Vitis vinifera cultivars (Blanco-Ward et al., 2021). It has been well-established that extensive leaf damage itself can induce a pathogenesis-like response mechanism and that O3-related defence mechanisms are highly similar to those induced by pathogen infections (Grulke and Heath, 2020). Indeed, the over-expression of resistance-related genes after O3 exposition has been reported in different species (Sharma et al., 1996; Heath, 2008). Therefore, O3 has been studied not only as an air pollutant, but also as an abiotic elicitor for different crops and plants (Sharma et al., 1996; Sandermann et al., 1998; Heidenreich et al., 2006; Modesti et al., 2018). To study the possible SAR activation, the expression level of resistance-related genes was determined in the present study. The up-regulation of related resistance genes after O3 exposition has already been demonstrated (Sharma et al., 1996; Langebartels et al., 2002) and reviewed by Heath (2008) and Grulke and Heath (2020). Langebartels et al. (2002) demonstrated that the biochemical response of tobacco plants to O3 exposition (Nicotiana tabacum L.) was the same as that observed after a pathogen attack. The underlying mechanism seems to be the production of ROS when O3 penetrates the stomata. Once ROS are produced, a hypersensitive pathogen-like response (HR) is activated (Heath, 2008; Grulke and Heath, 2020). HR is the biochemical mechanism which prevents the local spread of infection. In a first step, HR leads to rapid cell death in the affected area, thus limiting the growth of pathogens. After that, a systemic resistance is slowly activated (Grulke and Heath, 2020). The activation of this mechanism includes the activity of specific genes (Langebartels et al., 2002; Heath, 2018; Grulke and Heath, 2020). In plants, a large number of SAR-related genes take part in SAR activation involving two different mechanisms: i) the recognition of virulence products, or ii) direct interaction with the pathogen's biological structure (van Loon et al., 2006); in this study, the two genes categories seem to have reacted in slightly different ways. PR1 and PR2 showed more persistent over-expression when the vines were treated with 100 ppb for 6 h, reaching their maximum expression level 7 d after the treatment. Other studies have reported that the PR1 gene shows an expression peak 5 d after the infection/wounding (Brederode et al., 1991). In addition, it is well known that PR6 proteins are proteinase inhibitors, being highly stable plant tissue defensive proteins (Datta and Muthukrishnan, 1999). Moreover, the gene expression of this family has been correlated with the accumulation of two key systemic resistance hormones: salicylic acid and jasmonic acid (Datta and Muthukrishnan, 1999); this may explain the more persistent over-expression that was recorded in the present study. On the other hand, the genes involved in the cell wall degradation of fungi (Chit IV, Chit B and β-1,3 glucanase) showed a strong up-regulation in most cases and in both years after O3 exposition, but the over expression was transient and limited to the 12-hour post-treatment. An important aspect highlighted in some studies (Watanabe et al., 2005) is that the expression of these genes is rapidly stimulated within a few hours after O3 treatment, and then their expression level drops after a day, suggesting that they are more involved in the early and rapid defence response. The two-year results show that the GST gene, which is involved in the detoxification of foreign compounds (Watanabe et al., 2005), seems to be the least susceptible gene and 100 ppb of O3 for 3 h of exposition seems to be insufficient for the up-regulation of the expression of this gene. Taken together, these data indicate that the transient activation of molecular mechanisms resembling the pathogen-induced responses are present in O3 -treated grapevine leaves.

2. Physiological response

As far as the physiological response to O3 is concerned, a well-known physiological shift that plants make to “survive” the O3 effect is stomatal closure, thus limiting O3 uptake (Grulke and Heath, 2020). Here, the different stomatal behaviour observed in the two years is not completely surprising and might be the result of a number of factors. In fact, stomatal closure, density and dimension are strongly influenced by air humidity, temperature, daily sunlight exposition, internal CO2 and metabolic state (Turcsanyi et al., 2000; Grulke and Heath, 2020), as well as O3 uptake (Hetherington and Woodward, 2003). Many studies have reported a decrease in stomatal conductance in Vitis vinifera after O3 exposition, thus reducing its uptake (Pellegrini et al., 2015; Valletta et al., 2016; Geng et al., 2017). The results obtained in 2019 after the first and the second treatments, along with the trend observed in 2020, all confirm this behaviour. In contrast, the data obtained for the last treatment in 2019, after which the plants had higher stomatal conductance levels, refers to plants which havehad already been exposed to higher O3 concentrations for longer (i.e., first treatment of 300 ppb of O3 for 12 h and second treatment of 100 ppb of O3 for 6 h). Previous studies have reported that short term (few hours) exposure to low or moderate levels of O3 is generally associated with a rapid reduction in stomatal conductance. On the other hand, longer exposition to higher levels of O3 is translated into a sluggish stomatal response (Grulke and Heath, 2020). The induced sluggish response leads to an inefficient control of water loss, because the stomata will remain opened in undesirable environmental conditions and partially closed in external optimal conditions for accumulation (Patterson and Rundel, 1993). This could be the case of the already O3 stressed plants in 2019. In addition, after long and high-dose O3 exposition, the sluggish stomatal response is often persistent and leads to an incomplete closure during the night (Barnes et al., 1990). Interestingly, the contradictory results obtained in stomatal conductance suggest that leaf physiological response to O3 is strongly dependent on the time and the concentration of exposition. Another important consideration is related to the regressive model, performed on the NIR spectra, in the discriminating control and O3-exposed leaves. In 2019, the plants were exposed to higher concentrations and for a longer time which was probably the reason for the good discrimination obtained between spectra from the control and O3-treated leaves. This observation seems to be confirmed by the fact that, in 2020, leaves exposed to lower concentrations for a shorter time were not so different from their controls. This is of particular interest, considering the NIR spectra ability to give significant indications about internal changes occurring as a consequence of molecular modifications, even related to stress effects (Khaled et al., 2018). Furthermore, the clear separation obtained in 2019 after the different treatments (T1, T2 and T3) suggests that NIR spectra was affected - as a consequence of a vibrational response of the molecular overtones - by not only the different O3 concentrations but also the different treatment durations. Indeed, in 2019, the treatments were based on different exposition time (12, 6 and 3 h); therefore, the control plants were also kept at 10 °C for 12, 6 and 3 h in the first, second and third treatments respectively, and a good segregation among the different treatments is observed. On the other hand, in 2020, all three treatments were the same in terms of duration (3 hours), and it is difficult to differentiate them.

3. Structural response

Lastly, our NIR spectra findings seem to agree with the stomatal behaviour data: in 2019, the plants were more stressed, and stomatal conductance was found to have been affected by each treatment. The induced stomatal response leads to significant modifications in gas exchanges and, consequently, in water content (Patterson and Rundel, 1993). Hence, the identified VIP of the 2019 PLSDA model were associated with the spectral regions commonly combined with water content (Seeling et al., 2008; Zhang et al., 2012); the ability of the model to discriminate samples from spectra is therefore likely due to the sensitivity of spectra to water regulations that are influenced by stomatal behaviour, as already stated in other published work (Marchica et al., 2019). A prolonged and uncontrolled exposure of vines to O3 has been previously found to often lead to negative and deleterious effects (Pellegrini et al., 2015; Valletta et al., 2016; Blanco-Ward et al., 2021); however, the exposition to adequate O3 concentrations for a shorter time and under controlled conditions can be a useful tool for taking advantage of the O3-induced oxidative stress (Tonelli et al., 2015; Pellegrini et al., 2018; Modesti et al., 2019; Marchica et al., 2019). In the present study, this seems to be confirmed by the fact that in 2020 the plants were exposed to lower concentrations of O3 for a shorter duration, and therefore no significant alterations occurred in the O3-treated leaves, even though the SAR-related genes were still up-regulated.


Ozone treatments induced a temporary up-regulation of most of the systemic acquired resistance-related genes. The remarkable effect of the O3 treatments differed depending on exposition level. After being subjected to prolonged treatments comprising high levels of O3, the plants showed serious visible foliar damage, as well as an inefficient control of gaseous exchange due to a sluggish stomatal closure response. This set of data suggests that the higher the O3 concentration, the higher the possibility for the plant to develop signs of damage, thus demonstrating a strong relationship between plant physiological response and O3 accumulation. This observation is confirmed by the NIR spectra, which highlight the potential physiological and structural modifications mainly due to changes in water content. On the other hand, the induced oxidative stress and subsequent activation of a pathogen-like response were detected in both years, regardless of the dose and treatment duration. This indicates that when plants are in need of a more systemic defense mechanism, SAR-related processes are always activated. This study provides innovative results for the possible practical application of O3 on vines, revealing that the combination of 100 ppb for 3 h is effective in stimulating the expression of the SAR-related gene, even when plants remain healthy, as in the case of the control plants. Further investigations will be carried out to highlight the effect of O3 treatment (gaseous or ozonated water) on other processes/compounds related to SAR activation (such as ROS, salicylic and jasmonic acid biosynthesis) and on the direct oxidative effect on pathogen structure for the control of specific grapevine pathogens.


The authors gratefully acknowledge Vivai Cooperativi Rauscedo (Rauscedo, PN, Italy) and Aziende Vivaistiche Gini (Cenaia, PI, Italy) for supplying the potted plants.

This work has been carried out as part of the networking activities “Oxygen sensing a novel mean for biology and technology of fruit quality” (CA18210), implemented under the COST Action “RoxyCOST” and funded by the European Cooperation in Science & Technology (2019–2023).


  • Achen, M. & Yousef, A.E. (2001). Efficacy of Ozone Against Escherichia coli O157:H7 on Apples. Journal of food science, 66, 1380-1384. https://doi.org/10.1111/j.1365-2621.2001.tb15218.x
  • Ainsworth, E. A., Lemonnier, P. & Wedow, J. M. (2020). The influence of rising tropospheric carbon dioxide and ozone on plant productivity. Plant biology (Stuttgart, Germany), 22 Suppl 1(Suppl 1), 5-11. https://doi.org/10.1111/plb.12973
  • Arneth, A., Harrison, S. P., Zaehle, S., Tsigaridis, K., Menon, S., Bartlein, P. J., Feichter, J., Korhola, A., Kulmala, M., O'Donnell, D., Schurgers, G., Sorvari, S. & Vesala, T. (2010). Terrestrial biogeochemical feedbacks in the climate system. Nature Geoscience, 3 (8). 525-532. https://doi.org/10.1038/ngeo905
  • Barnaba, F. E., Bellincontro, A. & Mencarelli, F. (2014). Portable NIR‐AOTF spectroscopy combined with winery FTIR spectroscopy for an easy, rapid, in‐field monitoring of Sangiovese grape quality. Journal of the Science of Food and Agriculture, 94(6), 1071-1077. https://doi.org/10.1002/jsfa.6391
  • Barnes, D., Eamus A, Davison H., Ro-Poulsen & Mortensen, D. (1990). Persistent effects of ozone on needle water loss and wettability in Norway spruce. Environmental Pollution, 63 (4), 345-363. https://doi.org/10.1016/0269-7491(90)90140-8
  • Bellincontro, A., Catelli, C., Cotarella, R. & Mencarelli, F. (2017). Postharvest ozone fumigation of Petit Verdot grapes to prevent the use of sulftes and to increase anthocyanin in wine. Australian Journal of Grape and Wine Research, 23, 200–206. https://doi.org/10.1111/ajgw.12257
  • Bhadra, P. (2015). Using ozone for integrated pest management in viticulture. Engineering and Technology for Sustainable World 22(4):15-17
  • Blanco-Ward, D., Ribeiro, A., Paoletti, E. & Miranda, A.I. (2021). Assessment of tropospheric ozone phytotoxic effects on the grapevine (Vitis vinifera L.): A review. Atmospheric Environment, 244,1352-2310. https://doi.org/10.1016/j.atmosenv.2020.117924
  • Brace, S., Peterson, D.L. & Bowers, D. (1999). A guide to ozone injury in vascular plants of the Pacific Northwest. Gen. Tech. Rep. PNW-GTR-446. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. https://doi.org/10.2737/PNW-GTR-446
  • Brederode, F.T., Linthorst, H.J. & Bol, J.F. (1991). Differential induction of acquired resistance and PR gene expression in tobacco by virus infection, ethephon treatment, UV light and wounding. Plant Molecular and Biology 17(6), 1117-25. https://doi.org/10.1007/BF00028729
  • Campayo, A., Serrano, K., García-Martínez, M.M., Sánchez-Martínez J.F. & Salinas M.R. (2019). Spraying ozonated water on Bobal grapevines: Effect on grape quality. Food Research International, 125. https://doi.org/10.1016/j.foodres.2019.108540
  • Carbone, K. & Mencarelli, F. (2015). Influence of Short-Term Postharvest Ozone Treatments in Nitrogen or Air Atmosphere on the Metabolic Response of White Wine Grapes. Food Bioprocess Technology 8, 1739-1749. https://doi.org/10.1007/s11947-015-1515-y
  • Conklin, P.L. & Barth, C. (2004). Ascorbic Acid, a Familiar Small Molecule Intertwined in the Response of Plants to Ozone, Pathogens, and the Onset of Senescence. Plant, Cell & Environment, 27, 959-970. http://dx.doi.org/10.1111/j.1365-3040.2004.01203.x
  • Datta, S.K. & Muthukrishnan, S. (Eds.). (1999). Pathogenesis-Related Proteins in Plants (1st ed.). CRC Press. https://doi.org/10.1201/9781420049299
  • Desanctis, F., Ceccantoni, B., Bellincontro, A., Botondi, R., Mencarelli, F., D’onofrio, C., Ducci, E. & Catelli, C. (2015). Ozone Fumigation Postharvest Treatment for the Quality of Wine Grape. Acta Horticulture, 1071, 795-800. https://doi.org/10.17660/ActaHortic.2015.1071.105
  • Eurostat (2007). The use of plant protection products in the European Union. ISBN 92-79-03890-7
  • Fan, L., Song, J., McRae, K.B., Walker, B.A. & Sharpe, D. (2007). Gaseous ozone treatment inactivates Listeria innocua in vitro. Journal of Applied Microbiology, 103: 2657-2663. https://doi.org/10.1111/j.1365-2672.2007.03522.x
  • Fearn, T. (2002). Assessing Calibrations: SEP, RPD, RER and R2. NIR News, 13(6), 12–13. https://doi.org/10.1255/nirn.689
  • Feng, Z., Kobayashi, K. & Ainsworth, E.A. (2008). Impact of elevated ozone concentration on growth, physiology, and yield of wheat (Triticum aestivum L.): a meta-analysis. Global Change Biology, 14: 2696-2708. https://doi.org/10.1111/j.1365-2486.2008.01673.x
  • Fordellone, M., Bellincontro, A. & Mencarelli, F. (2018). Partial least squares discriminant analysis: A dimensionality reduction method to classify hyperspectral data. arXiv preprint arXiv:1806.09347.
  • Fuhrer, J. (2009). Ozone risk for crops and pastures in present and future climates. Die Naturwissenschaften, 96(2), 173–194. https://doi.org/10.1007/s00114-008-0468-7
  • Geng, Q., Xing, H., Sun, Y., Hao, G., Zhai, H. & Du, Y. (2017). Analysis of the interaction effects of light and Os3 on fluorescence properties of ‘Cabernet Sauvignon’ grapes based on response surface methodology, Scientia Horticulturae, 225. https://doi.org/10.1016/j.scienta.2017.07.030
  • Grulke, N.E. & Heath, R.L. (2020). Ozone effects on plants in natural ecosystems. Plant Biol J, 22: 12-37. https://doi.org/10.1111/plb.12971
  • Heath, LR (2007). Alterations of the Biochemical Pathways of Plants by the Air Pollutant Ozone: Which are the True Gauges of Injury? TheScientificWorld Journal, 7(S1), 110–118. https://doi.org/10.1100/tsw.2007.19
  • Heath, LR (2008). Modification of the biochemical pathways of plants induced by ozone: What are the varied routes to change?. Environmental Pollution,155 (3), 453-463, https://doi.org/10.1016/j.envpol.2008.03.010
  • Heidenreich, B., Bieber, E. & Sandermann, H. (2006). Identification of a new member of the WRKY family in tobacco. Involved in ozone-induced gene regulation?. Acta Physiol Plant 28, 117-125. https://doi.org/10.1007/s11738-006-0038-6
  • Hetherington, A. & Woodward, F. (2003). The role of stomata in sensing and driving environmental change. Nature 424, 901-908. https://doi.org/10.1038/nature01843
  • Kadinov, G., Ananieva, K., Gesheva, E., Doncheva-Boneva, M., Doncheva, S. & Bezlova, D. (2017). Visible foliar injury and response of antioxidant Defense system in beech (fagus sylvatica l.) To acute ozone exposure under controlled Conditions. Genetics and Plant Physiology, 7(1–2): 34–48.
  • Khaled, A. Y., Abd Aziz, S., Bejo, S. K., Nawi, N. M., Seman, I. A. & Onwude, D. I. (2018). Early detection of diseases in plant tissue using spectroscopy–applications and limitations. Applied Spectroscopy Reviews, 53(1), 36-64. https://doi.org/10.1080/05704928.2017.1352510
  • Langebartels, C., Wohlgemuth, H., Kschieschan, S., Grün, S. & Sandermann, H. (2002). Oxidative burst and cell death in ozone-exposed plants. Plant Physiology and Biochemistry, 40 (6–8), 67-575, https://doi.org/10.1016/S0981-9428(02)01416-X
  • Livak, K.J. & Schmittgen, T.D. (2001). Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2−ΔΔCT Method. Methods, 25 (4), 402-408. https://doi.org/10.1006/meth.2001.1262
  • Marchica, A., Loré, S., Cotrozzi, L., Lorenzini, G., Nali, C., Pellegrini, E. & Remorini, D. (2019). Early Detection of Sage (Salvia officinalis L.) Responses to Ozone Using Reflectance Spectroscopy. Plants 8(9):346. https://doi.org/10.3390/plants8090346
  • Mencarelli, F. & Bellincontro, A. (2018). J. Sci. Food Agric. doi:10.1002/jsfa.8910 (2018). https://doi.org/10.1002/jsfa.8910
  • Modesti, M., Baccelloni, S., Brizzolara, S., Aleandri, M.P., Bellincontro, A., Mencarelli, F. & Tonutti, P. (2019). Effects of treatments with ozonated water in the vineyard (cv Vermentino) on microbial population and fruit quality parameters. BIO Web of Conferences, 13, 040. https://doi.org/10.1051/bioconf/20191304011
  • Modesti, M., Petriccione, M., Forniti, R., Zampella L., Mastrobuoi F., Scortichini M. & Mencarelli, F. (2018). Methyl jasmonate and ozone affect the antioxidant system and the quality of wine grape during postharvest partial dehydration, Food Research International 112. 369-377. https://doi.org/10.1016/j.foodres.2018.06.061
  • Musselman, R.C. (1985). Protecting Grapevines from Ozone Injury with Ethylenediurea and Benomyl. American journal of enology and viticulture, Vol. 36, no. 1, pp. 38-42.
  • Padilla, F.M., Peña-fleitas, M.T., De Souza, R., Giménez, C., Thompson, R.B. & Gallardo, M. (2018). Evaluation of the sensitivity of portable chlorophyll meters to estimate leaf chlorophyll and n contents under excessive n conditions. 20th N Workshop and Side event – Rennes, France – June 25-27
  • Patterson, M.T. & Rundel, P.W. (1993) In: Ehlringer J.B., Hall A.E., Farquhar G.D. (Eds), Stable isotopes and plant carbon – water relations. Academic Press, San Diego, CA, USA, pp. 213-226. https://doi.org/10.1016/B978-0-08-091801-3.50022-2
  • Pellegrini, E., Campanella, A., Cotrozzi, L., Tonelli, M., Nali, C. & Lorenzini, G. (2018). What about the detoxification mechanisms underlying ozone-sensitivity in tulip tree (Liriodendron tulipifera)? Environmental Science and Pollution Research, 25, 8148–8160. https://doi.org/10.1007/s11356-017-8818-7
  • Pellegrini, E., Campanella, A., Paolocci, M., Trivellini, A., Gennai, C., Muganu, M., Nali, C. & Lorenzini, G. (2015). Functional Leaf Traits and Diurnal Dynamics of Photosynthetic Parameters Predict the Behavior of Grapevine Varieties Towards Ozone. PloS One, 10. https://doi.org/10.1371/journal.pone.0135056
  • Richards, B.L., Middleton, J.T. & Hewitt, W.B. (1959). Ozone stipple of grape leaf: Lesions on the upper leaf surfaces and premature leaf fall occur on grapevines in areas polluted by air-borne ozone. California Agriculture 13(12):4-11
  • Romeo-Oliván, A., Pagès, M., Breton, C., Lagarde, F., Cros, H., Yobrégat, O., Violleau, F. & Alban, J. (2021). Ozone Dissolved in Water: An Innovative Tool for the Production of Young Plants in Grapevine Nurseries? Ozone: Science & Engineering. https://doi.org/10.1080/01919512.2021.1984203
  • Sandermann, H.Jr., Ernst, D., Heller, W. & Langebartels, C. (1998). Ozone: An abiotic elicitor of plant defence reactions. Trends Plant Sciences 3, 47–50. https://doi.org/10.1016/S1360-1385(97)01162-X
  • Seeling, H.D., Hoehn, A., Stodieck, L.S., Klaus, D.M., Adams Lii, W.W. & Emery, W.J. (2008). International Journal of Remote Sensing, 29(13), 3701-3713. https://doi.org/10.1080/01431160701772500
  • Sharma, YK., Leon, J., Raskin, I. & Davis, KR. (1996). Ozone-induced responses in Arabidopsis thaliana: The role of salicylic acid in the accumulation of defense-related transcripts and induced resistance. Proceedings of the National Academy of Sciences, 93, 5099–5104. https://doi.org/10.1073/pnas.93.10.5099
  • Steenstrup, L.D. & Floros, J.D. (2004). Inactivation of e. coli 0157:h7 in apple cider by ozone at various temperatures and concentrations. Journal of Food Processing and Preservation, 28: 103-116. https://doi.org/10.1111/j.1745-4549.2004.tb00814.x
  • Thanomsub, B., Anupunpisit, V., Chanphetch, S., Watcharachaipong, T., Poonkhum, R., & Srisukonth, C. (2002). Effects of ozone treatment on cell growth and ultrastructural changes in bacteria. The Journal of general and applied microbiology, 48(4), 193–199. https://doi.org/10.2323/jgam.48.193
  • Tonelli, M., Pellegrini, E., D’Angiolillo, F., Petersen, M., Nali, C., Pistelli, L. & Lorenzini, G. (2015). Ozone-elicited secondary metabolites in shoot cultures of Melissa officinalis L. Plant Cell, Tissue and Organ Culture, 120, 617–629. https://doi.org/10.1007/s11240-014-0628-8
  • Turcsanyi, E., Lyon, T., Plochl, M. & Barnes, J. (2000) Does ascorbate in the mesophyll cell walls form the first line of defence against ozone? Testing the concept using broad bean (Vicia faba L.). Journal of Experimental Botany, 51, 901-910. https://doi.org/10.1093/jexbot/51.346.901
  • Tzortzakis, N.G., Singleton I. & Barnes, J. (2007). Impact of atmospheric ozone-enrichment on quality-related attributes of tomato fruit. Postharvest Biology and Technology, 43, 261-270. https://doi.org/10.1016/j.postharvbio.2007.03.004
  • Valletta, A., Salvatori, E., Rita Santamaria, A., Nicoletti, M., Toniolo, C., Caboni, E., Bernardini, A., Pasqua, G. & Manes, F. (2016). Ecophysiological and phytochemical response to ozone of wine grape cultivars of Vitis vinifera L., Natural Product Research, 30, 2514–2522. https://doi.org/10.1080/14786419.2015.1118631
  • van Loon, L.C., Rep, M. & Pieterse, C.M.J. (2006). Significance of Inducible Defense-related Proteins in Infected Plants. Annual Review Phytopathology, 44: 135-162. https://doi.org/10.1146/annurev.phyto.44.070505.143425
  • Westerhuis, J. A., Hoefsloot, H. C., Smit, S. & Vis, D. J. (2008). Assessment of PLSDA cross validation. Metabolomics, 4(1), 81-89. https://doi.org/10.1007/s11306-007-0099-6
  • Zhang, Q., Li, Q. & Zhang, G. (2012). Rapid Determination of Leaf Water Content Using VIS/NIR Spectroscopy Analysis with Wavelength Selection. Spectroscopy: An International Journal, 27(2), 93-105. https://doi.org/10.1155/2012/276795


Margherita Modesti



Affiliation : Department for innovation in biological, agro-food and forest system (DIBAF), Tuscia University, Via San Camillo de Lellis snc, 01100 Viterbo

Country : Italy

Roberto Forniti

Affiliation : Department for innovation in biological, agro-food and forest system (DIBAF), Tuscia University, Via San Camillo de Lellis snc, 01100 Viterbo

Country : Italy

Elena Brunori

Affiliation : Department for innovation in biological, agro-food and forest system (DIBAF), Tuscia University, Via San Camillo de Lellis snc, 01100 Viterbo

Country : Italy

Fabio Mencarelli

Affiliation : Department of Agriculture Food and Environment (DAFE), University of Pisa, Via del Borghetto 80, 56124 Pisa

Country : Italy

Andrea Bellincontro

Affiliation : Department for innovation in biological, agro-food and forest system (DIBAF), Tuscia University, Via San Camillo de Lellis snc, 01100 Viterbo

Country : Italy

Pietro Tonutti


Affiliation : Life Sciences Institute, Scuola Superiore Sant’Anna, Piazza Martiri della Libertà 33, 5612 Pisa

Country : Italy



Supplementary data


Article statistics

Views: 832


XML: 8