MRI and HR-MAS NMR spectroscopies to correlate structural characteristics and the metabolome of Fiano and Pallagrello grapes with the action of field spray preparation 500 and the soil spatial microvariability

MRI and HR-MAS NMR spectroscopies to correlate structural characteristics and the metabolome of Fiano and Pallagrello grapes with the action of field spray preparation 500 and the soil spatial microvariability | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article MRI and HR-MAS NMR spectroscopies to correlate structural characteristics and the metabolome of Fiano and Pallagrello grapes with the action of field spray preparation 500 and the soil spatial microvariability Pierluigi Mazzei, Andrea Sica, Claudio Migliaro, Gessica Altieri, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4351758/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract BACKGROUND A number of Italian grape berry varieties, such as Fiano (F) and Pallagrello nero (P), represent National strategic products. Therefore, it is important to identify soil conditions emphasizing their peculiar characteristics as well as find innovative and sustainable treatments improving their compositional and nutraceutical quality. The field spray preparation 500 is a biodynamic product that is presumed to serve as biostimulant on the vine. However, so far, the scientific results probing its effectiveness are still lacking. Moreover, it is necessary to establish a reliable relationship between the grape quality and the spatial microvariability of the vineyard’s soil. On this basis, the main objective of this work consisted in correlating structural and morphological characteristics (via MRI), the primary metabolome (via semi-solid state HRMAS NMR) and important nutraceutical parameters (total phenols and antioxidants via DPPH assay) of F and P grapes with both the action of preparation 500 biostimulant and the vineyard soil microvariability, based on soil apparent electrical conductivity. RESULTS HRMAS enabled the identification of the primary metabolome of F and P. The elaboration of 1 H NMR spectra through chemometrics revealed significant changes in F and P grapes, accounting for both soil microvariability and the application of field spray (the latter also confirmed by PLS-DA and Heat-map clustering). Interestingly, for both F and P it was observed a significantly lower content of carbohydrates after biostimulant treatment while MRI revealed structural characteristics and internal details of intact grapes. The combined use of proton parametric indices, such as relaxation times and diffusion coefficients, indicated alterations induced in grapes by both the spatial microvariability of the soil and the effects of investigated biostimulant. Interestingly, a tight correlation was found between MRI transverse relaxation time and the contents in total phenols and antioxidants. CONCLUSIONS Our results have proven that both soil spatial microvariability and the application of field spray preparation 500 significantly affect the structural, metabolomic and nutraceutical characteristics of grapes. Moreover, the Preparation 500 treatment has increased the nutraceutical value of grapes. Importantly, these data may be potentially used to promote and protect biodynamic grape and predict the quality of the resulting wines. primary grape metabolome proton relaxation times diffusion coefficients chemometrics antioxidants Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introducion About 7.5 million hectares of fertile soils, in the world, are vocated to the cultivation of Vitis Vinifera and are mostly concentrated in China, Italy, USA, France, Spain and Turkey [1]. Over the 93% of these vineyards is composed by vine cultivars producing quality grapes suitable for the wine industry. Italy represents the second world producer of wine, following the France, with an average production in 2021 of over 50.2 million hecto-liters [2]. From a qualitative point of view, many Italian wines are classified as excellent, as proved by the assignment of 329 D.O.C. (Controlled Designation of Origin) and 70 prestigious D.O.C.G. (Controlled and Guaranteed Designation of Origin) quality labels [3]. A large interest is thus addressed by grape and wine producers towards the identification of innovative agronomic strategies and advantageous soil conditions useful to improve vine productivity and promote the attainment of quality and excellent grapes. In this perspective, the use of efficient biostimulants can promote vines growth and increase their ability to deal with negative factors affecting their physiology and performances, such as deficiency of soil nutrients and water, patogens attack and adverse microclimatic conditions. In fact, biostimulants can activate mechanisms eliciting a response in vines, making them more efficient in exploiting soil resources [4–7]. It is noteworthy the fact that effective organic biostimulants may contribute to mitigate the adverse effects due to the incessant onset of climate changes [8]. Biodynamic products, which are obtained in respect of the principles of circular economy and sustainability, may represent suitable candidates for this purpose. For example, it has been proved that the application of a biodynamic treatment on soil and vines may improve grape production and quality, representing an alternative approach to traditional methods often responsible to determine relevant environmental impacts [9–10]. In particular, the field spray preparation 500 (p500) is an organic product deriving from the biochemical stabilization of cow manure, carried out in respect of biodynamic principles [ 11 ]. This product has a complex molecular structure, with an overall low hydrophobic character and composed mostly by aromatic derivatives, polysaccharides, and alkyl compounds [ 12 ]. It has been proved that the application of p500 can positively influence the wheat by promoting plant and root growth and increasing the seedling vigor [ 13 ]. Recently, it has been developed a model, based on proton liquid-state Nuclear Magnetic Resonance (NMR), which proves that the molecular composition of grapes, cultivated according to a biodynamic method including the p500 treatment, exhibits a profile significantly different than for grapes resulting from a traditional organic management [ 14 ]. However, so far, the literature probing the effectiveness of p500 as biodynamic biostimulant is still poor. Therefore, further investigations are required to confirm and consolidate the beneficial and effective role of this product. On the other hand, there is a growing interest related to the use of fast, reliable and georeferenceable methods to better understand the correlations existing between wine quality and the properties characterizing the territory of grape origin. In this context, the electromagnetic induction (EMI) method, typically adopted to conduct precision agriculture, represents a very promising technique allowing the evaluation of soil apparent electrical conductivity (ECa) and its representation through intuitive responsive maps. EMI is very advantageous because permits to predict several soil conditions and properties, such as the content in water, organic matter, active clays and salts, as well as individuate buried rocks at a soil depth which, depending on the selected frequency for the measurement, may explore up to 1.5-2 meters [ 15 – 16 ]. Conceptually, the EMI technique involves the generation of a primary magnetic field, developed from a moving transmitter and oriented towards the soil. The presence of conductive components in soil produces secondary currents that, in turn, develop a secondary induced magnetic field. The detection and the quantification of the latter is finally exploited to extrapolate the soil electrical conductivity [ 15 – 17 ]. Recently, ECa maps have been also used to correlate the spatial variability of soil physical properties with site-specific crop responses, including biological parameters [ 18 ] and total soil respiration [ 19 ]. It has been found that plant growth parameters, such as height, leaf area index and dry matter, well correlate with soil patterns identified through ECa maps for crops such as winter barley, winter wheat and sugar beet [ 20 ]. Similarly, a close relationship between ECa maps and maize plant height has recently been established in field experiments [ 21 ]. A significant correlation has been also found between soil microvariability detected via EMI and the molecular quality of Aglianicone grapes in different vintages [ 22 ]. On this basis, the purpose of this work was to investigate on the possible effects induced by the p500 treatment on model autochthonous Italian vine cultivars (Pallagrello Nero and Fiano), in open field tests, accounting also for the spatial microvariability of vineyards revealed by EMI method. The influence of both p500 treatment and soil-microariability was verified by examining (i) representative nutraceutical parameters, (ii) the primary metabolome and (iii) microstructure and morphological traits of studied grapes. In particular, grape metabolome was unraveled by means of High-Resolution Magic Angle Spinning (HRMAS) NMR spectroscopy, in the semi-solid state [ 23 – 24 ]. This technique permits to obtain NMR spectra through the direct exam of fresh and intact tissues, in a relatively rapid way, with a resolution very similar to that attainable via High Resolution liquid-state NMR [ 23 , 25 ]. HRMAS represents a very innovative and powerful technique which has been already used with success mostly to investigate on the quality and the origins of relevant agro-food products, such as tomato [ 26 ], persimmon [ 27 ], sweet pepper [ 28 ] and maize [ 29 ]. However, despite its undisputable potential, HRMAS is still underutilized, in the fields of food and agricultural chemistry, while it would be useful to explore its unexpressed potential. The analysis of grape microstructure and morphological traits was carried out by means of Magnetic Resonance Imaging [ 30 – 32 ]. This technique is based on the quantification and the spatial location of water hydrogens and enables the acquisition of detailed morphological images (resolution of tens of micrometers) of internal sections of intact tissues. In addition, the adoption of specific experiments permits to extrapolate multiple and complementary information informing directly on the composition and dynamics of the water molecules in the examined tissue under examination and indirectly on sample microstructure [ 30 , 33 ]. Up to date, MRI has been used to examine several important aspects of relevant agro-food products, such as tomato [ 34 – 35 ], radish [ 36 ], kiwi [ 33 , 37 – 38 ] and apple [ 39 – 41 ]. MRI is a very powerful spectroscopy which so far has been largely used in the medical diagnostics and pharmacological fields. However, its potential still remains almost totally unexplored for the analysis of agrifood. Therefore, a further objective of this work was to reveal and demonstrate the suitability and potential role of MRI and HRMAS NMR spectroscopies for investigating on grapes and providing useful information. Experimental section Fiano and Pallagrello nero vineyards and p500 treatment The grapes of white Fiano (F) and black Pallagrello (P) were produced by the biodynamic farm “I Cacciagalli”, located in Teano (Caserta, Italy). Vineyards soil is only few meters shallow, exhibits a slightly acid pH and is charaterized by a silty clay texture. F and P grapes were sampled on the 3rd and the 16th of September 2021, respectively, at the full maturation of fruits which was established on the basis of sugar content (16 °Bx for F and 21 °Bx for P). Half of F and P vineyards were treated with field spray preparation 500 (p500), purchased from the association “Agriculture living” and dynamized in the farm immediatly before the soil application. The latter occurred in full compliance with the guidelines provided by the Italian association DEMETER. The product was sprayed on the soil on the 1st of December 2020 and 14th of April 2021, using a dose of 250–300 g/ha ( Supporting Table 1 ). The grape samples were collected from plants insisting on soil sites treated (T, treated with 500) and untreated (C, control) with p500. For each cultivar and treatment, at least 100 berries were taken from 5 different plants. Samples were stored at the temperature of -20 C until the analysis. Table 1 Titratable acidity (g/l of tartaric acid equivalents) of Fiano (F) and Pallagrello nero (P) grape berries, collected from soil sites characterized by low (L) and high (H) ECa levels, and treated (T) or not (C) with p500. The results are expressed as average of 3 replicates and are associated to standard deviation. PC_H PC_L PT_H PT_L FC_H FC_L FT_H FT_L Titratable acidity 3,78 ± 0,002 3,81 ± 0,001 3,93 ± 0,0001 3,93 ± 0,0009 5,50 ± 0,02 5,04 ± 0,008 4,87 ± 0,0009 5,62 ± 0,0009 Soil mapping via electromagnetic induction technique Vineyard’s spatial variability was measured by using a multi-frequency EMI sensor (GSSI Profiler EMP-400) equipped with a 1.2 m coil spacing [ 15 , 22 ]. The measurements were performed on the 16th of September 2020, by setting the vertical dipole mode (VDP), and a frequency of 13 kHz. Such a frequency resulted the best compromise to examine that portion of soil typically explored by the roots of the vine (estimated magnetic field penetration depth of about 1.5 m). ECa values (expressed as mS/m) were obtained by collecting data every 0.75 s, in continuous mode, walking along each row of the vineyard and at a speed of about 4–5 km/h. The acquisition points were georeferenced using the Tripod Data System Recon Personal Digital Assistant, accompanied by both an integrated Bluetooth service and HoluxTM Wide Area Augmentation System Global Position, with a horizontal dilution of differential correction accuracy. Collected data were then used to develop ECa (apparent electrical conductivity) maps of F and P vineyards. To ensure maximum georeferencing accuracy, the coordinates of the ECa values were determined considering at least 8 satellites. ECa maps were generated by using MagMap2000c and Surfer Golden© and applying the kriging method (single cell 1x1 m). Abnormal ECa data acquired in proximity of vineyard metal rollers were removed. Grape berries, for both varieties and as a function of p500 treatment, were sampled by plants insisting on vineyards sites characterized by the highest (H, high, relative range 10.42–14.87 mS/m) and the lowest (L, low, relative range 8.7-11.45 mS/m) ECa values. Specifically, samples were collected at high ECa values (PC3 and PT1, 14.84 and 10.31 ms/m; FC1 and FT1, 12.42 and 13.27 ms/m) and at low ECa values (PC5 and PT1, 11.45 and 10.31 ms/m; FC3 and FT5, 8.7 and 9.55 ms/m) (Fig. 1 ). For each cultivar, at least 100 berries were taken from 5 different plants. Samples were stored at the temperature of -20 C until the analysis. All sample typologies and respective labels are listed in Supporting Table 1 . Titratable acidity For each thesis, 25 grapes were thawed and pressed until to obtain a juice, which was then filtered under vacuum (cellulose acetate filters, Whatman 41, 20 µm pores). Then, a volume of 2 ml of filtered juice was diluted with 98 ml of deionized water, added with phenolphthalein indicator and titrated, under magnetic stirring, with a solution of NaOH 0.1M. The titratable acidity was expressed as equivalents of tartaric acid (g/l). 3 replicates were analyzed for each grape type. Grape extraction and quantification of total phenols and antioxidants For each thesis, 20 grapes were thawed and blended, through an immersion mixer, until the material was completely crushed. Then, 5 g of blended material were put in contact with 20 ml of a 70% (v/v) ethanol solution, ultrasonicated (ARGO LAB DU-32) for 4 minutes at 30 ºC and left under magnetic stirring for 17 hours and 30 minutes, in absence of light. Afterwards, a centrifugation (10 minutes at 6000 rpm) was carried out to isolate supernatant which was finally filtered, under vacuum, and stored at the temperature of -20°C, until further analyses. The content in total phenols was determined by using the Folin-Ciocalteu assay and was expressed as Gallic Acid (GAE) equivalents. The reaction was performed by adding, progressively, 0.2 ml of ethanolic extract, 0.5 ml of Folin-Ciocalteu reagent (diluted 1:3 with bi-distilled water), and 0.5 ml of a Na 2 CO 3 solution (10% v/v). After the addition of each component, the mixture was agitated for about 10 seconds, through a vortex, and held for about 4 minutes, in absence of light. Finally, the mixture was diluted with 3.9 ml of bi-distilled water and the reaction was left to proceed for 2 hours and 30 minutes, at 25°C, in absence of light. Each experimental set consisted of 4 replicates and 2 blank samples. For the latter, the volume of grape extract was replaced by the same volume of ethanol 70% solution. The reaction product was examined via spectrophotometer (Thermo Spectronic 20 Genesys) by setting it at a wavelength of 765 nm. The absorbance values were then converted in GAE through a comparison with a calibration curve, built by launching the reaction with increasing amounts of gallic acid (range 0–0,2 mg GAE; r 2 = 0.9804). The values were expressed in mg of GAE per g of fresh mass. The antioxidants content was measured by the free radical scavenging assay via DPPH (2,2-diphenyl-1-picrylhydrazyl), and the results were expressed as ascorbic acid (AAE) equivalents. The procedure was carried out by diluting 0,125 ml of extract with 4,325 ml of methanol and adding 0,5 ml of DPPH solution (2.18 mM in MeOH). The mixture was agitated for about 10 seconds, and then the reaction occurred for 30 minutes, in absence of light. The results were then read spectrophotometrically (Thermo Spectronic 20 Genesys) at the wavelength of 517 nm. Each experimental set consisted of 4 replicates and 2 blank samples. For the latter, the volume of ethanolic extract was replaced by the same volume of ethanol 70% solution. The absorbance values were then converted in AAE through a comparison with a calibration curve, built by launching the the reaction with increasing amounts of ascorbic acid (range 0–120 µg AAE; r 2 = 0.9938). The values were expressed in µg of AAE per g of fresh mass. HRMAS NMR spectroscopy Per each representative sample, three berries were thawed before HRMAS NMR analysis. After few minutes, each berry was dissected, and few aliquots of grape pulp were sampled per berry. The material was merged and homogenized. About 30 ± 1 mg of homogenized sample was inserted into an HRMAS-NMR rotor (4 mm in zirconia; 80 µl). Few tens of microliters of a phosphate buffer solution (pH 6.8), prepared entirely with deuterated water (99.8% D 2 O/H 2 O), were then added to the rotor, by removing possible relatively large air bubbles. The latter was finally closed with a special external cap equipped with fins, serving as turbine and necessary to trigger sample rotation [ 23 – 24 ]. HRMAS NMR experiments were conducted on a 500 MHz Magnet (11.7 Tesla, Bruker Oxford/spectrospin cryomagnet) equipped with a 1 H – 13 C HRMAS probe. The experiments were conducted at a temperature of 22 ± 1°C, at a rotation rate of 5 kHz and examining at least 4 replicates for grape typology. 1 H NMR spectra were acquired by applying the on-resonance presaturation technique, to suppress the water signal, and setting 16k of acquisition points, 5 seconds of recycle delay, 8 dummy scans and 256 effective scans. To assign the most intense NMR signals and to support the identification of grapes metabolomic fingerprint [ 22 ], 1D proton-decoupled 13 C spectra and two-dimensional spectra were also acquired for several representative samples of F and P. The bidimensional spectra consisted in homonuclear 1 H- 1 H COSY (Correlation Spectroscopy), TOCSY (Total Correlation spectroscopy) e NOESY (Nuclear Overhauser Effect Spectroscopy) and heteronuclear 1 H- 13 C HSQC (Heteronuclear Single-Quantum Coherence) e HMBC (Heteronuclear Multiple-Bond Coherence) [ 42 ]. All 2D NMR spectra were set up with 16 dummy scans, 64 scans, 2048 acquisition points for 256 experiments, and spectral windows of 16 and 300 ppm, respectively, for hydrogen and carbon nuclei (in the case of heteronuclear experiments). All spectra were processed (Fourier transform, phase, and baseline corrections) by using Bruker TopSpin software (v. 4.1). Magnetic Resonance Imaging (MRI) of grape samples MRI experiments were performed at a temperature of 25 ± 1°C on a wide-bore Bruker Avance 300 MHz magnet (BrukerBiospin, Rheinstetten, Germany), equipped with a µ-imaging MICRO 5 probe and operating at a proton frequency of 300.13 MHz. For each analysis, one grape berry was loaded into a sterilized 25 mm tube (Norell, borosilicate). An inert and soft material was put on the bottom and on the top of the tube to stabilize vertically the grape, during the analysis, and avoid the possible interference of water exuded from grape. Then, each tube was tightly closed to minimize the grape water evaporation ( Supporting Fig. S1 ). 3 types of MRI experiments were launched to identify longitudinal spin-lattice (T 1 ) and transverse spin-spin (T 2 ) relaxation times as well as self-diffusion coefficients (DIFF), respectively. T 2 experiments were acquired by applying a Multi-Slice-Multi-Echo pulse sequence, setting a recycle delay of 6 s, 4 scans and 18 sub-experiments, each of them characterized by an increasing number of spin-echoes (the total spin-echo duration varied gradually and progressively from 12 to 216 ms, in 18 experiments) [ 30 – 32 ]. The data were fitted according to the following equation: M t = M 0 exp(-t/T 2 ) ( Eq. 1 ) where M 0 and M t represent the resulting magnetization intensity at the minimum spin-echo delay and after the i th spin-echo interval, respectively. The 2D images extrapolated from this experiment, at the shortest spin-echo duration (12 ms), was exploited for morphological evaluations. T 1 experiments were acquired by applying an inversion recovery pulse sequence and performing experiments at increasing repetition times, which gradually varied from 700 to 7500 ms. The experiments were set with an echo time of 65 ms and 4 scans. The data were fitted according to the following equation M t = M 0 [1-exp(-t/T 1 )] ( Eq. 2 ) where M 0 and M t represent the resulting magnetization intensity at the minimum repetition time and after the i − th delay, respectively. The self-diffusion coefficients (DIFF) were extrapolated from a pulse sequence based on the use of pulsed field gradients and integrated with stimulated echoes. The experiment was set with a recycle delay of 6 s, 6 scans, a delay of 4 ms for the two gradients (δ, little delta), 13 ms as interval (Δ, big elta) between the first (encoding) and second (decoding) gradient. The strength of gradients increased progressively (100, 400, 750, 1000, 1500, 2168 s mm − 2 ) in 6 experiments. The data were fitted according to the following equation M t = M 0 exp [-γ 2 G 2 δ 2 (Δ-δ/3) D] ( Eq. 3 ) where M 0 and M t represent the resulting magnetization intensities after the application of the weakest gradient and that at the i − th strength, respectively, γ is the gyromagnetic ratio of hydrogen, G is the intensity of the pulsed field, δ is the duration of the gradient, and Δ is the time interval between the first (encoding) and second (decoding) gradient. All MRI images resulted from the acquisition of 6 interlaced axial slices, with a thickness of 1 mm, a visual window of 18 × 18 mm 2 ( Supporting Fig. S2 , on the left). The images consisted of a matrix 256 x 256 for T 1 and T 2 experiments and a matrix 128 x 128 for DIFF experiments. Bruker’s Paravision 6.0.1 software was used to process MRI data, obtain images and perform the data fittings. Calculations for the fittings were carried out by examining and averaging, per sample, 12 regions of interest (ROI) distributed over 4 slices. Briefly, 4 representative slices, per sample, were considered and, for each of them, 3 circular ROIs (area ≈ 0.4 cm 2 ; Supporting Fig. S2 , on the right) were selected (3 ROIs x 4 slices x sample). At least 4 replicates were acquired for each type of grape. Multivariate analysis For each replicate, all HRMAS NMR proton signals included in the spectral range 7.766 − 0.764 ppm were assigned and integrated by creating buckets with variable width and sized to include whole peaks (either singlets or multiplets, depending on compound structure). For each grape variety (F and P), it was produced a data matrix composed of 43 variables (integrations) per a total number of observations accounting for both p500 treatment (Control vs Treatment), and the ECa values (L vs H) of soil sites (at least 4 replicates per sample typology). For each 1 H NMR spectrum, the area corresponding to each single integration was normalized, with respect to the sum of the individual areas (expressed in %), and Pareto-scaled to prevent the underestimation of relatively low intense NMR signals [ 43 – 44 ]. The F and P matrixes (either whole spectrum or containing only significant variables, depending on the elaboration phase) were elaborated through multivariate statistic techniques. Principal Component Analysis (PCA) was used to identify the variables (loading-vectors) discriminating different grape types. The ANOVA (ANalysis Of VAriance) test was applied to assess the significance (Tukey and Benjamini Hochberg FDR tests, both set at an α confidence level of 0.05) by which the most relevant variables (extrapolated through the evaluation of PCA loading-plots) contributed to differentiate the composition of grape types [ 43 – 44 ]. Heat-map variable analysis and Partial Least Square Discriminant Analysis (PLS-DA) were applied by considering the variables significantly involved in the discrimination among the samples. For Heat map clustering, the values were centred, reduced and displayed as colours ranging from red (relatively higher) to yellow (relatively lower) through orange (intermediate values). Samples were clustered using Euclidean’s distance. PLS-DA permitted to assess the ability to objectively and significantly (α confidence level of 0.05) discriminate the composition of different grapes by providing the % of correct sample classification, and the Receiver Operator Characteristics (ROC) curve [ 43 ]. It was also performed a cross-validation (CV) session based on the PLS-DA test. In particular, the method consisted in building a validation set, achieved by dividing all samples in either a training (discriminant model composed by samples which had been classified a-priori ) or a test (cross-validation composed by unclassified samples) set. The distribution of samples into the two different sets was conducted accounting for the number of replicates per sample type and aimed to maintain a number of replicates in the training set > 70%. This validation was repeated 5 times by randomly inverting, per each grape type, the samples included in the training and test sets. The larger the percentage of correct classification of test set samples, the better the discrimination extent among of samples. One representative score-plot, out of the five obtained, was shown for P and F PLS-DA cross-validations [ 43 ]. The values resulting from MRI experiments (T 1 , T 2 and DIFF) were subjected to the ANOVA (Analysis of Variance) statistical test to verify the significance (Tukey and Benjamini Hochberg FDR tests, both set at an α confidence level of 0.05) by which the variables contributed to differentiate the different types of grapes. PCA, ANOVA, PLS-DA and Heat map tests were carried out by the XLStat software (v.2019, Addinsoft, Paris, France). Results and discussions HRMAS NMR Spectroscopy Semi-solid state HRMAS NMR spectroscopy represents a very innovative and powerful technique which, despite its undisputable potential, it is still underutilized in the fields of food and agricultural chemistry. HRMAS permits to obtain NMR spectra by the direct exam of fresh and intact tissues, relatively rapidly and with a resolution very similar to that attainable via NMR in liquid-state and high resolution [ 24 – 25 ]. On this basis, such a technique was exploited to identify the primary metaboloma of Fiano and Pallagrello Nero grapes and research possible correlations with both soil electrical conductivity and the treatment based on the biodynamic preparation 500 biostimulant. Initially, the most appropriate rotor spin rate was established. In fact, if, on one hand, the resolution of an NMR spectrum of a sample analysed in the semi-solid state can be improved by increasing the spin-rate, on the other hand, an excessively high rotational speed can alter the integrity of the sample, penalizing reliability, and spectral quality and reproducibility [ 24 – 25 ]. Therefore, through a preliminary set of tests, we found that the best compromise in consisted in a spin rate of 5 kHz. The full 1 H HRMAS NMR spectrum of a representative F berry is shown in Fig. 2 a. It is evident the spectral region in which was performed the suppression of the residual solvent signal (at around 4.8 ppm) and which was digitally removed to eliminate the corresponding and non informative artifacts. The identification of the most intense proton signals found in both grapes types permitted to define their primary metaboloma [4, 22,44–46]. Figure 2 b reports the assignment of the main proton signals detected, almost exclusively, in the two regions of alkyl (0.5–2.5 ppm) and hydroxyalkyl (2.5–5.5 ppm) protons. Most of predominant signals were attributed to multiplets in carbohydrates such as glucose, fructose and sucrose, followed by xylose but with a content significantly lower. Several amino acids were found in P and F berries, and consisted in proline, asparagine, GABA, glutamate, arginine, lysine, alanine, threonine, and valine. In addition, the metabolomic profile was also composed by alcohols, like methanol and ethanol, organic acids, such as tartaric, succinic and malic acids, and other compounds such as choline and lipid molecules (broadened signals in the alkyl region). Except for very weak and broadened signals, which were attributed to tryptophan and phenylalanine, there were no relevant peaks and multiplets at low field frequencies, and, in particular, in the aromatic region. This was explained by the fact that the examined tissue consisted in grape mesocarp, which notoriously lacks of phenolic and polyphenolic compounds (i.e., tannins and anthocyanins). In fact, the latter ones are expected to be much more concentrated in the grape skin and pips. The profile of the primary metabolome of F and G grapes was thus defined and, from a qualitative point of view, the same compounds were detected in both grape varieties. Subsequently, a semiquantitative comparison among the integrations of F and G spectra was conducted aiming to find significant, objective, and diagnostic characteristics related to both the soil spatial variability (based on ECa values detected via EMI technique) and the action exerted by the p500 application. It is important to underline that the studied grapes were harvested in the same vintage (September 2021), from the same vineyards and the same type of soil, managed by the same producer, and grown, practically, in the same microclimatic conditions. Soil Spatial microvariability In order to correlate soil vineyard microvariability with the primary metabolome of studied grapes, we elaborated the integrations of their HRMAS NMR spectra via Principal Component Analysis (PCA). The latter is an unsupervised multivariate statistical analysis enabling the rapid identification of statistical groups of observations characterized by specific peculiarities. It allows an effective exploration of very large data matrices, providing, in a single output (namely score plot), clear information on systematic variations induced by specific conditions and/or treatments, and revealing, at the same time, the variables responsible for these discriminations [ 43 ]. Therefore, HRMAS 1 H NMR spectra of F samples collected from plants located in the soil sites characterized by a low (L, 9 mS/m) or high (H, 13 mS/m) ECa responses were thoroughly compared via PCA. Fig.s 3a and 3b show both the PCA score-plots and the most significant spectral comparisons between Fiano samples, for both control and p500 treated plants. The score-plot displayed in Fig. 3 a refers to FC samples and results from the linear combination of the line PC1 and PC2, explaining 44.18 and 24.03% of total system variability, respectively. The fact that both L and H groups were placed in markedly different regions of the score-plot, indicated neat and peculiar differences in their primary metabolomas. Firstly, the metabolites mostly representing the PC1 and PC2 were individuated by examining the respective PCA loading-plot. Then, the metabolites responsible to significantly discriminate L from H were selected via ANOVA test. FC collected in L sites exhibited a lower content in amino acids, such as proline, glutamate, GABA, alanine, valine, and threonine, along with a lower content in ethanol and lipids (differentiation expressed along the PC1). Also the lower content in xylose permitted a collocation of L samples at higher PC2 values, thus contributing to the discrimination. On the right-side of Fig. 3 a it is exhibited a comparison among the spectral regions (3 replicates per grape type) including the NMR signals mostly involved in the discrimination between the two types of grapes. Figure 3 b reports a comparison between L and H Fiano samples resulting from treatment with biodynamic p500. Again, the combination of PC1 and PC2 (respectively 40.93 and 23.93% of the total explained variability) implied a clear differentiation between FT_L and FT_H. The discrimination occurred only along the PC1 and was due to the fact that FT_H samples exhibited a higher content in fructose, lysine, lipids, and valine, accompanied by a lower amount of GABA, threonine, and alanine. Again, the spectral comparison in the right-side of the figure highlights the neat semiquantitative differences between the two types of grapes. These results proved that, in all cases, the metabolome of F samples changed significantly, as a function of soil electrical conductivity, thus proving that the factors which determined a difference of 4 mS/m between the ECa values of L and H sites are sufficient to affect the vine metabolism. The score-plot exhibited in Fig. 3 c informs on the metabolome of Pallagrello grapes (untreated with p500) as a function of different ECa responses. Only the combination PC4 and PC7 (explaining the 14,68% of total variability) allowed a discrimination between H and L, and was exclusively due to a significantly higher content of malic acid in PC_L samples. In case of P samples treated with the biostimulant p500, it was detected a neat discrimination as a function of soil ECa responses (Fig. 3 d). The discrimination occurred by combining the PC1 (43.47%) with the PC2 (27.41%) and was ascribed to the higher content in GABA, proline, asparagine, malate, glutamate, alanine, ethanol, lipids, and valine in PT_H samples. Interestingly and in agreement with the results concerning F berries treated with p500 (Fig. 3 b), also in PT samples it was recorded an over-expression of several amino acids. Another common aspect is that valine and lipid compounds resulted higher than in L, for both FT_H and PT_H. Also for P grapes, the different ECa values revealed soil sites with different characteristics and affecting differently the grape metabolism. The soil spatial variability identified by EMI technique is mainly attributable to the overall distribution in active clays, water, and organic matter which contribute to increase the apparent electrical conductivity [ 16 ]. Our results, based on sites with ECa values at least of 8 mS/m and characterized by an average difference of 4 mS/m, proved that investigated soil sites experimented specific soil conditions which, not only were indirectly revealed by EMI, but also significantly impacted on both vine physiology and grape metaboloma. In fact, in all of studied cases, it was verified that at different ECa values corresponded systematically a different metaboloma. In addition, such a discrimination resulted even more pronounced for samples subjected to biostimulant treatment. Interestingly, this outcoming wel l agrees with a recent paper focusing on Aglianicone black grapes [ 22 ] and proving the existence of a tight and systematic correlations between the composition of Aglianicone grapes, revealed via HRMAS NMR, and ECa soil data. Effects induced by p500 treatment on grape metabolome PCA was also exploited to investigate on possible effects induced by the action of the biostimulant p500 on the composition of the two varieties of grapes F (Fig. 4 a) and P (Fig. 4 b). Interestingly, both varieties of grapes showed an enhanced p500-dependent change in the primary metaboloma. The score-plot differentiating FC from FT grapes was obtained by combining PC1 and PC2 (56.67% of the total system variability explained; Fig. 4 a). The discrimination occurred along the bisector direction between the second and the fourth quadrant and was determined by a higher content in proline, glutamate, GABA, alanine, ethanol, and lipids, in samples resulting from p500 treatment, accompanied by a lower content in sucrose and fructose. For P samples, the neat differentiation occurred along the PC1 (48% of the total variability of the system; Fig. 4 b). Analysis of the loading plot, combined with the ANOVA test, revealed that the PC samples exhibited a significantly lower content in metabolites such as glucose, fructose, proline, glutamate, lysine, alanine, threonine, and lipids. These outcomes suggest that, for both P and F grapes, the p500 treatment may promote the expression of several amino acids as well as can impact on carbohydrates contents. As compared to the control, the p500 induced an increase of proline, glutamate and lipids for both FT and PT. However, the influence induced by p500 on vine metabolism seems to vary as a function of the variety. A certain influence of biodynamic products on the expression of amino acids have been already observed previously. For example, Picone and coworkers [ 14 ], in agreement with our results on FT, have demonstrated that the concentration of GABA, in treated grapes, was significantly higher than the grapes subjected to traditional organic treatment, as well as it was proved that biodynamic treatments positively elicited the production of isoleucine and valine [ 47 ]. Heatmap (Fig. 5 ) not only confirmed the discriminability of explored samples, by clusterizing control and p500 samples into two distinct groups for both P and F samples, but also highlighted, in a direct and simple way, the semi-quantitative response (red to yellow scale) of the variables significantly involved in the grape type discrimination. In case of F samples, the map underlined a lower content in sucrose and fructose in FT samples, accompained by a larger amount of glutamate, proline, lipids, alanine ethanol and GABA (Fig. 5 a). In case of Pallagrello grapes, the heat map confirmed the pronounced difference in the primary metabolome induced by p500 treatment. In fact, PT exhibited a lower abundance of fructose and glucose, while PC showed a pronounced expression of several compounds, including amino acids and lipids. Finally, it was applied the cross-validation test of PLS-DA to further prove the capacity of the used analytical approach to discriminate control from p500-treated Fiano and Pallagrello grapes in a reliable way. The test was successful since in each of five cross-validations, the "unknown" replicates were classified correctly at the 100%. In Supporting Fig. S3 they are reported the outputs of one representative cross-validation test for both Fiano ( Supporting Fig. S3 a-c) and Pallagrello grapes ( Supporting Fig. S3 d-f). The figure includes the PLS-DA Cross-validation score-plot, the PLS-DA Receiver Operator Characteristics (ROC) curve in which the Area Under the Curve corresponded to 1 and the PLS-DA cross-validation results. In conclusion, these outcomes showed unequivocally that biostimulant preparation 500, when applied with appropriate times, methods, and amounts, can influence the primary metabolism of vines, with effects which reflect on the berry composition and with possible implications on grape and wine quality. This finding is very important because permits to candidate the proposed HRMAS-based metabolomic approach as a reliable tool to prove objectively the biodynamic nature of the product, thus permitting to protect biodynamic producers as well as permit them to valorize and promote their products. Titratable acidity, contents in total phenols and antioxidant agents The titratable acidity of grape is an important parameter because is related to the pH, accounts for content in weak acids, impacts on organoleptic properties and influences the vinemaking process. It is mostly influenced by predominant organic acids which are represented by tartaric acid (0.2–1.0%, on fresh weight), followed by malic, succinic and citric acid [ 48 ]. It is reported that the content of tartaric acid, generally ranging between 3 and 9 g/l, depends on the variety, production area, vintage, and terroir [ 14 , 48 – 51 ]. F grapes exhibited a significantly higher titratable acidity than P one (Table 1 ). This was expected, due to the fact that the pH of white grape berries is typically more acid than red ones. Moreover, samples FT_L, PT_H and PT_L showed the highest values for F and P variety, respectively. Importantly, all of these samples resulted from the p500 treatment, thus suggesting a possible role of this biostimulant in eliciting an accumulation of organic acids in grapes. The titratable acidity did not vary among P samples, with an average value of 3.86 g/l. Conversely, this parameter varied markedly among F samples, as a function of both p500 treatment and soil ECa values. In detail, FT_L was characterized by the highest titratable acidity, which corresponded to 5.62 g/l. Interestingly, the slightly but significantly higher titratable acidity of PC_L than PC_H is in line with the highest content in malic acid for PC_L already observed by HRMAS NMR. The content in total phenols was evaluated in all investigated grapes through the Folin Ciocalteau assay. As expected, the black grape berries P exhibited significantly higher contents in total phenols than the white berries F, with average values of 1.455 and 0.655 mg GAE/g fw, respectively. These values are in line with the content in total phenols for red grapes, which generally ranges within 1.2 and 3.35 mg GAE/g fw, while, for white berries, the range is typically included between 0.32 and 2 mg GAE/g fw [ 52 – 55 ]. The highest contents in total phenols were observed for F and P grapes treated with p500, as compared to the respective control (Fig. 6 ). The assumption of food rich in total phenols is important due to the beneficial properties of these compounds. In fact, they may serve as powerful antioxidants, prevent inflammations, stimulate positively the nervous system, prevent cardiovascular diseases and seem to be related to anti-tumor activity [ 56 ]. On this basis, it is possible to conclude that the application of biodynamic treatment not only influenced the primary metabolome of F and P grapes, but also implied an increase in their nutraceutical and healthy value. In addition, it was found also a neat correlation between the phenols content and site-specific ECa responses. In fact, except for FT_H and FT_L which did not vary at all among each other, FC_L was significantly higher than FC_H. Conversely, in case of Pallagrello samples, the total phenols responses changed significantly, as a function of ECa values, by exhibiting the highest contents in case of PC_H and PT_L (Fig. 6 a). The DPPH assay was carried out to quantify the free radical scavenging activity in studied grapes. It was detected a significant amount of antioxidants in both varieties (Fig. 6 b) with higher average values for P grapes. This well agrees with the responses observed for the total phenols, since most of the antioxidant activity deriving from the assumption of grapes, is actually ascribable to phenolic and polyphenolic compounds. The antioxidant activity in red grape berries ranges between 2244 and 3154 µg of AEE/g fw [ 56 – 57 ], while the average content of antioxidant agents in white berried grapes typically is included within 1233 and 1408 µg of AEE/g fw. Again, in agreement with the total phenols, the samples FT_H and PT_L, both resulting from the treatment with the biodynamic product, gave, in absolute, the highest responses in antioxidant activity, with values of approximately 1718 and 3419 µg AAE/g fw, respectively. These results were even higher than those typical for red and white berries. About the correlation with ECa vineyard responses, in case of Pallagrello the values varied slightly, as a function of ECa values, with L samples giving the relatively higher values. In case of Fiano grapes, the changes ascribable to site-specific ECa values, were much more pronounced, with the highest values observed for FC_L and FT_H. MRI analysis of Fiano and Pallagrello grape berries MRI spectroscopy was exploited to examine the inner morphology of intact berries of Fiano and Pallagrello cultivars. In Fig. 7 they are shown the MRI images related to the central slices of representative Fiano (a) and Pallagrello nero (b) grape berries. MRI images clearly permitted to distinguish the three main morphological tissues composing this fruit. In fact, the thin outer darker layer is composed by the exocarp and the epidermis, whereas the mesocarp corresponds to the large area occupied by intense and whitish signals due to the high water content. The seeds are neatly identifiable in the endocarp by the presence of dark semi-ellipsoidal areas, characterized by a relatively darker and external area (the tegument) and relatively greyish and lighter area (the endosperm contained in the seeds). The latter ones appear significantly darker than the surrounding mesocarp tissue being them poorer in water and richer in oils and polyphenols. Furthermore, it was also possible to appreciate the vascular system and, in particular, the peripheral vascular bundles, branching out in proximity of the exocarp, the central fibrovascular bundles, developing below the cercine, and the bundles directly connected to the seeds (Fig. 7 ; supporting video 1 and 2). These images suggest that MRI can reveal anatomical details of intact in-vivo or ex-vivo samples, with a resolution of tens of microns. This is important because paves the way to its use to investigate on the early detection of treatment-related effects on inner grape morphological traits. It is important to underline that, to date, only one research group [ 58 ] has successfully attempted to use MRI to investigate on grape berries, by detecting interesting effects exerted on Nero di Troia grapes as a function of different fertilizers. Although this further proves the potential role of MRI in studying grapes, on the other hand, it certifies how the MRI potential to examine grape is still largely unexpressed and unexplored, thus requiring further validations. Firstly, MRI images of F and P were accurately compared. They were only appreciated morphometric differences between the two cultivars, with the diameter and volume of P berries and seeds (detected in integer berry) appearing significantly larger than F grapes. However, from a qualitative and morphological point of view, no relevant and reproducible differences were appreciated as a function of both p500 treatment and soil electrical conductivity. Therefore, the next step consisted in examining hydrogen relaxation times (T 1 and T 2 ) and self-diffusion coefficients to obtain information on grape microstructure and on behaviour, mobility and nature of the water molecules in the grapes mesocarp. Since these MRI parameters are notoriously very stable and constant, their possible and pronounced variation is diagnostic of an alteration in the tissue under examination [ 30 ]. These changes can be attributed, directly or indirectly, to factors such as: a sharp variation in the content of free water; change in microstructure and therefore in the confinement of water molecules; alteration in the consistency of the material; thickening of vascular bundles and so on [ 29 – 31 ]. In Fig. 8 and Sup. Table S2 are reported the results concerning T 1 , T 2 and self-diffusion coefficients of F grapes. Differently than for MR images, MRI parametric data revealed singular and interesting responses depending on both p500 treatment and soil microvariability ( Sup. Table S3 ). T 2 values ranged between 39.56 to 53.22 ms and the differences among the F types resulted statistically significant in all cases (Fig. 8 a, Sup. Table S2). Even though this is only a preliminary study, it suggests the potential use of this single parameter as an innovative and diagnostic element of quality, useful to serve as supporting data to discriminate, trace and identify grapes treated or not with preparation 500 and/or sampled from micro-areas characterized by different soil properties. Interestingly, while p500 led to the highest T 2 value, in case of high ECa values, it was detected the lowest value in case of grapes treated with p500 but sampled at soil sites with the lowest electrical conductivity. For T 1 relaxation times (Fig. 8 b, Sup. Table S2 ), the values ranged between 1220 and 1489 ms. The T 1 values for FT_H and FT_L did not differ from each other, while a neat difference was observed between FC_H and FC_L samples, which exhibited a gap of 269 ms, with FT_H characterized by a longitudinal relaxation time of 1489 ms. Finally, the diffusion coefficients varied from 1.213 to 1.413 mm 2 /s * 10 − 3 (Fig. 8 c, Sup. Table S2 ). Interestingly, the results followed a trend similar to that of T 2 results with the longest relaxation time recorded for samples collected at high ECa values and resulting from p500 treatment. For samples collected at points characterized by low ECa values, no differences were found between treated (FT_L) and untreated plants (FC_L), with values averaging around 1.3 mm 2 /s * 10 − 3 . Our results suggested that MRI proton relaxation times and diffusion coefficients are capable to appreciate grape structural properties and characteristics, which depend on conditions and factors affecting the vine phenological development during the fruit production. In fact, the application of p500 as well as different soil electrical conductivity were capable to induce pronounced changes in Fiano grapes, leading to relatively small but statistically significant inter-class differences (Sup. Table S3). In addition, the fact that all replicates (per berry and per ROI) exhibited a very low intra-class variance certified an excellent and promising reproducibility for such innovative analytical method. A variation in relaxation times and in water diffusivity may be explained by factors such as a change in the content and confinement of free water in the berries; alteration in the grape microstructure; development of a denser and thicker vascular system, with consequent impact on mesocarpic water mobility; modification in fruit consistency. The systematic change observed for these parameters permitted to conclude that they correlated with both p500 treatment and soil ECa, and in particular to conditions such as: (1) different bioaccessible water in soil, which directly influenced soil ECa values and indirectly the water content of the fruit, thus implying a higher dry weight; (2) a different and site-specific content of organic matter (excluding the role of active clayes content which were assumed constant within the examined F vineyard) (3) the impact of the biodynamic treatment on the bioavailability of water and/or nutrients, inducing an alteration that varied depending on spatial microvariability (4) the action of biodynamic treatment serving as a primer to shift and enrich the vine rhizospheric microbial community, with an influence on nutrient and water uptake as well as their respective physiology (5) the greater bioavailability of iron (presumably related to the induced presence of siderophore microorganisms) capable to imply a more efficient uptake of this ferromagnetic element, known to promote an artificial shortening (i.e. not related to the tissue itself) of relaxation times. Although, at the moment, we cannot discriminate among these factors, we can assume that most of these concurred to determine the observed results and hypothize to performe, in the future, futher experiments aimed to go deeper inside these important aspects. It is important to emphasize that the content in antioxidant activity and total phenols for FT_H sample resulted, absolutely, the highest. This outcome permits to infer that Fiano grapes treated with preparation 500 and developed in points characterized by relatively higher ECa values yielded grapes with a neatly superior nutraceutical quality. Consequently, we found that MRI T 2 relaxation times and diffusion coefficients of FT_H showed a similar trend, exhibiting the highest values for both parameters. This original evidence suggests that these MRI parameters may serve as indirect markers of nutraceutical quality. Consistently, the absolutely lowest value found in T 2 was that of FC_H samples, which was also the sample with the markedly poorest content in antioxidants and total phenols. Again, this finding suggests a possible correlation between nutraceutical quality of Fiano and MRI T 2 . The analytical approach conducted for Fiano grapes was attempted identically also for Pallagrello grapes. This notwithstanding, it was not possible to obtain reliable and useful MRI data with an unacceptable variance. In fact, differently than for the Fiano variety, all examined Pallagrello berries tended to collapse drastically in the MRI tube before the end of all MRI experiments (each series of MRI experiments required 8 hours of continuous acquisition time) ( Supporting Fig S4 ). At the end of the analysis we incurred in these problems: (a) a general collapse of the entire berry, whereas in MRI analysis it is required to mantain the sample motionless throughout the whole experiments [ 29 – 31 ] (b) a relevant amount of juice exuded from the berry on the bottom of the tube (c) a greater susceptibility to pathogens attack and (d) a drastically reduced consistency of the berry. Except for just few and not statistically relevant samples, all these conditions concurred to make Pallagrello samples (and presumably all grape varieties with similar characteristics) unsuitable for this type of MRI experiments. Conclusions In this work we used an innovative analytical approach to verify a possible impact exerted on grape fruit by the application of biodynamic p500 biostimulant and accounting for the vineyard spatial variability revealed by the soil electrical conductivity. We focused on two varieties of grapes very representative of the Italian excellent wine production, namely Pallagrello nero and Fiano, and evaluated (i) the primary metabolome of grapes via HRMAS NMR; (ii) the main parameters representative of chemical and nutraceutical quality of grapes (titratable acidity; content in total phenols and polyphenols via FOLIN; content in antioxidant compounds via DPPH) (iii) grape microstructural and morphological characteristics via MRI. It is noteworthy to point out that the use of HRMAS NMR and MRI spectroscopies represent a relevant technological innovation. In fact, despite their indisputable ability to enable the NMR analysis of fresh and intact samples without residing into preliminary sample manipulations, they are still inexplicably unexplored and underestimated in the field of food chemistry. This work confirmed the validity and reliability of the HRMAS technique in analyzing grape berry. In fact, permitted to detect the primary metaboloma of Fiano and Pallagrello grapes, identifying the main amino acids, alcohol, organic acids, carbohydrates, and nitrogen compounds related to the primary metabolism of grapes. The elaboration of proton spectra of the different types of grapes investigated through advanced and multivariate statistical techniques proved, objectively, that the proper application of the biostimulant p500 influences the primary metabolism of plants, with effects which affect the primary metabolism of grape as well as that grape berries collected in vineyard sites characterized by different ECa values exhibit a systematically different metaboloma. Also chemical and nutraceutical parameters were significantly impacted by these factors. In particular, the highest nutraceutical values were recorded for Pallagrello and Fiano grapes both treated with p500 and collected at points characterized by low and high ECa values, respectively. In fact, the former contained 1.76 mg GAE/g and 3372.45 ug AAE/g, while the latter 0.89 mg GAE/g and 1717.97 ug AAE/g. Importantly, this proved that, at certain soil conditions, the biodynamic p500 may significantly improve the nutraceutical quality of this fruit. MRI not only enabled to exam of the main structural characteristics of integer grape berries, but also permitted to extrapolate important water parametric data, such as proton relaxation times and diffusion coefficients, which were found to correlate with both p500 application and soil electrical conductivity. It was hypothesized that these variations in grape reflected the biotic and abiotic conditions experienced by the vine during the development of the examined fruit, such as different water and nutrients availability in soils, the action exerted by p500 as a primer to alter and enrich the microbial community at the rhizospheric level and so on. It is noteworthy that T 2 results showed a trend very similar with that of diffusion coefficients and that high values correlated with the highest content in total phenols and antioxidant. This outcome candidates this MRI parametric data as potential marker for grape nutraceutical quality. However, as demonstrated by the application of MRI on P grapes, a limitation of the method is that it can be applied, in a reliable way, only on grape varieties capable to remain mostly unalterated at 25°C for at least 8 hours (such as Fiano). In conclusion, our findings have shown how the spatial microvariability of the soil and the use of p500 can affect the molecular, structural and nutraceutical characteristics of Pallagrello nero and Fiano grapes, with effects that can presumably reflect on the quality of the resulting wines. The proposed approach represents an efficient solution to indirectly valorize biodynamic grapes and wines. Moreover the identified metabolomic fingerprint of p500 grapes may serve as a useful tool to help biodynamic producers to (i) promote their products via molecular traceability, (ii) contrast possible frauds and, importantly, (iii) objectively demonstrate the use of the declared biodynamic treatment. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests Availability of data and materials Not applicable. Funding Not applicable. A cknowledgements This work was financially supported by the Projects “modelli circolari - modelli di sistemi circolari multifunzionali per produzioni tipiche (MIPAAF)” and “Spettroscopie NMR HRMAS e MRI per correlare, rispettivamente, il metaboloma primario e parametri strutturali di uva Fiano e Pallagrello nero all'azione di biostimolanti e alla conducibilità elettrica apparente del suolo del vigneto (University of Salerno - orsa230438)” References Khan N, Fahad S, Faisal S, Naushad M. Grape production critical review in the world. SSRN Electr J. 2020; 10.2139/ssrn.3595842. Vinci G, Prencipe SA, Abbafati A, Filippi M. Environmental Impact Assessment of an Organic Wine Production in Central Italy: Case Study from Lazio. Sustain. 2022; 14(22):15483. Dordevic N, Wehrens R, Postma GJ, Buydens LMC, Camin F. Statistical methods for improving verification of claims of origin for Italian wines based on stable isotope ratios. Anal Chim Acta. 2012; 757, p. 19-25. 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Metabolomics. 2013; 1:92–107. Mulas G, Galuffi MG, Pretti L, Nieddu G. NMR Analysis of seven selections of Vermentino grape berry: metabolites composition and development. J Agr Food Chem. 2011;793-802. Hong YS. NMR-based metabolomics in wine science. Magnet Res Chem. 2011; 49, 13-21. Botelho RV, Roberti R, Tessarin P, Garcia-Mina JM, Rombolà AD. Physiological responses of grapevines to biodynamic management. Renew Agr Food Syst. 2016; 31(5):402. Sansavini S, Costa G, Gucci R, Inglese P, Ramina R, Xiloyannis C. Forme di allevamento e potatura della vite, Arboricoltura generale. Patron editori Bologna. 2012;383-386. Sicheri G. Le principali caratteristiche botaniche dell’uva. Il libro completo del Vino, 16A edizione, DeAgostini. 2017; 34-35(34-48). Tartian AC, Cotea V, Niculaua M, Zamfir CI, Colibaba CL, Morosanu AM. The influenze of the different techniques of maceration on the aromatic and phenolic profile of the Busuioaca de Bohotin wine. Bio Web Conf. 2017. . Cherviak SN, Anikina NS, Gnilomedova NV, Gnilomediva VG, Gerzhikova VG, Vesiutova AV. Study of physic-chemical and biochemical parameters of technical varieties of grapes. Earth Env Sci. 2021; 659: 012087. Zhang P, Needs S, Liu D, Fuentes S. The influence of apical and basal defoliation on the canopy structure and biochemical composition of vitis vinifera cv. Shiraz grapes and wine. Front Chem. 2017; 1-9. Ramirez-Lopez LM & DeWitt CAM. Analysis of phenolic compounds in commercial dried grape pomace by high-performance liquid chromatography electrospray ionization mass spectrometry. Food Sci Nutr. 2014; 470-477. Cadota Y, Minana Castello MT, Chevalier M. Flavan-3-ol compositional changes in grape berries (Vitis vinifera L. cv Cabernet Franc) before veraison, using two complementary analytical approaches, HPLC reversed phase and histochemistry. Anal Chim Acta. 2006; 563:65-75. Ivanova V, Stefova M, Vojnoski B, Dornyei A, Mark L, Dimovska V, Stafilov T, Kilar F. Identification of polyphenolic compounds in red and white grape varieties grown in R. Macedonia and changes of their content during ripening. Food Res Int. 2011; 44:2851-2860. Derradji-Benmeziane F, Djamai R, Cadot Y. Antioxidant capacity, total phenolic, carotenoid, and vitamin C contents of five table grape varieties from Algeria and their correlations. OENO One. 2014; 48(2):153-162. Montero GC, Minatel IO, Junior AP, Alonzo Gomez H, Correa de Camargo JP, et al. Bioactive compounds and antioxidant capacity of grape pomace flours. LWT. 2021;135. Ciampa et al. Combined magnetic resonance imaging and high-resolution spectroscopy approaches to study the fertilization effects on metabolome, morphology and yeast community of wine grape berries, cultivar Nero di Troia. Food Chem. 2019; 831-839. Additional Declarations No competing interests reported. Supplementary Files GraphicalAbstract.png MAZZEIetalManuscriptSupInf.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 May, 2024 Editor assigned by journal 04 May, 2024 Submission checks completed at journal 04 May, 2024 First submitted to journal 30 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4351758","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":298697929,"identity":"77de054f-b6d3-4fe9-88d8-326011f46532","order_by":0,"name":"Pierluigi Mazzei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYPACCTkGBsYGEIN4LcYwLcTrSWyAaSao1Fzs8OOPPyos0jfcbm7+8IPBoo6gFsvZaQYGEmckcjfcOdgm2UOMwwxuJxgkGLYBtdxIbGPgIU5L+ocDif8k0g1uJDZ//EOclhzDhoMNEglALQ3SRNliOTunmLHhmIThTKDDpGUMJCQbCGkxl07f/PFHTZ083430xx/fVNTxE3YYXi4xWkbBKBgFo2AUYAEAxUM5YexsZ5cAAAAASUVORK5CYII=","orcid":"","institution":"University of Salerno, Department of Pharmacy","correspondingAuthor":true,"prefix":"","firstName":"Pierluigi","middleName":"","lastName":"Mazzei","suffix":""},{"id":298697931,"identity":"926dce01-fb83-46a6-9fee-41b7967f8b91","order_by":1,"name":"Andrea Sica","email":"","orcid":"","institution":"University of Salerno, Department of Pharmacy","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Sica","suffix":""},{"id":298697933,"identity":"295518f7-cf86-4663-a84b-24daf4e3a014","order_by":2,"name":"Claudio Migliaro","email":"","orcid":"","institution":"University of Salerno, Department of Pharmacy","correspondingAuthor":false,"prefix":"","firstName":"Claudio","middleName":"","lastName":"Migliaro","suffix":""},{"id":298697935,"identity":"567ea8f3-7fa5-4c0f-a32a-0209e8c1332c","order_by":3,"name":"Gessica Altieri","email":"","orcid":"","institution":"University of Salerno, Department of Pharmacy","correspondingAuthor":false,"prefix":"","firstName":"Gessica","middleName":"","lastName":"Altieri","suffix":""},{"id":298697944,"identity":"a6bd2e3d-afc1-431b-98f8-2f0930f7e78a","order_by":4,"name":"Nicola Funicello","email":"","orcid":"","institution":"University of Salerno, Department of Physics Caianiello","correspondingAuthor":false,"prefix":"","firstName":"Nicola","middleName":"","lastName":"Funicello","suffix":""},{"id":298697951,"identity":"b0d628d2-db7e-4701-975c-9bbd42137959","order_by":5,"name":"Salvatore Pasquale","email":"","orcid":"","institution":"University of Salerno, Department of Physics Caianiello","correspondingAuthor":false,"prefix":"","firstName":"Salvatore","middleName":"","lastName":"Pasquale","suffix":""},{"id":298697953,"identity":"ce896f40-eb35-4128-b4c1-02448ce01d27","order_by":6,"name":"Alessandro Piccolo","email":"","orcid":"","institution":"University of Naples \"Federico II\", Department of Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Alessandro","middleName":"","lastName":"Piccolo","suffix":""},{"id":298697956,"identity":"f7fc7867-8fe7-4a82-be6a-f21a6a458b92","order_by":7,"name":"Giuseppe Celano","email":"","orcid":"","institution":"University of Salerno, Department of Pharmacy","correspondingAuthor":false,"prefix":"","firstName":"Giuseppe","middleName":"","lastName":"Celano","suffix":""}],"badges":[],"createdAt":"2024-04-30 23:39:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4351758/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4351758/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56396472,"identity":"431d1ecc-4c1c-45f0-8b81-639261c9ef5c","added_by":"auto","created_at":"2024-05-13 15:44:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1642408,"visible":true,"origin":"","legend":"\u003cp\u003eECa map of Pallagrello nero (P, on the left) and Fiano (F, on the right) vineyards obtained by electromagnetic induction technique (13 kHz). The color scale is described in the legend and shifts progressively from red (low ECa values, 5.37 mS/m) to blue (high ECa values 16.49 mS/m) as a function of site-specific electrical conductivity. The image also specify the plots either treated (T) or not (C) with p500.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4351758/v1/1fb7dd241dc70562744d0b7b.png"},{"id":56396476,"identity":"508ddb93-a1dc-4bb9-971b-609a42650a69","added_by":"auto","created_at":"2024-05-13 15:44:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":426799,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Full \u003csup\u003e1\u003c/sup\u003eH HRMAS NMR spectrum of a representative sample of FIANO grape berry (b) Expansion and magnification (entity of magnification is reported in parentheses) of the following spectral regions: 0.4-3.1 ppm (x16); 3.2-4.8 ppm; 5-5.6 ppm. Red boxes include an additional magnification of the regions 5.26-5.52 ppm (x128) and 4.35-4.55 ppm (x4). The figure reports the name of compounds attributed to the signals. The abbreviations G, F, S and Xil refer to carbohydrates glucose, fructose, sucrose and xylose, respectively.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4351758/v1/8e0ab56039fb76e4bb5d9eeb.png"},{"id":56398026,"identity":"56b0265f-9812-4f45-b47d-0d86850f6e2a","added_by":"auto","created_at":"2024-05-13 15:52:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2252173,"visible":true,"origin":"","legend":"\u003cp\u003ePCA score-plot obtained by comparing \u003csup\u003e1\u003c/sup\u003eH HRMAS NMR spectra of Fiano (F) and Pallagrello (P) mesocarp tissues, sampled from vineyard sites characterized by high (H) and low (L) ECa values, and collected from plants untreated (a and c) and treated with the biostimulant p500 (b and d). In green, along the edges of score plots, are reported the name and the orthogonal direction of the most significant loading vectors responsible for the discrimination between the compared types. On the right side of each score-plot they are shown the NMR peaks mostly involved in the discrimination between the grape types (3 replicates # per type).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4351758/v1/8ca82c28c557e68b090d767c.png"},{"id":56396479,"identity":"085cf9ac-ad50-4b28-a332-36c772ea380f","added_by":"auto","created_at":"2024-05-13 15:44:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1241383,"visible":true,"origin":"","legend":"\u003cp\u003ePCA score-plot obtained by comparing \u003csup\u003e1\u003c/sup\u003eH HRMAS NMR spectra of Fiano (F, a) and Pallagrello (P, b) mesocarp untreated (Control) and treated with p500 (p500). In green, along the edges, are reported the name and the orthogonal direction of the most significant loading vectors responsible for the discrimination between the compared types.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4351758/v1/33395fbc960f0951aaee37ac.png"},{"id":56396480,"identity":"61f1b2df-e590-4768-9b28-669020716b42","added_by":"auto","created_at":"2024-05-13 15:44:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":828708,"visible":true,"origin":"","legend":"\u003cp\u003eHeat-map of Fiano (F, a) and Pallagrello (P,b) grapes untreated (C) and treated (T) with p500. The values for variables were centered, reduced and displayed (see legend) as colors ranging from red (relatively higher) to yellow (relatively lower) through orange (intermediate values). Both samples and variables were clustered by applying the Euclidean’s distance. G, a-G, F and S refer to glucose, alfa-glucose, fructose and sucrose, respectively.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4351758/v1/9e8c1091289761df4c0a3515.png"},{"id":56396475,"identity":"6a521a91-364c-4cec-9345-6f4475aed75a","added_by":"auto","created_at":"2024-05-13 15:44:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":331515,"visible":true,"origin":"","legend":"\u003cp\u003eContents in total phenols (up, mg GAE/ g\u003csub\u003efw\u003c/sub\u003e) and antioxidants (down, mg AAE/ g\u003csub\u003efw\u003c/sub\u003e) of Fiano (F, on the left; a and c) and Pallagrello nero (P, on the right; b and d) grapes. The bars on the top of each histogram represent the respective standard deviations. The labels L, H, C and T refer to low soil ECa levels, high soil ECa levels, control and p500 treatment, respectively.\u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4351758/v1/56c6c5bc58b8866e5180d51a.png"},{"id":56396482,"identity":"b1cbe3bc-fa1c-4b2f-8ad8-fca1642fefa5","added_by":"auto","created_at":"2024-05-13 15:44:31","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1425592,"visible":true,"origin":"","legend":"\u003cp\u003eSpin-density images of two different slices of representative Fiano (a and b) and Pallagrello nero (c and d) grapes. The images were obtained by setting a total spin-echo duration of 12 ms.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4351758/v1/56be26f1308d1e1cf6f49f4a.png"},{"id":56398027,"identity":"e70f8406-ac73-40fd-aded-36e9c00377c2","added_by":"auto","created_at":"2024-05-13 15:52:31","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":195191,"visible":true,"origin":"","legend":"\u003cp\u003eAverage proton longitudinal spin-lattice (T1 in ms; a), transversal spin-spin (T2 in ms; b) and self-diffusion coefficients (DIFF in mm\u003csup\u003e2\u003c/sup\u003e/s * 10\u003csup\u003e-3\u003c/sup\u003e, c) of Fiano grapes (control, FC, and treated, FT), collected in vineyard sites characterized by low (L) and high (H) apparent soil electrical conductivity. The bars at the top of each histograms indicate the respective standard deviation.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4351758/v1/a5c8acbd9aa608994db16221.png"},{"id":56399104,"identity":"84185ca9-18b2-40a4-9e9d-7e0405a8e664","added_by":"auto","created_at":"2024-05-13 16:00:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12625254,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4351758/v1/b85ed562-e5ec-4c14-ba8a-1925af6a33ed.pdf"},{"id":56396474,"identity":"79b5c10b-af29-46d2-b8f9-57288b44ba6e","added_by":"auto","created_at":"2024-05-13 15:44:30","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":307266,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-4351758/v1/e4b7d4304c5cff0e4c23a2bb.png"},{"id":56398025,"identity":"4c1218fe-6756-4c10-8267-5d3b64f4c0b9","added_by":"auto","created_at":"2024-05-13 15:52:30","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3022905,"visible":true,"origin":"","legend":"","description":"","filename":"MAZZEIetalManuscriptSupInf.docx","url":"https://assets-eu.researchsquare.com/files/rs-4351758/v1/c53a5dad72bd86fb4f6131b4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"MRI and HR-MAS NMR spectroscopies to correlate structural characteristics and the metabolome of Fiano and Pallagrello grapes with the action of field spray preparation 500 and the soil spatial microvariability","fulltext":[{"header":"Introducion","content":"\u003cp\u003eAbout 7.5\u0026nbsp;million hectares of fertile soils, in the world, are vocated to the cultivation of \u003cem\u003eVitis Vinifera\u003c/em\u003e and are mostly concentrated in China, Italy, USA, France, Spain and Turkey [1]. Over the 93% of these vineyards is composed by vine cultivars producing quality grapes suitable for the wine industry. Italy represents the second world producer of wine, following the France, with an average production in 2021 of over 50.2\u0026nbsp;million hecto-liters [2]. From a qualitative point of view, many Italian wines are classified as excellent, as proved by the assignment of 329 D.O.C. (Controlled Designation of Origin) and 70 prestigious D.O.C.G. (Controlled and Guaranteed Designation of Origin) quality labels [3]. A large interest is thus addressed by grape and wine producers towards the identification of innovative agronomic strategies and advantageous soil conditions useful to improve vine productivity and promote the attainment of quality and excellent grapes.\u003c/p\u003e \u003cp\u003eIn this perspective, the use of efficient biostimulants can promote vines growth and increase their ability to deal with negative factors affecting their physiology and performances, such as deficiency of soil nutrients and water, patogens attack and adverse microclimatic conditions. In fact, biostimulants can activate mechanisms eliciting a response in vines, making them more efficient in exploiting soil resources [4\u0026ndash;7]. It is noteworthy the fact that effective organic biostimulants may contribute to mitigate the adverse effects due to the incessant onset of climate changes [8]. Biodynamic products, which are obtained in respect of the principles of circular economy and sustainability, may represent suitable candidates for this purpose. For example, it has been proved that the application of a biodynamic treatment on soil and vines may improve grape production and quality, representing an alternative approach to traditional methods often responsible to determine relevant environmental impacts [9\u0026ndash;10]. In particular, the field spray preparation 500 (p500) is an organic product deriving from the biochemical stabilization of cow manure, carried out in respect of biodynamic principles [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This product has a complex molecular structure, with an overall low hydrophobic character and composed mostly by aromatic derivatives, polysaccharides, and alkyl compounds [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. It has been proved that the application of p500 can positively influence the wheat by promoting plant and root growth and increasing the seedling vigor [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Recently, it has been developed a model, based on proton liquid-state Nuclear Magnetic Resonance (NMR), which proves that the molecular composition of grapes, cultivated according to a biodynamic method including the p500 treatment, exhibits a profile significantly different than for grapes resulting from a traditional organic management [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, so far, the literature probing the effectiveness of p500 as biodynamic biostimulant is still poor. Therefore, further investigations are required to confirm and consolidate the beneficial and effective role of this product.\u003c/p\u003e \u003cp\u003eOn the other hand, there is a growing interest related to the use of fast, reliable and georeferenceable methods to better understand the correlations existing between wine quality and the properties characterizing the territory of grape origin. In this context, the electromagnetic induction (EMI) method, typically adopted to conduct precision agriculture, represents a very promising technique allowing the evaluation of soil apparent electrical conductivity (ECa) and its representation through intuitive responsive maps. EMI is very advantageous because permits to predict several soil conditions and properties, such as the content in water, organic matter, active clays and salts, as well as individuate buried rocks at a soil depth which, depending on the selected frequency for the measurement, may explore up to 1.5-2 meters [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Conceptually, the EMI technique involves the generation of a primary magnetic field, developed from a moving transmitter and oriented towards the soil. The presence of conductive components in soil produces secondary currents that, in turn, develop a secondary induced magnetic field. The detection and the quantification of the latter is finally exploited to extrapolate the soil electrical conductivity [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Recently, ECa maps have been also used to correlate the spatial variability of soil physical properties with site-specific crop responses, including biological parameters [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eand total soil respiration [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. It has been found that plant growth parameters, such as height, leaf area index and dry matter, well correlate with soil patterns identified through ECa maps for crops such as winter barley, winter wheat and sugar beet [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Similarly, a close relationship between ECa maps and maize plant height has recently been established in field experiments [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A significant correlation has been also found between soil microvariability detected via EMI and the molecular quality of Aglianicone grapes in different vintages [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn this basis, the purpose of this work was to investigate on the possible effects induced by the p500 treatment on model autochthonous Italian vine cultivars (Pallagrello Nero and Fiano), in open field tests, accounting also for the spatial microvariability of vineyards revealed by EMI method. The influence of both p500 treatment and soil-microariability was verified by examining (i) representative nutraceutical parameters, (ii) the primary metabolome and (iii) microstructure and morphological traits of studied grapes.\u003c/p\u003e \u003cp\u003eIn particular, grape metabolome was unraveled by means of High-Resolution Magic Angle Spinning (HRMAS) NMR spectroscopy, in the semi-solid state [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This technique permits to obtain NMR spectra through the direct exam of fresh and intact tissues, in a relatively rapid way, with a resolution very similar to that attainable via High Resolution liquid-state NMR [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. HRMAS represents a very innovative and powerful technique which has been already used with success mostly to investigate on the quality and the origins of relevant agro-food products, such as tomato [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], persimmon [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], sweet pepper [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and maize [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, despite its undisputable potential, HRMAS is still underutilized, in the fields of food and agricultural chemistry, while it would be useful to explore its unexpressed potential.\u003c/p\u003e \u003cp\u003eThe analysis of grape microstructure and morphological traits was carried out by means of Magnetic Resonance Imaging [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This technique is based on the quantification and the spatial location of water hydrogens and enables the acquisition of detailed morphological images (resolution of tens of micrometers) of internal sections of intact tissues. In addition, the adoption of specific experiments permits to extrapolate multiple and complementary information informing directly on the composition and dynamics of the water molecules in the examined tissue under examination and indirectly on sample microstructure [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Up to date, MRI has been used to examine several important aspects of relevant agro-food products, such as tomato [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], radish [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], kiwi [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and apple [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. MRI is a very powerful spectroscopy which so far has been largely used in the medical diagnostics and pharmacological fields. However, its potential still remains almost totally unexplored for the analysis of agrifood. Therefore, a further objective of this work was to reveal and demonstrate the suitability and potential role of MRI and HRMAS NMR spectroscopies for investigating on grapes and providing useful information.\u003c/p\u003e"},{"header":"Experimental section","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eFiano and Pallagrello nero vineyards and p500 treatment\u003c/h2\u003e \u003cp\u003eThe grapes of white Fiano (F) and black Pallagrello (P) were produced by the biodynamic farm \u0026ldquo;I Cacciagalli\u0026rdquo;, located in Teano (Caserta, Italy). Vineyards soil is only few meters shallow, exhibits a slightly acid pH and is charaterized by a silty clay texture. F and P grapes were sampled on the 3rd and the 16th of September 2021, respectively, at the full maturation of fruits which was established on the basis of sugar content (16 \u0026deg;Bx for F and 21 \u0026deg;Bx for P).\u003c/p\u003e \u003cp\u003eHalf of F and P vineyards were treated with field spray preparation 500 (p500), purchased from the association \u0026ldquo;Agriculture living\u0026rdquo; and dynamized in the farm immediatly before the soil application. The latter occurred in full compliance with the guidelines provided by the Italian association DEMETER. The product was sprayed on the soil on the 1st of December 2020 and 14th of April 2021, using a dose of 250\u0026ndash;300 g/ha (\u003cb\u003eSupporting\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The grape samples were collected from plants insisting on soil sites treated (T, treated with 500) and untreated (C, control) with p500. For each cultivar and treatment, at least 100 berries were taken from 5 different plants. Samples were stored at the temperature of -20 C until the analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTitratable acidity (g/l of tartaric acid equivalents) of Fiano (F) and Pallagrello nero (P) grape berries, collected from soil sites characterized by low (L) and high (H) ECa levels, and treated (T) or not (C) with p500. The results are expressed as average of 3 replicates and are associated to standard deviation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC_H\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC_L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePT_H\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePT_L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFC_H\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFC_L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFT_H\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFT_L\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTitratable acidity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3,78\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003cp\u003e0,002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3,81\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003cp\u003e0,001\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3,93\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003cp\u003e0,0001\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3,93\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003cp\u003e0,0009\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e5,50\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003cp\u003e0,02\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e5,04\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003cp\u003e0,008\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e4,87\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003cp\u003e0,0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e5,62\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003cp\u003e0,0009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSoil mapping via electromagnetic induction technique\u003c/h2\u003e \u003cp\u003eVineyard\u0026rsquo;s spatial variability was measured by using a multi-frequency EMI sensor (GSSI Profiler EMP-400) equipped with a 1.2 m coil spacing [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe measurements were performed on the 16th of September 2020, by setting the vertical dipole mode (VDP), and a frequency of 13 kHz. Such a frequency resulted the best compromise to examine that portion of soil typically explored by the roots of the vine (estimated magnetic field penetration depth of about 1.5 m). ECa values (expressed as mS/m) were obtained by collecting data every 0.75 s, in continuous mode, walking along each row of the vineyard and at a speed of about 4\u0026ndash;5 km/h.\u003c/p\u003e \u003cp\u003eThe acquisition points were georeferenced using the Tripod Data System Recon Personal Digital Assistant, accompanied by both an integrated Bluetooth service and HoluxTM Wide Area Augmentation System Global Position, with a horizontal dilution of differential correction accuracy. Collected data were then used to develop ECa (apparent electrical conductivity) maps of F and P vineyards. To ensure maximum georeferencing accuracy, the coordinates of the ECa values were determined considering at least 8 satellites. ECa maps were generated by using MagMap2000c and Surfer Golden\u0026copy; and applying the kriging method (single cell 1x1 m). Abnormal ECa data acquired in proximity of vineyard metal rollers were removed. Grape berries, for both varieties and as a function of p500 treatment, were sampled by plants insisting on vineyards sites characterized by the highest (H, high, relative range 10.42\u0026ndash;14.87 mS/m) and the lowest (L, low, relative range 8.7-11.45 mS/m) ECa values. Specifically, samples were collected at high ECa values (PC3 and PT1, 14.84 and 10.31 ms/m; FC1 and FT1, 12.42 and 13.27 ms/m) and at low ECa values (PC5 and PT1, 11.45 and 10.31 ms/m; FC3 and FT5, 8.7 and 9.55 ms/m) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For each cultivar, at least 100 berries were taken from 5 different plants. Samples were stored at the temperature of -20 C until the analysis. All sample typologies and respective labels are listed in Supporting Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eTitratable acidity\u003c/h2\u003e \u003cp\u003eFor each thesis, 25 grapes were thawed and pressed until to obtain a juice, which was then filtered under vacuum (cellulose acetate filters, Whatman 41, 20 \u0026micro;m pores). Then, a volume of 2 ml of filtered juice was diluted with 98 ml of deionized water, added with phenolphthalein indicator and titrated, under magnetic stirring, with a solution of NaOH 0.1M. The titratable acidity was expressed as equivalents of tartaric acid (g/l). 3 replicates were analyzed for each grape type.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGrape extraction and quantification of total phenols and antioxidants\u003c/h2\u003e \u003cp\u003eFor each thesis, 20 grapes were thawed and blended, through an immersion mixer, until the material was completely crushed. Then, 5 g of blended material were put in contact with 20 ml of a 70% (v/v) ethanol solution, ultrasonicated (ARGO LAB DU-32) for 4 minutes at 30 \u0026ordm;C and left under magnetic stirring for 17 hours and 30 minutes, in absence of light. Afterwards, a centrifugation (10 minutes at 6000 rpm) was carried out to isolate supernatant which was finally filtered, under vacuum, and stored at the temperature of -20\u0026deg;C, until further analyses.\u003c/p\u003e \u003cp\u003eThe content in total phenols was determined by using the Folin-Ciocalteu assay and was expressed as Gallic Acid (GAE) equivalents. The reaction was performed by adding, progressively, 0.2 ml of ethanolic extract, 0.5 ml of Folin-Ciocalteu reagent (diluted 1:3 with bi-distilled water), and 0.5 ml of a Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e solution (10% v/v). After the addition of each component, the mixture was agitated for about 10 seconds, through a vortex, and held for about 4 minutes, in absence of light. Finally, the mixture was diluted with 3.9 ml of bi-distilled water and the reaction was left to proceed for 2 hours and 30 minutes, at 25\u0026deg;C, in absence of light. Each experimental set consisted of 4 replicates and 2 blank samples. For the latter, the volume of grape extract was replaced by the same volume of ethanol 70% solution. The reaction product was examined via spectrophotometer (Thermo Spectronic 20 Genesys) by setting it at a wavelength of 765 nm. The absorbance values were then converted in GAE through a comparison with a calibration curve, built by launching the reaction with increasing amounts of gallic acid (range 0\u0026ndash;0,2 mg GAE; r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.9804). The values were expressed in mg of GAE per g of fresh mass.\u003c/p\u003e \u003cp\u003eThe antioxidants content was measured by the free radical scavenging assay via DPPH (2,2-diphenyl-1-picrylhydrazyl), and the results were expressed as ascorbic acid (AAE) equivalents. The procedure was carried out by diluting 0,125 ml of extract with 4,325 ml of methanol and adding 0,5 ml of DPPH solution (2.18 mM in MeOH). The mixture was agitated for about 10 seconds, and then the reaction occurred for 30 minutes, in absence of light. The results were then read spectrophotometrically (Thermo Spectronic 20 Genesys) at the wavelength of 517 nm. Each experimental set consisted of 4 replicates and 2 blank samples. For the latter, the volume of ethanolic extract was replaced by the same volume of ethanol 70% solution. The absorbance values were then converted in AAE through a comparison with a calibration curve, built by launching the the reaction with increasing amounts of ascorbic acid (range 0\u0026ndash;120 \u0026micro;g AAE; r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.9938). The values were expressed in \u0026micro;g of AAE per g of fresh mass.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eHRMAS NMR spectroscopy\u003c/h2\u003e \u003cp\u003ePer each representative sample, three berries were thawed before HRMAS NMR analysis. After few minutes, each berry was dissected, and few aliquots of grape pulp were sampled per berry. The material was merged and homogenized. About 30\u0026thinsp;\u0026plusmn;\u0026thinsp;1 mg of homogenized sample was inserted into an HRMAS-NMR rotor (4 mm in zirconia; 80 \u0026micro;l). Few tens of microliters of a phosphate buffer solution (pH 6.8), prepared entirely with deuterated water (99.8% D\u003csub\u003e2\u003c/sub\u003eO/H\u003csub\u003e2\u003c/sub\u003eO), were then added to the rotor, by removing possible relatively large air bubbles. The latter was finally closed with a special external cap equipped with fins, serving as turbine and necessary to trigger sample rotation [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHRMAS NMR experiments were conducted on a 500 MHz Magnet (11.7 Tesla, Bruker Oxford/spectrospin cryomagnet) equipped with a \u003csup\u003e1\u003c/sup\u003eH \u0026ndash; \u003csup\u003e13\u003c/sup\u003eC HRMAS probe. The experiments were conducted at a temperature of 22\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C, at a rotation rate of 5 kHz and examining at least 4 replicates for grape typology.\u003c/p\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003eH NMR spectra were acquired by applying the on-resonance presaturation technique, to suppress the water signal, and setting 16k of acquisition points, 5 seconds of recycle delay, 8 dummy scans and 256 effective scans. To assign the most intense NMR signals and to support the identification of grapes metabolomic fingerprint [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], 1D proton-decoupled \u003csup\u003e13\u003c/sup\u003eC spectra and two-dimensional spectra were also acquired for several representative samples of F and P. The bidimensional spectra consisted in homonuclear \u003csup\u003e1\u003c/sup\u003eH-\u003csup\u003e1\u003c/sup\u003eH COSY (Correlation Spectroscopy), TOCSY (Total Correlation spectroscopy) e NOESY (Nuclear Overhauser Effect Spectroscopy) and heteronuclear \u003csup\u003e1\u003c/sup\u003eH-\u003csup\u003e13\u003c/sup\u003eC HSQC (Heteronuclear Single-Quantum Coherence) e HMBC (Heteronuclear Multiple-Bond Coherence) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. All 2D NMR spectra were set up with 16 dummy scans, 64 scans, 2048 acquisition points for 256 experiments, and spectral windows of 16 and 300 ppm, respectively, for hydrogen and carbon nuclei (in the case of heteronuclear experiments). All spectra were processed (Fourier transform, phase, and baseline corrections) by using Bruker TopSpin software (v. 4.1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMagnetic Resonance Imaging (MRI) of grape samples\u003c/h2\u003e \u003cp\u003eMRI experiments were performed at a temperature of 25\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C on a wide-bore Bruker Avance 300 MHz magnet (BrukerBiospin, Rheinstetten, Germany), equipped with a \u0026micro;-imaging MICRO 5 probe and operating at a proton frequency of 300.13 MHz. For each analysis, one grape berry was loaded into a sterilized 25 mm tube (Norell, borosilicate). An inert and soft material was put on the bottom and on the top of the tube to stabilize vertically the grape, during the analysis, and avoid the possible interference of water exuded from grape. Then, each tube was tightly closed to minimize the grape water evaporation (\u003cb\u003eSupporting Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e3 types of MRI experiments were launched to identify longitudinal spin-lattice (T\u003csub\u003e1\u003c/sub\u003e) and transverse spin-spin (T\u003csub\u003e2\u003c/sub\u003e) relaxation times as well as self-diffusion coefficients (DIFF), respectively. T\u003csub\u003e2\u003c/sub\u003e experiments were acquired by applying a Multi-Slice-Multi-Echo pulse sequence, setting a recycle delay of 6 s, 4 scans and 18 sub-experiments, each of them characterized by an increasing number of spin-echoes (the total spin-echo duration varied gradually and progressively from 12 to 216 ms, in 18 experiments) [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The data were fitted according to the following equation:\u003c/p\u003e \u003cp\u003eM\u003csub\u003et\u003c/sub\u003e = M\u003csub\u003e0\u003c/sub\u003e exp(-t/T\u003csub\u003e2\u003c/sub\u003e) (\u003cb\u003eEq.\u0026nbsp;1\u003c/b\u003e)\u003c/p\u003e \u003cp\u003ewhere M\u003csub\u003e0\u003c/sub\u003e and M\u003csub\u003et\u003c/sub\u003e represent the resulting magnetization intensity at the minimum spin-echo delay and after the i\u003csup\u003eth\u003c/sup\u003e spin-echo interval, respectively. The 2D images extrapolated from this experiment, at the shortest spin-echo duration (12 ms), was exploited for morphological evaluations.\u003c/p\u003e \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e experiments were acquired by applying an inversion recovery pulse sequence and performing experiments at increasing repetition times, which gradually varied from 700 to 7500 ms. The experiments were set with an echo time of 65 ms and 4 scans. The data were fitted according to the following equation\u003c/p\u003e \u003cp\u003eM\u003csub\u003et\u003c/sub\u003e = M\u003csub\u003e0\u003c/sub\u003e [1-exp(-t/T\u003csub\u003e1\u003c/sub\u003e)] (\u003cb\u003eEq.\u0026nbsp;2\u003c/b\u003e)\u003c/p\u003e \u003cp\u003ewhere M\u003csub\u003e0\u003c/sub\u003e and M\u003csub\u003et\u003c/sub\u003e represent the resulting magnetization intensity at the minimum repetition time and after the i\u003csup\u003e\u0026minus;\u0026thinsp;th\u003c/sup\u003e delay, respectively.\u003c/p\u003e \u003cp\u003eThe self-diffusion coefficients (DIFF) were extrapolated from a pulse sequence based on the use of pulsed field gradients and integrated with stimulated echoes. The experiment was set with a recycle delay of 6 s, 6 scans, a delay of 4 ms for the two gradients (δ, little delta), 13 ms as interval (Δ, big elta) between the first (encoding) and second (decoding) gradient. The strength of gradients increased progressively (100, 400, 750, 1000, 1500, 2168 s mm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) in 6 experiments. The data were fitted according to the following equation\u003c/p\u003e \u003cp\u003eM\u003csub\u003et\u003c/sub\u003e = M\u003csub\u003e0\u003c/sub\u003e exp [-γ\u003csup\u003e2\u003c/sup\u003eG\u003csup\u003e2\u003c/sup\u003eδ\u003csup\u003e2\u003c/sup\u003e (Δ-δ/3) D] (\u003cb\u003eEq.\u0026nbsp;3\u003c/b\u003e)\u003c/p\u003e \u003cp\u003ewhere M\u003csub\u003e0\u003c/sub\u003e and M\u003csub\u003et\u003c/sub\u003e represent the resulting magnetization intensities after the application of the weakest gradient and that at the i\u003csup\u003e\u0026minus;\u0026thinsp;th\u003c/sup\u003e strength, respectively, γ is the gyromagnetic ratio of hydrogen, G is the intensity of the pulsed field, δ is the duration of the gradient, and Δ is the time interval between the first (encoding) and second (decoding) gradient.\u003c/p\u003e \u003cp\u003eAll MRI images resulted from the acquisition of 6 interlaced axial slices, with a thickness of 1 mm, a visual window of 18 \u0026times; 18 mm\u003csup\u003e2\u003c/sup\u003e (\u003cb\u003eSupporting Fig. S2\u003c/b\u003e, on the left). The images consisted of a matrix 256 x 256 for T\u003csub\u003e1\u003c/sub\u003e and T\u003csub\u003e2\u003c/sub\u003e experiments and a matrix 128 x 128 for DIFF experiments.\u003c/p\u003e \u003cp\u003eBruker\u0026rsquo;s Paravision 6.0.1 software was used to process MRI data, obtain images and perform the data fittings. Calculations for the fittings were carried out by examining and averaging, per sample, 12 regions of interest (ROI) distributed over 4 slices. Briefly, 4 representative slices, per sample, were considered and, for each of them, 3 circular ROIs (area\u0026thinsp;\u0026asymp;\u0026thinsp;0.4 cm\u003csup\u003e2\u003c/sup\u003e; \u003cb\u003eSupporting Fig. S2\u003c/b\u003e, on the right) were selected (3 ROIs x 4 slices x sample). At least 4 replicates were acquired for each type of grape.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate analysis\u003c/h2\u003e \u003cp\u003eFor each replicate, all HRMAS NMR proton signals included in the spectral range 7.766\u0026thinsp;\u0026minus;\u0026thinsp;0.764 ppm were assigned and integrated by creating buckets with variable width and sized to include whole peaks (either singlets or multiplets, depending on compound structure). For each grape variety (F and P), it was produced a data matrix composed of 43 variables (integrations) per a total number of observations accounting for both p500 treatment (Control vs Treatment), and the ECa values (L vs H) of soil sites (at least 4 replicates per sample typology).\u003c/p\u003e \u003cp\u003eFor each \u003csup\u003e1\u003c/sup\u003eH NMR spectrum, the area corresponding to each single integration was normalized, with respect to the sum of the individual areas (expressed in %), and Pareto-scaled to prevent the underestimation of relatively low intense NMR signals [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The F and P matrixes (either whole spectrum or containing only significant variables, depending on the elaboration phase) were elaborated through multivariate statistic techniques. Principal Component Analysis (PCA) was used to identify the variables (loading-vectors) discriminating different grape types. The ANOVA (ANalysis Of VAriance) test was applied to assess the significance (Tukey and Benjamini Hochberg FDR tests, both set at an α confidence level of 0.05) by which the most relevant variables (extrapolated through the evaluation of PCA loading-plots) contributed to differentiate the composition of grape types [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHeat-map variable analysis and Partial Least Square Discriminant Analysis (PLS-DA) were applied by considering the variables significantly involved in the discrimination among the samples. For Heat map clustering, the values were centred, reduced and displayed as colours ranging from red (relatively higher) to yellow (relatively lower) through orange (intermediate values). Samples were clustered using Euclidean\u0026rsquo;s distance. PLS-DA permitted to assess the ability to objectively and significantly (α confidence level of 0.05) discriminate the composition of different grapes by providing the % of correct sample classification, and the Receiver Operator Characteristics (ROC) curve [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. It was also performed a cross-validation (CV) session based on the PLS-DA test. In particular, the method consisted in building a validation set, achieved by dividing all samples in either a training (discriminant model composed by samples which had been classified \u003cem\u003ea-priori\u003c/em\u003e) or a test (cross-validation composed by unclassified samples) set. The distribution of samples into the two different sets was conducted accounting for the number of replicates per sample type and aimed to maintain a number of replicates in the training set\u0026thinsp;\u0026gt;\u0026thinsp;70%. This validation was repeated 5 times by randomly inverting, per each grape type, the samples included in the training and test sets. The larger the percentage of correct classification of test set samples, the better the discrimination extent among of samples. One representative score-plot, out of the five obtained, was shown for P and F PLS-DA cross-validations [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe values resulting from MRI experiments (T\u003csub\u003e1\u003c/sub\u003e, T\u003csub\u003e2\u003c/sub\u003e and DIFF) were subjected to the ANOVA (Analysis of Variance) statistical test to verify the significance (Tukey and Benjamini Hochberg FDR tests, both set at an α confidence level of 0.05) by which the variables contributed to differentiate the different types of grapes.\u003c/p\u003e \u003cp\u003ePCA, ANOVA, PLS-DA and Heat map tests were carried out by the XLStat software (v.2019, Addinsoft, Paris, France).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and discussions","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHRMAS NMR Spectroscopy\u003c/h2\u003e \u003cp\u003eSemi-solid state HRMAS NMR spectroscopy represents a very innovative and powerful technique which, despite its undisputable potential, it is still underutilized in the fields of food and agricultural chemistry. HRMAS permits to obtain NMR spectra by the direct exam of fresh and intact tissues, relatively rapidly and with a resolution very similar to that attainable via NMR in liquid-state and high resolution [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. On this basis, such a technique was exploited to identify the primary metaboloma of Fiano and Pallagrello Nero grapes and research possible correlations with both soil electrical conductivity and the treatment based on the biodynamic preparation 500 biostimulant.\u003c/p\u003e \u003cp\u003eInitially, the most appropriate rotor spin rate was established. In fact, if, on one hand, the resolution of an NMR spectrum of a sample analysed in the semi-solid state can be improved by increasing the spin-rate, on the other hand, an excessively high rotational speed can alter the integrity of the sample, penalizing reliability, and spectral quality and reproducibility [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Therefore, through a preliminary set of tests, we found that the best compromise in consisted in a spin rate of 5 kHz.\u003c/p\u003e \u003cp\u003eThe full \u003csup\u003e1\u003c/sup\u003eH HRMAS NMR spectrum of a representative F berry is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea. It is evident the spectral region in which was performed the suppression of the residual solvent signal (at around 4.8 ppm) and which was digitally removed to eliminate the corresponding and non informative artifacts. The identification of the most intense proton signals found in both grapes types permitted to define their primary metaboloma [4, 22,44\u0026ndash;46]. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb reports the assignment of the main proton signals detected, almost exclusively, in the two regions of alkyl (0.5\u0026ndash;2.5 ppm) and hydroxyalkyl (2.5\u0026ndash;5.5 ppm) protons. Most of predominant signals were attributed to multiplets in carbohydrates such as glucose, fructose and sucrose, followed by xylose but with a content significantly lower. Several amino acids were found in P and F berries, and consisted in proline, asparagine, GABA, glutamate, arginine, lysine, alanine, threonine, and valine. In addition, the metabolomic profile was also composed by alcohols, like methanol and ethanol, organic acids, such as tartaric, succinic and malic acids, and other compounds such as choline and lipid molecules (broadened signals in the alkyl region). Except for very weak and broadened signals, which were attributed to tryptophan and phenylalanine, there were no relevant peaks and multiplets at low field frequencies, and, in particular, in the aromatic region. This was explained by the fact that the examined tissue consisted in grape mesocarp, which notoriously lacks of phenolic and polyphenolic compounds (i.e., tannins and anthocyanins). In fact, the latter ones are expected to be much more concentrated in the grape skin and pips. The profile of the primary metabolome of F and G grapes was thus defined and, from a qualitative point of view, the same compounds were detected in both grape varieties. Subsequently, a semiquantitative comparison among the integrations of F and G spectra was conducted aiming to find significant, objective, and diagnostic characteristics related to both the soil spatial variability (based on ECa values detected via EMI technique) and the action exerted by the p500 application. It is important to underline that the studied grapes were harvested in the same vintage (September 2021), from the same vineyards and the same type of soil, managed by the same producer, and grown, practically, in the same microclimatic conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSoil Spatial microvariability\u003c/h2\u003e \u003cp\u003eIn order to correlate soil vineyard microvariability with the primary metabolome of studied grapes, we elaborated the integrations of their HRMAS NMR spectra via Principal Component Analysis (PCA). The latter is an unsupervised multivariate statistical analysis enabling the rapid identification of statistical groups of observations characterized by specific peculiarities. It allows an effective exploration of very large data matrices, providing, in a single output (namely score plot), clear information on systematic variations induced by specific conditions and/or treatments, and revealing, at the same time, the variables responsible for these discriminations [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Therefore, HRMAS \u003csup\u003e1\u003c/sup\u003eH NMR spectra of F samples collected from plants located in the soil sites characterized by a low (L, 9 mS/m) or high (H, 13 mS/m) ECa responses were thoroughly compared via PCA. \u003cb\u003eFig.s 3a\u003c/b\u003e and \u003cb\u003e3b\u003c/b\u003e show both the PCA score-plots and the most significant spectral comparisons between Fiano samples, for both control and p500 treated plants. The score-plot displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea refers to FC samples and results from the linear combination of the line PC1 and PC2, explaining 44.18 and 24.03% of total system variability, respectively. The fact that both L and H groups were placed in markedly different regions of the score-plot, indicated neat and peculiar differences in their primary metabolomas. Firstly, the metabolites mostly representing the PC1 and PC2 were individuated by examining the respective PCA loading-plot. Then, the metabolites responsible to significantly discriminate L from H were selected via ANOVA test. FC collected in L sites exhibited a lower content in amino acids, such as proline, glutamate, GABA, alanine, valine, and threonine, along with a lower content in ethanol and lipids (differentiation expressed along the PC1). Also the lower content in xylose permitted a collocation of L samples at higher PC2 values, thus contributing to the discrimination. On the right-side of Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea it is exhibited a comparison among the spectral regions (3 replicates per grape type) including the NMR signals mostly involved in the discrimination between the two types of grapes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb reports a comparison between L and H Fiano samples resulting from treatment with biodynamic p500. Again, the combination of PC1 and PC2 (respectively 40.93 and 23.93% of the total explained variability) implied a clear differentiation between FT_L and FT_H. The discrimination occurred only along the PC1 and was due to the fact that FT_H samples exhibited a higher content in fructose, lysine, lipids, and valine, accompanied by a lower amount of GABA, threonine, and alanine. Again, the spectral comparison in the right-side of the figure highlights the neat semiquantitative differences between the two types of grapes. These results proved that, in all cases, the metabolome of F samples changed significantly, as a function of soil electrical conductivity, thus proving that the factors which determined a difference of 4 mS/m between the ECa values of L and H sites are sufficient to affect the vine metabolism.\u003c/p\u003e \u003cp\u003eThe score-plot exhibited in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec informs on the metabolome of Pallagrello grapes (untreated with p500) as a function of different ECa responses. Only the combination PC4 and PC7 (explaining the 14,68% of total variability) allowed a discrimination between H and L, and was exclusively due to a significantly higher content of malic acid in PC_L samples.\u003c/p\u003e \u003cp\u003eIn case of P samples treated with the biostimulant p500, it was detected a neat discrimination as a function of soil ECa responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). The discrimination occurred by combining the PC1 (43.47%) with the PC2 (27.41%) and was ascribed to the higher content in GABA, proline, asparagine, malate, glutamate, alanine, ethanol, lipids, and valine in PT_H samples. Interestingly and in agreement with the results concerning F berries treated with p500 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), also in PT samples it was recorded an over-expression of several amino acids. Another common aspect is that valine and lipid compounds resulted higher than in L, for both FT_H and PT_H. Also for P grapes, the different ECa values revealed soil sites with different characteristics and affecting differently the grape metabolism.\u003c/p\u003e \u003cp\u003eThe soil spatial variability identified by EMI technique is mainly attributable to the overall distribution in active clays, water, and organic matter which contribute to increase the apparent electrical conductivity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Our results, based on sites with ECa values at least of 8 mS/m and characterized by an average difference of 4 mS/m, proved that investigated soil sites experimented specific soil conditions which, not only were indirectly revealed by EMI, but also significantly impacted on both vine physiology and grape metaboloma. In fact, in all of studied cases, it was verified that at different ECa values corresponded systematically a different metaboloma. In addition, such a discrimination resulted even more pronounced for samples subjected to biostimulant treatment. Interestingly, this outcoming wel l agrees with a recent paper focusing on Aglianicone black grapes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and proving the existence of a tight and systematic correlations between the composition of Aglianicone grapes, revealed via HRMAS NMR, and ECa soil data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEffects induced by p500 treatment on grape metabolome\u003c/h2\u003e \u003cp\u003ePCA was also exploited to investigate on possible effects induced by the action of the biostimulant p500 on the composition of the two varieties of grapes F (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea) and P (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Interestingly, both varieties of grapes showed an enhanced p500-dependent change in the primary metaboloma. The score-plot differentiating FC from FT grapes was obtained by combining PC1 and PC2 (56.67% of the total system variability explained; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The discrimination occurred along the bisector direction between the second and the fourth quadrant and was determined by a higher content in proline, glutamate, GABA, alanine, ethanol, and lipids, in samples resulting from p500 treatment, accompanied by a lower content in sucrose and fructose. For P samples, the neat differentiation occurred along the PC1 (48% of the total variability of the system; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Analysis of the loading plot, combined with the ANOVA test, revealed that the PC samples exhibited a significantly lower content in metabolites such as glucose, fructose, proline, glutamate, lysine, alanine, threonine, and lipids.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese outcomes suggest that, for both P and F grapes, the p500 treatment may promote the expression of several amino acids as well as can impact on carbohydrates contents. As compared to the control, the p500 induced an increase of proline, glutamate and lipids for both FT and PT. However, the influence induced by p500 on vine metabolism seems to vary as a function of the variety. A certain influence of biodynamic products on the expression of amino acids have been already observed previously. For example, Picone and coworkers [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], in agreement with our results on FT, have demonstrated that the concentration of GABA, in treated grapes, was significantly higher than the grapes subjected to traditional organic treatment, as well as it was proved that biodynamic treatments positively elicited the production of isoleucine and valine [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHeatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) not only confirmed the discriminability of explored samples, by clusterizing control and p500 samples into two distinct groups for both P and F samples, but also highlighted, in a direct and simple way, the semi-quantitative response (red to yellow scale) of the variables significantly involved in the grape type discrimination. In case of F samples, the map underlined a lower content in sucrose and fructose in FT samples, accompained by a larger amount of glutamate, proline, lipids, alanine ethanol and GABA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). In case of Pallagrello grapes, the heat map confirmed the pronounced difference in the primary metabolome induced by p500 treatment. In fact, PT exhibited a lower abundance of fructose and glucose, while PC showed a pronounced expression of several compounds, including amino acids and lipids. Finally, it was applied the cross-validation test of PLS-DA to further prove the capacity of the used analytical approach to discriminate control from p500-treated Fiano and Pallagrello grapes in a reliable way. The test was successful since in each of five cross-validations, the \"unknown\" replicates were classified correctly at the 100%. In \u003cb\u003eSupporting Fig. S3\u003c/b\u003e they are reported the outputs of one representative cross-validation test for both Fiano (\u003cb\u003eSupporting Fig. S3 a-c)\u003c/b\u003e and Pallagrello grapes (\u003cb\u003eSupporting Fig. S3 d-f).\u003c/b\u003e The figure includes the PLS-DA Cross-validation score-plot, the PLS-DA Receiver Operator Characteristics (ROC) curve in which the Area Under the Curve corresponded to 1 and the PLS-DA cross-validation results. In conclusion, these outcomes showed unequivocally that biostimulant preparation 500, when applied with appropriate times, methods, and amounts, can influence the primary metabolism of vines, with effects which reflect on the berry composition and with possible implications on grape and wine quality. This finding is very important because permits to candidate the proposed HRMAS-based metabolomic approach as a reliable tool to prove objectively the biodynamic nature of the product, thus permitting to protect biodynamic producers as well as permit them to valorize and promote their products.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTitratable acidity, contents in total phenols and antioxidant agents\u003c/h2\u003e \u003cp\u003eThe titratable acidity of grape is an important parameter because is related to the pH, accounts for content in weak acids, impacts on organoleptic properties and influences the vinemaking process. It is mostly influenced by predominant organic acids which are represented by tartaric acid (0.2\u0026ndash;1.0%, on fresh weight), followed by malic, succinic and citric acid [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. It is reported that the content of tartaric acid, generally ranging between 3 and 9 g/l, depends on the variety, production area, vintage, and terroir [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR49 CR50\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eF grapes exhibited a significantly higher titratable acidity than P one (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This was expected, due to the fact that the pH of white grape berries is typically more acid than red ones. Moreover, samples FT_L, PT_H and PT_L showed the highest values for F and P variety, respectively. Importantly, all of these samples resulted from the p500 treatment, thus suggesting a possible role of this biostimulant in eliciting an accumulation of organic acids in grapes. The titratable acidity did not vary among P samples, with an average value of 3.86 g/l. Conversely, this parameter varied markedly among F samples, as a function of both p500 treatment and soil ECa values. In detail, FT_L was characterized by the highest titratable acidity, which corresponded to 5.62 g/l. Interestingly, the slightly but significantly higher titratable acidity of PC_L than PC_H is in line with the highest content in malic acid for PC_L already observed by HRMAS NMR.\u003c/p\u003e \u003cp\u003eThe content in total phenols was evaluated in all investigated grapes through the Folin Ciocalteau assay. As expected, the black grape berries P exhibited significantly higher contents in total phenols than the white berries F, with average values of 1.455 and 0.655 mg GAE/g fw, respectively. These values are in line with the content in total phenols for red grapes, which generally ranges within 1.2 and 3.35 mg GAE/g fw, while, for white berries, the range is typically included between 0.32 and 2 mg GAE/g fw [\u003cspan additionalcitationids=\"CR53 CR54\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The highest contents in total phenols were observed for F and P grapes treated with p500, as compared to the respective control (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The assumption of food rich in total phenols is important due to the beneficial properties of these compounds. In fact, they may serve as powerful antioxidants, prevent inflammations, stimulate positively the nervous system, prevent cardiovascular diseases and seem to be related to anti-tumor activity [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. On this basis, it is possible to conclude that the application of biodynamic treatment not only influenced the primary metabolome of F and P grapes, but also implied an increase in their nutraceutical and healthy value.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, it was found also a neat correlation between the phenols content and site-specific ECa responses. In fact, except for FT_H and FT_L which did not vary at all among each other, FC_L was significantly higher than FC_H. Conversely, in case of Pallagrello samples, the total phenols responses changed significantly, as a function of ECa values, by exhibiting the highest contents in case of PC_H and PT_L (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eThe DPPH assay was carried out to quantify the free radical scavenging activity in studied grapes. It was detected a significant amount of antioxidants in both varieties (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) with higher average values for P grapes. This well agrees with the responses observed for the total phenols, since most of the antioxidant activity deriving from the assumption of grapes, is actually ascribable to phenolic and polyphenolic compounds. The antioxidant activity in red grape berries ranges between 2244 and 3154 \u0026micro;g of AEE/g fw [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], while the average content of antioxidant agents in white berried grapes typically is included within 1233 and 1408 \u0026micro;g of AEE/g fw. Again, in agreement with the total phenols, the samples FT_H and PT_L, both resulting from the treatment with the biodynamic product, gave, in absolute, the highest responses in antioxidant activity, with values of approximately 1718 and 3419 \u0026micro;g AAE/g fw, respectively. These results were even higher than those typical for red and white berries. About the correlation with ECa vineyard responses, in case of Pallagrello the values varied slightly, as a function of ECa values, with L samples giving the relatively higher values. In case of Fiano grapes, the changes ascribable to site-specific ECa values, were much more pronounced, with the highest values observed for FC_L and FT_H.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMRI analysis of Fiano and Pallagrello grape berries\u003c/h2\u003e \u003cp\u003eMRI spectroscopy was exploited to examine the inner morphology of intact berries of Fiano and Pallagrello cultivars. In Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e they are shown the MRI images related to the central slices of representative Fiano (a) and Pallagrello nero (b) grape berries. MRI images clearly permitted to distinguish the three main morphological tissues composing this fruit. In fact, the thin outer darker layer is composed by the exocarp and the epidermis, whereas the mesocarp corresponds to the large area occupied by intense and whitish signals due to the high water content. The seeds are neatly identifiable in the endocarp by the presence of dark semi-ellipsoidal areas, characterized by a relatively darker and external area (the tegument) and relatively greyish and lighter area (the endosperm contained in the seeds). The latter ones appear significantly darker than the surrounding mesocarp tissue being them poorer in water and richer in oils and polyphenols. Furthermore, it was also possible to appreciate the vascular system and, in particular, the peripheral vascular bundles, branching out in proximity of the exocarp, the central fibrovascular bundles, developing below the cercine, and the bundles directly connected to the seeds (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e; supporting video 1 and 2). These images suggest that MRI can reveal anatomical details of intact in-vivo or ex-vivo samples, with a resolution of tens of microns. This is important because paves the way to its use to investigate on the early detection of treatment-related effects on inner grape morphological traits. It is important to underline that, to date, only one research group [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] has successfully attempted to use MRI to investigate on grape berries, by detecting interesting effects exerted on Nero di Troia grapes as a function of different fertilizers. Although this further proves the potential role of MRI in studying grapes, on the other hand, it certifies how the MRI potential to examine grape is still largely unexpressed and unexplored, thus requiring further validations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFirstly, MRI images of F and P were accurately compared. They were only appreciated morphometric differences between the two cultivars, with the diameter and volume of P berries and seeds (detected in integer berry) appearing significantly larger than F grapes. However, from a qualitative and morphological point of view, no relevant and reproducible differences were appreciated as a function of both p500 treatment and soil electrical conductivity.\u003c/p\u003e \u003cp\u003eTherefore, the next step consisted in examining hydrogen relaxation times (T\u003csub\u003e1\u003c/sub\u003e and T\u003csub\u003e2\u003c/sub\u003e) and self-diffusion coefficients to obtain information on grape microstructure and on behaviour, mobility and nature of the water molecules in the grapes mesocarp. Since these MRI parameters are notoriously very stable and constant, their possible and pronounced variation is diagnostic of an alteration in the tissue under examination [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. These changes can be attributed, directly or indirectly, to factors such as: a sharp variation in the content of free water; change in microstructure and therefore in the confinement of water molecules; alteration in the consistency of the material; thickening of vascular bundles and so on [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and \u003cb\u003eSup. Table S2\u003c/b\u003e are reported the results concerning T\u003csub\u003e1\u003c/sub\u003e, T\u003csub\u003e2\u003c/sub\u003e and self-diffusion coefficients of F grapes. Differently than for MR images, MRI parametric data revealed singular and interesting responses depending on both p500 treatment and soil microvariability (\u003cb\u003eSup. Table S3\u003c/b\u003e). T\u003csub\u003e2\u003c/sub\u003e values ranged between 39.56 to 53.22 ms and the differences among the F types resulted statistically significant in all cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea, Sup. Table S2). Even though this is only a preliminary study, it suggests the potential use of this single parameter as an innovative and diagnostic element of quality, useful to serve as supporting data to discriminate, trace and identify grapes treated or not with preparation 500 and/or sampled from micro-areas characterized by different soil properties. Interestingly, while p500 led to the highest T\u003csub\u003e2\u003c/sub\u003e value, in case of high ECa values, it was detected the lowest value in case of grapes treated with p500 but sampled at soil sites with the lowest electrical conductivity. For T\u003csub\u003e1\u003c/sub\u003e relaxation times (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb, \u003cb\u003eSup. Table S2\u003c/b\u003e), the values ranged between 1220 and 1489 ms. The T\u003csub\u003e1\u003c/sub\u003e values for FT_H and FT_L did not differ from each other, while a neat difference was observed between FC_H and FC_L samples, which exhibited a gap of 269 ms, with FT_H characterized by a longitudinal relaxation time of 1489 ms. Finally, the diffusion coefficients varied from 1.213 to 1.413 mm\u003csup\u003e2\u003c/sup\u003e/s * 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec, \u003cb\u003eSup. Table S2\u003c/b\u003e). Interestingly, the results followed a trend similar to that of T\u003csub\u003e2\u003c/sub\u003e results with the longest relaxation time recorded for samples collected at high ECa values and resulting from p500 treatment. For samples collected at points characterized by low ECa values, no differences were found between treated (FT_L) and untreated plants (FC_L), with values averaging around 1.3 mm\u003csup\u003e2\u003c/sup\u003e/s * 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOur results suggested that MRI proton relaxation times and diffusion coefficients are capable to appreciate grape structural properties and characteristics, which depend on conditions and factors affecting the vine phenological development during the fruit production. In fact, the application of p500 as well as different soil electrical conductivity were capable to induce pronounced changes in Fiano grapes, leading to relatively small but statistically significant inter-class differences (Sup. Table S3). In addition, the fact that all replicates (per berry and per ROI) exhibited a very low intra-class variance certified an excellent and promising reproducibility for such innovative analytical method. A variation in relaxation times and in water diffusivity may be explained by factors such as a change in the content and confinement of free water in the berries; alteration in the grape microstructure; development of a denser and thicker vascular system, with consequent impact on mesocarpic water mobility; modification in fruit consistency. The systematic change observed for these parameters permitted to conclude that they correlated with both p500 treatment and soil ECa, and in particular to conditions such as: (1) different bioaccessible water in soil, which directly influenced soil ECa values and indirectly the water content of the fruit, thus implying a higher dry weight; (2) a different and site-specific content of organic matter (excluding the role of active clayes content which were assumed constant within the examined F vineyard) (3) the impact of the biodynamic treatment on the bioavailability of water and/or nutrients, inducing an alteration that varied depending on spatial microvariability (4) the action of biodynamic treatment serving as a primer to shift and enrich the vine rhizospheric microbial community, with an influence on nutrient and water uptake as well as their respective physiology (5) the greater bioavailability of iron (presumably related to the induced presence of siderophore microorganisms) capable to imply a more efficient uptake of this ferromagnetic element, known to promote an artificial shortening (i.e. not related to the tissue itself) of relaxation times. Although, at the moment, we cannot discriminate among these factors, we can assume that most of these concurred to determine the observed results and hypothize to performe, in the future, futher experiments aimed to go deeper inside these important aspects. It is important to emphasize that the content in antioxidant activity and total phenols for FT_H sample resulted, absolutely, the highest. This outcome permits to infer that Fiano grapes treated with preparation 500 and developed in points characterized by relatively higher ECa values yielded grapes with a neatly superior nutraceutical quality. Consequently, we found that MRI T\u003csub\u003e2\u003c/sub\u003e relaxation times and diffusion coefficients of FT_H showed a similar trend, exhibiting the highest values for both parameters. This original evidence suggests that these MRI parameters may serve as indirect markers of nutraceutical quality. Consistently, the absolutely lowest value found in T\u003csub\u003e2\u003c/sub\u003e was that of FC_H samples, which was also the sample with the markedly poorest content in antioxidants and total phenols. Again, this finding suggests a possible correlation between nutraceutical quality of Fiano and MRI T\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eThe analytical approach conducted for Fiano grapes was attempted identically also for Pallagrello grapes. This notwithstanding, it was not possible to obtain reliable and useful MRI data with an unacceptable variance. In fact, differently than for the Fiano variety, all examined Pallagrello berries tended to collapse drastically in the MRI tube before the end of all MRI experiments (each series of MRI experiments required 8 hours of continuous acquisition time) (\u003cb\u003eSupporting Fig S4\u003c/b\u003e). At the end of the analysis we incurred in these problems: (a) a general collapse of the entire berry, whereas in MRI analysis it is required to mantain the sample motionless throughout the whole experiments [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] (b) a relevant amount of juice exuded from the berry on the bottom of the tube (c) a greater susceptibility to pathogens attack and (d) a drastically reduced consistency of the berry. Except for just few and not statistically relevant samples, all these conditions concurred to make Pallagrello samples (and presumably all grape varieties with similar characteristics) unsuitable for this type of MRI experiments.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this work we used an innovative analytical approach to verify a possible impact exerted on grape fruit by the application of biodynamic p500 biostimulant and accounting for the vineyard spatial variability revealed by the soil electrical conductivity. We focused on two varieties of grapes very representative of the Italian excellent wine production, namely Pallagrello nero and Fiano, and evaluated (i) the primary metabolome of grapes via HRMAS NMR; (ii) the main parameters representative of chemical and nutraceutical quality of grapes (titratable acidity; content in total phenols and polyphenols via FOLIN; content in antioxidant compounds via DPPH) (iii) grape microstructural and morphological characteristics via MRI. It is noteworthy to point out that the use of HRMAS NMR and MRI spectroscopies represent a relevant technological innovation. In fact, despite their indisputable ability to enable the NMR analysis of fresh and intact samples without residing into preliminary sample manipulations, they are still inexplicably unexplored and underestimated in the field of food chemistry.\u003c/p\u003e \u003cp\u003eThis work confirmed the validity and reliability of the HRMAS technique in analyzing grape berry. In fact, permitted to detect the primary metaboloma of Fiano and Pallagrello grapes, identifying the main amino acids, alcohol, organic acids, carbohydrates, and nitrogen compounds related to the primary metabolism of grapes. The elaboration of proton spectra of the different types of grapes investigated through advanced and multivariate statistical techniques proved, objectively, that the proper application of the biostimulant p500 influences the primary metabolism of plants, with effects which affect the primary metabolism of grape as well as that grape berries collected in vineyard sites characterized by different ECa values exhibit a systematically different metaboloma. Also chemical and nutraceutical parameters were significantly impacted by these factors. In particular, the highest nutraceutical values were recorded for Pallagrello and Fiano grapes both treated with p500 and collected at points characterized by low and high ECa values, respectively. In fact, the former contained 1.76 mg GAE/g and 3372.45 ug AAE/g, while the latter 0.89 mg GAE/g and 1717.97 ug AAE/g. Importantly, this proved that, at certain soil conditions, the biodynamic p500 may significantly improve the nutraceutical quality of this fruit.\u003c/p\u003e \u003cp\u003eMRI not only enabled to exam of the main structural characteristics of integer grape berries, but also permitted to extrapolate important water parametric data, such as proton relaxation times and diffusion coefficients, which were found to correlate with both p500 application and soil electrical conductivity. It was hypothesized that these variations in grape reflected the biotic and abiotic conditions experienced by the vine during the development of the examined fruit, such as different water and nutrients availability in soils, the action exerted by p500 as a primer to alter and enrich the microbial community at the rhizospheric level and so on. It is noteworthy that T\u003csub\u003e2\u003c/sub\u003e results showed a trend very similar with that of diffusion coefficients and that high values correlated with the highest content in total phenols and antioxidant. This outcome candidates this MRI parametric data as potential marker for grape nutraceutical quality. However, as demonstrated by the application of MRI on P grapes, a limitation of the method is that it can be applied, in a reliable way, only on grape varieties capable to remain mostly unalterated at 25\u0026deg;C for at least 8 hours (such as Fiano).\u003c/p\u003e \u003cp\u003eIn conclusion, our findings have shown how the spatial microvariability of the soil and the use of p500 can affect the molecular, structural and nutraceutical characteristics of Pallagrello nero and Fiano grapes, with effects that can presumably reflect on the quality of the resulting wines.\u003c/p\u003e \u003cp\u003eThe proposed approach represents an efficient solution to indirectly valorize biodynamic grapes and wines. Moreover the identified metabolomic fingerprint of p500 grapes may serve as a useful tool to help biodynamic producers to (i) promote their products via molecular traceability, (ii) contrast possible frauds and, importantly, (iii) objectively demonstrate the use of the declared biodynamic treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eA\u003cstrong\u003ecknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by the Projects \u0026ldquo;modelli circolari - modelli di sistemi circolari multifunzionali per produzioni tipiche (MIPAAF)\u0026rdquo; and \u0026ldquo;Spettroscopie NMR HRMAS e MRI per correlare, rispettivamente, il metaboloma primario e parametri strutturali di uva Fiano e Pallagrello nero all\u0026apos;azione di biostimolanti e alla conducibilit\u0026agrave; elettrica apparente del suolo del vigneto (University of Salerno - orsa230438)\u0026rdquo;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKhan N, Fahad S, Faisal S, Naushad M. 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Food Chem. 2019; 831-839.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"chemical-and-biological-technologies-in-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Chemical and Biological Technologies in Agriculture](https://chembioagro.springeropen.com/)","snPcode":"40538","submissionUrl":"https://submission.nature.com/new-submission/40538/3","title":"Chemical and Biological Technologies in Agriculture","twitterHandle":"@SpringerPlants","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"primary grape metabolome, proton relaxation times, diffusion coefficients, chemometrics, antioxidants","lastPublishedDoi":"10.21203/rs.3.rs-4351758/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4351758/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBACKGROUND\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA number of Italian grape berry varieties, such as Fiano (F) and Pallagrello nero (P), represent National strategic products. Therefore, it is important to identify soil conditions emphasizing their peculiar characteristics as well as find innovative and sustainable treatments improving their compositional and nutraceutical quality. The field spray preparation 500 is a biodynamic product that is presumed to serve as biostimulant on the vine. However, so far, the scientific results probing its effectiveness are still lacking. Moreover, it is necessary to establish a reliable relationship between the grape quality and the spatial microvariability of the vineyard’s soil. On this basis, the main objective of this work consisted in correlating structural and morphological characteristics (via MRI), the primary metabolome (via semi-solid state HRMAS NMR) and important nutraceutical parameters (total phenols and antioxidants via DPPH assay) of F and P grapes with both the action of preparation 500 biostimulant and the vineyard soil microvariability, based on soil apparent electrical conductivity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHRMAS enabled the identification of the primary metabolome of F and P. The elaboration of \u003csup\u003e1\u003c/sup\u003eH NMR spectra through chemometrics revealed significant changes in F and P grapes, accounting for both soil microvariability and the application of field spray (the latter also confirmed by PLS-DA and Heat-map clustering). Interestingly, for both F and P it was observed a significantly lower content of carbohydrates after biostimulant treatment while MRI revealed structural characteristics and internal details of intact grapes. The combined use of proton parametric indices, such as relaxation times and diffusion coefficients, indicated alterations induced in grapes by both the spatial microvariability of the soil and the effects of investigated biostimulant. Interestingly, a tight correlation was found between MRI transverse relaxation time and the contents in total phenols and antioxidants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONCLUSIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur results have proven that both soil spatial microvariability and the application of field spray preparation 500 significantly affect the structural, metabolomic and nutraceutical characteristics of grapes. Moreover, the Preparation 500 treatment has increased the nutraceutical value of grapes. Importantly, these data may be potentially used to promote and protect biodynamic grape and predict the quality of the resulting wines.\u003c/p\u003e","manuscriptTitle":"MRI and HR-MAS NMR spectroscopies to correlate structural characteristics and the metabolome of Fiano and Pallagrello grapes with the action of field spray preparation 500 and the soil spatial microvariability","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-13 15:44:24","doi":"10.21203/rs.3.rs-4351758/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-04T13:48:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-04T13:45:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-04T07:07:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Chemical and Biological Technologies in Agriculture","date":"2024-04-30T23:29:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"chemical-and-biological-technologies-in-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Chemical and Biological Technologies in Agriculture](https://chembioagro.springeropen.com/)","snPcode":"40538","submissionUrl":"https://submission.nature.com/new-submission/40538/3","title":"Chemical and Biological Technologies in Agriculture","twitterHandle":"@SpringerPlants","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"17ff353d-fee6-41e0-9fae-858f6a7e155c","owner":[],"postedDate":"May 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-07-18T17:37:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-13 15:44:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4351758","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4351758","identity":"rs-4351758","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}