Iron nuclearity in mineral fibres: Unravelling the catalytic activity for predictive modelling of toxicity.

Chronic inflammation induced in vivo by mineral fibres, such as asbestos, is sustained by the cyclic formation of cytotoxic/genotoxic oxidant species that are catalysed by iron. High catalytic activity is observed when iron atoms are isolated in the crystal lattice (nuclearity=1), whereas the catalytic activity is expected to be reduced or null when iron forms clusters of higher nuclearity. This study presents a novel approach for systematically measuring iron nuclearity across a large range of iron-containing standards and mineral fibres of social and economic importance, and for quantitatively assessing the relation between nuclearity and toxicity. The multivariate curve resolution (MCR) empirical approach and density functional theory (DFT) calculations were applied to the analysis of UV-Vis spectra to obtain information on the nature of iron and nuclearity. This approach led to the determination of the nuclearity of selected mineral fibres which was subsequently used to calculate a toxicity-related index. High nuclearity-related toxicity was estimated for chrysotile samples, fibrous glaucophane, asbestos tremolite, and fibrous wollastonite. Intermediate values of toxicity, corresponding to a mean nuclearity of 2, were assigned to actinolite asbestos, amosite, and crocidolite. Finally, a low nuclearity-related toxicity parameter, corresponding to an iron-cluster with a lower catalytic power to produce oxidants, was assigned to asbestos anthophyllite.

Clodronate Liposome-Mediated Phagocytic Hemocyte Depletion Affects the Regeneration of the Cephalic Tentacle of the Invasive Snail, Pomacea canaliculata.

After amputation, granular hemocytes infiltrate the blastema of regenerating cephalic tentacles of the freshwater snail Pomacea canaliculata. Here, the circulating phagocytic hemocytes were chemically depleted by injecting the snails with clodronate liposomes, and the effects on the cephalic tentacle regeneration onset and on Pc-Hemocyanin, Pc-transglutaminase (Pc-TG) and Pc-Allograft Inflammatory Factor-1 (Pc-AIF-1) gene expressions were investigated. Flow cytometry analysis demonstrated that clodronate liposomes targeted large circulating hemocytes, resulting in a transient decrease in their number. Corresponding with the phagocyte depletion, tentacle regeneration onset was halted, and it resumed at the expected pace when clodronate liposome effects were no longer visible. In addition to the regeneration progress, the expressions of Pc-Hemocyanin, Pc-TG, and Pc-AIF-1, which are markers of hemocyte-mediated functions like oxygen transport and immunity, clotting, and inflammation, were modified. After the injection of clodronate liposomes, a specific computer-assisted image analysis protocol still evidenced the presence of granular hemocytes in the tentacle blastema. This is consistent with reports indicating the large and agranular hemocyte population as the most represented among the professional phagocytes of P. canaliculata and with the hypothesis that different hemocyte morphologies could exert diverse biological functions, as it has been observed in other invertebrates.

Class modelling by Soft Independent Modelling of Class Analogy: why, when, how? A tutorial.

This article contains a comprehensive tutorial on classification by means of Soft Independent Modelling of Class Analogy (SIMCA). Such a tutorial was conceived in an attempt to offer pragmatic guidelines for a sensible and correct utilisation of this tool as well as answers to three basic questions: "why employing SIMCA?", "when employing SIMCA?" and "how employing/not employing SIMCA?". With this purpose in mind, the following points are here addressed: i) the mathematical and statistical fundamentals of the SIMCA approach are presented; ii) distinct variants of the original SIMCA algorithm are thoroughly described and compared in two different case-studies; iii) a flowchart outlining how to fine-tune the parameters of a SIMCA model for achieving an optimal performance is provided; iv) figures of merit and graphical tools for SIMCA model assessment are illustrated and v) computational details and rational suggestions about SIMCA model validation are given. Moreover, a novel Matlab toolbox, which encompasses routines and functions for running and contrasting all the aforementioned SIMCA versions is also made available.

A proficient multivariate approach for iron(II) spin crossover behaviour modelling in the solid state.

Iron(II) bis-pyrazolilpyridyl (bpp-R) complexes [Fe(bpp-R)2](X)2·solvent, R = substituent and X- = anion, can undergo a spin transition from high (S = 2, HS) to low spin (S = 0, LS), being spin crossover (SCO) in the solid state. The distortion of the octahedral coordination environment around the metal centre is governed by crystal packing, i.e. the intermolecular interactions among the substituent R of the bpp-R ligands, the anion X-, and the co-crystallized solvent, and this modulates the SCO behaviour. In this work, an innovative multivariate approach, through the combination of the chemometric tools Principal Component Analysis and Partial Least Squares regression, was applied on the coordination bond distances and angles and selected torsional angles of the available HS structures. The obtained results can efficiently model and rationalize the structural data distinguishing between SCO-active and HS-blocked complexes bearing different R groups, X- anions, and co-crystallized solvents and help predict the spin transition temperature T1/2.

Exploring the Effect of Different Storage Conditions on the Aroma Profile of Bread by Using Arrow-SPME GC-MS and Chemometrics.

In the present feasibility study, SPME Arrow-GC-MS method coupled with chemometric techniques, was used for investigating the impact of two different storage conditions, namely freezing and refrigeration, on volatile organic compounds (VOCs) of different commercial breads. The SPME Arrow technology was used as it is a novel extraction technique, able to address issues arising with traditional SPME fibers. Furthermore, the raw chromatographic signals were analysed by means of a PARAFAC2-based deconvolution and identification system (PARADISe approach). The use of PARADISe approach allowed for an efficient and rapid putative identification of 38 volatile organic compounds, including alcohols, esters, carboxylic acids, ketones, and aldehydes. Additionally, Principal Component Analysis, applied on the areas of the resolved compounds, was used to investigate the effects of storage conditions on the aroma profile of bread. The results revealed that the VOC profile of fresh bread is more similar to the one of bread stored in the fridge. Furthermore, there was a clear loss of aroma intensity in frozen samples, which could be explained by phenomena related to different starch retrogradation that occurs during freezing and refrigeration. However, considering the limited number of investigated samples, this study must be considered as a proof of concept; a more statistically representative sampling and further examinations of other properties, such as bread texture, need to be performed to better understand whether samples destined for eventual analysis should be frozen or refrigerated.

A Feasibility Study towards the On-Line Quality Assessment of Pesto Sauce Production by NIR and Chemometrics.

The food industry needs tools to improve the efficiency of their production processes by minimizing waste, detecting timely potential process issues, as well as reducing the efforts and workforce devoted to laboratory analysis while, at the same time, maintaining high-quality standards of products. This can be achieved by developing on-line monitoring systems and models. The present work presents a feasibility study toward establishing the on-line monitoring of a pesto sauce production process by means of NIR spectroscopy and chemometric tools. The spectra of an intermediate product were acquired on-line and continuously by a NIR probe installed directly on the process line. Principal Component Analysis (PCA) was used both to perform an exploratory data analysis and to build Multivariate Statistical Process Control (MSPC) charts. Moreover, Partial Least Squares (PLS) regression was employed to compute real time prediction models for two different pesto quality parameters, namely, consistency and total lipids content. PCA highlighted some differences related to the origin of basil plants, the main pesto ingredient, such as plant age and supplier. MSPC charts were able to detect production stops/restarts. Finally, it was possible to obtain a rough estimation of the quality of some properties in the early production stage through PLS.

Toward the Non-Targeted Detection of Adulterated Virgin Olive Oil with Edible Oils via FTIR Spectroscopy & Chemometrics: Research Methodology Trends, Gaps and Future Perspectives.

Fourier-Transform mid-infrared (FTIR) spectroscopy offers a strong candidate screening tool for rapid, non-destructive and early detection of unauthorized virgin olive oil blends with other edible oils. Potential applications to the official anti-fraud control are supported by dozens of research articles with a "proof-of-concept" study approach through different chemometric workflows for comprehensive spectral analysis. It may also assist non-targeted authenticity testing, an emerging goal for modern food fraud inspection systems. Hence, FTIR-based methods need to be standardized and validated to be accepted by the olive industry and official regulators. Thus far, several literature reviews evaluated the competence of FTIR standalone or compared with other vibrational techniques only in view of the chemometric methodology, regardless of the inherent characteristics of the product spectra or the application scope. Regarding authenticity testing, every step of the methodology workflow, and not only the post-acquisition steps, need thorough validation. In this context, the present review investigates the progress in the research methodology on FTIR-based detection of virgin olive oil adulteration over a period of more than 25 years with the aim to capture the trends, identify gaps or misuses in the existing literature and highlight intriguing topics for future studies. An extensive search in Scopus, Web of Science and Google Scholar, combined with bibliometric analysis, helped to extract qualitative and quantitative information from publication sources. Our findings verified that intercomparison of literature results is often impossible; sampling design, FTIR spectral acquisition and performance evaluation are critical methodological issues that need more specific guidance and criteria for application to product authenticity testing.

Application of experimental design in HPLC method optimisation for the simultaneous determination of multiple bioactive cannabinoids.

The scientific interest in Cannabis sativa L. analysis has been rapidly increasing in recent years, especially for what concerns cannabinoids, plant secondary metabolites which are well known for having many biological properties. High-performance liquid chromatography (HPLC) is frequently used for both the qualitative and quantitative analysis of cannabinoids in plant extracts from C. sativa and its derived products. Many studies have been focused on the main cannabinoids, such as ∆9-tetrahydrocannabinolic acid (∆9-THCA), cannabidiolic acid (CBDA), cannabigerolic acid (CBGA) and their decarboxylated derivatives, such as ∆9-tetrahydrocannabinol (∆9-THC), cannabidiol (CBD) and cannabigerol (CBG). In addition to the abovementioned compounds, the plant produces other metabolites of the same chemical class, and some of them have shown interesting biological activities. In the light of this, it is important to have efficient analytical methods for the simultaneous separation of cannabinoids, which is quite complex since they present similar chemical-physical characteristics. The present work is focused on the use of the Design of Experiments technique (DoE) to develop and optimise an HPLC method for the simultaneous separation of 14 cannabinoids. Experimental design optimisation was applied by using a Central Composite Face-Centered design to achieve the best resolution with minimum experimental trials. Five significant variables affecting the chromatographic separation, including ammonium formate concentration, gradient elution, run time and flow rate, were studied. A multivariate strategy, based on Principal Component Analysis (PCA) and Partial Least Squared (PLS) regression, was used to define the best operative conditions. The developed method allowed for the separation of 12 out of 14 cannabinoids. Due to co-elution phenomena, HPLC coupled with a triple quadrupole mass analyser (HPLC-ESI-MS/MS) was applied, monitoring the specific transitions of each compound in the multiple reaction monitoring (MRM) mode. Finally, the optimised method was applied to C. sativa extracts having a different cannabinoid profile to demonstrate its efficiency to real samples. The methodology applied in this study can be useful for the separation of other cannabinoid mixtures, by means of appropriate optimisation of the experimental conditions.

A Multiblock Approach to Fuse Process and Near-Infrared Sensors for On-Line Prediction of Polymer Properties.

Petrochemical companies aim at assessing final product quality in real time, in order to rapidly deal with possible plant faults and to reduce chemical wastes and staff effort resulting from the many laboratory analyses performed every day. In order to answer these needs, the main purpose of the current work is to explore the feasibility of multiblock regression methods to build real-time monitoring models for the prediction of two quality properties of Acrylonitrile-Butadiene-Styrene (ABS) by fusing near-infrared (NIR) and process sensors data. Data come from a production plant, which operates continuously, and where four NIR probes are installed on-line, in addition to standard process sensors. Multiblock-PLS (MB-PLS) and Response-Oriented Sequential Alternation (ROSA) methods were here utilized to assess which of such sensors and plant areas were the most relevant for the quality parameters prediction. Several prediction models were constructed exploiting measurements provided by sensors active at different ABS production process stages. Both methods provided good prediction performances and permitted identification of the most relevant data blocks for the quality parameters' prediction. Moreover, models built without considering recordings from the final stage of the process yielded prediction errors comparable to those involving all available data blocks. Thus, in principle, allowing final ABS quality to be estimated in real-time before the end of the process itself.

A novel proposal to investigate the interplay between the spatial and spectral domains in near-infrared spectral imaging data by means of Image Decomposition, Encoding and Localization (IDEL).

The emergence of new spectral imaging applications in many science fields and in industry has not come to be a surprise, considering the immense potential this technique has to map spectral information. In the case of near-infrared spectral imaging, a rapid evolution of the technology has made it more and more appealing in non-destructive analysis of food and materials as well as in process monitoring applications. However, despite its great diffusion, some challenges remain open from the data analysis point of view, with the aim to fully uncover patterns and unveil the interplay between both the spatial and spectral domains. Here we propose a new approach, called Image Decomposition, Encoding and Localization (IDEL), where a spatial perspective is taken for the analysis of spectral images, while maintaining the significant information within the spectral domain. The methodology benefits from wavelet transform to exploit spatial features, encoding the outcoming images into a set of descriptors and utilizing multivariate analysis to isolate and extract the significant spatial-spectral information. A forensic case study of near-infrared images of biological stains on cotton fabrics is used as a benchmark. The stain and fabric have hardly distinguishable spectral signatures due to strong scattering effects that originate from the rough surface of the fabric and the high spectral absorbance of cotton in the near-infrared range. There is no selective information that can isolate signals related to these two components in the spectral images under study, and the complex spatial structure is highly interconnected to the spectral signatures. IDEL was capable of isolating the stains, (spatial) scattering effects, and a possible drying effect from the stains. It was possible to recover, at the same time, specific spectral regions that mostly highlight these isolated spatial structures, which was previously unobtainable.

Fusing NIR and Process Sensors Data for Polymer Production Monitoring.

Process analytical technology and multivariate process monitoring are nowadays the most effective approaches to achieve real-time quality monitoring/control in production. However, their use is not yet a common practice, and industries benefit much less than they could from the outcome of the hundreds of sensors that constantly monitor production in industrial plants. The huge amount of sensor data collected are still mostly used to produce univariate control charts, monitoring one compartment at a time, and the product quality variables are generally used to monitor production, despite their low frequency (offline measurements at analytical laboratory), which is not suitable for real-time monitoring. On the contrary, it would be extremely advantageous to benefit from predictive models that, based on online sensors, will be able to return quality parameters in real time. As a matter of fact, the plant setup influences the product quality, and process sensors (flow meters, thermocouples, etc.) implicitly register process variability, correlation trends, drift, etc. When the available spectroscopic sensors, reflecting chemical composition and structure, consent to monitor the intermediate products, coupling process, and spectroscopic sensor and extracting/fusing information by multivariate analysis from this data would enhance the evaluation of the produced material features allowing production quality to be estimated at a very early stage. The present work, at a pilot plant scale, applied multivariate statistical process control (MSPC) charts, obtained by data fusion of process sensor data and near-infrared (NIR) probes, on a continuous styrene-acrylonitrile (SAN) production process. Furthermore, PLS regression was used for real-time prediction of the Melt Flow Index and percentage of bounded acrylonitrile (%AN). The results show that the MSPC model was able to detect deviations from normal operative conditions, indicating the variables responsible for the deviation, be they spectral or process. Moreover, predictive regression models obtained using the fused data showed better results than models computed using single datasets in terms of both errors of prediction and R 2. Thus, the fusion of spectra and process data improved the real-time monitoring, allowing an easier visualization of the process ongoing, a faster understanding of possible faults, and real-time assessment of the final product quality.

Characterization of Basil Volatile Fraction and Study of Its Agronomic Variation by ASCA.

Basil is a plant known worldwide for its culinary and health attributes. It counts more than a hundred and fifty species and many more chemo-types due to its easy cross-breeds. Each species and each chemo-type have a typical aroma pattern and selecting the proper one is crucial for the food industry. Twelve basil varieties have been studied over three years (2018-2020), as have four different cuts. To characterize the aroma profile, nine typical basil flavour molecules have been selected using a gas chromatography-mass spectrometry coupled with an olfactometer (GC-MS/O). The concentrations of the nine selected molecules were measured by an ultra-fast CG e-nose and Principal Component Analysis (PCA) was applied to detect possible differences among the samples. The PCA results highlighted differences between harvesting years, mainly for 2018, whereas no observable clusters were found concerning varieties and cuts, probably due to the combined effects of the investigated factors. For this reason, the ANOVA Simultaneous Component Analysis (ASCA) methodology was applied on a balanced a posteriori designed dataset. All the considered factors and interactions were statistically significant (p < 0.05) in explaining differences between the basil aroma profiles, with more relevant effects of variety and year.

A New Protocol of Computer-Assisted Image Analysis Highlights the Presence of Hemocytes in the Regenerating Cephalic Tentacles of Adult Pomacea canaliculata.

In humans, injuries and diseases can result in irreversible tissue or organ loss. This well-known fact has prompted several basic studies on organisms capable of adult regeneration, such as amphibians, bony fish, and invertebrates. These studies have provided important biological information and helped to develop regenerative medicine therapies, but important gaps concerning the regulation of tissue and organ regeneration remain to be elucidated. To this aim, new models for studying regenerative biology could prove helpful. Here, the description of the cephalic tentacle regeneration in the adult of the freshwater snail Pomacea canaliculata is presented. In this invasive mollusk, the whole tentacle is reconstructed within 3 months. Regenerating epithelial, connective, muscular and neural components are already recognizable 72 h post-amputation (hpa). Only in the early phases of regeneration, several hemocytes are retrieved in the forming blastema. In view of quantifying the hemocytes retrieved in regenerating organs, granular hemocytes present in the tentacle blastema at 12 hpa were counted, with a new and specific computer-assisted image analysis protocol. Since it can be applied in absence of specific cell markers and after a common hematoxylin-eosin staining, this protocol could prove helpful to evidence and count the hemocytes interspersed among regenerating tissues, helping to unveil the role of immune-related cells in sensory organ regeneration.

A Metabolomic Approach to Beer Characterization.

The consumers' interest towards beer consumption has been on the rise during the past decade: new approaches and ingredients get tested, expanding the traditional recipe for brewing beer. As a consequence, the field of "beeromics" has also been constantly growing, as well as the demand for quick and exhaustive analytical methods. In this study, we propose a combination of nuclear magnetic resonance (NMR) spectroscopy and chemometrics to characterize beer. 1H-NMR spectra were collected and then analyzed using chemometric tools. An interval-based approach was applied to extract chemical features from the spectra to build a dataset of resolved relative concentrations. One aim of this work was to compare the results obtained using the full spectrum and the resolved approach: with a reasonable amount of time needed to obtain the resolved dataset, we show that the resolved information is comparable with the full spectrum information, but interpretability is greatly improved.

Multi-sensor data fusion and parallel factor analysis reveals kinetics of wood weathering.

Understanding mechanisms of materials deterioration during service life is fundamental for their confident use in the building sector. This work presents analysis of time series of data related to wood weathering acquired at three scales (molecular, microscopic, macroscopic) with different sensors. By using several complementary techniques, the material description is precise and complete; however, the data provided by multiple equipment are often not directly comparable due to different resolution, sensitivity and/or data format. This paper presents an alternative approach for multi-sensor data fusion and modelling of the deterioration processes by means of PARAFAC model. Time series data generated within this research were arranged in a data cube of dimensions samples × sensors × measuring time. The original protocol for data fusion as well as novel meta parameters, such as cumulative nested biplot, was proposed and tested. It was possible to successfully differentiate weathering trends of diverse materials on the basis of the NIR spectra and selected surface appearance indicators. A unique advantage for such visualization of the PARAFAC model output is the possibility of straightforward comparison of the degradation kinetics and deterioration trends simultaneously for all tested materials.

Preliminary Assessment of Parmigiano Reggiano Authenticity by Handheld Raman Spectroscopy.

Raman spectroscopy, and handheld spectrometers in particular, are gaining increasing attention in food quality control as a fast, portable, non-destructive technique. Furthermore, this technology also allows for measuring the intact sample through the packaging and, with respect to near infrared spectroscopy, it is not affected by the water content of the samples. In this work, we evaluate the potential of the methodology to model, by multivariate data analysis, the authenticity of Parmigiano Reggiano cheese, which is one of the most well-known and appreciated hard cheeses worldwide, with protected denomination of origin (PDO). On the other hand, it is also highly subject to counterfeiting. In particular, it is critical to assess the authenticity of grated cheese, to which, under strictly specified conditions, the PDO is extended. To this aim, it would be highly valuable to develop an authenticity model based on a fast, non-destructive technique. In this work, we present preliminary results obtained by a handheld Raman spectrometer and class-modeling (Soft Independent Modeling of Class Analogy, SIMCA), which are extremely promising, showing sensitivity and specificity of 100% for the test set. Moreover, another salient issue, namely the percentage of rind in grated cheese, was addressed by developing a multivariate calibration model based on Raman spectra. It was possible to obtain a prediction error around 5%, with 18% being the maximum content allowed by the production protocol.

A metabolomic data fusion approach to support gliomas grading.

Magnetic resonance imaging (MRI) is the current gold standard for the diagnosis of brain tumors. However, despite the development of MRI techniques, the differential diagnosis of central nervous system (CNS) primary pathologies, such as lymphoma and glioblastoma or tumor-like brain lesions and glioma, is often challenging. MRI can be supported by in vivo magnetic resonance spectroscopy (MRS) to enhance its diagnostic power and multiproject-multicenter evaluations of classification of brain tumors have shown that an accuracy around 90% can be achieved for most of the pairwise discrimination problems. However, the survival rate for patients affected by gliomas is still low. The High-Resolution Magic-Angle-Spinning Nuclear Magnetic Resonance (HR-MAS NMR) metabolomics studies may be helpful for the discrimination of gliomas grades and the development of new strategies for clinical intervention. Here, we propose to use T2 -filtered, diffusion-filtered and conventional water-presaturated spectra to try to extract as much information as possible, fusing the data gathered by these different NMR experiments and applying a chemometric approach based on Multivariate Curve Resolution (MCR). Biomarkers important for glioma's discrimination were found. In particular, we focused our attention on cystathionine (Cyst) that shows promise as a biomarker for the better prognosis of glioma tumors.

Data fusion approaches in spectroscopic characterization and classification of PDO wine vinegars.

Spain is one of the major producers of high-quality wine vinegars having three protected designations of origin (a.k.a. PDOs): "Vinagre de Jerez", "Vinagre de Condado de Huelva" and "Vinagre de Montilla-Moriles". Their high prices due to their high quality and their high production costs explain the need for developing an adequate quality control technique and the interest in extensive characterization in order to capture the identity of each denomination. In this framework, methodologies based on non-targeted techniques, such as spectroscopies, are becoming popular in food authentication. Thus, for improving vinegar quality assessment, fusion of data blocks obtained from the same samples but different analytical techniques could be a good strategy, since the quantity and quality of sample knowledge could be enhanced providing new insights into the differentiation of vinegars. Therefore, the aim of this manuscript is the development of a multi-platform methodology and a model able to classify the Spanish wine vinegar PDOs. Sixty-five PDO wine vinegars were analyzed by four spectroscopic techniques: Fourier-transform mid-infrared spectroscopy (MIR), near infrared spectroscopy (NIR), multidimensional fluorescence spectroscopy (EEM) and proton nuclear magnetic resonance (1H-NMR). Two different data fusion strategies were evaluated: Mid-level data fusion with different preprocessing, and Common Component and Specific Weights analysis multiblock method. Exploratory and classification analysis on the data from individual techniques were also performed and compared with data fusion models. The data fusion models improved the classification, providing a more efficient differentiation, than the models based on single methods, and supporting the approach to combine these methods to achieve synergies for an optimized PDO differentiation.

Fused adjacency matrices to enhance information extraction: The beer benchmark.

Multivariate exploratory data analysis allows revealing patterns and extracting information from complex multivariate data sets. However, highly complex data may not show evident groupings or trends in the principal component space, e.g. because the variation of the variables are not grouped but rather continuous. In these cases, classical exploratory methods may not provide satisfactory results when the aim is to find distinct groupings in the data. To enhance information extraction in such situations, we propose a novel approach inspired by the concept of combining weak classifiers, but in the unsupervised context. The approach is based on the fusion of several adjacency matrices obtained by different distance measures on data from different analytical platforms. This paper is intended to present and discuss the potential of the approach through a benchmark data set of beer samples. The beer data were acquired using three spectroscopic techniques: Visible, near-Infrared and Nuclear Magnetic Resonance. The results of fusing the three data sets via the proposed approach are compared with those from the single data blocks (Visible, NIR and NMR) and from a standard mid-level data fusion methodology. It is shown that, with the suggested approach, groupings related to beer style and other features are efficiently recovered, and generally more evident.

Development of 87Sr/86Sr maps as targeted strategy to support wine quality.

This study summarizes the results obtained from a systematic and long-term project aimed at the development of tools to assess the provenance of food in the oenological sector. In particular, 87Sr/86Sr isotope ratios were measured on statistically representative set of soils, vine branches and wines sampled in the production district of Modena, worldwide known for the Lambrusco wines production. The obtained data were used to build strontium isotopic maps able to objectively support the Lambrusco PDO wines origin as well as other products of the Modena district. Finally, a strong relationship was found between the 87Sr/86Sr isotope ratios of soils and vine branches on a large scale, highlighting and confirming once more the idea that plants can also represent an optimal sampling device to support geographical traceability.