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Nutritional information provided on food labels can impact healthy dietary decisions of consumers. The accuracy of the information provided is of paramount importance to guide consumers’ food choices and prevent food-related chronic diseases. The present study aimed to verify the veracity of nutritional labels of 103 food products purchased online through well-known e-commerce websites (80 processed and 23 unprocessed items) using near infrared spectroscopy. Among processed food products, surprisingly, 28 food products out of 80 (35%) did not bear nutritional labels. Considering the European tolerances for nutrient values declared on a label, the comparison of experimental values with those reported on the labels showed that more than 74% of the values declared on the label were congruent with the NIR experimental data, whereas 7.5% of the label values were non-compliant with the tolerance limits, and about 11.3% were slightly outside the tolerance limits. Note that 6.6% of the values indicated in the labels did not abide the regulation at all. Finally, 35.8% of the samples showed at least one value outside the tolerance limits. The current study demonstrated the capability of NIR spectroscopy for monitoring the compliance of nutritional labels with EU tolerance limits and guiding the choice of reference methods for further confirmation purposes.

The modeling of the historical architecture can be characterized by different levels of geometric development, more or less advanced, which correspond to different values of deviation between the real object and its three-dimensional representation. The aim of the paper is a critical study of the different levels of geometry (LoG) that the architectural elements of a model can have in relation to the efforts required to achieve them and the modeling objectives. More specifically, the contribution of this study proposes—on the basis of a survey campaign of a case study conducted by drone—the evaluation of the deviation between the point cloud and HBIM models with the different levels of development in order to assess their reliability according to specific objectives, such as the documentation of the architectural asset, feasibility study, restoration project, etc.

This paper illustrates the goals, the achievements and the team of a new food chemistry laboratory of the Istituto Zooprofilattico Sperimentale delle Venezie. The laboratory equipment, the testing activity and the research network are also described.

Through the analysis of the vaulted system, the study aims to research the spatialities enclosed in the church of St. Michael the Archangel in Brixen (Bz): the current one surveyed, the gothic one and the painted baroque one, grafted onto the late-Gothic structure.

The potential of the combination of near infrared (NIR) spectroscopy and Raman spectroscopy to differentiate Italian and Greek extra virgin olive oil (EVOO) by geographical origin was evaluated. Near infrared spectroscopy and Raman fingerprints of both study groups (extra virgin olive oil from the two countries) were pre-processed, merged by low-level and mid-level data fusion strategies and submitted to partial least-squares discriminant analysis. The classification models were cross-validated. After low-level data fusion, the partial least-squares discriminant analysis correctly predicted the geographical origins of extra virgin olive oils in cross-validation with 93.9% accuracy, while sensitivity and specificity were 77.8% and 100%, respectively. After mid-level data fusion, the partial least-squares discriminant analysis correctly predicted the geographical origins of extra virgin olive oils in cross-validation with 97.0% accuracy, while sensitivity and specificity were 88.9% and 100%, respectively. In this preliminary study, improved discrimination of Italian extra virgin olive oils was achieved by the synergism of near infrared spectroscopy and Raman spectroscopy as compared to the discrimination obtained by the separate laboratory techniques. This pilot study shows encouraging results that could open a new avenue for the authentication of Italian extra virgin olive oil.

Neoadjuvant chemotherapy (NACT) is offered to patients with operable or inoperable breast cancer (BC) to downstage the disease. Clinical responses to NACT may vary depending on a few known clinical and biological features, but the diversity of responses to NACT is not fully understood. In this study, 80 women had their metabolite profiles of pre-treatment sera analyzed for potential NACT response biomarker candidates in combination with immunohistochemical parameters using Nuclear Magnetic Resonance (NMR). Sixty-four percent of the patients were resistant to chemotherapy. NMR, hormonal receptors (HR), human epidermal growth factor receptor 2 (HER2), and the nuclear protein Ki67 were combined through machine learning (ML) to predict the response to NACT. Metabolites such as leucine, formate, valine, and proline, along with hormone receptor status, were discriminants of response to NACT. The glyoxylate and dicarboxylate metabolism was found to be involved in the resistance to NACT. We obtained an accuracy in excess of 80% for the prediction of response to NACT combining metabolomic and tumor profile data. Our results suggest that NMR data can substantially enhance the prediction of response to NACT when used in combination with already known response prediction factors.

INTRODUCTION: The early diagnosis of Mycobacterium avium subsp. paratuberculosis (MAP) is one of the current challenges of farmers and veterinarians. OBJECTIVES: This work aimed to investigated the changes in metabolic levels associated with natural MAP infection in infected and infectious dairy cattle METHODS: The study included sera from 23 infectious/seropositive, 10 infected but non-infectious/seronegative, and 26 negative animals. The samples were selected from a collection of samples gathered during a prospective study. The samples were analyzed by quantitative nuclear magnetic resonance (NMR) spectroscopy and routine blood chemistry. The blood indices and the 1H NMR data were concatenated by low-level data fusion, resulting in a unique global fingerprint. Afterwards, the merged dataset was statistically analyzed by the least absolute shrinkage and selection operator (LASSO), which is a shrinkage and selection method for supervised learning. Finally, pathways analysis was performed to get more insights on the possible dysregulated metabolic pathways.RESULTS: The LASSO model achieved, in cross-validation, an overall accuracy of 91.5% with high values of sensitivity and specificity in classifying correctly the negative, infected, and infectious animals. The pathway analysis revealed MAP-infected cattle have increased tyrosine metabolism and enhanced phenylalanine, tyrosine and tryptophan biosynthesis. The enhanced synthesis and degradation of ketone bodies was observed both in infected and infectious cattle.CONCLUSION: In conclusion, fusing data from multiple sources has proved to be useful in exploring the altered metabolic pathways in MAP infection and potentially predicting negative animals within paratuberculosis-infected herds.

This review provides a summary of known molecular alterations in commonly used cancer models and strives to stipulate how they may affect ambient mass spectrometry profiles. Immortalized cell lines are known to accumulate mutations, and xenografts derived from cell lines are known to contain tumour microenvironment elements from the host animal. While the use of human specimens for mass spectrometry profiling studies is highly encouraged, patient-derived xenografts with low passage numbers could provide an alternative means of amplifying material for ambient MS research when needed. Similarly, genetic preservation of patient tissue seen in some organoid models, further verified by qualitative proteomic and transcriptomic analyses, may argue in favor of organoid suitability for certain ambient profiling studies. However, to choose the appropriate model, pre-evaluation of the model’s molecular characteristics in the context of the research question(s) being asked will likely provide the most appropriate strategy to move research forward. This can be achieved by performing comparative ambient MS analysis of the disease model of choice against a small amount of patient tissue to verify concordance. Disease models, however, will continue to be useful tools to orthogonally validate metabolic states of patient tissues through controlled genetic alterations that are not possible with patient specimens.

International audience; To address the issues related to high perishability and limited shelf life of food proteins from muscle origin, different innovative processing, and preservation techniques, as well as analytical methodological approaches have been developed to meet environmental challenges and consumer demand for food of high quality and sustainable production supported by circular economy principles. This development has been enhanced and increased during the ongoing age of the fourth industrial revolution (Industry 4.0), which has been gaining momentum since 2015, coming up with a range of automated and digitized technologies. This review provides an updated overview of the recently developed thermal and nonthermal processing and preservation technologies, along with selected advanced analytical techniques used in the industry of muscle foods. Particular attention has been given to Industry 4.0 technologies and their role in achieving smart production with high automation and digitalization rates. As in other industry sectors, technology breakthroughs are reshaping the food industry, including the muscle food sector. Emerging technologies, such as pulsed electric field, high-pressure processing, ohmic heating, nanotechnology, advanced mass spectrometry and hyperspectral imaging sensors are among the key elements in the current food revolution 4.0. Although Industry 4.0 offers countless possibilities, more studies are still needed to capture its full potential and further harness its technologies to solve current challenges and move forward toward Industry 5.0.

Aims The efficacy of ambient mass spectrometry to identify and serotype Legionella pneumophila was assessed. To this aim, isolated waterborne colonies were submitted to a rapid extraction method and analysed by direct analysis in real-time mass spectrometry (DART-HRMS). Methods and results The DART-HRMS profiles, coupled with partial least squares discriminant analysis (PLS-DA), were first evaluated for their ability to differentiate Legionella spp. from other bacteria. The resultant classification model achieved an accuracy of 98.1% on validation. Capitalising on these encouraging results, DART-HRMS profiling was explored as an alternative approach for the identification of L. pneumophila sg. 1, L. pneumophila sg. 2-15 and L. non-pneumophila; therefore, a different PLS-DA classifier was built. When tested on a validation set, this second classifier reached an overall accuracy of 95.93%. It identified the harmful L. pneumophila sg. 1 with an impressive specificity (100%) and slightly lower sensitivity (91.7%), and similar performances were reached in the classification of L. pneumophila sg. 2-15 and L. non-pneumophila. Conclusions The results of this study show the DART-HMRS method has good accuracy, and it is an effective method for Legionella serogroup profiling. Significance and impact of the study These preliminary findings could open a new avenue for the rapid identification and quick epidemiologic tracing of L. pneumophila, with a consequent improvement to risk assessment.

SummaryHuman embryo studies have proposed the use of additional morphological evaluations related to the moment of the first cell divisions as relevant to embryo viability. Nevertheless, there are still not enough data available related to morphokinetic analysis and its relationship with lipid composition in embryos. Therefore, the aim of this study was to address the lipid profile of bovine embryos with different developmental kinetics: fast (four or more cells) and slow (two or three cells) at 40 h post-insemination (hpi), at three time points of in vitro culture (40, 112 and 186 hpi) and compare these to profiles of in vivo embryos. The lipid profiles of embryos were analyzed by matrix-assisted laser desorption ionization mass spectrometry, which mainly detected pools of membrane lipids such as phosphatidylcholine and sphingomyelin. In addition to their structural function, these lipid classes have an important role in cell signalling, particularly regarding events such as stress and pregnancy. Different patterns of lipids in the fast and slow groups were revealed in all the analyzed stages. Also, differences between in vitro embryos were more pronounced at 112 hpi, a critical moment due to embryonic genome activation. At the blastocyst stage, in vitro-produced embryos, despite the kinetics, had a closer lipid profile when compared with in vivo blastocysts. In conclusion, the kinetics of development had a greater effect on the membrane lipid profiles throughout the embryo culture, especially at the 8–16-cell stage. The in vitro environment affects lipid composition and may compromise cell signalling and function in blastocysts.

Rapid characterization of tissue disorder using ambient mass spectrometry (MS) techniques, requiring little to no preanalytical preparations of sampled tissues, has been shown using a variety of ion sources and with many disease classes. A brief overview of ambient MS in clinical applications, the state of the art in regulatory affairs, and recommendations to facilitate adoption for use at the bedside are presented. Unique challenges in the validation of untargeted MS methods and additional safety and compliance requirements for deployment within a clinical setting are further discussed. Development of a harmonized validation strategy for ambient MS methods is emphasized.

Mycobacterium avium subsp. paratuberculosis (MAP) is the causative agent of paratuberculosis [Johne's disease (JD)], a chronic disease that causes substantial economic losses in the dairy cattle industry. The long incubation period means clinical signs are visible in animals only after years, and some cases remain undetected because of the subclinical manifestation of the disease. Considering the complexity of JD pathogenesis, animals can be classified as infected, infectious, or affected. The major limitation of currently available diagnostic tests is their failure in detecting infected non-infectious animals. The present study aimed to identify metabolic markers associated with infected and infectious stages of JD. Direct analysis in real time coupled with high resolution mass spectrometry (DART-HRMS) was, hence, applied in a prospective study where cohorts of heifers and cows were followed up annually for 2–4 years. The animals' infectious status was assigned based on a positive result of both serum ELISA and fecal PCR, or culture. The same animals were retrospectively assigned to the status of infected at the previous sampling for which all JD tests were negative. Stored sera from 10 infected animals and 17 infectious animals were compared with sera from 20 negative animals from the same herds. Two extraction protocols and two (-/+) ionization modes were tested. The three most informative datasets out of the four were merged by a mid-level data fusion approach and submitted to partial least squares discriminant analysis (PLS-DA). Compared to the MAP negative subjects, metabolomic analysis revealed the m/z signals of isobutyrate, dimethylethanolamine, palmitic acid, and rhamnitol were more intense in infected animals. Both infected and infectious animals showed higher relative intensities of tryptamine and creatine/creatinine as well as lower relative abundances of urea, glutamic acid and/or pyroglutamic acid. These metabolic differences could indicate altered fat metabolism and reduced energy intake in both infected and infectious cattle. In conclusion, DART-HRMS coupled to a mid-level data fusion approach allowed the molecular features that identified preclinical stages of JD to be teased out.

This review provides a summary of known molecular alterations in commonly used cancer models and strives to stipulate how they may affect ambient mass spectrometry profiles. Immortalized cell lines are known to accumulate mutations, and xenografts derived from cell lines are known to contain tumour microenvironment elements from the host animal. While the use of human specimens for mass spectrometry profiling studies is highly encouraged, patient-derived xenografts with low passage numbers could provide an alternative means of amplifying material for ambient MS research when needed. Similarly, genetic preservation of patient tissue seen in some organoid models, further verified by qualitative proteomic and transcriptomic analyses, may argue in favor of organoid suitability for certain ambient profiling studies. However, to choose the appropriate model, pre-evaluation of the model's molecular characteristics in the context of the research question(s) being asked will likely provide the most appropriate strategy to move research forward. This can be achieved by performing comparative ambient MS analysis of the disease model of choice against a small amount of patient tissue to verify concordance. Disease models, however, will continue to be useful tools to orthogonally validate metabolic states of patient tissues through controlled genetic alterations that are not possible with patient specimens.

The aim of the research is the realization of Mixed Reality experiences to narrate the history of architectural heritage. This work is related to the INCIPICT Project and to 5G experimentations conducted by L’Aquila University for the supporting of scientific research and productive activities through the realization of a smart city. In particular, the work roots on the 3D reconstruction of the no-more existing Baroque configuration of the Basilica of Collemaggio in L’Aquila (IT), made according to the analysis of archival documents and the surveying of the current building, with the integration of laser scanning and photogrammetry by drone pictures. The Baroque system was demolished in the Seventies for a stylistic restoration. ICT allows the ubiquitous visualization of real time renderings of the old historical phase on people’s personal devices, coherently superimposed to the surrounded reality framed by the users. The 3D model’s visualizations have an educational purpose, but also configure as visual-computing tool for historical-critical study and enhancement of architectural heritage.

Plasma and tissue from breast cancer patients are valuable for diagnostic/prognostic purposes and are accessible by multiple mass spectrometry (MS) tools. Liquid chromatography-mass spectrometry (LC-MS) and ambient mass spectrometry imaging (MSI) were shown to be robust and reproducible technologies for breast cancer diagnosis. Here, we investigated whether there is a correspondence between lipid cancer features observed by desorption electrospray ionization (DESI)-MSI in tissue and those detected by LC-MS in plasma samples. The study included 28 tissues and 20 plasma samples from 24 women with ductal breast carcinomas of both special and no special type (NST) along with 22 plasma samples from healthy women. The comparison of plasma and tissue lipid signatures revealed that each one of the studied matrices (i.e., blood or tumor) has its own specific molecular signature and the full interposition of their discriminant ions is not possible. This comparison also revealed that the molecular indicators of tissue injury, characteristic of the breast cancer tissue profile obtained by DESI-MSI, do not persist as cancer discriminators in peripheral blood even though some of them could be found in plasma samples.

Extra virgin olive oil (EVOO) is frequently adulterated by mixing it with soft refined oils (SROO). The differentiation of EVOO from its blends with SROO is not possible with the most common approaches, and, for this reason, the discriminating power of liquid chromatography-high resolution mass spectrometry (LC-MS), gas-chromatography ion mobility spectrometry (GC-IMS) and flash gas-chromatography electronic nose (FGC-Enose) was examined previously. Here, the combination of the above-mentioned techniques for an improvement in classification power of the methods is explored. A total of 43 commercial EVOOs and 18 illegal mixtures of SROO with EVOO were previously analysed by LC-(+/−)MS, GC-IMS and FGC-Enose. Low-level and mid-level data fusion of the four datasets were performed. The merged unique fingerprints were submitted to partial least squared discriminant analysis (PLS-DA), and the extrapolated most informative variables were used to build support vector machine (SVM) classifiers. Statistical indicators were calculated and compared to find out the best classifier. The results of PLS-DA-SVM strategies on the combination of datasets demonstrated that, after low-level data fusion, the discriminatory capability of the two merged GC-based techniques was remarkably improved as compared to the individual techniques. This indicates that merging the datasets before PLS-DA better retrieves the most informative variables and, thus, enhances group separation and classification of unknowns. The combination of LC(+/−)MS datasets, both by mid- and low-level data fusion, did not show significant enhancement in terms of discrimination of EVOO from SROO as compared to the individual LC(+)MS matrix. The low-level combination of the four datasets (LC(+/−)MS, GC-IMS, FGC-Enose) was successful, although this laborious option is not a viable path in industry quality assurance. This study primarily provides new paths for the authentication of EVOO, taking advantage of merging multimodal LC-(+/−)MS, GC-IMS and FGC-Enose data, with consequent improvement in the performances of the classification models. The most promising results were achieved by the low-level data fusion of GC-IMS and FGC-Enose data.

Analytical methods able to differentiate fresh from frozen/thawed fish products are needed for suppliers and official authorities to prevent food frauds. In this study we applied a metabolomics approach to identify markers of freezing/thawing in skinless European sea bass (Dicentrarchus labrax) fish fillets. To this end, one hundred and twenty fish samples (sixty fresh and sixty frozen/thawed) were analysed in positive and negative ion modes by liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS). A combination of a supervised classification method by partial least square-discriminant analysis (PLS-DA), and Recursive Feature Elimination (RFE) allowed us to discover the most informative metabolites useful to codify fresh and defrosted European sea bass. Eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) metabolites were identified as the most relevant predictors. These were used to build a model capable of classifying European sea bass samples according to their fresh or frozen/thawed condition. The model showed accuracy, sensitivity and specificity of 100 %, when tested to an independent set of European sea bass undergoing the same experimental conditions. EPA and DHA were found to be altered even in a set of twenty fresh and frozen/thawed Atlantic salmon (Salmo salar), opening the way for the development of a targeted method aimed at providing robust species-independent tests to be applied in the seafood chain by producers and competent authorities.

Despite the recognized advantages of the use of BIM in the field of architecture and engineering, the extension of this procedure to the architectural heritage is neither immediate nor critical. The uniqueness and irregularity of historical architecture, on the one hand, and the great quantity of information necessary for the knowledge of architectural heritage, on the other, require appropriate reflections. The aim of this paper is to define a general framework for the use of BIM procedures for architectural heritage. The proposed methodology consists of three different Level of Development (LoD), depending on the characteristics of the building and the objectives of the study: a simplified model with a low geometric accuracy and a minimum quantity of information (LoD 200); a model nearer to the reality but, however, with a high deviation between virtual and real model (LoD 300); a detailed BIM model that reproduce as much as possible the geometric irregularities of the building and is enriched by the maximum quantity of information available (LoD 400).

Ambient mass spectrometry (AMS) was applied for the first time to differentiate fresh and frozen/thawed Dicentrarchus labrax (European sea bass). One hundred and twenty samples were submitted to two extraction procedures and analyzed in positive and negative ion modes by direct analysis in real time-high resolution mass spectrometry (DART-HRMS). The four DART-HRMS datasets were concatenated with a low-level data fusion approach, and the resultant unique block of data was submitted to multivariate statistical analysis to tease out the most informative m/z values capable of codifying fresh and defrosted D. labrax. The statistical significance (p-value) and fold change (FC) of these informative signals were then evaluated with univariate analyses and tentatively assigned. The 25 features were then used to build a support vector machine (SVM) model capable of classifying D. labrax samples according to whether they were fresh or previously frozen. The SVM model has accuracy, sensitivity and specificity of 100%, both in training and test set. The SVM model was used to successfully classify an independent set of twenty fresh and frozen/thawed Salmo salar (Atlantic salmon). The concentrations of the most significant molecular features were quantified by gas-chromatography-mass spectrometry.

In this study, the ability of an unmodified ESI Z-spray source to carry out ambient ion soft landing (AISL) and microdroplet reactions (MR) was tested. To this aim, heme proteins were ionized and landed onto the working area of multi-walled carbon nanotubes electrodes. The voltammetric and spectroscopic analyses of the modified electrodes evidenced a strong interaction of unfolded structures of the heme proteins with the carbon nanotubes surface. Moreover, the possibility to use the ESI Z-spray source to study microdroplet reactions was attempted by exploring the accelerated acid-catalyzed decomposition of carbohydrates. The study demonstrated that a commercial ESI Z-spray source can provide successful modifications of surfaces by AISL, and represents a suitable platform for the study of accelerated microdroplet reactions.

Untargeted lipid fingerprinting with hand-held ambient mass spectrometry (MS) probes without chromatographic separation has shown promise in the rapid characterization of cancers. As human cancers present significant molecular heterogeneities, careful molecular modeling and data validation strategies are required to minimize late-stage performance variations of these models across a large population. This review utilizes parallels from the pitfalls of conventional protein biomarkers in reaching bedside utility and provides recommendations for robust modeling as well as validation strategies that could enable the next logical steps in large scale assessment of the utility of ambient MS profiling for cancer diagnosis. Six recommendations are provided that range from careful initial determination of clinical added value to moving beyond just statistical associations to validate lipid involvements in disease processes mechanistically. Further guidelines for careful selection of suitable samples to capture expected and unexpected intragroup variance are provided and discussed in the context of demographic heterogeneities in the lipidome, further influenced by lifestyle factors, diet, and potential intersect with cancer lipid pathways probed in ambient mass spectrometry profiling studies.

Economically motivated adulteration (EMA) of herbs and spices is very frequent and a major cause of concern to consumers, manufacturers and legislators. Rapid, non-targeted methods capable of distinguishing authentic herbs from those mixed with low cost materials are desirable. To this aim, ambient mass spectrometry (AMS) was applied to authenticate dried oregano leaves. Authentic and adulterated oregano samples were submitted to two extraction procedures and analysed in positive and negative ion modes by direct analysis in real time-high resolution mass spectrometry (DART-HRMS). Mid-level data fusion of the four blocks of DART-HRMS data was performed, and the resultant unique dataset was submitted to supervised statistical analysis to ascertain the signals that discriminate authentic from adulterated oregano. The fourteen most informative signals of authenticity were tentatively assigned and validated by support vector machine (SVM). In the final model, accuracy, sensitivity and specificity of >90% were obtained. Independent authentic and adulterated oregano samples were used to validate the discriminative m/z values and evaluate the classification capability of the model. To the best of our knowledge this is the first application of AMS coupled to mid-level data fusion for oregano authentication.

This chapter presents a process for the study of an architectural and archaeological heritage. It is an interdisciplinary work carried on with the integration of different digital systems for surveying and managing data. An innovative methodological approach is presented, based on the HBIM for 3D digital management of data, related to the analysis of masonry techniques, of conservation and damage, paying particular attention to the masonries 3D stratigraphic study. A specific focus is on the use of HBIM to design the restoration yard. The case study is the church of San Cipriano, located near the small country of Castelvecchio Calvisio in the province of L'Aquila (Italy).