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

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 R2. 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

Chemical Characterization and Temporal Variability of Pasta Condiment By-Products for Sustainable Waste Management

Sustainable waste management is an extremely important issue due to its environmental, economic, and social impacts. Knowledge of the chemical composition of the waste produced is a starting point for its valorization. This research focuses, for the first time, on the by-products of pasta condiment production, starting with their characterization. In particular, the presence of potential bioactive compounds and their variability over time have been studied. The latter aspect is crucial for the subsequent valorization of these by-products. In addition to acidity and total phenolic content, an untargeted strategy was adopted, using spectroscopic and chromatographic techniques coupled with chemometrics, to study waste samples coming from four single condiment production lines, i.e., Genoese pesto, tomato, ricotta, and rag & ugrave; sauces. The presence of lycopene, polyphenols, and several valuable volatile compounds was highlighted. Their presence and relative amounts vary mainly according to the presence of tomatoes in the sauce. The results obtained at different storage times (after 0, 7, 10, and 15 days) showed that the samples studied, despite having similar chemical characteristics, underwent changes after one week of storage and then presented a relatively stable chemical profile. A general decrease is observed after 7 days for almost all the chemical variables monitored, so careful planning within the first days is required to obtain a high recovery

Tracing the identity of Parmigiano Reggiano “Prodotto di Montagna - Progetto Territorio” cheese using NMR spectroscopy and multivariate data analysis

Background Nuclear magnetic resonance (NMR) spectroscopy is one of the well-established tools for food metabolomic analysis, as it proved to be very effective in authenticity and quality control of dairy products, as well as to follow product evolution during processing and storage. The analytical assessment of the EU mountain denomination label, specifically for Parmigiano Reggiano "Prodotto di Montagna - Progetto Territorio" (Mountain-CQ) cheese, has received limited attention. Although it was established in 2012 the EU mountain denomination label has not been much studied from an analytical point of view. Nonetheless, tracing a specific profile for the mountain products is essential to support the value chain of this specialty. Results The aim of the study was to produce an identity profile for Parmigiano Reggiano “Prodotto di Montagna - Progetto Territorio” (Mountain-CQ) cheese, and to differentiate it from Parmigiano Reggiano PDO samples (conventional-PDO) using 1H NMR spectroscopy coupled with multivariate data analysis. Three different approaches were applied and compared. First, the spectra-as-such were analysed after proper preprocessing. For the other two approaches, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) was used for signals resolution and features extraction, either individually on manually-defined spectral intervals or by reapplying MCR-ALS on the whole spectra with selectivity constraints using the reconstructed “pure profiles” as initial estimates and targets. All approaches provided comparable information regarding the samples’ distribution, as in all three cases the separation between the two product categories conventional-PDO and Mountain-CQ could be highlighted. Moreover, a novel MATLAB toolbox for features extraction via MCR-ALS was developed and used in synergy with the Chenomx library, allowing for a putative identification of the selected features. Significance A first identity profile for Parmigiano Reggiano “Prodotto di Montagna - Progetto Territorio” obtained by interpreting the metabolites signals in NMR spectroscopy was obtained. Our workflow and toolbox for generating the features dataset allows a more straightforward interpretation of the results, to overcome the limitations due to dimensionality and to peaks overlapping, but also to include the signals assignment and matching since the early stages of the data processing and analysis

An unprecedented case of cranial surgery in Longobard Italy (6th–8th century) using a cruciform incision

The Longobard necropolis of Castel Trosino dates from the 6th to the 8th century CE. Among the tombs excavated, the skull of an older female shows the first evidence of a cross-shaped bone modification on a living subject. Macroscopic, microscopic, and CT scan analyses revealed signs of at least two sets of scraping marks. Specifically, SEM analysis shows that perimortem bone-scraping traces are present on the skull. Both healed and non-healed defects suggest that the woman has received at least twice intentional bone modifications to address her condition. This is the first evidence of a cross-shaped therapeutic intervention on a living subject

Comparative analysis of features extraction protocols for LC-HRMS untargeted metabolomics in mountain cheese ‘identitation’

This study presents a comprehensive metabolomic analysis of Parmigiano Reggiano samples to differentiate between those designated as Mountain Quality Certification (QC) and conventional Protected Designation of Origin (PDO). Despite following the same production protocol, these cheese varieties differ in the cows’ feeding regimes and milk stable locations, with mountain-certified samples adhering to specific requirements regarding milk origin and feed composition. An untargeted approach with Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS) was proposed to characterize the cheese metabolome. High-resolution LC-MS data can generate gigabyte-sized files, making data compression essential for manageable multivariate analysis and noise reduction. This study employs the Region of Interest-Multivariate Curve Resolution (ROI-MCR) protocol to achieve effective data compression and chromatographic resolution, thereby extracting the most informative features. This method was compared with a classical approach for feature extraction from chromatographic data, namely Compound Discoverer (CD) software. The features extracted by both methods were analysed through Principal Component Analysis (PCA) and ASCA (ANOVA Simultaneous Component Analysis). The comparison of ROI-MCR and CD approaches demonstrated that while both methods yielded similar overall conclusions, ROI-MCR provided a more streamlined and manageable dataset, facilitating easier interpretation of the metabolic differences. Both approaches indicated that amino acids, fatty acids, and bacterial activity-related compounds played significant roles in distinguishing between the two sample types

Multilingualism and the Brexit referendum

This chapter argues that the (lack of) foreign language skills has contributed to the outcome of the Brexit referendum. Theory suggests that speaking foreign languages reduces perceptions of cultural distance and contributes to the formation of transnational identities. Research also shows a link between language skills and European identity (Kuhn 2015; Díez Medrano 2018). Did Britons’ relative lack of foreign language skills play a role in the Brexit decision? Using matching methods and data from the referendum wave of the British Election Study, it is possible to estimate the effect of foreign language skills on the referendum vote. The results suggest that a significant effect of foreign language skills remains, even when taking into account education, age, gender, income, and region, party preference, and personality differences

Evolution of the Family Equidae, Subfamily Equinae, in North, Central and South America, Eurasia and Africa during the Plio-Pleistocene

Studies of horse evolution arose during the middle of the 19th century, and several hypotheses have been proposed for their taxonomy, paleobiogeography, paleoecology and evolution. The present contribution represents a collaboration of 19 multinational experts with the goal of providing an updated summary of Pliocene and Pleistocene North, Central and South American, Eurasian and African horses. At the present time, we recognize 114 valid species across these continents, plus 4 North African species in need of further investigation. Our biochronology and biogeography sections integrate Equinae taxonomic records with their chronologic and geographic ranges recognizing regional biochronologic frameworks. The paleoecology section provides insights into paleobotany and diet utilizing both the mesowear and light microscopic methods, along with calculation of body masses. We provide a temporal sequence of maps that render paleoclimatic conditions across these continents integrated with Equinae occurrences. These records reveal a succession of extinctions of primitive lineages and the rise and diversification of more modern taxa. Two recent morphological-based cladistic analyses are presented here as competing hypotheses, with reference to molecular-based phylogenies. Our contribution represents a state-of-the art understanding of Plio-Pleistocene Equus evolution, their biochronologic and biogeographic background and paleoecological and paleoclimatic contexts