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Many of the rarest prehistoric bones found by archaeologists are enormously precious and are considered to be part of our cultural and historical patrimony. Radiocarbon dating is a well-established technique that estimates the ages of bones by analysing the collagen still present. However, this method is destructive, and its use must be limited. In this study, we used imaging technology to quantify the presence of collagen in bone samples in a non-destructive way to select the most suitable samples (or sample regions) to be submitted to radiocarbon dating analysis. Near-infrared spectroscopy (NIR) that was connected to a camera with hyperspectral imaging (HSI) was used along with a chemometric model to create chemical images of the distribution of collagen in ancient bones. This model quantifies the collagen at every pixel and thus provides a chemical mapping of collagen content. Our results will offer significant advances for the study of human evolution as we will be able to minimise the destruction of valuable bone material, which is under the protection and enhancement of European cultural heritage and thus allow us to contextualise the valuable object by providing an accurate calendar age.The collagen present in rare prehistoric bones allows for their age to be estimated by radiocarbon dating, but this method is destructive towards these precious archaeological remains. Here, the authors report a non-destructive method based on near-infrared hyperspectral imaging to precisely localize the collagen preserved in parts of ancient specimens suitable for radiocarbon dating.

From a criminalistic point of view, the accurate dating of biological traces found at the crime scene, together with its compatibility with the estimated crime perpetration timeframe, enables to limit the number of suspects by assessing their alibis and clarifying the sequence of events. The present study delineates, for the first time, the possibility of dating biological fluids such as semen and urine, as well as blood traces, by using a novel non-destructive analytical strategy based on hyperspectral imaging in the near infared region (HSI-NIR), coupled with multivariate regression methods. Investigated aspects of the present study include not only the progressive degradation of the biological trace itself, but also the effects of its interactions with the support on which it is absorbed, in particular the hydrophilic vs. hydrophobic character of fabric tissues. Results are critically discussed, highlighting potential and limitations of the proposed approach for a practical implementation.

In therapies involving therapeutic antibodies, therapeutic drug monitoring (TDM) has the potential to substantially increase clinical efficacy and to enable more economical drug use, because the clearance from the bloodstream after administration is patient dependent. Since TDM must be performed continuously and over extended time periods, it should ideally be achieved through an economical and easy to perform point-of-care (POC) assay. Here, we introduce a new analytical method for POC TDM of the clinically important therapeutic antibody cetuximab, using microfluidic thread-based analytical devices (µTADs) as a platform for a bioluminescence resonance energy-transfer (BRET) switching sensor protein for simple quantitative antibody detection. The µTAD is pre-treated with NaCl for blood plasma separation and a BRET switching sensor protein and its substrate for cetuximab detection, integrating all elements required for a reagent-free, single-step assay. A change in bioluminescence emission color depending on cetuximab concentrations occurs after whole blood application. The device design enables the quantification of cetuximab within the therapeutically relevant blood concentration range in 2.5 min using only 2 µL of whole blood. The results obtained are independent of the blood volume. This µTAD can be stored for about one month at − 20 °C, a temperature compatible with commonly available freezers. The application of mathematical morphology recognition (MMR)-integrated image processing techniques for the automatic selection of regions of interest (ROIs) yields results with minimal error. The simplicity of use, rapidity, and cost-effectiveness of this µTAD are expected to offer the possibility of achieving TDM of therapeutic antibodies at POC for individual patients.

SOURCE utility for reprocessing, calibration, and evaluation is a software designed for web applications that permits to calibrate and validate ocean models within a selected spatial domain using in-situ observations. Nowadays, in-situ observations can be freely accessed online through several marine data portals together with the metadata information about the data provenance and its quality. Metadata information and compliance with modern data standards allow the user to select and filter the data according to the level of quality required for the intended use and application. However, the available data sets might still contain anomalous data, bad data flagged as good, due to several reasons, i.e., the general quality assurance procedures adopted by the data infrastructure, the selected data type, the timeliness of delivery, etc. In order to provide accurate model skill scores, the SOURCE utility performs a secondary quality check, or re-processing, of observations through gross check tests and a recursive statistical quality control. This first and basic SOURCE implementation uses Near Real Time moored temperature and salinity observations distributed by the Copernicus Marine Environment and Monitoring Service (CMEMS) and two model products from Istituto Nazionale di Geofisica e Vulcanologia (INGV), the first an analysis and the second a reanalysis, distributed during CMEMS phase I for the Mediterranean Sea. The SOURCE tool is freely available to the scientific community through the ZENODO open access repository, consistent with the open science principles and for that it has been designed to be relocatable, to manage multiple model outputs, and different data types. Moreover, its observation reprocessing module provides the possibility to characterize temperature and salinity variability at each mooring site and continuously monitor the ocean state. Highest quality mooring time series at 90 sites and the corresponding model values have been obtained and used to compute model skill scores. The SOURCE output also includes mooring climatologies, trends, Probability Density Functions and averages at different time scales. Model skill scores and site statistics can be used to visually inspect both model and sensor performance in Near Real Time at the single site or at the basin scale. The SOURCE utility uptake allows the interested user to adapt it to its specific purpose or domain, including for example additional parameters and statistics for early warning applications.

The composition of exhaled breath derives from an intricate combination of normal and abnormal physiological processes that are modified by the consumption of food and beverages, circadian rhythms, bacterial infections, and genetics as well as exposure to xenobiotics. This complexity, which results wide intra- and inter-individual variability and is further influenced by sampling conditions, hinders the identification of specific biomarkers and makes it difficult to differentiate between pathological and nominally healthy subjects. The identification of a ‘normal’ breath composition and the relative influence of the aforementioned parameters would make breath analyses much faster for diagnostic applications. We thus compared, for the first time, the breath composition of age-matched volunteers following a vegan and a Mediterranean omnivorous diet in order to evaluate the impact of diet on breath composition. Mixed breath was collected from 38 nominally healthy volunteers who were asked to breathe into a 2 l handmade Nalophan bag. Exhalation flow rate and carbon dioxide values were monitored during breath sampling. An aliquot (100 ml) of breath was loaded into a sorbent tube (250 mg of Tenax GR, 60/80 mesh) before being analyzed by thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). Breath profiling using TD-GC-MS analysis identified five compounds (methanol, 1-propanol, pentane, hexane, and hexanal), thus enabling differentiation between samples collected from the different group members. Principal component analysis showed a clear separation between groups, suggesting that breath analysis could be used to study the influence of dietary habits in the fields of nutrition and metabolism. Surprisingly, one Italian woman and her brother showed extremely low breath isoprene levels (about 5 pbv), despite their normal lipidic profile and respiratory data, such as flow rate and pCO2. Further investigations to reveal the reasons behind low isoprene levels in breath would help reveal the origin of isoprene in breath.

The key role of inflammation in COVID-19 induced many authors to study the cytokine storm, whereas the role of other inflammatory mediators such as oxylipins is still poorly understood. IMPRECOVID was a monocentric retrospective observational pilot study with COVID-19 related pneumonia patients (n = 52) admitted to Pisa University Hospital between March and April 2020. Our MS-based analytical platform permitted the simultaneous determination of sixty plasma oxylipins in a single run at ppt levels for a comprehensive characterisation of the inflammatory cascade in COVID-19 patients. The datasets containing oxylipin and cytokine plasma levels were analysed by principal component analysis (PCA), computation of Fisher's canonical variable, and a multivariate receiver operating characteristic (ROC) curve. Differently from cytokines, the panel of oxylipins clearly differentiated samples collected in COVID-19 wards (n = 43) and Intensive Care Units (ICUs) (n = 27), as shown by the PCA and the multivariate ROC curve with a resulting AUC equal to 0.92. ICU patients showed lower (down to two orders of magnitude) plasma concentrations of anti-inflammatory and pro-resolving lipid mediators, suggesting an impaired inflammation response as part of a prolonged and unsolvable pro-inflammatory status. In conclusion, our targeted oxylipidomics platform helped shedding new light in this field. Targeting the lipid mediator class switching is extremely important for a timely picture of a patient's ability to respond to the viral attack. A prediction model exploiting selected lipid mediators as biomarkers seems to have good chances to classify patients at risk of severe COVID-19.

The European Food Safety Authority asked for novel approaches for identifying mechanically separated meat (MSM) in meat products, due to food safety concern. In this study, a novel approach based on multivariate analysis of 43 trace elements in meat products is described. Overall, 27 trace elements and 16 rare earth elements were determined by using ICP-MS after sample mineralization of 100 meat samples, composed of different percentages of MSM, obtained at low and high pressure, and without MSM. After development and optimization, the multivariate approach was validated by analyzing and then classifying 10 "blind" meat samples, obtaining method accuracy equal to 90%. Thus, the applicability of this new analytical approach was demonstrated. The method represents a significant improvement for this type of determination, especially when MSM is obtained at low pressure, since this product is characterized by chemical characteristics very similar to fresh meat.

Water activity is an important phenomenon not yet explained in the terms of water molecular structure. This paper aims to find the relationship between the water activity and water molecular structure of the rice germ, based on its spectral pattern which can be measured using non-destructive technology. Aquaphotomics near-infrared spectroscopy was used to study rice germ stored at different levels of water activity and atmosphere. The findings show that state of the rice germ is governed by the water activity upon storage, which is defined by the structure of water within germ matrix. The structure of water can be described solely by the absorbance spectral pattern at the following absorbance bands: proton hydrates, hydration shells and water vapor (1364, 1375 and 1382 nm), trapped water (1392 nm), free water (1410 nm), hydration water (1425 nm), adsorbed water (1455 nm), non-bonded hydroxyl (1436 nm) and bound water (1520 nm).

Thermohaline staircases are a well-known peculiar feature of the Tyrrhenian Sea. Generated by extensive double diffusion processes fueled by lateral intrusions, they are considered to be the most stable of all the staircases that have been detected in the world ocean, seeing their persistence of more than 40 years in the literature. Double diffusion leads to efficient vertical mixing, potentially playing a significant role in guiding the diapycnal mixing. The present study investigates this process of mixing in the case of the Tyrrhenian staircases by calculating the heat and salt fluxes in their gradient zones (interfaces) and the resulting net fluxes in adjacent layers using hydrological profiles collected from 2003 to 2016 at a station in the heart of the basin interior. The staircases favor downward fluxes of heat and salt, and the results of the calculations show that these are greater where temperature and salinity gradients are also high. This condition is more frequently encountered at thin and sharp interfaces, which sometimes appear as substructures of the thicker interfaces of the staircases. These substructures are hot spots where vertical fluxes are further accentuated. Due to the increasing salt and heat content of the Levantine Intermediate Water (LIW) during the observation period, a rise in the values of the fluxes was noted in the portion of the water column below it down to about 1800 m. The data furthermore show that internal gravity waves can modulate the structure of the staircases and very likely contribute to the mixing, too, but the sampling frequency of the time series is too large to permit a proper assessment of these processes. It is shown that, at least during the period of observation, the fluxes due to salt fingers do not reach the bottom layer but remain within the staircases.

SummaryBackgroundThe key role of inflammation in the progression of COVID-19 was evident right from the beginning of the pandemic. Consequently, many authors studied the onset and evolution of the cytokine storm. In contrast, other inflammatory mediators such as oxylipins have received almost no attention.MethodsWe conducted a monocentric observational and retrospective study (IMPRE-COVID-19) with patients (aged ≥ 18 years) admitted to Pisa University Hospital with COVID-19 related pneumonia. SARS-CoV-2 infection was confirmed by polymerase chain reaction in a nasopharyngeal swab, while pneumonia was demonstrated by CT scan. Oxylipin plasma levels were analysed in a convenient sample of 52 patients randomly selected from those that had a complete set of cytokine values evaluated for clinical purposes. In 12 cases, plasma samples collected on different days were available at the BMS Multispecialistic Biobank of Pisa University Hospital and were analysed to understand the evolution of the oxylipin levels during hospitalization. The two datasets that included oxylipin and cytokine values were analysed by principal component analysis (PCA), computation of Fisher’s canonical variable, and a multivariate receiver operating characteristic (ROC) curve.FindingsBetween March and April 2020, 52 patients were enrolled of which 28 were hospitalised in COVID-19 wards, 20 in ICUs, and 4 who were initially hospitalised in the wards and who were then transferred to the ICUs due to deteriorating health conditions. Plasma samples collected on different days were available from 7 patients in the wards, 1 from an ICU, and 4 from patients who were hospitalised in both. The mean age of participants was 61 years (SD 16), 11 were females (21%), and 41 were males (79%), without significant differences between groups in terms of age and gender. PCA of oxylipin data led to a clear differentiation of samples collected in COVID-19 wards and ICUs. This differentiation had not been obtained with cytokine data. In addition, borderline samples were from patients hospitalised in COVID-19 wards that were about to be transferred to the ICUs, thus suggesting that differentiation is not a consequence of a different treatment, but somehow related to a diverse evolution of the pathology. Computation of Fisher’s canonical variable identified the original input variables that were the most effective in discriminating between the two classes. The combination of the two datasets did not improve the discrimination, thus suggesting that oxylipins are more informative than cytokines for this purpose. A multivariate class model built using the four lowest-order principal components as the input variables, and COVID-19 ward samples as the target class, produced a ROC curve with a resulting area under the curve (AUC) equal to 0·92 – which is much higher than most AUC outcomes obtained for individual oxylipins.InterpretationAfter analysing the metabolic pathways of the most informative oxylipins, we speculate that more severe COVID-19 is associated with a selective deficiency of pro-resolving oxylipins leading to ineffective resolutive mechanisms of inflammation, likely worsened by endothelial damage. We believe that our oxylipin data suggest the possibility to predict the evolution of COVID-19 in individual patients at an early stage.FundingInstitutional funds from the University of Pisa supported the study.

Contamination by microplastics (MP) represents a critical environmental challenge with potential consequences at ecosystem, economic and societal levels. As the marine system is the final sink for MP, there is an urgent need to develop methods for the monitoring of synthetic particles in different marine compartments and sample matrices. Extensive evaluations are hindered by time and costs associated with to conventional MP spectroscopic analyses. The potential of near infrared hyperspectral imaging (NIR-HSI) has been recently evaluated. However, NIR-HSI has been poorly studied so far, limitedly to the detection of large particles (>300 μm), and its capability for direct characterization of MP in real marine matrices has not been considered yet. In the present study, a rapid near infrared hyperspectral imaging (NIR-HSI) method, coupled with a customised normalised difference image (NDI) strategy for data processing, is presented and used to detect MP down to 50 μm in environmental matrices. The proposed method is largely automated, without the need for extensive data processing, and enabled a successful identification of different polymers, both in surface water and mussel soft tissue samples, as well as on real field samples with environmentally occurring MP. NIR-HSI is applied directly on filters, without the need for particles pre-sorting or multiple sample purifications, avoiding time consuming procedures, airborne contaminations, particle degradation and loss. Thanks to the time and cost effectiveness, a large-scale implementation of this method would enable to extensively monitor the MP presence in natural environments for assessing the ecological risk related to MP contamination.

The experimental determination of water solubility (log S0) and Setschenow coefficient (km) of a compound is a time-consuming activity, which often needs large amounts of expensive substances. This work aims at establishing two “open-source” chemometric models based on a regression tree that is able to predict the two abovementioned quantities. The dataset used is the largest to appear up to now for the collection of km values, containing information on 295 molecules and it is relevant also for the collection of logS0 values (321 molecules); for each of them 32 descriptors were taken from freely available databases. Information about water solubility and Setschenow coefficients, necessary to train the models, were taken from available literature. Validation was performed on a separate test set of molecules. The precision reached in the prediction is fully satisfying, being RMSEP = 0.6086 and 0.0441 for logS0 and km, respectively.

Some place their faith in forensic science to the degree that they are under the impression that it is absolute, infallible and unassailable. In truth it is a manmade construct, dependent on manmade machinery, man-calibrated accuracy, man-led action under manmade protocols and analyzed by man – an altogether human construct (American Academy of Forensic Sciences cited in Pyrek, 2007). People have always strived to discover and understand the world, and the scientific quest to provide explanations fuels technological progress. This drive has fuelled forensic chemistry, where information is obtained through the examination of various evidentialmaterials to assist the justice systempiece together stories of the past. Concurrently, the validity and reliability of the information provided by forensic experts, its ability to discriminate between the standpoints of defense and prosecution, is being questioned and challenged as never before (Pyrek, 2007; Fraser andWilliams, 2009)(...)

Moisture content (MC) is one of the main issues that the coffee industry has to monitor. The international standards (ISO 11817 and ISO 11294) for MC evaluation are incompatible with the rhythm of a modern productive chain. As an alternative, thermogravimetric moisture analyzers (TMA) are widely applied, but they cannot provide a real-time monitoring and management of MC. The present research aims at evaluating the performance of a near-infrared (NIR) spectrophotometer in measuring the MC of roasted beans and ground coffee, in comparison with a TMA. PLS regression models provided high predictive performances (R2Pred = 0.95 and RMSEP = 0.15%, and R2Pred = 0.97 and RMSEC = 0.13% for roasted and ground coffee, respectively). Passing-Bablok regression was performed to compare NIR and TMA measurements, but no significant differences were highlighted. The residual dispersion index (RDI%) was proposed, showing the higher predictive accuracy of the NIR spectrophotometer, envisaging this technology as a routine standard method for coffee MC evaluation.

The accumulation of contaminants and their potential mobility represent two of the main environmental issues facing coastal environments. Sediments often act as “reservoirs” of contaminants, including potentially toxic trace elements, but they can also be considered a secondary source of contamination due to remobilisation processes at the sediment-water interface which may affect the quality of the coastal water and aquatic biota. This research aims to provide a geochemical characterisation of the estuarine system of the Timavo/Reka River, focusing on the occurrence of trace elements in different environmental matrices with the purpose of highlighting potential critical conditions in terms of environmental quality. The surface sediments were found to be enriched in several trace elements especially in the innermost sector of the area. There, sulphate-reductive conditions in the bottom saltwater testify to potential anoxia at the sediment-water interface, driving trace element accumulation in the residual fraction of the sediments. However, Fe and Mn redox behaviour appears to play a crucial role in the recycling of dissolved trace elements in the water column. With the lone exception of the saltwater in the innermost sector, trace elements were found to be mainly associated with suspended particles due to oxidation and precipitation processes, whereas a common lithogenic origin was identified for Cr, Ni, and Co, which are significantly correlated both in the surface sediments and in the suspended particles.

Qualitative pattern recognition methods find important applications in the chemometric sector to extract structured information from complex experimental data. Two main strategies can be distinguished: unsupervised analysis, aimed at investigating on the presence of groupings within the samples analysed, and supervised analysis, aimed at predicting the class membership of new samples. Supervised qualitative methods are, in turn, divided in two families: discriminant and class-modelling methods. The first ones require at least two classes to be defined, while the second ones are suitable also for one-class classification. The features of each strategy, with a focus on advantages and limitations, are described and compared. New trends in the methods, as well as recent attempts to force discriminant methods to behave as class-modelling ones, and vice versa, are also critically presented.

In the present study, an innovative and highly efficient near-infrared hyperspectral imaging (NIR-HSI) method is proposed to provide spectral maps able to reveal collagen distribution in large-size bones, also offering semi-quantitative estimations. A recently introduced method for the construction of chemical maps, based on Normalized Difference Images (NDI), is declined in an innovative approach, through the exploitation of the NDI values computed for each pixel of the hyperspectral image to localize collagen and to extract information on its content by a direct comparison with known reference samples. The developed approach addresses an urgent issue of the analytical chemistry applied to bioarcheology researches, which rely on well-preserved collagen in bones to obtain key information on chronology, paleoecology and taxonomy. Indeed, the high demand for large-sample datasets and the consequent application of a wide variety of destructive analytical methods led to the considerable destruction of precious bone samples. NIR-HSI pre-screening allows researchers to properly select the sampling points for subsequent specific analyses, to minimize costs and time and to preserve integrity of archaeological bones (which are available in a very limited amount), providing further opportunities to understand our past.

One of the main challenges for the present and future in ocean observations is to find best practices for data management: infrastructures like Copernicus and SeaDataCloud already take responsibility for assembly, archive, update and publish data. Here we present the strengths and weaknesses in a SeaDataCloud Temperature and Salinity time series data collections, in particular a tool able to recognize the different devices and platforms and to merge them with processed Copernicus platforms.While Copernicus has the main target to quickly acquire and publish data, SeaDataNet aims to publish data with the best quality available. This two data repository should be considered together, since the originator can ingest the data in both the infrastructures or only in one, or partially in both. This results sometimes in data partially available in Copernicus or SeaDataCloud, with great impact for the researcher who wants to access as much data as possible. The data reprocessing should not be loaded on researchers' shoulders, since only skilled users in all data management plan know how merge the data.The SeaDataCloud time series data collections is a Global Ocean soon-to-be-published dataset that will represent a reference for ocean researchers, released in binary, user friendly Ocean Data View format. The database management plan was originally for profiles, but had been adapted for time series, resolving several issues like the uniqueness of the identifiers (ID).Here we present an extension of the SOURCE (Sea Observations Utility for Reprocessing. Calibration and Evaluation) Python package, able to enhance the data quality with redundant sophisticated methods and simplify their usage. SOURCE increases quality control (Q/C) performances on observations using statistical quality check procedures that follows the ocean best practices guidelines, exploiting the following issues:Find and aggregate all broken time series using likeness in ID parameter strings; Find and organize in a dictionary all different metadata variables; Correct time series time to match simpler measure units; Filter devices that are outside of a selected horizontal rectangle; Give some information on original Q/C scheme by SeaDataCloud infrastructure; Give information tables on platforms and on the merged ID string duplicates together with an errors log file (missing time, depth, data, wrong Q/C variables, etc.). In particular, the duplicates table and the log file may be helpful to SeaDataCloud partners in order to update the data collection and make it finally available for the users.The reconstructed SeaDataCloud time series data, divided by parameter and stored in a more flexible dataset, give the possibility to ingest it in the main part of the software, allowing to compare it with Copernicus time series, find the same platform using horizontal and vertical surroundings (without looking to ID) find and cleanup duplicated data, merge the two databases to extend the data coverage.This allow researchers to have the most wide and the best quality possible data for the final users release and to to use these data to calibrate and validate models, in order to reach an idea of a whole area sea conditions.

The Arctic Oscillation (AO) is one of the main modes of variability of the Northern Hemisphere winter, also referred as Northern Annular Mode (NAM). The positive phase of the AO is characterized by warming/cooling over Northern Eurasia and the United States and cooling over Canada, especially over eastern Canada. Its positive phase is also characterized by very dry conditions over the Mediterranean and wet conditions over Northern Europe. A positive trend of the AO is observed for the period 1951-2011 and it is captured in CMIP5 models only when GHG-only forcing are included. In CMIP5 models the change expected is mostly mitigated by the effects of the aerosols. When considering AR5 scenarios, the AO is projected to become more positive in the future, though with a large spread among the models.Overall the spread in the representation of the AO variability and trend is large also in experiments with present-day conditions, likely associated with the large internal variability. Unique tools to identify and measure the role of the internal variability in the model representation of the large-scale modes of variability are large ensembles where multiple members are built with different initial conditions.Here we use the NCAR Community Model Large Ensemble (CESM-LE) composing the historical period (1920-2005) to the future (2006-2100) in a RCP8.5 scenario to measure the role of the internal variability in shaping AO variability and changes. Potential predictability of the AO index is quantified in the historical and future periods, evidencing how the members spread remain large without specific trends in these characteristics. Preliminary results indicate that the internal variability has large influence on the AO changes and related implications for the Northern Hemisphere climate.

Microplastic (MP) contamination is a critical environmental challenge with a strong impact on the ecosystems, economy and potentially for human health. The smaller the MP size, the greater is the environmental risks as well as the analytical difficulties in detecting and characterising the particles. .We propose a rapid near infrared hyperspectral imaging (NIR-HSI) method that enables the chemical identification and characterisation of small MP (down to 80 μm) in aquatic samples, directly on filters, with no pre-sorting step needed. By considerably reducing the procedural steps, the time of analysis and costs our method addresses the urgent need of cost-effective and robust tools for extensive monitoring of MP in natural systems.

Although fabrication of colorimetric paper-based analytical devices (PADs) has drawn increasing attention recently, the signal readout method is still a crucial challenge on the way to practical exploitations. We herein introduce an integration of digital image processing with a model PAD for easing and improving the signal readout procedure. The colorimetric detection mechanism of PAD relies on in-situ induced yellowish silver nanoparticles via the interaction of silver ions, poly(vinyl alcohol), and ammonia with isoniazid. The observed color value is related with the concentration of isoniazid. The proposed algorithm is based on mathematical morphology recognition, and minimizes the errors arising from manual area selection. Besides, it allows the recognition of both circle and square shapes in 96-well plate and A4 size array designs. Since this algorithm automatically provides the blank-corrected numerical matrixes and image profiles of red, green and blue channels, further investigations such as outlier classification, construction of regression/prediction models and calculation of detection limit can be easily performed. Comparison of signal readout results of the developed algorithm with ImageJ software demonstrates significant improvements in analysis speed, reproducibility, accuracy and color values. Therefore, application of the proposed algorithm is promising as a robust technique for practical applications.

In this paper we present a novel combined electrochemical-spectroscopic approach suitable to monitor trace levels of heavy metals directly in edible oils. The method is based on the electrochemical preconcentration/extraction of the analyte from the tested real matrix by cathodic deposition onto a Pt working electrode, then transfer and anodic re-oxidation of the metallic deposit to a “clean” aqueous solution, suitable for the subsequent spectroscopic analysis. The procedure has been here focused to the determination of lead in extra virgin olive oil (EVOO), performed by applying ICP-QMS or GFAAS techniques. To this aim, the EVOO samples were mixed with proper amounts of the room temperature ionic liquid (RTIL) [P14,6,6,6]+[NTf2]-, in order to obtain a non-aqueous supporting electrolyte suitable for the electrodeposition process. The feasibility and performance of the analytical strategy were at first tested in standard solutions of Pb(II) in RTIL, produced by anodic dissolution of lead in the RTIL, as well as in olive oil samples mixed with 0.5 M RTIL and spiked with known amounts of Pb(II). The optimisation of the electrochemical parameters was achieved by applying a D-Optimal Design, properly set up to optimise the efficiency of the deposition and re-oxidation steps, quantitative recovery and measurement time. Finally, the analytical procedure was applied to the determination of Pb content in some Italian EVOOs, without any need of performing mineralization pretreatments. Data obtained with the proposed procedure satisfactorily agree with those achieved by ICP-QMS analysis after microwave digestion, being differences between the two approaches within 10%, with the advantage of reducing to half the pretreatment time, operating at room temperature and avoiding the use of aggressive solvents.

Although microfluidic paper-based analytical devices (μPADs) get a lot of attention in the scientific literature, they rarely reach the level of commercialization. One possible reason for this is a lack of application of machine learning techniques supporting the design, optimization and fabrication of such devices. This work demonstrates the potential of two chemometric techniques including design of experiments (DoE) and digital image processing to support the production of μPADs. On the example of a simple colorimetric assay for isoniazid relying on the protonation equilibrium of methyl orange, the experimental conditions were optimized using a D-optimal design (DO) and the impact of multiple factors on the μPAD response was investigated. In addition, this work demonstrates the impact of automatic image processing on accelerating color value analysis and on minimizing errors caused by manual detection area selection. The employed algorithm is based on morphological recognition and allows the analysis of RGB (red, green, and blue) values in a repeatable way. In our belief, DoE and digital image processing methodologies are keys to overcome some of the remaining weaknesses in μPAD development to facilitate their future market entry.

Data mining is usually the last, but not for this less important, step of any food analysis process. It rather represents a critical phase: in fact, a proper data processing allows the extraction of useful information about the system under study from large amounts of collected data—and getting information is usually the main objective in analytical chemistry. The classical univariate approach, which considers one variable at a time, underutilizes the global data structure and offers just a partial image of it. Instead, multivariate strategies allow a more complete interpretation of data and exploitation of the information contained therein. Multivariate techniques can be used both for exploratory purposes and for qualitative or quantitative modeling. Generally, modeling is performed for predictive applications: in such cases, a thorough model validation is always required.

The importance of detecting minute biological traces in forensic evidences feeds the continuous interest towards the development of new dedicated technologies both sensitive and reliable. The present study describes the opportunity to combine chemical properties derived from NIR signals with spatial features typical of RGB images by means of hyperspectral imaging (HSI). An analytical procedure based on HSI data collection and their multivariate processing followed by normalized difference images (NDI) is proposed as a screening method to highlight otherwise invisible traces of biological fluids on different supports in view of their collection for DNA analysis. The pattern features identified inside the NDI provided insight into the nature of the biological trace, on the basis of the wavelength at which the stain is highlighted and irrespective of the support on which the stain is deposited. In particular, the procedure allowed to detect and distinguish traces (i.e., 10 and 20 μL volumes) of dehydrated blood, urine, and semen on glass, paper, cotton, denim and polyblend fabric. Beside the simulated specimens used to develop and test the protocol, its robustness was demonstrated also on real and unknown validation samples, confirming its feasibility in some real case studies. An interesting evolution of the proposed strategy is to lay the scientific foundations for the development of a handheld device directly applicable in field.

For the processing industry, it is crucial to know what effect the roasting process and conditions have on hazelnut quality. The present study investigates, for the first time, the effects of hot-air and infrared (IR) roasting at different time-temperature combinations on Tonda Gentile Trilobata hazelnut: whole kernels and derived processing products (paste and oil).; Results: The nutritional and physical characteristics of hazelnuts and processing products were investigated to determine the influence of the different roasting conditions as a function of intended use. The antioxidant profile (2.2-diphenyl-1-picrylhydrazyl radical, oxygen radical absorbance capacity and total phenolic content) were analyzed on roasted hazelnut and paste extracts. For a comprehensive understanding of the complex biochemical phenomena occurring during roasting, E-nose and near-IR spectroscopy were also applied. All analytical data were processed using univariate and multivariate data analyses. Hazelnuts derived from IR roasting at higher temperatures (195 °C) showed a richer antioxidant profile and a more intense flavour. On the other hand, the yield associated with the oil extraction under the same conditions was unsatisfactory, making this process completely inadequate for oil production. Oil obtained by hot-air roasting and IR roasting at lower temperature (135 °C) was found to be of good quality, showing rather similar acidity grade, peroxide number and acidic composition. In particular, a slightly but significantly lower acidity was related to lower roasting temperatures (0.21-0.22% versus 0.27% for higher temperatures). All roasting conditions tested allowed the quantitative homogeneous hazelnut paste to be obtained and, from a rheological point of view, a higher roasting temperatures resulted in pastes characterized by higher density and viscosity values.; Conclusion: The use of IR was found to be a promising alternative method for hazelnut roasting, as a result of its capability with respect to preserving nutritional values and enhancing organoleptic quality. © 2018 Society of Chemical Industry.; © 2018 Society of Chemical Industry.

In the field of applied researches in heritage science, the use of multivariate approach is still quite limited and often chemometric results obtained are often underinterpreted. Within this scenario, the present paper is aimed at disseminating the use of suitable multivariate methodologies and proposes a procedural workflow applied on a representative group of case studies, of considerable importance for conservation purposes, as a sort of guideline on the processing and on the interpretation of this FTIR data. Initially, principal component analysis (PCA) is performed and the score values are converted into chemical maps. Successively, the brushing approach is applied, demonstrating its usefulness for a deep understanding of the relationships between the multivariate map and PC score space, as well as for the identification of the spectral bands mainly involved in the definition of each area localised within the score maps.

In this paper, a novel voltammetric/amperometric approach for the direct determination of free acidity (FFA, expressed as mass percentage of free oleic acid) in olive oil samples is presented. The method is based on the reduction processes occurring at a platinum microdisk electrode involving the free fatty acids present in the matrices. To overcome problems related to the low conductivity of the samples investigated, olive oils were mixed with suitable amounts of the room temperature ionic liquid (RTIL), tri-hexyl(tetradecyl)phosphonium bis (trifluoromethylsulfonyl) imide ([P14,6,6,6]+[NTf2]-), which acted as a supporting electrolyte. Conditions for a reliable quantification of the acids were preliminarily investigated by performing voltammetric and chronoamperometric measurements in RTIL solutions containing oleic acid at different concentrations. Oleic acid (OA) was chosen as a model compound as it is the main component of the FFA content in olive oils. In order to establish the effect of oxygen on the electroanalytical responses, the reduction process of OA was investigated under both deoxygenated and oxygenated conditions. It was found that, in both situations, the current arising from the electrode process of OA depended linearly on the OA concentration over a wide range varying from 0.1% to 8% OA (w/w). This range includes FFA values which can be found on all categories of commercially available oil samples, including extra-virgin, virgin and lampante oils. Voltammetric and chronoamperometric experiments were also performed in oil/RTIL samples artificially acidified (extra-virgin olive oils with known addition of oleic acid) and in natural olive oils from some commercial categories. The results obtained indicated that the electrochemical procedure developed was satisfactory in terms of both sensitivity and detection limits. The reliability of the proposed approach for the detection of FFA was finally assessed by comparison of the voltammetric/chronoamperometric values with those obtained by the official method for quantification of olive oil acidity, which is an acid/base volumetric titration.

This chapter will be devoted to chemometrics in the multivariate design of the experiments and in data treatment in gas chromatography. Basic principles will be given on the most commonly used approaches, along with applications in the field of one-dimensional and multidimensional gas chromatography. This chapter shows how suitable multivariate statistical methods can be of great support in specific food applications such as confirmation of authenticity, geographical origin, quality, evaluation of technological processes, and so on, exploiting its potential also in the omics field, such as “foodomics”.

Evaluating the possibility of extending shelf life of rice germ (a by-product of rice milling process) by reducing water activity in combination with storage atmosphere packaging, without any heat treatment, is the aim of the present study. Samples at different water activities (0.55, 0.45 and 0.36) were packed in air, argon or under vacuum, and stored at 27 °C for 150 days. To the aim, a non-targeted approach was applied by means of an FT-NIR spectrometer in reflectance with a rotating sample holder and a portable electronic nose, equipped with 10 non-specific sensors. For understanding the impact of the factors under study on the rice germ shelf life, a modified mid-level data fusion approach was applied to enhance the information most correlated with time. Moreover, Principal Component Analysis was applied on fused data to follow samples evolution during storage and identify different clusters according to the storage conditions. The rice germ case study allowed to better understand the information captured by the non-specific sensors: a 2D correlation map was developed combining the e-nose data with the NIR spectral information, highlighting relationships among NIR absorption bands and classes of chemical compounds inducing e-nose responses. A data fusion approach highlighted the importance of water activity on rice germ storage, while no interesting differences were ascribable to storage atmosphere packaging systems. In terms of correlation, the sensors could be divided in two groups, negatively inter-correlated: sensors ascribable to aromatic compounds (WC) and correlated with the NIR band around 4800–4900 cm−1 (N–H bending of primary amides, typical for peptides coming from protein hydrolysis); broad-range response sensors (WS), linked with the NIR band at 5128 cm−1 (second overtone of C O stretching of esters).

This study represents the first attempt to combine mid infrared (MIR) spectroscopy and multivariate data processing for prediction of alcohol degree, sugars content and total acidity in straw wine. 302 Italian samples, representing different vintages, production regions and grape varieties, were analysed using FT-MIR spectroscopy and reference methods. New regression functions based on a combination of Orthogonal Signal Correction and Partial Least Squares regression are proposed for prediction of quality parameters: this approach allows overcoming the issue of matrix complexity, reducing spectral interferences and enhancing the information embodied in fingerprinting data. The models proposed are characterised by an excellent reliability, with low error in prediction (alcohol: 0.28%; sugars: 9.9 g/L; acidity: 0.29 g/L) comparable both to reference methods and table wine models. Results demonstrate that vibrational spectroscopy, combined with a proper multivariate data strategy, represents a suitable strategy for the quick and non-destructive assessment of quality parameters of straw wine.

In the present study, an advanced and original multivariate strategy for the processing of hyperspectral images in the near-infrared region is proposed to automatically detect physico-chemical defects in green coffee, which are similar one to each other by naked eye. An object-based approach for the characterization of individual beans, rather than single pixels, was adopted, calculating a series of descriptive parameters characterizing the distribution of scores on the lowest-order principal components. On such parameters, the k-nearest neighbors (k-NN) classification algorithm was applied and the predictive results on the test samples indicate that this approach is able not only to distinguish defective beans from non-defective ones, but also to differentiate the various types of defects. Hyperspectral imaging is demonstrated to be a valid alternative for the sorting of green beans - a crucial phase for coffee import/export.

Thirty-three Byzantine copper-based coins (so-called anonymous follis) from 970 to 1085 CE were investigated in order to determine the elemental composition both of the bulk and of the surface. The ancient coins analysed - belonging to the Thomas Bertele Collection, now located in the Bottacin Museum of Padova (Italy) - were studied by way of physical, chemical and chemometric testing techniques. This study aims to obtain information on socio-economic aspects of the Byzantine Empire using chemical and physical data of currency as indirect indicators. First, the weight, diameter and thickness of each piece were measured, then a micro-Energy Dispersive X-ray Fluorescence (μ-EDXRF) technique was applied on a cleaned and polished area of the surface of the coin to evaluate the presence of minor elements in a non-destructive fashion. A micro-destructive sampling method for both surface and bulk samples, based on mechanically drilling the edge, was finally employed, and Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES) was used for the quantitative determination of Cu (the major alloying element), Pb, As, Fe, Zn, Ag, Ni, Sn and Mn (minor elements) in the powder which resulted from drilling each coin, previously dissolved via aqua regia dissolution technique overnight at room temperature. The average copper concentration of the folles was 95.9 ± 3.1% for the bulk and 91.7 ± 3.5% for the surface. The Pb-Cu ratio appears to be an index which can be used to discriminate between different coins, in particular for Class B (from 1030 to 1042 CE), as confirmed by exploratory chemometric data analysis. According to our results, all the anonymous Byzantine folles under examination were minted in much the same way and using the same mineral raw materials. Using the chemical data measured on the bulk, we can grasp that the monetary choice of the period under study excluded silver, gold and bronze (concentrations of Pb is low, while Sn resulted undetectable or unquantifiable) to turn, substantially, to a copper-only coinage (deducted the impurities given by the gangue of the starting minerals). Moreover, along the observed period of 120 years, the mass of the coins was significantly reduced (with an overall loss of material of approximately 60%).

Cheese represents one of the most complex food matrices, for the high number of factors contributing to the chemical composition, and so its evaluation represents an important analytical challenge. The present study describes an innovative and non-destructive analytical approach, based on hyperspectral imaging in the near-infrared region (HSI-NIR) and multivariate pattern recognition, to study and monitor the extent - spatial and temporal - of biochemical phenomena responsible for cheese ripening. NIR spectral bands characterising dehydration, proteolysis and lipolysis were individuated and studied by exploiting a representative sample set of characteristic cheeses. The information obtained was employed to develop score maps based on principal component analysis (PCA), which permitted to monitor and visualise the ripening of Formaggetta, a commercial semi-hard cheese typical of Liguria, an Italian region, providing a deep understanding of the evolution of dehydration, proteolysis and lipolysis during the maturation period that precedes the placing on the market.

Boletus edulis and allied species (BEAS), known as "porcini mushrooms", represent almost the totality of wild mushrooms placed on the Italian market, both fresh and dehydrated. Furthermore, considerable amounts of these dried fungi are imported from China. The presence of Tylopilus spp. and other extraneous species (i.e., species edible but not belonging to BEAS) within dried porcini mushrooms - mainly from those imported from China and sold in Italy - may represent an evaluable problem from a commercial point of view. The purpose of the present study is to evaluate near-infrared spectroscopy (NIRS) as a rapid and effective alternative to classical methods for identifying extraneous species within dried porcini batches and detecting related commercial frauds. To this goal, 80 dried fungi including BEAS, Tylopilus spp., and Boletus violaceofuscus were analysed by NIRS. For each sample, 3 different parts of the pileus (pileipellis, flesh and hymenium) were analysed and a low-level strategy for data fusion, consisting of combining the signals obtained by the different parts before data processing, was applied. Then, NIR spectra were used to develop reliable and efficient class-models using a novel method, partial least squares density modelling (PLS-DM), and the two most commonly used class-modelling techniques, UNEQ and SIMCA. The results showed that NIR spectroscopy coupled with chemometric class-modelling technique can be suggested as an effective analytical strategy to check the authenticity of dried BEAS mushrooms.

The potential of FT-NIR and FT-Raman spectroscopies to characterise the gum applied on the backside of ancient stamps was investigated for the first time. This represents a very critical issue for the collectors' market, since gum conditions heavily influence stamp quotations, and fraudulent application of synthetic gum onto damaged stamp backsides to increase their desirability is a well-documented practice. Spectral data were processed by exploratory pattern recognition tools. In particular, application of principal component analysis (PCA) revealed that both of the spectroscopic techniques provide information useful to characterise stamp gum. Examination of PCA loadings and their chemical interpretation confirmed the robustness of the outcomes. Fusion of FT-NIR and FT-Raman spectral data was performed, following both a low-level and a mid-level procedure. The results were critically compared with those obtained separately for the two spectroscopic techniques.

UNEQ is a parametric probabilisic class-modelling technique that makes use of the Hotelling's T2 distribution and can be considered as the modelling version of quadratic discriminant analysis. The class of interest is described by an elliptical space built around the barycentre of training data points of the class, namely the centroid vector. Orientation and eccentricity of the elliptical class space, respectively, describe correlation between the variables and their dispersion, while the width of the class space is determined according to the critical value of Hotelling’s T2 statistics at a pre-determined confidence level.

In the present study, a chemometric strategy was deployed for comparing the analytical performances of quartz cuvettes and disposable glass vials in the analysis of olive oil by near infrared spectroscopy (NIRS). Nowadays, laboratories that perform ‘highly frequent’ analysis on extra virgin olive oil (EVOO) by NIRS usually employ quartz cuvettes. This results in time-consuming measurements, especially in the cleaning phase, and an increased cost for non-green cleaning solvents. The use of mono-use glass vials may reduce time and costs significantly, but their analytical performances in EVOO analysis, have not yet been investigated. In order to reach this goal, a set of 106 EVOO samples from different Italian olive-growing areas were collected and analysed using both quartz cuvettes and mono-use glass vials. From spectral data, multivariate calibration models were developed to estimate quality parameters of extra virgin olive oil: methyl esters of fatty acids (FAMEs) and triacylglycerols (TAGs) determined by a fast-GC approach and an UHPLC system, respectively. An optimisation procedure was performed in order to individuate the best pre-treatment and the optimal complexity for each model. The predictive ability of each PLS model was evaluated on an independent test set. The Passing-Bablok linear regression was lastly used to statistically compare the performances of the two different types of cuvettes. In light of the outcomes of the present study, analytical performances of quartz cuvettes and disposable glass vials were considered not significantly different in predicting the olive oil quality parameters taken into account.

The present study describes a multivariate strategy that can be used for automatic on-site processing of reflection mode macro FTIR mapping (MA-rFTIR) data obtained during investigation of artworks. The chemometric strategy is based on the integration of principal component analysis (PCA) with a clustering approach in the space subtended by the three lowest-order principal components and allows to automatically identify the regions of interest (ROIs) of the area scanned and to extract the average FTIR spectra related to each ROI. Thanks to the automatic data management, in-field HSI (hyperspectral imaging)-based analyses may be performed even by staff lacking specific advanced chemometric expertise, as it is sometimes the case for conservation scientists or conservators with a scientific background. MA-rFTIR was only recently introduced in the conservation field and, in this work the technique was employed to characterize the surface of metallic artefacts. The analytical protocol was employed as part of a rapid procedure to evaluate the conservation state and the performance of cleaning methods on bronze objects. Both activities are commonly part of restoration campaigns of bronzes and require an on-site analytical procedure for efficient and effective diagnosis. The performance of the method was first evaluated on aged standard samples (bronzes with a layer of green basic copper hydroxysulphate, treated with different organic coatings) and then scrutinized in situ on areas of the 16th century Neptune fountain statue (Piazza del Nettuno, Bologna, Italy) by Gianbologna.

The present tutorial paper is aimed to analyse and critically discuss the consequences of row pre-processing (conversion of measurement units, derivatives, and standard normal variate transform) on the evaluation of final outcomes of chemometric data analysis. An in-depth focus on pre-processing effects both on the signal shape and on misinterpretation of results – a crucial and disregarded issue in the analytical field – is presented. It is shown how this preliminary step of data processing may lead, in many cases, to draw incongruous conclusions, not actually based on real information embodied within data, but on artefacts arising from the mathematical transforms. This tutorial is not limited to a description of the problem, it also introduces strategies and tools for overcoming such unwanted effects, allowing a direct interpretation of the importance of original variables to be performed, explaining the chemical information that actually characterises samples. The dangerous implications of row pre-processing on instrumental signals is demonstrated on real datasets coming from different analytical techniques: transmission and attenuated total reflection infrared spectroscopy, cyclic voltammetry, X-ray fluorescence spectroscopy, Raman spectroscopy, and ultraviolet–visible spectroscopy. Hence, the impact of this widespread problem in most of the branches of analytical chemistry is illustrated.

In this paper, an approach for the detection of extra-virgin olive oil (EVOO) free-acidity, based on combination of voltammetric profiles (Voltammetry) and Partial Least Squares (PLS) multivariate regression, is described. Voltammetric measurements are performed with a 12.5 μm radius platinum microdisk, directly in the oil samples mixed with 0.5 M of the room temperature ionic liquid (RTIL) tri-hexyl(tetradecyl)phosphonium bis(trifluoromethylsulfonyl)imide, which acted as a supporting electrolyte, and allows achieving a suitable conductivity in the matrices. Multivariate regression is performed directly on full voltammetric responses recorded over a properly chosen negative potential range and scan rate, where essentially all free fatty acids, characterizing EVOOs, can be reproducibly reduced. PLS regression models are built by employing Italian EVOO samples training sets at different acidity levels (over the range 0.2% w/w - 1.5% w/w; (% w/w) represents mass percentage) and optimized by choosing the optimal complexity, in terms of number of latent variables (LVs). The free-acidity prediction is made through a multivariate model, constructed by using standards of known acidity (determined by the official volumetric titration method) and validated on an external sample set. To show the validity of the proposed method, the PLS/Voltammetry predictions of the free-acidity of a series of commercially available Italian EVOOs, ranging from 0.2 to 0.41 %w/w, are obtained and the values compared with those determined by the official titration approach. Differences found between the two methods are within 5% RSD.

This chapter discusses a deep understanding of the correct approaches for applying exploratory principal component analysis (PCA) on hyperspectral data, which is a fundamental step for every multivariate data processing protocol. Since PCA is applicable only on two-dimensional data matrices, hypercubes have to be reorganized in a bi-dimensional structure prior to their processing. When a hyperspectral image is submitted to multivariate data processing, including PCA, several approaches can be applied, depending not only on the type of data but also on the specific task that has to be achieved. In a multivariate system, such as hyperspectral images, it is fundamental to deal with methods that allow exploration of the data structure in a visual way understanding typical characteristics, in terms of sample groupings and variable intercorrelations. Exploratory methods belong to the family of unsupervised strategies in which a priori information about sample identity is neither required nor used for building models.