Nuova ricerca

LORENZO STRANI

Assegnista di ricerca
Dipartimento di Scienze Chimiche e Geologiche - Sede Dipartimento di Scienze Chimiche e Geologiche

Home | Didattica |


Pubblicazioni

2024 - Iron nuclearity in mineral fibres: Unravelling the catalytic activity for predictive modelling of toxicity [Articolo su rivista]
Gualtieri, Alessandro F.; Cocchi, Marina; Muniz-Miranda, Francesco; Pedone, Alfonso; Castellini, Elena; Strani, Lorenzo
abstract

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

2023 - A Feasibility Study towards the On-Line Quality Assessment of Pesto Sauce Production by NIR and Chemometrics [Articolo su rivista]
Tanzilli, Daniele; D'Alessandro, Alessandro; Tamelli, Samuele; Durante, Caterina; Cocchi, Marina; Strani, Lorenzo
abstract

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

2023 - Exploring the Effect of Different Storage Conditions on the Aroma Profile of Bread by Using Arrow-SPME GC-MS and Chemometrics [Articolo su rivista]
Pellacani, Samuele; Cocchi, Marina; Durante, Caterina; Strani, Lorenzo
abstract

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

2023 - Near Infrared and UV-Visible Spectroscopy Coupled with Chemometrics for the Characterization of Flours from Different Starch Origins [Articolo su rivista]
Pellacani, Samuele; Borsari, Marco; Cocchi, Marina; D'Alessandro, Alessandro; Durante, Caterina; Farioli, Giulia; Strani, Lorenzo
abstract

2023 - Optimization of an analytical method based on SPME-Arrow and chemometrics for the characterization of the aroma profile of commercial bread [Articolo su rivista]
Pellacani, Samuele; Durante, Caterina; Celli, Silvia; Mariani, Manuel; Marchetti, Andrea; Cocchi, Marina; Strani, Lorenzo
abstract

A SPME-Arrow GC-MS approach, coupled with chemometrics, was used to thoroughly investigate the impact of different types of yeast (sourdough, bear's yeast and a mixture of both) and their respective leaving time (one, three and five hours) on VOCs of commercial bread samples. This aspect is of paramount importance for the baking industry to adjust recipe modifications and production parameters, as well as to meet consumer needs in formulating new products. A deep learning approach, PARADISe (PARAFAC2-based deconvolution and identification system), was used to analyse the obtained chromatograms in an untargeted manner. In particular, PARADISe, was able to perform a fast deconvolution of the chromatographic peaks directly from raw chromatographic data to allow a putatively identification of 66 volatile organic compounds, including alcohols, esters, carboxylic acids, ketones, aldehydes. Finally, Principal Component Analysis, applied on the areas of the resolved compounds, showed that bread samples differentiate according to their recipe and highlighted the most relevant volatile compounds responsible for the observed differences.

2023 - Real Time Quality Assessment of General Purpose Polystyrene (GPPS) by means of Multiblock-PLS Applied on On-line Sensors Data [Articolo su rivista]
Strani, Lorenzo; Bonacini, Francesco; Ferrando, Angelo; Perolo, Andrea; Tanzilli, Daniele; Vitale, Raffaele; Cocchi, Marina
abstract

In the petrochemical industry, in order to control the final product quality over time and to detect potential plant failures, the amount of lab (off-line) analysis performed every day is very demanding in terms of resources and time. Hence, at/in-line monitoring can be an efficient solution to decrease chemical wastes and operators’ efforts and to perform a fast detection of deviations from normal operative conditions. Moving toward this implementation requires both installation of analytical sensors and the development of models capable to predict in real time the quality parameters of the polymers based on both process and analytical sensors. The primary aim of the current work has been the development of real time monitoring models by advanced chemometric tools for the prediction of a General Purpose PolyStyrene (GPPS) quality property, fusing Near Infrared (NIR) and process sensors data. In the plant considered, in addition to standard process sensors, along the GPPS production line, operating in continuous, two NIR probes are installed in-line. After the arrangement of the available data in different blocks, aiming at studying the specific contribution of the two types of sensors and of the main phases of the process, Multiblock-PLS (MB-PLS) method was employed to fuse the different blocks and to assess which were the most relevant sensors and plant phases for the prediction of the two quality parameters. Good prediction performances were achieved, allowing identifying the most significant data blocks for the GPPS quality prediction. Moreover, prediction errors obtained by models computed without considering blocks of data belonging to the final stages of the process were similar to those involving all the available data blocks. Therefore, a good real time assessment of the GPPS quality can be obtained even before the production is completed, which is very promising in view of minimizing the number of off-line laboratory analyses

2023 - Toward the Non-Targeted Detection of Adulterated Virgin Olive Oil with Edible Oils via FTIR Spectroscopy & Chemometrics: Research Methodology Trends, Gaps and Future Perspectives [Articolo su rivista]
Ordoudi, S. A.; Strani, L.; Cocchi, M.
abstract

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.

2023 - Tracing the identity of Parmigiano Reggiano “Prodotto di Montagna - Progetto Territorio” cheese using NMR spectroscopy and multivariate data analysis [Articolo su rivista]
Cavallini, N.; Strani, L.; Becchi, P. P.; Pizzamiglio, V.; Michelini, S.; Savorani, F.; Cocchi, M.; Durante, C.
abstract

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.

2022 - A Multiblock Approach to Fuse Process and Near-Infrared Sensors for On-Line Prediction of Polymer Properties [Articolo su rivista]
Strani, Lorenzo; Vitale, Raffaele; Tanzilli, Daniele; Bonacini, Francesco; Perolo, Andrea; Mantovani, Erik; Ferrando, Angelo; Cocchi, Marina
abstract

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

2022 - Candying process for enhancing pre-waste watermelon rinds to increase food sustainability [Articolo su rivista]
Maletti, Laura; D'Eusanio, Veronica; Lancellotti, Lisa; Marchetti, Andrea; Pincelli, Luca; Strani, Lorenzo; Tassi, Lorenzo
abstract

This work describes two alternative laboratory methods ’candying fruit’ methods of fresh mesocarp of Crimson sweet watermelon, a typical waste and unappetizing material. Our experimental candying process was conducted by slow osmosis, lasting 24 weeks at room temperature. It was activated with granular sucrose according to our two alternative laboratory methods, WET and DRY. Fresh watermelon rinds were transformed into candied fruit with excellent flavor and aromas. The aromatic profile of all the materials was characterized with HS-SPME-GC– MS technique. The results highlighted some significant differences in the Volatile Organic Compounds fraction, probably attributable both to the cultivar and to the two candying methods, as verified also by a panel test. The class of alcohols remains almost constant in all the samples. Terpenoids are highly present in FRESH samples, while they disappear in DRY candied ones. Acetate esters are absent in FRESH rinds, they reach the maximum level in WET, and stop at the middle in DRY samples. The trend of the values relating to the class of acids is opposite: absent in the FRESH aromatic profile, maximum and average for DRY and WET samples, respectively.

2022 - Fast GC E-Nose and Chemometrics for the Rapid Assessment of Basil Aroma [Articolo su rivista]
Strani, Lorenzo; D'Alessandro, Alessandro; Ballestrieri, Daniele; Durante, Caterina; Cocchi, Marina
abstract

The aim of this work is to assess the potentialities of the synergistic combination of an ultra-fast chromatography-based electronic nose as a fingerprinting technique and multivariate data analysis in the context of food quality control and to investigate the influence of some factors, i.e., basil variety, cut, and year of crop, in the final aroma of the samples. A low = level data fusion approach coupled with Principal Component Analysis (PCA) and ANOVA—Simultaneous Component Analysis (ASCA) was used in order to analyze the chromatographic signals acquired with two different columns (MXT-5 and MXT-1701). While the PCA analysis results highlighted the peculiarity of some basil varieties, differing either by a higher concentration of some of the detected chemical compounds or by the presence of different compounds, the ASCA analysis pointed out that variety and year are the most relevant effects, and also confirmed the results of previous investigations.

2021 - Characterization of Basil Volatile Fraction and Study of its Agronomic Variation by ASCA [Articolo su rivista]
D'Alessandro, Alessandro; Ballestrieri, Daniele; Strani, Lorenzo; Cocchi, Marina; Durante, Caterina
abstract

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

2021 - Fusing NIR and Process Sensors Data for Polymer Production Monitoring [Articolo su rivista]
Strani, Lorenzo; Mantovani, Erik; Bonacini, Francesco; Marini, Federico; Cocchi, Marina
abstract

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.