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 ragù 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.

Implementing multiblock techniques in a full-scale plant scenario: On-line prediction of quality parameters in a continuous process for different acrylonitrile butadiene styrene (ABS) products.

BACKGROUND: The study explores the challenges of handling multiblock data of different natures (process and NIR sensors) for on-line quality prediction in a full-scale plant scenario, namely a plant operating in continuous on an industrial scale and producing different grade Acrylonitrile Butadiene Styrene (ABS) products. This environment is an ideal scenario to evaluate the use of multiblock data analysis methods, which can enhance data interpretation, visualization, and predictive performances. In particular, a novel multiblock extension of Locally Weighted PLS has been proposed by the authors, namely Locally Weighted Multiblock Partial Least Squares (LW-MB-PLS). Response-Oriented Sequential Alternation (ROSA) has also been employed to evaluate the diverse block relevance for the prediction of two quality parameters associated with the polymer. Data are split in blocks both according to sensor type and different plant sections, and different models have been built by incremental addition of data blocks to evaluate if early estimation of product quality is feasible. RESULTS: ROSA method showed promising predictive performance for both quality parameters, highlighting the most influential plant sections through the selection of data blocks. The results suggested that both early and late-stage sensors play crucial roles in predicting product quality. A reasonable estimation of quality parameters before production completion has been achieved. On the other hand, the proposed LW-MB-PLS, while comparable in predictive performances, allowed reducing systematic prediction errors for specific products. SIGNIFICANCE: This study contributes valuable insights for continuous production processes, aiding plant operators and paving the way for advancements in online quality prediction and control. Furthermore, it is implemented as a locally weighted extension of MB-PLS.

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

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

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

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

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.

A metabolomic data fusion approach to support gliomas grading.

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

Optimization of a Dynamic Headspace-Thermal Desorption-Gas Chromatography/Mass Spectrometry procedure for the determination of furfurals in vinegars.

The use of a Dynamic Headspace System (DHS) device combined with a Thermal Desorption Unit (TDU) interfaced to a Gas Chromatography/Mass Spectrometry (GC/MS) system is proposed for the determination of furfurals in oenological products. An experimental design protocol has been employed for the optimization of the instrumental settings concerning DHS and TDU extraction and desorption steps. It has been possible to individuate the following optimized conditions: incubation temperature 40°C, purge volume 800 mL, dry volume 1500 mL, TDU hold time 5 min and incubation time 10 min. The performance of two different SPE sorbents, namely Tenax TA and Tenax GR used for the furfurals trapping, was investigated too. The developed DHS sampling procedure showed good reproducibility values with a RSD% lower than 10% for all the monitored species. The optimized experimental settings have been used to determine furfurals in several vinegar samples obtained by traditional procedure starting from cooked grape musts, i.e. in Aceto Balsamico Tradizionale di Modena (ABTM). In fact, the control of these species is extremely important for quality and safety issues.

Discrimination of healthy and neoplastic human colon tissues by ex vivo HR-MAS NMR spectroscopy and chemometric analyses.

The metabolic profile of human healthy and neoplastic colorectal tissues was obtained using ex vivo High-Resolution Magic Angle Spinning (HR-MAS) NMR spectroscopy. Principal Components Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were applied to NMR data in order to highlight the biochemical differences between healthy and neoplastic colorectal tissues. The synergic combination of ex vivo HR-MAS NMR spectroscopy with Multivariate Data Analysis enables discrimination between healthy and tumoral colorectal tissues and identification of the increase of taurine, acetate, lactate, and lipids, and the decrease of polyols and sugars as tumoral characteristics. Moreover, it was found that macroscopically/histologically normal colorectal tissues, collected at least 15 cm from the adenocarcinoma, are characterized by a metabolic pattern quite similar to that typical of tumoral lesions. It was shown that ex vivo HR-MAS NMR spectroscopy, performed on intact specimens, may be of great potentiality in the clinical evaluation of human neoplastic colorectal tissues and that the biochemical data represent the molecular basis for an accurate and noninvasive clinical applications of in vivo NMR spectroscopy.

Subject classification obtained by cluster analysis and principal component analysis applied to flow cytometric data.

BACKGROUND: Polychromatic flow cytometry (PFC) allows the simultaneous determination of multiple antigens in the same cell, resulting in the generation of a high number of subsets. As a consequence, data analysis is the main difficulty with this technology. Here we show the use of cluster analysis (CA) and principal component analyses (PCA) to simplify multicolor data visualization and to allow subjects' classification. METHODS: By eight-colour cytofluorimetric analysis, we investigated the T cell compartment in donors of different age (young, middle-aged, and centenarians). T cell subsets were identified by combining positive and negative expression of antigens. The resulting data set was organized into a matrix and subjected to CA and PCA. RESULTS: CA clustered people of different ages on the basis of cytofluorimetric profile. PCA of the cellular subsets identified centenarians within a different cluster from young donors, while middle-aged donors were scattered between these groups. These approaches identified T cell phenotypes that changed with increasing age. In young donors, memory T cell subsets tended to be CD127+ and CD95- whereas CD127-, CD95+ phenotypes were found at higher frequencies in people with advanced age. CONCLUSIONS: Our data suggest the use of bioinformatic approaches to analyze large data-sets generated by PFC and to obtain the rapid identification of key populations that best characterize a group of subjects.