On the role of surface functional groups in Pt carbon interaction

The interaction between platinum crystallites and surface functional groups of carbon in a homologously prepared series of Pt/C catalysts for phosphoric acid fuel cell (PAFC) applications has been studied by X-ray photoelectron spectroscopy (XPS) and potentiometric titration techniques. It has been found that the platinum surface area depends on the amount of oxygenated groups on the carbon support. In addition, relationships between the platinum electroactive surface area and the acid-base nature of support functionalities have been found. The carbon support functional groups have been shown to affect the electronic nature of the platinum states.     

Influence of olive cultivars and period of harvest on the contents of Cu,Cd, Pb,and Zn in virgin olive oils

Levels of four of the major pollutant heavy metals were assessed by ICP-OES in virgin olive oil monocultivar samples. The data showed high variability within cultivars for lead and zinc, whereas, for cadmium and copper, no statistical difference was observed. The influence of the cultivar and the stage of ripening of olives on heavy metal content was assessed; zinc was the only metal with a great variability within the first and the second harvest. All olive samples were processed with the same milling apparatus.     

Combined approach to the analysis of recombinant protein drugs using hollow-fiber flow field-flow fractionation, mass spectrometry, and chemiluminescence detection

The impurities present in recombinant protein drugs produced by large-scale refolding processes can not only affect the product safety but also interact with the expressed protein. To relate the impurity profile to conformation and functionality of the protein drug, analytical methods able not to degrade the sample components should be preferred. In this work, an urate oxidase (uricase) drug from Aspergillus flavus expressed in Saccharomyces cerevisiae, and a reagent-grade uricase from Candida sphaerica expressed in Escherichia coli, are analyzed by combining hollow-fiber flow field-flow fractionation with matrix-assisted laser desorption-ionization time-of-flight mass spectrometry (MALDI/TOFMS) and with chemiluminescence enzyme activity assay. Preliminary detection and identification of sample impurities is performed by means of conventional methods such as RP HPLC with electrospray ionization quadrupole-TOF MS and MALDI/TOFMS with SDS PAGE and 2D SDS PAGE. Results show that the recombinant uricase samples obtained from different microorganisms have different impurities and different enzymatic activity and that different uricase oligomers are present in solution.     

Matrix modelling of small-scale trigeneration systems and application to operational optimization

Combined production of electricity, heat and cooling power in trigeneration represents a key option for the development of high-efficiency and cost-effective integrated energy systems. The complexity of the possible plant schemes calls for the adoption of general models handling multiple interconnected components and energy flows of various typologies. This paper presents a comprehensive input–output matrix approach aimed at modelling small-scale trigeneration equipment taking into account the interactions among plant components and external energy networks. Starting from the definitions of specific efficiency matrices for each plant component and from a matrix representation of the relevant interconnections, an overall efficiency matrix representing the whole plant is constructed. This construction is carried out by means of an original procedure, suitable for automatic and symbolic implementation, which, exploiting graph theory concepts, explores the tree formed by the backward paths from outputs to inputs. The proposed formulation maintains the separation among the individual energy vectors, each of which can be associated to its time-dependent price, providing the basic framework for formulating optimization problems concerning management of trigeneration systems within an energy market context. A numerical example referred to the optimal operation of a composite scheme with absorption and electric chillers is illustrated and discussed. The results obtained show the modelling effectiveness of the proposed matrix formulation.     

An Advanced Colour Calibration Method for Fish Freshness Assessment: a Comparison Between Standard and Passive Refrigeration Modalities

Freshness represents a pivotal aspect in fish product for both security and quality. Its evaluation still represents the key factor driving the consumer’ choices. Fish appearance is affected by many different factors that demand the contribution of different disciplines to be understood: from the physical and optical properties to the slaughtering and post-slaughtering conditions. An innovative preservation system is represented by the Passive Refrigeration PRS? developed for the preservation and transport of perishable food products. Scientific methods for product freshness evaluation may be conveniently divided into two categories: sensorial and instrumental. In this study, an instrumental method of colour calibration and discrimination is proposed at pilot scale for automatic evaluation of gilthead seabream (Sparus aurata) freshness. We propose a non-destructive method based on the colorimetric imaging of the whole external body of seabreams to evaluate through multivariate partial least squares which approach the differences in the freshness preservation under four refrigeration modalities. The matrix of the independent variables is represented by RGB values for each pixel belonging to an extracted region of interest (129,633 values). The dependent variable is composed by two dummy variable corresponding to fresh (T₀) or non-fresh (T₂) individuals. T₁ individuals were used as external test. The results quantified significant colorimetric differences between fresh and non-fresh fish. All fish used to create the model (T₀ and T₂) were correctly classified as fresh or non-fresh, while external test individuals (T₁) were all classified as fresh. The proposed imaging method merges different image analysis techniques: (a) colorimetric calibration, (b) morphometric superimposition and (c) partial least square discriminant analysis modelling. This innovative and non-destructive approach allows the automatic assessment of fish freshness.     

Non-destructive Estimation of Mandarin Maturity Status Through Portable VIS-NIR Spectrophotometer

Sugar content is one of the most important quality attributes of citrus fruit, either for fresh or for processing market. Since sugars in citrus juice are highly correlated with total soluble solids (TSS) content, which can be determined easily even by the means of a hand refractometer, TSS is one of the most frequently used quality index. Since TSS can be measured only destructively, the results are representative only if carried out on large samples and do not allow classifying marketable fruit one by one according to their specific sugar content. Objective of this experiment was to assess possibility and limits of a non-destructive estimation of citrus fruits internal quality parameters (TSS and titratable acidity) presenting thick peel by the use of a spectrophotometric portable VIS-NIR system. Four hundred fruit of “Miho” satsuma and 150 fruit of “Page” tangelo were used. Each fruit was first subjected to spectrophotometric acquisition and soon after was juiced and TSS and titratable acidity (TA) determined. Partial least squares (PLS) regression analysis was applied for constructing a predictive model based on the spectral normalized response, constructing the model on a sub-sample and verifying the model (prediction test) on independent ones. The TA relative to Page mandarin was predicted in the test with an r = 0.88 and a standard error of prevision (SEP) coefficient of variability of 3.8% while the TSS scored an r = 0.85 and a SEP coefficient of variability equal to 4%. The TA of Miho mandarin was predicted in the test with an r = 0.81 and a SEP coefficient of Variability of 8.3% while the TSS scored an r = 0.84 and a SEP coefficient of variability equal to 5.6%.     

Spectrophotometric Qualification of Italian Pasta Produced by Traditional or Industrial Production Parameters

Wheat durum pasta represents one of the most typical Italian food products. Many factories realize different class products regarding the use of organic wheat or not and the application of traditional or industrial production parameters. Being not subordinate to regulations, these classifications are only indicative of the real processes representing different quantitative levels for the same class. Aim of this study is to set up a rapid and nondestructive method to distinguish between different declared production parameters of pasta brands (spaghetti) such as industrial (high temperature, short-time drying, Teflon drawn) and traditional (low temperature, long-time drying, bronze drawn) processes and the use of organic wheat or not. A hyperspectral system operating within visible and near-infrared spectra was used to acquire images of spaghetti bundles (of two different years). Hyperspectral information was statistically analyzed by multivariate provisional soft independent modeling of class analogy (SIMCA). The results report a percentage of correct classification equal to 75.3 % for the first year and to 73.9 % for the second year. For both sampling years, all the traditional brands are the most distant from the origin (i.e., full industrial model). The Spearman's cross-correlation test performed on the SIMCA distances indicates a statistically significant correlation between the 2 years of analysis confirming the system repeatability. The results demonstrate that the differentiation of pasta does not depend only on the raw material (i.e., organic and not) but also on the times and types of processing (i.e., short- and long-time drying, bronze or Teflon wire drawing).     

Bibliometric analysis and mapping of publications on brewing science from 1940 to 2018

Over the last 80 years, malting and brewing has evolved through increased scientific understanding, driven by numerous factors including efficiency improvement, cost reduction, quality enhancement, risk minimisation, brand marketing and image enhancement. The aim of this work was to review the historical trends in global brewing research in order to identify current opportunities and emerging trends, as well as uncover future key drivers. A bibliometric analysis was performed with English language peer reviewed literature from the Scopus database together with mapping using the VOSviewer clustering software. The bibliometric results were structured to identify which affiliations, authors, and journals have been involved in brewing research, as well as the average number of citations per paper. The bibliometric mapping yielded ‘research term maps’, where the words occurring more frequently in the research papers were grouped in coloured clusters by area of interest, and their relationships indicated. The year maps revealed which average terms tended to emerge in chronological order. The estimated average citation number per paper was about nine. Countries, including Spain, Czech Republic, Italy and Belgium displayed higher average citation scores than the world average. In the period 1940-79, brewing publications were mainly process oriented whereas between 2008-18, there was a shift in focus to the consumer (health and social responsibility) and to environmental sustainability. © 2020 The Institute of Brewing & Distilling     

H HRMAS-NMR metabolomic to assess quality and traceability of mozzarella cheese from Campania buffalo milk

The production of Mozzarella di Bufala Campana (MBC) is relevant for the agro-food economy of the Campania Region of Italy and the mark of Protected Designation of Origin (PDO) has been assigned to MBC in relation to its geographical origin. Advanced analytical methods must be then employed to assess authenticity, traceability, and quality of MBC. ¹H HRMAS-NMR (High Resolution Magic Angle Spinning Nuclear Magnetic Resonance) spectroscopy was applied here to directly identify specific metabolites in MBC intact samples without time-consuming sample pre-treatments. Overcrowded conventional ¹H HRMAS-NMR spectra were selectively simplified with two NMR pulse sequences: eCPMG and eDiff, by modulating spin-spin relaxation times and diffusion of MBC molecular components, respectively. Signal elaboration of edited spectra was combined with multivariate analyses to enable significant metabolic differentiation between MBC samples from two different production sites in Campania. Principal Components Analysis (PCA) for eCPMG spectra explained 97.54% of total variance between the two MBC groups for four metabolites (β-galactose, β-lactose, acetic acid, and glycerol). Less efficient was groups distinction by PCA for eDiff spectra, although differences in polyunsaturated acids, such as linoleic and linolenic acids, were highlighted. Similarly, Discriminant Analysis (DA) provided MBC group classification with 100% success in validation tests for eCPMG spectra, while DA prediction ability was reduced to 94.12% for eDiff spectra. Hierarchical Cluster Analysis (HCA) gave a totally correct classification between the two MBC groups only for eCPMG spectra. eCPMG spectra were also used to identify metabolites during MBC aging. As compared to fresh samples, 2 days old MBC samples showed increasing signals for isobutylic alcohol, lactic acid, and acetic acid. This work shows that ¹H HRMAS-NMR spectroscopy can rapidly characterise the metabolic profile of intact MBC samples and statistically distinguish the geographical origin of buffalo milk mozzarella and its freshness.