Fish freshness consists of complex endogenous and exogenous processes; therefore, the use of a few parameters to unravel illicit practices could be insufficient. Moreover, the development of strategies for the identification of such practices based on additives known to prevent and/or delay fish spoilage is still limited. The paper deals with the identification of the effect played by a Cafodos solution on the conservation state of sea bass at both short-term (3 h) and long-term (24 h). Controls and treated samples were characterized by a multi-omic approach involving proteomics, lipidomics, metabolomics, and metagenomics. Different parts of the fish samples were studied (muscle, skin, eye, and gills) and sampled through a non-invasive procedure based on EVA strips functionalized by ionic exchange resins. Data fusion methods were then applied to build models able to discriminate between controls and treated samples and identify the possible markers of the applied treatment. The approach was effective in the identification of the effect played by Cafodos that proved to be different in the short- and long-term and complex, involving proteins, lipids, and small molecules to a different extent.

In the field of food control for fresh products, the identification of foods subjected to illicit conservation treatments to extend their shelf life is fundamental. Fresh fish products are particularly subjected to this type of fraud due to their high commercial value and the fact that they often have to be transported over a long distance, keeping their organoleptic characteristics unaltered. Treatments of this type involve, e.g., the bleaching of the meat and/or the momentary abatement of the microbial load, while the degradation process continues. It is therefore important to find rapid methods that allow the identification of illicit treatments. The study presented here was performed on 24 sea bass samples divided into four groups: 12 controls (stored on ice in the fridge for 3 or 24 h), and 12 treated with a Cafodos-like solution for 3 or 24 h. Muscle and skin samples were then characterized using micro-Raman spectroscopy. The data were pre-processed by smoothing and taking the first derivative and then PLS-DA models were built to identify short- and long- term effects on the fish’s muscle and skin. All the models provided the perfect classification of the samples both in fitting and cross-validation and an analysis of the bands responsible for the effects was also reported. To the best of the authors’ knowledge, this is the first time Raman spectroscopy has been applied for the identification of a Cafodos-like illicit treatment, focusing on both fish muscle and skin evaluation. The procedure could pave the way for a future application directly on the market through the use of a portable device.

This study is focused on the implementation of statistical learning methods for the prediction of the mechanical properties of steel products from the chemical profile of the raw material and the process parameters. The integration of this model into the production process allows a large-scale steel industry to predict steel properties with heightened accuracy, optimizing the manufacturing process for minimal waste and improved consistency. A workflow for process data analysis has been developed, based on the use of machine learning algorithms to build an interface for data treatment to be directly used online. The proposed approach has a comprehensive connotation, starting from data pre-treatment and cleaning, to model building and prediction. Different machine learning algorithms are compared (Polynomial Regression, LASSO, Random Forests and Gradient Boosting, ANN, SVM, and k-NN), to provide the best predictive ability, also exploiting human reinforcement. The results proved to be very promising for all the types of steel investigated, with very good RMSE and R² values both in fitting and in prediction. The application here presented is being integrated into Total Quality Tutor (TQT) software, developed in-house in C# language, for predicting the mechanical properties of steel.

μRaman spectroscopy measurements were used to study the resistivity in 4H-SiC samples by intercalibrating with Eddy current measurements (eddy-current probe that accurately measures bulk resistivity of wafers). The position and line width associated with the Raman longitudinal optical phonon-plasmon coupled (LOPC) mode were used since their variation from the reference values of a material in the absence of dopant-generated defects is proportional to the amount of the free carrier concentration in the conduction band present in the semiconductor. Using wafers of known resistivity to calibrate the model and deconvolving the individual recorded spectra, a multi-variable model was created to predict the resistivity of individual map points. Resistivity was thus predicted in a pointwise manner resulting in maps of 92 points over a 6-inch diameter area of a wafer, from which false-colour images were created showing the spatial distribution along the X and Y axes, and in the bulk, along the Z axis of the resistivity. The analysis procedure was automated by creating suitable R-language codes that extract the necessary information on the individual aspects of the analysis and create the images described above from a single dataset.

Global fish and seafood consumption is increasing annually, frequently leading to the emergence of food fraud, mainly related to mislabeling and adulteration like, for example, the use of illicit/unauthorized food additives to mask or delay fish spoilage. Among the available diagnostic tools for control purposes, spectroscopic techniques have often been proposed to identify these kinds of illicit practices in fish and seafood products. The presented study aims to test two cheap and portable near infrared (NIR) spectrometers, a handheld MicroNIR and a pocket-sized SCiO, to uncover use of the illicit food additive Cafodos, a mixture of sodium citrate and hydrogen peroxide used to preserve some fish characteristics (like smell, color, na dtexture). The NIR spectroscopy in combination with chemometric approaches, allowed the successfully classification of (81–100%) samples of sea bass (Dicentrarchus labrax) treated with Cafodos. The study highlights the potential of this technique that, by not requiring pre-treatment of samples with further reagents, is cheaper and safer for the environment. In conclusion, the study confirmed the potential of portable devices for rapid NIR spectroscopy analysis to identify food fraud and ensure consumer safety.