In-Line Monitoring and Control of Rheological Properties through Data-Driven Ultrasound Soft-Sensors.

The use of continuous processing is replacing batch modes because of their capabilities to address issues of agility, flexibility, cost, and robustness. Continuous processes can be operated at more extreme conditions, resulting in higher speed and efficiency. The issue when using a continuous process is to maintain the satisfaction of quality indices even in the presence of perturbations. For this reason, it is important to evaluate in-line key performance indicators. Rheology is a critical parameter when dealing with the production of complex fluids obtained by mixing and filling. In this work, a tomographic ultrasonic velocity meter is applied to obtain the rheological curve of a non-Newtonian fluid. Raw ultrasound signals are processed using a data-driven approach based on principal component analysis (PCA) and feedforward neural networks (FNN). The obtained sensor has been associated with a data-driven decision support system for conducting the process.

Statistical Health Monitoring Applied to a Metabolomic Study of Experimental Hepatocarcinogenesis: An Alternative Approach to Supervised Methods for the Identification of False Positives.

In a typical metabolomics experiment, two or more conditions (e.g., treated versus untreated) are compared, in order to investigate the potential differences in the metabolic profiles. When dealing with complex biological systems, a two-class classification is often unsuitable, since it does not consider the unpredictable differences between samples (e.g., nonresponder to treatment). An approach based on statistical process control (SPC), which is able to monitor the response to a treatment or the development of a pathological condition, is proposed here. Such an approach has been applied to an experimental hepatocarcinogenesis model to discover early individual metabolic variations associated with a different response to the treatment. Liver study was performed by nuclear magnetic resonance (NMR) spectroscopy, followed by multivariate statistical analysis. By this approach, we were able to (1) identify which treated samples have a significantly different metabolic profile, compared to the control (in fact, as confirmed by immunohistochemistry, the method correctly classified 7 responders and 3 nonresponders among the 10 treated animals); (2) recognize, for each individual sample, the metabolites that are out of control (e.g., glutathione, acetate, betaine, and phosphocholine). The first point could be used for classification purposes, and the second point could be used for a better understanding of the mechanisms underlying the early phase of carcinogenesis. The statistical control approach can be used for diagnosis (e.g., healthy versus pathological, responder versus nonresponder) and for generation of an individual metabolic profile, leading to a better understanding of the individual pathological processes and to a personalized diagnosis and therapy.

Metabolomic profiling of Fiore Sardo cheese: Investigation of the influence of thermal treatment and ripening time using univariate and multivariate classification techniques.

The effect of different sub-pasteurization heat treatments and different ripening times was investigated in this work. The metabolite profiles of 95 cheese samples were analyzed using GC-MS in order to determine the effects of thermal treatment (raw milk, 57 °C and 68 °C milk thermization) and ripening time (105 and 180 days). ANOVA test on GC-MS peaks complemented with false discovery rate correction was employed to identify the compounds whose levels significantly varied over different ripening times and thermal treatments. The univariate t-test classifier and Partial Least Square Discriminant Analysis (PLS-DA) provided acceptable classification results, with an overall accuracy in cross-validation of 76% for the univariate model and 72% from the PLS-DA. The metabolites that mostly changed with ripening time were amino acids and one endocannabinoid (i.e., arachidonoyl amide), while compounds belonging to the classes of biogenic amines and saccharides resulted in being strongly affected by the thermization process.

Investigation of Seasonal Variation in Fatty Acid and Mineral Concentrations of Pecorino Romano PDO Cheese: Imputation of Missing Values for Enhanced Classification and Metabolic Profile Reconstruction.

Seasonal variation in fatty acids and minerals concentrations was investigated through the analysis of Pecorino Romano cheese samples collected in January, April, and June. A fraction of samples contained missing values in their fatty acid profiles. Probabilistic principal component analysis, coupled with Linear Discriminant Analysis, was employed to classify cheese samples on a production season basis while accounting for missing data and quantifying the missing fatty acid concentrations for the samples in which they were absent. The levels of rumenic acid, vaccenic acid, and omega-3 compounds were positively correlated with the spring season, while the length of the saturated fatty acids increased throughout the production seasons. Concerning the classification performances, the optimal number of principal components (i.e., 5) achieved an accuracy in cross-validation equal to 98%. Then, when the model was tasked with imputing the lacking fatty acid concentration values, the optimal number of principal components resulted in an R2 value in cross-validation of 99.53%.

Rheology of Conductive High Reactivity Carbonaceous Material (HRCM)-Based Ink Suspensions: Dependence on Concentration and Temperature.

The present case study reports a shear rheological characterization in the temperature domain of inks and pastes loaded with conductive High Reactivity Carbonaceous Material (HRCM) consisting mainly of few-layers graphene sheets. The combined effect of filler concentration and applied shear rate is investigated in terms of the shear viscosity response as a function of testing temperature. The non-Newtonian features of shear flow ramps at constant temperature are reported to depend on both the HRCM load and the testing temperature. Moreover, temperature ramps at a constant shear rate reveal a different viscosity-temperature dependence from what is observed in shear flow ramps while maintaining the same filler concentration. An apparent departure from the well-known Vogel-Fulcher-Tamman relationship as a function of the applied shear rate is also reported.

Fourier transform rheology as a universal non-linear mechanical characterization of droplet size and interfacial tension of dilute monodisperse emulsions.

A new protocol to gain interfacial tension and droplet size of dilute monodisperse emulsions from Fourier Transform Rheology (FTR), is proposed. Specifically, a universal dimensionless quantity E was found at small strain amplitudes to correlate with the droplet size of the emulsion where E is inversely related to the square of the capillary number Ca and directly proportional to the relative intensities of the fifth and third harmonics, I(5)/I(3). The limiting value E(0) at small strain deformations can be used as a universal parameter to calculate different emulsion properties. Different morphological constitutive models for emulsions were used to establish the universality of the parameter E(0). Preliminary analysis on experimental data confirms the validity of this approach for the characterization of emulsion properties, including the estimation of interfacial tension and droplet radius.