ABSTRACT
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.
ABSTRACT
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%.
ABSTRACT
This paper explores the transformation of biowastes from food industry and agriculture into high-value products through four examples. The objective is to provide insight into the principles of green transition and a circular economy. The first two case studies focus on the waste generated from the production of widely consumed food items, such as beer and coffee, while the other two examine the potential of underutilized plants, such as burdock and willow, as sources of valuable compounds. Phenolic compounds are the main target in the case of brewer's spent grain, with p-coumaric acid and ferulic acid being the most common. Lipids are a possible target in the case of spent coffee grounds with palmitic (C16:0) and linoleic (C18:2) acid being the major fatty acids among those recovered. In the case of burdock, different targets are reported based on which part of the plant is used. Extracts rich in linoleic and oleic acids are expected from the seeds, while the roots extracts are rich in sugars, phenolic acids such as chlorogenic, caffeic, o-coumaric, syringic, cinnamic, gentisitic, etc. acids, and, interestingly, the high-value compound epicatechin gallate. Willow is well known for being rich in salicin, but picein, (+)-catechin, triandrin, glucose, and fructose are also obtained from the extracts. The study thoroughly analyzes different extraction methods, with a particular emphasis on cutting-edge green technologies. The goal is to promote the sustainable utilization of biowaste and support the green transition to a more environmentally conscious economy.
ABSTRACT
An experimental investigation is here presented on the photo-electrochemical removal of Methyl Orange (MO), selected as a model of the organic dyes, contained in wastewaters. The process is carried out in an electrochemical flow reactor, in which titania nanotubular electrode is irradiated with a simulated solar light. Design of Experiments (DOE) technique is used to plan the experimental campaign and investigate on the single and combined effects of applied current, electrolyte flow rate, and initial MO concentration, on the specific reaction rate. The results of the DOE analysis, also combined with the study of the distribution of the intermediate products, confirm a reaction mechanism mediated by OH radicals; high applied current and low reactant concentration resulted as favourable conditions to achieve high specific reaction rate of color removal.
Subject(s)
Azo Compounds/chemistry , Titanium/chemistry , Waste Disposal, Fluid/methods , Water Pollutants, Chemical/chemistry , Color , Coloring Agents , Electrodes , Nanotubes , Oxidation-Reduction , WastewaterABSTRACT
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.
