Assessment of building cooling energy need through a quasi-steady state model: Simplified correlation for gain-loss mismatch

The objective of this work is to implement a simplified calculation procedure for building net energy need, based on a quasi-steady state model and on a monthly data set. In particular, it is intended to supply a formulation of the dynamic parameters and to adapt them to Italian climatic, typological, constructive and user data. The method was validated by determining the numerical correlations of the gain/loss utilization factor, through a comparison with a detailed building energy simulation software (EnergyPlus). The simulation was run on some test rooms defined by CEN (European Committee for Standardization) and on some real buildings that are representative of the Italian building stock, assuming weather data from different Italian locations (Torino, Roma, Palermo). The work shows that the accuracy of results is greatly affected by nonlinearities in the determination of the heat transfer and that the dynamic parameters are sensitive to some building features which are not taken into account in the CEN correlations.     

Flavone-di-C-glycosides in citrus juices from Southern Italy

6,8-Di-C-glucopyranosylapigenin and 6,8-di-C-glucopyranosyldiosmetin were detected by HPLC–DAD–ESI–MS–MS in a variety of Southern Italian Citrus juices (orange, lemon, bergamot, citron, mandarin, clementine). In some juices, the amounts of C-glycosides detected were significant. 6,8-Di-C-glucopyranosylapigenin is characteristic of orange juice, while 6,8-di-C-glucopyranosyldiosmetin is the most important C-glycoside in lemon and citron juice. In bergamot juice the concentrations of 6,8-di-C-glucopyranosylapigenin and 6,8-di-C-glucopyranosyldiosmetin are similar. Clementine juice is distinctive as the amounts of both C-glycosides present are negligible.     

Electronic nose to study postharvest dehydration of wine grapes

Montepulciano grapes were dehydrated at 10 and 20 °C, 45% RH and 1–1.5 m/s of air flow. Samplings were performed at 10%, 20%, 30%, and 40% of weight loss (wl). TSS (total solids content) increased up to 43° and 36° Brix at 20 and 10 °C, respectively, in 27 and 48 days. A straight regression line between M (kg water/kg dry matter) loss and days of dehydration with R² values, equal to 0.97, was found but an angular coefficient of −0.048 and −0.038, respectively at 20 and 10 °C indicated a different rate of weight loss. Volatiles compounds profile changed significantly with the temperature during the dehydration process. Anaerobic metabolites such as ethanol, acetaldehyde, and ethylacetate were much higher at 20 °C than at 10 °C and rose progressively, while at 10 °C, the significant rise was observed between 30% and 40% weight loss. Alcohols and esters prevailed at 20 °C while at 10 °C larger abundances of aldehydes and terpene alcohols are observed. PCA analysis of GC/MS confirmed the volatiles compounds separation between the samples held at 20 and 10 °C. A progressive evolution of the profile is observed for the samples held at 20 °C while those held at 10 °C show a change of volatiles compounds profile only at 40% wl. Loadings analysis show that aldehydes and terpenols are oriented towards the direction of 10 °C samples while alcohols and esters in that of 20 °C samples. The behaviours of GC/MS data were partially corroborated by the data of electronic nose. Electronic nose was able to follow the progressive change of aroma profile at 20 °C while at 10 °C grouped the responses obtained at 10%, 20%, 30% wl all together but at 40% wl, it discriminated the aroma response. This result opens a window on the potential use to monitor commercially the grape dehydration.     

Near-infrared spectroscopy: A possible routine analysis to detect the use of frozen curds in the production of Buffalo Mozzarella cheese

Mozzarella di Bufala Campana is a pasta filata cheese with a Protected Designation of Origin whose specifications require the use of fresh milk, forbidding the use of frozen curds. The goal is to develop a routine analysis to identify frozen curd presence. The Buffalo Mozzarella samples were analysed by the near-infrared analytical technique, and the spectral data were processed through an artificial neural network. The results make it possible to identify the use of frozen curd in samples of Buffalo Mozzarella for up to 9 days of storage. The model reported very high accuracy either in training (0.5% of bad prediction) or in tests (6.8% of bad prediction).