Nowadays, brewers’ spent grains (BSG) is considered the most abundant and low-cost brewing by-products, presenting a great potential as a functional food ingredient. Since BSG is rich in dietary fiber and protein, it can be a raw material of interest in bakery products. However, blending wheat flour with BSG can affect dough rheology and the structural and sensorial properties of products. In this context, BSG flour at different levels (0%, 5%, and 10%) was used to enrich three commercial soft wheat flours, and to develop new formulations for bakery products (bread, breadsticks and pizza). As expected, the enrichment caused a significant increase of proteins, dietary fibers, lipids, and ash related to the BSG enrichment level. Significant changes in dough rheological properties (e.g., higher water absorption, lower development time and stability, dough strength, and tenacity) and in the color of the crust and crumbs of bakery products were also observed. At last, the consumer test pointed out that the 5% BSG enrichment showed the higher overall acceptability of proposed bakery products.

A few concurrent and/or counteracting factors (increase of average air temperature, rainfall shortage, drought, CO2 enrichment, ozone, nitrogen fertilization, sulphate deposition) drive today the soil-tree-atmosphere relationships in the Mediterranean area. Radial stem growth measured within the ICP-Forests level II Italian network provides a sensitive response to these occurrences. Climate fluctuations and repeated anomalous seasons or extreme events are the major evidences of the change in progress. The 2003 heat wave is the main event occurred in this decade over large part of Europe. In Italy, it provoked a marked water deficit coupled to high air temperature, which resulted in a heavy water stress. The growth performance in different forest types was analyzed in this paper: growth rate in 2000-2004, compared with 1997-1999, showed reductions up to 50% on half of the plots examined. Most of them were: (i) in northern-central Italy within the southern continental border of the climatic deviation; (ii) at low elevation, which is more sensitive to high air temperature and drought; (iii) made up of deciduous broadleaved forests (beech and oaks), i.e., species showing growth effects also in the following year. Over the time-window 2005-2009, a significant growth decrease was vice-versa detected within the coniferous spruce forests located at medium-high elevation in the Alps. Repeated seasonal deviations in temperature and rainfall were recorded in the Alps in 2005-2009. Climate effects at local scale were examined in a site where two oak species with a different auto-ecology (sessile oak and Turkey oak) grow together. The on-going change produced heavy mortality and reduced the growth of the more demanding and less drought-tolerant sessile oak; in a few years Turkey oak became prevailing in the stand composition and structure.

Brewers’ spent grain (BSG) is the main solid by-product from the brewery industry, rich in valuable nutrients and bioactive compounds. The aim of this study was to valorize this by-product, recovering phenolic compounds from BSG using ultrasound-assisted extraction (UAE) and chemometric techniques, such as the response surface methodology (RSM). Therefore, UAE process parameters (temperature and time) and solvent composition (ethanol aqueous mixtures) were optimized using a three-level Box–Behnken design, in order to carry out the maximum yield in phenols. Then, the extract obtained under optimal conditions was characterized for the total phenolic content and antioxidant capacity (2,20-azino-bis(3-ethylbenothiazoline-6-sulphonic acid, ABTS, and 2,2-diphenyl-1-picrylhydrazyl, DPPH), and individual phenolic compounds were identified using HPLC-DAD. The results show the highest level of total soluble phenolic content (4.1 ± 0.1 mg GAE/g d.w.) at 80 °C, 50 min and 65:35% ethanol:water, with a high goodness of fit between experimental and predicted values (R² = 0.987), and a high antioxidant potential (DPPH: 0.42 ± 0.01 mg TE eq/g d.w.; ABTS: 5.82 ± 0.04 mg TE eq/g d.w.). A comparison between the classic extraction techniques and the UAE with the same solvent showed an increase of 156% in the phenol yield. The characterization of phenolic profile revealed that ferulic acid (1.5 ± 0.2 mg/L), vanillic acid (0.78 ± 0.18 mg/L) and p-coumaric acid (0.12 ± 0.03 mg/L) were the prevalent ones. UAE coupled with RSM was a useful tool to inexpensively and quickly recover bioactive phenolic compounds from BSG, which can be used in the food, pharmaceutical or cosmetic industries.

The fruit supply chain requires simple, non-destructive, and fast tools for quality evaluation both in the field and during the post-harvest phase. In this study, a portable visible and near-infrared (Vis/NIR) spectrophotometer and a portable Vis/NIR hyperspectral imaging (HSI) device were tested to highlight genetic differences among apricot cultivars, and to develop multi-cultivar and multi-year models for the most important marketable attributes (total soluble solids, TSS; titratable acidity, TA; dry matter, DM). To do this, the fruits of seventeen cultivars from a single experimental orchard harvested at the commercial maturity stage were considered. Spectral data emphasized genetic similarities and differences among the cultivars, capturing changes in the pigment content and macro components of the apricot samples. In recent years, machine learning techniques, such as artificial neural networks (ANNs), have been successfully applied to more efficiently extract valuable information from spectral data and to accurately predict quality traits. In this study, prediction models were developed based on a multilayer perceptron artificial neural network (ANN-MLP) combined with the Levenberg–Marquardt learning algorithm. Regarding the Vis/NIR spectrophotometer dataset, good predictive performances were achieved for TSS (R² = 0.855) and DM (R² = 0.857), while the performance for TA was unsatisfactory (R² = 0.681). In contrast, the optimal predictive ability was found for models of the HSI dataset (TSS: R² = 0.904; DM: R² = 0.918, TA: R² = 0.811), as confirmed by external validation. Moreover, the ANN allowed us to identify the most predictive input spectral regions for each model. The results showed the potential of Vis/NIR spectroscopy as an alternative to traditional destructive methods to monitor the qualitative traits of apricot fruits, reducing the time and costs of analyses.

Strawberry is a very popular fruit, appreciated for its unique flavor and many beneficial traits such as antioxidants and useful amino acids, which strongly contribute to the overall quality of the product. Indeed, the quality of fresh fruit is a fundamental aspect for consumers, and it is crucial for the success of breeding activities as well as for enhancing the competitiveness and profitability of the fruit industry. Nowadays, the entire supply chain requires simple and fast systems for quality evaluation. In this context, the pomological and chemical traits (i.e., soluble solids, firmness, titratable acidity, dry matter) as well as nutritional ones such as total phenols, total anthocyanins and antioxidant potential were evaluated and compared for seven strawberry cultivars and three harvest times. The prediction of the qualitative traits was carried out using color space coordinates (L*, a* and b*) and two statistical techniques, i.e., the multiple linear regression models (MLR) and artificial neural networks (ANNs). Unsatisfactory prediction performances were obtained for all parameters when MLR was applied. On the contrary, the good prediction of the internal quality attributes, using ANN, was observed, especially for both antioxidant activity and the total monomeric anthocyanin (R² = 0.906, and R² = 0.943, respectively). This study highlighted that color coordinates coupled with ANN can be successfully used to evaluate the quality of strawberry.