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.

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.