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.

Processing sustainability and the concept of zero waste discharge are of great interest for many industries. Every year, fruit and vegetable processing industries generate huge amounts of by-products, which are often intended for animal feed or discarded as waste, posing a problem to both environmental and economic points of view. However, to minimize the waste burden, the valorization of these residues received increased interest. In fact, fruit and vegetable by-products are an excellent source of valuable compounds, such as proteins, dietary fibers, lipids, minerals, vitamins, phenolic acids, flavonoids, anthocyanins, carotenoids, and pigments, which can be recovered and reused, creating new business prospects from a circular economy perspective. Understanding the chemical characteristics of these materials is a key concern for their valorization and the identification of their most appropriate intended use. In this study, the phytochemical and functional properties of fruit and vegetable processing by-products (peel and pomace) were investigated. Samples of different plants (i.e., apple, black and orange carrot, cucumber, kumquat, mango, parsnip, peach, black plum) were analyzed using chemical analytical methods and characterized using Fourier Transform Mid-Infrared spectroscopy (FT-MIR). The results highlighted their high nutritional composition in terms of protein, lipids, fiber, and ash, as well as bioactive and antioxidant profiles. These characteristics make these residues suitable as natural ingredients for the development of high-added-value products in food, cosmetic, and pharmaceutical industries.

Pomological traits are the major factors determining the quality and price of fresh fruits. This research was aimed to investigate the feasibility of using two hyperspectral imaging (HSI) systems in the wavelength regions comprising visible to near infrared (VisNIR) (400−1000 nm) and short-wave infrared (SWIR) (935−1720 nm) for predicting four strawberry quality attributes (firmness—FF, total soluble solid content—TSS, titratable acidity—TA, and dry matter—DM). Prediction models were developed based on artificial neural networks (ANN). The entire strawberry VisNIR reflectance spectra resulted in accurate predictions of TSS (R² = 0.959), DM (R² = 0.947), and TA (R² = 0.877), whereas good prediction was observed for FF (R² = 0.808). As for models from the SWIR system, good correlations were found between each of the physicochemical indices and the spectral information (R² = 0.924 for DM; R² = 0.898 for TSS; R² = 0.953 for TA; R² = 0.820 for FF). Finally, data fusion demonstrated a higher ability to predict fruit internal quality (R² = 0.942 for DM; R² = 0. 981 for TSS; R² = 0.976 for TA; R² = 0.951 for FF). The results confirmed the potential of these two HSI systems as a rapid and nondestructive tool for evaluating fruit quality and enhancing the product’s marketability.

In recent years, the growing demand for algae in Western countries is due to their richness in nutrients and bioactive compounds, and their use as ingredients for foods, cosmetics, nutraceuticals, fertilizers, biofuels,, etc. Evaluation of the qualitative characteristics of algae involves assessing their physicochemical and nutritional components to determine their suitability for specific end uses, but this assessment is generally performed using destructive, expensive, and time-consuming traditional chemical analyses, and requires sample preparation. The hyperspectral imaging (HSI) technique has been successfully applied in food quality assessment and control and has the potential to overcome the limitations of traditional biochemical methods. In this study, the nutritional profile (proteins, lipids, and fibers) of seventeen edible macro- and microalgae species widely grown throughout the world were investigated using traditional methods. Moreover, a shortwave infrared (SWIR) hyperspectral imaging device and artificial neural network (ANN) algorithms were used to develop multi-species models for proteins, lipids, and fibers. The predictive power of the models was characterized by different metrics, which showed very high predictive performances for all nutritional parameters (for example, R² = 0.9952, 0.9767, 0.9828 for proteins, lipids, and fibers, respectively). Our results demonstrated the ability of SWIR hyperspectral imaging coupled with ANN algorithms in quantifying biomolecules in algal species in a fast and sustainable way.

In this research, ultrasound-assisted extraction (UAE) coupled with response surface methodology was used to identify the optimal process parameters (temperature, time, and solvent composition (ethanol–water ratio)) for bioactive compounds extraction from stalks and debranning products. Extraction efficiency was assessed in terms of total phenolic compounds (TPCs) and antioxidant activity (AA), and phenolic profiles were identified using HPLC-DAD for the four samples of byproducts (two samples from stalks, C1 and C2, and two samples for debranning products, C3 and C4, from two different farms). The optimized extract containing the highest amount of TPC at different levels was used to enrich bread to evaluate the ability of extending their shelf life using a hyperspectral imaging device (935–1720 nm). Under the optimal conditions, the amounts of phenolics on average in stalk increased by about 79.5% and 47.0% in debranning products, whereas the relative AA increased by about 28.5% (C1 and C2 samples) and 63.0% (C3 and C4 samples) when UAE was applied with respect to the traditional technique. The characterization of stalk phenolic acids profile revealed that gallic, ferulic, and 4-coumaric acids were the prevalent ones. Otherwise, caffeic, syringic, and 3-coumaric acids were the most abundant in debranning products. As expected, enriched bread showed a significant increase in TPC and AA values without influence on organoleptic characteristics. Finally, enriched bread showed a shelf-life increase of about five days.