Hazelnuts are one of the most widely consumed nuts, but their production creates large quantities of by-products, especially shells, that could be upcycled into much more valuable products. Recent studies have shown that hazelnut shell hemicellulose is particularly rich in compounds that are potential precursors of xylooligosaccharides and arabino-xylooligosaccharides ((A)XOS), previously defined as emerging prebiotics very beneficial for human health. The production of these compounds on an industrial scale-up could have big consequences on the functional foods market. However, to produce (A)XOS from a lignocellulosic biomass, such as hazelnut shell, is not easy. Many methods for the extraction and the purification of these prebiotics have been developed, but they all have different efficiencies and consequences, including on the chemical structure of the obtained (A)XOS. The latter, in turn, is strongly correlated to the nutritional effects they have on health, which is why the optimization of the structural characterization process is also necessary. Therefore, this review aims to summarize the progress made by research in this field, so as to contribute to the exploitation of hazelnut waste streams through a circular economy approach, increasing the value of this biomass through the production of new functional ingredients.

Hydrothermal treatment is commonly used for hemicelluloses extraction from lignocellulosic materials. In this study, we thoroughly investigated with a novel approach the metabolomics of degradation compounds formed when hazelnut shells are subjected to this type of treatment. Three different complementary techniques were combined, namely GC-MS, ¹H NMR, and UHPLC-IM-Q-TOF-MS. Organic acids, modified sugars and aromatic compounds, likely to be the most abundant chemical classes, were detected and quantified by NMR, whereas GC- and LC-MS-based techniques allowed to detect many molecules with low and higher Mw, respectively. Furans, polyols, N-heterocyclic compounds, aldehydes, ketones, and esters appeared, among others. Ion mobility-based LC-MS method was innovatively used for this purpose and could allow soon to create potentially useful datasets for building specific databases relating to the formation of these compounds in different process conditions and employing different matrices. This could be a very intelligent approach especially in a risk assessment perspective.