Using Science and Technology to Unveil The Hidden Delicacy Terfezia arenaria, a Desert Truffle

Abstract

Terfezia arenaria is a desert truffle native to the Mediterranean Basin region, highly appreciated for its nutritional and aromatic properties. Despite the increasing interest in this desert truffle, T. arenaria is not listed as an edible truffle authorized for trade in the European Union. Therefore, our objective was to showcase T. arenaria’s nutritional and chemical composition and volatile profile. The nutritional analysis showed that T. arenaria is a good source of carbohydrates (67%), proteins (14%), and dietary fibre (10%), resulting in a Nutri-Score A. The truffle’s volatile profile was dominated by eight-carbon volatile compounds, with 1-octen-3-ol being the most abundant (64%), and 29 compounds were reported for the first time for T. arenaria. T. arenaria’s nutritional and chemical compositions were similar to those of four commercial mushroom and truffle species, while the aromatic profile was not. An electronic nose corroborated that T. arenaria‘s aromatic profile differs from that of the other four tested mushroom and truffle species. Our data showed that T. arenaria is a valuable food resource with a unique aroma and an analogous composition to meat, which makes it an ideal source for plant-based meat products. Our findings could help promote a sustainable future exploitation of T. arenaria and ensure the quality and authenticity of this delicacy.

Comparative analysis of volatile components of fresh and dried Tuber indicum truffles with different extraction methods

“…C 8 compounds are important characteristic flavour compounds of edible mushrooms, such as 1-octen-3-ol, 3-octone, trans-2-octenal, etc. They can present a strong mushroom flavour and a green grass fragrance (Molinier et al, 2015;Ferreira et al, 2023). Similar to most mushrooms, a large amount of alcohols, ketones, sulfur compounds and C 8 compounds are lost during the hot air drying process.…”

First Steps in Developing a Fast, Cheap, and Reliable Method to Distinguish Wild Mushroom and Truffle Species

“…Being a belowground fruiting fungus, Terfezia arenaria's unique aroma plays an important ecological role by mediating this truffle's communication with below-and above-ground communities [59], including attracting animals that help disperse the truffle spores [60]. Despite the variability in the aromatic profile, this truffle species can have volatile organic compounds that act as a species-specific fingerprint [16,60], which can help explain its distinctive smellprint when compared to that of the wild mushrooms.…”

“…Since the incubation temperature has been shown to influence the compounds emitted by the MT samples [16], the fact that we used a fixed ambient temperature (24 • C) for incubating the wild MT samples must have contributed to our method's accuracy. However, when the temperature or humidity changes (i.e., when identifying the wild MT species in the forest), the e-nose response will drift, which may deteriorate the detection performance.…”

“…Although wild MT constitute a highly esteemed delicacy in specific parts of the world [15,16], they face great skepticism in other regions and, therefore, they are not valued as an important forest resource. The valorization of wild MT has the potential to promote the socioeconomic sustainability of rural communities and forests [17,18].…”

“…The e-nose has been used to assess the volatile profiles of mushrooms for several applications in the food industry [43,44], including quality control during postharvest processing [45][46][47], monitoring of the maturation process [48], and assessment of the shelf life and packaging [49,50]. Recently, several studies have investigated the e-nose's capacity to accurately distinguish different wild MT species by analyzing their volatile profiles [16,40,41,43,44,51,52]. While the findings obtained using the e-nose are promising, a wild MT species identification based on the e-nose requires further research to expand the datasets, standardize the methodology, and explore the integration of machine learning algorithms to maximize its potential in wild MT species identification.…”