| Summary: | Truffles represent the best known and most expensive edible mushroom. Known as <i>Ascomycetes</i>, they belong to the genus <i>Tuber</i> and live in symbiosis with plant host roots. Due to their extraordinary taste and smell, truffles are sold worldwide for high prices of up to 3000–5000 euros per kilogram (<i>Tuber magnatum</i> <span style="font-variant: small-caps;">PICO</span>). Amongst black truffles, the species <i>Tuber melanosporum</i> <span style="font-variant: small-caps;">VITTAD</span>. is highly regarded for its organoleptic properties. Nonetheless, numerous different sorts of black truffle are offered at lower prices, including <i>Tuber aestivum</i> <span style="font-variant: small-caps;">VITTAD</span>., <i>Tuber indicum</i> and <i>Tuber uncinatum</i>, which represent the most frequently consumed types. Because truffles do not differ visually for inexperienced consumers, food fraud is likely to occur. In particular, for the highly prized <i>Tuber melanosporum</i>, which morphologically forms very similar fruiting bodies to those of <i>Tuber indicum</i>, there is a risk of fraud via imported truffles from Asia. In this study, 126 truffle samples belonging to the four mentioned species were investigated by four different NIR instruments, including three miniaturized devices—the Tellspec Enterprise Sensor, the VIAVI solutions MicroNIR 1700 and the Consumer Physics SCiO—working on different technical principles. Three different types of measurement techniques were applied for all instruments (outer shell, rotational device and fruiting body) in order to identify the best results for classification and quality assurance in a non-destructive manner. Results provided differentiation with an accuracy up to 100% for the expensive <i>Tuber melanosporum</i> from <i>Tuber indicum</i>. Classification between <i>Tuber melanosporum</i>, <i>Tuber indicum</i>, <i>Tuber aestivum</i> and <i>Tuber uncinatum</i> could also be achieved with success of 100%. In addition, quality monitoring including discrimination between fresh and frozen/thawed, and prediction of the approximate date of harvesting, was performed. Furthermore, feasibility studies according to the geographical origin of the truffle were attempted. The presented work compares the performance for prediction and quality monitoring of portable vs. benchtop NIR devices and applied measurement techniques in order to be able to present a suitable, accurate, fast, non-destructive and reliable method for consumers.
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