Corylus avellana L. Aroma Blueprint: Potent Odorants Signatures in the Volatilome of High Quality Hazelnuts

Terahertz imaging for non-invasive classification of healthy and cimiciato-infected hazelnuts

The development of new non-invasive approaches able to recognize defective food is currently a lively field of research. In particular, a simple and non-destructive method able to recognize defective hazelnuts, such as cimiciato-infected ones, in real-time is still missing. This study has been designed to detect the presence of such damaged hazelnuts. To this aim, a measurement setup based on terahertz (THz) radiation has been developed. Images of a sample of 150 hazelnuts have been acquired in the low THz range by a compact and portable active imaging system equipped with a 0.14 THz source and identified as Healthy Hazelnuts (HH) or Cimiciato Hazelnut (CH) after visual inspection. All images have been analyzed to find the average transmission of the THz radiation within the sample area. The differences in the distribution of the two populations have been used to set up a classification scheme aimed at the discrimination between healthy and injured samples. The performance of the classification scheme has been assessed through the use of the confusion matrix on 50 samples. The False Positive Rate (FPR) and True Negative Rate (TNR) are 0% and 100%, respectively. On the other hand, the True Positive Rate (TPR) and False Negative Rate (FNR) are 75% and 25%, respectively. These results are relevant from the perspective of the development of a simple, automatic, real-time method for the discrimination of cimiciato-infected hazelnuts in the processing industry.

Artificial Intelligence decision-making tools based on comprehensive two-dimensional gas chromatography data: the challenge of quantitative volatilomics in food quality assessment

Effective investigation of food volatilome by comprehensive two-dimensional gas chromatography with parallel detection by mass spectrometry and flame ionization detector (GC×GC-MS/FID) gives access to valuable information related to industrial quality. However, without accurate quantitative data, results transferability over time and across laboratories is prevented. The study applies quantitative volatilomics by multiple headspace solid phase microextraction (MHS-SPME) to a large selection of hazelnut samples (Corylus avellana L. n = 207) representing the top-quality selection of interest for the confectionery industry. By untargeted and targeted fingerprinting, performant classification models validate the role of chemical patterns strongly correlated to quality parameters (i.e., botanical/geographical origin, post-harvest practices, storage time and conditions). By quantification of marker analytes, Artificial Intelligence (AI) tools are derived: the augmented smelling based on sensomics with blueprint related to key-aroma compounds and spoilage odorant; decision-makers for rancidity level and storage quality; origin tracers. By reliable quantification AI can be applied with confidence and could be the driver for industrial strategies.

Mycotoxin Contamination in Hazelnut: Current Status, Analytical Strategies, and Future Prospects

Hazelnuts represent a potential source of mycotoxins that pose a public health issue due to their increasing consumption as food ingredients worldwide. Hazelnuts contamination by mycotoxins may derive from fungal infections occurring during fruit development, or in postharvest. The present review considers the available data on mycotoxins detected in hazelnuts, on fungal species reported as infecting hazelnut fruit, and general analytical approaches adopted for mycotoxin investigation. Prompted by the European safety regulation concerning hazelnuts, many analytical methods have focused on the determination of levels of aflatoxin B1 (AFB1) and total aflatoxins. An overview of the available data shows that a multiplicity of fungal species and further mycotoxins have been detected in hazelnuts, including anthraquinones, cyclodepsipeptides, ochratoxins, sterigmatocystins, trichothecenes, and more. Hence, the importance is highlighted in developing suitable methods for the concurrent detection of a broad spectrum of these mycotoxins. Moreover, control strategies to be employed before and after harvest in the aim of controlling the fungal contamination, and in reducing or inactivating mycotoxins in hazelnuts, are discussed.

Multidimensional Gas Chromatography Moves Forward

In this extended special feature to celebrate the 35th anniversary edition of LCGC Europe, leading figures from the separation science community explore contemporary trends in separation science and identify possible future developments.