Fourier-transform-near infrared (FT-NIR) spectroscopy has been used to develop quantitative and classification models for the prediction of deoxynivalenol (DON) levels in durum wheat samples. Partial least-squares (PLS) regression analysis was used to determine DON in wheat samples in the range of <50–16,000 µg/kg DON. The model displayed a large root mean square error of prediction value (1,977 µg/kg) as compared to the EU maximum limit for DON in unprocessed durum wheat (i.e., 1,750 µg/kg), thus making the PLS approach unsuitable for quantitative prediction of DON in durum wheat. Linear discriminant analysis (LDA) was successfully used to differentiate wheat samples based on their DON content. A first approach used LDA to group wheat samples into three classes: A (DON ≤ 1,000 µg/kg), B (1,000 < DON ≤ 2,500 µg/kg), and C (DON > 2,500 µg/kg) (LDA I). A second approach was used to discriminate highly contaminated wheat samples based on three different cut-off limits, namely 1,000 (LDA II), 1,200 (LDA III) and 1,400 µg/kg DON (LDA IV). The overall classification and false compliant rates for the three models were 75%–90% and 3%–7%, respectively, with model LDA IV using a cut-off of 1,400 µg/kg fulfilling the requirement of the European official guidelines for screening methods. These findings confirmed the suitability of FT-NIR to screen a large number of wheat samples for DON contamination and to verify the compliance with EU regulation.

Fourier transform near infrared (FT-NIR) spectroscopy, in combination with principal component-linear discriminant analysis (PC-LDA), was used for tracing the geographical origin of durum wheat samples. The classification model PC-LDA was applied to discriminate durum wheat samples originating from Northern, Central, and Southern Italy (n = 181), and to differentiate Italian durum wheat samples from those cultivated in other countries across the world (n = 134). Developed models were validated on a separated set of wheat samples. Different pre-treatments of spectral data and different spectral regions were selected and compared in terms of overall discrimination (OD) rates obtained in validation. The LDA models were able to correctly discriminate durum Italian wheat samples according to their geographical origin (i.e., North, Central, and South) with OD rates of up of 96.7%. Better results were obtained when LDA models were applied to the discrimination of Italian durum wheat samples from those originating from other countries across the world, having OD rates of up to 100%. The excellent results obtained herein clearly indicate the potential of FT-NIR spectroscopy to be used for the discrimination of durum wheat samples according to their geographical origin.

The antimicrobial effect of eight essential oils’ vapors against pathogens and spoilage bacteria was assayed. Oreganum vulgare L. essential oil (OVO) showed a broad antibacterial effect, with Minimum Inhibitory Concentration (MIC) values ranging from 94 to 754 µg cm⁻³ air, depending on the bacterial species. Then, gaseous OVO was used for the treatment of stainless steel, polypropylene, and glass surfaces contaminated with four bacterial pathogens at 6–7 log cfu coupon⁻¹. No viable cells were found after OVO treatment on all food-contact surfaces contaminated with all pathogens, with the exception of Sta. aureus DSM 799 on the glass surface. The antimicrobial activity of OVO after the addition of beef extract as a soiling agent reduced the Sta. aureus DSM 799 viable cell count by more than 5 log cfu coupon⁻¹ on polypropylene and glass, while no viable cells were found in the case of stainless steel. HS-GC-MS analysis of the headspace of the boxes used for the antibacterial assay revealed 14 different volatile compounds with α-Pinene (62–63%), and p-Cymene (21%) as the main terpenes. In conclusion, gaseous OVO could be used for the microbial decontamination of food-contact surfaces, although its efficacy needs to be evaluated since it depends on several parameters such as target microorganisms, food-contact material, temperature, time of contact, and relative humidity.