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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.

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BACKGROUND Deoxynivalenol (DON) is the most common Fusarium mycotoxin occurring in wheat and wheat‐derived products, with several adverse and toxic effects in animals and humans. Although bran fractions produced by milling wheat have numerous health benefits, cereal bran is the part of the grain with the highest concentration of DON, thus representing a risk for consumers. Increased efforts have been made to develop analytical methods suitable for rapid DON screening. RESULTS The applicability of Fourier transform near‐infrared (FTNIR), or mid‐infrared (FTMIR) spectroscopy, and their combination for rapid analysis of DON in wheat bran, was investigated for the classification of samples into compliant and non‐compliant groups regarding the EU legal limit of 750 µg kg−1. Partial least squares‐discriminant analysis (PLS‐DA) and principal component‐linear discriminant analysis (PC‐LDA) were employed as classification techniques using a cutoff value of 400 µg kg−1 DON to distinguish the two classes. Depending on the classification model, overall discrimination rates were from 87% to 91% for FTNIR and from 86% to 87% for the FTMIR spectral range. The FTNIR spectroscopy gave the highest overall classification rate of wheat bran samples, with no false compliant samples and 18% false noncompliant samples when the PC‐LDA classification model was applied. The combination of the two spectral ranges did not provide a substantial improvement in classification results in comparison with FTNIR. CONCLUSIONS Fourier transform near‐infrared spectroscopy in combination with classification models was an efficient tool to screen many DON‐contaminated wheat bran samples and assess their compliance with EU regulations. © 2018 Society of Chemical Industry

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.

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