Natural toxins include a wide range of toxic metabolites also occurring in food and products, thus representing a risk for consumer health. In the last few decades, several robust and sensitive analytical methods able to determine their occurrence in food have been developed. Liquid chromatography mass spectrometry is the most powerful tool for the simultaneous detection of these toxins due to its advantages in terms of sensitivity and selectivity. A comprehensive review on the most relevant papers on methods based on liquid chromatography mass spectrometry for the analysis of mycotoxins, alkaloids, marine toxins, glycoalkaloids, cyanogenic glycosides and furocoumarins in food is reported herein. Specifically, a literature search from 2011 to 2021 was carried out, selecting a total of 96 papers. Different approaches to sample preparation, chromatographic separation and detection mode are discussed. Particular attention is given to the analytical performance characteristics obtained in the validation process and the relevant application to real samples.

Fourier transform near-infrared spectroscopy (FT-NIR) was used for rapid and non-invasive analysis of deoxynivalenol (DON) in durum and common wheat. The relevance of using ground wheat samples with a homogeneous particle size distribution to minimize measurement variations and avoid DON segregation among particles of different sizes was established. Calibration models for durum wheat, common wheat and durum + common wheat samples, with particle size <500 µm, were obtained by using partial least squares (PLS) regression with an external validation technique. Values of root mean square error of prediction (RMSEP, 306–379 µg kg^–1) were comparable and not too far from values of root mean square error of cross-validation (RMSECV, 470–555 µg kg^–1). Coefficients of determination (r ²) indicated an “approximate to good” level of prediction of the DON content by FT-NIR spectroscopy in the PLS calibration models (r ² = 0.71–0.83), and a “good” discrimination between low and high DON contents in the PLS validation models (r ² = 0.58–0.63). A “limited to good” practical utility of the models was ascertained by range error ratio (RER) values higher than 6. A qualitative model, based on 197 calibration samples, was developed to discriminate between blank and naturally contaminated wheat samples by setting a cut-off at 300 µg kg^–1 DON to separate the two classes. The model correctly classified 69% of the 65 validation samples with most misclassified samples (16 of 20) showing DON contamination levels quite close to the cut-off level. These findings suggest that FT-NIR analysis is suitable for the determination of DON in unprocessed wheat at levels far below the maximum permitted limits set by the European Commission.