Each year, mycotoxins cause economic losses of several billion US dollars worldwide. Consequently, methods must be developed, for producers and cereal manufacturers, to detect these toxins and to comply with regulations. Chromatographic reference methods are time consuming and costly. Thus, alternative methods such as infrared spectroscopy are being increasingly developed to provide simple, rapid, and nondestructive methods to detect mycotoxins. This article reviews research conducted over the last eight years into the use of near-infrared and mid-infrared spectroscopy to monitor mycotoxins in corn, wheat, and barley. More specifically, we focus on the Fusarium species and on the main fusariotoxins of deoxynivalenol, zearalenone, and fumonisin B1 and B2. Quantification models are insufficiently precise to satisfy the legal requirements. Sorting models with cutoff levels are the most promising applications.

Aflatoxins are carcinogenic mycotoxins that may contaminate many crops and more especially maize. To protect consumers from these contaminants, many countries set up low regulatory thresholds of few µg/kg. The control of food requires time-consuming analysis for which sampling is a key step. It would therefore of key sanitary and economic relevance to develop rapid, sensitive and accurate methods that could even be applied on line at harvest, to identify batches to be excluded as soon as possible. In this study, we analyzed more than 500 maize samples taken at harvest during 3 years for their aflatoxin contamination using HPLC-MS. Among them, only 7% were contaminated but sometimes at levels largely exceeding European regulations. We demonstrate that Near InfraRed Spectroscopy (NIRS) could be of great help to classify cereal samples according to their level of aflatoxin contamination (below or higher than E.U. regulation). To build the model, all AF contaminated samples as well as an equivalent number of AF free samples were used. NIRS performance was not sufficient to quantify the toxins with adequate precision. However, its ability to discriminate naturally contaminated maize samples according to their level of contamination with aflatoxins in relation to European regulations using a quadratic PCA-DA model was excellent. Accuracy of the model was 97.4% for aflatoxin B1 and 100% for total aflatoxins.

Fumonisins are major mycotoxins found worldwide in maize and maize products. Because of their toxicity for both human and animals, European Union regulations were created to fix the maximal fumonisin B1 and B2 content allowed in foods and feeds. Unfortunately, directly measuring these mycotoxins by current analytical techniques is tedious and expensive and most measurement methods do not lend themselves to online control. Alternative approaches to chemical analysis have been developed and involve models that allow the mycotoxin contamination to be predicted based on environmental conditions and analysis by near‐infrared (NIR) spectroscopy. In the present work, we use NIR spectroscopy to determine the fumonisin and fungal contents of 117 samples of maize. The determination coefficient between fumonisin and fungal‐biomass content was 0.44. We establish herein a threshold for the number of CFUs for fungal biomass beyond which the fumonisin content is likely to exceed the European regulatory level of 4000 μ g/kg. In addition, we determine the fungal content by using a NIR‐spectroscopy model that allows us to sort samples of maize. Upon calibration, the percentage of well‐classified samples was 96%, which compares favorably to the 82% obtained by independent verification.

Vine fertilization is a tool that allows winegrowers to influence and regulate the quality of their wine. Today, nutritional analysis is done by using a CHNS analyzer and mass spectroscopy. However, these methods are destructive and time consuming. Another approach is to use near-infrared (NIR) spectroscopy, which, when coupled with chemometric tools, allows users to develop prediction models. This approach is widely used today in agriculture. In this study, we focus on the relative amount of carbon (C), hydrogen (H), nitrogen (N), and sulfur (S), in dry matter (DM), and on the C:N ratio. The relative amount of these elements was obtained by applying NIR spectroscopy to 252 samples of various fresh and dried vine organs. Each partial least squares model was tested on an external prediction set. The coefficient of determination for prediction (r²), the root-mean-square error of prediction (RMSEP), and the ratio of performance of prediction (RPD) were obtained for C (0.49, 2.24% of DM, and 1.33 for fresh material with MSC; 0.45, 2.37% of DM, and 1.26 for dry material with MSC, respectively), H (0.56, 0.27% of DM, and 1.45 for fresh material with D1; 0.49, 0.30% of DM, and 1.32 for dry material with D1, respectively), N (0.91, 0.17% of DM, and 3.32 for fresh material with raw spectra; 0.95, 0.13% of DM, and 4.39 for dry material with MSC, respectively), S (0.47, 0.046% of DM, and 1.31 for fresh material with MSC; 0.46, 0.046% of DM, and 1.30 for dry material with D2, respectively), and the C:N ratio (0.85, 8.20, and 2.58 for raw spectra of fresh material; 0.87, 7.55, and 2.80 for dry material with D2, respectively). These results show that NIR spectroscopy can be used to assess the status of nitrogen nutrition in vines and to monitor the C:N ratio.

Deoxynivalenol and fumonisins pose a health concern and have economic consequences, so the European regulation CE 1126/2007 dictates the maximal content allowed in cereals. The direct measurement of mycotoxin content using the established method is not only time-consuming and tedious, but also destructive and cannot be used in a silo. Alternative tools such as infrared spectroscopy are therefore being studied. For the present investigation, spectral data collected from maize kernels contaminated naturally by mycotoxins were studied to predict the risk of deoxynivalenol and fumonisins. Discriminant models were used to create and identify batches that satisfy regulations for animal or human consumption.