**1. Introduction**

Over the last several years, mycotoxins, which are metabolites secreted by fungi, have been the subject of numerous studies. These eukaryotes play a major ecological role in the life cycle of plants. Indeed, for some fungi, the role of saprophyte places them at the heart of ecosystem dynamics [Alexopoulous et al., 1996].

Some 350 mold species produce a large range of secondary metabolites (over 300, of which ~30 are toxic) [Fremy et al., 2009] and represent a potential danger for animal and human health and cause significant losses for the cereals industry [Le Bars et al., 1996]. The effects of mold are well illustrated by decreases in crop and livestock yields, public health problems, or write-offs on the international cereal market [Le Bars, et al., 1996]. The United Nations Food and Agriculture Organization estimates annual global losses from mycotoxins at 1 billion tons of foodstuffs [Fao, 2001]. The primary organisms impacted by mycotoxins are plants. Currently, about 25% of agricultural crops worldwide are contaminated by these metabolites [Charmley et al., 2006].

In response to these significant economic and health risks, global non-tariff barriers (i.e., specific food-safety standards imposed on imported products) were erected to control commercial trade based on the mycotoxic quality of foodstuffs. These measures generate significant economic and material losses for countries that export contaminated foodstuffs, either because their cargo is refused or because of a reduction in prices. To limit these consequences, farmers and the food industry strive to reduce the presence of mycotoxins in their products. Therefore, producers and processors are searching for alternative analytical methods to determine, in a quick, simple, and inexpensive manner, the risk of their products containing fungi or mycotoxins. The use of infrared spectroscopy—a mature technology—to monitor foodstuffs could respond to this need.

In this chapter, we focus on mycotoxins found mainly in wheat, barley, and corn and that have been studied in the international literature; namely, deoxynivalenol, fumonisins, and aflatoxin B1.

Infrared Spectroscopy Applied to Identification and Detection

must be less than a given threshold; the limit for corn is 8 µg/g.

**wheat, barley, and corn 3.1 Background and methods** 

of Microorganisms and Their Metabolites on Cereals (Corn, Wheat, and Barley) 187

Ergosterol, however, is more specific to mold. This molecule, which may still be called provitamin D2, is a C24-methylated sterol (and is part of the subgroup of organic compounds that are soluble in lipids) and is found in the cell membranes of yeasts and filamentous fungi. This molecule is not found in animal cells [Verscheure et al., 2002] and is in the minority among the sterols found in higher plants [Pitt et al., 1997] and insects [Weete, 1980]. Griffiths et al. [Griffiths et al., 2003] demonstrated that ergosterol is the primary sterol found in molds: it represents 95% of the total sterols, with the remaining 5% being ergosterol precursors from *Leptosphaeria maculans*. This specificity makes this molecule a potential tracer of fungal activity. It is generally agreed that the ergosterol content of grains

**3. Infrared spectroscopy to detect fungal and mycotoxic contamination of** 

with the quantification of other parameters such as protein content, humidity, etc.

Nowicki 2007; Penteado Moretzsohn De Castro et al., 2002; Perkowski,et al., 1995].

Covering the last 20 years, we count over 20 articles dealing with the use of infrared spectroscopy (primarily near-infrared) to detect molds and mycotoxins in wheat, barley, and corn. Because some of the work in infrared spectroscopy deals both with the detection and the identification of mycotoxins, we separate the articles into three groups. Table 1 is for molds, Table 2 compiles the trials dealing with deoxynivalenol (DON), fumonisins (FUMs), and B1 aflatoxins (AF1). Finally, Table 3 contains articles in which the authors worked

The first application of infrared spectroscopy to detect microorganisms dates from the 1950s [Miguel Gomez et al., 2003]. In these applications, the spectrometers were calibrated depending on the method of dosing the fungi or mycotoxins. In the 1980s, Fraenkel *et al.* [Fraenkel et al., 1980] and Davies *et al.* [Davies et al., 1987] published their first works on the detection by infrared spectroscopy of fungal contamination (*Botrytis cinerea* and *Alternaria tenuissima*), but the application of this tool to detecting mold really grew in the 1990s. This growth was due to the fact the existing agronomic models required collecting a significant amount of data in the field, making this approach unsuitable for routine use. In addition, industry required nondestructive techniques to assess the health safety of crops. Therefore, several research teams used infrared spectroscopy to detect mold and mycotoxins on cereals, which could be done concomitantly

One method proposed to determine the fungal or mycotoxin content is to quantify the total fungal biomass. Toward this end, ergosterol is used as a fungus marker [Castro et al., 2002; Saxena et al., 2001; Seitz et al., 1977; Seitz et al., 1979]. Very often, this type of study is coupled with a study of the mycotoxin content and fungal units (colony-forming units or CFU). Indeed, the quantity of fungi is not proportional to the quantity of mycotoxins; it is possible to have small quantities of fungi but large quantities of mycotoxins, and vice versa. Indeed, fungi may disappear after secreting its toxins, either because of the evolution of the mycoflora or because of the application of chemical treatments. In addition, certain strains are more toxic than others. Two conclusions exist from the work on this subject: some researchers find a correlation between the mycotoxin content, the ergosterol content, and/or the fungal units [Lamper et al., 2000; Le Bouquin et al., 2007; Miedaner et al., 2000; Seitz et al., 1977; Wanyoike et al., 2002; Zill et al., 1988] whereas the others find no correlation or cannot make categoric conclusions [Beyer et al., 2007; Diener et al., 1982; Gilbert et al., 2002;
