Kamut, the brand name of a variety of wheat that is believed to have been cultivated in ancient times and that reportedly has a higher nutritional value than traditional wheat, commands a premium price. As a result, there is a desire among suppliers and consumers to verify the quality and integrity of products made from the cereal.Eric Dufour of Ecole Nationale d’Ingénieurs des Travaux Agricoles de Clermont-Ferrand, an agricultural and engineering university in France, thus has investigated whether a nondestructive spectroscopy technique he had used to categorize milk and cheese would be suitable for the identification of Kamut. Front-face fluorescence spectroscopy nondestructively identifies varieties of cereal, including “King Tut’s Wheat,” Kamut. Dufour, along with Romdhane Karoui of the university and Gerald Cartaud of Inter Bio Bretagne of Rennes, France, employed front-face fluorescence spectroscopy — in which the excitation source and detector are positioned on the same side of the sample — to test samples of Kamut and two other varieties of wheat provided by Kamut Enterprises of Europe BVBA of Ghent, Belgium. They employed a Fluoro-Max-2 spectrofluorometer from Horiba Jobin Yvon SAS of Longjumeau, France, in the experiments to excite an amino acid native to the grain and to observe the emitted fluorescence.For the work, they selected tryptophan, which is excited by 290-nm radiation and produces a 305- to 400-nm fluorescent signal. The tryptophan spectrum offers a good signal-to-noise ratio and serves as a sensitive probe of such factors as the hydrophobic or hydrophilic nature of its environment and other factors that can change molecular conformation. Similarly, differences in tryptophan fluorescence reflect both the normal variation between grains and the changes due to processing.To generate a straightforward classification method, Dufour used principal component analysis, a technique of identifying features that vary the most in the target population. The expectation is that features with the highest variance will indicate differences in sample characteristics. Rather than performing a point-by-point spectral analysis, principal component analysis reduces the comparison to a handful of features that can be quantified and rapidly compared.The method was applied to flour, semolina (essentially, the residue that remains after flour has been ground and removed), and ground pasta prepared from whole-grain and processed Kamut and other wheat varieties. The results were encouraging.Using the first two principal components, all processed soft wheat flours were identified, and 80 percent of the whole-grain Kamut was categorized correctly. Similar results were obtained for other categorization tests. For example, the method accurately identified the form of 92 percent of the analytes as being flour, semolina or pasta, regardless of the variety of grain used.The results did not come as a surprise to Dufour, who has used the technique to trace the origin of cheese, to quantify the texture of cake and to measure the tenderness of meat. By using additional intrinsic molecular probes, increasing the size of the training set of grains and producing a laptop spectrofluorometer, he anticipates that the method will become accurate and easy to use for food authentication. Journal of Agricultural and Food Chemistry, online Feb. 28, 2006, doi:10.1021/jf053010y.