The presence of aflatoxins in food and feed products is considered as one of the most important food safety problems in the world. Aflatoxins occur in a wide range of food products and can cause serious health risks. Moreover, they can nowadays only be detected by the use of destructive, time-consuming and expensive chemical analyses. We investigate the use of one-and two-photon induced fluorescence spectroscopy as nondestructive detection methods for the identification of aflatoxins. Particularly, as the samples under test, we consider the aflatoxin-contamination of different maize batches since maize is the staple food in many countries and cultivates in climates that show an extensive presence of the fungi. We first characterize the one-and two-photon induced fluorescence spectrum of pure aflatoxin B1, when excited with 365 nm and 730 nm laser light respectively. Subsequently, we experimentally investigate the fluorescence spectrum of various healthy and aflatoxin-contaminated maize samples, when excited with 365 nm, 405 nm, 730 nm, 750 nm, 780 nm and 810 nm laser light. For all excitation wavelengths, an intrinsic fluorescence signal of the maize grains is observed. However, for the contaminated maize grains, the present aflatoxin B1 significantly influences the intrinsic fluorescence. Depending on the excitation wavelength, we observe a different spectral contrast between the healthy and contaminated samples. The largest optical difference is observed for excitation with 365 nm and 780 nm, during the one-and two-photon induced fluorescence measurements respectively. The comparison of the measured fluorescence signals allows us to define a detection criterion for the optical identification of the contaminated maize samples. We can conclude that fluorescence spectroscopy can be a valuable tool for the measurement of aflatoxin-contents in maize, paving the way for real-time nondestructive industrial scanning-based detection.
Abstract. This paper introduces a novel method for learning a wrapper for extraction of text nodes from web pages based upon (k, l)-contextual tree languages. It also introduces a method to learn good values of k and l based on a few positive and negative examples. Finally, it describes how the algorithm can be integrated in a tool for information extraction.
This paper introduces a novel method for learning a wrapper for extraction of information from web pages, based upon (k, l)-contextual tree languages. It also introduces a method to learn good values of k and l based on a few positive and negative examples. Finally, it describes how the algorithm can be integrated in a tool for information extraction.
The presence of carcinogenic aflatoxins in food and feed products is a major worldwide problem. To date, the aflatoxin contamination can only be detected by the use of destructive sample-based chemical analyses. Therefore, we developed an optical setup able to detect the localized aflatoxin contamination in individual maize kernels, on the basis of one-and two-photon induced fluorescence spectroscopy. Our developed optical configuration comprises a tunable titaniumsapphire laser (710nm-830nm) in combination with second harmonic wavelength generation (355nm-415nm), enabling the measurement of both one-and two-photon induced fluorescence spectra. Moreover, an accurate scanning of the kernel's surface was induced by the use of automated translation stages, allowing to study the localized maize contamination. First, the operation of the setup is validated by the characterization of pure aflatoxin B1 powder. Second, the fluorescence spectra of healthy (< 1ppb aflatoxin B1) and contaminated maize kernels (>70ppb aflatoxin B1) were measured, after excitation with 365nm, 730nm, 750nm and 780nm. For both the one-and two-photon induced fluorescence processes, the presence of the aflatoxin inside the contaminated maize kernels influenced the intrinsic fluorescence signals. Based on the fluorescence spectrum between 400nm and 550nm, we defined a detection criterion to identify the contaminated maize kernels. Furthermore, we demonstrate the sensing of the localized contamination level, indicating both contaminated maize kernels with a high contamination level in a limited surface area (as small as 1mm²) as with a lower contamination spread over a large surface area (up to 20mm²). As a result, our developed measurement methodology allows the identification of the localized aflatoxin contamination, paving the way to the non-destructive, real-time and high-sensitive industrial scanning-based detection of aflatoxins in food products.
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