Natural Occurrence of Alternaria Toxins in Agricultural Products and Processed Foods Marketed in South Korea by LC-MS/MS.

Alternaria mycotoxins including alternariol (AOH), alternariol monomethyl ether (AME), altenuene (ALT), altertoxin-I (ATX-I), tentoxin (TEN), and tenuazonic acid (TeA), are ubiquitous contaminants in agricultural products. A method for the simultaneous determination of these six toxins by ultrahigh performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) with solid phase extraction (SPE) was validated in rice, sesame, tomato, and apple juice matrices. The performance of the method was evaluated in terms of linearity (R2 > 0.999), the limit of detection (0.04-1.67 µg/kg), the limit of quantification (0.12-5.06 µg/kg), recovery (80.0-114.7%), and precision (<17.7%). The validated method was applied to monitor 152 marketed food samples in South Korea, as well as to investigate the co-occurrence and correlation between Alternaria toxins. The mean occurrence levels were 2.77 µg/kg for AOH, 4.36 µg/kg for AME, 0.14 µg/kg for ALT, 0.11 µg/kg for ATX-I, 0.43 µg/kg for TEN, and 104.56 µg/kg for TeA. Mean and extreme (95th percentile) daily dietary exposures of South Koreans to Alternaria toxins were estimated to be 22.93 ng/kg b.w./day and 86.07 ng/kg b.w./day, respectively.

Simple chromatographic determination of aflatoxins in Korean fermented soybean products doenjang, ganjang, and gochujang, with comparison of derivatization methods.

Korean fermented soybean products doenjang, ganjang, and gochujang are vulnerable to contamination with aflatoxigenic fungi in the open fermentation environment. Therefore, simple and effective methods to determine aflatoxins (AFs) in these complex food matrices are needed. High-performance liquid chromatography with fluorescence detection using two derivatization methods for AF determination in three fermented soybean products was optimized and validated. Pre-column derivatization (preCD) of the AF extracts was performed using trifluoroacetic acid, and post-column derivatization (PCD) was performed in a photochemical reactor for enhanced detection. Both derivatization methods resulted in acceptable performances for linearity (R2 > 0.999), recovery (71-118%), and precision (< 10.6%) values. Recovery and precision with preCD and PCD were similar, but the limit of detection was superior with PCD. When these analytical methods were applied to commercially available fermented soybean products, the AF levels of all commercial products were ranged from not detected to 6.06 µg/kg.

Complex-bilinear recurrent neural network for equalization of a digital satellite channel.

Equalization of satellite communication using complex-bilinear recurrent neural network (C-BLRNN) is proposed. Since the BLRNN is based on the bilinear polynomial, it can be used in modeling highly nonlinear systems with time-series characteristics more effectively than multilayer perceptron type neural networks (MLPNN). The BLRNN is first expanded to its complex value version (C-BLRNN) for dealing with the complex input values in the paper. C-BLRNN is then applied to equalization of a digital satellite communication channel for M-PSK and QAM, which has severe nonlinearity with memory due to traveling wave tube amplifier (TWTA). The proposed C-BLRNN equalizer for a channel model is compared with the currently used Volterra filter equalizer or decision feedback equalizer (DFE), and conventional complex-MLPNN equalizer. The results show that the proposed C-BLRNN equalizer gives very favorable results in both the MSE and BER criteria over Volterra filter equalizer, DFE, and complex-MLPNN equalizer.