A robust method was developed using LC-ESI-MS/MS-based identification and quantification of 103 fortified pesticides in a mango fruit drink. Variations in QuEChERS extraction (without buffer, citrate, and/or acetate buffered) coupled with dispersive clean-up combinations were evaluated. Results showed 5 mL dilution and citrate buffered QuEChERS extraction with anhydrous (anhy) MgSO clean-up gave acceptable recovery for 100 pesticides @ 1 μg mL fortification.
View Article and Find Full Text PDFData-driven dictionaries have produced the state-of-the-art results in various classification tasks. However, when the target data has a different distribution than the source data, the learned sparse representation may not be optimal. In this paper, we investigate if it is possible to optimally represent both source and target by a common dictionary.
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January 2014
Traditional biometric recognition systems rely on a single biometric signature for authentication. While the advantage of using multiple sources of information for establishing the identity has been widely recognized, computational models for multimodal biometrics recognition have only recently received attention. We propose a multimodal sparse representation method, which represents the test data by a sparse linear combination of training data, while constraining the observations from different modalities of the test subject to share their sparse representations.
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