ABSTRACT
This study introduces two-dimensional (2-D) wavelet analysis to the classification of gas chromatogram differential mobility spectrometry (GC/DMS) data which are composed of retention time, compensation voltage, and corresponding intensities. One reported method to process such large data sets is to convert 2-D signals to 1-D signals by summing intensities either across retention time or compensation voltage, but it can lose important signal information in one data dimension. A 2-D wavelet analysis approach keeps the 2-D structure of original signals, while significantly reducing data size. We applied this feature extraction method to 2-D GC/DMS signals measured from control and disordered fruit and then employed two typical classification algorithms to testify the effects of the resultant features on chemical pattern recognition. Yielding a 93.3% accuracy of separating data from control and disordered fruit samples, 2-D wavelet analysis not only proves its feasibility to extract feature from original 2-D signals but also shows its superiority over the conventional feature extraction methods including converting 2-D to 1-D and selecting distinguishable pixels from training set. Furthermore, this process does not require coupling with specific pattern recognition methods, which may help ensure wide applications of this method to 2-D spectrometry data.
Subject(s)
Chromatography, Gas/methods , Algorithms , Chromatography, Gas/classification , Fruit/chemistry , Pattern Recognition, Automated , Principal Component AnalysisABSTRACT
For practical chromatographers it is extremely difficult to judge the merits and limitations of new technological developments. On the other hand, it is nearly impossible for those at the forefront of technology to judge the implications of their efforts for all specific applications of chromatography. Both chromatographers and researchers can be aided by a classification of the numerous specific applications into a few well-defined categories. In this paper, we propose such a classification of all chemical analysis by chromatography into three generic types of applications, viz. target-compound analysis, group-type separation, and fingerprinting. The requirements for each type are discussed in general terms. The classification scheme is applied to assess the benefits and limitations of comprehensive two-dimensional gas chromatography (GC x GC) and the possible additional benefits of using multivariate-analysis (MVA) techniques for each type of application. The conclusions pertaining to the generic types of applications are indicative for the implications of new developments for specific chemical analysis by chromatography.
Subject(s)
Chromatography, Gas/methods , Chromatography, Gas/classification , Multivariate AnalysisABSTRACT
Los plaguicidas organofosforados han sido ampliamente utilizados en la agricultura desde hace más de 50 años, proporcionado tratamientos económicos y efectivos para un gran número de cultivos. Sin embargo, algunos residuos de estos plaguicidas persisten en los alimentos y constituyen un riesgo importante para la salud humana. Los métodos de análisis de residuos deberán proporcionar una precisión y exactitud adecuadas siendo a la vez rápidos y simples de manera que proporcionen resultados fiables para el análisis de alimentos complejos en un tiempo razonable. Se presenta una revisión relativa a la determinación de plaguicidas organofosforados en alimentos donde se discuten las ventajas e inconvenientes de los métodos de extracción y determinación junto con los desarrollos más recientes en estos campos. Actualmente, la extracción con disolventes orgánicos y la extracción en fase sólida, como partes fundamentales de las tecnologías de preparación de la muestra, están en continua evolución. La cromatografia de gases utilizando detectores selectivos es la técnica más utilizada para la determinación de plaguicidas organofosforados debido a sus cualidades sobre otras técnicas-analíticas (AU)
