Self-Organizing Maps and Support Vector Regression as aids to coupled chromatography: Illustrated by predicting spoilage in apples using volatile organic compounds

Predicting the Weathering Time by the Empty Puparium of Sarcophaga peregrina (Diptera: Sarcophagidae) with the ANN Models

Empty puparium are frequently collected at crime scenes and may provide valuable evidence in cases with a long postmortem interval (PMI). Here, we collected the puparium of Sarcophaga peregrina (Diptera: Sarcophagidae) (Robineau-Desvoidy, 1830) for 120 days at three temperatures (10 °C, 25 °C, and 40 °C) with the aim to estimate the weathering time of empty puparium. The CHC profiles were analyzed by gas chromatography-mass spectrometry (GC-MS). The partial least squares (PLS), support vector regression (SVR), and artificial neural network (ANN) models were used to estimate the weathering time. This identified 49 CHCs with a carbon chain length between 10 and 33 in empty puparium. The three models demonstrate that the variation tendency of hydrocarbon could be used to estimate the weathering time, while the ANN models show the best predictive ability among these three models. This work indicated that puparial hydrocarbon weathering has certain regularity with weathering time and can gain insight into estimating PMI in forensic investigations.

Data visualization of Salmonella Typhimurium contamination in packaged fresh alfalfa sprouts using a Kohonen network

Class visualization of multi-dimensional data from analysis of volatile metabolic compounds monitored using an electronic nose based on metal oxide sensor array was attained using a Kohonen network. An array of 12 metal oxide based chemical sensors was used to monitor changes in the volatile compositions from the headspace of packaged fresh sprouts with and without Salmonella Typhimurium contamination. Kohonen׳s self-organizing map (SOM) was then created for learning different patterns of volatile metabolites. The Kohonen network comprising 225 nodes arranged into a two-dimensional hexagonal map was used to locate the samples on the map to facilitate sample classification. Graphical maps including the unified matrix, component planes, and hit histograms were described to characterize the relation between samples. The clustering of samples with different levels of S. Typhimurium contamination could be visually distinguishable on the SOM. The Kohonen network proved to be advantageous in visualization of multi-dimensional nonlinear data and provided a clearer separation of different sample groups than a conventional linear principal component analysis (PCA) approach. The sensor array integrated with the Kohonen network could be used as a rapid and nondestructive method to distinguish samples with different levels of S. Typhimurium contamination. Although the analyses were performed on samples with natural background microbiota of about 7 Log(CFU/g), this microbiota did not affect the S. Typhimurium detection. The proposed method has potential to rapidly detect a target foodborne pathogen in real-life food samples instantaneously without subsequently culturing stages.

Quantification of animal fat biodiesel in soybean biodiesel and B20 diesel blends using near infrared spectroscopy and synergy interval support vector regression

In this work, multivariate calibration based on partial least squares (PLS) and support vector regression (SVR) using the whole spectrum and variable selection by synergy interval (siPLS and siSVR) were applied to NIR spectra for the determination of animal fat biodiesel content in soybean biodiesel and B20 diesel blends. For all models, prediction errors, bias test for systematic errors and permutation test for trends in the residuals were calculated. The siSVR produced significantly lower prediction errors compared to the full spectrum methods and siPLS, with a root mean squares error (RMSEP) of 0.18%(w/w) (concentration range: 0.00%-69.00%(w/w)) in the soybean biodiesel blend and 0.10%(w/w) in the B20 diesel (concentration range: 0.00%-13.80%(w/w)). Additionally, in the models for the determination of animal fat biodiesel in blends with soybean diesel, PLS and SVR showed evidence of systematic errors, and PLS/siPLS presented trends in residuals based on the permutation test. For the B20 diesel, PLS presented evidence of systematic errors, and siPLS presented trends in the residuals.

Microextraction techniques for the determination of volatile and semivolatile organic compounds from plants: a review

Vegetables and fruits are necessary for human health, and traditional Chinese medicine that uses plant materials can cure diseases. Thus, understanding the composition of plant matrix has gained increased attention in recent years. Since plant matrix is very complex, the extraction, separation and quantitation of these chemicals are challenging. In this review we focus on the microextraction techniques used in the determination of volatile and semivolatile organic compounds (such as esters, alcohols, aldehydes, hydrocarbons, ketones, terpenes, sesquiterpene, phenols, acids, plant secondary metabolites and pesticides) from plants (e.g., fruits, vegetables, medicinal plants, tree leaves, etc.). These microextraction techniques include: solid phase microextraction (SPME), stir-bar sorptive extraction (SBSE), single drop microextraction (SDME), hollow fiber liquid phase microextraction (HF-LPME), dispersive liquid liquid microextraction (DLLME), and gas purge microsyringe extraction (GP-MSE). We have taken into consideration papers published from 2008 to the end of January 2013, and provided critical and interpretative review on these techniques, and formulated future trends in microextraction for the determination of volatile and semivolatile compounds from plants.

Reproducible vapor-time profiles using solid-phase microextraction with an externally sampled internal standard

Determination of the dynamic nature of vapor/odor release has application in a wide variety of systems. This study applies automated solid-phase microextraction (SPME) utilizing an externally sampled internal standard (ESIS) to determine the vapor-time profile of odor delivery devices for three classes of explosive compounds. The profiles of delivery systems for target odorants 2,4-dinitrotoluene (2,4-DNT), 2-ethyl-1-hexanol (2-EH), and triacetone triperoxide (TATP) as canine training aids were compared over a period of 70 h. Strategies for evaluating the vapor-time profile of components with widely differing volatility are considered. An approach to quantifying the vapor concentration is described. The differences in the vapor-time profiles are examined and suggestions for selecting the best representative odor delivery technique are outlined.