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This study optimized the headspace solid phase microextraction (HS-SPME) conditions for the analysis of the volatile flavor compounds of Chinese south stinky tofu brine by gas chromatography-mass spectrometry (GC-MS). The optimum HS-SPME conditions established were as follows: polar column CD-WAX, white 85 μm polyella extractor, extraction temperature 60 °C, equilibrium time 20 min, extraction time 40 min. Under these conditions, a total of 63 volatile flavor compounds in five stinky tofu brines were identified. The offensive odor of the stinky tofu may be derived from some of the volatile flavor compounds such as phenol, p-cresol, 3-methylindole, indole, acetic acid, propionic acid, isobutyric acid, n-butyric acid and 3-methylbutanoic acid. The volatile flavor substances data was examined by principal component analysis (PCA) to visualize the response patterns in the feature space of principal components (PC). PCA analysis results revealed that the Chengshifu brine (STB1) and Baise jingdian brine (STB4) are similar in PC 1, 2, and 3, and the two brines have a similar flavor. Results also indicate that the Huogongdian brine (STB2) and Wangcheng brine (STB3) can be grouped in the same class as they are similar in PC 3. However, PC 1, 2, and 3 of the Luojia brine (STB5) and other brands of brine are different as is the flavor.

A method for the fast determination of the components in a complex sample by using gas chromatography with mass spectrometry was developed and used for the quantitative analysis of phthalic acid esters in environmental water. In the method, the adaptively corrected mass spectra were used to compensate for the differences between the library spectra and the measured ones in the experiment. The correction was obtained by the iterative transformation of the library spectra using iterative target transformation factor analysis, and the resolution was performed by non-negative immune algorithm using the corrected spectra. Rapid analysis of 16 phthalic acid esters in water samples was achieved using fast elution gas chromatography with mass spectrometry measurements. The results show that the mass spectra and chromatographic profiles of the phthalic acid esters can be obtained from the overlapping signal of 13 min elution, and accurate quantitative analysis can be obtained. The recoveries of the phthalic acid esters obtained by standard addition are between 90.3 and 107.4%, and the relative standard deviations obtained in repeated measurements are less than 9%., (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

To the increasing difficulty of the detection of multi-pesticide residues in cowpea, optimized moving window target conversion factor analysis (OMWTTFA) algorithm was proposed, in which an adaptive iterative re-weighted penalized least squares calculation had been added to the moving window target transfer factor analysis (MWTTFA) algorithm. And a new method for non-targeted detection of multi-pesticide residues in cowpea was established by combining with gas chromatography-mass spectrometry (GC-MS). A mass spectrometry database of 500 common pesticides was established and high throughput analysis of cowpea GC-MS data was realized by using OMWTTFA algorithm. The method was verified with actual cowpea and spiked samples. The results showed that the method could realize non-targeted detection of all pesticide residues in the two groups of spiked cowpea samples within 30 min retention time, while no pesticide residues were detected in the actual cowpea samples. The matching values of all components were larger than 800‰, and this method was more accurate than MWTTFA algorithm. The above results showed that this method promised to become a new way for non-targeted and high-throughput detection of multi-pesticide residues in fruits and vegetables. [ABSTRACT FROM AUTHOR]

In chromatographic analysis of multicomponent real samples, peak overlapping, high level of noise and background are frequently encountered, making the qualitative and quantitative analysis difficult or even impossible. In this work, an algorithm named as generalized window factor analysis (GWFA) was proposed for quantitative analysis of the target components in the samples with complex matrices by gas chromatography–mass spectrometry (GC–MS). The theory and calculation of GWFA are just similar with the conventional window factor analysis (WFA), but the “window” is defined as the selected channels (mass-to-charge ratios) in the mass spectral dimension of the data matrix, instead of a continuous region in chromatographic dimension along the retention time. Therefore, the generalized window for a target component can be easily determined with the help of the mass spectrum. Then, the calculated mass spectrum can be obtained with the window and quantitative determination can be achieved with the help of the standard. Both simulated and experimental data were investigated with the proposed method. Whether or not a peak shift occurs during the test, accurate results were obtained from the overlapping GC–MS signals with high level of noise and background. [ABSTRACT FROM AUTHOR]

A second-order method, standard signal extraction was proposed for quantitative analysis of target analytes in the samples with complex matrices by gas chromatography-mass spectrometry. In the method, standard addition was adopted. The data of the pure standard, sample, and spiked sample were used in the calculation. By performing principal component analysis on the data aligned with the measured signals of the sample and standard, the standardized signal that is orthogonal to the signals of interferences in the sample can be extracted. Then, the signal of spiked sample was projected to the standardized signal. The concentration ratio of the target analyte in the sample and spiked sample can be obtained. Finally, the concentration of the target analyte in the sample can be calculated with the added concentration in the spiked sample. Both simulated and experimental data were investigated with the proposed approach, compared with rank annihilation factor analysis. The recoveries of standard addition were found in a range of 99-105%, and the relative standard deviations obtained in three repeated measurements were less than 3%. Results show that the method can accurately estimate the quantitative information of analytes of interest in the presence of interferents and can be applied more widely compared with rank annihilation factor analysis. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

[Display omitted] An automatic, rapid and non-targeted detection method for multi-pesticides in plant-derived foods was developed by gas chromatography–mass spectrometry and chemometrics. In this method, a novel algorithm named moving window iterative target transformation factor analysis was proposed. Although there are challenges of peak overlapping and background interference, the retention time and corrected mass spectra of unknown pesticides can be automatically obtained through iteration calculation in the 'moving window' with reference to the pesticide mass spectral library. One mixed pesticide standard and nine varieties of plant-derived foods were investigated with the proposed method. By contrast, a fast temperature programme was used to shorten detection time compared to the standard temperature programme. For the mixed standard, the mass spectra and retention times of all 39 pesticides were successfully obtained from the overlapping signal. Furthermore, all spiked pesticides were successfully detected in plant-derived foods within 10 min using a fast temperature programme. [ABSTRACT FROM AUTHOR]