Quantitative assessment of moisture damage for cacao bean quality using two-dimensional gas chromatography combined with time-of-flight mass spectrometry and chemometrics

Quality control of cacao beans is a significant issue in the chocolate industry. In this report, we describe how moisture damage to cacao beans alters the volatile chemical signature of the beans in a way that can be tracked quantitatively over time. The chemical signature of the beans is monitored via sampling the headspace of the vapor above a given bean sample. Headspace vapor sampled with solid-phase micro-extraction (SPME) was detected and analyzed with comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC × GC–TOFMS). Cacao beans from six geographical origins (Costa Rica, Ghana, Ivory Coast, Venezuela, Ecuador, and Panama) were analyzed. Twenty-nine analytes that change in concentration levels via the time-dependent moisture damage process were measured using chemometric software. Biomarker analytes that were independent of geographical origin were found. Furthermore, prediction algorithms were used to demonstrate that moisture damage could be verified before there were visible signs of mold by analyzing subsets of the 29 analytes. Thus, a quantitative approach to quality screening related to the identification of moisture damage in the absence of visible mold is presented.     

Low-lying (K π = 0+) states of gadolinium isotopes

The sd-interacting boson approximation (sd-IBA) and the df-interacting boson approximation (df-IBA) can be related to each other and the states of the interacting boson approximation model can be identified with the fully symmetric states in the sdf interacting boson approximation model. A systematic study of the sdf-IBA model showed that the constructed Hamiltonian can successfully describe the strong octupole correlations in the deformed nuclei. We showed that the interacting boson approximation may account for many of these K ^π ?=?0⁺ states. It was found that the calculated energy spectra of the gadolinium isotopes agree quite well with the experimental data. The observed B(E2) values were also calculated and compared with the experimental data.     

Constituents with independence from growth temperature for bacteria using pyrolysis-gas chromatography/differential mobility spectrometry with analysis of variance and principal component analysis

Four bacteria, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus warneri, and Micrococcus luteus, were grown at temperatures of 23, 30, and 37 degrees C and were characterized by pyrolysis-gas chromatography/differential mobility spectrometry (Py-GC/DMS) providing, with replicates, 120 data sets of retention time, compensation voltage, and ion intensity, each for negative and positive polarity. Principal component analysis (PCA) for 96 of these data sets exhibited clusters by temperature of culture growth and not by genus. Analysis of variance was used to isolate the constituents with dependences on growth temperature. When these were subtracted from the data sets, Fisher ratios with PCA resulted in four clusters according to genus at all temperatures for ions in each polarity. Comparable results were obtained from unsupervised PCA with 24 of the original data sets. The ions with taxonomic features were reconstructed into 3D plots of retention time, compensation voltage, and Fisher ratio and were matched, through GC-mass spectrometry (MS), with chemical standards attributed to the thermal decomposition of proteins and lipid A. Results for negative ions provided simpler data sets than from positive ions, as anticipated from selectivity of gas phase ion-molecule reactions in air at ambient pressure.     

A principal component analysis based method to discover chemical differences in comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GCxGC-TOFMS) separations of metabolites in plant samples

Comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC × GC-TOFMS) provides high resolution separations of complex samples with a mass spectrum at every point in the separation space. The large volumes of multidimensional data obtained by GC × GC-TOFMS analysis are analyzed using a principal component analysis (PCA) method described herein to quickly and objectively discover differences between complex samples. In this work, we submitted 54 chromatograms to PCA to automatically compare the metabolite profiles of three different species of plants, namely basil (Ocimum basilicum), peppermint (Mentha piperita), and sweet herb stevia (Stevia rebaudiana), where there were 18 chromatograms for each type of plant. The 54 scores of the m/z 73 data set clustered in three groups according to the three types of plants. Principal component 1 (PC 1) separated the stevia cluster from the basil and peppermint clusters, capturing 61.84% of the total variance. Principal component 2 (PC 2) separated the basil cluster from the peppermint cluster, capturing 16.78% of the total variance. The PCA method revealed that relative abundances of amino acids, carboxylic acids, and carbohydrates were responsible for differentiating the three plants. A brief list of the 16 most significant metabolites is reported. After PCA, the 54 scores of the m/z 217 data set clustered in three groups according to the three types of plants, as well, yielding highly loaded variables corresponding with chemical differences between plants that were complementary to the m/z 73 information. The PCA data mining method is applicable to all of the monitored selective mass channels, utilizing all of the collected data, to discover unknown differences in complex sample profiles.     

Separation and determination of denatured caseins by hydrophobic interaction chromatography Part II. Method validation and applications

A method recently described for the separation of denatured alpha-, beta- and kappa-caseins by hydrophobic interaction chromatography was validated by the analysis of reference skim milk powder (BCR-063R) certificated for total nitrogen content. The method is based on fast and easy solubilization of commercial and real samples by 4.0 M guanidine thiocyanate and elution on a TSK-Gel Phenyl-5PW column (TosoHaas) in the presence of 8.0 M urea in the mobile phase. No preliminary precipitation or separation of the casein fraction is required. A linear relationship between the concentration of casein and peak area (UV absorbance detector at 280 nm) was obtained over the concentration range 0.5-60 microM. The detection limits for alpha-, beta- and kappa-caseins ranged between 0.30 and 0.65 microM. The precision of the method was evaluated; the relative standard deviation for alpha-, beta- and kappa-casein determination ranged between 2.2 and 2.7% for standard solutions and between 3.5 and 6.2% for real sample solutions. The mean casein content found in 10 aliquots of BCR-063R calculated with respect to the total protein content (estimated on the basis of certified total nitrogen content) was 79.1+/-2.7%. Results of linear fitting of standard additions data for alpha-, beta- and kappa-caseins to BCR-063R were compared with linear fitting of alpha-, beta- and kappa-casein calibration data. The method was applied to commercial caseins and to 31 real, raw samples [processed cow's milk (pasteurised, UHT-treated), follow-up milk powders, cream, cheeses, casein-free infant formulae, cookies for babies containing milk proteins] with the aim of showing the wide applicability of the method in order to determine alpha-, beta- and kappa-caseins.     

A dynamic liquid-liquid interfacial pressure detector for the rapid analysis of surfactants in a flowing organic liquid

Design and development of a dynamic interfacial pressure detector (DIPD) is reported. The DIPD measures the differential pressure as a function of time across the liquid-liquid interface of organic liquid drops (i.e., n-hexane) that repeatedly grow in water at the end of a capillary tip. Using a calibration technique based on the Young-Laplace equation, the differential pressure signal is converted, in real-time, to a relative interfacial pressure. This allows the DIPD to monitor the interfacial tension of surface active species at liquid-liquid interfaces in flow-based analytical techniques, such as flow injection analysis (FIA), sequential injection analysis (SIA) and high performance liquid chromatography (HPLC). The DIPD is similar in principle to the dynamic surface tension detector (DSTD), which monitors the surface tension at the air-liquid interface. In this report, the interfacial pressure at the hexane-water interface was monitored as analytes in the hexane phase diffused to and arranged at the hexane-water interface. The DIPD was combined with FIA to analytically measure the interfacial properties of cholesterol and Brij^®30 at the hexane-water interface. Results show that both cholesterol and Brij^®30 exhibit a dynamic interfacial pressure signal during hexane drop growth. A calibration curve demonstrates that the relative interfacial pressure of cholesterol in hexane increases as the cholesterol concentration increases from 100 to 10,000 μg ml⁻¹. An example of the utility of the DIPD as a selective detector for a chromatographic separation of interface-active species is also presented in the analysis of cholesterol in egg yolk by normal-phase HPLC-DIPD.     

Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry detection: analysis of amino acid and organic acid trimethylsilyl derivatives, with application to the analysis of metabolites in rye grass samples

First, standard mixtures of trimethylsilyl (TMS) derivatives of amino acid and organic acid are analyzed by comprehensive two-dimensional (2D) gas chromatography (GC) coupled to time-of-flight mass spectrometry (GC × GC/TOFMS) in order to illustrate important issues regarding application of the technique. Specifically of interest is the extent to which the peak capacity of the 2D separation space has been utilized and the procedure by which the derivative standards are identified in the 2D separations using the mass spectral information. The resulting 2D separation is found to make extensive use of the GC × GC separation space provided by the complementary stationary phases employed.Second, in order to demonstrate GC × GC/TOFMS on two real sample types, trimethylsilyl metabolite derivatives were analyzed from extracts of common lawn grass samples (i.e., perennial rye grass), as a means to provide insight into both the pre and post harvest physiology. Various chemical components in the two rye grass extract samples were found to either emerge or disappear in relation to the trauma response. For example, a significant difference in the peak for the TMS derivative of malic acid was found. The successful analysis of various components was readily facilitated by the 2D separation, while a 1D separation would have produced too much peak overlap, thus impeding the analysis. The importance of using a GC × GC separation approach for the analysis of complex samples, such as metabolite extracts, is therefore demonstrated. The real-time analysis capability of GC × GC/TOFMS for multidimensional metabolite analysis makes this technique well suited to the high-throughput analysis of metabolomic samples, especially compared to slower, stopped-flow type separation approaches.     

Chromatographic determination of copper speciation in jet fuel

A method is described that allows one to distinguish and quantitate two different classes of copper compounds in the same hydrocarbon sample. This will enable the study of the effects of different copper compounds on the performance and stability of petroleum samples. Copper N,N'-disalicylidene-1,2-propylenediamine (CuDMD) and several copper carboxylates were preconcentrated from a hydrocarbon matrix using a column packed with polyvinylpyrrolidone, (C(6)H(9)NO)(x), a novel polymeric stationary phase. The copper complexes were then sequentially eluted using a step gradient program beginning with hexane/isopropyl alcohol as the eluent and ending with an acetic acid/isopropyl alcohol eluent. The copper complexes were detected by serial UV absorbance and flame atomic absorbance (FAA) detection. With on-column preconcentration and FAA detection, the limits of detection were 7 and 40 ppb copper for CuDMD and the copper carboxylates respectively. With this method, it was possible to distinguish between the two different classes of copper compounds in the same hydrocarbon sample, which will help to provide an understanding of the catalytic activity of different copper compounds, leading to a better understanding of the factors causing fuel instability. The method promises to be a valuable tool in the analysis and characterization of copper compounds in petroleum samples.