Project description:Aroma is a key factor in milk powder quality evaluation and consumer choice. However, research has mostly focused on processing effects, with little on flavor differences among milk powders. This study analysed and identified the flavor characteristics of five common types of milk powders in China, including yak milk powder, donkey milk powder, camel milk powder, goat milk powder, and cow milk powder, using Headspace-Gas Chromatography-Ion Mobility Spectrometry (HS-GC-IMS), Headspace Solid-Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS), and multivariate statistical analysis. Results identified 55 and 86 volatile compounds via HS-GC-IMS and HS-SPME-GC-MS, respectively, revealing significant differences between milk powders. PCA, OPLS-DA, PLS-DA, and heatmaps further distinguished the sources. Based on VIP values, 27 and 24 key compounds were identified. These results underscored the potential of utilizing these combined techniques for quick flavor analysis and detecting adulteration in milk powder.
Project description:Morchella sextelata and Morchella importuna are the main cultivars of morel. However, the key compounds affecting their flavors (taste and odor) are currently unknown. Here, an ultra performance tandem mass spectrometry combined with two-dimensional gas chromatography-time-of-flight mass spectrometry method was used to detect and relatively quantify the metabolites in both morel cultivars. A total of 631 non-volatile compounds and 242 volatile compounds were identified. The odor activity value was calculated to assess the contribution of key odor volatile. The results indicated that M. importuna had a sweeter flavor than M. sextelata. The former posed more prominent mushroom flavor than the latter based on the correlation analysis of the metabolites. The flavor differences of the two morel cultivars are highly relevant with the content of lipids, carbohydrates, amino acids and derivatives, alcohols and ketones. This study provides new insights into the theoretical basis for the flavor differences in both morel cultivars.
Project description:Polymeric coatings are used as a protective layer to preserve food or beverage quality and protect it from corrosion and avoid a metallic taste. These types of materials can contain some chemicals that are susceptible to migrate to food and constitute a risk for consumers' health. This study is focused on the identification of volatile and semi-volatile low molecular weight compounds present in polymeric coatings used for metal food and beverage cans. A method based on solid-liquid extraction followed by gas chromatography-mass spectrometry (GC-MS) was optimized for the semi-volatile compounds. Different solvents were tried with the aim of extracting compounds with different polarities. Furthermore, a method based on solid-phase microextraction (SPME) in headspace (HS) mode and gas chromatography coupled with mass spectrometry (HSSPME-GC-MS) was developed for the identification of potential volatile migrants in polymeric coatings. Some parameters such as extraction time, equilibrium temperature, or the type of fiber were optimized. Different compounds, including aldehydes such as octanal or nonanal, alcohols such as α-terpineol or 2-butoxyethanol, ethers, alkenes, or phthalic compounds, among others, were identified and confirmed with analytical standards both via SPME analysis as well after solvent extraction.
Project description:Ship emissions contribute substantial air pollutants when at berth. However, the complexity and diversity of the marine fuels utilized hinder our understanding and mapping of the characteristics of ship emissions. Herein, we applied GC × GC-MS to analyze the components of marine fuel oils. Owing to the high separation capacity of GC × GC-MS, 11 classes of organic compounds, including b-alkanes, alkenes, and cyclo-alkanes, which can hardly be resolved by traditional one-dimensional GC-MS, were detected. Significant differences are observed between light (-10# and 0#) and heavy (120# and 180#) fuels. Notably, -10# and 0# diesel fuels are more abundant in b-alkanes (44~49%), while in 120# and 180#, heavy fuels b-alkanes only account for 8%. Significant enhancement of naphthalene proportions is observed in heavy fuels (20%) compared to diesel fuels (2~3%). Hopanes are detected in all marine fuels and are especially abundant in heavy marine fuels. The volatility bins, one-dimensional volatility-based set (VBS), and two-dimensional VBS (volatility-polarity distributions) of marine fuel oils are investigated. Although IVOCs still take dominance (62-66%), the proportion of SVOCs in heavy marine fuels is largely enhanced, accounting for ~30% compared to 6~12% in diesel fuels. Furthermore, the SVOC/IVOC ratio could be applied to distinguish light and heavy marine fuel oils. The SVOC/IVOC ratios for -10# diesel fuel, 0# diesel fuel, 120# heavy marine fuel, and 180# heavy marine fuel are 0.085 ± 0.046, 0.168 ± 0.159, 0.504, and 0.439 ± 0.021, respectively. Our work provides detailed information on marine fuel compositions and could be further implemented in estimating organic emissions and secondary organic aerosol (SOA) formation from marine fuel storage and evaporation processes.
Project description:Volatile organic compounds (VOCs) play an important role in the biological activities of the medicinal Zingiberaceae species. In commercial preparations of VOCs from Kaempferia parviflora rhizomes, its leaves are wasted as by-products. The foliage could be an alternative source to rhizome, but its VOCs composition has not been explored previously. In this study, the VOCs in the leaves and rhizomes of K. parviflora plants grown in a growth room and in the field were analyzed using the headspace solid-phase microextraction (HS-SPME) method coupled with gas chromatography and time-of-flight mass spectrometry (GC-TOF-MS). The results showed a total of 75 and 78 VOCs identified from the leaves and rhizomes, respectively, of plants grown in the growth room. In the field samples, 96 VOCs were detected from the leaves and 98 from the rhizomes. These numbers are higher compared to the previous reports, which can be attributed to the analytical techniques used. It was also observed that monoterpenes were dominant in leaves, whereas sesquiterpenes were more abundant in rhizomes. Principal component analysis (PCA) revealed significantly higher abundance and diversity of VOCs in plants grown in the field than in the growth room. A high level of similarity of identified VOCs between the two tissues was also observed, as they shared 68 and 94 VOCs in the growth room and field samples, respectively. The difference lies in the relative abundance of VOCs, as most of them are abundant in rhizomes. Overall, the current study showed that the leaves of K. parviflora, grown in any growth conditions, can be further utilized as an alternative source of VOCs for rhizomes.
Project description:The efficacy of using human volatile organic compounds (VOCs) as a form of forensic evidence has been well demonstrated with canines for crime scene response, suspect identification, and location checking. Although the use of human scent evidence in the field is well established, the laboratory evaluation of human VOC profiles has been limited. This study used Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) to analyze human hand odor samples collected from 60 individuals (30 Females and 30 Males). The human volatiles collected from the palm surfaces of each subject were interpreted for classification and prediction of gender. The volatile organic compound (VOC) signatures from subjects' hand odor profiles were evaluated with supervised dimensional reduction techniques: Partial Least Squares-Discriminant Analysis (PLS-DA), Orthogonal-Projections to Latent Structures Discriminant Analysis (OPLS-DA), and Linear Discriminant Analysis (LDA). The PLS-DA 2D model demonstrated clustering amongst male and female subjects. The addition of a third component to the PLS-DA model revealed clustering and minimal separation of male and female subjects in the 3D PLS-DA model. The OPLS-DA model displayed discrimination and clustering amongst gender groups with leave one out cross validation (LOOCV) and 95% confidence regions surrounding clustered groups without overlap. The LDA had a 96.67% accuracy rate for female and male subjects. The culminating knowledge establishes a working model for the prediction of donor class characteristics using human scent hand odor profiles.
Project description:The growing demand for plant-based beverages has underscored the importance of investigating their volatile profiles, which play a crucial role in sensory perception and consumer acceptance. This is especially true for plant-based milks (PBMs) that have a clear reference model in bovine milk. This study characterises the volatile organic compounds (VOCs) in soy, almond and oat beverages compared to bovine milk using proton transfer reaction-time of flight-mass spectrometry (PTR-ToF-MS) as a rapid and noninvasive screening tool, complemented by gas chromatography-mass spectrometry (GC-MS) for compound identification. A total of 188 mass peaks were detected by PTR-ToF-MS, all showing significant differences from the blank, while GC-MS allowed the identification of 50 compounds, supporting the tentative identifications performed with PTR-MS analysis. In order to facilitate a comparison of different milks, after statistical analysis, these 188 mass peaks were further categorised into two groups: one consisting of VOCs with minimal variability across all samples and another comprising VOCs with significantly different abundances, distinctly characterising each beverage. Principal component analysis revealed a clear separation between bovine milk and PBMs, with almond beverages exhibiting the richest volatilome, while oat beverages displayed a more homogeneous volatile profile. PTR-ToF-MS demonstrated its ability to analyse volatile profiles rapidly, with excellent complementarity to GC-MS in terms of analytical versatility. The results provided a valuable basis for testing new experimental designs aimed to characterise and enhance flavour profiles in plant-based beverages, also after processing, in case of new product development that considers using these milks as raw materials.
Project description:In the present work, a novel infrared-assisted extraction coupled to headspace solid-phase microextraction (IRAE-HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) was developed for rapid determination of the volatile components in green tea. The extraction parameters such as fiber type, sample amount, infrared power, extraction time, and infrared lamp distance were optimized by orthogonal experimental design. Under optimum conditions, a total of 82 volatile compounds in 21 green tea samples from different geographical origins were identified. Compared with classical water-bath heating, the proposed technique has remarkable advantages of considerably reducing the analytical time and high efficiency. In addition, an effective classification of green teas based on their volatile profiles was achieved by partial least square-discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). Furthermore, the application of a dual criterion based on the variable importance in the projection (VIP) values of the PLS-DA models and on the category from one-way univariate analysis (ANOVA) allowed the identification of 12 potential volatile markers, which were considered to make the most important contribution to the discrimination of the samples. The results suggest that IRAE-HS-SPME/GC-MS technique combined with multivariate analysis offers a valuable tool to assess geographical traceability of different tea varieties.
Project description:Black tea, a widely popular non-alcoholic beverage, is renowned for its unique aroma and has attracted significant attention due to its complex composition. However, the chemical profile of Iranian tea remains largely unexplored. In this research, black tea samples from key tea cultivation regions in four geographical areas in northern Iran were firstly analyzed using headspace solid-phase microextraction followed by gas chromatography-mass spectrometry (HS-SPME-GC-MS) to separate, identify, and quantify their volatile organic compounds. Subsequently, employing a robust investigative strategy, we utilized for the first time the well-known multivariate curve resolution-alternating least square (MCR-ALS) method as a deconvolution technique to analyze the complex GC-MS peak clusters of tea samples. This approach effectively addressed challenges such as severe baseline drifts, overlapping peaks, and background noise, enabling the identification of minor components responsible for the distinct flavors and tastes across various samples. The MCR-ALS technique significantly improved the resolution of spectral and elution profiles, enabling both qualitative and semi-quantitative analysis of tea constituents. Qualitative analysis involved comparing resolved peak profiles to theoretical spectra, along with retention indices, while semi-quantification was conducted using the overall volume integration (OVI) approach for volatile compounds, providing a more accurate correlation between peak areas and concentrations. The application of chemometric tools in GC-MS analysis increased the number of recognized components in four tea samples, expanding from 54 to 256 components, all with concentrations exceeding 0.1 %. Among them, 32 volatile compounds were present in every tea sample. Hydrocarbons (including alkenes, alkanes, cycloalkanes, monoterpenes and sesquiterpenes), esters and alcohols were the three major chemical classes, comprising 78 % of the total relative content of volatile compounds. Analyzing black teas from four distinct regions revealed variations not only in their volatile components but also in their relative proportions. This integrated approach provides a comprehensive understanding of the volatile chemical profiles in Iranian black teas, enhances knowledge about their unique characteristics across diverse geographical origin, and lays the groundwork for quality improvement.