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13 pages, 2327 KiB  
Article
Variation in the Aroma Composition of Jasmine Tea with Storage Duration
by Zihao Qi, Wenjing Huang, Qiuyan Liu and Jingming Ning
Foods 2024, 13(16), 2524; https://doi.org/10.3390/foods13162524 (registering DOI) - 13 Aug 2024
Abstract
This study investigated the changes in the aroma of jasmine tea during storage. Solid-phase micro-extraction (SPME)–gas chromatography (GC)-mass spectrometry (MS) and stir bar sorptive extraction (SBSE)-GC-MS were combined to detect all volatile compounds. GC-olfactometry (GC-O), odor activity value (OAV), and p-value were [...] Read more.
This study investigated the changes in the aroma of jasmine tea during storage. Solid-phase micro-extraction (SPME)–gas chromatography (GC)-mass spectrometry (MS) and stir bar sorptive extraction (SBSE)-GC-MS were combined to detect all volatile compounds. GC-olfactometry (GC-O), odor activity value (OAV), and p-value were employed to analyze and identify the key aroma compounds in six jasmine tea samples stored for different durations. Nine key aroma compounds were discovered, namely (Z)-3-hexen-1-yl acetate, methyl anthranilate, methyl salicylate, trans-β-ionone, linalool, geraniol, (Z)-4-heptenal, benzoic acid methyl ester, and benzoic acid ethyl ester. The importance of these compounds was confirmed through the aroma addition experiment. Correlation analysis showed that (Z)-4-heptenal might be the main reason for the increase in the stale aroma of jasmine tea. Through sensory evaluation and specific experimental analysis, it can be concluded that jasmine tea had the best aroma after 3 years of storage, and too long a storage time may cause the overall aroma of the tea to weaken and produce an undesirable odor. The findings can provide a reference for the change in aroma during the storage of jasmine tea and provide the best storage time (3 years) in terms of jasmine tea aroma. Full article
(This article belongs to the Section Food Quality and Safety)
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Graphical abstract

Graphical abstract
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<p>Variations in the volatile components of jasmine tea throughout storage. (<b>A</b>) Relative concentrations of overall and several classes of volatile compounds. (<b>B</b>) Volatiles shared and not shared among the six samples. (<b>C</b>) Principal component analysis (PCA). (<b>D</b>) Hierarchical clustering analysis (HCA).</p>
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<p>Heat map of all volatiles in six jasmine tea samples. Gray represents volatiles that were not detected by gas chromatograph-mass spectrometry (GC-MS) in the samples.</p>
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<p>(<b>A</b>) Quantitative descriptive analysis (QDA) radar map of six sample infusions. (<b>B</b>) QDA radar map of six sample infusions after adding nine key aroma compound standard solutions.</p>
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16 pages, 2108 KiB  
Article
A Comparative Analysis of Cold Brew Coffee Aroma Using the Gas Chromatography–Olfactometry–Mass Spectrometry Technique: Headspace–Solid-Phase Extraction and Headspace Solid-Phase Microextraction Methods for the Extraction of Sensory-Active Compounds
by Esteban Narváez, Esteban Zapata, Juan David Dereix, Carlos Lopez, Sandra Torijano-Gutiérrez and Julián Zapata
Molecules 2024, 29(16), 3791; https://doi.org/10.3390/molecules29163791 - 10 Aug 2024
Viewed by 310
Abstract
Coffee, one of the most widely consumed commodities globally, embodies a sensory experience deeply rooted in social, cultural, and hedonic contexts. The cold brew (CB) method, characterized by cold extraction, is a refreshing and unique alternative to traditional coffee. Despite its growing popularity, [...] Read more.
Coffee, one of the most widely consumed commodities globally, embodies a sensory experience deeply rooted in social, cultural, and hedonic contexts. The cold brew (CB) method, characterized by cold extraction, is a refreshing and unique alternative to traditional coffee. Despite its growing popularity, CB lacks defined preparation parameters and comprehensive analysis of its aromatic composition. In this study, we aimed to obtain a representative extract of the volatile matrix of CB and characterize the aroma of sensory-active compounds using advanced techniques such as headspace–solid-phase Microextraction (HS-SPME) and headspace-solid-phase extraction (HS-SPE) for volatile compound extraction, followed by gas chromatography–olfactometry–mass Spectrometry (GC-O-MS) for compound identification. Optimization of the HS-SPME parameters resulted in the identification of 36 compounds, whereas HS-SPE identified 28 compounds, which included both complementary and similar compounds. In HS-SPME, 15 compounds exhibited sensory activity with descriptors such as floral, caramel, sweet, and almond, whereas seven exhibited sensory activity with descriptors such as chocolate, floral, coffee, and caramel. This comprehensive approach to HS-SPME and HS-SPE aroma extraction with GC-O-MS offers an efficient methodology for characterizing the aroma profile of CB, paving the way for future research and quality standards for this innovative coffee beverage. Full article
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<p>Optimization of HS-SPME variables (Ext.time: extraction time, Cond.time: conditioning time, Sample Vol.: sample volume, and Fiber: fiber type) based on response factors (total area and number of peaks).</p>
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<p>Chromatograms obtained from experiments E3 (extraction time = 50 min; sample volume = 4 mL; conditioning time = 15 min; fiber type = DVB/PDMS/CAR); E14 (extraction time = 50 min; sample volume = 4 mL; equilibrium time = 15 min; fiber type = PDMS); E29 (extraction time = 50 min; sample volume = 4 mL; equilibrium time = 15 min; fiber type = DVB/PDMS) with the fiber type optimization graph.</p>
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<p>The optimization of the extraction time for HS-SPME was analyzed based on the response factors (total area and number of peaks).</p>
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<p>Chromatograms obtained from experiments E3 (extraction time = 50 min; sample volume = 4 mL; conditioning time = 15 min; fiber type = DVB/PDMS/CAR) and E41 (extraction time = 10 min; sample volume = 4 mL; conditioning time = 15 min; fiber type = DVB/PDMS/CAR) with interaction graphs of extraction times for the total area and number of peaks.</p>
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<p>Olfactogram obtained through HS-SPME-GC-O-MS with Modified Frequencies.</p>
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12 pages, 1626 KiB  
Article
Odor Dilution Assessment for Explosive Detection
by Dillon E. Huff, Ariela Cantu, Sarah A. Kane, Lauren S. Fernandez, Jaclyn E. Cañas-Carrell, Nathaniel J. Hall and Paola A. Prada-Tiedemann
Analytica 2024, 5(3), 402-413; https://doi.org/10.3390/analytica5030025 (registering DOI) - 9 Aug 2024
Viewed by 329
Abstract
Canine olfaction is a highly developed sense and is utilized for the benefit of detection applications, ranging from medical diagnostics to homeland security and defense prevention strategies. Instrumental validation of odor delivery methods is key to standardize canine olfaction research to establish baseline [...] Read more.
Canine olfaction is a highly developed sense and is utilized for the benefit of detection applications, ranging from medical diagnostics to homeland security and defense prevention strategies. Instrumental validation of odor delivery methods is key to standardize canine olfaction research to establish baseline data for explosive detection applications. Solid-phase microextraction gas chromatography (SPME/GC-MS) was used to validate the odor delivery of an olfactometer. Three explosive classes were used in this study: composition C-4 (C-4), trinitrotoluene (TNT), and ammonium nitrate (AN). Dynamic airflow sampling yielded the successful detection of previously reported target volatile organic compounds (VOCs): 2,3-dimethyl-2,3-dinitrobutane (DMNB) in C-4 and 2-ethylhexan-1-ol (2E1H) in ammonium nitrate and TNT across odor dilutions of 80%, 50%, 25%, 12%, and 3%. C-4 highlighted the most reliable detection from the olfactometer device, depicting a response decrease as a function of dilution factor of its key odor volatile DMNB across the entire range tested. TNT only portrayed 2-ethylhexan-1-ol as a detected odor volatile with a detection response as a function of dilution from 80% down to 12%. Comparatively, ammonium nitrate also depicted 2-ethylhexan-1-ol as an odor volatile in the dynamic airflow sampling but with detection only within the upper scale of the dilution range (80% and 50%). The results suggest the importance of monitoring odor delivery across different dilution ranges to provide quality control for explosive odor detection using dynamic airflow systems. Full article
(This article belongs to the Special Issue Feature Papers in Analytica)
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<p>Olfactometer six-channel manifold and sampling. (<b>a</b>) Olfactometer six-channel solenoid valve manifold (inside olfactometer). (<b>b</b>) Solid-phase microextraction (SPME) fiber attached to odor port and secured with parafilm to entrap exiting volatiles from olfactometer.</p>
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<p>Instrumental delivery validation results for composition C-4 odor dilution; Interday mean concentrations (n = 6) of DMNB for 80%, 50%, 25%, 12%, and 3% dilutions.</p>
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<p>Instrumental delivery validation results for TNT odor dilution; Interday concentrations (n = 6) of 2E1H for 80%, 50%, 25%, 12%, and 3% dilutions.</p>
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<p>Instrumental delivery validation results for AN Prill odor dilution; Interday concentrations of 2E1H for 80%, 50%, 25%, 12%, and 3% dilutions (<b>top</b>); * statistically significant difference at 80% dilution; ** statistically significant difference at 50% dilution. Relative concentrations are reported for numerical comparison (<b>bottom</b>).</p>
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<p>Mean peak area counts for explosive bulk material by olfactometer dilution (n = 6); mean peak area for bulk explosives at 80%, 50%, 25%, 12%, and 3% dilutions.</p>
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17 pages, 2349 KiB  
Article
Influence of Penicillium lanosum and Staphylococcus equorum on Microbial Diversity and Flavor of Mianning Hams
by Wenli Wang, Yanli Zhu, Wei Wang, Jiamin Zhang, Daolin He, Lili Ji and Lin Chen
Foods 2024, 13(16), 2494; https://doi.org/10.3390/foods13162494 - 8 Aug 2024
Viewed by 335
Abstract
Mianning ham is a traditional meat product in China. In this experiment, solid-phase microextraction–gas chromatography (SPME-GC-MS) and high-throughput sequencing were used to study the effects of adding Penicillium lanosum and adding the mixture of Penicillium lanosum and Staphylococcus equorum on the flavor and [...] Read more.
Mianning ham is a traditional meat product in China. In this experiment, solid-phase microextraction–gas chromatography (SPME-GC-MS) and high-throughput sequencing were used to study the effects of adding Penicillium lanosum and adding the mixture of Penicillium lanosum and Staphylococcus equorum on the flavor and microbiology of Mianning ham. The results showed that the addition of the ferments resulted in an increase in the abundance of both the dominant bacterial phylum (Thick-walled Bacteria) and the dominant fungal phylum (Ascomycota). The variety of volatile flavor substances and key flavor substances increased after adding fermentation agents. A free amino acid analysis showed that hams from the Penicillium lanosum and Staphylococcus equorum group had significantly higher umami flavor amino acids than the control group and Penicillium lanosum group. Therefore, inoculation with Penicillium lanosum and Staphylococcus equorum favored the dominant bacteria and flavor of Mianning ham. Full article
(This article belongs to the Section Meat)
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Figure 1
<p>Venn diagram based on ASVs at the phylum (<b>A</b>) level and genus (<b>B</b>) level.</p>
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<p>Venn diagram based on ASVs at the phylum (<b>A</b>) level and genus (<b>B</b>) level.</p>
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<p>The relative abundance between bacteria groups at the phylum (<b>A</b>) level and genus (<b>B</b>) level.</p>
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<p>The relative abundance between bacteria groups at the phylum (<b>A</b>) level and genus (<b>B</b>) level.</p>
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<p>The relative abundance between fungi groups at the phylum (<b>A</b>) level and genus (<b>B</b>) level.</p>
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<p>The relative abundance between fungi groups at the phylum (<b>A</b>) level and genus (<b>B</b>) level.</p>
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<p>Heatmap of differences in bacterial community abundance at the phylum (<b>A</b>) level and genus (<b>B</b>) level.</p>
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<p>Heatmap of differences in abundance of fungal communities at the phylum (<b>A</b>) level and genus (<b>B</b>) level.</p>
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<p>Plot of principal component analysis scores of flavor substances of Mianning ham at fermentation maturation stage. The numbers 1-3 stand for RG-1, RG-2, RG-3, 4-6 for YM-1, YM-2, YM-3,7-9 for YMMW-1, YMMW-2, YMMW-3.</p>
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<p>Heatmap of differences in key amino acid content.</p>
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19 pages, 2657 KiB  
Article
Effects of Wickerhamomyces anomalus Co-Fermented with Saccharomyces cerevisiae on Volatile Flavor Profiles during Steamed Bread Making Using Electronic Nose and HS-SPME-GC-MS
by Xialiang Ding, Meixiang Yue, Henghao Gu, Suyang Li, Shiyi Chen, Liang Wang and Ling Sun
Foods 2024, 13(16), 2490; https://doi.org/10.3390/foods13162490 - 8 Aug 2024
Viewed by 287
Abstract
Steamed bread is a traditional staple food in China, and it has gradually become loved by people all over the world because of its healthy production methods. With the improvement in people’s living standards, the light flavor of steamed bread fermented by single [...] Read more.
Steamed bread is a traditional staple food in China, and it has gradually become loved by people all over the world because of its healthy production methods. With the improvement in people’s living standards, the light flavor of steamed bread fermented by single yeast cannot meet people’s needs. Multi-strain co-fermentation is a feasible way to improve the flavor of steamed bread. Here, the dynamic change profiles of volatile substances in steamed bread co-fermented by Saccharomyces cerevisiae SQJ20 and Wickerhamomyces anomalus GZJ2 were analyzed using the electronic nose (E-nose) and headspace solid-phase microextraction combined with gas chromatography–mass spectrometry (HS-SPME-GC-MS). The five detectors of the E-nose rapidly detected the changes in volatile substances in different dough or steamed bread with the highest response value in co-fermented dough. A total of 236 volatile substances were detected in all the samples using HS-SPME-GC-MS, and alcohols were the most variable component, especially Phenylethyl alcohol. Significantly, more alcohols and esters were upregulated in co-fermented dough, and the addition of W. anomalus GZJ2 improved the key volatile aroma compounds of steamed bread using the relative odor activity value method (ROAV), especially the aldehydes and alcohols. Moreover, these key volatile aroma compounds can be quickly distinguished using the W2S detector of the E-nose, which can be used for the rapid detection of aroma components in steamed bread. Full article
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<p>E-nose analysis of different dough and steamed bread fermented by <span class="html-italic">S. cerevisiae</span> SQJ20 with or without <span class="html-italic">W. anomalus</span> GZJ2. (<b>A</b>) Principal component diagram; (<b>B</b>) Radar map.</p>
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<p>Dynamic changes in volatile substances and their contents in the dough or steamed bread fermented by <span class="html-italic">S. cerevisiae</span> SQJ20 with or without <span class="html-italic">W. anomalus</span> GZJ2. (<b>A</b>) The amount of various volatile substances; (<b>B</b>) Stack diagram of the relative abundance of volatile substances.</p>
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<p>Effect of <span class="html-italic">S. cerevisiae</span> SQJ20 (In short: SQJ20) on the formation of volatile substances in fermented dough compared with the unfermented dough (Control). (<b>A</b>) OPLS-DA score chart; (<b>B</b>) Volcanic maps; (<b>C</b>) Heat map.</p>
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<p>Effect of <span class="html-italic">W. anomalus</span> GZJ2 co-fermented with <span class="html-italic">S. cerevisiae</span> SQJ20 (In short: SQJ20 + GZJ2) on the formation of volatile substances in fermented dough compared with the unfermented dough (Control). (<b>A</b>) OPLS-DA score chart; (<b>B</b>) Volcanic maps; (<b>C</b>) Heat map.</p>
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<p>Phenylalanine metabolic pathways during dough fermentation. Note: Red represents upregulated metabolites.</p>
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<p>Effect of steaming on the formation of volatile substances in co-fermented steamed bread compared to dough co-fermented by <span class="html-italic">W. anomalus</span> GZJ2 and <span class="html-italic">S. cerevisiae</span> SQJ20. (<b>A</b>) OPLS-DA score chart; (<b>B</b>) Volcanic maps; (<b>C</b>) Heat map.</p>
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<p>Correlation network diagram of sensory flavor characteristics and flavor substances in co-fermented steamed bread.</p>
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19 pages, 649 KiB  
Entry
Biological Pollution of Indoor Air, Its Assessment and Control Methods
by Natalia Stocka, Andrzej Butarewicz, Marcin Stocki, Piotr Borowik and Tomasz Oszako
Encyclopedia 2024, 4(3), 1217-1235; https://doi.org/10.3390/encyclopedia4030079 - 5 Aug 2024
Viewed by 517
Definition
The aim of the entry was to write a substantial contribution that analyses and compares the biological pollution of indoor air, the possibilities of its assessment and the control methods. In addition, the aim of our entry was to review journals covering both [...] Read more.
The aim of the entry was to write a substantial contribution that analyses and compares the biological pollution of indoor air, the possibilities of its assessment and the control methods. In addition, the aim of our entry was to review journals covering both commercial and residential buildings. By analysing the above topics from the existing articles, one can have the impression that air pollution is one of the most important problems that need to be solved in the modern world. Adequate air quality is important for maintaining human health, affects the health of ecosystems, including animals, and determines crop production. With the development of civilisation, the quality of air in the atmosphere and indoors is constantly deteriorating. Indoor air pollution can be divided into physical (e.g., noise, inadequate lighting, ionising radiation), chemical (e.g., tobacco smoke, household products) and microbiological (bacteria, viruses, fungi and products of their metabolism) factors. Each of these factors can have a negative impact on a person’s health or cause premature death. The entry deals with indoor air pollution, focussing on biological pollutants. It compares different methods available and describes the method of sampling to analyse indoor air pollution and ways to reduce it. Full article
(This article belongs to the Section Engineering)
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<p>Photography of Supelco SPME (Merck, Darmstadt, Germany) needle (<b>a</b>) the examples of the results of the gas chromatography (<b>b</b>) and mass spectrometry (<b>c</b>) output of the GCMS measurements.</p>
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14 pages, 13447 KiB  
Article
Transcriptome and Metabolome Analyses of Aroma Differences between Chardonnay and a Chardonnay Bud Sport
by Xiaoqin Bao, Jin Dong, Min Niu, Zhilei Wang and Guoqian Xu
Molecules 2024, 29(15), 3671; https://doi.org/10.3390/molecules29153671 - 2 Aug 2024
Viewed by 405
Abstract
Chardonnay is one of the most popular white grape wine varieties in the world, but this wine lacks typical aroma, considered a sensory defect. Our research group identified a Chardonnay bud sport with typical muscat characteristics. The goal of this work was to [...] Read more.
Chardonnay is one of the most popular white grape wine varieties in the world, but this wine lacks typical aroma, considered a sensory defect. Our research group identified a Chardonnay bud sport with typical muscat characteristics. The goal of this work was to discover the key candidate genes related to muscat characteristics in this Chardonnay bud sport to reveal the mechanism of muscat formation and guide molecular design breeding. To this end, HS−SPME−GC−MS and RNA−Seq were used to analyze volatile organic compounds and the differentially expressed genes in Chardonnay and its aromatic bud sport. Forty-nine volatiles were identified as potential biomarkers, which included mainly aldehydes and terpenes. Geraniol, linalool, and phenylacetaldehyde were identified as the main aroma components of the mutant. The GO, KEGG, GSEA, and correlation analysis revealed HMGR, TPS1, TPS2, TPS5, novel.939, and CYP450 as key genes for terpene synthesis. MAO1 and MAO2 were significantly downregulated, but there was an increased content of phenylacetaldehyde. These key candidate genes provide a reference for the development of functional markers for muscat varieties and also provide insight into the formation mechanism of muscat aroma. Full article
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Graphical abstract
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<p>Fruit characterization of CH and CH09 at the mature stage.</p>
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<p>Volatile profiles of CH and CH09. The total concentration of each class (<b>A</b>) and the Venn diagram of VOCs (<b>B</b>).</p>
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<p>Principal component and multivariate analysis of differential VOCs. The OPLS−DA plot of CH09 (green) and CH (blue) (<b>A</b>), permutation test of OPLS−DA model (<b>B</b>), PCA plot of CH and CH09 (<b>C</b>), and cluster heat map of different aroma components in CH09 and CH grape fruits (<b>D</b>).</p>
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<p>The volcano plot of up−(red) and downregulated (green) DEGs in CH09 vs. CH grape fruits (<b>A</b>), the KEGG pathway enrichment bubble chart (<b>B</b>), and the GSEA analysis results of KEGG pathway (<b>C</b>).</p>
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<p>The terpene synthesis-related gene heat map (<b>A</b>), phenylalanine metabolism-related gene heat map (<b>B</b>), the schematic diagram of terpene synthesis pathway and the expression heat map of terpenoid synthesis pathway genes in CH09 and CH (<b>C</b>), and the schematic diagram of phenylacetaldehyde synthesis pathway and the expression heat map of phenylacetaldehyde synthesis pathway genes in CH09 and CH (<b>D</b>).</p>
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<p>The correlation analysis of terpenoids and terpenoid synthesis−related genes in grapes (<b>A</b>); the correlation analysis of genes related to aldehyde and aldehyde compound synthesis in grape fruit (<b>B</b>); and the qRT−PCR validation of seven genes related to VOC biosynthesis in CH and CH09 and the correlation analysis of qRT−PCR and RNA−seq (<b>C</b>).</p>
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34 pages, 2751 KiB  
Article
Characterisation of the Volatile Compounds and Key Odourants in Japanese Mandarins by Gas Chromatography–Mass Spectrometry and Gas Chromatography–Olfactometry
by Lingyi Li, Rui Min Vivian Goh, Yunle Huang, Kim-Huey Ee, Aileen Pua, Daphne Tan, Shanbo Zhang, Lionel Jublot, Shao Quan Liu and Bin Yu
Separations 2024, 11(8), 237; https://doi.org/10.3390/separations11080237 - 1 Aug 2024
Viewed by 395
Abstract
Japanese mandarins are becoming increasingly popular due to their pleasant aroma. The volatiles in four varieties of Japanese mandarins (Iyokan, Ponkan, Shiranui, and Unshiu mikan) were extracted by headspace solid-phase microextraction (HS-SPME) and solvent extraction, then analysed by gas chromatography–mass spectrometry (GC-MS). Principal [...] Read more.
Japanese mandarins are becoming increasingly popular due to their pleasant aroma. The volatiles in four varieties of Japanese mandarins (Iyokan, Ponkan, Shiranui, and Unshiu mikan) were extracted by headspace solid-phase microextraction (HS-SPME) and solvent extraction, then analysed by gas chromatography–mass spectrometry (GC-MS). Principal component analysis (PCA) of the GC-MS data demonstrated distinct segregation of all four Japanese mandarin varieties. Esters, such as neryl acetate, distinguished Iyokan. Methylthymol uniquely characterised Ponkan, valencene was exclusive to Shiranui, and acids like hexanoic acid and heptanoic acid differentiated Unshiu mikan from the other three varieties. Aroma extract dilution analysis (AEDA) revealed 131 key odourants across four Japanese mandarins, including myrcene (peppery, terpenic), perillyl alcohol (green, spicy, floral), trans-nerolidol (sweet, floral), and trans-farnesol (woody, floral, green). Finally, sensory evaluation was conducted on the four Japanese mandarin peel extracts to describe the distinct aroma profile of each variety of Japanese mandarin: Iyokan had higher floral and juicy notes, Ponkan showed higher sulphury notes, Shiranui was perceived to have more albedo notes, and Unshiu mikan exhibited higher peely, green, and woody notes. Full article
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Figure 1
<p>PCA scores and loadings plots of volatile compounds in four varieties of Japanese Mandarin (Iyokan (<span style="color:#ef8c31">▲</span>), Ponkan (<span style="color:#4ab5d6">■</span>), Shiranui (<span style="color:#9463ad">●</span>), and Unshiu mikan (<span style="color:#6bbe8c">★</span>)): (<b>a</b>) Scores plot of volatiles in juices extracted by HS-SPME; (<b>b</b>) Loadings plot of volatiles in juices extracted by HS-SPME; (<b>c</b>) Scores plot of volatiles in peels extracted by HS-SPME; (<b>d</b>) Loadings plot of volatiles in peels extracted by HS-SPME; (<b>e</b>) Scores plot of volatiles in peels extracted by solvent extraction; (<b>f</b>) Loadings plot of volatiles in peels extracted by solvent extraction. The numbers denote the corresponding volatiles reported in <a href="#separations-11-00237-t001" class="html-table">Table 1</a> (juice HS-SPME plots), <a href="#separations-11-00237-t002" class="html-table">Table 2</a> (peel HS-SPME plots), and <a href="#separations-11-00237-t003" class="html-table">Table 3</a> (solvent extraction plots). The black squares indicate the contribution magnitude and direction of variables to the principal components, with their position reflecting the loading value.</p>
Full article ">Figure 1 Cont.
<p>PCA scores and loadings plots of volatile compounds in four varieties of Japanese Mandarin (Iyokan (<span style="color:#ef8c31">▲</span>), Ponkan (<span style="color:#4ab5d6">■</span>), Shiranui (<span style="color:#9463ad">●</span>), and Unshiu mikan (<span style="color:#6bbe8c">★</span>)): (<b>a</b>) Scores plot of volatiles in juices extracted by HS-SPME; (<b>b</b>) Loadings plot of volatiles in juices extracted by HS-SPME; (<b>c</b>) Scores plot of volatiles in peels extracted by HS-SPME; (<b>d</b>) Loadings plot of volatiles in peels extracted by HS-SPME; (<b>e</b>) Scores plot of volatiles in peels extracted by solvent extraction; (<b>f</b>) Loadings plot of volatiles in peels extracted by solvent extraction. The numbers denote the corresponding volatiles reported in <a href="#separations-11-00237-t001" class="html-table">Table 1</a> (juice HS-SPME plots), <a href="#separations-11-00237-t002" class="html-table">Table 2</a> (peel HS-SPME plots), and <a href="#separations-11-00237-t003" class="html-table">Table 3</a> (solvent extraction plots). The black squares indicate the contribution magnitude and direction of variables to the principal components, with their position reflecting the loading value.</p>
Full article ">Figure 1 Cont.
<p>PCA scores and loadings plots of volatile compounds in four varieties of Japanese Mandarin (Iyokan (<span style="color:#ef8c31">▲</span>), Ponkan (<span style="color:#4ab5d6">■</span>), Shiranui (<span style="color:#9463ad">●</span>), and Unshiu mikan (<span style="color:#6bbe8c">★</span>)): (<b>a</b>) Scores plot of volatiles in juices extracted by HS-SPME; (<b>b</b>) Loadings plot of volatiles in juices extracted by HS-SPME; (<b>c</b>) Scores plot of volatiles in peels extracted by HS-SPME; (<b>d</b>) Loadings plot of volatiles in peels extracted by HS-SPME; (<b>e</b>) Scores plot of volatiles in peels extracted by solvent extraction; (<b>f</b>) Loadings plot of volatiles in peels extracted by solvent extraction. The numbers denote the corresponding volatiles reported in <a href="#separations-11-00237-t001" class="html-table">Table 1</a> (juice HS-SPME plots), <a href="#separations-11-00237-t002" class="html-table">Table 2</a> (peel HS-SPME plots), and <a href="#separations-11-00237-t003" class="html-table">Table 3</a> (solvent extraction plots). The black squares indicate the contribution magnitude and direction of variables to the principal components, with their position reflecting the loading value.</p>
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<p>PCA scores and loadings plots of volatile compounds in four varieties of Japanese Mandarin (Iyokan (<span style="color:#ef8c31">▲</span>), Ponkan (<span style="color:#4ab5d6">■</span>), Shiranui (<span style="color:#9463ad">●</span>), and Unshiu mikan (<span style="color:#6bbe8c">★</span>)): (<b>a</b>) Scores plot of volatiles in juices extracted by HS-SPME; (<b>b</b>) Loadings plot of volatiles in juices extracted by HS-SPME; (<b>c</b>) Scores plot of volatiles in peels extracted by HS-SPME; (<b>d</b>) Loadings plot of volatiles in peels extracted by HS-SPME; (<b>e</b>) Scores plot of volatiles in peels extracted by solvent extraction; (<b>f</b>) Loadings plot of volatiles in peels extracted by solvent extraction. The numbers denote the corresponding volatiles reported in <a href="#separations-11-00237-t001" class="html-table">Table 1</a> (juice HS-SPME plots), <a href="#separations-11-00237-t002" class="html-table">Table 2</a> (peel HS-SPME plots), and <a href="#separations-11-00237-t003" class="html-table">Table 3</a> (solvent extraction plots). The black squares indicate the contribution magnitude and direction of variables to the principal components, with their position reflecting the loading value.</p>
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<p>Heatmap of the concentrations and FD factors of the key odourants of four Japanese mandarin peel extracts. “NA” means the odourant was not detected by AEDA via GC-O/MS. For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.</p>
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<p>Sensory profiles of four varieties of Japanese mandarin (Iyokan, Ponkan, Shiranui, and Unshiu mikan) peel extracts.</p>
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23 pages, 8696 KiB  
Article
Unraveling the Chicken Meat Volatilome with Nanostructured Sensors: Impact of Live and Dehydrated Insect Larvae Feeding
by Dario Genzardi, Estefanía Núñez Carmona, Elisabetta Poeta, Francesco Gai, Immacolata Caruso, Edoardo Fiorilla, Achille Schiavone and Veronica Sberveglieri
Sensors 2024, 24(15), 4921; https://doi.org/10.3390/s24154921 - 29 Jul 2024
Viewed by 570
Abstract
Incorporating insect meals into poultry diets has emerged as a sustainable alternative to conventional feed sources, offering nutritional, welfare benefits, and environmental advantages. This study aims to monitor and compare volatile compounds emitted from raw poultry carcasses and subsequently from cooked chicken pieces [...] Read more.
Incorporating insect meals into poultry diets has emerged as a sustainable alternative to conventional feed sources, offering nutritional, welfare benefits, and environmental advantages. This study aims to monitor and compare volatile compounds emitted from raw poultry carcasses and subsequently from cooked chicken pieces from animals fed with different diets, including the utilization of insect-based feed ingredients. Alongside the use of traditional analytical techniques, like solid-phase microextraction combined with gas chromatography-mass spectrometry (SPME-GC-MS), to explore the changes in VOC emissions, we investigate the potential of S3+ technology. This small device, which uses an array of six metal oxide semiconductor gas sensors (MOXs), can differentiate poultry products based on their volatile profiles. By testing MOX sensors in this context, we can develop a portable, cheap, rapid, non-invasive, and non-destructive method for assessing food quality and safety. Indeed, understanding changes in volatile compounds is crucial to assessing control measures in poultry production along the entire supply chain, from the field to the fork. Linear discriminant analysis (LDA) was applied using MOX sensor readings as predictor variables and different gas classes as target variables, successfully discriminating the various samples based on their total volatile profiles. By optimizing feed composition and monitoring volatile compounds, poultry producers can enhance both the sustainability and safety of poultry production systems, contributing to a more efficient and environmentally friendly poultry industry. Full article
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<p>S3+ setup representation.</p>
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<p>Graphical representation of the output of a single sensor. The <span class="html-italic">y</span>-axis shows the resistance value (Ω), while the <span class="html-italic">x</span>-axis shows time (s).</p>
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<p>(<b>A,B</b>) Chemical classes in cooked and raw samples from poultry fed with different diets.</p>
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<p>Aldehydes in cooked samples.</p>
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<p>Aldehydes in raw samples.</p>
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<p>Alcohols in cooked samples.</p>
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<p>Alcohols in raw samples.</p>
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<p>Carboxylic acids in cooked samples.</p>
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<p>Carboxylic acids in raw samples.</p>
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<p>Alkanes in cooked samples.</p>
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<p>Alkanes in raw samples.</p>
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<p>LDA in 3D, representing cooked samples with CONTROL diet (blue points), ST (red points), LD diet (green points), and LL diet (purple points).</p>
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<p>LDA in 3D, representing raw samples with the CONTROL diet (blue points), ST diet (red points), LD diet (green points), and LL diet (purple points).</p>
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<p>Confusion Matrix on cooked samples.</p>
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<p>Confusion Matrix on RAW samples.</p>
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<p>ROC Curve of cooked samples.</p>
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<p>ROC Curve of raw samples.</p>
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20 pages, 11397 KiB  
Article
Valorization of Residual Babassu Mesocarp Biomass to Obtain Aroma Compounds by Solid-State Fermentation
by Tamires N. dos Anjos, Robert Wojcieszak, Selma G. F. Leite and Ivaldo Itabaiana Jr
Microbiol. Res. 2024, 15(3), 1386-1405; https://doi.org/10.3390/microbiolres15030093 - 29 Jul 2024
Viewed by 277
Abstract
In this work, solid-state fermentation (SSF) was applied to babassu mesocarp (BM) for the low-cost bioproduction of natural aroma compounds having Trichoderma harzianum (IOC 4042) and Geotrichum candidum (CCT 1205) as microbial agents. Fermentation was carried out using in natura babassu mesocarp (IN-BM) [...] Read more.
In this work, solid-state fermentation (SSF) was applied to babassu mesocarp (BM) for the low-cost bioproduction of natural aroma compounds having Trichoderma harzianum (IOC 4042) and Geotrichum candidum (CCT 1205) as microbial agents. Fermentation was carried out using in natura babassu mesocarp (IN-BM) and defatted babassu mesocarp through soxhlet extraction (DEF-BM) as support, impregnated with hydration solutions of three and seven salts. The compounds produced were analyzed using solid phase microextraction (SPME) and gas chromatography coupled with a mass spectrometer (GC-MS). Among several aroma compounds detected, 6-pentyl-α-pyrone (6-PP)—GRAS 3696, coconut aroma; 2-phenylethanol (2-PE)—GRAS 2858, rose and honey aroma; and hexanal—GRAS 2557, green apple aroma, were the compounds that that were detected with the greatest intensity. The highest concentrations (ppm (w/w)) of 6-PP and 2-PE were obtained in DEF-BM using NS7SG (308.17 ± 3.18 and 414.53 ± 1.96), respectively, while for hexanal, the highest concentration (ppm (w/w)) was obtained in IN-BM using NS7SG (210.83 ± 2.14). The results indicate that producing aroma compounds by G. candidum and T. harzianum through BM SSF is viable, generating value-added compounds. Full article
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<p>Obtaining babassu mesocarp: (<b>A</b>) babassu palm; (<b>B</b>) babassu coconut bunch; (<b>C</b>) sectioned babassu coconut; (<b>D</b>) mesocarp extracted from several babassu coconuts, showing several lumps; (<b>E</b>) crushed and sieved babassu mesocarp with particles smaller than 1.19 mm in diameter. Adapted from Santos and Muniz, 2017 [<a href="#B22-microbiolres-15-00093" class="html-bibr">22</a>].</p>
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<p>Babassu mesocarp after the pre-treatment process: (<b>A</b>) babassu mesocarp washed with distilled water (IN-BM) and (<b>B</b>) babassu mesocarp treated with 95% hexane (DEF-BM).</p>
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<p>Scanning electron micrographs of babassu mesocarp: (<b>a</b>) in natura babassu mesocarp (IN-BM), (<b>b</b>) defatted babassu mesocarp (DEF-BM).</p>
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<p>Visual and sensory inspection of fermentations with <span class="html-italic">T. harzianum</span> and <span class="html-italic">G. candidum</span> in babassu mesocarp. Both the growth and production of aroma compounds were monitored qualitatively at this stage, where a scale ranging from 0 (−) to 5 (+++++) denotes intensity.</p>
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<p>Quantifying the 6 pentyl-alpha-pyrone (6-PP) aroma in solid-state fermentation (SSF) with <span class="html-italic">T. harzianum</span>. (<b>a</b>) SSF with greased babassu mesocarp (IN-BM) using hydration solutions with seven salts. (<b>b</b>) SSF with defatted babassu mesocarp (DEF-BM) using hydration solutions with seven salts. (<b>c</b>) SSF with defatted babassu mesocarp (IN-BM) using hydration solutions with three salts. (<b>d</b>) SSF with defatted babassu mesocarp (DEF-BM) using hydration solutions with three salts.</p>
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<p>Quantifying the aroma of 2-phenylethanol (2-PE) and hexanal in SSF with <span class="html-italic">G. candidum</span>. (<b>a</b>) SSF with IN-BM using hydration solutions with seven salts. (<b>b</b>) SSF with DEF-BM using hydration solutions with seven salts. (<b>c</b>) SSF with IN-BM using hydration solutions with three salts. (<b>d</b>) SSF with DEF-BM using hydration solutions with three salts.</p>
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<p>GC-MS chromatogram of the aroma compounds 6-pentyl-alpha-pyrone (6-PP), 2-phenylethanol (2-PE), and hexanal synthesized from babassu mesocarp by SSF.</p>
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<p>Scanning electron microscopy of BM supports after 7 days of fermentation with <span class="html-italic">T. harzianum</span> and <span class="html-italic">G. candidum</span>. (<b>a</b>) IN-BM fermented with <span class="html-italic">G. candidum</span>; (<b>b</b>) DEF-BM fermented with <span class="html-italic">G. candidum</span>; (<b>c</b>) IN-BM fermented with <span class="html-italic">T. harzianum</span>; (<b>d</b>) DEF-BM fermented with <span class="html-italic">T. harzianum</span>.</p>
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<p>Surface analysis of BM supports after 7 days of fermentation with <span class="html-italic">T. harzianum</span> and <span class="html-italic">G. candidum</span>. (<b>a</b>) IN-BM fermented with <span class="html-italic">G. candidum</span>; (<b>b</b>) DEF-BM fermented with <span class="html-italic">G. candidum</span>; (<b>c</b>) IN-BM fermented with <span class="html-italic">T. harzianum</span>; (<b>d</b>) DEF-BM fermented with <span class="html-italic">T. harzianum</span>.</p>
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17 pages, 1910 KiB  
Article
Innovative Approach for Human Semen Quality Assessment Based on Volatilomics
by Simonetta Capone, Angiola Forleo, Antonio Vincenzo Radogna, Valentina Longo, Giulia My, Alessandra Genga, Alessandra Ferramosca, Giuseppe Grassi, Flavio Casino, Pietro Siciliano, Tiziana Notari, Sebastiana Pappalardo, Marina Piscopo and Luigi Montano
Toxics 2024, 12(8), 543; https://doi.org/10.3390/toxics12080543 - 27 Jul 2024
Viewed by 463
Abstract
The volatilome profile of some biofluids (blood, urine, and human semen) identified by Solid-Phase Microextraction–Gas Chromatography/Mass Spectrometry (SPME-GC/MS) and collected from young men living in two high-pollution areas in Italy, i.e., Land of Fires and Valley of Sacco River, have been coupled to [...] Read more.
The volatilome profile of some biofluids (blood, urine, and human semen) identified by Solid-Phase Microextraction–Gas Chromatography/Mass Spectrometry (SPME-GC/MS) and collected from young men living in two high-pollution areas in Italy, i.e., Land of Fires and Valley of Sacco River, have been coupled to sperm parameters obtained by spermiogram analysis to build general multiple regression models. Panels of volatile organic compounds (VOCs) have been selected to optimize the models and used as predictive variables to estimate the different sperm quality parameters (sperm cell concentration, total and progressive motility/immotile cells, total/head/neck/tail morphology anomalies, semen round cell concentration). The results of the multiple linear regression models based on the different subgroups of data joining VOCs from one/two or three biofluids have been compared. Surprisingly, the models based on blood and urine VOCs have allowed an excellent estimate of spermiogram values, paving the way towards a new method of indirect evaluation of semen quality and preventive screening. The significance of VOCs in terms of toxicity and dangerousness was discussed with the support of chemical databases available online. Full article
(This article belongs to the Special Issue Endocrine-Disrupting Chemicals and Reproductive Toxicology)
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<p>Box and whisker plots displaying the dispersion of the seminogram parameters related to sperm count and motility: (<b>a</b>) sperm cell concentration; (<b>b</b>) progressive motility; (<b>c</b>) total motility; (<b>d</b>) immotile cells in the subgroups of the population living in <span class="html-italic">Land of Fires</span> (LF) and in <span class="html-italic">Valley of Sacco river</span> (VSR) and the total population (all = LF + VSR).</p>
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<p>(<b>a</b>) Overall fit of the MLR model described by the test of the SS whole model vs. SS residual for sperm concentration (in 10<sup>6</sup>/mL); (<b>b</b>) observed vs. predicted values for sperm concentration as a result of the MLR analysis for the data group B + U based on selected VOC predictors; (<b>c</b>) pattern of VOCs used as predictor variables in the MLR model as selected by a Pareto chart; the order in the list reflects the greatest predictive contribution to the model (VOC name_X; X = B, blood, U, urine, or HS, human semen). Notation for numeric values: comma “,” is the decimal separator (SI).</p>
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<p>(<b>a</b>) Overall fit of the MLR model described by the test of the SS whole model vs. SS residual for sperm progressive motility (in percentage); (<b>b</b>) observed vs. predicted values for sperm progressive motility as a result of the MLR analysis for the data group U based on selected VOC predictors; (<b>c</b>) pattern of VOCs used as predictor variables in the MLR model as selected by a Pareto chart; the order in the list reflects the greatest predictive contribution to the model (VOC name_X; X = B, blood, U, urine, or HS, human semen). Notation for numeric values: comma “,” is the decimal separator (SI).</p>
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<p>(<b>a</b>) Overall fit of the MLR model described by the test of the SS whole model vs. SS residual for sperm total motility (in percentage); (<b>b</b>) observed vs. predicted values for sperm total motility as a result of the MLR analysis for the data group U based on selected VOC predictors; (<b>c</b>) pattern of VOCs used as predictor variables in the MLR model as selected by a Pareto chart; the order in the list reflects the greatest predictive contribution to the model (VOC name_X; X = B, blood, U, urine, or HS, human semen). Notation for numeric values: comma “,” is the decimal separator (SI).</p>
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<p>(<b>a</b>) Overall fit of the MLR model described by the test of the SS whole model vs. SS residual for sperm immotiles (in percentage); (<b>b</b>) observed vs. predicted values for sperm immotiles as a result of the MLR analysis for the data group U based on selected VOC predictors; (<b>c</b>) pattern of VOCs used as predictor variables in the MLR model as selected by a Pareto chart; the order in the list reflects the greatest predictive contribution to the model (VOC name_X; X = B, blood, U, urine, or HS, human semen). Notation for numeric values: comma “,” is the decimal separator (SI).</p>
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17 pages, 1014 KiB  
Article
Impact of Thyme Essential Oil on the Aroma Profile and Shelf Life of Vacuum-Packed Minced Turkey Meat
by Paweł Satora, Magdalena Michalczyk and Joanna Banaś
Molecules 2024, 29(15), 3524; https://doi.org/10.3390/molecules29153524 - 26 Jul 2024
Viewed by 440
Abstract
There is considerable interest in the use of essential oils for food preservation, but their effect on the aroma profile of a product is poorly understood. This study investigated the effect of thyme essential oil (EO) addition at increasing concentrations (0.005, 0.01, 0.02, [...] Read more.
There is considerable interest in the use of essential oils for food preservation, but their effect on the aroma profile of a product is poorly understood. This study investigated the effect of thyme essential oil (EO) addition at increasing concentrations (0.005, 0.01, 0.02, and 0.03% v/w) on the volatile compound composition of vacuum-packed minced turkey meat after storage for 8 days at 1–2 °C. The aroma profile of the meat was determined using the HS-SPME/GCMS (headspace solid-phase microextraction/gas chromatography–mass spectrometry) method. The results were also analysed by PCA (principal component analysis). The addition of thyme EO had a modifying effect on the aroma profile of meat-derived components, e.g., the formation of benzeneacetaldehyde, benzyl alcohol, 4,7-dimethylbenzofuran, hexathiane, hexanal, and 1-hexanol was reduced and the appearance of 9-hexadecenoic acid was observed in the stored samples. The increase in EO concentration affected the levels of its individual components in the meat headspace in different ways. In terms of fat rancidity indices, even a 0.005% addition of this essential oil significantly reduced the peroxide value. Quantitative descriptive analysis (QDA) showed that the addition of thyme EO reduced or masked the intensity of unpleasant odours associated with meat spoilage. In the aroma analysis, the turkey with 0.02% v/w EO scored highest, and pleasant citrus notes were found. Full article
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<p>Principal component analysis (PCA) scores and loadings plots of the volatile compounds of the turkey meat after the addition of different concentrations of thyme essential oil. (<b>a</b>) F—fresh meat; S—stored meat; 0.005, 0.01, 0.02, 0.03—percentage concentration of thyme essential oil in sample. (<b>b</b>) 1—acetaldehyde; 2—ethyl acetate; 3—3-propoxy-1-propene; 4—dimethyl disulfide; 5—1-pentanol; 6—hexanal; 7—4-ethylbenzamide; 8—1-hexanol; 9—butyrolactone; 10—dimethyl sulfone; 11—heptanal; 12—benzaldehyde; 13—2-heptenal; 14—dimethyl trisulfide; 15—1-octen-3-one; 16—octen-3-ol; 17—hexanoic acid; 18—2-pentylfuran; 19—octanal; 20—benzeneacetaldehyde; 21—2-octenal; 22—benzyl alcohol; 23—2-octen-1-ol; 24—nonanal; 25—octanoic acid; 26—ethyl octanoate; 27—dimethyl tetrasulfide; 28—decanal; 29—4,7-dimethyl-benzofuran; 30—ethyl 2-methyloctanoate; 31—methyl diethyldithiocarbamate; 32—n-decanoic acid; 33—ethyl 9-decenoate; 34—ethyl decanoate; 35—dodecanal; 36—hexathiane; 37—2-methyl-decanoic acid; 38—dodecanoic acid; 39—ethyl dodecanoate; 40—hexyl octanoate; 41—tetradecanal; 42—octyl ether; 43—2-dodecen-1-ol; 44—ethyl tetradecanoate; 45—hexadecanal; 46—9-hexadecenoic acid; 47—cyclic octaatomic sulfur; 48—ethyl hexadecanoate.</p>
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<p>Principal component analysis (PCA) scores and loadings plots of the volatile compounds from thyme essential oil. (<b>a</b>) 0.005%, 0.01%, 0.02%, 0.03%—concentration of thyme essential oil in samples; (<b>b</b>), 1—α-phellandrene; 2—isothymol methyl ether; 3—β-myrcene; 4—terpinolene; 5—δ-cadinene; 6—α-terpinene; 7—β-pinene; 8—α-pinene; 9—camphene; 10—humulene; 11—trans-β-ocimene; 12—cis-β-ocimene; 13—thujene; 14—caryophyllene oxide; 15—carvacrol; 16—thymol; 17—α-copaene; 18—caryophyllene; 19—γ-terpienene; 20—limonene; 21—isocaryophyllene; 22—cymene; 23—linalool; 24—cymenene; 25—borneol; 26—terpinen-4-ol; 27—terpineol; 28—linalool oxide; 29—aromadendrene; 30—γ-muurolene; 31—germacrene D; 32—carvone; 33—eugenol; 34—caryophyllene alcohol; 35—camphore; 36—α-muurolene; 37—β-farnesene.</p>
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<p>The spider web diagrams visualise the aroma qualities of stored, vacuum-packed minced turkey meat without and with different concentrations of thyme EO added. *, **, ***—the significance at 0.05, 0.01, and 0.005 by least significant difference, respectively.</p>
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20 pages, 1802 KiB  
Article
Comparative Analysis of Hydrosol Volatile Components of Citrus × Aurantium ‘Daidai’ and Citrus × Aurantium L. Dried Buds with Different Extraction Processes Using Headspace-Solid-Phase Microextraction with Gas Chromatography–Mass Spectrometry
by Xinyue Xie, Huiling Xue, Baoshan Ma, Xiaoqian Guo, Yanli Xia, Yuxia Yang, Ke Xu, Ting Li and Xia Luo
Molecules 2024, 29(15), 3498; https://doi.org/10.3390/molecules29153498 - 26 Jul 2024
Viewed by 515
Abstract
This work used headspace solid-phase microextraction with gas chromatography–mass spectrometry (HS-SPME-GC–MS) to analyze the volatile components of hydrosols of Citrus × aurantium ‘Daidai’ and Citrus × aurantium L. dried buds (CAVAs and CADBs) by immersion and ultrasound–microwave synergistic-assisted steam distillation. The results show [...] Read more.
This work used headspace solid-phase microextraction with gas chromatography–mass spectrometry (HS-SPME-GC–MS) to analyze the volatile components of hydrosols of Citrus × aurantium ‘Daidai’ and Citrus × aurantium L. dried buds (CAVAs and CADBs) by immersion and ultrasound–microwave synergistic-assisted steam distillation. The results show that a total of 106 volatiles were detected in hydrosols, mainly alcohols, alkenes, and esters, and the high content components of hydrosols were linalool, α-terpineol, and trans-geraniol. In terms of variety, the total and unique components of CAVA hydrosols were much higher than those of CADB hydrosols; the relative contents of 13 components of CAVA hydrosols were greater than those of CADB hydrosols, with geranyl acetate up to 15-fold; all hydrosols had a citrus, floral, and woody aroma. From the pretreatment, more volatile components were retained in the immersion; the relative contents of linalool and α-terpineol were increased by the ultrasound–microwave procedure; and the ultrasound–microwave procedure was favorable for the stimulation of the aroma of CAVA hydrosols, but it diminished the aroma of the CADB hydrosols. This study provides theoretical support for in-depth exploration based on the medicine food homology properties of CAVA and for improving the utilization rate of waste resources. Full article
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<p>Comparison of the amounts of volatile components obtained in CAVA hydrosols and CADB hydrosols.</p>
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<p>Venn diagram of volatile components obtained in CAVA hydrosols and CADB hydrosols. Different colors correspond to different hydrosols, consistent with the textual identification on the figure; numbers indicate the number of unique and shared components in the four hydrosols.</p>
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<p>Heatmap analysis of volatile components of hydrosols. In the figure, 1-106 are the hydrosol compound numbers, while the specific names are shown in <a href="#molecules-29-03498-t006" class="html-table">Table 6</a> (compounds).</p>
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<p>PCA analysis of main volatile components of hydrosols, biplot. A1-CAVA IH; A2-CAVA UH; B1-CADB IH; B2-CADB UH. The number 26 and other numbers indicate the main volatile components of the hydrosols (the triangles show the location of the components); specific names are shown in <a href="#molecules-29-03498-t006" class="html-table">Table 6</a> (compounds).</p>
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<p>OPLS-DA model cross-validation results.</p>
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<p>VIP diagram of OPLS-DA model. The number 26 and other numbers indicate the main volatile components of the hydrosols, with specific names shown in <a href="#molecules-29-03498-t006" class="html-table">Table 6</a> (compounds); red indicates that the VIP value &gt; 1, green indicates that the VIP value &lt; 1.</p>
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13 pages, 2803 KiB  
Article
The Light-Intensity-Affected Aroma Components of Green Tea during Leaf Spreading
by Youyue He, Shujing Liu, Yuzhong Kang, Rajiv Periakaruppan, Jing Zhuang, Yuhua Wang, Xuan Chen, Xinqiu Liu and Xinghui Li
Foods 2024, 13(15), 2349; https://doi.org/10.3390/foods13152349 - 25 Jul 2024
Viewed by 428
Abstract
Leaf spreading is a key processing step that affects the aroma formation of green tea. The effects of a single-light wavelength on the aroma and taste of tea have been extensively studied. Less attention has been paid to the effect of different complex [...] Read more.
Leaf spreading is a key processing step that affects the aroma formation of green tea. The effects of a single-light wavelength on the aroma and taste of tea have been extensively studied. Less attention has been paid to the effect of different complex light intensities on the formation of green tea’s volatile aroma during leaf spreading. The current study was designed to evaluate how leaf spreading under different complex light intensities relates to the quality of green tea. Using headspace solid-phase micro-extraction and gas chromatography-mass spectrometry (HS-SPME/GC-MS), volatile flavor compounds in green tea were analyzed during leaf spreading in five different light conditions. Multivariate statistical analysis and odor activity values (OAVs) were used to classify these samples and identify key odors. Eight distinct groups, including ninety volatile compounds, were detected. The most prevalent volatile compounds found in green tea samples were hydrocarbons and alcohols, which accounted for 29% and 22% of the total volatile compounds, respectively. Fourteen volatile compounds (OAV > 1) were identified as key active differential odorants. The chestnut-like aroma in green tea was mostly derived from 3-methyl-butanal and linalool, which were significantly accumulated in medium-intensity light (ML). Full article
(This article belongs to the Special Issue Tea Technology and Resource Utilization)
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<p>LED photon-distribution spectrum of full-spectrum composite light. Low-intensity light (LL; full-spectrum composite light, 400–750 nm; 75 μmol∙m<sup>−2</sup>∙s<sup>−1</sup>), medium-intensity light ((ML) 150 μmol∙m<sup>−2</sup>∙s<sup>−1</sup>), and high-intensity light ((HL) 300 μmol∙m<sup>−2</sup>∙s<sup>−1</sup>).</p>
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<p>Analysis of volatile substances in green tea. (<b>A</b>) The proportion of volatile compounds classified according to functional groups. (<b>B</b>) The variation in content of volatile compounds between different treatment groups. Different letters in the same volatile categories indicate significant differences at <span class="html-italic">p</span> &lt; 0.05 level.</p>
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<p>The distribution of volatile compounds in different treatment groups. (<b>A</b>) The content of total volatile compounds (<b>upper</b>, µg∙kg<sup>−1</sup>) and the number of volatile compounds (<b>lower</b>). Different letters in the same volatile categories indicate significant differences at <span class="html-italic">p</span> &lt; 0.05 level. (<b>B</b>) Ninety distinct volatile chemicals are shown in the Venn diagram.</p>
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<p>Distinctive volatile components of green tea in various conditions of light intensity. (<b>A</b>) Green tea treated under varying light-intensity settings shows a score plot of volatile chemicals derived from principal component analysis. (<b>B</b>) Biplot of discrimination analysis using orthogonal partial least squares for volatile chemicals in green tea treated under various light conditions. The serial numbers of the chemicals in <a href="#app1-foods-13-02349" class="html-app">Table S1</a> correspond to the numbers in the figure. (<b>C</b>) Significant variables in the projection (VIP) plot containing volatile chemicals found in green tea are orthogonal partial least squares discriminating analysis variables. Blue bars show 1 &lt; VIP, and red bars show volatile chemicals with VIP &gt; 1. (<b>D</b>) The depth of the color indicates the degree. Blank indicates a lack of enrichment. Cluster analysis using a heatmap. In the illustration, a volatile component is represented by each column and the treatment condition by each row. The abundance of information about the associated volatile components in the related green tea samples is represented by the color’s depth. Red denotes upregulation and blue denotes downregulation. The degree is indicated by the color’s depth. A blank represents a deficiency in enrichment.</p>
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<p>The impact of varying light-intensity levels on the concentrations of 14 significant differential volatile compounds that contribute to the distinctive green tea aroma. (<b>A</b>–<b>C</b>) Volatile substances that give green tea its grassy flavor. (<b>D</b>–<b>F</b>) Volatile substances that give green tea its chestnut-like aroma. (<b>G</b>–<b>N</b>) Volatile substances that give green tea its floral and fruit aroma. Values below the instrument detection limit are indicated by N.D. Different letters in the same volatile categories indicate significant differences at <span class="html-italic">p</span> &lt; 0.05 level.</p>
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16 pages, 5338 KiB  
Article
Phytochemical Constituents and Biological Properties of Finger Lime (Citrus australasica F. Muell.) Peel, Pulp and Seeds
by Daniela De Vita, Anna Rita Stringaro, Marisa Colone, Maria Luisa Dupuis, Fabio Sciubba, Luigi Scipione and Stefania Garzoli
Appl. Sci. 2024, 14(15), 6498; https://doi.org/10.3390/app14156498 - 25 Jul 2024
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Abstract
In this work, for the first time, different parts of the Finger Lime (Citrus australasica F. Muell.), such as pulp, peel and seeds, were analyzed by HS-SPME-GC/MS, and NMR techniques in order to describe its volatile and non-volatile chemical profile. The results [...] Read more.
In this work, for the first time, different parts of the Finger Lime (Citrus australasica F. Muell.), such as pulp, peel and seeds, were analyzed by HS-SPME-GC/MS, and NMR techniques in order to describe its volatile and non-volatile chemical profile. The results highlighted the presence of a high number of terpenes with limonene as principal component in all investigated parts (ranging from 40.4% to 62.6%) and molecules belonging to the classes of amino acids, organic acids, carbohydrates, fatty acids, phenols and miscellaneous compounds that followed a different trend between the investigated different parts. In this study, the inhibition of ChEs (AChE and BChE) was evaluated using the spectrophotometric method of Ellman. The results showed that only peel extract weakly inhibited AChE (14%). Based on these data, this extract was further investigated by GC/MS after derivatization. Furthermore, peel extract was chosen to evaluate the in vitro effects on two human glioblastoma cells lines (U87 and LN18). Flow cytometry results showed that citrus extract was more effective in down-regulating the expression of the adhesion molecule CD44. In fact, after 72 h with 400 µg/mL of citrus extract, CD44 expression levels were reduced in both U87 and LN18 glioblastoma cell lines. This was confirmed by immunofluorescence analysis, which also showed a modification of CD44 antigen localization in both U87 and LN18 cell lines. Moreover, wound assay data supported its ability to reduce glioblastoma cell’s motility. The migration ability of U87 cells decreased (85% control vs. 50% at 400 μg/mL), while it was even more pronounced in resistant LN18 cells (93% control vs. 15% at 400 μg/mL). The findings highlighted that citrus peel extract could have an anti-invasive activity for glioma management. Full article
(This article belongs to the Special Issue Natural Products and Bioactive Compounds)
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Figure 1

Figure 1
<p>Illustration of <span class="html-italic">Citrus australsica</span> fruit.</p>
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<p>Cell viability analysis in U87 cells treated with peel extract at 48 h. All experiments were conducted in triplicate and the results are expressed as mean ± SD values. According to GraphPad Prism10, unpaired <span class="html-italic">t</span>-test, * <span class="html-italic">p</span>-value &lt; 0.05.</p>
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<p>Cell viability analysis in LN18 cells treated with peel extract at 48 h. All experiments were conducted in triplicate and the results are expressed as mean ± SD values. According to GraphPad Prism10, unpaired <span class="html-italic">t</span>-test, * <span class="html-italic">p</span>-value &lt; 0.05, ** <span class="html-italic">p</span>-value 0.01.</p>
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<p>Cell cycle analysis. Percentages of U87 (<b>A</b>) and LN18 (<b>B</b>) cells in the different phases of the cell cycle after treatment with 100, 200, and 400 µg/mL of peel extract at 48 and 72 h by DNA flow cytometry analysis using Kaluza software for analysis v. 2.2 (Beckman Coulter). Values are the means ± SD of three individual measurements.</p>
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<p>(<b>A</b>) Representative wound-healing profiles in U87 cells after <span class="html-italic">C. australasica</span> peel extract treatments (Control, 100, 200, and 400 μg/mL, respectively). (<b>B</b>) Graphical profile of the migration ability of U87 cells. All experiments were conducted in triplicate and the results are expressed as mean ± SD values. According to GraphPad Prism10, unpaired <span class="html-italic">t</span>-test, * <span class="html-italic">p</span>-value &lt; 0.05, ** <span class="html-italic">p</span>-value ˂ 0.01.</p>
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<p>(<b>A</b>) Representative wound-healing profiles in LN18 cells after peel extract treatments (Control, 100, 200, and 400 μg/mL, respectively). (<b>B</b>) Graphical profile of the migration ability of LN18 cells. All experiments were conducted in triplicate and the results are expressed as mean ± SD values. According to GraphPad Prism10, unpaired <span class="html-italic">t</span>-test, * <span class="html-italic">p</span>-value &lt; 0.05, ** <span class="html-italic">p</span>-value ˂ 0.01, *** <span class="html-italic">p</span>-value ˂ 0.001.</p>
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<p>Flow cytometric analysis of CD44 expression. U87 (<b>A</b>) and LN18 (<b>B</b>) cells after treatment with 100, 200, and 400 µg/mL of peel extract at 48 and 72 h, were stained with Alexa-Fluor 488-conjugated anti-CD44 antibody. Negative controls are cells labeled with secondary goat–anti-mouse IgG Alexa-Fluor 488-conjugate. Flow cytometric analysis was performed as described in the Materials and Methods. Change in mean fluorescence intensity is depicted as mean ± SD of three independent experiments. According to GraphPad Prism10, unpaired <span class="html-italic">t</span>-test, * <span class="html-italic">p</span>-value &lt; 0.05, ** <span class="html-italic">p</span>-value 0.001 to 0.01, *** <span class="html-italic">p</span>-value 0.0001 to 0.001.</p>
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<p>Expression of CD44 molecules on U87 human glioma cells. Immunofluorescence observations on (<b>A</b>) (Control), (<b>B</b>) (200 µg/mL), and (<b>C</b>) (400 µg/mL) of peel extract after 48 h of treatment. Green color indicates CD44 molecule, red color indicates actin filaments stained by Alexa Fluor™ Plus 555 Phalloidin and blue color indicates nuclei stained by Hoechst 33258.</p>
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<p>Expression of CD44 molecules on LN18 human glioma cells. Immunofluorescence observations on (<b>A</b>) (control), (<b>B</b>) (200 µg/mL), and (<b>C</b>) (400 µg/mL) of peel extract after 48 h of treatment. Green color indicates CD44 molecule, red color indicates actin filaments stained by Alexa Fluor™ Plus 555 Phalloidin and blue color indicates nuclei stained by Hoechst 33258.</p>
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