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Metabolomic Technology in Quality and Safety of Agricultural Products and...
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Metabolomic Technology in Quality and Safety of Agricultural Products and Foods

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Food Metabolomics".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 7944

Special Issue Editor

Dr. Yasuhiro Date

E-Mail Website
Guest Editor
Research Center for Advanced Analysis, National Agriculture and Food Research Organization (NARO), Tsukuba 305-8642, Japan
Interests: metabolomics; NMR; data science; food chemistry; analytical chemistry; bioinformatics

Special Issue Information

Dear Colleagues,

Metabolomics is a powerful tool for the analysis and evaluation of the complex and diverse metabolite mixtures found in agricultural products and foods. Metabolomic studies provide useful information, e.g., how to improve nutritional and functional qualities, prevent food poisoning, and understand the processing and storage effects. In addition, the metabolomic technology in this field is constantly being advanced owing to the recent developments in computer science and information technology.

This Special Issue focuses on the recent advancements in metabolomic technology that has been made to address agricultural products and foods. The topics to be covered include an application of metabolomic technology for the evaluation and analysis of agricultural products and foods as well as the methodological advancements in metabolomic analysis using agricultural and food big data.

Dr. Yasuhiro Date
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • metabolomics
  • metabolic profiling
  • metabolic fingerprinting
  • foodomics
  • nuclear magnetic resonance spectroscopy
  • mass spectrometry
  • multivariate analysis
  • machine learning

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Published Papers (6 papers)

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Research

18 pages, 2892 KiB  
Article
Effects of Roasting Process on Sensory Qualities, Color, Physicochemical Components, and Identification of Key Aroma Compounds in Hubei Strip-Shaped Green Tea
by Fei Ye, Anhui Gui, Xiaoyan Qiao, Panpan Liu, Xueping Wang, Shengpeng Wang, Lin Feng, Jin Teng, Jinjin Xue, Xun Chen, Yuanhong Mei, Binghua Zhang, Hanshan Han, Anhua Liao, Pengcheng Zheng and Shiwei Gao
Metabolites 2025, 15(3), 155; https://doi.org/10.3390/metabo15030155 - 25 Feb 2025
Viewed by 142
Abstract
Background: Roasting conditions significantly influence the sensory profile of Hubei strip-shaped green tea (HSSGT). Methods: This study examined the effects of roast processing on the sensory attributes, color qualities, physicochemical properties, and key aroma compounds of HSSGT. Sensory evaluation, color qualities determination, principal [...] Read more.
Background: Roasting conditions significantly influence the sensory profile of Hubei strip-shaped green tea (HSSGT). Methods: This study examined the effects of roast processing on the sensory attributes, color qualities, physicochemical properties, and key aroma compounds of HSSGT. Sensory evaluation, color qualities determination, principal component analysis of physicochemical components (PCA), HS-SPME (headspace solid-phase microextraction) coupled with GC-MS (gas chromatography–mass spectrometry), relative odor activity value (ROAV), gas chromatography–olfactometry (GC-O), and absolute quantification analysis were employed to identify the critical difference in compounds that influence HSSGT desirability. Results: The results indicated that HSSGT roasted at 110 °C for 14 min achieved the highest sensory scores, superior physicochemical qualities, and an enhanced aroma index, which was attributed to shifting the proportion of chestnut to floral volatile compounds. Additionally, sensory-guided ROAV, GC-O, and absolute quantification revealed that linalool, octanal, nonanal, and hexanal were the most significant volatile compounds. The variations in these four critical compounds throughout the roasting process were further elucidated, showing that the ideal roasting conditions heightened floral aromas while diminishing the presence of less desirable green odors. These findings offer technical guidance and theoretical support for producing HSSGT with a more desirable balance of chestnut and floral aroma characteristics. Full article
(This article belongs to the Special Issue Metabolomic Technology in Quality and Safety of Agricultural Products and Foods)
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Figure 1

Figure 1
<p>Hubei strip-shaped green tea (HSSGT) manufacturing process. (<b>A</b>) Flow chart of HSSGT processing and determination of optimal roasting temperature. (<b>B</b>) Flow chart outlining the determination of optimal roasting time and roasting parameter.</p> Full article ">Figure 2
<p>Sensory quality scores of roasted HSSGT. (<b>A</b>) Sensory scores at different temperatures. (<b>B</b>) Sensory scores for varying roasting times at 110 °C.</p> Full article ">Figure 3
<p>Color quality scores of HSSGT roasted at 110 °C for different durations. (<b>A</b>) Dry tea color analysis. (<b>B</b>) Brew color analysis. Each sample was performed in triplicate. Columns labeled with ‘a’, ‘b’, and ‘c’ had significant differences (<span class="html-italic">p</span> &lt; 0.05) from each other.</p> Full article ">Figure 4
<p>Changes in bioactive compound content of HSSGT roasted at 110 °C for varying durations. (<b>A</b>) Tea polyphenol; (<b>B</b>) Free amino acids; (<b>C</b>) Soluble sugar; (<b>D</b>) Gallic acid; (<b>E</b>) Caffeine; (<b>F</b>) GC; (<b>G</b>) C; (<b>H</b>) EC; (<b>I</b>) EGC; (<b>J</b>) ECG; (<b>K</b>) EGCG; (<b>L</b>) GCG; (<b>M</b>) Ester catechins; (<b>N</b>) Total catechins. Columns labeled with ‘a’, ‘b’, and ‘c’ had significant differences (<span class="html-italic">p</span> &lt; 0.05) from each other.</p> Full article ">Figure 5
<p>Characterization of HSSGT VOCs. (<b>A</b>) The abundance of specific categories of VOCs in HSSGT roasted for 6, 14, and 22 min. (<b>B</b>) Principal component analysis. (<b>C</b>) Heat map of main differential alcohol compounds. (<b>D</b>) Aroma index. Columns labeled with ‘a’, ‘b’, and ‘c’ had significant differences (<span class="html-italic">p</span> &lt; 0.05) from each other.</p> Full article ">Figure 6
<p>Changes in the abundance of nonanal, linalool, hexanal, and octanal in the HSSGT roasting process. The different letters (‘a’, ‘b’, and ‘c’) indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 7
<p>Proposed pathways for molecular formation of the four characteristic HSSGT aroma compounds: (<b>A</b>) linalool, (<b>B</b>) nonanal, (<b>C</b>) octanal, (<b>D</b>) hexanal.</p> Full article ">
18 pages, 7282 KiB  
Article
Untargeted Metabolite Profiling Reveals Acute Toxicity of Pentosidine on Adipose Tissue of Rats
by Chuanqin Hu, Zhenzhen Shao, Wei Wu and Jing Wang
Metabolites 2024, 14(10), 539; https://doi.org/10.3390/metabo14100539 - 9 Oct 2024
Viewed by 1117
Abstract
Background: Pentosidine is an advanced glycation end product that is commonly found in heat-processed foods. Pentosidine has been involved in the occurrence and development of some chronic diseases. It was reported that pentosidine exposure can impair the function of the liver and [...] Read more.
Background: Pentosidine is an advanced glycation end product that is commonly found in heat-processed foods. Pentosidine has been involved in the occurrence and development of some chronic diseases. It was reported that pentosidine exposure can impair the function of the liver and kidneys. Adipose tissue, as an active endocrine organ, plays an important role in maintaining the normal physiological function of cells. However, the metabolic mechanism that causes pentosidine to induce toxicity in adipose tissue remains unclear. Methods: In the study, thirty male Sprague-Dawley rats were divided into a normal diet group, low dose group, and high dose group. A non-targeted metabolomics approach was used to compare the metabolic profiles of adipose tissue between the pentosidine and normal diet groups. Furthermore, histopathological observation and body weight change analysis were performed to test the results of the metabolomics analysis. Results: A total of forty-two differential metabolites were identified. Pentosidine mainly disturbed twelve metabolic pathways, such as ascorbate and aldarate metabolism, glycine, serine, and threonine metabolism, sulfur metabolism, pyruvate metabolism, etc. Additionally, pyruvic acid was identified as a possible key upregulated metabolite involved in thirty-four metabolic pathways. α-Ketoglutaric acid was named as a probable key downregulated metabolite involved in nineteen metabolic pathways based on enrichment network analysis. In addition, histopathological analysis and body weight changes confirmed the results of the metabolomics analysis. Conclusions: These results provided a new perspective for the molecular mechanisms of adipose tissue toxicity induced by pentosidine. Full article
(This article belongs to the Special Issue Metabolomic Technology in Quality and Safety of Agricultural Products and Foods)
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Figure 1
<p>Weight changes in rats in ND, LD, and HD groups. Data were expressed as mean ± standard deviation (SD). Differences in different groups were evaluated by <span class="html-italic">t</span>-test, “*” represents <span class="html-italic">p</span> &lt; 0.05, “**” represents <span class="html-italic">p</span> &lt; 0.01. ND group: normal diet group, n = 10; LD group: low dose group, n = 10; HD group: high dose group, n = 10.</p> Full article ">Figure 2
<p>H&amp;E staining images of adipose tissue from ND, LD, and HD groups (original magnification: 400×). Black arrows: damaged cell membranes; barred arrows: distorted cell contours; doubleheaded arrows: blurred cell contours; dashed arrows: inflammatory cell. ND: normal diet group; LD: low dose group; HD: high dose group.</p> Full article ">Figure 3
<p>Multivariate statistical analysis of results from GC−MS analysis. (<b>A</b>) PCA score plot analysis (R<sup>2</sup>X =74.2%, Q<sup>2</sup> = 49.9%); (<b>B</b>) PLS−DA score plot analysis (R<sup>2</sup>X = 81.9%, R<sup>2</sup>Y = 99.4%, Q<sup>2</sup> = 92.8%); (<b>C</b>) permutation plot for PLS−DA model (n = 999), R<sup>2</sup> = (0.0, 0.253), Q<sup>2</sup> = (0.0, − 0.177). PCA: principal component analysis; PLS−DA: partial least squares discriminant analysis; ND: normal diet group, n = 10; LD: low dose group, n = 10; HD: high dose group, n = 10; QC: quality control.</p> Full article ">Figure 4
<p>Boxplots of differential metabolites in adipose tissue of rats in ND, LD, and HD groups. Differences in different groups were evaluated by Mann–Whitney U test. “*” means <span class="html-italic">p</span> &lt; 0.05, “**” means <span class="html-italic">p</span> &lt; 0.01, “***” means <span class="html-italic">p</span> &lt; 0.001. ND group: normal diet group, n = 10; LD group: low dose group, n = 10; HD group: high dose group, n = 10.</p> Full article ">Figure 5
<p>Disturbed pathways in adipose tissue of rats from ND, LD, and HD groups. Node color in pathway analysis represented its <span class="html-italic">p</span>-value; node radius reflected their pathway impact values. (1) Ascorbate and aldarate metabolism, (2) glycine, serine, and threonine metabolism, (3) sulfur metabolism, (4) pyruvate metabolism, (5) aminoacyl−tRNA biosynthesis, (6) alanine, aspartate, and glutamate metabolism, (7) glyoxylate and dicarboxylate metabolism, (8) citrate cycle (TCA cycle), (9) glycolysis/gluconeogenesis, (10) inositol phosphate metabolism, (11) cysteine and methionine metabolism, (12) pentose and glucuronate interconversions. ND group: normal diet group, n = 10; LD group: low dose group, n = 10; HD group: high dose group, n = 10.</p> Full article ">Figure 6
<p>Pathway mapping of differential metabolites detected in HD group compared to ND group. Metabolic pathway generated through MetaMapp and drawn by Cytoscape. The depicted network reveals that red nodes represent significantly upregulated metabolites, blue nodes show remarkably downregulated metabolites, and gray nodes reveal no significant changes in metabolites. Size of node is positively correlated with fold change between HD group and ND group. ND group: normal diet group, n = 10; LD group: low dose group, n = 10; HD group: high dose group.</p> Full article ">Figure 7
<p>Network analysis of differential metabolites and metabolic pathways in pentosidine-exposed groups shows that there are forty-two differential metabolites. “*” represents <span class="html-italic">p</span> &lt; 0.05, “**” represents <span class="html-italic">p</span> &lt; 0.01, “***” represents <span class="html-italic">p</span> &lt; 0.001. The red circles and black circles show upregulated and downregulated metabolites, respectively. Intensity of colors indicates fold changes in metabolites. A total of seventy-two metabolic pathways are classified as eight metabolic pathways (<a href="#app1-metabolites-14-00539" class="html-app">Table S3</a>) and connected to different metabolites by the red line (upregulated) and black line (downregulated).</p> Full article ">Figure 8
<p>Interplay between differential metabolites in HD group compared to ND group. Metabolic pathways are illustrated based on information obtained from KEGG database. Red boxes represent increased metabolites, blue boxes show decreased metabolites, blank boxes reveal no significant changes in metabolites. Numbers 1 to 12 represent metabolic pathways with impact value larger than 0.1 (<a href="#metabolites-14-00539-f005" class="html-fig">Figure 5</a>). ND group: normal diet group, n = 10; LD group: low dose group, n = 10; HD group: high dose group, n = 10.</p> Full article ">
13 pages, 5376 KiB  
Article
An Exploratory Study on the Rapid Detection of Volatile Organic Compounds in Gardenia Fruit Using the Heracles NEO Ultra-Fast Gas Phase Electronic Nose
by Wenjing Cai, Wei Zhou, Jiayao Liu, Jing Wang, Ding Kuang, Jian Wang, Qing Long and Dan Huang
Metabolites 2024, 14(8), 445; https://doi.org/10.3390/metabo14080445 - 11 Aug 2024
Viewed by 1004
Abstract
Gardenia fruit is a popular functional food and raw material for natural pigments. It comes from a wide range of sources, and different products sharing the same name are very common. Volatile organic compounds (VOCs) are important factors that affect the flavor and [...] Read more.
Gardenia fruit is a popular functional food and raw material for natural pigments. It comes from a wide range of sources, and different products sharing the same name are very common. Volatile organic compounds (VOCs) are important factors that affect the flavor and quality of gardenia fruit. This study used the Heracles NEO ultra-fast gas phase electronic nose with advanced odor analysis performance and high sensitivity to analyze six batches of gardenia fruit from different sources. This study analyzed the VOCs to find a way to quickly identify gardenia fruit. The results show that this method can accurately distinguish the odor characteristics of various gardenia fruit samples. The VOCs in gardenia fruit are mainly organic acid esters, ketones, and aldehyde compounds. By combining principal component analysis (PCA) and discriminant factor analysis (DFA), this study found that the hexanal content varied the most in different gardenia fruit samples. The VOCs allowed for the fruit samples to be grouped into two main categories. One fruit sample was quite different from the fruits of other origins. The results provide theoretical support for feasibility of rapid identification and quality control of gardenia fruit and related products in the future. Full article
(This article belongs to the Special Issue Metabolomic Technology in Quality and Safety of Agricultural Products and Foods)
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Figure 1

Figure 1
<p>MXT-5 gas chromatogram overlay diagram.</p> Full article ">Figure 2
<p>MXT-1701 gas chromatogram overlay diagram.</p> Full article ">Figure 3
<p>Principal component analysis of gardenia fruit samples.</p> Full article ">Figure 4
<p>Principal component analysis and loading diagram of gardenia fruit samples.</p> Full article ">Figure 5
<p>Histogram of differential compound contents.</p> Full article ">Figure 6
<p>Discriminant factor analysis chart of different gardenia fruit samples.</p> Full article ">
23 pages, 5065 KiB  
Article
Adenosine Triphosphate and Adenylate Energy Charge in Ready-to-Eat Food
by Georgii Konoplev, Alar Sünter, Artur I. Kuznetsov, Piret Raudsepp, Tõnu Püssa, Lauri Toom, Linda Rusalepp, Dea Anton, Oksana V. Stepanova, Daniil Lyalin, Liubov Abramova, Andrey Kozin, Oksana S. Stepanova, Aleksandr Frorip and Mati Roasto
Metabolites 2024, 14(8), 440; https://doi.org/10.3390/metabo14080440 - 7 Aug 2024
Viewed by 1225
Abstract
It is commonly accepted that dietary nucleotides should be considered as essential nutrients originating mainly but not exclusively from meat and fish dishes. Most research in food science related to nutrition nucleotides is focused on raw products, while the effects of thermal processing [...] Read more.
It is commonly accepted that dietary nucleotides should be considered as essential nutrients originating mainly but not exclusively from meat and fish dishes. Most research in food science related to nutrition nucleotides is focused on raw products, while the effects of thermal processing of ready-to-eat food on nucleotide content are largely overlooked by the scientific community. The aim of this study is to investigate the impact of thermal processing and cold storage on the content of dietary nucleotides in freshly prepared and canned ready-to-eat meat and fish food. The concentrations of ATP, ADP, AMP, IMP, Ino, and Hx were determined using NMR, HPLC, FPMLC, and ATP bioluminescence analytical techniques; freshness indices K and K1 and adenylate energy charge (AEC) values were estimated to assess the freshness status and confirm a newly unveiled phenomenon of the reappearance of adenylate nucleotides. It was found that in freshly prepared at 65 °C ≤ T ≤ +100 °C and canned food, the concentration of free nucleotides was in the range of 0.001–0.01 µmol/mL and remained unchanged for a long time during cold storage; the correct distribution of mole fractions of adenylates corresponding to 0 < AEC < 0.5 was observed compared to 0.2 < AEC < 1.0 in the original raw samples, with either a high or low content of residual adenylates. It could be assumed that heating at nonenzymatic temperatures T > 65 °C can rupture cell membranes and release residual intracell nucleotides in quite a meaningful concentration. These findings may lead to a conceptual change in the views on food preparation processes, taking into account the phenomenon of the free adenylates renaissance and AEC bioenergetics. Full article
(This article belongs to the Special Issue Metabolomic Technology in Quality and Safety of Agricultural Products and Foods)
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Figure 1

Figure 1
<p>Distribution of adenylates as the mole fractions of ATP, ADP and AMP on the AEC values in 19 samples of very fresh raw pork (<b>a</b>) and 61 samples of fish flesh (<b>b</b>) with the quadratic polynomial approximations of the experimental curves for ATP (the equation on the right), ADP (in the middle) and AMP (on the left). The diagrams were produced purely by the re-analysis of the experimental data from the original papers [<a href="#B24-metabolites-14-00440" class="html-bibr">24</a>,<a href="#B35-metabolites-14-00440" class="html-bibr">35</a>,<a href="#B36-metabolites-14-00440" class="html-bibr">36</a>,<a href="#B37-metabolites-14-00440" class="html-bibr">37</a>,<a href="#B38-metabolites-14-00440" class="html-bibr">38</a>,<a href="#B39-metabolites-14-00440" class="html-bibr">39</a>,<a href="#B40-metabolites-14-00440" class="html-bibr">40</a>,<a href="#B41-metabolites-14-00440" class="html-bibr">41</a>,<a href="#B42-metabolites-14-00440" class="html-bibr">42</a>,<a href="#B43-metabolites-14-00440" class="html-bibr">43</a>,<a href="#B44-metabolites-14-00440" class="html-bibr">44</a>,<a href="#B45-metabolites-14-00440" class="html-bibr">45</a>,<a href="#B46-metabolites-14-00440" class="html-bibr">46</a>,<a href="#B47-metabolites-14-00440" class="html-bibr">47</a>,<a href="#B48-metabolites-14-00440" class="html-bibr">48</a>].</p> Full article ">Figure 1 Cont.
<p>Distribution of adenylates as the mole fractions of ATP, ADP and AMP on the AEC values in 19 samples of very fresh raw pork (<b>a</b>) and 61 samples of fish flesh (<b>b</b>) with the quadratic polynomial approximations of the experimental curves for ATP (the equation on the right), ADP (in the middle) and AMP (on the left). The diagrams were produced purely by the re-analysis of the experimental data from the original papers [<a href="#B24-metabolites-14-00440" class="html-bibr">24</a>,<a href="#B35-metabolites-14-00440" class="html-bibr">35</a>,<a href="#B36-metabolites-14-00440" class="html-bibr">36</a>,<a href="#B37-metabolites-14-00440" class="html-bibr">37</a>,<a href="#B38-metabolites-14-00440" class="html-bibr">38</a>,<a href="#B39-metabolites-14-00440" class="html-bibr">39</a>,<a href="#B40-metabolites-14-00440" class="html-bibr">40</a>,<a href="#B41-metabolites-14-00440" class="html-bibr">41</a>,<a href="#B42-metabolites-14-00440" class="html-bibr">42</a>,<a href="#B43-metabolites-14-00440" class="html-bibr">43</a>,<a href="#B44-metabolites-14-00440" class="html-bibr">44</a>,<a href="#B45-metabolites-14-00440" class="html-bibr">45</a>,<a href="#B46-metabolites-14-00440" class="html-bibr">46</a>,<a href="#B47-metabolites-14-00440" class="html-bibr">47</a>,<a href="#B48-metabolites-14-00440" class="html-bibr">48</a>].</p> Full article ">Figure 2
<p>Comparison of the empirical–theoretical approximation of the adenylates distributions for raw pork and fish flesh in the pairs ATP (fish)—ATP (pork), ADP (fish)—ADP (pork) and AMP (fish)—AMP (pork). The equations of the polynomial approximations are given inside the diagram, for fish (above the curves) and pork (below the curves).</p> Full article ">Figure 3
<p><sup>1</sup>H NMR spectra for 4 meat samples: (<b>a</b>) ready-to-eat chicken schnitzel; (<b>b</b>)—the same schnitzel after irradiation in microwave oven at 400 W for 1 min; (<b>c</b>)—broiler filet boiled in broth at 75 °C for 20 min; (<b>d</b>)—broiler filet raw at 24 °C.</p> Full article ">Figure 4
<p>Real distribution of adenylates (dots) and its extrapolation for thermally processed pork, beef, poultry, and fish. In addition to the datapoints obtained in this work by the NMR method (36 triads), 4 triads of datapoints for AEC = 0.218; 0.098; 0.0920; 0875 (triangular markers) for minced boiled beef are adopted from the HPLC results presented in [<a href="#B52-metabolites-14-00440" class="html-bibr">52</a>].</p> Full article ">Figure 5
<p>Comparison of the combined empirical–theoretical approximations of the adenylate distributions for raw and heated fish and meat: one triple dataset is based on the combined data for raw fish and meat (<a href="#metabolites-14-00440-f001" class="html-fig">Figure 1</a>) produced from literature data (based on 80 samples in total) and the other is for heated fish and meat (<a href="#metabolites-14-00440-f004" class="html-fig">Figure 4</a>) produced from the experimental data in the framework of this research (based on 36 samples in total).</p> Full article ">Figure 6
<p>HPLC chromatograms of four canned fish samples: (<b>a</b>) Iwashi (544); (<b>b</b>) Mediterranean Sardine (545); (<b>c</b>) Rainbow Trout (546); (<b>d</b>) Pink Salmon (547).</p> Full article ">Figure 6 Cont.
<p>HPLC chromatograms of four canned fish samples: (<b>a</b>) Iwashi (544); (<b>b</b>) Mediterranean Sardine (545); (<b>c</b>) Rainbow Trout (546); (<b>d</b>) Pink Salmon (547).</p> Full article ">Figure 7
<p><sup>1</sup>H NMR spectra of two canned fish samples: Iwashi (544); Mediterranean Sardine (545).</p> Full article ">Figure 8
<p>The comparison of the index Time measured by the FPMLC method, and the <span class="html-italic">K</span> and <span class="html-italic">K</span><sub>I</sub> indices measured by the HPLC for four samples of canned fish (the sample codes are indicated next to each point).</p> Full article ">Figure 9
<p>The comparison of the index Time measured by the FPMLC method, and the <span class="html-italic">K</span><sub>I</sub> index measured by the NMR spectroscopy for four samples of canned fish (the sample codes are indicated next to each point).</p> Full article ">Figure 10
<p>Comparison of two FPMLC chromatograms processed on days 1 and 9 of the cold storage for the samples of the raw carp fish.</p> Full article ">Figure 11
<p>Dynamics of the index Time for the raw carp fish in the process of the prolonged cold storage and steam-cooked carp fish (immediately prepared from the cold stored raw fish and stored refrigerated after heating of the fresh fish at the beginning of the experiment); the experimental datasets are approximated with polynomial functions for an easier interpretation.</p> Full article ">Figure 12
<p>Dynamics of the ATP concentration determined by the bioluminescence assay for the raw carp fish in the process of prolonged cold storage and steam-cooked carp fish (immediately prepared from the cold stored raw fish and stored refrigerated after heating of the fresh fish in the beginning of the experiment); the experimental datasets are approximated with linear functions for an easier interpretation.</p> Full article ">Figure 13
<p>Thermal degradation of ATP at +80 °C and relative distribution of ADP and AMP as the products of this reaction. The dots from the right to the left correspond to the values measured at the very beginning of the experiment (AEC = 1.0) and at 8, 16 and 24 (AEC = 0.129) hours later on.</p> Full article ">Figure 14
<p>Combination of the molar distributions of adenylates in the enzyme-free model solution and in the intercellular fluid of absolutely fresh pork according to the data in <a href="#metabolites-14-00440-f001" class="html-fig">Figure 1</a>a. The experimental points are given together with the extrapolation curves; the approximation equations are given for the pork datasets.</p> Full article ">
13 pages, 13204 KiB  
Article
Explorative Study on Volatile Organic Compounds of Cinnamon Based on GC-IMS
by Yu Pan, Liya Qiao, Shanshuo Liu, Ye He, Danna Huang, Wuwei Wu, Yingying Liu, Lu Chen and Dan Huang
Metabolites 2024, 14(5), 274; https://doi.org/10.3390/metabo14050274 - 9 May 2024
Cited by 2 | Viewed by 1765
Abstract
Cinnamon is one of the most popular spices worldwide, and volatile organic compounds (VOCs) are its main metabolic products. The misuse or mixing of cinnamon on the market is quite serious. This study used gas chromatography-ion migration spectroscopy (GC-IMS) technology to analyze the [...] Read more.
Cinnamon is one of the most popular spices worldwide, and volatile organic compounds (VOCs) are its main metabolic products. The misuse or mixing of cinnamon on the market is quite serious. This study used gas chromatography-ion migration spectroscopy (GC-IMS) technology to analyze the VOCs of cinnamon samples. The measurement results showed that 66 VOCs were detected in cinnamon, with terpenes being the main component accounting for 45.45%, followed by aldehydes accounting for 21.21%. The content of esters and aldehydes was higher in RG-01, RG-02, and RG-04; the content of alcohols was higher in RG-01; and the content of ketones was higher in RG-02. Principal component analysis, cluster analysis, and partial least squares regression analysis can be performed on the obtained data to clearly distinguish cinnamon. According to the VIP results of PLS-DA, 1-Hexanol, 2-heptanone, ethanol, and other substances are the main volatile substances that distinguish cinnamon. This study combined GC-IMS technology with chemometrics to accurately identify cinnamon samples, providing scientific guidance for the efficient utilization of cinnamon. At the same time, this study is of great significance for improving the relevant quality standards of spices and guiding the safe use of spices. Full article
(This article belongs to the Special Issue Metabolomic Technology in Quality and Safety of Agricultural Products and Foods)
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Figure 1
<p>The 3D spectrum of the VOCs of four groups of cinnamon.</p> Full article ">Figure 2
<p>The 2D spectrum of the VOCs of four groups of cinnamon.</p> Full article ">Figure 3
<p>Analysis of the spectral differences between RG-01 and the other three groups of cinnamon.</p> Full article ">Figure 4
<p>Characteristic peak position plot of VOCs of cinnamon.</p> Full article ">Figure 5
<p>Gallery plot of VOCs selected via GC-IMS.</p> Full article ">Figure 6
<p>Plot of the PCA scores of VOCs in four species of cinnamon.</p> Full article ">Figure 7
<p>Cluster heat map of VOCs in four species of cinnamon.</p> Full article ">Figure 8
<p>PLS−DA analysis of VOCs in four groups of cinnamon.</p> Full article ">Figure 9
<p>VIP diagram of the PLS-DA model.</p> Full article ">Figure 10
<p>Permutation test results of VOCs in four groups of cinnamon.</p> Full article ">
17 pages, 2604 KiB  
Article
A Data-Driven Approach to Sugarcane Breeding Programs with Agronomic Characteristics and Amino Acid Constituent Profiling
by Chiaki Ishikawa, Yasuhiro Date, Makoto Umeda, Yusuke Tarumoto, Megumi Okubo, Yasujiro Morimitsu, Yasuaki Tamura, Yoichi Nishiba and Hiroshi Ono
Metabolites 2024, 14(4), 243; https://doi.org/10.3390/metabo14040243 - 21 Apr 2024
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Abstract
Sugarcane (Saccharum spp. hybrids) and its processed products have supported local industries such as those in the Nansei Islands, Japan. To improve the sugarcane quality and productivity, breeders select better clones by evaluating agronomic characteristics, such as commercially recoverable sugar and cane [...] Read more.
Sugarcane (Saccharum spp. hybrids) and its processed products have supported local industries such as those in the Nansei Islands, Japan. To improve the sugarcane quality and productivity, breeders select better clones by evaluating agronomic characteristics, such as commercially recoverable sugar and cane yield. However, other constituents in sugarcane remain largely unutilized in sugarcane breeding programs. This study aims to establish a data-driven approach to analyze agronomic characteristics from breeding programs. This approach also determines a correlation between agronomic characteristics and free amino acid composition to make breeding programs more efficient. Sugarcane was sampled in clones in the later stage of breeding selection and cultivars from experimental fields on Tanegashima Island. Principal component analysis and hierarchical cluster analysis using agronomic characteristics revealed the diversity and variability of each sample, and the data-driven approach classified cultivars and clones into three groups based on yield type. A comparison of free amino acid constituents between these groups revealed significant differences in amino acids such as asparagine and glutamine. This approach dealing with a large volume of data on agronomic characteristics will be useful for assessing the characteristics of potential clones under selection and accelerating breeding programs. Full article
(This article belongs to the Special Issue Metabolomic Technology in Quality and Safety of Agricultural Products and Foods)
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Figure 1

Figure 1
<p>Principal component analysis (PCA) of data for the agronomic characteristics of sugarcane: (<b>A</b>) PCA score plot based on 12 agronomic characteristics of samples from 2018 to 2020 (<span class="html-italic">n</span> = 460); (<b>B</b>) loading plot based on the same analysis as the score plot A.</p> Full article ">Figure 2
<p>A dendrogram based on Hierarchical cluster analysis (HCA), calculated from 12 agronomic characteristics.</p> Full article ">Figure 3
<p>Comparison of 12 parameters between groups classified using HCA. We illustrate the parameters of agronomic characteristics used for HCA as box plots, arranged from left to right by groups A–C. Group A comprised 6 cultivars and clones, including KTn03-54; group B comprised 5 cultivars and clones, including Harunoogi; and group C comprised the remaining 16 cultivars and clones. Sample numbers of agronomic characteristics without length of millable stalks were <span class="html-italic">n</span> = 99, 75, and 290 for A, B, and C, respectively. Missing data for length of millable stalks are shown in <a href="#app1-metabolites-14-00243" class="html-app">Table S2</a>. Diamonds indicate the means of each group. Statistical significance between groups was calculated using the Steel–Dwass test, except for the diameter of millable stalks, Brix, and average Brix in autumn. For these, the Tukey–Kramer test was used; *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 4
<p>Comparison of amino acid content among the three groups. Eleven free amino acids and total amino acid concentrations in sugarcane juice are shown as box plots by groups, which were classified using HCA. Groups A, B, and C are arranged from left to right. Sample numbers of amino acids without asparagine, glutamate, and total amino acids are <span class="html-italic">n</span> = 99, 75, and 290 for A, B, and C, respectively. Missing data for asparagine, glutamate, and total amino acids are shown in <a href="#app1-metabolites-14-00243" class="html-app">Table S2</a>. Diamonds indicate the means of each group. Statistical significance between groups was calculated using the Steel–Dwass test; *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05. Abbreviation: GABA, γ-aminobutyric acid.</p> Full article ">Figure 5
<p>Comparison of amino acid content between cultivars and clones. Five free amino acids and total concentration in sugarcane juice are shown as box plots by groups classified using HCA, as well as by cultivars and clones. Groups A–C are arranged from top to bottom. The groups are represented by colors corresponding to the grouping in <a href="#metabolites-14-00243-f002" class="html-fig">Figure 2</a>. Sampled sugarcane were all cropping types, harvested from 2018 to 2021. Sample numbers differed depending on cultivars and clones, <span class="html-italic">n</span> = 5–39. The number of data for each cultivar or clone is shown in <a href="#app1-metabolites-14-00243" class="html-app">Table S2</a>. Diamonds indicate the means of each cultivar or clone.</p> Full article ">Figure 6
<p>Differences in amino acid composition in sugarcane cultivars and clones. The stacked bar graph illustrates the average content of 17 amino acids in sugarcane juice from 6 cultivars and 21 clones. Sampled sugarcanes were newly planted and harvested in January 2019. <span class="html-italic">n</span> = 5; NiF8, Ni22, and NCo310 contained duplicate test plots at the fourth selection. <span class="html-italic">n</span> = 3; other cultivars and clones.</p> Full article ">
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