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Journal articles on the topic 'Foodomics'

To see the other types of publications on this topic, follow the link: Foodomics.

Author: Grafiati

Published: 10 March 2023

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1

Si, Wenjin, Yangdong Zhang, Xiang Li, Yufeng Du, and Qingbiao Xu. "Understanding the Functional Activity of Polyphenols Using Omics-Based Approaches." Nutrients 13, no. 11 (November 5, 2021): 3953. http://dx.doi.org/10.3390/nu13113953.

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Plant polyphenols are the main category of natural active substances, and are distributed widely in vegetables, fruits, and plant-based processed foods. Polyphenols have a beneficial performance in preventing diseases and maintaining body health. However, its action mechanism has not been well understood. Foodomics is a novel method to sequence and widely used in nutrition, combining genomics, proteomics, transcriptomics, microbiome, and metabolomics. Based on multi-omics technologies, foodomics provides abundant data to study functional activities of polyphenols. In this paper, physiological functions of various polyphenols based on foodomics and microbiome was discussed, especially the anti-inflammatory and anti-tumor activities and gut microbe regulation. In conclusion, omics (including microbiomics) is a useful approach to explore the bioactive activities of polyphenols in the nutrition and health of human and animals.

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2

Cifuentes, Alejandro. "Recent developments in foodomics." INFORM International News on Fats, Oils, and Related Materials 29, no. 4 (April 1, 2018): 26–29. http://dx.doi.org/10.21748/inform.04.2018.26.

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3

Bordoni, Alessandra, and Francesco Capozzi. "Foodomics for healthy nutrition." Current Opinion in Clinical Nutrition and Metabolic Care 17, no. 5 (September 2014): 418–24. http://dx.doi.org/10.1097/mco.0000000000000089.

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4

Giacometti, Jasminka, and Djuro Josic. "Foodomics in microbial safety." TrAC Trends in Analytical Chemistry 52 (December 2013): 16–22. http://dx.doi.org/10.1016/j.trac.2013.09.003.

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5

Xu, Yong-Jiang, and Xi Wu. "Foodomics in microbiological investigations." Current Opinion in Food Science 4 (August 2015): 51–55. http://dx.doi.org/10.1016/j.cofs.2015.05.001.

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6

Herrero, Miguel. "Editorial overview: Foodomics technologies." Current Opinion in Food Science 22 (August 2018): iii—iv. http://dx.doi.org/10.1016/j.cofs.2018.10.010.

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7

Munekata, Paulo ES, Mirian Pateiro, María López-Pedrouso, Mohammed Gagaoua, and José M. Lorenzo. "Foodomics in meat quality." Current Opinion in Food Science 38 (April 2021): 79–85. http://dx.doi.org/10.1016/j.cofs.2020.10.003.

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8

Mahmoodpoor, Ata, Ali Shamekh, and Sarvin Sanaie. "Foodomics and COVID-19." Clinical Nutrition ESPEN 38 (August 2020): 283. http://dx.doi.org/10.1016/j.clnesp.2020.05.017.

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9

Cifuentes, Alejandro. "Food analysis and Foodomics." Journal of Chromatography A 1216, no. 43 (October 2009): 7109. http://dx.doi.org/10.1016/j.chroma.2009.09.018.

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10

Bordoni, Alessandra, Francesco Capozzi, and Pasquale Ferranti. "FOODOMICS. Food to life." Food Research International 89 (November 2016): 1047. http://dx.doi.org/10.1016/j.foodres.2016.11.007.

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11

Cifuentes, Alejandro, and Miguel Herrero. "Foodomics: Surfing the (analytical) wave." ELECTROPHORESIS 43, no. 18-19 (October 2022): 1813. http://dx.doi.org/10.1002/elps.202270114.

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12

Valdés, Alberto, Gerardo Álvarez-Rivera, Bárbara Socas-Rodríguez, Miguel Herrero, Elena Ibáñez, and Alejandro Cifuentes. "Foodomics: Analytical Opportunities and Challenges." Analytical Chemistry 94, no. 1 (November 23, 2021): 366–81. http://dx.doi.org/10.1021/acs.analchem.1c04678.

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13

D’Alessandro, Angelo, and Lello Zolla. "Foodomics to investigate meat tenderness." TrAC Trends in Analytical Chemistry 52 (December 2013): 47–53. http://dx.doi.org/10.1016/j.trac.2013.05.017.

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14

Bevilacqua, Marta, Rasmus Bro, Federico Marini, Åsmund Rinnan, Morten Arendt Rasmussen, and Thomas Skov. "Recent chemometrics advances for foodomics." TrAC Trends in Analytical Chemistry 96 (November 2017): 42–51. http://dx.doi.org/10.1016/j.trac.2017.08.011.

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15

Yuan, Lei, Fedrick C. Mgomi, Zhenbo Xu, Ni Wang, Guoqing He, and Zhenquan Yang. "Understanding of food biofilms by the application of omics techniques." Future Microbiology 16, no. 4 (March 2021): 257–69. http://dx.doi.org/10.2217/fmb-2020-0218.

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Biofilms constitute a protective barrier for foodborne pathogens to survive under stressful food processing conditions. Therefore, studies into the development and control of biofilms by novel techniques are vital for the food industry. In recent years, foodomics techniques have been developed for biofilm studies, which contributed to a better understanding of biofilm behavior, physiology, composition, as well as their response to antibiofilm methods at different molecular levels including genes, RNA, proteins and metabolic metabolites. Throughout this review, the main studies where foodomics tools used to explore the mechanisms for biofilm formation, dispersal and elimination were reviewed. The data summarized from relevant studies are important to design novel and appropriate biofilm elimination methods for enhancing food safety at any point of food processing lines.

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16

Cifuentes, Alejandro. "Food Analysis: Present, Future, and Foodomics." ISRN Analytical Chemistry 2012 (November 26, 2012): 1–16. http://dx.doi.org/10.5402/2012/801607.

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This paper presents a revision on the instrumental analytical techniques and methods used in food analysis together with their main applications in food science research. The present paper includes a brief historical perspective on food analysis, together with a deep revision on the current state of the art of modern analytical instruments, methodologies, and applications in food analysis with a special emphasis on the works published on this topic in the last three years (2009–2011). The article also discusses the present and future challenges in food analysis, the application of “omics” in food analysis (including epigenomics, genomics, transcriptomics, proteomics, and metabolomics), and provides an overview on the new discipline of Foodomics.

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17

Cifuentes, Alejandro. "Foodomics, foodome and modern food analysis." TrAC Trends in Analytical Chemistry 96 (November 2017): 1. http://dx.doi.org/10.1016/j.trac.2017.09.001.

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18

Rešetar, Dina, Sandra Kraljević Pavelić, and Djuro Josić. "Foodomics for investigations of food toxins." Current Opinion in Food Science 4 (August 2015): 86–91. http://dx.doi.org/10.1016/j.cofs.2015.05.004.

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19

Klampfl, Christian W. "Ambient mass spectrometry in foodomics studies." Current Opinion in Food Science 22 (August 2018): 137–44. http://dx.doi.org/10.1016/j.cofs.2018.03.014.

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20

Bordoni, Alessandra, Francesco Capozzi, and Pasquale Ferranti. "Preface: FoodOmics. The science for discovering." Food Research International 63 (September 2014): 125. http://dx.doi.org/10.1016/j.foodres.2014.07.007.

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21

Cifuentes, Alejandro. "Advanced food analysis, foodome and foodomics." ELECTROPHORESIS 39, no. 13 (July 2018): 1525–26. http://dx.doi.org/10.1002/elps.201870106.

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22

Corsaro, Carmelo, Nicola Cicero, Domenico Mallamace, Sebastiano Vasi, Clara Naccari, Andrea Salvo, Salvatore Vincenzo Giofrè, and Giacomo Dugo. "HR-MAS and NMR towards Foodomics." Food Research International 89 (November 2016): 1085–94. http://dx.doi.org/10.1016/j.foodres.2016.09.033.

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23

Cifuentes, Alejandro. "Editorial overview: Foodomics technologies: Foodomics: exploring safety, quality and bioactivity of foods in the 21st century." Current Opinion in Food Science 4 (August 2015): 136–38. http://dx.doi.org/10.1016/j.cofs.2015.07.006.

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24

IIJIMA, Yoko. "Evaluation of Food Quality by Foodomics Approach: How Are the Characteristics of Each Food Cleared by Foodomics?" KAGAKU TO SEIBUTSU 58, no. 4 (April 1, 2020): 210–16. http://dx.doi.org/10.1271/kagakutoseibutsu.58.210.

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25

Nazzaro, Filomena, Pierangelo Orlando, Florinda Fratianni, Aldo Di Luccia, and Raffaele Coppola. "Protein Analysis-on-Chip Systems in Foodomics." Nutrients 4, no. 10 (October 16, 2012): 1475–89. http://dx.doi.org/10.3390/nu4101475.

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26

Hu, Chunxiu, and Guowang Xu. "Mass-spectrometry-based metabolomics analysis for foodomics." TrAC Trends in Analytical Chemistry 52 (December 2013): 36–46. http://dx.doi.org/10.1016/j.trac.2013.09.005.

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27

Khakimov, Bekzod, and Søren Balling Engelsen. "Resveratrol in the foodomics era: 1:25,000." Annals of the New York Academy of Sciences 1403, no. 1 (September 2017): 48–58. http://dx.doi.org/10.1111/nyas.13425.

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28

Xu, Yong-Jiang. "Foodomics: A novel approach for food microbiology." TrAC Trends in Analytical Chemistry 96 (November 2017): 14–21. http://dx.doi.org/10.1016/j.trac.2017.05.012.

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29

Valdés, Alberto, Alejandro Cifuentes, and Carlos León. "Foodomics evaluation of bioactive compounds in foods." TrAC Trends in Analytical Chemistry 96 (November 2017): 2–13. http://dx.doi.org/10.1016/j.trac.2017.06.004.

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30

Gilbert-López, Bienvenida, José A. Mendiola, and Elena Ibáñez. "Green foodomics. Towards a cleaner scientific discipline." TrAC Trends in Analytical Chemistry 96 (November 2017): 31–41. http://dx.doi.org/10.1016/j.trac.2017.06.013.

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31

Ferranti, Pasquale. "The future of analytical chemistry in foodomics." Current Opinion in Food Science 22 (August 2018): 102–8. http://dx.doi.org/10.1016/j.cofs.2018.02.005.

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32

Jimenez-Carvelo, Ana M., and Luis Cuadros-Rodríguez. "Data mining/machine learning methods in foodomics." Current Opinion in Food Science 37 (February 2021): 76–82. http://dx.doi.org/10.1016/j.cofs.2020.09.008.

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33

Ren, Xinmin, and Xiangdong Li. "Advances in Research on Diabetes by Human Nutriomics." International Journal of Molecular Sciences 20, no. 21 (October 29, 2019): 5375. http://dx.doi.org/10.3390/ijms20215375.

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The incidence and prevalence of diabetes mellitus (DM) have increased rapidly worldwide over the last two decades. Because the pathogenic factors of DM are heterogeneous, determining clinically effective treatments for DM patients is difficult. Applying various nutrient analyses has yielded new insight and potential treatments for DM patients. In this review, we summarized the omics analysis methods, including nutrigenomics, nutritional-metabolomics, and foodomics. The list of the new targets of SNPs, genes, proteins, and gut microbiota associated with DM has been obtained by the analysis of nutrigenomics and microbiomics within last few years, which provides a reference for the diagnosis of DM. The use of nutrient metabolomics analysis can obtain new targets of amino acids, lipids, and metal elements, which provides a reference for the treatment of DM. Foodomics analysis can provide targeted dietary strategies for DM patients. This review summarizes the DM-associated molecular biomarkers in current applied omics analyses and may provide guidance for diagnosing and treating DM.

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34

Opetz, Danielle, Alison Beloshapka, Patrícia M. Oba, Maria R. de Godoy, and Kelly S. Swanson. "PSXI-4 Use of Foodomics Analysis to Biochemically Compare Different pet Food Ingredient Categories." Journal of Animal Science 100, Supplement_3 (September 21, 2022): 276–77. http://dx.doi.org/10.1093/jas/skac247.502.

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Abstract With an increased focus on pet health and longevity, ingredient nutritional quality and functionality is of great interest to the pet food industry. Characterizing the natural bioactive molecules present in different ingredient categories may identify those most likely to support health and/or manage disease. Over the past couple decades, high-throughput ‘omics’ technologies have been used to characterize the DNA, RNA, proteins, and metabolite profiles of dogs and cats. Similar technologies may now be applied to foods, a process referred to as ‘foodomics’. The objective of this study was to use foodomics analysis to characterize and compare various fruit-, vegetable-, tuber-, legume-, and grain-based ingredients. A total of 35 ingredients were subsampled, ground with liquid nitrogen, and sent to Metabolon, Inc. (Durham, NC) for untargeted high-throughput metabolomics analysis using gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry. Over 640 named bioactive molecules were identified in the samples. Principal component analysis showed that ingredient classes tended to cluster together and separately from others, with distinct fruit-, vegetable-, and grain-based clusters identified. Legumes clustered most closely to tubers and vegetables. Random forest analysis had a nearly perfect predictive accuracy and was used to identify molecules most influential in separating ingredient classes. This dataset has provided a foundation from which the pet foodomics field may grow and has identified molecule signatures specific to ingredient type. Consequent studies may identify bioactive molecules possessing beneficial properties (e.g., antioxidant, antibacterial) that may be used to target obesity or support digestive health, skin and coat health, and joint health. Such analysis may also identify ingredient-specific biomarkers that may increase food consumption accuracy.

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35

Uršulin-Trstenjak, Natalija, Ivana Dodlek Šarkanj, Melita Sajko, David Vitez, and Ivana Živoder. "Determination of the Personal Nutritional Status of Elderly Populations Based on Basic Foodomics Elements." Foods 10, no. 10 (October 9, 2021): 2391. http://dx.doi.org/10.3390/foods10102391.

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Nutritional status is a series of related parameters collected using different available methods. In order to determine the nutritional status of elderly populations and ensure nutritional support based on an individual approach, the implementation of the increasingly used foodomics approach is available; this approach plays a key role in personalized diets and in the optimization of diets for a population group, such as an elderly population. The Mini Nutritional Assessment (MNA) method and the Nottingham Screening Tool (NST) form were used on 50 users in a home for the elderly in northwest Croatia. A loss of body mass (BM) was statistically significantly higher for those who had the following: decreased food intake in the last week and users who had complete and partial feeding autonomy. Additionally, the obtained data on drug intake, fluid, individual nutrients, and physical activity are based on an individual approach. The available documentation provides insight into nutritional values and food preparation in an attempt to satisfy a holistic approach in the evaluation of exposure while trying to achieve as many elements of foodomics as possible.

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36

Rodríguez-Carrasco, Yelko. "Foodomics: Current and Future Perspectives in Food Analysis." Foods 11, no. 9 (April 26, 2022): 1238. http://dx.doi.org/10.3390/foods11091238.

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37

Rodríguez-Carrasco, Yelko. "Foodomics: Current and Future Perspectives in Food Analysis." Foods 11, no. 9 (April 26, 2022): 1238. http://dx.doi.org/10.3390/foods11091238.

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38

do Prado Apparecido, Rafael, Thiago Inácio Barros Lopes, and Glaucia Braz Alcantara. "NMR-based foodomics of common tubers and roots." Journal of Pharmaceutical and Biomedical Analysis 209 (February 2022): 114527. http://dx.doi.org/10.1016/j.jpba.2021.114527.

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39

Cifuentes, Alejandro, and Elena Ibáñez. "Exploration of Foods and Foodomics: a new adventure." Exploration of Foods and Foodomics 1, no. 1 (September 21, 2022): 1–4. http://dx.doi.org/10.37349/eff.2022.00001.

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40

Valdés, Alberto, Carolina Simó, Clara Ibáñez, and Virginia García-Cañas. "Foodomics strategies for the analysis of transgenic foods." TrAC Trends in Analytical Chemistry 52 (December 2013): 2–15. http://dx.doi.org/10.1016/j.trac.2013.05.023.

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41

Laghi, Luca, Gianfranco Picone, and Francesco Capozzi. "Nuclear magnetic resonance for foodomics beyond food analysis." TrAC Trends in Analytical Chemistry 59 (July 2014): 93–102. http://dx.doi.org/10.1016/j.trac.2014.04.009.

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42

García-Cañas, Virginia, Carolina Simó, Miguel Herrero, Elena Ibáñez, and Alejandro Cifuentes. "Present and Future Challenges in Food Analysis: Foodomics." Analytical Chemistry 84, no. 23 (September 19, 2012): 10150–59. http://dx.doi.org/10.1021/ac301680q.

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43

Ferranti, Pasquale, Paola Roncada, and Andrea Scaloni. "Foodomics - Novel insights in food and nutrition domains." Journal of Proteomics 147 (September 2016): 1–2. http://dx.doi.org/10.1016/j.jprot.2016.07.016.

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44

Cozzolino, Daniel. "Foodomics and infrared spectroscopy: from compounds to functionality." Current Opinion in Food Science 4 (August 2015): 39–43. http://dx.doi.org/10.1016/j.cofs.2015.05.003.

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45

Trimigno, Alessia, Flaminia Cesare Marincola, Nicolò Dellarosa, Gianfranco Picone, and Luca Laghi. "Definition of food quality by NMR-based foodomics." Current Opinion in Food Science 4 (August 2015): 99–104. http://dx.doi.org/10.1016/j.cofs.2015.06.008.

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46

Vimaleswaran, Karani S., Caroline Ivanne Le Roy, and Sandrine Paule Claus. "Foodomics for personalized nutrition: how far are we?" Current Opinion in Food Science 4 (August 2015): 129–35. http://dx.doi.org/10.1016/j.cofs.2015.07.001.

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47

Braconi, Daniela, Giulia Bernardini, Lia Millucci, and Annalisa Santucci. "Foodomics for human health: current status and perspectives." Expert Review of Proteomics 15, no. 2 (December 29, 2017): 153–64. http://dx.doi.org/10.1080/14789450.2018.1421072.

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48

Gallo, Monica, and Pasquale Ferranti. "The evolution of analytical chemistry methods in foodomics." Journal of Chromatography A 1428 (January 2016): 3–15. http://dx.doi.org/10.1016/j.chroma.2015.09.007.

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49

Canela, Núria, Miguel Ángel Rodríguez, Isabel Baiges, Pedro Nadal, and Lluís Arola. "Foodomics imaging by mass spectrometry and magnetic resonance." ELECTROPHORESIS 37, no. 13 (July 2016): 1748–67. http://dx.doi.org/10.1002/elps.201500494.

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50

Khakimov, B., G. Gürdeniz, and S. B. Engelsen. "Trends in the application of chemometrics to foodomics studies." Acta Alimentaria 44, no. 1 (March 2015): 4–31. http://dx.doi.org/10.1556/aalim.44.2015.1.1.

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