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Theodore Armand TP, Nfor KA, Kim JI, Kim HC. Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review. Nutrients 2024; 16:1073. [PMID: 38613106 PMCID: PMC11013624 DOI: 10.3390/nu16071073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 04/02/2024] [Accepted: 04/03/2024] [Indexed: 04/14/2024] Open
Abstract
In industry 4.0, where the automation and digitalization of entities and processes are fundamental, artificial intelligence (AI) is increasingly becoming a pivotal tool offering innovative solutions in various domains. In this context, nutrition, a critical aspect of public health, is no exception to the fields influenced by the integration of AI technology. This study aims to comprehensively investigate the current landscape of AI in nutrition, providing a deep understanding of the potential of AI, machine learning (ML), and deep learning (DL) in nutrition sciences and highlighting eventual challenges and futuristic directions. A hybrid approach from the systematic literature review (SLR) guidelines and the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines was adopted to systematically analyze the scientific literature from a search of major databases on artificial intelligence in nutrition sciences. A rigorous study selection was conducted using the most appropriate eligibility criteria, followed by a methodological quality assessment ensuring the robustness of the included studies. This review identifies several AI applications in nutrition, spanning smart and personalized nutrition, dietary assessment, food recognition and tracking, predictive modeling for disease prevention, and disease diagnosis and monitoring. The selected studies demonstrated the versatility of machine learning and deep learning techniques in handling complex relationships within nutritional datasets. This study provides a comprehensive overview of the current state of AI applications in nutrition sciences and identifies challenges and opportunities. With the rapid advancement in AI, its integration into nutrition holds significant promise to enhance individual nutritional outcomes and optimize dietary recommendations. Researchers, policymakers, and healthcare professionals can utilize this research to design future projects and support evidence-based decision-making in AI for nutrition and dietary guidance.
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Affiliation(s)
- Tagne Poupi Theodore Armand
- Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Republic of Korea; (T.P.T.A.); (J.-I.K.)
| | - Kintoh Allen Nfor
- Department of Computer Engineering, Inje University, Gimhae 50834, Republic of Korea;
| | - Jung-In Kim
- Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Republic of Korea; (T.P.T.A.); (J.-I.K.)
| | - Hee-Cheol Kim
- Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Republic of Korea; (T.P.T.A.); (J.-I.K.)
- Department of Computer Engineering, Inje University, Gimhae 50834, Republic of Korea;
- College of AI Convergence, u-AHRC, Inje University, Gimhae 50834, Republic of Korea
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2
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Aspromonte J, Mascrez S, Eggermont D, Purcaro G. Solid-phase microextraction coupled to comprehensive multidimensional gas chromatography for food analysis. Anal Bioanal Chem 2024; 416:2221-2246. [PMID: 37999723 DOI: 10.1007/s00216-023-05048-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/22/2023] [Accepted: 11/09/2023] [Indexed: 11/25/2023]
Abstract
Solid-phase microextraction and comprehensive multidimensional gas chromatography represent two milestone innovations that occurred in the field of separation science in the 1990s. They have a common root in their introduction and have found a perfect coupling in their evolution and applications. This review will focus on food analysis, where the paradigm has changed significantly over time, moving from a targeted analysis, focusing on a limited number of analytes at the time, to a more holistic approach for assessing quality in a larger sense. Indeed, not only some major markers or contaminants are considered, but a large variety of compounds and their possible interaction, giving rise to the field of foodomics. In order to obtain such detailed information and to answer more sophisticated questions related to food quality and authenticity, the use of SPME-GC × GC-MS has become essential for the comprehensive analysis of volatile and semi-volatile analytes. This article provides a critical review of the various applications of SPME-GC × GC in food analysis, emphasizing the crucial role this coupling plays in this field. Additionally, this review dwells on the importance of appropriate data treatment to fully harness the results obtained to draw accurate and meaningful conclusions.
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Affiliation(s)
- Juan Aspromonte
- Laboratorio de Investigación y Desarrollo de Métodos Analíticos, LIDMA, Facultad de Ciencias Exactas (Universidad Nacional de La Plata, CIC-PBA, CONICET), Calle 47 Esq. 115, 1900, La Plata, Argentina
| | - Steven Mascrez
- Gembloux Agro-Bio Tech, University of Liège, Passage Des Déportés, 2, B-5030, Gembloux, Belgium
| | - Damien Eggermont
- Gembloux Agro-Bio Tech, University of Liège, Passage Des Déportés, 2, B-5030, Gembloux, Belgium
| | - Giorgia Purcaro
- Gembloux Agro-Bio Tech, University of Liège, Passage Des Déportés, 2, B-5030, Gembloux, Belgium.
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3
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Cozzolino D, Chapman J. Advances, limitations, and considerations on the use of vibrational spectroscopy towards the development of management decision tools in food safety. Anal Bioanal Chem 2024; 416:611-620. [PMID: 37542534 DOI: 10.1007/s00216-023-04849-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 08/07/2023]
Abstract
Food safety and food security are two of the main concerns for the modern food manufacturing industry. Disruptions in the food supply and value chains have created the need to develop agile screening tools that will allow the detection of food pathogens, spoilage microorganisms, microbial contaminants, toxins, herbicides, and pesticides in agricultural commodities, natural products, and food ingredients. Most of the current routine analytical methods used to detect and identify microorganisms, herbicides, and pesticides in food ingredients and products are based on the use of reliable and robust immunological, microbiological, and biochemical techniques (e.g. antigen-antibody interactions, extraction and analysis of DNA) and chemical methods (e.g. chromatography). However, the food manufacturing industries are demanding agile and affordable analytical methods. The objective of this review is to highlight the advantages and limitations of the use of vibrational spectroscopy combined with chemometrics as proxy to evaluate and quantify herbicides, pesticides, and toxins in foods.
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Affiliation(s)
- Daniel Cozzolino
- The University of Queensland, Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation, St. Lucia, Brisbane, QLD, 4072, Australia.
| | - James Chapman
- School of Science, RMIT University, GPO Box 2476, Melbourne, VIC, 3001, Australia
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4
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Gaida M, Stefanuto PH, Focant JF. Theoretical modeling and machine learning-based data processing workflows in comprehensive two-dimensional gas chromatography-A review. J Chromatogr A 2023; 1711:464467. [PMID: 37871505 DOI: 10.1016/j.chroma.2023.464467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 10/15/2023] [Accepted: 10/17/2023] [Indexed: 10/25/2023]
Abstract
In recent years, comprehensive two-dimensional gas chromatography (GC × GC) has been gradually gaining prominence as a preferred method for the analysis of complex samples due to its higher peak capacity and resolution power compared to conventional gas chromatography (GC). Nonetheless, to fully benefit from the capabilities of GC × GC, a holistic approach to method development and data processing is essential for a successful and informative analysis. Method development enables the fine-tuning of the chromatographic separation, resulting in high-quality data. While generating such data is pivotal, it does not necessarily guarantee that meaningful information will be extracted from it. To this end, the first part of this manuscript reviews the importance of theoretical modeling in achieving good optimization of the separation conditions, ultimately improving the quality of the chromatographic separation. Multiple theoretical modeling approaches are discussed, with a special focus on thermodynamic-based modeling. The second part of this review highlights the importance of establishing robust data processing workflows, with a special emphasis on the use of advanced data processing tools such as, Machine Learning (ML) algorithms. Three widely used ML algorithms are discussed: Random Forest (RF), Support Vector Machine (SVM), and Partial Least Square-Discriminate Analysis (PLS-DA), highlighting their role in discovery-based analysis.
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Affiliation(s)
- Meriem Gaida
- Organic and Biological Analytical Chemistry Group (OBiAChem), MolSys Research Unit, Liège University, Belgium
| | - Pierre-Hugues Stefanuto
- Organic and Biological Analytical Chemistry Group (OBiAChem), MolSys Research Unit, Liège University, Belgium
| | - Jean-François Focant
- Organic and Biological Analytical Chemistry Group (OBiAChem), MolSys Research Unit, Liège University, Belgium
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5
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Markos MU, Tola Y, Kebede BT, Ogah O. Metabolomics: A suitable foodomics approach to the geographical origin traceability of Ethiopian Arabica specialty coffees. Food Sci Nutr 2023; 11:4419-4431. [PMID: 37576063 PMCID: PMC10420859 DOI: 10.1002/fsn3.3434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 05/01/2023] [Accepted: 05/04/2023] [Indexed: 08/15/2023] Open
Abstract
Coffee arabica, originated in Ethiopia, is considered a quality bean for its high sensory qualities, and has a special price in the world coffee market. The country is a pool of genetic diversity for Arabica coffee, and coffee from different regions has a distinct flavor profile. Their exceptional quality is attributed to their genetic diversity, favorable environmental conditions, and agroforestry-based production system. However, the country still needs to benefit from its single-origin product due to a lack of appropriate traceability information to register for its geographical indication. Certification of certain plants or plant-derived products emerged to inform consumers about their exceptional qualities due to their geographical origin and protect the product from fraud. The recently emerging foodomics approaches, namely proteomics, genomics, and metabolomics, are reported as suitable means of regional agri-food product authentication and traceability. Particularly, the metabolomics approach provides truthful information on product traceability. Despite efforts by some researchers to trace the geographical origin of Ethiopian Arabica coffees through stable isotope and phenolic compound profiling and elemental analysis, foodomics approaches are not used to trace the geographical origin of Arabica specialty coffees from various parts of the country. A metabolomics-based traceability system that demonstrates the connection between the exceptional attributes of Ethiopian Arabica specialty coffees and their geographic origin is recommended to maximize the benefit of single-origin coffees.
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Affiliation(s)
- Makiso Urugo Markos
- Department of Food Science and Postharvest Technology, College of Agricultural SciencesWachemo UniversityHosannaEthiopia
- Department of Postharvest Management, College of Agriculture and Veterinary MedicineJimma UniversityJimmaEthiopia
| | - Yetenayet Tola
- Department of Postharvest Management, College of Agriculture and Veterinary MedicineJimma UniversityJimmaEthiopia
| | | | - Onwuchekwa Ogah
- Department of BiotechnologyEbonyi State UniversityAbakalikiNigeria
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6
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Zhao H, Kim Y, Avena-Bustillos RJ, Nitin N, Wang SC. Characterization of California olive pomace fractions and their in vitro antioxidant and antimicrobial activities. Lebensm Wiss Technol 2023. [DOI: 10.1016/j.lwt.2023.114677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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7
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Development of Non-Targeted Mass Spectrometry Method for Distinguishing Spelt and Wheat. Foods 2022; 12:foods12010141. [PMID: 36613357 PMCID: PMC9818861 DOI: 10.3390/foods12010141] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 12/13/2022] [Accepted: 12/21/2022] [Indexed: 12/29/2022] Open
Abstract
Food fraud, even when not in the news, is ubiquitous and demands the development of innovative strategies to combat it. A new non-targeted method (NTM) for distinguishing spelt and wheat is described, which aids in food fraud detection and authenticity testing. A highly resolved fingerprint in the form of spectra is obtained for several cultivars of spelt and wheat using liquid chromatography coupled high-resolution mass spectrometry (LC-HRMS). Convolutional neural network (CNN) models are built using a nested cross validation (NCV) approach by appropriately training them using a calibration set comprising duplicate measurements of eleven cultivars of wheat and spelt, each. The results reveal that the CNNs automatically learn patterns and representations to best discriminate tested samples into spelt or wheat. This is further investigated using an external validation set comprising artificially mixed spectra, samples for processed goods (spelt bread and flour), eleven untypical spelt, and six old wheat cultivars. These cultivars were not part of model building. We introduce a metric called the D score to quantitatively evaluate and compare the classification decisions. Our results demonstrate that NTMs based on NCV and CNNs trained using appropriately chosen spectral data can be reliable enough to be used on a wider range of cultivars and their mixes.
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Rivera-Pérez A, García-Pérez P, Romero-González R, Garrido Frenich A, Lucini L. An untargeted strategy based on UHPLC-QTOF-HRMS metabolomics to identify markers revealing the terroir and processing effect on thyme phenolic profiling. Food Res Int 2022; 162:112081. [DOI: 10.1016/j.foodres.2022.112081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 10/18/2022] [Accepted: 10/22/2022] [Indexed: 11/24/2022]
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9
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Sobolev AP, Ingallina C, Spano M, Di Matteo G, Mannina L. NMR-Based Approaches in the Study of Foods. Molecules 2022; 27:7906. [PMID: 36432006 PMCID: PMC9697393 DOI: 10.3390/molecules27227906] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/07/2022] [Accepted: 11/14/2022] [Indexed: 11/17/2022] Open
Abstract
In this review, the three different NMR-based approaches usually used to study foodstuffs are described, reporting specific examples. The first approach starts with the food of interest that can be investigated using different complementary NMR methodologies to obtain a comprehensive picture of food composition and structure; another approach starts with the specific problem related to a given food (frauds, safety, traceability, geographical and botanical origin, farming methods, food processing, maturation and ageing, etc.) that can be addressed by choosing the most suitable NMR methodology; finally, it is possible to start from a single NMR methodology, developing a broad range of applications to tackle common food-related challenges and different aspects related to foods.
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Affiliation(s)
- Anatoly P. Sobolev
- Magnetic Resonance Laboratory “Segre-Capitani”, Institute for Biological Systems, CNR, Via Salaria, Km 29.300, 00015 Monterotondo, Italy
| | - Cinzia Ingallina
- Laboratory of Food Chemistry, Department of Chemistry and Technology of Drugs, Sapienza University of Rome, P.le Aldo Moro 5, 00185 Rome, Italy
| | - Mattia Spano
- Laboratory of Food Chemistry, Department of Chemistry and Technology of Drugs, Sapienza University of Rome, P.le Aldo Moro 5, 00185 Rome, Italy
| | - Giacomo Di Matteo
- Laboratory of Food Chemistry, Department of Chemistry and Technology of Drugs, Sapienza University of Rome, P.le Aldo Moro 5, 00185 Rome, Italy
| | - Luisa Mannina
- Laboratory of Food Chemistry, Department of Chemistry and Technology of Drugs, Sapienza University of Rome, P.le Aldo Moro 5, 00185 Rome, Italy
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10
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A Coding Basis and Three-in-One Integrated Data Visualization Method 'Ana' for the Rapid Analysis of Multidimensional Omics Dataset. LIFE (BASEL, SWITZERLAND) 2022; 12:life12111864. [PMID: 36430999 PMCID: PMC9698950 DOI: 10.3390/life12111864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/02/2022] [Accepted: 11/10/2022] [Indexed: 11/16/2022]
Abstract
With innovations and advancements in analytical instruments and computer technology, omics studies based on statistical analysis, such as phytochemical omics, oilomics/lipidomics, proteomics, metabolomics, and glycomics, are increasingly popular in the areas of food chemistry and nutrition science. However, a remaining hurdle is the labor-intensive data process because learning coding skills and software operations are usually time-consuming for researchers without coding backgrounds. A MATLAB® coding basis and three-in-one integrated method, 'Ana', was created for data visualizations and statistical analysis in this work. The program loaded and analyzed an omics dataset from an Excel® file with 7 samples * 22 compounds as an example, and output six figures for three types of data visualization, including a 3D heatmap, heatmap hierarchical clustering analysis, and principal component analysis (PCA), in 18 s on a personal computer (PC) with a Windows 10 system and in 20 s on a Mac with a MacOS Monterey system. The code is rapid and efficient to print out high-quality figures up to 150 or 300 dpi. The output figures provide enough contrast to differentiate the omics dataset by both color code and bar size adjustments per their higher or lower values, allowing the figures to be qualified for publication and presentation purposes. It provides a rapid analysis method that would liberate researchers from labor-intensive and time-consuming manual or coding basis data analysis. A coding example with proper code annotations and completed user guidance is provided for undergraduate and postgraduate students to learn coding basis statistical data analysis and to help them utilize such techniques for their future research.
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11
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PÉREZ-BELTRÁN CH, PÉREZ–CABALLERO G, ANDRADE JM, CUADROS-RODRÍGUEZ L, JIMÉNEZ-CARVELO AM. Non-targeted Spatially Offset Raman Spectroscopy-based vanguard analytical method to authenticate spirits: White Tequilas as a case study. Microchem J 2022. [DOI: 10.1016/j.microc.2022.108126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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12
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Applications of Advanced Data Analytic Techniques in Food Safety and Risk Assessment. Curr Opin Food Sci 2022. [DOI: 10.1016/j.cofs.2022.100937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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13
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Pérez-Beltrán CH, Jiménez-Carvelo AM, Martín-Torres S, Ortega-Gavilán F, Cuadros-Rodríguez L. Instrument-agnostic multivariate models from normal phase liquid chromatographic fingerprinting. A case study: Authentication of olive oil. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.108957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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14
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Sun Y, Zhao Y, Wu J, Liu N, Kang X, Wang S, Zhou D. An explainable machine learning model for identifying geographical origins of sea cucumber Apostichopus japonicus based on multi-element profile. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108753] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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15
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Identification of Potential Valid Clients for a Sustainable Insurance Policy Using an Advanced Mixed Classification Model. SUSTAINABILITY 2022. [DOI: 10.3390/su14073964] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Due to the social awareness of risk control, we are witnessing the popularization of the insurance concept and the rapid development of financial insurance. The performance of the insurance industry is highly competitive; thus, in order to develop new and old business from existing clients, information on the renewal of client premiums, purchase of new policies, and new client referrals has become an important research topic in this field. However, based on a review of published literature, few scholars have engaged in relevant research on the above topics by data mining, which motivated the formation of this study, hoping to bridge this gap. We constructed 10 mixed classification prediction models (called Models A–J) using advanced data mining techniques. Moreover, 19 conditional attributes (coded as X1–X19) were selected from the collected insurance client database, plus three different decision attributes (coded as X20–X22): whether to pay the renewal insurance premium, whether to buy a new insurance policy, and whether to introduce new clients. In terms of technical methods, we used two data pretreatment techniques, attribute selection and data discretization, combined with different methods of disassembly in proportion and data cross-validation to conduct data analysis of the collected experimental data set. We also combined and calculated 23 important classification algorithms (or classifiers) in seven different classifications of data mining techniques (i.e., decision tree, Bayes, Function, Lazy, Meta, Mise, and Rule). In terms of the experimental results of insurance data, this study has the following important contributions and findings: (1) finding the best classifier; (2) finding the optimal mixed classification model; (3) determining the best disassembly in proportion; (4) comparing the performance of different disassembly in proportion and data cross-validation methods; (5) determining the important factors influencing the decision attribute “whether to purchase a new insurance policy”, including the time interval to the first purchase, the number of valid policies, the total number of purchased policies, the family salary structure, and gender; and (6) building a knowledge base of decision rules and criteria with the decision tree C4.5 technology, which shall be provided to relevant stakeholders such as insurance dealers and insurance salespeople as a reference for looking for valid clients in the future, and is conducive to the rapid expansion of insurance business. Finally, the important research findings and management implications of this study can serve as a basis for further study of sustainable insurance by academic researchers.
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Martín-Torres S, Ruiz-Castro L, Jiménez-Carvelo AM, Cuadros-Rodríguez L. Applications of multivariate data analysis in shelf life studies of edible vegetal oils – A review of the few past years. Food Packag Shelf Life 2022. [DOI: 10.1016/j.fpsl.2021.100790] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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17
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Zhang X, Li H, Mu W, Gecevska V, Zhang X, Feng J. Sensory evaluation and prediction of bulk wine by physicochemical indicators based on PCA‐PSO‐LSSVM method. J FOOD PROCESS PRES 2022. [DOI: 10.1111/jfpp.16343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Xu Zhang
- College of Information and Electrical Engineering China Agricultural University Beijing China
| | - Hao Li
- College of Information and Electrical Engineering China Agricultural University Beijing China
| | - Weisong Mu
- College of Information and Electrical Engineering China Agricultural University Beijing China
| | | | - Xiaoshuan Zhang
- College of Engineering China Agricultural University Beijing China
| | - Jianying Feng
- College of Information and Electrical Engineering China Agricultural University Beijing China
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18
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Valdés A, Álvarez-Rivera G, Socas-Rodríguez B, Herrero M, Ibáñez E, Cifuentes A. Foodomics: Analytical Opportunities and Challenges. Anal Chem 2022; 94:366-381. [PMID: 34813295 PMCID: PMC8756396 DOI: 10.1021/acs.analchem.1c04678] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Alberto Valdés
- Laboratory of Foodomics, Institute
of Food Science Research, CIAL, CSIC, Nicolas Cabrera 9, Madrid, 28049, Spain
| | - Gerardo Álvarez-Rivera
- Laboratory of Foodomics, Institute
of Food Science Research, CIAL, CSIC, Nicolas Cabrera 9, Madrid, 28049, Spain
| | - Bárbara Socas-Rodríguez
- Laboratory of Foodomics, Institute
of Food Science Research, CIAL, CSIC, Nicolas Cabrera 9, Madrid, 28049, Spain
| | - Miguel Herrero
- Laboratory of Foodomics, Institute
of Food Science Research, CIAL, CSIC, Nicolas Cabrera 9, Madrid, 28049, Spain
| | - Elena Ibáñez
- Laboratory of Foodomics, Institute
of Food Science Research, CIAL, CSIC, Nicolas Cabrera 9, Madrid, 28049, Spain
| | - Alejandro Cifuentes
- Laboratory of Foodomics, Institute
of Food Science Research, CIAL, CSIC, Nicolas Cabrera 9, Madrid, 28049, Spain
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19
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Rivera-Pérez A, Romero-González R, Garrido Frenich A. Application of an innovative metabolomics approach to discriminate geographical origin and processing of black pepper by untargeted UHPLC-Q-Orbitrap-HRMS analysis and mid-level data fusion. Food Res Int 2021; 150:110722. [PMID: 34865751 DOI: 10.1016/j.foodres.2021.110722] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/10/2021] [Accepted: 09/22/2021] [Indexed: 12/16/2022]
Abstract
An untargeted metabolomics approach based on ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) fingerprinting was applied to investigate the metabolic differences of black pepper among three geographical origins (Sri Lanka, Vietnam, and Brazil) and two post-harvest processing (sterilized and non-sterilized spice). Principal component analysis (PCA) was employed to assess the overall clustering of samples, whereas supervised orthogonal partial least squares discriminant analysis (OPLS-DA) was effectively used for discrimination purposes. OPLS-DA models were fully validated (R2Y and Q2 values > 0.5) and the variable importance in projection (VIP) approach was employed to provide valuable data about differential metabolites with high discrimination potential (8 markers were putatively identified). For origin differentiation, three markers were highlighted with VIP values > 1.5 (i.e. reynosin, artabsinolide D, and tatridin B). Fatty acid derivates were the most frequent markers within the metabolites annotated for processing discrimination (e.g. 10,16-dihydroxyhexadecanoic acid and 9-hydroperoxy-10E-octadecenoic acid). Additionally, different combinations of mid-level data fusion of chromatographic-mass spectrometric techniques (UHPLC and gas chromatography coupled to HRMS) and proton nuclear magnetic resonance spectroscopy (1H NMR) were evaluated for the first time for geographical and processing discrimination of black pepper. The NMR-UHPLC-GC mid-level fused model was preferred among the tested fusion approaches since good sample clustering and no misclassification were achieved. Enhanced correct classification rate was achieved by mid-level data fusion compared with the findings obtained for one of the individual techniques (1H NMR fingerprinting) (from 92% to 100% of samples correctly classified). This study opens the path to new metabolomics approaches for black pepper authentication and quality control.
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Affiliation(s)
- Araceli Rivera-Pérez
- Research Group "Analytical Chemistry of Contaminants", Department of Chemistry and Physics, Research Centre for Mediterranean Intensive Agrosystems and Agrifood Biotechnology (CIAIMBITAL), Agrifood Campus of International Excellence (ceiA3), University of Almeria, E-04120 Almeria, Spain.
| | - Roberto Romero-González
- Research Group "Analytical Chemistry of Contaminants", Department of Chemistry and Physics, Research Centre for Mediterranean Intensive Agrosystems and Agrifood Biotechnology (CIAIMBITAL), Agrifood Campus of International Excellence (ceiA3), University of Almeria, E-04120 Almeria, Spain.
| | - Antonia Garrido Frenich
- Research Group "Analytical Chemistry of Contaminants", Department of Chemistry and Physics, Research Centre for Mediterranean Intensive Agrosystems and Agrifood Biotechnology (CIAIMBITAL), Agrifood Campus of International Excellence (ceiA3), University of Almeria, E-04120 Almeria, Spain.
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20
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Full Workflows for the Analysis of Gas Chromatography-Ion Mobility Spectrometry in Foodomics: Application to the Analysis of Iberian Ham Aroma. SENSORS 2021; 21:s21186156. [PMID: 34577363 PMCID: PMC8469025 DOI: 10.3390/s21186156] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 09/08/2021] [Accepted: 09/10/2021] [Indexed: 11/24/2022]
Abstract
Gas chromatography—ion mobility spectrometry (GC-IMS) allows the fast, reliable, and inexpensive chemical composition analysis of volatile mixtures. This sensing technology has been successfully employed in food science to determine food origin, freshness and preventing alimentary fraud. However, GC-IMS data is highly dimensional, complex, and suffers from strong non-linearities, baseline problems, misalignments, peak overlaps, long peak tails, etc., all of which must be corrected to properly extract the relevant features from samples. In this work, a pipeline for signal pre-processing, followed by four different approaches for feature extraction in GC-IMS data, is presented. More precisely, these approaches consist of extracting data features from: (1) the total area of the reactant ion peak chromatogram (RIC); (2) the full RIC response; (3) the unfolded sample matrix; and (4) the ion peak volumes. The resulting pipelines for data processing were applied to a dataset consisting of two different quality class Iberian ham samples, based on their feeding regime. The ability to infer chemical information from samples was tested by comparing the classification results obtained from partial least-squares discriminant analysis (PLS-DA) and the samples’ variable importance for projection (VIP) scores. The choice of a feature extraction strategy is a trade-off between the amount of chemical information that is preserved, and the computational effort required to generate the data models.
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Lorenzo JM. Editorial overview: Strategies for obtaining healthy meat products. Curr Opin Food Sci 2021. [DOI: 10.1016/j.cofs.2021.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Cuadros-Rodríguez L, Jiménez-Carvelo AM, Fernández-Ramos M. Multivariate thinking for optical microfluidic analytical devices – A tutorial review. Microchem J 2021. [DOI: 10.1016/j.microc.2021.105959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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