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Giussani B, Gorla G, Riu J. Analytical Chemistry Strategies in the Use of Miniaturised NIR Instruments: An Overview. Crit Rev Anal Chem 2024; 54:11-43. [PMID: 35286178 DOI: 10.1080/10408347.2022.2047607] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Miniaturized NIR instruments have been increasingly used in the last years, and they have become useful tools for many applications on a broad variety of samples. This review focuses on miniaturized NIR instruments from an analytical point of view, to give an overview of the analytical strategies used in order to help the reader to set up their own analytical methods, from the sampling to the data analysis. It highlights the uses of these instruments, providing a critical discussion including current and future trends.
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Affiliation(s)
- Barbara Giussani
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, Como, Italy
| | - Giulia Gorla
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, Como, Italy
| | - Jordi Riu
- Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Tarragona, Spain
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2
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Kozub A, Nikolaichuk H, Przykaza K, Tomaszewska-Gras J, Fornal E. Lipidomic characteristics of three edible cold-pressed oils by LC/Q-TOF for simple quality and authenticity assurance. Food Chem 2023; 415:135761. [PMID: 36881959 DOI: 10.1016/j.foodchem.2023.135761] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 02/12/2023] [Accepted: 02/18/2023] [Indexed: 03/07/2023]
Abstract
Distinguishing oil samples from each other is challenging but it is crucial for ensuring food quality, and for detecting and preventing the possible adulteration of these products. Lipidomic profiling is believed to provide sufficient information to get fit-to-purpose confidence of oil identification as well as to deliver oil-specific lipid features which could be used as targets for routine authenticity testing of camelina, flax, and hemp oil in food control laboratories. Conducted di- and triacylglycerol profiling by LC/Q-TOFMS yielded successful differentiation of the oils. A marker panel consisting of 27 lipids (both DAGs and TAGs) useful for quality verification and authenticity assurance of the oils was established. Moreover, sunflower, rapeseed, and soybean oils were analysed as potential adulterants. We identified 6 lipid markers (DAGs 34:6, 35:2, 40:1, 40:2, 42:2, and TAG 63:1) which can be used for revealing the adulteration of camelina, hemp, and flax seed oils with these oils.
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Affiliation(s)
- Anna Kozub
- Department of Bioanalytics, Faculty of Biomedicine, Medical University of Lublin, Jaczewskiego 8b, 20-090 Lublin, Poland
| | - Hanna Nikolaichuk
- Department of Bioanalytics, Faculty of Biomedicine, Medical University of Lublin, Jaczewskiego 8b, 20-090 Lublin, Poland.
| | - Kacper Przykaza
- Department of Bioanalytics, Faculty of Biomedicine, Medical University of Lublin, Jaczewskiego 8b, 20-090 Lublin, Poland
| | - Jolanta Tomaszewska-Gras
- Department of Food Safety and Quality Management, Faculty of Food Science and Nutrition, Poznan University of Life Sciences, Wojska Polskiego 31/33, 60-624 Poznan, Poland
| | - Emilia Fornal
- Department of Bioanalytics, Faculty of Biomedicine, Medical University of Lublin, Jaczewskiego 8b, 20-090 Lublin, Poland
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3
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Zhang W, Kasun LC, Wang QJ, Zheng Y, Lin Z. A Review of Machine Learning for Near-Infrared Spectroscopy. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22249764. [PMID: 36560133 PMCID: PMC9784128 DOI: 10.3390/s22249764] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 12/04/2022] [Accepted: 12/05/2022] [Indexed: 06/01/2023]
Abstract
The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided. Secondly, the public NIR spectroscopy datasets are briefly discussed, with links provided. Thirdly, the widely used data preprocessing and feature selection algorithms that have been reported for NIR spectroscopy are presented. Then, the majority of the traditional machine learning methods and deep network architectures that are commonly employed are covered. Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction.
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Affiliation(s)
- Wenwen Zhang
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
| | | | - Qi Jie Wang
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
| | - Yuanjin Zheng
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Zhiping Lin
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
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4
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Pastore TC, Braga LR, da C. Kunze DC, Soares LF, Pastore F, de O. Moreira AC, dos Anjos PV, Lara CS, Coradin VT, W. B. Braga J. A green and direct method for authentication of rosewood essential oil by handheld near infrared spectrometer and one-class classification modeling. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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5
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Multivariate analysis of food fraud: A review of NIR based instruments in tandem with chemometrics. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2021.104343] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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6
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Rifna EJ, Pandiselvam R, Kothakota A, Subba Rao KV, Dwivedi M, Kumar M, Thirumdas R, Ramesh SV. Advanced process analytical tools for identification of adulterants in edible oils - A review. Food Chem 2022; 369:130898. [PMID: 34455326 DOI: 10.1016/j.foodchem.2021.130898] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 07/16/2021] [Accepted: 08/16/2021] [Indexed: 12/16/2022]
Abstract
This review summarizes the use of spectroscopic processes-based analytical tools coupled with chemometric techniques for the identification of adulterants in edible oil. Investigational approaches of process analytical tools such asspectroscopy techniques, nuclear magnetic resonance (NMR), hyperspectral imaging (HSI), e-tongue and e-nose combined with chemometrics were used to monitor quality of edible oils. Owing to the variety and intricacy of edible oil properties along with the alterations in attributes of the PAT tools, the reliability of the tool used and the operating factors are the crucial components which require attention to enhance the efficiency in identification of adulterants. The combination of process analytical tools with chemometrics offers a robust technique with immense chemotaxonomic potential. These involves identification of adulterants, quality control, geographical origin evaluation, process evaluation, and product categorization.
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Affiliation(s)
- E J Rifna
- Department of Food Process Engineering, National Institute of Technology, Rourkela 769008, Odisha, India
| | - R Pandiselvam
- Physiology, Biochemistry and Post-Harvest Technology Division, ICAR - Central Plantation Crops Research Institute, Kasaragod 671 124, Kerala, India.
| | - Anjineyulu Kothakota
- Agro-Processing & Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (NIIST), Trivandrum 695 019, Kerala, India.
| | - K V Subba Rao
- Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Madhuresh Dwivedi
- Department of Food Process Engineering, National Institute of Technology, Rourkela 769008, Odisha, India
| | - Manoj Kumar
- Chemical and Biochemical Processing Division, ICAR-Central Institute for Research on Cotton Technology, Matunga, Mumbai 400019, India
| | - Rohit Thirumdas
- Department of Food Process Technology, College of Food Science and Technology, PJTSAU, Telangana, India
| | - S V Ramesh
- Physiology, Biochemistry and Post-Harvest Technology Division, ICAR - Central Plantation Crops Research Institute, Kasaragod 671 124, Kerala, India
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8
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de Oliveira Moreira AC, Braga JWB. Authenticity Identification of Copaiba Oil Using a Handheld NIR Spectrometer and DD-SIMCA. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-020-01933-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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9
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Li X, Zhang L, Zhang Y, Wang D, Wang X, Yu L, Zhang W, Li P. Review of NIR spectroscopy methods for nondestructive quality analysis of oilseeds and edible oils. Trends Food Sci Technol 2020. [DOI: 10.1016/j.tifs.2020.05.002] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Yan J, Stuijvenberg L, Ruth SM. Handheld Near‐Infrared Spectroscopy for Distinction of Extra Virgin Olive Oil from Other Olive Oil Grades Substantiated by Compositional Data. EUR J LIPID SCI TECH 2019. [DOI: 10.1002/ejlt.201900031] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Jing Yan
- Food Quality and Design GroupWageningen University and Research P.O. Box 17 6700 AA Wageningen The Netherlands
- Wageningen Food Safety Research P.O. Box 230 6700 AE Wageningen The Netherlands
| | - Louka Stuijvenberg
- Food Quality and Design GroupWageningen University and Research P.O. Box 17 6700 AA Wageningen The Netherlands
| | - Saskia M. Ruth
- Food Quality and Design GroupWageningen University and Research P.O. Box 17 6700 AA Wageningen The Netherlands
- Wageningen Food Safety Research P.O. Box 230 6700 AE Wageningen The Netherlands
- Institute for Global Food SecuritySchool of Biological SciencesQueen's University Belfast BT7 1NN Northern Ireland UK
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