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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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Zhang Z, Li Y, Zhao S, Qie M, Bai L, Gao Z, Liang K, Zhao Y. Rapid analysis technologies with chemometrics for food authenticity field: A review. Curr Res Food Sci 2024; 8:100676. [PMID: 38303999 PMCID: PMC10830540 DOI: 10.1016/j.crfs.2024.100676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 12/15/2023] [Accepted: 01/07/2024] [Indexed: 02/03/2024] Open
Abstract
In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.
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Affiliation(s)
- Zixuan Zhang
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yalan Li
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Shanshan Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Mengjie Qie
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lu Bai
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Zhiwei Gao
- Hangzhou Nutritome Biotech Co., Ltd., Hangzhou, China
| | - Kehong Liang
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Yan Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
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3
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Shi S, Tang Z, Ma Y, Cao C, Jiang Y. Application of spectroscopic techniques combined with chemometrics to the authenticity and quality attributes of rice. Crit Rev Food Sci Nutr 2023:1-23. [PMID: 38010116 DOI: 10.1080/10408398.2023.2284246] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Rice is a staple food for two-thirds of the world's population and is grown in over a hundred countries around the world. Due to its large scale, it is vulnerable to adulteration. In addition, the quality attribute of rice is an important factor affecting the circulation and price, which is also paid more and more attention. The combination of spectroscopy and chemometrics enables rapid detection of authenticity and quality attributes in rice. This article described the application of seven spectroscopic techniques combined with chemometrics to the rice industry. For a long time, near-infrared spectroscopy and linear chemometric methods (e.g., PLSR and PLS-DA) have been widely used in the rice industry. Although some studies have achieved good accuracy, with models in many studies having greater than 90% accuracy. However, higher accuracy and stability were more likely to be obtained using multiple spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. Future research should develop larger rice databases to include more rice varieties and larger amounts of rice depending on the type of rice, and then combine various spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. This article provided a reference for a more efficient and accurate determination of rice quality and authenticity.
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Affiliation(s)
- Shijie Shi
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Zihan Tang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Yingying Ma
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Cougui Cao
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
- Shuangshui Shuanglü Institute, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Yang Jiang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
- Shuangshui Shuanglü Institute, Huazhong Agricultural University, Wuhan, Hubei, China
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4
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Aznan A, Gonzalez Viejo C, Pang A, Fuentes S. Review of technology advances to assess rice quality traits and consumer perception. Food Res Int 2023; 172:113105. [PMID: 37689840 DOI: 10.1016/j.foodres.2023.113105] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 06/02/2023] [Accepted: 06/09/2023] [Indexed: 09/11/2023]
Abstract
The increase in rice consumption and demand for high-quality rice is impacted by the growth of socioeconomic status in developing countries and consumer awareness of the health benefits of rice consumption. The latter aspects drive the need for rapid, low-cost, and reliable quality assessment methods to produce high-quality rice according to consumer preference. This is important to ensure the sustainability of the rice value chain and, therefore, accelerate the rice industry toward digital agriculture. This review article focuses on the measurements of the physicochemical and sensory quality of rice, including new and emerging technology advances, particularly in the development of low-cost, non-destructive, and rapid digital sensing techniques to assess rice quality traits and consumer perceptions. In addition, the prospects for potential applications of emerging technologies (i.e., sensors, computer vision, machine learning, and artificial intelligence) to assess rice quality and consumer preferences are discussed. The integration of these technologies shows promising potential in the forthcoming to be adopted by the rice industry to assess rice quality traits and consumer preferences at a lower cost, shorter time, and more objectively compared to the traditional approaches.
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Affiliation(s)
- Aimi Aznan
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia; Department of Agrotechnology, Faculty of Mechanical Engineering and Technology, Universiti Malaysia Perlis, 02600 Perlis, Malaysia
| | - Claudia Gonzalez Viejo
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia
| | - Alexis Pang
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia
| | - Sigfredo Fuentes
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia; Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey, N.L., México 64849, Mexico.
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5
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Pan P, Xing Y, Zhang D, Wang J, Liu C, Wu D, Wang X. A review on the identification of transgenic oilseeds and oils. J Food Sci 2023; 88:3189-3203. [PMID: 37458291 DOI: 10.1111/1750-3841.16705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/26/2023] [Accepted: 06/29/2023] [Indexed: 08/05/2023]
Abstract
Transgenic technology can increase the quantity and quality of vegetable oils worldwide. However, people are skeptical about the safety of transgenic oil-bearing crops and the oils they produce. In order to protect consumers' rights and avoid transgenic oils being adulterated or labeled as nontransgenic oils, the transgenic detection technology of oilseeds and oils needs careful consideration. This paper first summarized the current research status of transgenic technologies implemented at oil-bearing crops. Then, an inspection process was proposed to detect a large number of samples to be the subject rapidly, and various inspection strategies for transgenic oilseeds and oils were summarized according to the process sequence. The detection indicators included oil content, fatty acid, triglyceride, tocopherol, and nucleic acid. The detection technologies involved chromatography, spectroscopy, nuclear magnetic resonance, and polymerase chain reaction. It is hoped that this article can provide crucial technical reference and support for staff engaging in the supervision of transgenic food and for researchers developing fast and efficient monitoring methods in the future.
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Affiliation(s)
- Pengyuan Pan
- College of Food Science and Engineering, Jilin Agricultural University, Changchun, China
- National Engineering Laboratory of Wheat and Corn Deep Processing, Changchun, China
| | - Yihang Xing
- College of Food Science and Engineering, Jilin Agricultural University, Changchun, China
- National Engineering Laboratory of Wheat and Corn Deep Processing, Changchun, China
| | - Dingwen Zhang
- College of Food Science and Engineering, Jilin Agricultural University, Changchun, China
- National Engineering Laboratory of Wheat and Corn Deep Processing, Changchun, China
| | - Ji Wang
- College of Food Science and Engineering, Jilin Agricultural University, Changchun, China
- National Engineering Laboratory of Wheat and Corn Deep Processing, Changchun, China
| | - Chunlei Liu
- College of Food Science and Engineering, Jilin Agricultural University, Changchun, China
- National Engineering Laboratory of Wheat and Corn Deep Processing, Changchun, China
| | - Dan Wu
- College of Food Science and Engineering, Jilin Agricultural University, Changchun, China
- National Engineering Laboratory of Wheat and Corn Deep Processing, Changchun, China
| | - Xiyan Wang
- College of Food Science and Engineering, Jilin Agricultural University, Changchun, China
- National Engineering Laboratory of Wheat and Corn Deep Processing, Changchun, China
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6
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Wu D, Liu X, Bai B, Li J, Wang R, Zhang Y, Deng Q, Huang H, Wu J. Determining farming methods and geographical origin of chinese rice using NIR combined with chemometrics methods. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2023. [DOI: 10.1007/s11694-023-01901-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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7
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Identification of Transgenic Agricultural Products and Foods Using NIR Spectroscopy and Hyperspectral Imaging: A Review. Processes (Basel) 2023. [DOI: 10.3390/pr11030651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023] Open
Abstract
Spectroscopy and its imaging techniques are now popular methods for quantitative and qualitative analysis in fields such as agricultural products and foods, and combined with various chemometric methods. In fact, this is the application basis for spectroscopy and spectral imaging techniques in other fields such as genetics and transgenic monitoring. To date, there has been considerable research using spectroscopy and its imaging techniques (especially NIR spectroscopy, hyperspectral imaging) for the effective identification of agricultural products and foods. There have been few comprehensive reviews that cover the use of spectroscopic and imaging methods in the identification of genetically modified organisms. Therefore, this paper focuses on the application of NIR spectroscopy and its imaging techniques (including NIR spectroscopy and hyperspectral imaging techniques) in transgenic agricultural product and food detection and compares them with traditional detection methods. A large number of studies have shown that the application of NIR spectroscopy and imaging techniques in the detection of genetically modified foods is effective when compared to conventional approaches such as polymerase chain reaction and enzyme-linked immunosorbent assay.
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8
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Mata MMD, Rocha PD, Farias IKTD, Silva JLBD, Medeiros EP, Silva CS, Simões SDS. Distinguishing cotton seed genotypes by means of vibrational spectroscopic methods (NIR and Raman) and chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 266:120399. [PMID: 34597869 DOI: 10.1016/j.saa.2021.120399] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 09/07/2021] [Accepted: 09/10/2021] [Indexed: 06/13/2023]
Abstract
The use of vibrational spectroscopy, such as near infrared (NIR) and Raman, combined with multivariate analysis methods to analyze agricultural products are promising for investigating genetically modified organisms (GMO). In Brazil, cotton is grown under humid tropical conditions and is highly affected by pests and diseases, requiring the use of large amounts of phytosanitary chemicals. To avoid the use of those pesticides, genetic improvement can be carried out to produce species tolerant to herbicides, resistant to fungi and insects, or even to provide greater productivity and better quality. Even with these advantages, it is necessary to manage and limit the contact of transgenic species with native ones, avoiding possible contamination or even extinction of conventional species. The identification of the presence of GMOs is based on complex DNA-based analysis, which is usually laborious, expensive, time-consuming, destructive, and generally unavailable. In the present study, a new methodology to identify GMOs using partial least squares discriminant analysis (PLS-DA) on NIR and Raman data is proposed to distinguish conventional and transgenic cotton seed genotypes, providing classification errors for prediction set of 2.23% for NIR and 0.0% for Raman.
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Affiliation(s)
- Mayara Macedo da Mata
- Graduate Program in Chemistry, State University of Paraiba, Rua Baraúnas, 351, Bairro Universitário, Bodocongó, Campina Grande, Paraiba, 58429-500, Brazil
| | - Priscila Dantas Rocha
- Graduate Program in Chemistry, State University of Paraiba, Rua Baraúnas, 351, Bairro Universitário, Bodocongó, Campina Grande, Paraiba, 58429-500, Brazil
| | - Ingrid Kelly Teles de Farias
- Graduate Program in Chemistry, State University of Paraiba, Rua Baraúnas, 351, Bairro Universitário, Bodocongó, Campina Grande, Paraiba, 58429-500, Brazil
| | - Juliana Lima Brasil da Silva
- Graduate Program in Chemistry, State University of Paraiba, Rua Baraúnas, 351, Bairro Universitário, Bodocongó, Campina Grande, Paraiba, 58429-500, Brazil
| | - Everaldo Paulo Medeiros
- Department of Chemistry Engineering, Federal University of Pernambuco, Av. da Arquitetura, Cidade Universitária, Recife, Pernambuco, 50740-540, Brazil
| | - Carolina Santos Silva
- Department of Food Sciences and Nutrition, Faculty of Health Sciences, University of Malta, Msida, Malta; Brazilian Agricultural Research Corporation, Embrapa Cotton, Rua Osvaldo Cruz, 1143, Bairro Centenário, Campina Grande, Paraiba, 58428-095, Brazil
| | - Simone da Silva Simões
- Graduate Program in Chemistry, State University of Paraiba, Rua Baraúnas, 351, Bairro Universitário, Bodocongó, Campina Grande, Paraiba, 58429-500, Brazil.
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Sohn SI, Pandian S, Zaukuu JLZ, Oh YJ, Park SY, Na CS, Shin EK, Kang HJ, Ryu TH, Cho WS, Cho YS. Discrimination of Transgenic Canola ( Brassica napus L.) and their Hybrids with B. rapa using Vis-NIR Spectroscopy and Machine Learning Methods. Int J Mol Sci 2021; 23:ijms23010220. [PMID: 35008646 PMCID: PMC8745187 DOI: 10.3390/ijms23010220] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 12/22/2021] [Accepted: 12/22/2021] [Indexed: 12/19/2022] Open
Abstract
In recent years, the rapid development of genetically modified (GM) technology has raised concerns about the safety of GM crops and foods for human health and the ecological environment. Gene flow from GM crops to other crops, especially in the Brassicaceae family, might pose a threat to the environment due to their weediness. Hence, finding reliable, quick, and low-cost methods to detect and monitor the presence of GM crops and crop products is important. In this study, we used visible near-infrared (Vis-NIR) spectroscopy for the effective discrimination of GM and non-GM Brassica napus, B. rapa, and F1 hybrids (B. rapa X GM B. napus). Initially, Vis-NIR spectra were collected from the plants, and the spectra were preprocessed. A combination of different preprocessing methods (four methods) and various modeling approaches (eight methods) was used for effective discrimination. Among the different combinations, the Savitzky-Golay and Support Vector Machine combination was found to be an optimal model in the discrimination of GM, non-GM, and hybrid plants with the highest accuracy rate (100%). The use of a Convolutional Neural Network with Normalization resulted in 98.9%. The same higher accuracy was found in the use of Gradient Boosted Trees and Fast Large Margin approaches. Later, phenolic acid concentration among the different plants was assessed using GC-MS analysis. Partial least squares regression analysis of Vis-NIR spectra and biochemical characteristics showed significant correlations in their respective changes. The results showed that handheld Vis-NIR spectroscopy combined with chemometric analyses could be used for the effective discrimination of GM and non-GM B. napus, B. rapa, and F1 hybrids. Biochemical composition analysis can also be combined with the Vis-NIR spectra for efficient discrimination.
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Affiliation(s)
- Soo-In Sohn
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea; (S.P.); (S.-Y.P.); (E.-K.S.); (H.-J.K.); (T.-H.R.); (W.-S.C.); (Y.-S.C.)
- Correspondence: ; Tel.: +82-063-238-4712
| | - Subramani Pandian
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea; (S.P.); (S.-Y.P.); (E.-K.S.); (H.-J.K.); (T.-H.R.); (W.-S.C.); (Y.-S.C.)
| | - John-Lewis Zinia Zaukuu
- Department of Food Science and Technology, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi AK-039-5028, Ghana;
| | - Young-Ju Oh
- Institute for Future Environmental Ecology Co., Ltd., Jeonju 54883, Korea;
| | - Soo-Yun Park
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea; (S.P.); (S.-Y.P.); (E.-K.S.); (H.-J.K.); (T.-H.R.); (W.-S.C.); (Y.-S.C.)
| | - Chae-Sun Na
- Seed Conservation Research Division, Baekdudewgan National Arboretum, Bonghwa 36209, Korea;
| | - Eun-Kyoung Shin
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea; (S.P.); (S.-Y.P.); (E.-K.S.); (H.-J.K.); (T.-H.R.); (W.-S.C.); (Y.-S.C.)
| | - Hyeon-Jung Kang
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea; (S.P.); (S.-Y.P.); (E.-K.S.); (H.-J.K.); (T.-H.R.); (W.-S.C.); (Y.-S.C.)
| | - Tae-Hun Ryu
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea; (S.P.); (S.-Y.P.); (E.-K.S.); (H.-J.K.); (T.-H.R.); (W.-S.C.); (Y.-S.C.)
| | - Woo-Suk Cho
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea; (S.P.); (S.-Y.P.); (E.-K.S.); (H.-J.K.); (T.-H.R.); (W.-S.C.); (Y.-S.C.)
| | - Youn-Sung Cho
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea; (S.P.); (S.-Y.P.); (E.-K.S.); (H.-J.K.); (T.-H.R.); (W.-S.C.); (Y.-S.C.)
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10
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Yang H, Bao L, Liu Y, Luo S, Zhao F, Chen G, Liu F. Identification and quantitative analysis of salt-adulterated honeysuckle using infrared spectroscopy coupled with multi-chemometrics. Microchem J 2021. [DOI: 10.1016/j.microc.2021.106829] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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11
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Portable spectroscopy for high throughput food authenticity screening: Advancements in technology and integration into digital traceability systems. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.11.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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12
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Sohn SI, Pandian S, Oh YJ, Zaukuu JLZ, Kang HJ, Ryu TH, Cho WS, Cho YS, Shin EK, Cho BK. An Overview of Near Infrared Spectroscopy and Its Applications in the Detection of Genetically Modified Organisms. Int J Mol Sci 2021; 22:ijms22189940. [PMID: 34576101 PMCID: PMC8469702 DOI: 10.3390/ijms22189940] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 09/09/2021] [Accepted: 09/11/2021] [Indexed: 01/12/2023] Open
Abstract
Near-infrared spectroscopy (NIRS) has become a more popular approach for quantitative and qualitative analysis of feeds, foods and medicine in conjunction with an arsenal of chemometric tools. This was the foundation for the increased importance of NIRS in other fields, like genetics and transgenic monitoring. A considerable number of studies have utilized NIRS for the effective identification and discrimination of plants and foods, especially for the identification of genetically modified crops. Few previous reviews have elaborated on the applications of NIRS in agriculture and food, but there is no comprehensive review that compares the use of NIRS in the detection of genetically modified organisms (GMOs). This is particularly important because, in comparison to previous technologies such as PCR and ELISA, NIRS offers several advantages, such as speed (eliminating time-consuming procedures), non-destructive/non-invasive analysis, and is inexpensive in terms of cost and maintenance. More importantly, this technique has the potential to measure multiple quality components in GMOs with reliable accuracy. In this review, we brief about the fundamentals and versatile applications of NIRS for the effective identification of GMOs in the agricultural and food systems.
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Affiliation(s)
- Soo-In Sohn
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea; (S.P.); (H.-J.K.); (T.-H.R.); (W.-S.C.); (Y.-S.C.); (E.-K.S.)
- Correspondence: (S.-I.S.); (B.-K.C.)
| | - Subramani Pandian
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea; (S.P.); (H.-J.K.); (T.-H.R.); (W.-S.C.); (Y.-S.C.); (E.-K.S.)
| | - Young-Ju Oh
- Institute for Future Environmental Ecology Co., Ltd., Jeonju 54883, Korea;
| | - John-Lewis Zinia Zaukuu
- Department of Measurements and Process Control, Szent István University, H-1118 Budapest, Hungary;
| | - Hyeon-Jung Kang
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea; (S.P.); (H.-J.K.); (T.-H.R.); (W.-S.C.); (Y.-S.C.); (E.-K.S.)
| | - Tae-Hun Ryu
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea; (S.P.); (H.-J.K.); (T.-H.R.); (W.-S.C.); (Y.-S.C.); (E.-K.S.)
| | - Woo-Suk Cho
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea; (S.P.); (H.-J.K.); (T.-H.R.); (W.-S.C.); (Y.-S.C.); (E.-K.S.)
| | - Youn-Sung Cho
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea; (S.P.); (H.-J.K.); (T.-H.R.); (W.-S.C.); (Y.-S.C.); (E.-K.S.)
| | - Eun-Kyoung Shin
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea; (S.P.); (H.-J.K.); (T.-H.R.); (W.-S.C.); (Y.-S.C.); (E.-K.S.)
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Korea
- Correspondence: (S.-I.S.); (B.-K.C.)
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Authentication of Rice (Oryza sativa L.) Using Near Infrared Spectroscopy Combined with Different Chemometric Classification Strategies. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11010362] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Rice is a staple food in Vietnam, and the concern about rice is much greater than that for other foods. Preventing fraud against this product has become increasingly important in order to protect producers and consumers from possible economic losses. The possible adulteration of this product is done by mixing, or even replacing, high-quality rice with cheaper rice. This highlights the need for analytical methodologies suitable for its authentication. Given this scenario, the present work aims at testing a rapid and non-destructive approach to detect adulterated rice samples. To fulfill this purpose, 200 rice samples (72 authentic and 128 adulterated samples) were analyzed by near infrared (NIR) spectroscopy coupled, with partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogies (SIMCA). The two approaches provided different results; while PLS-DA analysis was a suitable approach for the purpose of the work, SIMCA was unable to solve the investigated problem. The PLS-DA approach provided satisfactory results in discriminating authentic and adulterated samples (both 5% and 10% counterfeits). Focusing on authentic and 10%-adulterated samples, the accuracy of the approach was even better (with a total classification rate of 82.6% and 82.4%, for authentic and adulterated samples, respectively).
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14
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Non-Destructive Identification and Estimation of Granulation in Honey Pomelo Using Visible and Near-Infrared Transmittance Spectroscopy Combined with Machine Vision Technology. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10165399] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Granulation is a physiological disorder of juice sacs in citrus fruit, causing juice sacs to become hard and dry and resulting in decreased internal quality of citrus fruit. Honey pomelo is a thick-skinned citrus fruit, and it is difficult to identify the extent of granulation by observation of the outer peel and fruit shape. In this study, a rapid and non-destructive testing method using visible and near-infrared transmittance spectroscopy combined with machine vision technology was applied to identify and estimate granulation inside fruit. A total of 600 samples in different growth periods was harvested, and fruit were divided into five classes according to five granulation levels. Spectral data were obtained for two ranges of 400–1100 nm and 900–1700 nm by visible and near-infrared transmittance spectroscopy. In addition, chemometrics were used to measure the chemical changes of soluble solid content (SSC), titratable acidity (TA), and moisture content (MC) caused by different granulation levels. Machine vision technology can rapidly estimate the external characteristics of samples and measure the physical changes in mass and volume caused by different granulation levels. Compared with using a single or traditional methods, the predictive performances of multi-category classification models (PCA-SVM and PCA-GRNN) were significantly enhanced. In particular, the model accuracy rate (ARM) was 99% for PCA-GRNN, with classification accuracy (CA), classification sensitivity (CS), and classification specificity (CSP) of 0.9950, 0.9750, and 0.9934, respectively. The results showed that this method has great potential for the identification and estimation of granulation. Multi-source data fusion and application of a multi-category classification model with the smallest number of input layers and acceptable high predictive performances are proposed for on-line applications. This method can be effectively used on-line for the non-destructive detection of fruits with granulation.
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