Discrimination of tea seed oil adulteration based on near-infrared spectroscopy and combined preprocessing method
Near-infrared spectroscopy and chemometrics was used to qualitatively distinguish the types of adulterated oils in binary adulteration of tea seed oil in this study. To address the limitations of a single preprocessing method, nine preprocessing methods from four categories were combined, and the im...
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Published in | Journal of food composition and analysis Vol. 134; p. 106560 |
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Format | Journal Article |
Language | English |
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01.10.2024
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Abstract | Near-infrared spectroscopy and chemometrics was used to qualitatively distinguish the types of adulterated oils in binary adulteration of tea seed oil in this study. To address the limitations of a single preprocessing method, nine preprocessing methods from four categories were combined, and the impact of preprocessing method order on model accuracy was assessed. Additionally, variable iterative space shrinkage approach (VISSA), interval combinatorial optimization (ICO), and uninformative variables elimination (UVE) were used to screen characteristic wavelengths. Subsequently, a discriminative model for tea seed oil adulteration was constructed using two strategies. The results indicate that the order of preprocessing methods significantly influences model accuracy, and combining preprocessing methods can effectively enhance model accuracy. All three characteristic wavelength selection methods effectively screened characteristic variables. Both two strategies demonstrate good discriminant capabilities for binary adulteration in tea seed oil. In strategy 1, identification accuracies for the calibration, prediction and external datasets are 98.67 %, 100 % and 94.44 %, respectively. In strategy 2, identification accuracies for the calibration, prediction and external datasets are 100 %, 98 % and 94.44 %, respectively. Therefore, integrating NIRS with combined preprocessing and variable screening can effectively discern the types of adulterated oils in tea seed oil, serving as a potent detection tool.
•A five-category model of tea seed oil adulteration were established by two strategies.•Combined preprocessing method was proposed instead of single preprocessing method.•The impact of the order and quantity of combined preprocessing method was explored.•VISSA, ICO and UVE were used for characteristic wavelength selection. |
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AbstractList | Near-infrared spectroscopy and chemometrics was used to qualitatively distinguish the types of adulterated oils in binary adulteration of tea seed oil in this study. To address the limitations of a single preprocessing method, nine preprocessing methods from four categories were combined, and the impact of preprocessing method order on model accuracy was assessed. Additionally, variable iterative space shrinkage approach (VISSA), interval combinatorial optimization (ICO), and uninformative variables elimination (UVE) were used to screen characteristic wavelengths. Subsequently, a discriminative model for tea seed oil adulteration was constructed using two strategies. The results indicate that the order of preprocessing methods significantly influences model accuracy, and combining preprocessing methods can effectively enhance model accuracy. All three characteristic wavelength selection methods effectively screened characteristic variables. Both two strategies demonstrate good discriminant capabilities for binary adulteration in tea seed oil. In strategy 1, identification accuracies for the calibration, prediction and external datasets are 98.67 %, 100 % and 94.44 %, respectively. In strategy 2, identification accuracies for the calibration, prediction and external datasets are 100 %, 98 % and 94.44 %, respectively. Therefore, integrating NIRS with combined preprocessing and variable screening can effectively discern the types of adulterated oils in tea seed oil, serving as a potent detection tool.
•A five-category model of tea seed oil adulteration were established by two strategies.•Combined preprocessing method was proposed instead of single preprocessing method.•The impact of the order and quantity of combined preprocessing method was explored.•VISSA, ICO and UVE were used for characteristic wavelength selection. |
ArticleNumber | 106560 |
Author | Sun, Tong Kong, Lingfei Li, Hanlin Wu, Chengzhao Yuan, Ming'an |
Author_xml | – sequence: 1 givenname: Lingfei surname: Kong fullname: Kong, Lingfei organization: College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, PR China – sequence: 2 givenname: Chengzhao surname: Wu fullname: Wu, Chengzhao organization: College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, PR China – sequence: 3 givenname: Hanlin surname: Li fullname: Li, Hanlin organization: College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, PR China – sequence: 4 givenname: Ming'an surname: Yuan fullname: Yuan, Ming'an email: minganyuan@126.com organization: Jinhua Academy of Agricultural Sciences, Jinhua 321000, PR China – sequence: 5 givenname: Tong surname: Sun fullname: Sun, Tong email: suntong@zafu.edu.cn organization: College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, PR China |
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Cites_doi | 10.1186/s13195-023-01268-9 10.1016/j.lwt.2020.109247 10.1016/j.saa.2021.120841 10.1016/j.saa.2023.122482 10.1016/j.tifs.2020.05.002 10.1080/10408390802606790 10.1016/j.trac.2020.116045 10.1016/j.trac.2022.116648 10.1016/j.foodcont.2020.107577 10.1016/j.saa.2020.118973 10.1007/s12161-021-02034-z 10.1002/ejlt.201900355 10.1016/j.lwt.2021.111168 10.1039/C4AN00730A 10.1007/s13197-019-03697-7 10.1080/01621459.2022.2093206 10.1016/j.arabjc.2017.12.025 10.1016/j.chemolab.2022.104497 10.1142/S1793545818500062 10.1007/s13320-022-0652-y 10.1007/s12647-022-00558-1 10.1016/j.foodcont.2018.11.055 10.1016/j.talanta.2020.120748 10.1016/j.microc.2020.105544 10.3390/molecules23020241 10.1016/j.foodchem.2022.134828 10.3389/fphy.2022.1047466 10.1016/j.postharvbio.2020.111271 10.3390/molecules28165943 10.1016/j.foodcont.2019.06.013 10.3390/foods11152221 10.1016/j.foodcont.2020.107145 10.1016/j.jfca.2023.105965 10.1016/j.infrared.2021.103824 10.1007/s13197-020-04375-9 10.3390/foods11081134 10.1109/TNNLS.2021.3105196 |
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Keywords | External validation Modeling strategy Adulteration Combined preprocessing Tea seed oil Near-infrared spectroscopy |
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References | Mishra, Biancolillo, Roger, Marini, Rutledge (bib27) 2020; 132 Yu, Yan, Wu, Wang, Xia (bib38) 2022; 11 Chen, Fu, Dou, Deng, Wang, Ma, Yu, Yun, Li, Zhang (bib4) 2023; 18 Kim, Lee, Lee, Rhee, Shin, Lee, Cho, Min, Kwon, Kim, Yon (bib16) 2023; 15 Liu, Gong, Li, Wen, Guan, Zheng (bib22) 2023; 28 Mishra, Roger, Rutledge, Woltering (bib29) 2020; 168 Mei, Wang, Zhang, Shi, Jiang (bib25) 2021; 143 Cheng, Yang, Wang, Zhou, Yan, Teng, Wang, Chen, He, Guo, Zhang (bib5) 2018; 11 Li, Zhang, Zhang (bib20) 2023; 34 Arslan, Akin, Karuk Elmas, Yilmaz, Janssen, Kenar (bib2) 2019; 98 Gao, Lu, Guo, Zhang, Lin (bib11) 2022; 10 Dou, Mao, Zhang, Xie, Chen, Yu, Ma, Wang, Zhang, Li (bib8) 2018; 23 Mishra, Roger, Marini, Biancolillo, Rutledge (bib28) 2022; 222 Du, Zhu, Shi, Luo, Gan, Tang, Chen (bib10) 2021; 121 Meng, Yin, Yuan, Zhang, Ju, Xin, Chen, Lv, Hu (bib26) 2023; 405 Puspita, Irawati, Madurani, Kurniawan, Koentjoro, Hatta (bib30) 2022; 12 Li, Zhang, Zhang, Wang, Wang, Yu, Zhang, Li (bib21) 2020; 101 Xie, Qiao, Yang, Zhang, Cui, He, Du, Xiao, Li (bib37) 2024; 127 Li, Su, Sejdinovic (bib19) 2022; 118 Guo, ZHU, ZHANG, DU (bib13) 2019; 51 Sohng, Park, Jang, Cha, Jung, Chung (bib33) 2020; 212 Chu, Wang, Li, Zhao, Jiang (bib6) 2018; 11 Jin, Wang, Lv, Zhang, Zhu, Zhao, Li (bib15) 2022; 11 Uncu, Ozen (bib35) 2019; 105 Gertz, Matthäus, Willenberg (bib12) 2020; 122 Liu, Wang, Zhao (bib23) 2023; 294 Borghi, Santos, Santos, Nascimento, Corrêa, Cesconetto, Pires, Ribeiro, Lacerda, Romão, Filgueiras (bib3) 2020; 159 Li, G., Mu, L., Zhou, M., Zhao, J., Wu, S., Lin, L., (2021). New strategy of sample set division in spectroscopy analysis——SWNW. Infrared Physics & Technology 117. Rashvand, Akbarnia (bib31) 2019; 4 Srinath, Kiranmayee, Bhanot, Panchariya (bib34) 2022; 37 Armenta, Moros, Garrigues, de la Guardia (bib1) 2010; 50 Liu, Y., Yao, L., Xia, Z., Gao, Y., Gong, Z., (2021). Geographical discrimination and adulteration analysis for edible oils using two-dimensional correlation spectroscopy and convolutional neural networks (CNNs). Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 246. Deng, Yun, Liang, Yi (bib7) 2014; 139 Li, Huang, Song, Zhang, Min (bib17) 2019; 56 Drira, Guclu, Portolés, Jabeur, Kelebek, Selli, Bouaziz (bib9) 2021; 14 Heidari, Talebpour, Abdollahpour, Adib, Ghanavi, Aboul-Enein (bib14) 2020; 57 Rodionova, Oliveri, Malegori, Pomerantsev (bib32) 2024 Yuan, Wang, Wang, Cheng, Wu, Kong (bib39) 2020; 112 Zhang, Hu, Liu, Wei, Bian (bib41) 2022; 270 Yuan, Zhang, Wang, Jiang, Harrington, Mao, Zhang, Li (bib40) 2020; 125 Wang, Chen, Dai, Liu, Li, Xu, Chu (bib36) 2022; 153 Armenta (10.1016/j.jfca.2024.106560_bib1) 2010; 50 Du (10.1016/j.jfca.2024.106560_bib10) 2021; 121 Liu (10.1016/j.jfca.2024.106560_bib23) 2023; 294 Xie (10.1016/j.jfca.2024.106560_bib37) 2024; 127 Dou (10.1016/j.jfca.2024.106560_bib8) 2018; 23 Mei (10.1016/j.jfca.2024.106560_bib25) 2021; 143 10.1016/j.jfca.2024.106560_bib18 Liu (10.1016/j.jfca.2024.106560_bib22) 2023; 28 Guo (10.1016/j.jfca.2024.106560_bib13) 2019; 51 Uncu (10.1016/j.jfca.2024.106560_bib35) 2019; 105 Li (10.1016/j.jfca.2024.106560_bib17) 2019; 56 Li (10.1016/j.jfca.2024.106560_bib20) 2023; 34 Meng (10.1016/j.jfca.2024.106560_bib26) 2023; 405 Mishra (10.1016/j.jfca.2024.106560_bib29) 2020; 168 Srinath (10.1016/j.jfca.2024.106560_bib34) 2022; 37 Deng (10.1016/j.jfca.2024.106560_bib7) 2014; 139 Mishra (10.1016/j.jfca.2024.106560_bib27) 2020; 132 Rashvand (10.1016/j.jfca.2024.106560_bib31) 2019; 4 Yu (10.1016/j.jfca.2024.106560_bib38) 2022; 11 Jin (10.1016/j.jfca.2024.106560_bib15) 2022; 11 Rodionova (10.1016/j.jfca.2024.106560_bib32) 2024 Arslan (10.1016/j.jfca.2024.106560_bib2) 2019; 98 Gertz (10.1016/j.jfca.2024.106560_bib12) 2020; 122 Puspita (10.1016/j.jfca.2024.106560_bib30) 2022; 12 Wang (10.1016/j.jfca.2024.106560_bib36) 2022; 153 Yuan (10.1016/j.jfca.2024.106560_bib39) 2020; 112 Li (10.1016/j.jfca.2024.106560_bib21) 2020; 101 Zhang (10.1016/j.jfca.2024.106560_bib41) 2022; 270 Drira (10.1016/j.jfca.2024.106560_bib9) 2021; 14 Kim (10.1016/j.jfca.2024.106560_bib16) 2023; 15 Chen (10.1016/j.jfca.2024.106560_bib4) 2023; 18 10.1016/j.jfca.2024.106560_bib24 Yuan (10.1016/j.jfca.2024.106560_bib40) 2020; 125 Borghi (10.1016/j.jfca.2024.106560_bib3) 2020; 159 Cheng (10.1016/j.jfca.2024.106560_bib5) 2018; 11 Heidari (10.1016/j.jfca.2024.106560_bib14) 2020; 57 Mishra (10.1016/j.jfca.2024.106560_bib28) 2022; 222 Sohng (10.1016/j.jfca.2024.106560_bib33) 2020; 212 Chu (10.1016/j.jfca.2024.106560_bib6) 2018; 11 Gao (10.1016/j.jfca.2024.106560_bib11) 2022; 10 Li (10.1016/j.jfca.2024.106560_bib19) 2022; 118 |
References_xml | – volume: 98 start-page: 323 year: 2019 end-page: 332 ident: bib2 article-title: Rapid detection of authenticity and adulteration of cold pressed black cumin seed oil: a comparative study of ATR–FTIR spectroscopy and synchronous fluorescence with multivariate data analysis publication-title: Food Control contributor: fullname: Kenar – volume: 51 start-page: 350 year: 2019 end-page: 357 ident: bib13 article-title: Detection on adulterated oil-tea camellia seed oil based on near-infrared spectroscopy publication-title: Trans. Chin. Soc. Agric. Mach. contributor: fullname: DU – volume: 168 year: 2020 ident: bib29 article-title: SPORT pre-processing can improve near-infrared quality prediction models for fresh fruits and agro-materials publication-title: Postharvest Biol. Technol. contributor: fullname: Woltering – volume: 112 year: 2020 ident: bib39 article-title: Rapid detection of the authenticity and adulteration of sesame oil using excitation-emission matrix fluorescence and chemometric methods publication-title: Food Control contributor: fullname: Kong – volume: 11 start-page: 815 year: 2018 end-page: 826 ident: bib5 article-title: New method for effective identification of adulterated Camellia oil basing on Camellia oleifera-specific DNA publication-title: Arab. J. Chem. contributor: fullname: Zhang – volume: 153 year: 2022 ident: bib36 article-title: Recent advances of chemometric calibration methods in modern spectroscopy: algorithms, strategy, and related issues publication-title: TrAC Trends Anal. Chem. contributor: fullname: Chu – volume: 121 year: 2021 ident: bib10 article-title: Adulteration detection of corn oil, rapeseed oil and sunflower oil in camellia oil by in situ diffuse reflectance near-infrared spectroscopy and chemometrics publication-title: Food Control contributor: fullname: Chen – volume: 23 year: 2018 ident: bib8 article-title: Multispecies adulteration detection of camellia oil by chemical markers publication-title: Molecules contributor: fullname: Li – volume: 294 year: 2023 ident: bib23 article-title: Direct observation on argon tagging nitrobenzene radical anion in gas phase: infrared photodissociation spectroscopy and theoretical calculation publication-title: Spectrochim. Acta Part A: Mol. Biomol. Spectrosc. contributor: fullname: Zhao – volume: 56 start-page: 2158 year: 2019 end-page: 2166 ident: bib17 article-title: Spectral interval combination optimization (ICO) on rapid quality assessment of Solanaceae plant: a validation study publication-title: J. Food Sci. Technol. contributor: fullname: Min – volume: 105 start-page: 209 year: 2019 end-page: 218 ident: bib35 article-title: A comparative study of mid-infrared, UV–Visible and fluorescence spectroscopy in combination with chemometrics for the detection of adulteration of fresh olive oils with old olive oils publication-title: Food Control contributor: fullname: Ozen – volume: 12 year: 2022 ident: bib30 article-title: Graphene- and multi-walled carbon nanotubes-coated tapered plastic optical fiber for detection of lard adulteration in olive oil publication-title: Photon. Sens. contributor: fullname: Hatta – volume: 4 start-page: 237 year: 2019 end-page: 243 ident: bib31 article-title: The feasibility of using image processing and artificial neural network for detecting the adulteration of sesame oil publication-title: AIMS Agric. Food contributor: fullname: Akbarnia – volume: 28 year: 2023 ident: bib22 article-title: Rapid and Low-cost quantification of adulteration content in camellia oil utilizing UV-Vis-NIR spectroscopy combined with feature selection methods publication-title: Molecules contributor: fullname: Zheng – volume: 57 start-page: 3415 year: 2020 end-page: 3425 ident: bib14 article-title: Discrimination between vegetable oil and animal fat by a metabolomics approach using gas chromatography–mass spectrometry combined with chemometrics publication-title: J. Food Sci. Technol. contributor: fullname: Aboul-Enein – volume: 143 year: 2021 ident: bib25 article-title: Fast detection of adulteration of aromatic peanut oils based on alpha-tocopherol and gamma-tocopherol contents and ratio publication-title: Lwt contributor: fullname: Jiang – volume: 405 year: 2023 ident: bib26 article-title: Rapid detection of adulteration of olive oil with soybean oil combined with chemometrics by Fourier transform infrared, visible-near-infrared and excitation-emission matrix fluorescence spectroscopy: a comparative study publication-title: Food Chem. contributor: fullname: Hu – volume: 50 start-page: 567 year: 2010 end-page: 582 ident: bib1 article-title: The use of near-infrared spectrometry in the olive oil industry publication-title: Crit. Rev. Food Sci. Nutr. contributor: fullname: de la Guardia – start-page: 147 year: 2024 ident: bib32 article-title: Chemometrics as an efficient tool for food authentication: golden pillars for building reliable models publication-title: Trends Food Sci. Technol. contributor: fullname: Pomerantsev – volume: 14 start-page: 2121 year: 2021 end-page: 2135 ident: bib9 article-title: Safe and fast fingerprint aroma detection in adulterated extra virgin olive oil using gas chromatography–olfactometry-mass spectrometry combined with chemometrics publication-title: Food Anal. Methods contributor: fullname: Bouaziz – volume: 222 year: 2022 ident: bib28 article-title: Pre-processing ensembles with response oriented sequential alternation calibration (PROSAC): a step towards ending the pre-processing search and optimization quest for near-infrared spectral modelling publication-title: Chemom. Intell. Lab. Syst. contributor: fullname: Rutledge – volume: 270 year: 2022 ident: bib41 article-title: Near infrared spectroscopy combined with chemometrics for quantitative analysis of corn oil in edible blend oil publication-title: Spectrochim. Acta A Mol. Biomol. Spectrosc. contributor: fullname: Bian – volume: 15 start-page: 127 year: 2023 ident: bib16 article-title: Quantification of identifying cognitive impairment using olfactory-stimulated functional near-infrared spectroscopy with machine learning: a post hoc analysis of a diagnostic trial and validation of an external additional trial publication-title: Alzheimers Res Ther. contributor: fullname: Yon – volume: 34 start-page: 1627 year: 2023 end-page: 1632 ident: bib20 article-title: Self-weighted unsupervised LDA publication-title: IEEE Trans. Neural Netw. Learn. Syst. contributor: fullname: Zhang – volume: 132 year: 2020 ident: bib27 article-title: New data preprocessing trends based on ensemble of multiple preprocessing techniques publication-title: TrAC Trends Anal. Chem. contributor: fullname: Rutledge – volume: 11 year: 2018 ident: bib6 article-title: Identifying camellia oil adulteration with selected vegetable oils by characteristic near-infrared spectral regions publication-title: J. Innov. Opt. Health Sci. contributor: fullname: Jiang – volume: 10 year: 2022 ident: bib11 article-title: Quantum K-nearest neighbors classification algorithm based on Mahalanobis distance publication-title: Front. Phys. contributor: fullname: Lin – volume: 122 year: 2020 ident: bib12 article-title: Detection of soft-deodorized olive oil and refined vegetable oils in virgin olive oil using near infrared spectroscopy and traditional analytical parameters publication-title: Eur. J. Lipid Sci. Technol. contributor: fullname: Willenberg – volume: 159 year: 2020 ident: bib3 article-title: Quantification and classification of vegetable oils in extra virgin olive oil samples using a portable near-infrared spectrometer associated with chemometrics publication-title: Microchem. J. contributor: fullname: Filgueiras – volume: 37 start-page: 483 year: 2022 end-page: 493 ident: bib34 article-title: Detection of palm oil adulteration in sunflower oil using ATR-MIR spectroscopy coupled with chemometric algorithms publication-title: Mapan contributor: fullname: Panchariya – volume: 11 year: 2022 ident: bib15 article-title: Rapid detection of avocado oil adulteration using low-field nuclear magnetic resonance publication-title: Foods contributor: fullname: Li – volume: 212 year: 2020 ident: bib33 article-title: Incorporation of two-dimensional correlation analysis into discriminant analysis as a potential tool for improving discrimination accuracy: near-infrared spectroscopic discrimination of adulterated olive oils publication-title: Talanta contributor: fullname: Chung – volume: 127 year: 2024 ident: bib37 article-title: Establishment of a general prediction model for protein content in various varieties and colors of peas using visible-near-infrared spectroscopy publication-title: J. Food Compos. Anal. contributor: fullname: Li – volume: 118 start-page: 2876 year: 2022 end-page: 2888 ident: bib19 article-title: Benign overfitting and noisy features publication-title: J. Am. Stat. Assoc. contributor: fullname: Sejdinovic – volume: 11 year: 2022 ident: bib38 article-title: Quality evaluation of the oil of camellia spp publication-title: Foods contributor: fullname: Xia – volume: 101 start-page: 172 year: 2020 end-page: 181 ident: bib21 article-title: Review of NIR spectroscopy methods for nondestructive quality analysis of oilseeds and edible oils publication-title: . Trends Food Sci. Technol. contributor: fullname: Li – volume: 139 year: 2014 ident: bib7 article-title: A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling publication-title: Analyst contributor: fullname: Yi – volume: 18 year: 2023 ident: bib4 article-title: Comprehensive adulteration detection of sesame oil based on characteristic markers publication-title: Food Chem.: X contributor: fullname: Zhang – volume: 125 year: 2020 ident: bib40 article-title: Detection of flaxseed oil multiple adulteration by near-infrared spectroscopy and nonlinear one class partial least squares discriminant analysis publication-title: Lwt contributor: fullname: Li – volume: 15 start-page: 127 issue: 1 year: 2023 ident: 10.1016/j.jfca.2024.106560_bib16 article-title: Quantification of identifying cognitive impairment using olfactory-stimulated functional near-infrared spectroscopy with machine learning: a post hoc analysis of a diagnostic trial and validation of an external additional trial publication-title: Alzheimers Res Ther. doi: 10.1186/s13195-023-01268-9 contributor: fullname: Kim – volume: 4 start-page: 237 issue: 2 year: 2019 ident: 10.1016/j.jfca.2024.106560_bib31 article-title: The feasibility of using image processing and artificial neural network for detecting the adulteration of sesame oil publication-title: AIMS Agric. Food contributor: fullname: Rashvand – volume: 125 year: 2020 ident: 10.1016/j.jfca.2024.106560_bib40 article-title: Detection of flaxseed oil multiple adulteration by near-infrared spectroscopy and nonlinear one class partial least squares discriminant analysis publication-title: Lwt doi: 10.1016/j.lwt.2020.109247 contributor: fullname: Yuan – volume: 270 year: 2022 ident: 10.1016/j.jfca.2024.106560_bib41 article-title: Near infrared spectroscopy combined with chemometrics for quantitative analysis of corn oil in edible blend oil publication-title: Spectrochim. Acta A Mol. Biomol. Spectrosc. doi: 10.1016/j.saa.2021.120841 contributor: fullname: Zhang – volume: 294 year: 2023 ident: 10.1016/j.jfca.2024.106560_bib23 article-title: Direct observation on argon tagging nitrobenzene radical anion in gas phase: infrared photodissociation spectroscopy and theoretical calculation publication-title: Spectrochim. Acta Part A: Mol. Biomol. Spectrosc. doi: 10.1016/j.saa.2023.122482 contributor: fullname: Liu – volume: 101 start-page: 172 year: 2020 ident: 10.1016/j.jfca.2024.106560_bib21 article-title: Review of NIR spectroscopy methods for nondestructive quality analysis of oilseeds and edible oils publication-title: . Trends Food Sci. Technol. doi: 10.1016/j.tifs.2020.05.002 contributor: fullname: Li – start-page: 147 year: 2024 ident: 10.1016/j.jfca.2024.106560_bib32 article-title: Chemometrics as an efficient tool for food authentication: golden pillars for building reliable models publication-title: Trends Food Sci. Technol. contributor: fullname: Rodionova – volume: 50 start-page: 567 issue: 6 year: 2010 ident: 10.1016/j.jfca.2024.106560_bib1 article-title: The use of near-infrared spectrometry in the olive oil industry publication-title: Crit. Rev. Food Sci. Nutr. doi: 10.1080/10408390802606790 contributor: fullname: Armenta – volume: 132 year: 2020 ident: 10.1016/j.jfca.2024.106560_bib27 article-title: New data preprocessing trends based on ensemble of multiple preprocessing techniques publication-title: TrAC Trends Anal. Chem. doi: 10.1016/j.trac.2020.116045 contributor: fullname: Mishra – volume: 153 year: 2022 ident: 10.1016/j.jfca.2024.106560_bib36 article-title: Recent advances of chemometric calibration methods in modern spectroscopy: algorithms, strategy, and related issues publication-title: TrAC Trends Anal. Chem. doi: 10.1016/j.trac.2022.116648 contributor: fullname: Wang – volume: 121 year: 2021 ident: 10.1016/j.jfca.2024.106560_bib10 article-title: Adulteration detection of corn oil, rapeseed oil and sunflower oil in camellia oil by in situ diffuse reflectance near-infrared spectroscopy and chemometrics publication-title: Food Control doi: 10.1016/j.foodcont.2020.107577 contributor: fullname: Du – ident: 10.1016/j.jfca.2024.106560_bib24 doi: 10.1016/j.saa.2020.118973 – volume: 14 start-page: 2121 issue: 10 year: 2021 ident: 10.1016/j.jfca.2024.106560_bib9 article-title: Safe and fast fingerprint aroma detection in adulterated extra virgin olive oil using gas chromatography–olfactometry-mass spectrometry combined with chemometrics publication-title: Food Anal. Methods doi: 10.1007/s12161-021-02034-z contributor: fullname: Drira – volume: 122 issue: 6 year: 2020 ident: 10.1016/j.jfca.2024.106560_bib12 article-title: Detection of soft-deodorized olive oil and refined vegetable oils in virgin olive oil using near infrared spectroscopy and traditional analytical parameters publication-title: Eur. J. Lipid Sci. Technol. doi: 10.1002/ejlt.201900355 contributor: fullname: Gertz – volume: 143 year: 2021 ident: 10.1016/j.jfca.2024.106560_bib25 article-title: Fast detection of adulteration of aromatic peanut oils based on alpha-tocopherol and gamma-tocopherol contents and ratio publication-title: Lwt doi: 10.1016/j.lwt.2021.111168 contributor: fullname: Mei – volume: 18 year: 2023 ident: 10.1016/j.jfca.2024.106560_bib4 article-title: Comprehensive adulteration detection of sesame oil based on characteristic markers publication-title: Food Chem.: X contributor: fullname: Chen – volume: 51 start-page: 350 issue: 9 year: 2019 ident: 10.1016/j.jfca.2024.106560_bib13 article-title: Detection on adulterated oil-tea camellia seed oil based on near-infrared spectroscopy publication-title: Trans. Chin. Soc. Agric. Mach. contributor: fullname: Guo – volume: 139 issue: 19 year: 2014 ident: 10.1016/j.jfca.2024.106560_bib7 article-title: A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling publication-title: Analyst doi: 10.1039/C4AN00730A contributor: fullname: Deng – volume: 56 start-page: 2158 issue: 4 year: 2019 ident: 10.1016/j.jfca.2024.106560_bib17 article-title: Spectral interval combination optimization (ICO) on rapid quality assessment of Solanaceae plant: a validation study publication-title: J. Food Sci. Technol. doi: 10.1007/s13197-019-03697-7 contributor: fullname: Li – volume: 118 start-page: 2876 issue: 544 year: 2022 ident: 10.1016/j.jfca.2024.106560_bib19 article-title: Benign overfitting and noisy features publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.2022.2093206 contributor: fullname: Li – volume: 11 start-page: 815 issue: 6 year: 2018 ident: 10.1016/j.jfca.2024.106560_bib5 article-title: New method for effective identification of adulterated Camellia oil basing on Camellia oleifera-specific DNA publication-title: Arab. J. Chem. doi: 10.1016/j.arabjc.2017.12.025 contributor: fullname: Cheng – volume: 222 year: 2022 ident: 10.1016/j.jfca.2024.106560_bib28 article-title: Pre-processing ensembles with response oriented sequential alternation calibration (PROSAC): a step towards ending the pre-processing search and optimization quest for near-infrared spectral modelling publication-title: Chemom. Intell. Lab. Syst. doi: 10.1016/j.chemolab.2022.104497 contributor: fullname: Mishra – volume: 11 issue: 02 year: 2018 ident: 10.1016/j.jfca.2024.106560_bib6 article-title: Identifying camellia oil adulteration with selected vegetable oils by characteristic near-infrared spectral regions publication-title: J. Innov. Opt. Health Sci. doi: 10.1142/S1793545818500062 contributor: fullname: Chu – volume: 12 issue: 4 year: 2022 ident: 10.1016/j.jfca.2024.106560_bib30 article-title: Graphene- and multi-walled carbon nanotubes-coated tapered plastic optical fiber for detection of lard adulteration in olive oil publication-title: Photon. Sens. doi: 10.1007/s13320-022-0652-y contributor: fullname: Puspita – volume: 37 start-page: 483 issue: 3 year: 2022 ident: 10.1016/j.jfca.2024.106560_bib34 article-title: Detection of palm oil adulteration in sunflower oil using ATR-MIR spectroscopy coupled with chemometric algorithms publication-title: Mapan doi: 10.1007/s12647-022-00558-1 contributor: fullname: Srinath – volume: 98 start-page: 323 year: 2019 ident: 10.1016/j.jfca.2024.106560_bib2 article-title: Rapid detection of authenticity and adulteration of cold pressed black cumin seed oil: a comparative study of ATR–FTIR spectroscopy and synchronous fluorescence with multivariate data analysis publication-title: Food Control doi: 10.1016/j.foodcont.2018.11.055 contributor: fullname: Arslan – volume: 212 year: 2020 ident: 10.1016/j.jfca.2024.106560_bib33 article-title: Incorporation of two-dimensional correlation analysis into discriminant analysis as a potential tool for improving discrimination accuracy: near-infrared spectroscopic discrimination of adulterated olive oils publication-title: Talanta doi: 10.1016/j.talanta.2020.120748 contributor: fullname: Sohng – volume: 159 year: 2020 ident: 10.1016/j.jfca.2024.106560_bib3 article-title: Quantification and classification of vegetable oils in extra virgin olive oil samples using a portable near-infrared spectrometer associated with chemometrics publication-title: Microchem. J. doi: 10.1016/j.microc.2020.105544 contributor: fullname: Borghi – volume: 23 issue: 2 year: 2018 ident: 10.1016/j.jfca.2024.106560_bib8 article-title: Multispecies adulteration detection of camellia oil by chemical markers publication-title: Molecules doi: 10.3390/molecules23020241 contributor: fullname: Dou – volume: 405 year: 2023 ident: 10.1016/j.jfca.2024.106560_bib26 article-title: Rapid detection of adulteration of olive oil with soybean oil combined with chemometrics by Fourier transform infrared, visible-near-infrared and excitation-emission matrix fluorescence spectroscopy: a comparative study publication-title: Food Chem. doi: 10.1016/j.foodchem.2022.134828 contributor: fullname: Meng – volume: 10 year: 2022 ident: 10.1016/j.jfca.2024.106560_bib11 article-title: Quantum K-nearest neighbors classification algorithm based on Mahalanobis distance publication-title: Front. Phys. doi: 10.3389/fphy.2022.1047466 contributor: fullname: Gao – volume: 168 year: 2020 ident: 10.1016/j.jfca.2024.106560_bib29 article-title: SPORT pre-processing can improve near-infrared quality prediction models for fresh fruits and agro-materials publication-title: Postharvest Biol. Technol. doi: 10.1016/j.postharvbio.2020.111271 contributor: fullname: Mishra – volume: 28 issue: 16 year: 2023 ident: 10.1016/j.jfca.2024.106560_bib22 article-title: Rapid and Low-cost quantification of adulteration content in camellia oil utilizing UV-Vis-NIR spectroscopy combined with feature selection methods publication-title: Molecules doi: 10.3390/molecules28165943 contributor: fullname: Liu – volume: 105 start-page: 209 year: 2019 ident: 10.1016/j.jfca.2024.106560_bib35 article-title: A comparative study of mid-infrared, UV–Visible and fluorescence spectroscopy in combination with chemometrics for the detection of adulteration of fresh olive oils with old olive oils publication-title: Food Control doi: 10.1016/j.foodcont.2019.06.013 contributor: fullname: Uncu – volume: 11 issue: 15 year: 2022 ident: 10.1016/j.jfca.2024.106560_bib38 article-title: Quality evaluation of the oil of camellia spp publication-title: Foods doi: 10.3390/foods11152221 contributor: fullname: Yu – volume: 112 year: 2020 ident: 10.1016/j.jfca.2024.106560_bib39 article-title: Rapid detection of the authenticity and adulteration of sesame oil using excitation-emission matrix fluorescence and chemometric methods publication-title: Food Control doi: 10.1016/j.foodcont.2020.107145 contributor: fullname: Yuan – volume: 127 year: 2024 ident: 10.1016/j.jfca.2024.106560_bib37 article-title: Establishment of a general prediction model for protein content in various varieties and colors of peas using visible-near-infrared spectroscopy publication-title: J. Food Compos. Anal. doi: 10.1016/j.jfca.2023.105965 contributor: fullname: Xie – ident: 10.1016/j.jfca.2024.106560_bib18 doi: 10.1016/j.infrared.2021.103824 – volume: 57 start-page: 3415 issue: 9 year: 2020 ident: 10.1016/j.jfca.2024.106560_bib14 article-title: Discrimination between vegetable oil and animal fat by a metabolomics approach using gas chromatography–mass spectrometry combined with chemometrics publication-title: J. Food Sci. Technol. doi: 10.1007/s13197-020-04375-9 contributor: fullname: Heidari – volume: 11 issue: 8 year: 2022 ident: 10.1016/j.jfca.2024.106560_bib15 article-title: Rapid detection of avocado oil adulteration using low-field nuclear magnetic resonance publication-title: Foods doi: 10.3390/foods11081134 contributor: fullname: Jin – volume: 34 start-page: 1627 issue: 3 year: 2023 ident: 10.1016/j.jfca.2024.106560_bib20 article-title: Self-weighted unsupervised LDA publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2021.3105196 contributor: fullname: Li |
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Snippet | Near-infrared spectroscopy and chemometrics was used to qualitatively distinguish the types of adulterated oils in binary adulteration of tea seed oil in this... |
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SubjectTerms | Adulteration Combined preprocessing External validation Modeling strategy Near-infrared spectroscopy Tea seed oil |
Title | Discrimination of tea seed oil adulteration based on near-infrared spectroscopy and combined preprocessing method |
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