Vis/NIR spectroscopy and machine learning model for counterfeit Citri Reticulatae Pericarpium identification
Citri Reticulatae Pericarpium(CRP), due to its market demand and high price, is commonly subject to counterfeiting, with various methods of counterfeiting. In this study, visible/near-infrared (Vis/NIR) spectral images of authentic and counterfeited CRP were collected using a Vis/NIR spectrometer. S...
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| Vydáno v: | Journal of food composition and analysis Ročník 148; s. 108240 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Inc
01.12.2025
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| ISSN: | 0889-1575 |
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| Abstract | Citri Reticulatae Pericarpium(CRP), due to its market demand and high price, is commonly subject to counterfeiting, with various methods of counterfeiting. In this study, visible/near-infrared (Vis/NIR) spectral images of authentic and counterfeited CRP were collected using a Vis/NIR spectrometer. Spectral data processing and machine learning classification models were utilized for classification. Preprocessing, dimensionality reduction, feature wavelength extraction, and machine learning were applied to classify CRP spectral data to address this issue. We apply the Seagull Optimization Algorithm to optimize SVM parameters, thereby proposing the SOASVM model. The results demonstrated that the proposed model could effectively and accurately distinguish between authentic and counterfeited CRP, as well as different methods of counterfeiting. Linear discriminant analysis(LDA) after data processing achieved the best performance, with the classification accuracy of up to 99.3% in test set when combined with the SOASVM model via cross-validation. This study provides optimized models for CRP counterfeiting classification, offering a non-destructive, precise, and effective method for distinguishing authentic from counterfeited CRP.
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•The Spectra model non-destructively detects Citri Reticulatae Pericarpium counterfeits.•MSC and LDA are the optimal preprocessing and dimensionality reduction methods.•MSC-LDA-SOASVM combined with Vis/NIR achieves effective accuracy detection. |
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| AbstractList | Citri Reticulatae Pericarpium(CRP), due to its market demand and high price, is commonly subject to counterfeiting, with various methods of counterfeiting. In this study, visible/near-infrared (Vis/NIR) spectral images of authentic and counterfeited CRP were collected using a Vis/NIR spectrometer. Spectral data processing and machine learning classification models were utilized for classification. Preprocessing, dimensionality reduction, feature wavelength extraction, and machine learning were applied to classify CRP spectral data to address this issue. We apply the Seagull Optimization Algorithm to optimize SVM parameters, thereby proposing the SOASVM model. The results demonstrated that the proposed model could effectively and accurately distinguish between authentic and counterfeited CRP, as well as different methods of counterfeiting. Linear discriminant analysis(LDA) after data processing achieved the best performance, with the classification accuracy of up to 99.3% in test set when combined with the SOASVM model via cross-validation. This study provides optimized models for CRP counterfeiting classification, offering a non-destructive, precise, and effective method for distinguishing authentic from counterfeited CRP.
[Display omitted]
•The Spectra model non-destructively detects Citri Reticulatae Pericarpium counterfeits.•MSC and LDA are the optimal preprocessing and dimensionality reduction methods.•MSC-LDA-SOASVM combined with Vis/NIR achieves effective accuracy detection. |
| ArticleNumber | 108240 |
| Author | Yuan, Yunxia Ma, Chao Ma, Jianwei Yan, Yongyi Zhang, Mingkun Huang, Jiayu |
| Author_xml | – sequence: 1 givenname: Mingkun orcidid: 0000-0003-2797-8781 surname: Zhang fullname: Zhang, Mingkun email: zhangmkluoy@163.com organization: College of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China – sequence: 2 givenname: Chao orcidid: 0000-0003-2597-1316 surname: Ma fullname: Ma, Chao organization: College of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China – sequence: 3 givenname: Jianwei surname: Ma fullname: Ma, Jianwei email: majianwei_haust@163.com organization: College of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China – sequence: 4 givenname: Yunxia surname: Yuan fullname: Yuan, Yunxia organization: College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, 471023, China – sequence: 5 givenname: Jiayu orcidid: 0009-0007-4778-6361 surname: Huang fullname: Huang, Jiayu organization: College of Electronics and Information Technology, South China University of Technology, Guangzhou, 510641, China – sequence: 6 givenname: Yongyi surname: Yan fullname: Yan, Yongyi organization: College of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China |
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| Cites_doi | 10.1016/j.foodchem.2024.138531 10.1016/j.jfca.2024.105996 10.3390/foods10051042 10.1016/j.lwt.2024.116518 10.3389/fnut.2022.973457 10.3390/foods13233856 10.1016/j.foodchem.2024.138408 10.1177/09670335231194737 10.1016/j.foodchem.2024.141631 10.1016/j.jfca.2024.106765 10.1016/j.biosystemseng.2023.12.011 10.1016/j.saa.2024.125426 10.15586/qas.v16i1.1392 10.1016/j.foodcont.2023.110189 10.1016/j.foodchem.2021.131822 10.1016/j.tifs.2024.104377 10.1002/fsn3.2059 10.3390/foods12112192 10.1002/fsn3.4154 10.1016/j.knosys.2018.11.024 10.3390/foods13223630 10.1016/j.postharvbio.2024.112990 10.1016/j.microc.2023.109190 10.1016/j.compag.2024.109037 10.1016/j.saa.2024.125215 10.1016/j.foodchem.2022.135210 10.1080/10408398.2023.2222834 10.3390/foods13213469 10.1016/j.jfca.2024.106637 10.1016/j.foodchem.2025.142930 10.1038/s41538-025-00376-0 10.1016/j.saa.2022.120936 10.1016/j.postharvbio.2023.112706 10.1007/s11947-011-0697-1 10.1016/j.compag.2024.108730 10.3390/foods12152904 10.1016/j.jfca.2024.106700 10.1016/j.jfca.2024.106259 10.1117/1.JBO.20.3.030901 |
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| Keywords | Spectral Counterfeit identification Vis-NIR Citri Reticulatae Pericarpium Machine learning |
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| References | Fatchurrahman, Marini, Nosrati, Peruzzi, Castellano, Amodio, Colelli (b9) 2024; 13 Wilson, Nadeau, Jaworski, Tromberg, Durkin (b41) 2015; 20 Chen, Fu, Shi, Liu, Yang, Su, Lu, Zhou, He, Guo (b3) 2024; 442 Yang, Jiang, Wen, Wu, Zha, Xu, Zhang (b43) 2022; 9 Xu, Lu, He, Liang (b42) 2024; 214 Geană, Isopescu, Ciucure, Gîjiu, Joşceanu (b11) 2024; 13 Deng, Zheng, Lan, Zhang, Yun, Song (b6) 2025; 472 Shi, Peng, Chen, Qiao, Wang, Xu, Cheng (b35) 2024; 12 Wang, Qiu, Chen, Song, Zhou, Cao, Xiao, Xiao, Song (b40) 2024; 64 Guo, Yu, Hong, Cai, Xu, Gu (b15) 2023; 31 Malavi, Nikkhah, Alighaleh, Einafshar, Raes, Van Haute (b26) 2024; 157 Magwaza, Opara, Nieuwoudt, Cronje, Saeys, Nicolaï (b25) 2012; 5 Jiang, Lv, Zhong, Yang, Xu, Li, Liu, Fan, Shao, Zhang (b17) 2023; 12 Kganyago, Adjorlolo, Mhangara, Tsoeleng (b19) 2024; 218 Dhiman, Kumar (b7) 2019; 165 Li, Zhang, Zheng, Yang, Jiang, Liu, Ding, Shan (b22) 2021; 9 Chen, Li, Jia, Sun, Cui, Xu, Shi, Tang (b4) 2024; 24 Qiu, Dong, Jiang, Fan, Du, Li (b31) 2024; 205 Peng, Yu, Lu, Ye, Zhong, Hu, Chen, Song, Cai, Yin (b29) 2025; 326 Pandiselvam, Prithviraj, Manikantan, Kothakota, Rusu, Trif, Mousavi Khaneghah (b28) 2022; 9 Alighaleh, Pakdel, Ghanei Ghooshkhaneh, Einafshar, Rohani, Saeidirad (b1) 2023; 12 Dai, Wu, Zhao, Guan, Zeng, Zong, Fu, Du (b5) 2023; 408 Gul, Muzaffar, Shah, Assad, Makroo, Dar (b14) 2024; 16 Tang, Li, Huang, Zhang, Li (b38) 2024; 23 Jiang, Zhong, Xue, Lv, Zhou, Zhou, Xu, Shao, Zhang (b18) 2023; 193 Tan, Liu, Tang, Fan, Jiang, Li (b37) 2024; 13 Grassi, Jolayemi, Giovenzana, Tugnolo, Squeo, Conte, De Bruno, Flamminii, Casiraghi, Alamprese (b13) 2021; 10 Qin, Zhao, Zhou, Shi, Shou, Li, Zhang, Jiang (b30) 2024; 21 Donald, Emil (b8) 2001 Zhang, Ma, Yi, Wu (b44) 2024; 136 Ram, Oduor, Igathinathane, Howatt, Sun (b32) 2024; 222 Pan, Zhang, Xu, Yin, Gu, Yu (b27) 2022; 271 Semyalo, Kwon, Wakholi, Min, Cho (b34) 2024; 209 Li, Tang, Li, Qiu, Yang, Zhang, Zhang, Guo, Li (b21) 2025; 9 Wang, Bai, Long, Wan, Zhao, Fu, Yang (b39) 2025; 327 Ruggiero, Amalfitano, Di Vaio, Adamo (b33) 2022; 375 Cevoli, Iaccheri, Fabbri, Ragni (b2) 2024; 237 Fu, Wang, Gao, Liao, Peng, Fu, Li, Su, Guo, Shan (b10) 2025; 464 Goyal, Singha, Singh (b12) 2024; 146 Steidle Neto, Lopes (b36) 2024; 135 Jiang, Liu, Yan, Cao, Chen, Wei, Wang, Xing (b16) 2024; 132 Lanjewar, Asolkar, Parab, Morajkar (b20) 2024; 136 Luo, Xu, Xing, Yang, Sun (b24) 2024; 128 Li, Zhao, Qian, Wen, Bai, Zeng, Wang, Xian, Dong (b23) 2024; 442 Tan (10.1016/j.jfca.2025.108240_b37) 2024; 13 Guo (10.1016/j.jfca.2025.108240_b15) 2023; 31 Chen (10.1016/j.jfca.2025.108240_b3) 2024; 442 Li (10.1016/j.jfca.2025.108240_b22) 2021; 9 Li (10.1016/j.jfca.2025.108240_b23) 2024; 442 Pan (10.1016/j.jfca.2025.108240_b27) 2022; 271 Fu (10.1016/j.jfca.2025.108240_b10) 2025; 464 Luo (10.1016/j.jfca.2025.108240_b24) 2024; 128 Lanjewar (10.1016/j.jfca.2025.108240_b20) 2024; 136 Shi (10.1016/j.jfca.2025.108240_b35) 2024; 12 Steidle Neto (10.1016/j.jfca.2025.108240_b36) 2024; 135 Jiang (10.1016/j.jfca.2025.108240_b17) 2023; 12 Qiu (10.1016/j.jfca.2025.108240_b31) 2024; 205 Wilson (10.1016/j.jfca.2025.108240_b41) 2015; 20 Grassi (10.1016/j.jfca.2025.108240_b13) 2021; 10 Ram (10.1016/j.jfca.2025.108240_b32) 2024; 222 Magwaza (10.1016/j.jfca.2025.108240_b25) 2012; 5 Alighaleh (10.1016/j.jfca.2025.108240_b1) 2023; 12 Li (10.1016/j.jfca.2025.108240_b21) 2025; 9 Tang (10.1016/j.jfca.2025.108240_b38) 2024; 23 Peng (10.1016/j.jfca.2025.108240_b29) 2025; 326 Cevoli (10.1016/j.jfca.2025.108240_b2) 2024; 237 Goyal (10.1016/j.jfca.2025.108240_b12) 2024; 146 Gul (10.1016/j.jfca.2025.108240_b14) 2024; 16 Wang (10.1016/j.jfca.2025.108240_b40) 2024; 64 Jiang (10.1016/j.jfca.2025.108240_b18) 2023; 193 Geană (10.1016/j.jfca.2025.108240_b11) 2024; 13 Zhang (10.1016/j.jfca.2025.108240_b44) 2024; 136 Xu (10.1016/j.jfca.2025.108240_b42) 2024; 214 Dai (10.1016/j.jfca.2025.108240_b5) 2023; 408 Wang (10.1016/j.jfca.2025.108240_b39) 2025; 327 Malavi (10.1016/j.jfca.2025.108240_b26) 2024; 157 Yang (10.1016/j.jfca.2025.108240_b43) 2022; 9 Chen (10.1016/j.jfca.2025.108240_b4) 2024; 24 Semyalo (10.1016/j.jfca.2025.108240_b34) 2024; 209 Donald (10.1016/j.jfca.2025.108240_b8) 2001 Jiang (10.1016/j.jfca.2025.108240_b16) 2024; 132 Qin (10.1016/j.jfca.2025.108240_b30) 2024; 21 Ruggiero (10.1016/j.jfca.2025.108240_b33) 2022; 375 Fatchurrahman (10.1016/j.jfca.2025.108240_b9) 2024; 13 Dhiman (10.1016/j.jfca.2025.108240_b7) 2019; 165 Pandiselvam (10.1016/j.jfca.2025.108240_b28) 2022; 9 Deng (10.1016/j.jfca.2025.108240_b6) 2025; 472 Kganyago (10.1016/j.jfca.2025.108240_b19) 2024; 218 |
| References_xml | – volume: 12 year: 2023 ident: b17 article-title: Rapid prediction of adulteration content in atractylodis rhizoma based on data and image features fusions from near-infrared spectroscopy and hyperspectral imaging techniques publication-title: Foods – volume: 128 year: 2024 ident: b24 article-title: Principles and applications of convolutional neural network for spectral analysis in food quality evaluation: A review publication-title: J. Food Comp. Anal. – volume: 408 year: 2023 ident: b5 article-title: Classification of Pericarpium Citri Reticulatae (Chenpi) age using surface-enhanced Raman spectroscopy publication-title: Food Chem. – volume: 472 year: 2025 ident: b6 article-title: Detection of camellia oil adulteration based on near-infrared spectroscopy and smartphone combined with deep learning and multimodal fusion publication-title: Food Chem. – volume: 464 year: 2025 ident: b10 article-title: Microbes: Drivers of chenpi manufacturing, biotransformation, and physiological effects publication-title: Food Chem. – volume: 135 year: 2024 ident: b36 article-title: Chemometrics coupled with near infrared spectroscopy for detecting adulteration levels in herbal teas publication-title: J. Food Comp. Anal. – volume: 13 year: 2024 ident: b11 article-title: Honey adulteration detection via ultraviolet–Visible spectral investigation coupled with chemometric analysis publication-title: Foods – volume: 132 year: 2024 ident: b16 article-title: Hyperspectral imaging combined with spectral-imagery feature fusion convolutional neural network to discriminate different geographical origins of wolfberries publication-title: J. Food Comp. Anal. – volume: 209 year: 2024 ident: b34 article-title: Nondestructive online measurement of pineapple maturity and soluble solids content using visible and near-infrared spectral analysis publication-title: Postharvest Biology Technol. – volume: 136 year: 2024 ident: b44 article-title: Research on the adulteration of publication-title: J. Food Comp. Anal. – volume: 24 year: 2024 ident: b4 article-title: FT-NIR combined with machine learning was used to rapidly detect the adulteration of Pericarpium Citri Reticulatae (Chenpi) and predict the adulteration concentration publication-title: Food Chem.: X – volume: 165 start-page: 169 year: 2019 end-page: 196 ident: b7 article-title: Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems publication-title: Knowl.-Based Syst. – volume: 442 year: 2024 ident: b23 article-title: Recent advances in the authentication (geographical origins, varieties and aging time) of tangerine peel (Citri reticulatae pericarpium):A review publication-title: Food Chem. – volume: 16 start-page: 78 year: 2024 end-page: 97 ident: b14 article-title: Deep learning hyperspectral imaging: a rapid and reliable alternative to conventional techniques in the testing of food quality and safety publication-title: Qual. Assur. Saf. Crop. & Foods – volume: 157 year: 2024 ident: b26 article-title: Detection of saffron adulteration with crocus sativus style using NIR-hyperspectral imaging and chemometrics publication-title: Food Control – volume: 20 year: 2015 ident: b41 article-title: Review of short-wave infrared spectroscopy and imaging methods for biological tissue characterization publication-title: J. Biomed. Opt. – volume: 12 start-page: 5036 year: 2024 end-page: 5051 ident: b35 article-title: Evaluation of chemical components and quality in Xinhui Chenpi (Citrus reticulata ‘Chachi’) with two different storage times by GC–MS and UPLC publication-title: Food Sci. Nutr. – volume: 21 year: 2024 ident: b30 article-title: Application of flash GC e-nose and FT-NIR combined with deep learning algorithm in preventing age fraud and quality evaluation of pericarpium citri reticulatae publication-title: Food Chem.: X – volume: 214 year: 2024 ident: b42 article-title: Non-destructive determination of internal soluble solid content in pomelo using visible/near infrared full-transmission spectroscopy publication-title: Postharvest Biology Technol. – volume: 222 year: 2024 ident: b32 article-title: A systematic review of hyperspectral imaging in precision agriculture: Analysis of its current state and future prospects publication-title: Comput. Electron. Agric. – volume: 237 start-page: 157 year: 2024 end-page: 169 ident: b2 article-title: Data fusion of FT-NIR spectroscopy and Vis/NIR hyperspectral imaging to predict quality parameters of yellow flesh ’Jintao’ kiwifruit publication-title: Biosyst. Eng. – volume: 442 year: 2024 ident: b3 article-title: Optimizing BP neural network algorithm for Pericarpium Citri Reticulatae (Chenpi) origin traceability based on computer vision and ultra-fast gas-phase electronic nose data fusion publication-title: Food Chem. – volume: 9 start-page: 943 year: 2021 end-page: 951 ident: b22 article-title: A novel method for the nondestructive classification of different-age Citri Reticulatae Pericarpium based on data combination technique publication-title: Food Sci. Nutr. – volume: 5 start-page: 425 year: 2012 end-page: 444 ident: b25 article-title: NIR spectroscopy applications for internal and external quality analysis of citrus fruit—a review publication-title: Food Bioprocess Technol. – year: 2001 ident: b8 article-title: Handbook of Near-Infrared Analysis – volume: 9 year: 2022 ident: b28 article-title: Recent advancements in NIR spectroscopy for assessing the quality and safety of horticultural products: A comprehensive review publication-title: Front. Nutr. – volume: 31 start-page: 263 year: 2023 end-page: 270 ident: b15 article-title: On-site rapid detection of aging of Pericarpium Citri Reticulatae using multispectral imaging publication-title: J. Near Infrared Spectrosc. – volume: 12 year: 2023 ident: b1 article-title: Detection and classification of saffron adulterants by vis-nir imaging, chemical analysis, and soft computing publication-title: Foods – volume: 13 year: 2024 ident: b9 article-title: The potential application of visible-near infrared (Vis-NIR) hyperspectral imaging for classifying typical defective goji berry ( publication-title: Foods – volume: 136 year: 2024 ident: b20 article-title: Detecting starch-adulterated turmeric using Vis-NIR spectroscopy and multispectral imaging with machine learning publication-title: J. Food Comp. Anal. – volume: 375 year: 2022 ident: b33 article-title: Use of near-infrared spectroscopy combined with chemometrics for authentication and traceability of intact lemon fruits publication-title: Food Chem. – volume: 218 year: 2024 ident: b19 article-title: Optical remote sensing of crop biophysical and biochemical parameters: An overview of advances in sensor technologies and machine learning algorithms for precision agriculture publication-title: Comput. Electron. Agric. – volume: 9 start-page: 17 year: 2025 ident: b21 article-title: Geographical origin discrimination of Chenpi using machine learning and enhanced mid-level data fusion publication-title: Npj Sci. Food – volume: 13 start-page: 3856 year: 2024 ident: b37 article-title: Accurate discrimination of mold-damaged Citri Reticulatae Pericarpium using partial least-squares discriminant analysis and selected wavelengths publication-title: Foods – volume: 64 start-page: 10332 year: 2024 end-page: 10360 ident: b40 article-title: Review of recent advances on health benefits, microbial transformations, and authenticity identification of Citri reticulatae Pericarpium bioactive compounds publication-title: Crit. Rev. Food Sci. Nutr. – volume: 9 year: 2022 ident: b43 article-title: Chemical variation of Chenpi (Citrus peels) and corresponding correlated bioactive compounds by LC-MS metabolomics and multibioassay analysis publication-title: Front. Nutr. – volume: 10 start-page: 1042 year: 2021 ident: b13 article-title: Near infrared spectroscopy as a green technology for the quality prediction of intact olives publication-title: Foods – volume: 193 year: 2023 ident: b18 article-title: Data fusion based on near-infrared spectroscopy and hyperspectral imaging technology for rapid adulteration detection of Ganoderma lucidum spore powder publication-title: Microchem. J. – volume: 205 year: 2024 ident: b31 article-title: Portable near-infrared spectroscopy with variable selection-linear discriminant analysis technology for accurate and nondestructive detection of sulfur-fumigated Citri Reticulatae Pericarpium publication-title: LWT – volume: 327 year: 2025 ident: b39 article-title: Rapid qualitative and quantitative detection for adulteration of atractylodis rhizoma using hyperspectral imaging combined with chemometric methods publication-title: Spectrochim. Acta Part A: Mol. Biomol. Spectrosc. – volume: 146 year: 2024 ident: b12 article-title: Spectroscopic food adulteration detection using machine learning: Current challenges and future prospects publication-title: Trends Food Sci. Technol. – volume: 23 year: 2024 ident: b38 article-title: Accurate and visualiable discrimination of Chenpi age using 2D-CNN and grad-cam++ based on infrared spectral images publication-title: Food Chem.: X – volume: 271 year: 2022 ident: b27 article-title: Rapid on-site identification of geographical origin and storage age of tangerine peel by Near-infrared spectroscopy publication-title: Spectrochim. Acta Part A: Mol. Biomol. Spectrosc. – volume: 326 year: 2025 ident: b29 article-title: Application of handheld near infrared spectrometer in quality control of traditional Chinese medicine: Rapid screening and quantitative analysis of Lonicerae Japonicae Flos adulteration publication-title: Spectrochim. Acta Part A: Mol. Biomol. Spectrosc. – volume: 442 year: 2024 ident: 10.1016/j.jfca.2025.108240_b23 article-title: Recent advances in the authentication (geographical origins, varieties and aging time) of tangerine peel (Citri reticulatae pericarpium):A review publication-title: Food Chem. doi: 10.1016/j.foodchem.2024.138531 – volume: 128 year: 2024 ident: 10.1016/j.jfca.2025.108240_b24 article-title: Principles and applications of convolutional neural network for spectral analysis in food quality evaluation: A review publication-title: J. Food Comp. Anal. doi: 10.1016/j.jfca.2024.105996 – volume: 10 start-page: 1042 issue: 5 year: 2021 ident: 10.1016/j.jfca.2025.108240_b13 article-title: Near infrared spectroscopy as a green technology for the quality prediction of intact olives publication-title: Foods doi: 10.3390/foods10051042 – volume: 205 year: 2024 ident: 10.1016/j.jfca.2025.108240_b31 article-title: Portable near-infrared spectroscopy with variable selection-linear discriminant analysis technology for accurate and nondestructive detection of sulfur-fumigated Citri Reticulatae Pericarpium publication-title: LWT doi: 10.1016/j.lwt.2024.116518 – volume: 9 year: 2022 ident: 10.1016/j.jfca.2025.108240_b28 article-title: Recent advancements in NIR spectroscopy for assessing the quality and safety of horticultural products: A comprehensive review publication-title: Front. Nutr. doi: 10.3389/fnut.2022.973457 – volume: 13 start-page: 3856 issue: 23 year: 2024 ident: 10.1016/j.jfca.2025.108240_b37 article-title: Accurate discrimination of mold-damaged Citri Reticulatae Pericarpium using partial least-squares discriminant analysis and selected wavelengths publication-title: Foods doi: 10.3390/foods13233856 – volume: 442 year: 2024 ident: 10.1016/j.jfca.2025.108240_b3 article-title: Optimizing BP neural network algorithm for Pericarpium Citri Reticulatae (Chenpi) origin traceability based on computer vision and ultra-fast gas-phase electronic nose data fusion publication-title: Food Chem. doi: 10.1016/j.foodchem.2024.138408 – year: 2001 ident: 10.1016/j.jfca.2025.108240_b8 – volume: 31 start-page: 263 issue: 5 year: 2023 ident: 10.1016/j.jfca.2025.108240_b15 article-title: On-site rapid detection of aging of Pericarpium Citri Reticulatae using multispectral imaging publication-title: J. Near Infrared Spectrosc. doi: 10.1177/09670335231194737 – volume: 464 year: 2025 ident: 10.1016/j.jfca.2025.108240_b10 article-title: Microbes: Drivers of chenpi manufacturing, biotransformation, and physiological effects publication-title: Food Chem. doi: 10.1016/j.foodchem.2024.141631 – volume: 136 year: 2024 ident: 10.1016/j.jfca.2025.108240_b44 article-title: Research on the adulteration of Lycium barbarum based on hyperspectral imaging technology combined with deep learning algorithm publication-title: J. Food Comp. Anal. doi: 10.1016/j.jfca.2024.106765 – volume: 237 start-page: 157 year: 2024 ident: 10.1016/j.jfca.2025.108240_b2 article-title: Data fusion of FT-NIR spectroscopy and Vis/NIR hyperspectral imaging to predict quality parameters of yellow flesh ’Jintao’ kiwifruit publication-title: Biosyst. Eng. doi: 10.1016/j.biosystemseng.2023.12.011 – volume: 327 year: 2025 ident: 10.1016/j.jfca.2025.108240_b39 article-title: Rapid qualitative and quantitative detection for adulteration of atractylodis rhizoma using hyperspectral imaging combined with chemometric methods publication-title: Spectrochim. Acta Part A: Mol. Biomol. Spectrosc. doi: 10.1016/j.saa.2024.125426 – volume: 16 start-page: 78 issue: 1 year: 2024 ident: 10.1016/j.jfca.2025.108240_b14 article-title: Deep learning hyperspectral imaging: a rapid and reliable alternative to conventional techniques in the testing of food quality and safety publication-title: Qual. Assur. Saf. Crop. & Foods doi: 10.15586/qas.v16i1.1392 – volume: 157 year: 2024 ident: 10.1016/j.jfca.2025.108240_b26 article-title: Detection of saffron adulteration with crocus sativus style using NIR-hyperspectral imaging and chemometrics publication-title: Food Control doi: 10.1016/j.foodcont.2023.110189 – volume: 375 year: 2022 ident: 10.1016/j.jfca.2025.108240_b33 article-title: Use of near-infrared spectroscopy combined with chemometrics for authentication and traceability of intact lemon fruits publication-title: Food Chem. doi: 10.1016/j.foodchem.2021.131822 – volume: 146 year: 2024 ident: 10.1016/j.jfca.2025.108240_b12 article-title: Spectroscopic food adulteration detection using machine learning: Current challenges and future prospects publication-title: Trends Food Sci. Technol. doi: 10.1016/j.tifs.2024.104377 – volume: 9 start-page: 943 issue: 2 year: 2021 ident: 10.1016/j.jfca.2025.108240_b22 article-title: A novel method for the nondestructive classification of different-age Citri Reticulatae Pericarpium based on data combination technique publication-title: Food Sci. Nutr. doi: 10.1002/fsn3.2059 – volume: 12 year: 2023 ident: 10.1016/j.jfca.2025.108240_b1 article-title: Detection and classification of saffron adulterants by vis-nir imaging, chemical analysis, and soft computing publication-title: Foods doi: 10.3390/foods12112192 – volume: 12 start-page: 5036 issue: 7 year: 2024 ident: 10.1016/j.jfca.2025.108240_b35 article-title: Evaluation of chemical components and quality in Xinhui Chenpi (Citrus reticulata ‘Chachi’) with two different storage times by GC–MS and UPLC publication-title: Food Sci. Nutr. doi: 10.1002/fsn3.4154 – volume: 165 start-page: 169 year: 2019 ident: 10.1016/j.jfca.2025.108240_b7 article-title: Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2018.11.024 – volume: 13 year: 2024 ident: 10.1016/j.jfca.2025.108240_b11 article-title: Honey adulteration detection via ultraviolet–Visible spectral investigation coupled with chemometric analysis publication-title: Foods doi: 10.3390/foods13223630 – volume: 214 year: 2024 ident: 10.1016/j.jfca.2025.108240_b42 article-title: Non-destructive determination of internal soluble solid content in pomelo using visible/near infrared full-transmission spectroscopy publication-title: Postharvest Biology Technol. doi: 10.1016/j.postharvbio.2024.112990 – volume: 21 year: 2024 ident: 10.1016/j.jfca.2025.108240_b30 article-title: Application of flash GC e-nose and FT-NIR combined with deep learning algorithm in preventing age fraud and quality evaluation of pericarpium citri reticulatae publication-title: Food Chem.: X – volume: 193 year: 2023 ident: 10.1016/j.jfca.2025.108240_b18 article-title: Data fusion based on near-infrared spectroscopy and hyperspectral imaging technology for rapid adulteration detection of Ganoderma lucidum spore powder publication-title: Microchem. J. doi: 10.1016/j.microc.2023.109190 – volume: 222 year: 2024 ident: 10.1016/j.jfca.2025.108240_b32 article-title: A systematic review of hyperspectral imaging in precision agriculture: Analysis of its current state and future prospects publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2024.109037 – volume: 326 year: 2025 ident: 10.1016/j.jfca.2025.108240_b29 article-title: Application of handheld near infrared spectrometer in quality control of traditional Chinese medicine: Rapid screening and quantitative analysis of Lonicerae Japonicae Flos adulteration publication-title: Spectrochim. Acta Part A: Mol. Biomol. Spectrosc. doi: 10.1016/j.saa.2024.125215 – volume: 408 year: 2023 ident: 10.1016/j.jfca.2025.108240_b5 article-title: Classification of Pericarpium Citri Reticulatae (Chenpi) age using surface-enhanced Raman spectroscopy publication-title: Food Chem. doi: 10.1016/j.foodchem.2022.135210 – volume: 64 start-page: 10332 issue: 28 year: 2024 ident: 10.1016/j.jfca.2025.108240_b40 article-title: Review of recent advances on health benefits, microbial transformations, and authenticity identification of Citri reticulatae Pericarpium bioactive compounds publication-title: Crit. Rev. Food Sci. Nutr. doi: 10.1080/10408398.2023.2222834 – volume: 13 year: 2024 ident: 10.1016/j.jfca.2025.108240_b9 article-title: The potential application of visible-near infrared (Vis-NIR) hyperspectral imaging for classifying typical defective goji berry (Lycium barbarum L.) publication-title: Foods doi: 10.3390/foods13213469 – volume: 135 year: 2024 ident: 10.1016/j.jfca.2025.108240_b36 article-title: Chemometrics coupled with near infrared spectroscopy for detecting adulteration levels in herbal teas publication-title: J. Food Comp. Anal. doi: 10.1016/j.jfca.2024.106637 – volume: 24 year: 2024 ident: 10.1016/j.jfca.2025.108240_b4 article-title: FT-NIR combined with machine learning was used to rapidly detect the adulteration of Pericarpium Citri Reticulatae (Chenpi) and predict the adulteration concentration publication-title: Food Chem.: X – volume: 472 year: 2025 ident: 10.1016/j.jfca.2025.108240_b6 article-title: Detection of camellia oil adulteration based on near-infrared spectroscopy and smartphone combined with deep learning and multimodal fusion publication-title: Food Chem. doi: 10.1016/j.foodchem.2025.142930 – volume: 9 start-page: 17 issue: 1 year: 2025 ident: 10.1016/j.jfca.2025.108240_b21 article-title: Geographical origin discrimination of Chenpi using machine learning and enhanced mid-level data fusion publication-title: Npj Sci. Food doi: 10.1038/s41538-025-00376-0 – volume: 271 year: 2022 ident: 10.1016/j.jfca.2025.108240_b27 article-title: Rapid on-site identification of geographical origin and storage age of tangerine peel by Near-infrared spectroscopy publication-title: Spectrochim. Acta Part A: Mol. Biomol. Spectrosc. doi: 10.1016/j.saa.2022.120936 – volume: 209 year: 2024 ident: 10.1016/j.jfca.2025.108240_b34 article-title: Nondestructive online measurement of pineapple maturity and soluble solids content using visible and near-infrared spectral analysis publication-title: Postharvest Biology Technol. doi: 10.1016/j.postharvbio.2023.112706 – volume: 23 year: 2024 ident: 10.1016/j.jfca.2025.108240_b38 article-title: Accurate and visualiable discrimination of Chenpi age using 2D-CNN and grad-cam++ based on infrared spectral images publication-title: Food Chem.: X – volume: 5 start-page: 425 year: 2012 ident: 10.1016/j.jfca.2025.108240_b25 article-title: NIR spectroscopy applications for internal and external quality analysis of citrus fruit—a review publication-title: Food Bioprocess Technol. doi: 10.1007/s11947-011-0697-1 – volume: 218 year: 2024 ident: 10.1016/j.jfca.2025.108240_b19 article-title: Optical remote sensing of crop biophysical and biochemical parameters: An overview of advances in sensor technologies and machine learning algorithms for precision agriculture publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2024.108730 – volume: 9 year: 2022 ident: 10.1016/j.jfca.2025.108240_b43 article-title: Chemical variation of Chenpi (Citrus peels) and corresponding correlated bioactive compounds by LC-MS metabolomics and multibioassay analysis publication-title: Front. Nutr. – volume: 12 year: 2023 ident: 10.1016/j.jfca.2025.108240_b17 article-title: Rapid prediction of adulteration content in atractylodis rhizoma based on data and image features fusions from near-infrared spectroscopy and hyperspectral imaging techniques publication-title: Foods doi: 10.3390/foods12152904 – volume: 136 year: 2024 ident: 10.1016/j.jfca.2025.108240_b20 article-title: Detecting starch-adulterated turmeric using Vis-NIR spectroscopy and multispectral imaging with machine learning publication-title: J. Food Comp. Anal. doi: 10.1016/j.jfca.2024.106700 – volume: 132 year: 2024 ident: 10.1016/j.jfca.2025.108240_b16 article-title: Hyperspectral imaging combined with spectral-imagery feature fusion convolutional neural network to discriminate different geographical origins of wolfberries publication-title: J. Food Comp. Anal. doi: 10.1016/j.jfca.2024.106259 – volume: 20 issue: 3 year: 2015 ident: 10.1016/j.jfca.2025.108240_b41 article-title: Review of short-wave infrared spectroscopy and imaging methods for biological tissue characterization publication-title: J. Biomed. Opt. doi: 10.1117/1.JBO.20.3.030901 |
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