{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T18:24:35Z","timestamp":1783016675561,"version":"3.54.6"},"reference-count":35,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,20]],"date-time":"2022-02-20T00:00:00Z","timestamp":1645315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Plan Projects of Sichuan Province","award":["2018GZDZX0045, 22ZDYF0891(application number),  2020ZHCG0040"],"award-info":[{"award-number":["2018GZDZX0045, 22ZDYF0891(application number),  2020ZHCG0040"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Quality identification of multi-component mixtures is essential for production process control. Artificial sensory evaluation is a conventional quality evaluation method of multi-component mixture, which is easily affected by human subjective factors, and its results are inaccurate and unstable. This study developed a near-infrared (NIR) spectral characteristic extraction method based on a three-dimensional analysis space and establishes a high-accuracy qualitative identification model. First, the Norris derivative filtering algorithm was used in the pre-processing of the NIR spectrum to obtain a smooth main absorption peak. Then, the third-order tensor robust principal component analysis (TRPCA) algorithm was used for characteristic extraction, which effectively reduced the dimensionality of the raw NIR spectral data. Finally, on this basis, a qualitative identification model based on support vector machines (SVM) was constructed, and the classification accuracy reached 98.94%. Therefore, it is possible to develop a non-destructive, rapid qualitative detection system based on NIR spectroscopy to mine the subtle differences between classes and to use low-dimensional characteristic wavebands to detect the quality of complex multi-component mixtures. This method can be a key component of automatic quality control in the production of multi-component products.<\/jats:p>","DOI":"10.3390\/s22041654","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T08:34:47Z","timestamp":1645432487000},"page":"1654","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Near-Infrared Spectral Characteristic Extraction and Qualitative Analysis Method for Complex Multi-Component Mixtures Based on TRPCA-SVM"],"prefix":"10.3390","volume":"22","author":[{"given":"Guiyu","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information Engineering, Southwest University of Science and Technology, No. 59 Qinglong Road, Mianyang 621010, China"},{"name":"School of Automation & Information Engineering, Sichuan University of Science & Engineering, No. 1 Baita Road, Yibin 644000, China"},{"name":"Artificial Intelligence Key Laboratory of Sichuan Province, No. 1 Baita Road, Yibin 644000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianguo","family":"Tuo","sequence":"additional","affiliation":[{"name":"School of Automation & Information Engineering, Sichuan University of Science & Engineering, No. 1 Baita Road, Yibin 644000, China"},{"name":"Artificial Intelligence Key Laboratory of Sichuan Province, No. 1 Baita Road, Yibin 644000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang","family":"Zhai","sequence":"additional","affiliation":[{"name":"School of Automation & Information Engineering, Sichuan University of Science & Engineering, No. 1 Baita Road, Yibin 644000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuemei","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Automation & Information Engineering, Sichuan University of Science & Engineering, No. 1 Baita Road, Yibin 644000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Automation & Information Engineering, Sichuan University of Science & Engineering, No. 1 Baita Road, Yibin 644000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianglin","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Automation & Information Engineering, Sichuan University of Science & Engineering, No. 1 Baita Road, Yibin 644000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1021\/acs.analchem.0c04291","article-title":"96-Well Microtiter Plate Made of Paper: A Printed Chemosensor Array for Quantitative Detection of Saccharides","volume":"93","author":"Lyu","year":"2021","journal-title":"Anal. Chem."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7769","DOI":"10.1016\/j.tetlet.2007.09.032","article-title":"A quinoline\u2013polyamine conjugate as a fluorescent chemosensor for quantitative detection of Zn(II) in water","volume":"48","author":"Shiraishi","year":"2007","journal-title":"Tetrahedron Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"040501","DOI":"10.1117\/1.JBO.25.4.040501","article-title":"Quantitative spectral quality assessment technique validated using intraoperative in vivo Raman spectroscopy measurements","volume":"25","author":"Dallaire","year":"2020","journal-title":"J. Biomed. Opt."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"101","DOI":"10.3788\/COL202018.043001","article-title":"Rapid quantitative detection of mineral oil contamination in vegetable oil by near-infrared spectroscopy","volume":"18","author":"Zhao","year":"2020","journal-title":"Chin. Opt. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"946","DOI":"10.1021\/ac00032a020","article-title":"Heuristic evolving latent projections resolving two-way multicomponent data. 2. Detection and resolution of minor constituents","volume":"64","author":"Liang","year":"1992","journal-title":"Anal. Chem."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1021\/ac00279a020","article-title":"Spectrophotometric multicomponent analysis applied to trace metal determinations","volume":"57","author":"Otto","year":"1985","journal-title":"Anal. Chem."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6727","DOI":"10.1364\/AO.55.006727","article-title":"Design and daytime performance of laser-induced fluorescence spectrum lidar for simultaneous detection of multiple components, dissolved organic matter, phycocyanin, and chlorophyll in river water","volume":"55","author":"Saito","year":"2016","journal-title":"Appl. Opt."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2555","DOI":"10.4315\/0362-028X-67.11.2555","article-title":"Detection and Identification of Bacteria in a Juice Matrix with Fourier Transform\u2013Near Infrared Spectroscopy and Multivariate Analysis","volume":"67","author":"Khambaty","year":"2004","journal-title":"J. Food Prot."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"20190386","DOI":"10.1515\/ijfe-2019-0386","article-title":"Functional principal component analysis for near-infrared spectral data: A case study on Tricholoma matsutakeis","volume":"16","author":"Li","year":"2020","journal-title":"Int. J. Food Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"193","DOI":"10.4103\/0973-1296.196310","article-title":"Quality-by-Design: Multivariate Model for Multicomponent Quantification in Refining Process of Honey","volume":"13","author":"Li","year":"2017","journal-title":"Pharmacogn. Mag."},{"key":"ref_11","first-page":"1099","article-title":"Multiproduct, Multicomponent and Multivariate Calibration: A Case Study by Using Vis-NIR Spectroscopy","volume":"11","author":"Santos","year":"2017","journal-title":"Food Anal. Methods"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1007\/s10812-016-0368-0","article-title":"Near Infrared Spectrometry of Clinically Significant Fatty Acids Using Multicomponent Regression","volume":"83","author":"Kalinin","year":"2016","journal-title":"J. Appl. Spectrosc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.foodchem.2014.07.150","article-title":"Development of a method for identification and accurate quantitation of aroma compounds in Chinese Daohuaxiang liquors based on SPME using a sol\u2013gel fibre","volume":"169","author":"Wang","year":"2015","journal-title":"Food Chem."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4096","DOI":"10.1111\/1750-3841.15536","article-title":"Basic flavor types and component characteristics of Chinese traditional liquors: A review","volume":"85","author":"Wei","year":"2020","journal-title":"J. Food Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1906","DOI":"10.1039\/C4AY02580F","article-title":"Analysis of Volatile Compounds in Chinese Laobaigan Liquor using Headspace Solid-phase Microextraction Coupled with GC-MS","volume":"7","author":"Du","year":"2015","journal-title":"Anal. Methods"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1002\/jib.443","article-title":"Rapid quantitative analysis of Chinese Gu-Jing-Gong spirit for its quality control","volume":"123","author":"Zhang","year":"2017","journal-title":"J. Inst. Brew."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1016\/j.addr.2005.01.020","article-title":"Near-infrared spectroscopy and imaging: Basic principles and pharmaceutical applications","volume":"57","author":"Reich","year":"2005","journal-title":"Adv. Drug Deliv. Rev."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103687","DOI":"10.1016\/j.infrared.2021.103687","article-title":"Non-invasive detection of medicines and edible products by direct measurement through vials using near-infrared spectroscopy: A review","volume":"115","author":"Cui","year":"2021","journal-title":"Infrared Phys. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.foodchem.2017.11.015","article-title":"New PLS analysis approach to wine volatile compounds characterization by near infrared spectroscopy (NIR)","volume":"246","author":"Genisheva","year":"2018","journal-title":"Food Chem."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.foodchem.2018.09.116","article-title":"Predicting calcium in grape must and base wine by FT-NIR spectroscopy","volume":"276","author":"Vestia","year":"2018","journal-title":"Food Chem."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.jfoodeng.2008.05.011","article-title":"A feasibility study on the use of a miniature fiber optic NIR spectrometer for the prediction of volumic mass and reducing sugars in white wine fermentations","volume":"89","author":"Morales","year":"2008","journal-title":"J. Food Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1080\/19476337.2014.908955","article-title":"Application of artificial neural networks coupled to UV\u2013VIS\u2013NIR spectroscopy for the rapid quantification of wine compounds in aqueous mixtures","volume":"13","year":"2015","journal-title":"CyTA-J. Food"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.aca.2006.05.007","article-title":"Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration","volume":"572","author":"Chen","year":"2006","journal-title":"Anal. Chim. Acta"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.foodcont.2018.01.031","article-title":"NIR spectroscopy and chemometrics for the typification of Spanish wine vinegars with a protected designation of origin","volume":"89","author":"Amigo","year":"2018","journal-title":"Food Control"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1007\/s12161-013-9755-9","article-title":"Application of Vis\/NIR spectroscopy for Chinese liquor discrimination","volume":"7","author":"Li","year":"2014","journal-title":"Food Anal. Methods"},{"key":"ref_26","first-page":"2000","article-title":"Classification and Identification of Plant Fibrous Material with Different Species Using near Infrared Technique\u2014A New Way to Approach Determining Biomass Properties Accurately within Different Species","volume":"7","author":"Wei","year":"2016","journal-title":"Front. Plant Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1177\/0003702816685569","article-title":"A Rapid Identification Method for Calamine Using Near-Infrared Spectroscopy Based on Multi-Reference Correlation Coefficient Method and Back Propagation Artificial Neural Network","volume":"71","author":"Sun","year":"2017","journal-title":"Appl. Spectrosc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"S18","DOI":"10.1111\/j.1365-2621.2006.tb12400.x","article-title":"Rapid Near Infrared Spectroscopic Method for the Detection of Spoilage in Rainbow Trout (Oncorhynchus mykiss) Fillet","volume":"71","author":"Lin","year":"2006","journal-title":"J. Food Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"100378","DOI":"10.1016\/j.cosrev.2021.100378","article-title":"Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE)","volume":"40","author":"Anowar","year":"2021","journal-title":"Comput. Sci. Rev."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lu, C.Y., Feng, J.S., Chen, Y.D., Liu, W., Lin, Z., and Yan, S. (July, January 26). Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.567"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cai, S.T., Luo, Q.L., Yang, M., Li, W., and Xiao, M. (2019). Tensor Robust Principal Component Analysis via Non-Convex Low Rank Approximation. Appl. Sci., 9.","DOI":"10.3390\/app9071411"},{"key":"ref_32","unstructured":"Driggs, D., Becker, S., and Boyd-Graber, J. (2019). Tensor Robust Principal Component Analysis: Better recovery with atomic norm regularization. arXiv."},{"key":"ref_33","unstructured":"Bai, J.S., and Feng, J.L. (2019). Robust Principal Component Analysis with Non-Sparse Errors. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"39671","DOI":"10.1038\/srep39671","article-title":"Characterization of Chinese liquor aroma components during aging process and liquor age discrimination using gas chromatography combined with multivariable statistics","volume":"7","author":"Xu","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1080\/05704928608060440","article-title":"Near-Infrared Analysis (NIRA): A Technology for Quantitative and Qualitative Analysis","volume":"22","author":"Stark","year":"1986","journal-title":"Appl. Spectrosc. Rev."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/4\/1654\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:23:28Z","timestamp":1760135008000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/4\/1654"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,20]]},"references-count":35,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22041654"],"URL":"https:\/\/doi.org\/10.3390\/s22041654","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,20]]}}}