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DOI10.1038/s41598-024-59151-y
Quantitative measurement of internal quality of carrots using hyperspectral imaging and multivariate analysis
发表日期2024
ISSN2045-2322
起始页码14
结束页码1
卷号14期号:1
英文摘要The study aimed to measure the carotenoid (Car) and pH contents of carrots using hyperspectral imaging. A total of 300 images were collected using a hyperspectral imaging system, covering 472 wavebands from 400 to 1000 nm. Regions of interest (ROIs) were defined to extract average spectra from the hyperspectral images (HIS). We developed two models: least squares support vector machine (LS-SVM) and partial least squares regression (PLSR) to establish a quantitative analysis between the pigment amounts and spectra. The spectra and pigment contents were predicted and correlated using these models. The selection of EWs for modeling was done using the Successive Projections Algorithm (SPA), regression coefficients (RC) from PLSR models, and LS-SVM. The results demonstrated that hyperspectral imaging could effectively evaluate the internal attributes of carrot cortex and xylem. Moreover, these models accurately predicted the Car and pH contents of the carrot parts. This study provides a valuable approach for variable selection and modeling in hyperspectral imaging studies of carrots.
英文关键词Hyperspectral imaging; Internal attribute evaluation; Carrot; Variable selection; Quantitative analysis model
语种英语
WOS研究方向Science & Technology - Other Topics
WOS类目Multidisciplinary Sciences
WOS记录号WOS:001201873900035
来源期刊SCIENTIFIC REPORTS
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/293565
作者单位Fujian Agriculture & Forestry University; Fujian Jiangxia University
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GB/T 7714
. Quantitative measurement of internal quality of carrots using hyperspectral imaging and multivariate analysis[J],2024,14(1).
APA (2024).Quantitative measurement of internal quality of carrots using hyperspectral imaging and multivariate analysis.SCIENTIFIC REPORTS,14(1).
MLA "Quantitative measurement of internal quality of carrots using hyperspectral imaging and multivariate analysis".SCIENTIFIC REPORTS 14.1(2024).
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