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Establishment of a multi-position general model for evaluation of watercore and soluble solid content in ?Fuji? apples using on-line full-transmittance visible and near infrared spectroscopy

文献类型： 外文期刊

作者： Li, Jiangbo 1 ; Zhang, Yifei 1 ; Zhang, Qian 2 ; Duan, Dandan 3 ; Chen, Liping 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

2.Beijing Technol & Business Univ, Beijing Key Lab Big Data Technol Food Safety, Beijing 100048, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

关键词： Apple; Internal quality; On-line detection; Multi-position general model; Model optimization

期刊名称：JOURNAL OF FOOD COMPOSITION AND ANALYSIS （ 影响因子：4.3； 五年影响因子：4.6 ）

ISSN： 0889-1575

年卷期： 2023 年 117 卷

页码：

收录情况： SCI

摘要： Watercore and soluble solid content (SSC) are important indicators for assessment of the internal quality of apples. Visible and near infrared spectroscopy (Vis-NIRS) has been successfully applied for on-line analysis of internal quality of apples. In practical application, the position of the detected apples is usually uncertain, which limits the effective application of traditional model developed based on the fixed position or the optimal position. This study established a multi-position general model for evaluation of watercore and SSC in 'Fuji' apples using on-line Vis-NIRS. First, the full-transmittance spectra of three positions (P1-P3) of each apple were acquired, and subsequently, LS-SVM and PLS-DA models were constructed based on full wavelength data of single and multiple positions for watercore classification, and LS-SVM and PLS models were also constructed based on the same data for the quantitative prediction of SSC. Then, the detection ability of the constructed single position and the multi -position general models for three independent position samples was compared to determine the optimal full wavelength models; Finally, different wavelength selection algorithms including MC-UVE, BOSS, SPA and their combinations were employed to optimize the full wavelength models. Results showed that the multi-position general MC-UVE-SPA-PLS-DA model was the optimal for classification of watercore apples with average accu-racy of 95.96 %, and the multi-position general MC-UV-BOSS-LS-SVM model was the optimal for quantitative detection of SSC with RP of 0.856 and RMSEP of 0.723 %. The overall study revealed that the multi-position general model, combined with full-transmittance Vis-NIRS, was a better choice for on-line detection of water -core and SSC in 'Fuji' apples.

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