Detection of soluble solids content in tomatoes using full transmission Vis-NIR spectroscopy and combinatorial algorithms
文献类型： 外文期刊
作者： Cai, Letian 1 ; Zhang, Yizhi 1 ; Cai, Zhonglei 1 ; Shi, Ruiyao 1 ; Li, Sheng 1 ; Li, Jiangbo 1 ;
作者机构： 1.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing, Peoples R China
2.Natl Agr Intelligent Equipment Engn Technol Res Ct, Beijing, Peoples R China
关键词： tomato; online detection; feature selection; internal quality assessment; soluble solids content
期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：4.8； 五年影响因子：5.7 ）
ISSN： 1664-462X
年卷期： 2024 年 15 卷
页码：
收录情况： SCI
摘要： Introduction Soluble solids content (SSC) is an important indicator for evaluating tomato flavor, and general physical and chemical methods are time-consuming and destructive.Methods This study utilized full transmittance visible and near infrared (Vis-NIR) spectroscopy for multi-posed data acquisition of tomatoes in different orientations. The role of two directions (Z1 and Z2) and four preprocessing techniques, as well as three wavelength selection methods in the exploitation of SSC regression models was investigated.Results After using the Outlier elimination method, the spectra acquired in the Z2 direction and the raw spectral data processed by preprocessing methods gave the best result by the PLSR model (Rp = 0.877, RMSEP = 0.417 %). Compared to the model built using the full 2048 spectral wavelengths, the prediction accuracy using 20 wavelengths obtained by a combination wavelength selection: backward variable selection - partial least squares and simulated annealing (BVS-PLS and SA) was further improved (Rp = 0.912, RMSEP = 0.354 %).Discussion The findings of this research demonstrate the efficacy of full-transmission visible-near infrared (Vis-NIR) spectroscopy in forecasting SSC of tomatoes, and most importantly, the combination of the packing method in wavelength selection with an intelligent optimization algorithm provides a viable idea for accurately and rapidly assessing the SSC of tomatoes.
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