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Online detection of lycopene content in the two cultivars of tomatoes by multi-point full transmission Vis-NIR spectroscopy

文献类型： 外文期刊

作者： Li, Sheng 1 ; Wang, Qingyan 1 ; Yang, Xuhai 2 ; Zhang, Qian 2 ; Shi, Ruiyao 1 ; Li, Jiangbo 1 ;

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

2.Shihezi Univ, Coll Mech & Elect Engn, Shihezi, Peoples R China

关键词： Tomato quality; Nondestructive evaluation; Chemometrics; Least angle regression; Model optimization

期刊名称：POSTHARVEST BIOLOGY AND TECHNOLOGY （ 影响因子：7.0； 五年影响因子：6.9 ）

ISSN： 0925-5214

年卷期： 2024 年 211 卷

页码：

收录情况： SCI

摘要： Lycopene content is one of the most important indicators for tomato quality evaluation. Traditional detection methods are destructive and time-consuming. This study firstly used the multi-point full transmission visible and near-infrared spectroscopy for online detection of lycopene in two cultivars of tomatoes ('Provence' and 'Jingcai No.8 '). The weighted average was applied to process multi-point spectral data. Two orientations (O1 and O2) and three preprocessing methods were considered and least angle regression (LARS) with L1 and L2 norms was used for wavelength selection. The independent partial least squares regression (PLSR) model was established. The PLSR approach combined with LARS-L1 and O2 yielded the best lycopene prediction for 'Provence' tomato (Rp = 0.96, RMSEP = 13.44 mg kg-1) and 'Jingcai No.8 ' tomato (Rp = 0.95, RMSEP = 7.43 mg kg-1). As an extension, a general model was also established and proved its feasibility. This study provides a novel methodology for accurate and rapid detection of lycopene in tomatoes.

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