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Determination of soluble solids content of multiple varieties of tomatoes by full transmission visible-near infrared spectroscopy

文献类型： 外文期刊

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

作者机构： 1.Shihezi Univ, Coll Mech & Elect Engn, Shihezi, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing, Peoples R China

3.Xinjiang Prod & Construct Corps Key Lab Modern Agr, Shihezi, Peoples R China

4.Minist Agr & Rural Affairs, Key Lab Northwest Agr Equipment, Shihezi, Peoples R China

5.Minist Educ, Engn Res Ctr Prod Mechanizat Oasis Characterist Ca, Shihezi, Peoples R China

关键词： tomato; soluble solids content; online detection; full transmission; quantitative analysis model

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：4.1； 五年影响因子：5.3 ）

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 a pivotal parameter for assessing tomato quality. Traditional measurement methods are both destructive and time-consuming.Methods To enhance accuracy and efficiency in SSC assessment, this study employs full transmission visible and near-infrared (Vis-NIR) spectroscopy and multi-point spectral data collection techniques to quantitatively analyze SSC in two tomato varieties ('Provence' and 'Jingcai No.8' tomatoes). Preprocessing of the multi-point spectra is carried out using a weighted averaging approach, aimed at noise reduction, signal-to-noise ratio improvement, and overall data quality enhancement. Taking into account the potential influence of various detection orientations and preprocessing methods on model outcomes, we investigate the combination of partial least squares regression (PLSR) with two orientations (O1 and O2) and two preprocessing techniques (Savitzky-Golay smoothing (SG) and Standard Normal Variate transformation (SNV)) in the development of SSC prediction models.Results The model achieved the best results in the O2 orientation and SNV pretreatment as follows: 'Provence' tomato (Rp = 0.81, RMSEP = 0.69 degrees Brix) and 'Jingcai No.8' tomatoes (Rp = 0.84, RMSEP = 0.64 degrees Brix). To further optimize the model, characteristic wavelength selection is introduced through Least Angle Regression (LARS) with L1 and L2 regularization. Notably, when lambda=0.004, LARS-L1 produces superior results ('Provence' tomato: Rp = 0.95, RMSEP = 0.35 degrees Brix; 'Jingcai No.8' tomato: Rp = 0.96, RMSEP = 0.33 degrees Brix).Discussion This study underscores the effectiveness of full transmission Vis-NIR spectroscopy in predicting SSC in different tomato varieties, offering a viable method for accurate and swift SSC assessment in tomatoes.

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