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

dc.contributor.authorSheng Li
dc.contributor.authorSheng Li
dc.contributor.authorSheng Li
dc.contributor.authorSheng Li
dc.contributor.authorSheng Li
dc.contributor.authorJiangbo Li
dc.contributor.authorQingyan Wang
dc.contributor.authorRuiyao Shi
dc.contributor.authorXuhai Yang
dc.contributor.authorXuhai Yang
dc.contributor.authorXuhai Yang
dc.contributor.authorXuhai Yang
dc.contributor.authorQian Zhang
dc.contributor.authorQian Zhang
dc.contributor.authorQian Zhang
dc.contributor.authorQian Zhang
dc.contributor.otherCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
dc.contributor.otherIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
dc.contributor.otherXinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi, China
dc.contributor.otherKey Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China
dc.contributor.otherEngineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi, China
dc.contributor.otherIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
dc.contributor.otherIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
dc.contributor.otherIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
dc.contributor.otherCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
dc.contributor.otherXinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi, China
dc.contributor.otherKey Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China
dc.contributor.otherEngineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi, China
dc.contributor.otherCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
dc.contributor.otherXinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi, China
dc.contributor.otherKey Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China
dc.contributor.otherEngineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi, China
dc.date.accessioned2024-01-23T04:31:58Z
dc.date.accessioned2025-10-08T08:47:08Z
dc.date.available2025-10-08T08:47:08Z
dc.date.issued01-01-2024
dc.identifier.urihttp://digilib.fisipol.ugm.ac.id/repo/handle/15717717/37199
dc.description.abstractIntroductionSoluble solids content (SSC) is a pivotal parameter for assessing tomato quality. Traditional measurement methods are both destructive and time-consuming.MethodsTo 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.ResultsThe model achieved the best results in the O2 orientation and SNV pretreatment as follows: ‘Provence’ tomato (Rp = 0.81, RMSEP = 0.69°Brix) and ‘Jingcai No.8’ tomatoes (Rp = 0.84, RMSEP = 0.64°Brix). To further optimize the model, characteristic wavelength selection is introduced through Least Angle Regression (LARS) with L1 and L2 regularization. Notably, when λ=0.004, LARS-L1 produces superior results (‘Provence’ tomato: Rp = 0.95, RMSEP = 0.35°Brix; ‘Jingcai No.8’ tomato: Rp = 0.96, RMSEP = 0.33°Brix).DiscussionThis 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.
dc.language.isoEN
dc.publisherFrontiers Media S.A.
dc.subject.lccPlant culture
dc.titleDetermination of soluble solids content of multiple varieties of tomatoes by full transmission visible-near infrared spectroscopy
dc.typeArticle
dc.description.keywordstomato
dc.description.keywordssoluble solids content
dc.description.keywordsonline detection
dc.description.keywordsfull transmission
dc.description.keywordsquantitative analysis model
dc.description.doi10.3389/fpls.2024.1324753
dc.title.journalFrontiers in Plant Science
dc.identifier.e-issn1664-462X
dc.identifier.oaioai:doaj.org/journal:877ca821aa8c455e8c4a3b8f119e4245


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