Determination of sugar in tangerines by fluorescence with an Improved partial least squares (PLS) algorithm
The accurate determination of sugar content in tangerines plays a pivotal role in assessing their quality, nutritional value, and marketability. Traditional methods for sugar quantification often involve time-consuming and resource-intensive processes. In this paper, we introduce a novel approach fo...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2024-12-01
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| Series: | Measurement + Control |
| Online Access: | https://doi.org/10.1177/00202940241240896 |
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| author | Lei Liu Chunzhong Li Haiyi Bian Ahmed N Abdalla Hua Yao Wen Li |
| author_facet | Lei Liu Chunzhong Li Haiyi Bian Ahmed N Abdalla Hua Yao Wen Li |
| author_sort | Lei Liu |
| collection | DOAJ |
| description | The accurate determination of sugar content in tangerines plays a pivotal role in assessing their quality, nutritional value, and marketability. Traditional methods for sugar quantification often involve time-consuming and resource-intensive processes. In this paper, we introduce a novel approach for sugar determination in tangerines utilizing fluorescence spectroscopy in conjunction with an improved Partial Least Squares (iPLS) algorithm. A robust testing model was developed, incorporating a diverse dataset of tangerine samples with known sugar concentrations. Fluorescence spectra were acquired for 80 samples, of which 37 were used to build the iPLS model and were considered as the training dataset. The remaining 43 samples served as the validation dataset and were used to show the model’s efficacy. The training dataset was evaluated using cross-validation, and F -values were computed to determine how many main components should be utilized to build the model. The result approved validation dataset’s R -square and root-mean-square error were 0.9777 and 0.002992, respectively. These findings open the door to broader applications in the citrus industry and beyond, with the potential for automating the analysis process and improving overall quality control. |
| format | Article |
| id | doaj-art-dd66e3d0b04c43b58e0b40d1f42bcc0b |
| institution | OA Journals |
| issn | 0020-2940 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Measurement + Control |
| spelling | doaj-art-dd66e3d0b04c43b58e0b40d1f42bcc0b2025-08-20T02:13:54ZengSAGE PublishingMeasurement + Control0020-29402024-12-015710.1177/00202940241240896Determination of sugar in tangerines by fluorescence with an Improved partial least squares (PLS) algorithmLei Liu0Chunzhong Li1Haiyi Bian2Ahmed N Abdalla3Hua Yao4Wen Li5Jiangsu Engineering Laboratory for Lake Environment Remote Sensing Technologies, Huai’an, Jiangsu, ChinaFaculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu, ChinaJiangsu Engineering Laboratory for Lake Environment Remote Sensing Technologies, Huai’an, Jiangsu, ChinaFaculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu, ChinaFaculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu, ChinaFaculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu, ChinaThe accurate determination of sugar content in tangerines plays a pivotal role in assessing their quality, nutritional value, and marketability. Traditional methods for sugar quantification often involve time-consuming and resource-intensive processes. In this paper, we introduce a novel approach for sugar determination in tangerines utilizing fluorescence spectroscopy in conjunction with an improved Partial Least Squares (iPLS) algorithm. A robust testing model was developed, incorporating a diverse dataset of tangerine samples with known sugar concentrations. Fluorescence spectra were acquired for 80 samples, of which 37 were used to build the iPLS model and were considered as the training dataset. The remaining 43 samples served as the validation dataset and were used to show the model’s efficacy. The training dataset was evaluated using cross-validation, and F -values were computed to determine how many main components should be utilized to build the model. The result approved validation dataset’s R -square and root-mean-square error were 0.9777 and 0.002992, respectively. These findings open the door to broader applications in the citrus industry and beyond, with the potential for automating the analysis process and improving overall quality control.https://doi.org/10.1177/00202940241240896 |
| spellingShingle | Lei Liu Chunzhong Li Haiyi Bian Ahmed N Abdalla Hua Yao Wen Li Determination of sugar in tangerines by fluorescence with an Improved partial least squares (PLS) algorithm Measurement + Control |
| title | Determination of sugar in tangerines by fluorescence with an Improved partial least squares (PLS) algorithm |
| title_full | Determination of sugar in tangerines by fluorescence with an Improved partial least squares (PLS) algorithm |
| title_fullStr | Determination of sugar in tangerines by fluorescence with an Improved partial least squares (PLS) algorithm |
| title_full_unstemmed | Determination of sugar in tangerines by fluorescence with an Improved partial least squares (PLS) algorithm |
| title_short | Determination of sugar in tangerines by fluorescence with an Improved partial least squares (PLS) algorithm |
| title_sort | determination of sugar in tangerines by fluorescence with an improved partial least squares pls algorithm |
| url | https://doi.org/10.1177/00202940241240896 |
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