Near-Infrared Spectroscopy and Machine Learning for Fast Quality Prediction of Bottle Gourd
Protein and amino acid content are the crucial quality parameters in bottle gourd, and traditional measurement methods for detecting those parameters are complicated, time-consuming, and costly. In this study, we employed NIRS along with machine learning and neural network-based methods to model and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/14/14/2503 |
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| author | Xiao Guo Hongyu Huang Haiyan Wang Chang Cai Ying Wang Xiaohua Wu Jian Wang Baogen Wang Biao Zhu Yun Xiang |
| author_facet | Xiao Guo Hongyu Huang Haiyan Wang Chang Cai Ying Wang Xiaohua Wu Jian Wang Baogen Wang Biao Zhu Yun Xiang |
| author_sort | Xiao Guo |
| collection | DOAJ |
| description | Protein and amino acid content are the crucial quality parameters in bottle gourd, and traditional measurement methods for detecting those parameters are complicated, time-consuming, and costly. In this study, we employed NIRS along with machine learning and neural network-based methods to model and predict protein and free amino acids (FAAs) of bottle gourd. Specifically, the content of protein and FAAs were measured through conventional methods. Then a near-infrared analyzer was utilized to obtain the spectral data, which were processed using multiple scattering correction (MSC) and standard normalized variate (SNV). The processed spectral data were further processed using feature importance selection to select the feature bands that had the highest correlation with protein and FAAs, respectively. The models for protein and FAAs estimation were developed using support vector regression (SVR), ridge regression (RR), random forest regression (RFR), and fully connected neural networks (FCNNs). Among them, ridge regression achieved the optimal performance, with determination coefficients (R<sup>2</sup>) of 0.96 and 0.77 on the protein and FAAs test sets, respectively, and root mean square error (RMSE) values of 0.23 and 0.5, respectively. Based on this, we developed a precise and rapid prediction model for the important quality indices of bottle gourd. |
| format | Article |
| id | doaj-art-ca342e5d844949e8ae4209049f4c9c0d |
| institution | Kabale University |
| issn | 2304-8158 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj-art-ca342e5d844949e8ae4209049f4c9c0d2025-08-20T03:58:30ZengMDPI AGFoods2304-81582025-07-011414250310.3390/foods14142503Near-Infrared Spectroscopy and Machine Learning for Fast Quality Prediction of Bottle GourdXiao Guo0Hongyu Huang1Haiyan Wang2Chang Cai3Ying Wang4Xiaohua Wu5Jian Wang6Baogen Wang7Biao Zhu8Yun Xiang9College of Horticulture Science, Zhejiang A&F University, Hangzhou 311300, ChinaInstitute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, ChinaInstitute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, ChinaInstitute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, ChinaInstitute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, ChinaInstitute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, ChinaInstitute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, ChinaInstitute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, ChinaCollege of Horticulture Science, Zhejiang A&F University, Hangzhou 311300, ChinaInstitute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, ChinaProtein and amino acid content are the crucial quality parameters in bottle gourd, and traditional measurement methods for detecting those parameters are complicated, time-consuming, and costly. In this study, we employed NIRS along with machine learning and neural network-based methods to model and predict protein and free amino acids (FAAs) of bottle gourd. Specifically, the content of protein and FAAs were measured through conventional methods. Then a near-infrared analyzer was utilized to obtain the spectral data, which were processed using multiple scattering correction (MSC) and standard normalized variate (SNV). The processed spectral data were further processed using feature importance selection to select the feature bands that had the highest correlation with protein and FAAs, respectively. The models for protein and FAAs estimation were developed using support vector regression (SVR), ridge regression (RR), random forest regression (RFR), and fully connected neural networks (FCNNs). Among them, ridge regression achieved the optimal performance, with determination coefficients (R<sup>2</sup>) of 0.96 and 0.77 on the protein and FAAs test sets, respectively, and root mean square error (RMSE) values of 0.23 and 0.5, respectively. Based on this, we developed a precise and rapid prediction model for the important quality indices of bottle gourd.https://www.mdpi.com/2304-8158/14/14/2503bottle gourdprotein and amino acid contentnear-infrared spectroscopymachine learningquality prediction |
| spellingShingle | Xiao Guo Hongyu Huang Haiyan Wang Chang Cai Ying Wang Xiaohua Wu Jian Wang Baogen Wang Biao Zhu Yun Xiang Near-Infrared Spectroscopy and Machine Learning for Fast Quality Prediction of Bottle Gourd Foods bottle gourd protein and amino acid content near-infrared spectroscopy machine learning quality prediction |
| title | Near-Infrared Spectroscopy and Machine Learning for Fast Quality Prediction of Bottle Gourd |
| title_full | Near-Infrared Spectroscopy and Machine Learning for Fast Quality Prediction of Bottle Gourd |
| title_fullStr | Near-Infrared Spectroscopy and Machine Learning for Fast Quality Prediction of Bottle Gourd |
| title_full_unstemmed | Near-Infrared Spectroscopy and Machine Learning for Fast Quality Prediction of Bottle Gourd |
| title_short | Near-Infrared Spectroscopy and Machine Learning for Fast Quality Prediction of Bottle Gourd |
| title_sort | near infrared spectroscopy and machine learning for fast quality prediction of bottle gourd |
| topic | bottle gourd protein and amino acid content near-infrared spectroscopy machine learning quality prediction |
| url | https://www.mdpi.com/2304-8158/14/14/2503 |
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