Comprehensive Multi-indicator Prediction Model for Storage Quality of Multi-cultivar Kiwifruit Based on Visible-Near Infrared Spectroscopy
Multi-cultivar modeling using visible-near infrared spectroscopy (Vis-NIR) was explored for the rapid non-destructive detection of the internal quality of kiwifruit during storage. In this study, 'Hayward' 'Jin Tao' and 'Xu Xiang' kiwifruit were used as the experimental...
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The editorial department of Science and Technology of Food Industry
2025-07-01
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| Series: | Shipin gongye ke-ji |
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| Online Access: | http://www.spgykj.com/cn/article/doi/10.13386/j.issn1002-0306.2024070318 |
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| author | Zizhao LIANG Xin LI Pu LIU Wenqiang GUAN Ming LI |
| author_facet | Zizhao LIANG Xin LI Pu LIU Wenqiang GUAN Ming LI |
| author_sort | Zizhao LIANG |
| collection | DOAJ |
| description | Multi-cultivar modeling using visible-near infrared spectroscopy (Vis-NIR) was explored for the rapid non-destructive detection of the internal quality of kiwifruit during storage. In this study, 'Hayward' 'Jin Tao' and 'Xu Xiang' kiwifruit were used as the experimental subjects to assess changes in hardness, soluble solids, titratable acid, and flesh color under different storage times. Spectral data were collected at wavelengths ranging from 592~1102 nm. After the use of different preprocessing algorithms, such as first-order derivatives (FD), standard normal variate (SNV), second-order derivatives, convolutional smoothing, and FD+SNV, the data were combined with competitive adaptive reweighted sampling (CARS) for feature wavelength selection. A quality prediction model based on partial least squares (PLS) and multiple linear regression (MLR) was developed for kiwifruit physicochemical indices. The results showed that the FD and SNV-preprocessed models had the highest prediction accuracies. The relative prediction deviations (RPDs) of SSC in the single-cultivar model all exceeded 2.3. For hardness, while the RPD of 'Xu Xiang' was 1.8, those of other cultivars exceeded 2.3. CARS was used to extract the 600~700, 930~990, and 1000~1100 nm bands with high correlation. The PLS model predicted relatively better performance than the MLR model for each indicator. The establishment of a generalized model for mixed cultivars resulted in significantly enhanced predictive performance with FD+SNV combined with preprocessing, yielding RPDs of 2.280, 2.183, and 3.425 for the SSC, TA, and a* models, respectively, which demonstrated superior accuracy compared to the single-cultivar model. These findings indicate that Vis-NIR spectroscopy can be used for the quantitative detection of internal quality of kiwifruit during storage, providing a basis and reference for the application of non-destructive testing technology in kiwifruit. |
| format | Article |
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| institution | DOAJ |
| issn | 1002-0306 |
| language | zho |
| publishDate | 2025-07-01 |
| publisher | The editorial department of Science and Technology of Food Industry |
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| series | Shipin gongye ke-ji |
| spelling | doaj-art-e97fc36bd0ec48beb78fe939acbe7b342025-08-20T03:16:17ZzhoThe editorial department of Science and Technology of Food IndustryShipin gongye ke-ji1002-03062025-07-01461328229110.13386/j.issn1002-0306.20240703182024070318-13Comprehensive Multi-indicator Prediction Model for Storage Quality of Multi-cultivar Kiwifruit Based on Visible-Near Infrared SpectroscopyZizhao LIANG0Xin LI1Pu LIU2Wenqiang GUAN3Ming LI4Tianjin Key Laboratory of Food Biotechnology, School of Biotechnology and Food Science, Tianjin University of Commerce, Tianjin 300134, ChinaTianjin Key Laboratory of Food Biotechnology, School of Biotechnology and Food Science, Tianjin University of Commerce, Tianjin 300134, ChinaHenan Guoran Fengqing Fruit Industry Co., Ltd., Nanyang 474550, ChinaTianjin Key Laboratory of Food Biotechnology, School of Biotechnology and Food Science, Tianjin University of Commerce, Tianjin 300134, ChinaTianjin Key Laboratory of Food Biotechnology, School of Biotechnology and Food Science, Tianjin University of Commerce, Tianjin 300134, ChinaMulti-cultivar modeling using visible-near infrared spectroscopy (Vis-NIR) was explored for the rapid non-destructive detection of the internal quality of kiwifruit during storage. In this study, 'Hayward' 'Jin Tao' and 'Xu Xiang' kiwifruit were used as the experimental subjects to assess changes in hardness, soluble solids, titratable acid, and flesh color under different storage times. Spectral data were collected at wavelengths ranging from 592~1102 nm. After the use of different preprocessing algorithms, such as first-order derivatives (FD), standard normal variate (SNV), second-order derivatives, convolutional smoothing, and FD+SNV, the data were combined with competitive adaptive reweighted sampling (CARS) for feature wavelength selection. A quality prediction model based on partial least squares (PLS) and multiple linear regression (MLR) was developed for kiwifruit physicochemical indices. The results showed that the FD and SNV-preprocessed models had the highest prediction accuracies. The relative prediction deviations (RPDs) of SSC in the single-cultivar model all exceeded 2.3. For hardness, while the RPD of 'Xu Xiang' was 1.8, those of other cultivars exceeded 2.3. CARS was used to extract the 600~700, 930~990, and 1000~1100 nm bands with high correlation. The PLS model predicted relatively better performance than the MLR model for each indicator. The establishment of a generalized model for mixed cultivars resulted in significantly enhanced predictive performance with FD+SNV combined with preprocessing, yielding RPDs of 2.280, 2.183, and 3.425 for the SSC, TA, and a* models, respectively, which demonstrated superior accuracy compared to the single-cultivar model. These findings indicate that Vis-NIR spectroscopy can be used for the quantitative detection of internal quality of kiwifruit during storage, providing a basis and reference for the application of non-destructive testing technology in kiwifruit.http://www.spgykj.com/cn/article/doi/10.13386/j.issn1002-0306.2024070318kiwifruitvisible-near infrared spectroscopystored qualityprediction model |
| spellingShingle | Zizhao LIANG Xin LI Pu LIU Wenqiang GUAN Ming LI Comprehensive Multi-indicator Prediction Model for Storage Quality of Multi-cultivar Kiwifruit Based on Visible-Near Infrared Spectroscopy Shipin gongye ke-ji kiwifruit visible-near infrared spectroscopy stored quality prediction model |
| title | Comprehensive Multi-indicator Prediction Model for Storage Quality of Multi-cultivar Kiwifruit Based on Visible-Near Infrared Spectroscopy |
| title_full | Comprehensive Multi-indicator Prediction Model for Storage Quality of Multi-cultivar Kiwifruit Based on Visible-Near Infrared Spectroscopy |
| title_fullStr | Comprehensive Multi-indicator Prediction Model for Storage Quality of Multi-cultivar Kiwifruit Based on Visible-Near Infrared Spectroscopy |
| title_full_unstemmed | Comprehensive Multi-indicator Prediction Model for Storage Quality of Multi-cultivar Kiwifruit Based on Visible-Near Infrared Spectroscopy |
| title_short | Comprehensive Multi-indicator Prediction Model for Storage Quality of Multi-cultivar Kiwifruit Based on Visible-Near Infrared Spectroscopy |
| title_sort | comprehensive multi indicator prediction model for storage quality of multi cultivar kiwifruit based on visible near infrared spectroscopy |
| topic | kiwifruit visible-near infrared spectroscopy stored quality prediction model |
| url | http://www.spgykj.com/cn/article/doi/10.13386/j.issn1002-0306.2024070318 |
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