Machine learning-enhanced near-infrared spectroscopy for high-throughput phenotyping of sweetpotato sugars across raw and cooked states
Sweetpotato is a major root crop with high yield and nutritional benefits. However, existing methods for evaluating sugars level are inefficient, limiting the breeding and processing of high-quality varieties. This study utilized near-infrared spectroscopy (NIRS) coupled with machine learning algori...
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Elsevier
2025-06-01
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| Series: | Journal of Agriculture and Food Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666154325003059 |
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| author | Chaochen Tang Xueying Mo Zhimin Ma Zhangying Wang |
| author_facet | Chaochen Tang Xueying Mo Zhimin Ma Zhangying Wang |
| author_sort | Chaochen Tang |
| collection | DOAJ |
| description | Sweetpotato is a major root crop with high yield and nutritional benefits. However, existing methods for evaluating sugars level are inefficient, limiting the breeding and processing of high-quality varieties. This study utilized near-infrared spectroscopy (NIRS) coupled with machine learning algorithms to develop a high-throughput assay for fructose, glucose, sucrose, and maltose in sweetpotatoes across their raw, steamed, and baked states. Leveraging representative samples, characteristic spectral variables, and advanced learning algorithms, twelve optimal models were established for the four sugar indicators under three processing states. These models exhibited outstanding performance in calibration (R2C: 0.941–0.984), cross-validation (R2CV: 0.926–0.976), external validation (R2V: 0.898–0.971), and the ratio of prediction to deviation (RPD: 5.83–10.3), confirming their robust predictive capacity. The findings suggest that these machine learning-enhanced NIRS models enable rapid, high-throughput analysis of sweetpotato sugars, significantly benefiting both breeding programs and food processing applications. |
| format | Article |
| id | doaj-art-bc2ff844dffa41bab803f4ff02a02fc6 |
| institution | DOAJ |
| issn | 2666-1543 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Agriculture and Food Research |
| spelling | doaj-art-bc2ff844dffa41bab803f4ff02a02fc62025-08-20T03:10:25ZengElsevierJournal of Agriculture and Food Research2666-15432025-06-012110193410.1016/j.jafr.2025.101934Machine learning-enhanced near-infrared spectroscopy for high-throughput phenotyping of sweetpotato sugars across raw and cooked statesChaochen Tang0Xueying Mo1Zhimin Ma2Zhangying Wang3Crops Research Institute, Guangdong Academy of Agricultural Sciences & Key Laboratory of Crop Genetic Improvement of Guangdong Province, Guangzhou, 510640, ChinaCrops Research Institute, Guangdong Academy of Agricultural Sciences & Key Laboratory of Crop Genetic Improvement of Guangdong Province, Guangzhou, 510640, ChinaInstitution of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang, 050035, China; Institute of Cereal and Oil Crops, HAAFS/Hebei Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China; Corresponding author. Institution of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang, 050035, China.Crops Research Institute, Guangdong Academy of Agricultural Sciences & Key Laboratory of Crop Genetic Improvement of Guangdong Province, Guangzhou, 510640, China; Corresponding author.Sweetpotato is a major root crop with high yield and nutritional benefits. However, existing methods for evaluating sugars level are inefficient, limiting the breeding and processing of high-quality varieties. This study utilized near-infrared spectroscopy (NIRS) coupled with machine learning algorithms to develop a high-throughput assay for fructose, glucose, sucrose, and maltose in sweetpotatoes across their raw, steamed, and baked states. Leveraging representative samples, characteristic spectral variables, and advanced learning algorithms, twelve optimal models were established for the four sugar indicators under three processing states. These models exhibited outstanding performance in calibration (R2C: 0.941–0.984), cross-validation (R2CV: 0.926–0.976), external validation (R2V: 0.898–0.971), and the ratio of prediction to deviation (RPD: 5.83–10.3), confirming their robust predictive capacity. The findings suggest that these machine learning-enhanced NIRS models enable rapid, high-throughput analysis of sweetpotato sugars, significantly benefiting both breeding programs and food processing applications.http://www.sciencedirect.com/science/article/pii/S2666154325003059Sugar contentChemometric algorithmsModeling methodCrop breedingFood processing |
| spellingShingle | Chaochen Tang Xueying Mo Zhimin Ma Zhangying Wang Machine learning-enhanced near-infrared spectroscopy for high-throughput phenotyping of sweetpotato sugars across raw and cooked states Journal of Agriculture and Food Research Sugar content Chemometric algorithms Modeling method Crop breeding Food processing |
| title | Machine learning-enhanced near-infrared spectroscopy for high-throughput phenotyping of sweetpotato sugars across raw and cooked states |
| title_full | Machine learning-enhanced near-infrared spectroscopy for high-throughput phenotyping of sweetpotato sugars across raw and cooked states |
| title_fullStr | Machine learning-enhanced near-infrared spectroscopy for high-throughput phenotyping of sweetpotato sugars across raw and cooked states |
| title_full_unstemmed | Machine learning-enhanced near-infrared spectroscopy for high-throughput phenotyping of sweetpotato sugars across raw and cooked states |
| title_short | Machine learning-enhanced near-infrared spectroscopy for high-throughput phenotyping of sweetpotato sugars across raw and cooked states |
| title_sort | machine learning enhanced near infrared spectroscopy for high throughput phenotyping of sweetpotato sugars across raw and cooked states |
| topic | Sugar content Chemometric algorithms Modeling method Crop breeding Food processing |
| url | http://www.sciencedirect.com/science/article/pii/S2666154325003059 |
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