NIRS as an alternative method for table grapes Seedlessness sorting
Seedlessness in table grapes is a desirable trait for consumers. Plant growth regulators (PGRs) have been extensively utilized to induce seedlessness. However, the efficacy of these PGRs is not uniformly successful. In addition, the seedlessness is difficult to detect by cutting and counting techniq...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016124005405 |
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| Summary: | Seedlessness in table grapes is a desirable trait for consumers. Plant growth regulators (PGRs) have been extensively utilized to induce seedlessness. However, the efficacy of these PGRs is not uniformly successful. In addition, the seedlessness is difficult to detect by cutting and counting technique. The shortwave-near infrared spectroscopy (SW-NIRS), coupled with suitable chemometric analysis, is a non-destructive method for sorting and prediction of seedlessness grapes. The NIRS is higher efficiency than original technique in term of accuracy, measuring time and waste reduction. • The SW-NIR spectra of 240 grape berries were recorded. Each reflectance spectrum was acquired in the wavenumber of 3996–12,489 cm−1. After that all grape berries were cut and count for seedlessness sorting.All spectral together with seedlessness sorting were be analysis by chemometrics. • The NIR spectral data were analyzed using principal component analysis (PCA). In addition, supervised self-organizing map (SSOM) and quadratic discriminant analysis (QDA) were applied to classify the seedlessness. • The PCA results represented a negative tendency to classify the seedlessness. Clear classification tendency can be obtained from SOMs. Good predictive results from SSOM were obtained, as it gave a percentage correctly classified of 97.14 and 94.64% for training and test sample sets, respectively. |
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| ISSN: | 2215-0161 |