Single seed near-infrared hyperspectral imaging in determining tomato (Solanum lycopersicum L.) seed quality in association with multivariate data analysis
By S. Shrestha, Matej Knapic, Uros Zibrat, L. Deleuran, R. Gislum. 2016
65 citations recorded by the source corpus.
Abstract Near-infrared (NIR) hyperspectral imaging was explored as a rapid and non-destructive method of investigating seed quality parameters such as seed viability and variation in tomato seed lots. The seed lots differed with year of production and variety. Four tomato varieties: Cal J, Monprecus, NCL and Chiuri from 2013, 2014 and 2015 were used in the study. The extracted NIR hyperspectral data from 975 to 2500 nm were analysed by principal component analysis (PCA) and partial least squares- discriminant analysis (PLS-DA). No distinct patterns of separation between viable and non-viable tomato seeds were revealed by the PCA. Our findings showed a pattern of separation in the tomato seed lots due to production years and varieties. The PLS-DA showed the ability to predict with ∼100 percent accuracy for varietal class membership when only the seeds of a single harvest year were included in the model. The accuracy from PLS-DA on pooled samples (all seeds from all varieties) predicted varietal class membership in the range from 34 to 88 percent. High variation in the seed lots could have caused high variation in the predicted varietal class membership. The NIR regions with chemical information from C H, N H and O H had influence on the PCA and PLS-DA models. The study presents the prospects of using NIR hyperspectral imaging in varietal identification studies of tomato seeds though we recommend a thorough validation of models.
Subfields: Analytical Chemistry, Plant Science
View Paper: https://www.semanticscholar.org/paper/7808d1ba7a47c4b964267dedb8570f885cced9c4
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In Analytical Chemistry
In Plant Science
Citations in the corpus
- Utilisation of visible/near-infrared hyperspectral images to classify aflatoxin B1 contaminated maize kernels (2018, background): “The clustering of different groups of kernels suggests that kernels of the same cluster may have similar physical or chemical characteristics (Shrestha et al., 2016).”