Food security is an emerging problem that is faced by our civilization. There are millions of people around the world suffering from various kinds of malnutrition. The number of people that starve will only increase considering the continuous growth of the world’s population. The problem of food security can be addressed by timely detection and identification biotic and abiotic stresses in plants that drastically reduce the crop yield. A growing body of evidence suggests that Raman spectroscopy (RS), an emerging analytical technique, can be used for the confirmatory and non-invasive diagnostics of plant stresses. However, it remains unclear whether RS can efficiently disentangle biotic and abiotic stresses, as well as detect both of them simultaneously in plants. In this work, we modeled a stalk rot disease in corn by inoculating the plant stalks with Colletotrichum graminicola. In parallel, we subjected plants to salt stress, as well as challenging plants with both stalk rot disease and salinity stress simultaneously. After the stresses were introduced, Raman spectra were collected from the stalks to reveal stress-specific changes in the plant biochemistry. We found that RS was able to differentiate between stalk rot disease and salinity stresses with 100% accuracy, as well as predict presence of both of those stresses in plants on early and late stages. These results demonstrate that RS is a robust and reliable approach that can be used for confirmatory, non-destructive and label-free diagnostics of biotic and abiotic stresses in plants.
This research was intended to define and interpret cell wall attributes and other chemical composition of eight different varieties of tomato plants by utilizing fiber optic Fourier-transform near-infrared spectroscopy (FT-NIR) to acquire in situ chemical signatures of leaf, flower, fruit, and stem of tomato plant and cell wall at different developmental stages. Chemical spectral signatures of the tomato’s leaf, flower, fruit, and stem were only acquired during its session and in live mode such as green, yellow, and red in cell wall color. The spectral signature analysis of each tomato plant was performed to see substantial differences in chemical compositions using chemometric data modeling of FT-NIR spectra. In addition, principal component analysis (PCA) was performed to discriminate leaf, flower, fruit, and stem from the same variety. PCA was also performed to differentiate eight different varieties of tomato plants. The study showed how in situ FT-NIR could distinguish eight types of tomato leaf, flower, fruit, and stem chemical composition at different developmental stages related to cell wall and other attributes. This study has also demonstrated how in situ FT-NIR can discriminate between rusty vs. healthy leaf and intact fruit vs. off-the-plant fruit. The main objective of this study is to present the chemical signature differences in the live and developing tomato plants to improve crucial factors of tomatoes that would benefit plant breeding, tomato cell wall study, and ultimately human health.
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