Application of Infrared and Raman Spectroscopy for the Identification of Disease Resistant Trees

Conrad, Anna O.; Bonello, Pierluigi

doi:10.3389/fpls.2015.01152

Cited by 35 publications

(33 citation statements)

References 49 publications

(99 reference statements)

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“…4), we inferred that this is likely also associated with ethylene emission induced by GPD treatment36. Similar results were also obtained in Populus and oil palm, which showed that PtIDA and PtIDL1 were expressed more highly in leaves after shade treatment (a similar treatment to GPD resulting in carbohydrate stress), and EgIDA2 and EgIDA5 could be induced by ethylene23, further supporting that IDA functions in the ethylene-response pathway.…”

Section: Discussionsupporting

confidence: 82%

“…Briefly, IDA encodes a peptide ligand that forms a complex with redundant receptor-like kinases HAE and HSL2, which presumably activates a MITOGEN-ACTIVATED PROTEIN KINASE (MAPK) cascade that acts to regulate downstream KNOX-like transcription factor BP/KNAT1. Recently, it has been proposed that the involvement of this signaling module in abscission processes is conserved in other plant species2223. However, so far, the characterization of key regulators in control of abscission has been reported mainly in the model plants tomato and Arabidopsis ; these factors are largely unknown in perennial woody plants, especially in important horticultural cultivated fruit crops, such as litchi.…”

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confidence: 99%

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Identification and molecular characterization of an IDA-like gene from litchi, LcIDL1, whose ectopic expression promotes floral organ abscission in Arabidopsis

Ying

Liu

et al. 2016

Sci Rep

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Unexpected abscission of flowers or fruits is a major limiting factor for crop productivity. Key genes controlling abscission in plants, especially in popular fruit trees, are largely unknown. Here we identified a litchi (Litchi chinensis Sonn.) IDA-like (INFLORESCENCE DEFICIENT IN ABSCISSION-like) gene LcIDL1 as a potential key regulator of abscission. LcIDL1 encodes a peptide that shows the closest homology to Arabidopsis IDA, and is localized in cell membrane and cytoplasm. Real-time PCR analysis showed that the expression level of LcIDL1 accumulated gradually following flower abscission, and it was obviously induced by fruit abscission-promoting treatments. Transgenic plants expressing LcIDL1 in Arabidopsis revealed a role of LcIDL1 similar to IDA in promoting floral organ abscission. Moreover, ectopic expression of LcIDL1 in Arabidopsis activated the expression of abscission-related genes. Taken together, our findings provide evidence that LcIDL1 may act as a key regulator in control of abscission.

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Section: Discussionsupporting

confidence: 82%

mentioning

confidence: 99%

Identification and molecular characterization of an IDA-like gene from litchi, LcIDL1, whose ectopic expression promotes floral organ abscission in Arabidopsis

Ying

Liu

et al. 2016

Sci Rep

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“…Zhang Keqin's research further indicated the specific locations of Raman characteristic peak position and demonstrated the group vibration form of sugars, proteins, and carbohydrates in cereals . Raman spectroscopy can also be used directly in plants to determine the bacterial and viral diseases …”

Section: Introductionmentioning

confidence: 99%

Early detection of zinc deficit with confocal Raman spectroscopy

Zhao

Cai

2018

J Raman Spectroscopy

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Zinc deficit is one of the most important reasons for botanical disease. It can occur in many forms, for example, as rice blast, bacterial stripe, bacterial blight, and false smut. All these diseases will greatly lead to lower yields during rice harvest. In these paper, we show that Raman spectroscopy provides a single, fast, and sensitive method for detecting zinc deficit, especially during early forms of zinc deficiency. When a plant lacks zinc, the carotenoid content will increase, and many other components' content will decrease, such as starch, proteins, sugars, and amide. All of these changes will be brought out in the Raman spectrum of the rice leaves. In this study, we scanned the healthy leaves, the leaves with early zinc deficiency, and the leaves with zinc deficiency by confocal Raman spectrometer to get the Raman spectra within the wavenumber 200-1,200 cm −1 . First, the ensemble empirical mode decomposition algorithm was used to remove the baseline and the noise in the Raman spectra.Then principal components selected by the successive projections algorithm (PC1, PC2, PC3, and PC6) and characteristic parameters of the spectra S 2 S 1 (the area of 690-780 cm −1 S 1 is divided by the area of 1,080-1,200 cm −1 S 2 ) selected by one-way variance of analysis and related with the content of carotenoid, starch, carbohydrate, soluble sugar, and so forth were the inputs of the least squares support vector machine model. The model is able to determine from a variety of samples whether the sample has zinc deficiency, early zinc deficiency, or if it is healthy, with a 100% accuracy. These results illustrate that early forms of zinc deficit can be detected before the plants show any physical symptoms. This is groundbreaking news for the future development of zinc deficiency monitoring equipment, as well as for other types of deficiency monitoring equipment in general.

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“…In addition, the quality of agricultural and forestry products has been assessed by FTIR (Bureau et al, 2019). FTIR has also been used for the detection of plant pathogens such as Fusarium in maize (Kos et al, 2002), huanglongbing disease in citrus (Hawkins et al, 2010), Geosmithia-Ophistoma complex in trees (Conrad and Bonello, 2016), and different leaf viruses in grapevines (Topală et al, 2017).…”

mentioning

confidence: 99%

The use of infrared spectroscopy and machine learning tools for detection ofMeloidogyneinfestations

San‐Blas¹,

Paba

Cubillán

et al. 2020

Plant Pathology

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Plant parasitic nematodes are generally soilborne pathogens that attack plants and cause economic losses in many crops. The infested plants show nonspecific symptoms or, often, are symptomless; therefore, diagnosis is performed by taking soil and root tissue samples. Here, we show that a combination of different infrared spectra analysis and machine learning algorithms can be used to detect plant parasitic nematode infestations before symptoms become visible, using leaves instead of roots and soil as samples. We found that tomato and guava plants infested with Meloidogyne enterorlobii produced different spectral patterns compared to uninfested plants. Using partial spectra from 1,450 to 900/cm as the "fingerprint region", principal component analyses indicated that after 5 (tomatoes) or 8 weeks (guava), plants with no visible symptoms of infestations were positively diagnosed. To improve the early detection response, we used machine learning modelling. A support vector machine (SVM) was used to obtain more robust, accurate models. The SVM model contained 34 support vectors, 17 for each level. The overall performance of the model was >97% and the total accuracy was significantly higher, demonstrating the absence of chance prediction. The best prediction of infestation was obtained at the second and fourth weeks for tomatoes and guavas, respectively, reducing the diagnostic time by half. The combined application of these techniques reduces the processing time from field to laboratory and shows enormous advantages by avoiding root and soil sampling.

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Application of Infrared and Raman Spectroscopy for the Identification of Disease Resistant Trees

Cited by 35 publications

References 49 publications

Identification and molecular characterization of an IDA-like gene from litchi, LcIDL1, whose ectopic expression promotes floral organ abscission in Arabidopsis

Identification and molecular characterization of an IDA-like gene from litchi, LcIDL1, whose ectopic expression promotes floral organ abscission in Arabidopsis

Early detection of zinc deficit with confocal Raman spectroscopy

The use of infrared spectroscopy and machine learning tools for detection ofMeloidogyneinfestations

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