Handheld Raman spectroscopy for the early detection of plant diseases: Abutilon mosaic virus infecting Abutilon sp.

Yeturu, Sivaprasad; Jentzsch, Paul Vargas; Ciobotă, Valerian; Guerrero, Ricardo; Garrido, Patricia; Ramos, Luis A.

doi:10.1039/c6ay00381h

Cited by 60 publications

(60 citation statements)

References 55 publications

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“…Utilization of spectral information was important for hyperspectral imaging application. Average spectra of each sample have been widely used in detecting plant diseases [ 40 ]. Use of average spectra of hyperspectral imaging was the same as Vis/NIR spectroscopy.…”

Section: Discussionmentioning

confidence: 99%

“…The differences was that the spectra of Vis/NIR spectroscopy were collected from a small part of the leaves [ 41 , 42 ] and each sample has one spectra averaged by several times of scans, while average spectra of hyperspectral imaging was acquired from a predefined region of interest (ROI) in the sample. The use of average spectra of infected samples had two situations, average spectra of the entire sample including the infected region and the healthy region within the sample, and average spectra of only the infected region [ 40 , 41 ]. The former was more effective, samples with or without visible symptoms could be predicted in this situation.…”

Section: Discussionmentioning

confidence: 99%

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Application of Hyperspectral Imaging to Detect Sclerotinia sclerotiorum on Oilseed Rape Stems

Kong

Zhang

Huang

et al. 2018

Sensors

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Hyperspectral imaging covering the spectral range of 384–1034 nm combined with chemometric methods was used to detect Sclerotinia sclerotiorum (SS) on oilseed rape stems by two sample sets (60 healthy and 60 infected stems for each set). Second derivative spectra and PCA loadings were used to select the optimal wavelengths. Discriminant models were built and compared to detect SS on oilseed rape stems, including partial least squares-discriminant analysis, radial basis function neural network, support vector machine and extreme learning machine. The discriminant models using full spectra and optimal wavelengths showed good performance with classification accuracies of over 80% for the calibration and prediction set. Comparing all developed models, the optimal classification accuracies of the calibration and prediction set were over 90%. The similarity of selected optimal wavelengths also indicated the feasibility of using hyperspectral imaging to detect SS on oilseed rape stems. The results indicated that hyperspectral imaging could be used as a fast, non-destructive and reliable technique to detect plant diseases on stems.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Application of Hyperspectral Imaging to Detect Sclerotinia sclerotiorum on Oilseed Rape Stems

Kong

Zhang

Huang

et al. 2018

Sensors

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“…The analysis of Raman spectral peak position, amplitude, and area help understand the structure and content of organic molecules in plants . Yeturu et al studied the Raman spectra of velvetleaf and located its characteristic peaks using one‐way analysis of variance (ANOVA) method . Baranski et al analyzed carotenoid Raman peaks of vegetable tissue and found that the content of carotenoids in virus‐infected leaves were lower than that of healthy leaves .…”

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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“…14 Some salient applications of this technique include the study on the composition of materials in different areas, such as microbiology, arts, archaeology, geology, atmospheric sciences, and even planetary exploration, among many others. [15][16][17][18][19][20][21][22][23] Currently, also portable Raman spectrometers are available, with short acquisition times and reasonable spectral resolutions, thus providing reliable information on the characteristics of the measured material in short time. In a recent article, Morillas et al 24 have used a portable Raman spectrometer to evaluate the deterioration of building materials.…”

Section: Introductionmentioning

confidence: 99%

Adulteration of clove essential oil: Detection using a handheld Raman spectrometer

Jentzsch

Gualpa²,

Ramos³

et al. 2017

Flavour & Fragrance J

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Essential oils are highly appreciated substances used as row materials in the food, cosmetics and pharmaceutical industry. Due to its biological activity and properties, the oil extracted from clove (Eugenia caryophyllus) has gained economic importance, and this is the main reason why adulterated clove essential oils have appeared in the market. The detection of adulteration is a fundamental task to ensure the quality of industrial products and, therefore, to protect the consumers from potential harmful products. In order to test the suitability of Raman spectroscopy to detect adulteration in essential oils, nineteen samples of commercial clove essential oil were measured with a handheld device and their Raman spectra analyzed. The Raman spectrum of a sample of authentic clove essential oil is dominated by bands of its main component eugenol. Principal component analysis (PCA) and independent component analysis (ICA) were applied in order to identify the adulterated samples and to assess the quality of the authentic samples. It was found that two samples were adulterated by the addition of benzyl alcohol and a third one by the addition of a vegetable oil. The adulteration by addition of benzyl alcohol is easy to detect by a quick inspection of the Raman spectrum, since this compound shows an intense band at 1003 cm‐1. However, the detection of adulteration by the addition of other substances (e.g., vegetable oils) requires the application of multivariable analysis, i.e., PCA or ICA. The results show that Raman spectroscopy is an appropriate technique for quality control of clove essential oil.

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Handheld Raman spectroscopy for the early detection of plant diseases: Abutilon mosaic virus infecting Abutilon sp.

Abstract: Plant diseases have a direct impact on the productivity of crops, and therefore the early detection of diseases is crucial.

Cited by 60 publications

References 55 publications

Application of Hyperspectral Imaging to Detect Sclerotinia sclerotiorum on Oilseed Rape Stems

Application of Hyperspectral Imaging to Detect Sclerotinia sclerotiorum on Oilseed Rape Stems

Early detection of zinc deficit with confocal Raman spectroscopy

Adulteration of clove essential oil: Detection using a handheld Raman spectrometer

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