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Clark KR, Goldberg Oppenheimer P. Vibrational spectroscopic profiling of biomolecular interactions between oak powdery mildew and oak leaves. SOFT MATTER 2024; 20:959-970. [PMID: 38189096 PMCID: PMC10828924 DOI: 10.1039/d3sm01392h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/02/2024] [Indexed: 01/09/2024]
Abstract
Oak powdery mildew, caused by the biotrophic fungus Erysiphe alphitoides, is a prevalent disease affecting oak trees, such as English oak (Quercus robur). While mature oak populations are generally less susceptible to this disease, it can endanger young oak seedlings and new leaves on mature trees. Although disruptions of photosynthate and carbohydrate translocation have been observed, accurately detecting and understanding the specific biomolecular interactions between the fungus and the leaves of oak trees is currently lacking. Herein, via hybrid Raman spectroscopy combined with an advanced artificial neural network algorithm, the underpinning biomolecular interactions between biological soft matter, i.e., Quercus robur leaves and Erysiphe alphitoides, are investigated and profiled, generating a spectral library and shedding light on the changes induced by fungal infection and the tree's defence response. The adaxial surfaces of oak leaves are categorised based on either the presence or absence of Erysiphe alphitoides mildew and further distinguishing between covered or not covered infected leaf tissues, yielding three disease classes including healthy controls, non-mildew covered and mildew-covered. By analysing spectral changes between each disease category per tissue type, we identified important biomolecular interactions including disruption of chlorophyll in the non-vein and venule tissues, pathogen-induced degradation of cellulose and pectin and tree-initiated lignification of cell walls in response, amongst others, in lateral vein and mid-vein tissues. Via our developed computational algorithm, the underlying biomolecular differences between classes were identified and allowed accurate and rapid classification of disease with high accuracy of 69.6% for non-vein, 73.5% for venule, 82.1% for lateral vein and 85.6% for mid-vein tissues. Interfacial wetting differences between non-mildew covered and mildew-covered tissue were further analysed on the surfaces of non-vein and venule tissue. The overall results demonstrated the ability of Raman spectroscopy, combined with advanced AI, to act as a powerful and specific tool to probe foliar interactions between forest pathogens and host trees with the simultaneous potential to probe and catalogue molecular interactions between biological soft matter, paving the way for exploring similar relations in broader forest tree-pathogen systems.
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Affiliation(s)
- Kieran R Clark
- School of Chemical Engineering, Advanced Nanomaterials Structures and Applications Laboratories, College of Engineering and Physical Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
- Birmingham Institute of Forest Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Pola Goldberg Oppenheimer
- School of Chemical Engineering, Advanced Nanomaterials Structures and Applications Laboratories, College of Engineering and Physical Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
- Healthcare Technologies Institute, Institute of Translational Medicine, Mindelsohn Way, Birmingham, B15 2TH, UK
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2
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Chauhan S, Sharma S. Applications of Raman spectroscopy in the analysis of biological evidence. Forensic Sci Med Pathol 2023:10.1007/s12024-023-00660-z. [PMID: 37878163 DOI: 10.1007/s12024-023-00660-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2023] [Indexed: 10/26/2023]
Abstract
During the past few decades, Raman spectroscopy has progressed and captivated added attention in the field of science. However, the application of Raman spectroscopy is not limited to the field of forensic science and analytical chemistry; it is one of the emerging spectroscopic techniques, utilized in the field of forensic science which in turn could be a supporting tool in the law and justice system. The advantage of Raman spectroscopy over the other conventional techniques is that it is rapid, reliable, and non-destructive in nature with minimal or no sample preparation. The quantitative and qualitative analysis of evidence from biological and non-biological origins could easily be performed by using Raman spectroscopy. The forensic domain is highly complex with multidisciplinary branches, and therefore a plethora of techniques are utilized for the detection, identification, and differentiation of innumerable pieces of evidence for the purpose of law and justice. Herein, a systematic review is carried out on the application of Raman spectroscopy in the realm of forensic biology and serology considering its usefulness in practical perspectives. This review paper highlights the significance of modern techniques, including micro-Raman spectroscopy, confocal Raman spectroscopy, surface-enhanced Raman spectroscopy, and paper-based surface-enhanced Raman spectroscopy, in the field of Raman spectroscopy. These techniques have demonstrated notable advancements in terms of their applications and capabilities. Furthermore, to comprehensively capture the progress in the development of Raman spectroscopy, all the published papers which could be retrieved from the available databases from the year 2007 to 2022 were incorporated.
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Affiliation(s)
- Samiksha Chauhan
- LNJN NICFS, School of Forensic Sciences, National Forensic Science University, An Institute of National Importance, Ministry of Home Affairs, Govt. of India, Delhi Campus, Delhi, 110085, India
| | - Sweety Sharma
- LNJN NICFS, School of Forensic Sciences, National Forensic Science University, An Institute of National Importance, Ministry of Home Affairs, Govt. of India, Delhi Campus, Delhi, 110085, India.
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Terentev A, Dolzhenko V. Can Metabolomic Approaches Become a Tool for Improving Early Plant Disease Detection and Diagnosis with Modern Remote Sensing Methods? A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:5366. [PMID: 37420533 DOI: 10.3390/s23125366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/25/2023] [Accepted: 06/04/2023] [Indexed: 07/09/2023]
Abstract
The various areas of ultra-sensitive remote sensing research equipment development have provided new ways for assessing crop states. However, even the most promising areas of research, such as hyperspectral remote sensing or Raman spectrometry, have not yet led to stable results. In this review, the main methods for early plant disease detection are discussed. The best proven existing techniques for data acquisition are described. It is discussed how they can be applied to new areas of knowledge. The role of metabolomic approaches in the application of modern methods for early plant disease detection and diagnosis is reviewed. A further direction for experimental methodological development is indicated. The ways to increase the efficiency of modern early plant disease detection remote sensing methods through metabolomic data usage are shown. This article provides an overview of modern sensors and technologies for assessing the biochemical state of crops as well as the ways to apply them in synergy with existing data acquisition and analysis technologies for early plant disease detection.
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Affiliation(s)
- Anton Terentev
- All-Russian Institute of Plant Protection, 196608 Saint Petersburg, Russia
| | - Viktor Dolzhenko
- All-Russian Institute of Plant Protection, 196608 Saint Petersburg, Russia
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4
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Higgins S, Joshi R, Juarez I, Bennett JS, Holman AP, Kolomiets M, Kurouski D. Non-invasive identification of combined salinity stress and stalk rot disease caused by Colletotrichum graminicola in maize using Raman spectroscopy. Sci Rep 2023; 13:7661. [PMID: 37169839 PMCID: PMC10175297 DOI: 10.1038/s41598-023-34937-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 05/10/2023] [Indexed: 05/13/2023] Open
Abstract
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.
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Affiliation(s)
- Samantha Higgins
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, 77843, USA
| | - Ritu Joshi
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, 77843, USA
| | - Isaac Juarez
- Department of Toxicology, Texas A&M University, College Station, TX, 77843, USA
| | - John S Bennett
- Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, 77843, USA
| | - Aidan P Holman
- Department of Entomology, Texas A&M University, College Station, TX, 77843, USA
| | - Michael Kolomiets
- Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, 77843, USA.
| | - Dmitry Kurouski
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, 77843, USA.
- Department of Toxicology, Texas A&M University, College Station, TX, 77843, USA.
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, 77843, USA.
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5
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Yao L, Jiang Y, Tan Z, Wu W. Construction of Very Low-Cost Loop Polymerase Chain Reaction System Based on Proportional-Integral-Derivative Temperature Control Optimization Algorithm and Its Application in Gene Detection. ACS OMEGA 2022; 7:46003-46011. [PMID: 36570205 PMCID: PMC9773339 DOI: 10.1021/acsomega.2c02975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 08/12/2022] [Indexed: 06/17/2023]
Abstract
Real-time polymerase chain reaction (PCR) technology is essential in nucleic acid detection and point-of-care testing (POCT). However, nowadays, the classical qPCR instrument has the deficiency of its bulky volume, high cost, and inconvenience to use; hence, a low-cost and easy-to-use PCR equipment was thus developed consisting of a hardware subsystem as well as a software subsystem based on an improved proportional-integral-derivative (PID) system. The proposed system not only could hold self-setting reaction cycles of temperature rising and falling automatically but also the temperature during the constant temperature stage was regulated steady based on improved temperature control algorithm, which proved its great effect compared with the reaction temperature derived from an infrared thermal imaging camera. The experimental results in gene detection research also could indicate its applicability and stability of our developed PCR system by using the amplification curve analysis, the melting curve analysis, and agarose gel electrophoresis analysis compared with the commercial PCR instrument, which illustrates the great potential application value of the proposed PCR system.
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Affiliation(s)
- Liping Yao
- Institute
of Biological and Medical Engineering, Guangdong
Academy of Sciences, Guangzhou510500, China
| | - Yangyang Jiang
- Institute
of Biological and Medical Engineering, Guangdong
Academy of Sciences, Guangzhou510500, China
| | - Zhongwei Tan
- Institute
of Biological and Medical Engineering, Guangdong
Academy of Sciences, Guangzhou510500, China
| | - Wenming Wu
- Institute
of Biological and Medical Engineering, Guangdong
Academy of Sciences, Guangzhou510500, China
- State
Key Laboratory of Microelectronics and Integrated Circuits, Fudan University, Shanghai200433, China
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6
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Yang B, Li X, Wu L, Chen Y, Zhong F, Liu Y, Zhao F, Ye D, Weng H. Citrus Huanglongbing detection and semi-quantification of the carbohydrate concentration based on micro-FTIR spectroscopy. Anal Bioanal Chem 2022; 414:6881-6897. [PMID: 35947156 DOI: 10.1007/s00216-022-04254-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/08/2022] [Accepted: 07/25/2022] [Indexed: 12/01/2022]
Abstract
Citrus Huanglongbing (HLB) is nowadays one of the most fatal citrus diseases worldwide. Once the citrus tree is infected by the HLB disease, the biochemistry of the phloem region in midribs would change. In order to investigate the carbohydrate changes in phloem region of citrus midrib, the semi-quantification models were established to predict the carbohydrate concentration in it based on Fourier transform infrared microscopy (micro-FTIR) spectroscopy coupled with chemometrics. Healthy, asymptomatic-HLB, symptomatic-HLB, and nutrient-deficient citrus midribs were collected in this study. The results showed that the intensity of the characteristic peak varied with the carbohydrate (starch and soluble sugar) concentration in citrus midrib, especially at the fingerprint regions of 1175-900 cm-1, 1500-1175 cm-1, and 1800-1500 cm-1. Furthermore, semi-quantitative prediction models of starch and soluble sugar were established using the full micro-FTIR spectra and selected characteristic wavebands. The least squares support vector machine regression (LS-SVR) model combined with the random frog (RF) algorithm achieved the best prediction result with the determination coefficient of prediction ([Formula: see text]) of 0.85, the root mean square error of prediction (RMSEP) of 0.36%, residual predictive deviation (RPD) of 2.54, and [Formula: see text] of 0.87, RMSEP of 0.37%, RPD of 2.76, for starch and soluble sugar concentration prediction, respectively. In addition, multi-layer perceptron (MLP) classification models were established to identify HLB disease, achieving the overall classification accuracy of 94% and 87%, based on the full-range spectra and the optimal wavenumbers selected by the random frog (RF) algorithm, respectively. The results demonstrated that micro-FTIR spectroscopy can be a valuable tool for the prediction of carbohydrate concentration in citrus midribs and the detection of HLB disease, which would provide useful guidelines to detect citrus HLB disease.
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Affiliation(s)
- Biyun Yang
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.,Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou, 350002, China
| | - Xiaobin Li
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.,Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou, 350002, China
| | - Lianwei Wu
- Fujian Institute of Testing Technology, Fuzhou, 350003, China
| | - Yayong Chen
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.,Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou, 350002, China
| | - Fenglin Zhong
- College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Yunshi Liu
- College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Fei Zhao
- Fujian Institute of Testing Technology, Fuzhou, 350003, China
| | - Dapeng Ye
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China. .,Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou, 350002, China.
| | - Haiyong Weng
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China. .,Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou, 350002, China.
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7
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Goff NK, Guenther JF, Roberts JK, Adler M, Molle MD, Mathews G, Kurouski D. Non-Invasive and Confirmatory Differentiation of Hermaphrodite from Both Male and Female Cannabis Plants Using a Hand-Held Raman Spectrometer. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27154978. [PMID: 35956927 PMCID: PMC9370318 DOI: 10.3390/molecules27154978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/15/2022] [Accepted: 08/03/2022] [Indexed: 11/19/2022]
Abstract
Cannabis (Cannabis sativa L.) is a dioecious plant that produces both male and female inflorescences. In nature, male and female plants can be found with nearly equal frequency, which determines species out-crossing. In cannabis farming, only female plants are preferred due to their high yield of cannabinoids. In addition to unfavorable male plants, commercial production of cannabis faces the appearance of hermaphroditic inflorescences, species displaying both pistillate flowers and anthers. Such plants can out-cross female plants, simultaneously producing undesired seeds. The problem of hermaphroditic cannabis triggered a search for analytical tools that can be used for their rapid detection and identification. In this study, we investigate the potential of Raman spectroscopy (RS), an emerging sensing technique that can be used to probe plant biochemistry. Our results show that the biochemistry of male, female and hermaphroditic cannabis plants is drastically different which allows for their confirmatory identification using a hand-held Raman spectrometer. Furthermore, the coupling of machine learning approaches enables the identification of hermaphrodites with 98.7% accuracy, whereas both male and female plants can be identified with 100% accuracy. Considering the label-free, non-invasive and non-destructive nature of RS, the developed optical sensing approach can transform cannabis farming in the U.S. and overseas.
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Affiliation(s)
- Nicolas K. Goff
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA
| | | | | | | | | | | | - Dmitry Kurouski
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA
- Correspondence: ; Tel.: +979-458-3778
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8
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Raman Method in Identification of Species and Varieties, Assessment of Plant Maturity and Crop Quality—A Review. Molecules 2022; 27:molecules27144454. [PMID: 35889327 PMCID: PMC9322835 DOI: 10.3390/molecules27144454] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/08/2022] [Accepted: 07/11/2022] [Indexed: 02/05/2023] Open
Abstract
The present review covers reports discussing potential applications of the specificity of Raman techniques in the advancement of digital farming, in line with an assumption of yield maximisation with minimum environmental impact of agriculture. Raman is an optical spectroscopy method which can be used to perform immediate, label-free detection and quantification of key compounds without destroying the sample. The authors particularly focused on the reports discussing the use of Raman spectroscopy in monitoring the physiological status of plants, assessing crop maturity and quality, plant pathology and ripening, and identifying plant species and their varieties. In recent years, research reports have presented evidence confirming the effectiveness of Raman spectroscopy in identifying biotic and abiotic stresses in plants as well as in phenotyping and digital selection of plants in farming. Raman techniques used in precision agriculture can significantly improve capacities for farming management, crop quality assessment, as well as biological and chemical contaminant detection, thereby contributing to food safety as well as the productivity and profitability of agriculture. This review aims to increase the awareness of the growing potential of Raman spectroscopy in agriculture among plant breeders, geneticists, farmers and engineers.
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9
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Wang Z, Niu Y, Vashisth T, Li J, Madden R, Livingston TS, Wang Y. Nontargeted metabolomics-based multiple machine learning modeling boosts early accurate detection for citrus Huanglongbing. HORTICULTURE RESEARCH 2022; 9:uhac145. [PMID: 36061619 PMCID: PMC9433982 DOI: 10.1093/hr/uhac145] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 06/20/2022] [Indexed: 06/15/2023]
Abstract
Early accurate detection of crop disease is extremely important for timely disease management. Huanglongbing (HLB), one of the most destructive citrus diseases, has brought about severe economic losses for the global citrus industry. The direct strategies for HLB identification, such as quantitative real-time polymerase chain reaction (qPCR) and chemical staining, are robust for the symptomatic plants but powerless for the asymptomatic ones at the early stage of affection. Thus, it is very necessary to develop a practical method used for the early detection of HLB. In this study, a novel method combining ultra-high performance liquid chromatography/mass spectrometry (UHPLC/MS)-based nontargeted metabolomics and machine learning (ML) was developed for conducting the early detection of HLB for the first time. Six ML algorithms were selected to build the classifiers. Regularized logistic regression (LR-L2) and gradient-boosted decision tree (GBDT) outperformed with the highest average accuracy of 95.83% to not only classify healthy and infected plants but identify significant features. The proposed method proved to be practical for early detection of HLB, which tackled the shortcomings of low sensitivity in the conventional methods and avoid the problems such as lighting condition interference in spectrum/image recognition-based ML methods. Additionally, the discovered biomarkers were verified by the metabolic pathway analysis and content change analysis, which was remarkably consistent with the previous reports.
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Affiliation(s)
- Zhixin Wang
- Citrus Research & Education Center, Institute of Food and Agricultural Sciences, University of Florida, Lake Alfred, Florida 33850-2299, U.S.A
| | - Yue Niu
- Department of Mathematics, University of Arizona, Tucson, Arizona 85721-0089, U.S.A
| | - Tripti Vashisth
- Citrus Research & Education Center, Institute of Food and Agricultural Sciences, University of Florida, Lake Alfred, Florida 33850-2299, U.S.A
| | - Jingwen Li
- Citrus Research & Education Center, Institute of Food and Agricultural Sciences, University of Florida, Lake Alfred, Florida 33850-2299, U.S.A
| | - Robert Madden
- Citrus Research & Education Center, Institute of Food and Agricultural Sciences, University of Florida, Lake Alfred, Florida 33850-2299, U.S.A
| | - Taylor Shea Livingston
- Citrus Research & Education Center, Institute of Food and Agricultural Sciences, University of Florida, Lake Alfred, Florida 33850-2299, U.S.A
| | - Yu Wang
- Corresponding author: E-mail:
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10
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Parlamas S, Goetze PK, Humpal D, Kurouski D, Jo YK. Raman Spectroscopy Enables Confirmatory Diagnostics of Fusarium Wilt in Asymptomatic Banana. FRONTIERS IN PLANT SCIENCE 2022; 13:922254. [PMID: 35837469 PMCID: PMC9275401 DOI: 10.3389/fpls.2022.922254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 05/27/2022] [Indexed: 06/15/2023]
Abstract
Fusarium oxysporum f. sp. cubense (FOC) causes Fusarium wilt, one of the most concerning diseases in banana (Musa spp.), compromising global banana production. There are limited curative management options after FOC infections, and early Fusarium wilt symptoms are similar with other abiotic stress factors such as drought. Therefore, finding a reliable and timely form of early detection and proper diagnostics is critical for disease management for FOC. In this study, Portable Raman spectroscopy (handheld Raman spectrometer equipped with 830 nm laser source) was applied for developing a confirmatory diagnostic tool for early infection of FOC on asymptomatic banana. Banana plantlets were inoculated with FOC; uninoculated plants exposed to a drier condition were also prepared compared to well-watered uninoculated control plants. Subsequent Raman readings from the plant leaves, without damaging or destroying them, were performed weekly. The conditions of biotic and abiotic stresses on banana were modeled to examine and identify specific Raman spectra suitable for diagnosing FOC infection. Our results showed that Raman spectroscopy could be used to make highly accurate diagnostics of FOC at the asymptomatic stage. Based on specific Raman spectra at vibrational bands 1,155, 1,184, and 1,525 cm-1, Raman spectroscopy demonstrated nearly 100% accuracy of FOC diagnosis at 40 days after inoculation, differentiating FOC-infected plants from uninoculated plants that were well-watered or exposed to water deficit condition. This study first reported that Raman spectroscopy can be used as a rapid and non-destructive tool for banana Fusarium wilt diagnostics.
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Affiliation(s)
- Stephen Parlamas
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
| | - Paul K. Goetze
- Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, United States
| | - Dillon Humpal
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
| | - Dmitry Kurouski
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
- Institute for Advancing Health Through Agriculture, Texas A&M University, College Station, TX, United States
| | - Young-Ki Jo
- Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, United States
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11
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Higgins S, Biswas S, Goff NK, Septiningsih EM, Kurouski D. Raman Spectroscopy Enables Non-invasive and Confirmatory Diagnostics of Aluminum and Iron Toxicities in Rice. FRONTIERS IN PLANT SCIENCE 2022; 13:754735. [PMID: 35651767 PMCID: PMC9149412 DOI: 10.3389/fpls.2022.754735] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 03/28/2022] [Indexed: 05/26/2023]
Abstract
Metal toxicities can be detrimental to a plant health, as well as to the health of animals and humans that consume such plants. Metal content of plants can be analyzed using colorimetric, atomic absorption- or mass spectroscopy-based methods. However, these techniques are destructive, costly and laborious. In the current study, we investigate the potential of Raman spectroscopy (RS), a modern spectroscopic technique, for detection and identification of metal toxicities in rice. We modeled medium and high levels of iron and aluminum toxicities in hydroponically grown plants. Spectroscopic analyses of their leaves showed that both iron and aluminum toxicities can be detected and identified with ∼100% accuracy as early as day 2 after the stress initiation. We also showed that diagnostics accuracy was very high not only on early, but also on middle (day 4-day 8) and late (day 10-day 14) stages of the stress development. Importantly this approach only requires an acquisition time of 1 s; it is non-invasive and non-destructive to plants. Our findings suggest that if implemented in farming, RS can enable pre-symptomatic detection and identification of metallic toxins that would lead to faster recovery of crops and prevent further damage.
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Affiliation(s)
- Samantha Higgins
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
| | - Sudip Biswas
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, United States
| | - Nicolas K. Goff
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
| | - Endang M. Septiningsih
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, United States
| | - Dmitry Kurouski
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
- Institute for Quantum Science and Engineering, Texas A&M University, College Station, TX, United States
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12
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Tanner F, Tonn S, de Wit J, Van den Ackerveken G, Berger B, Plett D. Sensor-based phenotyping of above-ground plant-pathogen interactions. PLANT METHODS 2022; 18:35. [PMID: 35313920 PMCID: PMC8935837 DOI: 10.1186/s13007-022-00853-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 02/08/2022] [Indexed: 05/20/2023]
Abstract
Plant pathogens cause yield losses in crops worldwide. Breeding for improved disease resistance and management by precision agriculture are two approaches to limit such yield losses. Both rely on detecting and quantifying signs and symptoms of plant disease. To achieve this, the field of plant phenotyping makes use of non-invasive sensor technology. Compared to invasive methods, this can offer improved throughput and allow for repeated measurements on living plants. Abiotic stress responses and yield components have been successfully measured with phenotyping technologies, whereas phenotyping methods for biotic stresses are less developed, despite the relevance of plant disease in crop production. The interactions between plants and pathogens can lead to a variety of signs (when the pathogen itself can be detected) and diverse symptoms (detectable responses of the plant). Here, we review the strengths and weaknesses of a broad range of sensor technologies that are being used for sensing of signs and symptoms on plant shoots, including monochrome, RGB, hyperspectral, fluorescence, chlorophyll fluorescence and thermal sensors, as well as Raman spectroscopy, X-ray computed tomography, and optical coherence tomography. We argue that choosing and combining appropriate sensors for each plant-pathosystem and measuring with sufficient spatial resolution can enable specific and accurate measurements of above-ground signs and symptoms of plant disease.
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Affiliation(s)
- Florian Tanner
- Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA Australia
| | - Sebastian Tonn
- Department of Biology, Plant-Microbe Interactions, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Jos de Wit
- Department of Imaging Physics, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, The Netherlands
| | - Guido Van den Ackerveken
- Department of Biology, Plant-Microbe Interactions, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Bettina Berger
- Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA Australia
| | - Darren Plett
- Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA Australia
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13
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Higgins S, Jessup R, Kurouski D. Raman spectroscopy enables highly accurate differentiation between young male and female hemp plants. PLANTA 2022; 255:85. [PMID: 35279786 DOI: 10.1007/s00425-022-03865-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 03/01/2022] [Indexed: 06/14/2023]
Abstract
Hand-held Raman spectroscopy can be used for highly accurate differentiation between young male and female hemp plants. This differentiation is based on significantly different concentration of lutein in these plants. Last year, a global market of only industrial hemp attained the value of USD 4.7 billion. It is by far the fastest growing market with projected growth of 22.5% between 2021 and 2026. Hemp (Cannabis sativa L.) is a dioecious species that has separate male and female plants. In hemp farming, female plants are strongly preferred because male plants do not produce sufficient amount of cannabinoids. Male plants are also eliminated to minimize a possibility of uncontrolled cross-fertilization of plants. Silver treatments can induce development of male flowers on genetically female plants in order to produce feminized seed. Resulting cannabinoid hemp production fields should contain 100% female plants. However, any unintended pollination from male plants can produce unwanted males in production fields. Therefore, there is a growing demand for a label-free, non-invasive, and confirmatory approach that can be used to differentiate between male and female plants before flowering. In this study, we examined the extent to which Raman spectroscopy, an emerging optical technique, can be used for the accurate differentiation between young male and female hemp plants. Our findings show that Raman spectroscopy enables differentiation between male and female plants with 90% and 94% accuracy on the level of young and mature plants, respectively. Such analysis is entirely non-invasive and non-destructive to plants and can be performed in seconds using a hand-held spectrometer. High-performance liquid chromatography (HPLC) analysis and collected Raman spectra demonstrate that this spectroscopic differentiation is based on significantly different concentrations of carotenoids in male vs female plants. These findings open up a new avenue for quality control of plants grown in both field and a greenhouse.
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Affiliation(s)
- Samantha Higgins
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, 77843, USA
| | - Russell Jessup
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, 77843, USA
| | - Dmitry Kurouski
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, 77843, USA.
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14
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Payne WZ, Dou T, Cason JM, Simpson CE, McCutchen B, Burow MD, Kurouski D. A Proof-of-Principle Study of Non-invasive Identification of Peanut Genotypes and Nematode Resistance Using Raman Spectroscopy. FRONTIERS IN PLANT SCIENCE 2022; 12:664243. [PMID: 35058940 PMCID: PMC8765701 DOI: 10.3389/fpls.2021.664243] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 11/24/2021] [Indexed: 05/11/2023]
Abstract
Identification of peanut cultivars for distinct phenotypic or genotypic traits whether using visual characterization or laboratory analysis requires substantial expertise, time, and resources. A less subjective and more precise method is needed for identification of peanut germplasm throughout the value chain. In this proof-of-principle study, the accuracy of Raman spectroscopy (RS), a non-invasive, non-destructive technique, in peanut phenotyping and identification is explored. We show that RS can be used for highly accurate peanut phenotyping via surface scans of peanut leaves and the resulting chemometric analysis: On average 94% accuracy in identification of peanut cultivars and breeding lines was achieved. Our results also suggest that RS can be used for highly accurate determination of nematode resistance and susceptibility of those breeding lines and cultivars. Specifically, nematode-resistant peanut cultivars can be identified with 92% accuracy, whereas susceptible breeding lines were identified with 81% accuracy. Finally, RS revealed substantial differences in biochemical composition between resistant and susceptible peanut cultivars. We found that resistant cultivars exhibit substantially higher carotenoid content compared to the susceptible breeding lines. The results of this study show that RS can be used for quick, accurate, and non-invasive identification of genotype, nematode resistance, and nutrient content. Armed with this knowledge, the peanut industry can utilize Raman spectroscopy for expedited breeding to increase yields, nutrition, and maintaining purity levels of cultivars following release.
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Affiliation(s)
- William Z. Payne
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
| | - Tianyi Dou
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
| | - John M. Cason
- Texas A&M AgriLife Research, Stephenville, TX, United States
| | | | - Bill McCutchen
- Texas A&M AgriLife Research, Stephenville, TX, United States
| | - Mark D. Burow
- Department of Plant and Soil Science, Texas Tech University, Lubbock, TX, United States
- Texas A&M AgriLife Research, Lubbock, TX, United States
| | - Dmitry Kurouski
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
- The Institute for Quantum Science and Engineering, Texas A&M University, College Station, TX, United States
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15
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Chung PJ, Singh GP, Huang CH, Koyyappurath S, Seo JS, Mao HZ, Diloknawarit P, Ram RJ, Sarojam R, Chua NH. Rapid Detection and Quantification of Plant Innate Immunity Response Using Raman Spectroscopy. FRONTIERS IN PLANT SCIENCE 2021; 12:746586. [PMID: 34745179 PMCID: PMC8566886 DOI: 10.3389/fpls.2021.746586] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 09/17/2021] [Indexed: 06/13/2023]
Abstract
We have developed a rapid Raman spectroscopy-based method for the detection and quantification of early innate immunity responses in Arabidopsis and Choy Sum plants. Arabidopsis plants challenged with flg22 and elf18 elicitors could be differentiated from mock-treated plants by their Raman spectral fingerprints. From the difference Raman spectrum and the value of p at each Raman shift, we derived the Elicitor Response Index (ERI) as a quantitative measure of the response whereby a higher ERI value indicates a more significant elicitor-induced immune response. Among various Raman spectral bands contributing toward the ERI value, the most significant changes were observed in those associated with carotenoids and proteins. To validate these results, we investigated several characterized Arabidopsis pattern-triggered immunity (PTI) mutants. Compared to wild type (WT), positive regulatory mutants had ERI values close to zero, whereas negative regulatory mutants at early time points had higher ERI values. Similar to elicitor treatments, we derived an analogous Infection Response Index (IRI) as a quantitative measure to detect the early PTI response in Arabidopsis and Choy Sum plants infected with bacterial pathogens. The Raman spectral bands contributing toward a high IRI value were largely identical to the ERI Raman spectral bands. Raman spectroscopy is a convenient tool for rapid screening for Arabidopsis PTI mutants and may be suitable for the noninvasive and early diagnosis of pathogen-infected crop plants.
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Affiliation(s)
- Pil Joong Chung
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
- Disruptive and Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
| | - Gajendra P. Singh
- Disruptive and Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
| | - Chung-Hao Huang
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
- Disruptive and Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
| | - Sayuj Koyyappurath
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
- Disruptive and Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
| | - Jun Sung Seo
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
| | - Hui-Zhu Mao
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
| | - Piyarut Diloknawarit
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
| | - Rajeev J. Ram
- Disruptive and Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Rajani Sarojam
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
- Disruptive and Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
| | - Nam-Hai Chua
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
- Disruptive and Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
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16
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Dou T, Sanchez L, Irigoyen S, Goff N, Niraula P, Mandadi K, Kurouski D. Biochemical Origin of Raman-Based Diagnostics of Huanglongbing in Grapefruit Trees. FRONTIERS IN PLANT SCIENCE 2021; 12:680991. [PMID: 34489991 PMCID: PMC8417418 DOI: 10.3389/fpls.2021.680991] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 05/31/2021] [Indexed: 05/29/2023]
Abstract
Biotic and abiotic stresses cause substantial changes in plant biochemistry. These changes are typically revealed by high-performance liquid chromatography (HPLC) and mass spectroscopy-coupled HPLC (HPLC-MS). This information can be used to determine underlying molecular mechanisms of biotic and abiotic stresses in plants. A growing body of evidence suggests that changes in plant biochemistry can be probed by Raman spectroscopy, an emerging analytical technique that is based on inelastic light scattering. Non-invasive and non-destructive detection and identification of these changes allow for the use of Raman spectroscopy for confirmatory diagnostics of plant biotic and abiotic stresses. In this study, we couple HPLC and HPLC-MS findings on biochemical changes caused by Candidatus Liberibacter spp. (Ca. L. asiaticus) in citrus trees to the spectroscopic signatures of plant leaves derived by Raman spectroscopy. Our results show that Ca. L. asiaticus cause an increase in hydroxycinnamates, the precursors of lignins, and flavones, as well as a decrease in the concentration of lutein that are detected by Raman spectroscopy. These findings suggest that Ca. L. asiaticus induce a strong plant defense response that aims to exterminate bacteria present in the plant phloem. This work also suggests that Raman spectroscopy can be used to resolve stress-induced changes in plant biochemistry on the molecular level.
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Affiliation(s)
- Tianyi Dou
- Department of Biochemistry and Biophysicsw, Texas A&M University, College Station, TX, United States
| | - Lee Sanchez
- Department of Biochemistry and Biophysicsw, Texas A&M University, College Station, TX, United States
| | - Sonia Irigoyen
- Texas A&M AgriLife Research and Extension Center at Weslaco, Weslaco, TX, United States
| | - Nicolas Goff
- Department of Biochemistry and Biophysics, Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
| | - Prakash Niraula
- Texas A&M AgriLife Research and Extension Center at Weslaco, Weslaco, TX, United States
| | - Kranthi Mandadi
- Texas A&M AgriLife Research and Extension Center at Weslaco, Weslaco, TX, United States
- Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, United States
| | - Dmitry Kurouski
- Department of Biochemistry and Biophysicsw, Texas A&M University, College Station, TX, United States
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
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17
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Morey R, Farber C, McCutchen B, Burow MD, Simpson C, Kurouski D, Cason J. Raman spectroscopy-based diagnostics of water deficit and salinity stresses in two accessions of peanut. PLANT DIRECT 2021; 5:e342. [PMID: 34458666 PMCID: PMC8377774 DOI: 10.1002/pld3.342] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 06/24/2021] [Accepted: 07/23/2021] [Indexed: 05/17/2023]
Abstract
Water deficit and salinity are two major abiotic stresses that have tremendous effect on crop yield worldwide. Timely identification of these stresses can help limit associated yield loss. Confirmatory detection and identification of water deficit stress can also enable proper irrigation management. Traditionally, unmanned aerial vehicle (UAV)-based imaging and satellite-based imaging, together with visual field observation, are used for diagnostics of such stresses. However, these approaches can only detect salinity and water deficit stress at the symptomatic stage. Raman spectroscopy (RS) is a noninvasive and nondestructive technique that can identify and detect plant biotic and abiotic stress. In this study, we investigated accuracy of Raman-based diagnostics of water deficit and salinity stresses on two greenhouse-grown peanut accessions: tolerant and susceptible to water deficit. Plants were grown for 76 days prior to application of the water deficit and salinity stresses. Water deficit treatments received no irrigation for 5 days, and salinity treatments received 1.0 L of 240-mM salt water per day for the duration of 5-day sampling. Every day after the stress was imposed, plant leaves were collected and immediately analyzed by a hand-held Raman spectrometer. RS and chemometrics could identify control and stressed (either water deficit or salinity) susceptible plants with 95% and 80% accuracy just 1 day after treatment. Water deficit and salinity stressed plants could be differentiated from each other with 87% and 86% accuracy, respectively. In the tolerant accessions at the same timepoint, the identification accuracies were 66%, 65%, 67%, and 69% for control, combined stresses, water deficit, and salinity stresses, respectively. The high selectivity and specificity for presymptomatic identification of abiotic stresses in the susceptible line provide evidence for the potential of Raman-based surveillance in commercial-scale agriculture and digital farming.
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Affiliation(s)
- Rohini Morey
- Department of Biochemistry and BiophysicsTexas A&M UniversityCollege StationTexasUSA
| | - Charles Farber
- Department of Biochemistry and BiophysicsTexas A&M UniversityCollege StationTexasUSA
| | | | | | | | - Dmitry Kurouski
- Department of Biochemistry and BiophysicsTexas A&M UniversityCollege StationTexasUSA
- Department of Biomedical EngineeringTexas A&M UniversityCollege StationTexasUSA
| | - John Cason
- Texas A&M AgriLife ResearchStephenvilleTexasUSA
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18
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Payne WZ, Kurouski D. Raman spectroscopy enables phenotyping and assessment of nutrition values of plants: a review. PLANT METHODS 2021; 17:78. [PMID: 34266461 PMCID: PMC8281483 DOI: 10.1186/s13007-021-00781-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 07/11/2021] [Indexed: 05/23/2023]
Abstract
Our civilization has to enhance food production to feed world's expected population of 9.7 billion by 2050. These food demands can be met by implementation of innovative technologies in agriculture. This transformative agricultural concept, also known as digital farming, aims to maximize the crop yield without an increase in the field footprint while simultaneously minimizing environmental impact of farming. There is a growing body of evidence that Raman spectroscopy, a non-invasive, non-destructive, and laser-based analytical approach, can be used to: (i) detect plant diseases, (ii) abiotic stresses, and (iii) enable label-free phenotyping and digital selection of plants in breeding programs. In this review, we critically discuss the most recent reports on the use of Raman spectroscopy for confirmatory identification of plant species and their varieties, as well as Raman-based analysis of the nutrition value of seeds. We show that high selectivity and specificity of Raman makes this technique ideal for optical surveillance of fields, which can be used to improve agriculture around the world. We also discuss potential advances in synergetic use of RS and already established imaging and molecular techniques. This combinatorial approach can be used to reduce associated time and cost, as well as enhance the accuracy of diagnostics of biotic and abiotic stresses.
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Affiliation(s)
- William Z Payne
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, 77843, USA
| | - Dmitry Kurouski
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, 77843, USA.
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, 77843, USA.
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19
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Farber C, Islam ASMF, Septiningsih EM, Thomson MJ, Kurouski D. Non-Invasive Identification of Nutrient Components in Grain. Molecules 2021; 26:3124. [PMID: 34073711 PMCID: PMC8197263 DOI: 10.3390/molecules26113124] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 05/19/2021] [Accepted: 05/22/2021] [Indexed: 12/03/2022] Open
Abstract
Digital farming is a modern agricultural concept that aims to maximize the crop yield while simultaneously minimizing the environmental impact of farming. Successful implementation of digital farming requires development of sensors to detect and identify diseases and abiotic stresses in plants, as well as to probe the nutrient content of seeds and identify plant varieties. Experimental evidence of the suitability of Raman spectroscopy (RS) for confirmatory diagnostics of plant diseases was previously provided by our team and other research groups. In this study, we investigate the potential use of RS as a label-free, non-invasive and non-destructive analytical technique for the fast and accurate identification of nutrient components in the grains from 15 different rice genotypes. We demonstrate that spectroscopic analysis of intact rice seeds provides the accurate rice variety identification in ~86% of samples. These results suggest that RS can be used for fully automated, fast and accurate identification of seeds nutrient components.
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Affiliation(s)
- Charles Farber
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA;
| | - A. S. M. Faridul Islam
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA; (A.S.M.F.I.); (E.M.S.); (M.J.T.)
| | - Endang M. Septiningsih
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA; (A.S.M.F.I.); (E.M.S.); (M.J.T.)
| | - Michael J. Thomson
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA; (A.S.M.F.I.); (E.M.S.); (M.J.T.)
| | - Dmitry Kurouski
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA;
- The Institute for Quantum Science and Engineering, Texas A&M University, College Station, TX 77843, USA
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20
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Payne WZ, Kurouski D. Raman-Based Diagnostics of Biotic and Abiotic Stresses in Plants. A Review. FRONTIERS IN PLANT SCIENCE 2021; 11:616672. [PMID: 33552109 PMCID: PMC7854695 DOI: 10.3389/fpls.2020.616672] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 12/17/2020] [Indexed: 05/11/2023]
Abstract
Digital farming is a novel agricultural philosophy that aims to maximize a crop yield with the minimal environmental impact. Digital farming requires the development of technologies that can work directly in the field providing information about a plant health. Raman spectroscopy (RS) is an emerging analytical technique that can be used for non-invasive, non-destructive, and confirmatory diagnostics of diseases, as well as the nutrient deficiencies in plants. RS is also capable of probing nutritional content of grains, as well as highly accurate identification plant species and their varieties. This allows for Raman-based phenotyping and digital selection of plants. These pieces of evidence suggest that RS can be used for chemical-free surveillance of plant health directly in the field. High selectivity and specificity of this technique show that RS may transform the agriculture in the US. This review critically discusses the most recent research articles that demonstrate the use of RS in diagnostics of abiotic and abiotic stresses in plants, as well as the identification of plant species and their nutritional analysis.
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Affiliation(s)
| | - Dmitry Kurouski
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
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21
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Farber C, Bryan R, Paetzold L, Rush C, Kurouski D. Non-Invasive Characterization of Single-, Double- and Triple-Viral Diseases of Wheat With a Hand-Held Raman Spectrometer. FRONTIERS IN PLANT SCIENCE 2020; 11:01300. [PMID: 33013951 PMCID: PMC7495046 DOI: 10.3389/fpls.2020.01300] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 08/11/2020] [Indexed: 05/10/2023]
Abstract
Plant diseases can reduce crop yield by up to 100%. Therefore, timely and confirmatory diagnosis of plant diseases is strongly desired. Typical pathogen assaying methods include polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA). These approaches are quite useful but are also time-consuming and destructive to the sample. Raman spectroscopy (RS) is a modern analytical technique that enables non-invasive plant disease detection. In this study, we report on Raman-based detection of wheat diseases caused by wheat streak mosaic virus (WSMV) and barley yellow dwarf virus (BYDV). Our results show that RS can be used to differentiate between healthy wheat and wheat infected by these two viruses. We also show that RS can be used to identify whether wheat is infected by these individual viruses or by a combination of WSMV and BYDV, as well as WSMV, BYDV, and Triticum mosaic virus (TriMV). We found that wheat spectra showed non-linear spectroscopic responses to coinfection by different viruses. These results suggest that RS can be used to probe pathogen-specific changes in plant metabolism. The portable nature of this approach opens the possibility of RS directly in the field for confirmatory diagnostics of viral diseases.
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Affiliation(s)
- Charles Farber
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
| | - Rebecca Bryan
- Department of Plant Pathology, Texas A&M AgriLife Research and Extension Center at Amarillo, Amarillo, TX, United States
| | - Li Paetzold
- Department of Plant Pathology, Texas A&M AgriLife Research and Extension Center at Amarillo, Amarillo, TX, United States
| | - Charles Rush
- Department of Plant Pathology, Texas A&M AgriLife Research and Extension Center at Amarillo, Amarillo, TX, United States
| | - Dmitry Kurouski
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
- The Institute for Quantum Science and Engineering, Texas A&M University, College Station, TX, United States
- *Correspondence: Dmitry Kurouski,
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