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Ahmed E, Musio B, Todisco S, Mastrorilli P, Gallo V, Saponari M, Nigro F, Gualano S, Santoro F. Non-Targeted Spectranomics for the Early Detection of Xylella fastidiosa Infection in Asymptomatic Olive Trees, cv. Cellina di Nardò. Molecules 2023; 28:7512. [PMID: 38005234 PMCID: PMC10672767 DOI: 10.3390/molecules28227512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 10/31/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
Olive quick decline syndrome (OQDS) is a disease that has been seriously affecting olive trees in southern Italy since around 2009. During the disease, caused by Xylella fastidiosa subsp. pauca sequence type ST53 (Xf), the flow of water and nutrients within the trees is significantly compromised. Initially, infected trees may not show any symptoms, making early detection challenging. In this study, young artificially infected plants of the susceptible cultivar Cellina di Nardò were grown in a controlled environment and co-inoculated with additional xylem-inhabiting fungi. Asymptomatic leaves of olive plants at an early stage of infection were collected and analyzed using nuclear magnetic resonance (NMR), hyperspectral reflectance (HSR), and chemometrics. The application of a spectranomic approach contributed to shedding light on the relationship between the presence of specific hydrosoluble metabolites and the optical properties of both asymptomatic Xf-infected and non-infected olive leaves. Significant correlations between wavebands located in the range of 530-560 nm and 1380-1470 nm, and the following metabolites were found to be indicative of Xf infection: malic acid, fructose, sucrose, oleuropein derivatives, and formic acid. This information is the key to the development of HSR-based sensors capable of early detection of Xf infections in olive trees.
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Affiliation(s)
- Elhussein Ahmed
- Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Polytechnic University of Bari, Via Orabona, 4, I-70125 Bari, Italy; (E.A.); (S.T.); (P.M.); (V.G.)
- International Centre for Advanced Mediterranean Agronomic Studies of Bari (CIHEAM Bari), Via Ceglie 9, 70010 Valenzano, Italy;
| | - Biagia Musio
- Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Polytechnic University of Bari, Via Orabona, 4, I-70125 Bari, Italy; (E.A.); (S.T.); (P.M.); (V.G.)
| | - Stefano Todisco
- Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Polytechnic University of Bari, Via Orabona, 4, I-70125 Bari, Italy; (E.A.); (S.T.); (P.M.); (V.G.)
| | - Piero Mastrorilli
- Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Polytechnic University of Bari, Via Orabona, 4, I-70125 Bari, Italy; (E.A.); (S.T.); (P.M.); (V.G.)
- Innovative Solutions S.r.l.—Spin-Off Company of Polytechnic University of Bari, Zona H 150/B, 70015 Noci, Italy
| | - Vito Gallo
- Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Polytechnic University of Bari, Via Orabona, 4, I-70125 Bari, Italy; (E.A.); (S.T.); (P.M.); (V.G.)
- Innovative Solutions S.r.l.—Spin-Off Company of Polytechnic University of Bari, Zona H 150/B, 70015 Noci, Italy
| | - Maria Saponari
- Istituto Per la Protezione Sostenibile Delle Piante, CNR, Via Amendola 122/D, I-70126 Bari, Italy;
| | - Franco Nigro
- Department of Soil, Plant and Food Sciences, University of Bari Aldo Moro, Via Orabona, 4, I-70125 Bari, Italy;
| | - Stefania Gualano
- International Centre for Advanced Mediterranean Agronomic Studies of Bari (CIHEAM Bari), Via Ceglie 9, 70010 Valenzano, Italy;
| | - Franco Santoro
- International Centre for Advanced Mediterranean Agronomic Studies of Bari (CIHEAM Bari), Via Ceglie 9, 70010 Valenzano, Italy;
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Falcioni R, Antunes WC, Demattê JAM, Nanni MR. Biophysical, Biochemical, and Photochemical Analyses Using Reflectance Hyperspectroscopy and Chlorophyll a Fluorescence Kinetics in Variegated Leaves. BIOLOGY 2023; 12:704. [PMID: 37237516 PMCID: PMC10215320 DOI: 10.3390/biology12050704] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/05/2023] [Accepted: 05/09/2023] [Indexed: 05/28/2023]
Abstract
The adjustments that occur during photosynthesis are correlated with morphological, biochemical, and photochemical changes during leaf development. Therefore, monitoring leaves, especially when pigment accumulation occurs, is crucial for monitoring organelles, cells, tissue, and whole-plant levels. However, accurately measuring these changes can be challenging. Thus, this study tests three hypotheses, whereby reflectance hyperspectroscopy and chlorophyll a fluorescence kinetics analyses can improve our understanding of the photosynthetic process in Codiaeum variegatum (L.) A. Juss, a plant with variegated leaves and different pigments. The analyses include morphological and pigment profiling, hyperspectral data, chlorophyll a fluorescence curves, and multivariate analyses using 23 JIP test parameters and 34 different vegetation indexes. The results show that photochemical reflectance index (PRI) is a useful vegetation index (VI) for monitoring biochemical and photochemical changes in leaves, as it strongly correlates with chlorophyll and nonphotochemical dissipation (Kn) parameters in chloroplasts. In addition, some vegetation indexes, such as the pigment-specific simple ratio (PSSRc), anthocyanin reflectance index (ARI1), ratio analysis of reflectance spectra (RARS), and structurally insensitive pigment index (SIPI), are highly correlated with morphological parameters and pigment levels, while PRI, moisture stress index (MSI), normalized difference photosynthetic (PVR), fluorescence ratio (FR), and normalized difference vegetation index (NDVI) are associated with photochemical components of photosynthesis. Combined with the JIP test analysis, our results showed that decreased damage to energy transfer in the electron transport chain is correlated with the accumulation of carotenoids, anthocyanins, flavonoids, and phenolic compounds in the leaves. Phenomenological energy flux modelling shows the highest changes in the photosynthetic apparatus based on PRI and SIPI when analyzed with Pearson's correlation, the hyperspectral vegetation index (HVI) algorithm, and the partial least squares (PLS) to select the most responsive wavelengths. These findings are significant for monitoring nonuniform leaves, particularly when leaves display high variation in pigment profiling in variegated and colorful leaves. This is the first study on the rapid and precise detection of morphological, biochemical, and photochemical changes combined with vegetation indexes for different optical spectroscopy techniques.
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Affiliation(s)
- Renan Falcioni
- Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil; (W.C.A.); (M.R.N.)
| | - Werner Camargos Antunes
- Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil; (W.C.A.); (M.R.N.)
| | - José A. M. Demattê
- Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Av. Pádua Dias, 11, Piracicaba 13418-260, São Paulo, Brazil;
| | - Marcos Rafael Nanni
- Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil; (W.C.A.); (M.R.N.)
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3
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Falcioni R, Gonçalves JVF, de Oliveira KM, de Oliveira CA, Demattê JAM, Antunes WC, Nanni MR. Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms. PLANTS (BASEL, SWITZERLAND) 2023; 12:1333. [PMID: 36987021 PMCID: PMC10059284 DOI: 10.3390/plants12061333] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/03/2023] [Accepted: 03/08/2023] [Indexed: 06/19/2023]
Abstract
In this study, we investigated the use of artificial intelligence algorithms (AIAs) in combination with VIS-NIR-SWIR hyperspectroscopy for the classification of eleven lettuce plant varieties. For this purpose, a spectroradiometer was utilized to collect hyperspectral data in the VIS-NIR-SWIR range, and 17 AIAs were applied to classify lettuce plants. The results showed that the highest accuracy and precision were achieved using the full hyperspectral curves or the specific spectral ranges of 400-700 nm, 700-1300 nm, and 1300-2400 nm. Four models, AdB, CN2, G-Boo, and NN, demonstrated exceptional R2 and ROC values, exceeding 0.99, when compared between all models and confirming the hypothesis and highlighting the potential of AIAs and hyperspectral fingerprints for efficient, precise classification and pigment phenotyping in agriculture. The findings of this study have important implications for the development of efficient methods for phenotyping and classification in agriculture and the potential of AIAs in combination with hyperspectral technology. To advance our understanding of the capabilities of hyperspectroscopy and AIs in precision agriculture and contribute to the development of more effective and sustainable agriculture practices, further research is needed to explore the full potential of these technologies in different crop species and environments.
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Affiliation(s)
- Renan Falcioni
- Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil; (J.V.F.G.); (K.M.d.O.); (C.A.d.O.); (W.C.A.); (M.R.N.)
| | - João Vitor Ferreira Gonçalves
- Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil; (J.V.F.G.); (K.M.d.O.); (C.A.d.O.); (W.C.A.); (M.R.N.)
| | - Karym Mayara de Oliveira
- Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil; (J.V.F.G.); (K.M.d.O.); (C.A.d.O.); (W.C.A.); (M.R.N.)
| | - Caio Almeida de Oliveira
- Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil; (J.V.F.G.); (K.M.d.O.); (C.A.d.O.); (W.C.A.); (M.R.N.)
| | - José A. M. Demattê
- Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Av. Pádua Dias, 11, Piracicaba 13418-260, São Paulo, Brazil;
| | - Werner Camargos Antunes
- Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil; (J.V.F.G.); (K.M.d.O.); (C.A.d.O.); (W.C.A.); (M.R.N.)
| | - Marcos Rafael Nanni
- Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil; (J.V.F.G.); (K.M.d.O.); (C.A.d.O.); (W.C.A.); (M.R.N.)
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Falcioni R, Moriwaki T, Gibin MS, Vollmann A, Pattaro MC, Giacomelli ME, Sato F, Nanni MR, Antunes WC. Classification and Prediction by Pigment Content in Lettuce ( Lactuca sativa L.) Varieties Using Machine Learning and ATR-FTIR Spectroscopy. PLANTS (BASEL, SWITZERLAND) 2022; 11:3413. [PMID: 36559526 PMCID: PMC9783279 DOI: 10.3390/plants11243413] [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: 10/21/2022] [Revised: 12/01/2022] [Accepted: 12/06/2022] [Indexed: 05/14/2023]
Abstract
Green or purple lettuce varieties produce many secondary metabolites, such as chlorophylls, carotenoids, anthocyanins, flavonoids, and phenolic compounds, which is an emergent search in the field of biomolecule research. The main objective of this study was to use multivariate and machine learning algorithms on Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR)-based spectra to classify, predict, and categorize chemometric attributes. The cluster heatmap showed the highest efficiency in grouping similar lettuce varieties based on pigment profiles. The relationship among pigments was more significant than the absolute contents. Other results allow classification based on ATR-FTIR fingerprints of inflections associated with structural and chemical components present in lettuce, obtaining high accuracy and precision (>97%) by using principal component analysis and discriminant analysis (PCA-LDA)-associated linear LDA and SVM machine learning algorithms. In addition, PLSR models were capable of predicting Chla, Chlb, Chla+b, Car, AnC, Flv, and Phe contents, with R2P and RPDP values considered very good (0.81−0.88) for Car, Anc, and Flv and excellent (0.91−0.93) for Phe. According to the RPDP metric, the models were considered excellent (>2.10) for all variables estimated. Thus, this research shows the potential of machine learning solutions for ATR-FTIR spectroscopy analysis to classify, estimate, and characterize the biomolecules associated with secondary metabolites in lettuce.
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Affiliation(s)
- Renan Falcioni
- Plant Ecophysiology Laboratory, Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Thaise Moriwaki
- Plant Ecophysiology Laboratory, Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Mariana Sversut Gibin
- Optical Spectroscopy and Thermophysical Properties Research Group, Graduate Program in Physics, Department of Physics, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Alessandra Vollmann
- Plant Ecophysiology Laboratory, Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Mariana Carmona Pattaro
- Plant Ecophysiology Laboratory, Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Marina Ellen Giacomelli
- Plant Ecophysiology Laboratory, Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Francielle Sato
- Optical Spectroscopy and Thermophysical Properties Research Group, Graduate Program in Physics, Department of Physics, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Marcos Rafael Nanni
- Plant Ecophysiology Laboratory, Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Werner Camargos Antunes
- Plant Ecophysiology Laboratory, Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
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Abiotic Stress and Belowground Microbiome: The Potential of Omics Approaches. Int J Mol Sci 2022; 23:ijms23031091. [PMID: 35163015 PMCID: PMC8835006 DOI: 10.3390/ijms23031091] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/11/2022] [Accepted: 01/17/2022] [Indexed: 02/06/2023] Open
Abstract
Nowadays, the worldwide agriculture is experiencing a transition process toward more sustainable production, which requires the reduction of chemical inputs and the preservation of microbiomes’ richness and biodiversity. Plants are no longer considered as standalone entities, and the future of agriculture should be grounded on the study of plant-associated microorganisms and all their potentiality. Moreover, due to the climate change scenario and the resulting rising incidence of abiotic stresses, an innovative and environmentally friendly technique in agroecosystem management is required to support plants in facing hostile environments. Plant-associated microorganisms have shown a great attitude as a promising tool to improve agriculture sustainability and to deal with harsh environments. Several studies were carried out in recent years looking for some beneficial plant-associated microbes and, on the basis of them, it is evident that Actinomycetes and arbuscular mycorrhizal fungi (AMF) have shown a considerable number of positive effects on plants’ fitness and health. Given the potential of these microorganisms and the effects of climate change, this review will be focused on their ability to support the plant during the interaction with abiotic stresses and on multi-omics techniques which can support researchers in unearthing the hidden world of plant–microbiome interactions. These associated microorganisms can increase plants’ endurance of abiotic stresses through several mechanisms, such as growth-promoting traits or priming-mediated stress tolerance. Using a multi-omics approach, it will be possible to deepen these mechanisms and the dynamic of belowground microbiomes, gaining fundamental information to exploit them as staunch allies and innovative weapons against crop abiotic enemies threatening crops in the ongoing global climate change context.
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Hudon SF, Zaiats A, Roser A, Roopsind A, Barber C, Robb BC, Pendleton BA, Camp MJ, Clark PE, Davidson MM, Frankel‐Bricker J, Fremgen‐Tarantino M, Forbey JS, Hayden EJ, Richards LA, Rodriguez OK, Caughlin TT. Unifying community detection across scales from genomes to landscapes. OIKOS 2021. [DOI: 10.1111/oik.08393] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
| | | | - Anna Roser
- Boise State Univ. Boise ID USA
- Center for Natural Climate Solutions, Conservation International Arlington VA USA
| | | | | | | | | | | | - Patrick E. Clark
- Northwest Watershed Research Center, USDA Agricultural Research Service Boise ID USA
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