Chemometric development using portable molecular vibrational spectrometers for rapid evaluation of AVC (Valsa mali Miyabe et Yamada) infection of apple trees

Abstract Detecting of apple valsa canker (AVC), which is caused by Valsa mali Miyabe et Yamada, at early infection stage is beneficial to disease prevention and control for ensuring yield and quality of apples. Applying simple, economical and non-destructive rapid detection method for the early diagnosis of AVC is of great significance for precision management of orchards. In this research, near-infrared (NIR) spectroscopy (900-1700 nm) and Raman scattering (0-2000 cm-1) combined with machine learning algorithms were employed to diagnose 3 infection degrees of AVC (healthy, disease 1, disease 2) based on optimal variables which were selected by chemometric methods. NIR and Raman spectroscopy were obtained using portable spectrometers in reflection mode, respectively. Firstly, adaptive iterative reweighting partial least squares (air-PLS) was utilized to remove fluorescence background in Raman spectra. Secondly, clustering analysis was developed using principal component analysis (PCA). After that, optimal variables were selected by x-loadings (XLs) of PCA and competitive adaptive reweighted sampling (CARS) algorithm, respectively. Four optimal wavelengths at 983, 1156, 1395, and 1457 nm from NIR spectra and five optimal wavenumbers at 175, 229, 326, 409, and 1523 cm-1 from Raman spectra were identified by XLs. Six optimal wavelengths at 943, 949, 967, 1240, 1632, 1666 nm from NIR spectra and eight optimal wavelengths at 412, 1500, 1585, 1596, 1599, 1602, 1672 and 1709 cm-1 from Raman spectra were selected according to CARS. Finally, AVC diagnosing models were developed using least square support vector machine (LS-SVM), and receiver operating characteristic (ROC) curves were applied to evaluate the performance of the classification models. Overall, the relatively good classification results on optimal variables (94.67% and 97.33% in NIR, 89.33% and 89.33% in Raman) were obtained using LS-SVM. This study confirmed that molecular vibrational spectroscopy techniques (NIR and Raman) is promising for early detecting AVC and providing a practical way for diagnosing diseases in large-scale orchards.

[1]  G. Qiu,et al.  Discriminating growth stages of an endangered Mediterranean relict plant (Ammopiptanthus mongolicus) in the arid Northwest China using hyperspectral measurements. , 2019, The Science of the total environment.

[2]  Gregory J. Matthews,et al.  Confidence Intervals for the Area Under the Receiver Operating Characteristic Curve in the Presence of Ignorable Missing Data , 2018, International statistical review = Revue internationale de statistique.

[3]  Dong Hyuk Lee,et al.  Survey on the Occurrence of Apple Diseases in Korea from 1992 to 2000 , 2006 .

[4]  Patricia Garrido,et al.  Handheld Raman spectroscopy for the early detection of plant diseases: Abutilon mosaic virus infecting Abutilon sp. , 2016 .

[5]  Zhang Jianqiang,et al.  Detection of Thrips Defect on Green-Peel Citrus Using Hyperspectral Imaging Technology Combining PCA and B-Spline Lighting Correction Method , 2014 .

[6]  Yi-Zeng Liang,et al.  Baseline correction using adaptive iteratively reweighted penalized least squares. , 2010, The Analyst.

[7]  I. Rauschenbach,et al.  Detection and identification of salts and frozen salt solutions combining laser-induced breakdown spectroscopy and multivariate analysis methods: A study for future martian exploration , 2013 .

[8]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[9]  Hongdong Li,et al.  Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. , 2009, Analytica chimica acta.

[10]  Pei-Shih Liang,et al.  Nondestructive detection of zebra chip disease in potatoes using near-infrared spectroscopy , 2018 .

[11]  V. Botero-Fernández,et al.  Linking physiological parameters with visible/near-infrared leaf reflectance in the incubation period of vascular wilt disease , 2019, Saudi journal of biological sciences.

[12]  O. Adebo,et al.  Metabolomic approaches for the determination of metabolites from pathogenic microorganisms: A review. , 2021, Food research international.

[13]  Z. Nie,et al.  Direct identification and metabolomic analysis of Huanglongbing associated with Candidatus Liberibacter spp. in navel orange by MALDI-TOF-MS , 2020, Analytical and Bioanalytical Chemistry.

[14]  Charles Farber,et al.  Detection and Identification of Plant Pathogens on Maize Kernels with a Hand-Held Raman Spectrometer. , 2018, Analytical chemistry.

[15]  Qianqian Li,et al.  Moving window smoothing on the ensemble of competitive adaptive reweighted sampling algorithm. , 2019, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[16]  Artur Zdunek,et al.  Sensing the Structural Differences in Cellulose from Apple and Bacterial Cell Wall Materials by Raman and FT-IR Spectroscopy , 2011, Sensors.

[17]  Ryan Martin,et al.  Model-free posterior inference on the area under the receiver operating characteristic curve , 2019, 1906.08296.

[18]  Johan A. K. Suykens,et al.  Support Vector Machines: A Nonlinear Modelling and Control Perspective , 2001, Eur. J. Control.

[19]  Qi Wang,et al.  Isolation and characterization of Bacillus amyloliquefaciens PG12 for the biological control of apple ring rot , 2016 .

[20]  A. Synytsya.  Fourier transform Raman and infrared spectroscopy of pectins , 2003 .

[21]  D. Kurouski,et al.  Rapid and noninvasive diagnostics of Huanglongbing and nutrient deficits on citrus trees with a handheld Raman spectrometer , 2019, Analytical and Bioanalytical Chemistry.

[22]  Dmitry Kurouski,et al.  Detection and Identification of Fungal Infections in Intact Wheat and Sorghum Grain Using a Hand-Held Raman Spectrometer. , 2018, Analytical chemistry.

[23]  J. Hussain,et al.  Application of reflectance spectroscopies (FTIR-ATR & FT-NIR) coupled with multivariate methods for robust in vivo detection of begomovirus infection in papaya leaves. , 2018, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[24]  Zhen‐Chuan Fan,et al.  Apple phlorizin reduce plasma cholesterol by down-regulating hepatic HMG-CoA reductase and enhancing the excretion of fecal sterols , 2019, Journal of Functional Foods.

[25]  Lili Huang,et al.  A Nested PCR Assay for Detecting Valsa mali var. mali in Different Tissues of Apple Trees. , 2012, Plant disease.

[26]  Anna O. Conrad,et al.  Fourier-transform infrared (FT-IR) spectroscopy analysis discriminates asymptomatic and symptomatic Norway spruce trees. , 2019, Plant science : an international journal of experimental plant biology.

[27]  S. Kale,et al.  Identification of different species of Bacillus isolated from Nisargruna Biogas Plant by FTIR, UV-Vis and NIR spectroscopy. , 2015, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[28]  Zhenyi Chen,et al.  Surface-enhanced Raman scattering spectra revealing the inter-cultivar differences for Chinese ornamental Flos Chrysanthemum: a new promising method for plant taxonomy , 2017, Plant Methods.

[29]  Qi Wang,et al.  Fengycin produced by Bacillus subtilis 9407 plays a major role in the biocontrol of apple ring rot disease. , 2017, Microbiological research.

[30]  R. Ramasamy,et al.  Current and Prospective Methods for Plant Disease Detection , 2015, Biosensors.

[31]  Francisco Javier González,et al.  Raman Spectroscopy an Option for the Early Detection of Citrus Huanglongbing , 2016, Applied spectroscopy.

[32]  M. Eberlin,et al.  Major phytopathogens and strains from cocoa (Theobroma cacao L.) are differentiated by MALDI-MS lipid and/or peptide/protein profiles , 2017, Analytical and Bioanalytical Chemistry.

[33]  Yanbo Huang,et al.  Monitoring plant diseases and pests through remote sensing technology: A review , 2019, Comput. Electron. Agric..

[34]  Yufeng Ge,et al.  High-throughput analysis of leaf physiological and chemical traits with VIS–NIR–SWIR spectroscopy: a case study with a maize diversity panel , 2019, Plant Methods.

[35]  K. Elsayad.  Optical imaging spectroscopy for plant research: more than a colorful picture. , 2019, Current opinion in plant biology.

[36]  Da-Wen Sun,et al.  Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry , 2014, Food and Bioprocess Technology.