scholarly journals Application of Near-Infrared Spectroscopy to Quantitatively Determine Relative Content of Puccnia striiformis f. sp. tritici DNA in Wheat Leaves in Incubation Period

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yaqiong Zhao ◽  
Yilin Gu ◽  
Feng Qin ◽  
Xiaolong Li ◽  
Zhanhong Ma ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Incubation Period ◽  
Stripe Rust ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Puccinia Striiformis ◽  
Quantitative Detection ◽  
Support Vector ◽  
Infected Leaf ◽  
Wheat Leaves

Stripe rust caused by Puccinia striiformis f. sp. tritici (Pst) is a devastating wheat disease worldwide. Potential application of near-infrared spectroscopy (NIRS) in detection of pathogen amounts in latently Pst-infected wheat leaves was investigated for disease prediction and control. A total of 300 near-infrared spectra were acquired from the Pst-infected leaf samples in an incubation period, and relative contents of Pst DNA in the samples were obtained using duplex TaqMan real-time PCR arrays. Determination models of the relative contents of Pst DNA in the samples were built using quantitative partial least squares (QPLS), support vector regression (SVR), and a method integrated with QPLS and SVR. The results showed that the kQPLS-SVR model built with a ratio of training set to testing set equal to 3 : 1 based on the original spectra, when the number of the randomly selected wavelength points was 700, the number of principal components was 8, and the number of the built QPLS models was 5, was the best. The results indicated that quantitative detection of Pst DNA in leaves in the incubation period could be implemented using NIRS. A novel method for determination of latent infection levels of Pst and early detection of stripe rust was provided.

scholarly journals Quantitative Determination of Germinability ofPuccinia striiformisf. sp.triticiUrediospores Using Near Infrared Spectroscopy Technology

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yaqiong Zhao ◽  
Feng Qin ◽  
Pei Cheng ◽  
Xiaolong Li ◽  
Zhanhong Ma ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Quantitative Determination ◽  
Near Infrared Spectroscopy ◽  
Spectral Region ◽  
Near Infrared ◽  
Puccinia Striiformis ◽  
Support Vector ◽  
Near Infrared Reflectance ◽  
Preprocessing Method

Stripe rust caused byPuccinia striiformisf. sp.tritici(Pst) is an important disease on wheat. In this study, quantitative determination of germinability ofPsturediospores was investigated by using near infrared reflectance spectroscopy (NIRS) combined with quantitative partial least squares (QPLS) and support vector regression (SVR). The near infrared spectra of the urediospore samples were acquired using FT-NIR MPA spectrometer and the germination rate of each sample was measured using traditional spore germination method. The best QPLS model was obtained with vector correction as the preprocessing method of the original spectra and 4000–12000 cm−1as the modeling spectral region while the modeling ratio of the training set to the testing set was 4 : 1. The best SVR model was built when vector normalization was used as the preprocessing method, the modeling ratio was 5 : 1 and the modeling spectral region was 8000–11000 cm−1. The results showed that the effect of the best model built using QPLS or SVR was satisfactory. This indicated that quantitative determination of germinability ofPsturediospores using near infrared spectroscopy technology is feasible. A new method based on NIRS was provided for rapid, automatic, and nondestructive determination of germinability ofPsturediospores.

scholarly journals Effects of UV-B Radiation on Near-Infrared Spectroscopy and Identification ofPuccinia striiformisf. sp.tritici

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Pei Cheng ◽  
Xiaolong Li ◽  
Feng Qin ◽  
Longlian Zhao ◽  
Junhui Li ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Puccinia Striiformis ◽  
Control Treatment ◽  
Support Vector ◽  
Physiological Races ◽  
Spectral Curves ◽  
Near Infrared Spectra ◽  
Radiation Treatments

Based on near-infrared spectra of three physiological races ofPuccinia striiformisf. sp.tritici(i.e., CYR31, CYR32, and CYR33) irradiated under four UV-B intensities (i.e., 0, 150, 200, and 250 μw/cm2), the effects of UV-B radiation on near-infrared spectroscopy of the pathogen were investigated in spectral region 4000–10000 cm−1, and support vector machine models were built to identify UV-B radiation intensities and physiological races, respectively. The results showed that the spectral curves under UV-B radiation treatments exhibited great differences compared with the corresponding control treatment (0 μw/cm2) in the spectral regions 5300–5600 cm−1and 7000–7400 cm−1and that the absorbance values of all the three physiological races increased with the enhancement of UV-B radiation intensity. Based on near-infrared spectroscopy, different UV-B radiation intensities could be identified and different physiological races could be distinguished from each other with high accuracies. The results demonstrated the utility and stability of the proposed method to identify the physiological races.

scholarly journals Classification of Prefrontal Cortex Activity Based on Functional Near-Infrared Spectroscopy Data upon Olfactory Stimulation

2021 ◽  
Vol 11 (6) ◽  
pp. 701
Author(s):  
Cheng-Hsuan Chen ◽  
Kuo-Kai Shyu ◽  
Cheng-Kai Lu ◽  
Chi-Wen Jao ◽  
Po-Lei Lee
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Kernel Functions ◽  
Olfactory Stimulation ◽  
Support Vector ◽  
Hemodynamic Response Function ◽  
Functional Near Infrared Spectroscopy ◽  
Data Set

The sense of smell is one of the most important organs in humans, and olfactory imaging can detect signals in the anterior orbital frontal lobe. This study assessed olfactory stimuli using support vector machines (SVMs) with signals from functional near-infrared spectroscopy (fNIRS) data obtained from the prefrontal cortex. These data included odor stimuli and air state, which triggered the hemodynamic response function (HRF), determined from variations in oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb) levels; photoplethysmography (PPG) of two wavelengths (raw optical red and near-infrared data); and the ratios of data from two optical datasets. We adopted three SVM kernel functions (i.e., linear, quadratic, and cubic) to analyze signals and compare their performance with the HRF and PPG signals. The results revealed that oxyHb yielded the most efficient single-signal data with a quadratic kernel function, and a combination of HRF and PPG signals yielded the most efficient multi-signal data with the cubic function. Our results revealed superior SVM analysis of HRFs for classifying odor and air status using fNIRS data during olfaction in humans. Furthermore, the olfactory stimulation can be accurately classified by using quadratic and cubic kernel functions in SVM, even for an individual participant data set.

scholarly journals Analysis of Different Classification Techniques for Two-Class Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Noman Naseer ◽  
Nauman Khalid Qureshi ◽  
Farzan Majeed Noori ◽  
Keum-Shik Hong
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Continuous Wave ◽  
Mental Arithmetic ◽  
Statistical Significance ◽  
Support Vector ◽  
Functional Near Infrared Spectroscopy ◽  
Linear Discriminant

We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest) using functional near-infrared spectroscopy (fNIRS) signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO) signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks. In the classification, six different modalities, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA),k-nearest neighbour (kNN), the Naïve Bayes approach, support vector machine (SVM), and artificial neural networks (ANN), were utilized. With these classifiers, the average classification accuracies among the seven subjects for the 2- and 3-dimensional combinations of features were 71.6, 90.0, 69.7, 89.8, 89.5, and 91.4% and 79.6, 95.2, 64.5, 94.8, 95.2, and 96.3%, respectively. ANN showed the maximum classification accuracies: 91.4 and 96.3%. In order to validate the results, a statistical significance test was performed, which confirmed that thepvalues were statistically significant relative to all of the other classifiers (p< 0.005) using HbO signals.

scholarly journals Food powders classification using handheld Near-Infrared Spectroscopy and Support Vector Machine

2019 ◽  
Vol 1367 ◽  
pp. 012029 ◽  
Author(s):  
Mohamed Yasser Mohamed ◽  
Mahmud Iwan Solihin ◽  
Winda Astuti ◽  
Chun Kit Ang ◽  
Wan Zailah
Keyword(s):  
Support Vector Machine ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Support Vector ◽  
Food Powders

scholarly journals Identification of Tilletia foetida, Ustilago tritici, and Urocystis tritici Based on Near-Infrared Spectroscopy

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Yaqiong Zhao ◽  
Feng Qin ◽  
Fei Xu ◽  
Jinxing Ma ◽  
Zhenyu Sun ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Disease Control ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Smut Fungi ◽  
Pathogen Identification ◽  
Ustilago Tritici ◽  
Tilletia Foetida

Identifying plant pathogens for disease diagnosis and disease control strategy making is of great significance. In this study, based on near-infrared spectroscopy, a method for identifying three kinds of pathogens causing wheat smuts, including Tilletia foetida, Ustilago tritici, and Urocystis tritici, was investigated. Based on the acquired near-infrared spectral data of the teliospore samples of the three pathogens, pathogen identification models were built in different spectral regions using distinguished partial least squares (DPLS), backpropagation neural network (BPNN), and support vector machine (SVM). Satisfactory identification results were achieved using the DPLS, BPNN, and SVM models built in each of the 22 spectral regions. By contrast, the modeling effects of DPLS and SVM were better than those of BPNN. The modeling ratio of the training set to the testing set affected the identification results of the BPNN models more than those obtained using the DPLS and SVM models. In this study, a rapid, accurate, and nondestructive method was provided for plant pathogen identification, and some basis was provided for disease diagnosis, pathogen monitoring, and disease control. Moreover, some methodological references and supports were provided for identification of quarantine wheat smut fungi in plant quarantine.

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