scholarly journals Rapid identification of Lactobacillus species using near infrared spectral features of bacterial colonies

2019 ◽  
Vol 27 (4) ◽  
pp. 302-313
Author(s):  
Jiyong Shi ◽  
Xuetao Hu ◽  
Xiaobo Zou ◽  
Zhiming Guo ◽  
Mel Holmes ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Near Infrared ◽  
Rapid Identification ◽  
Lactobacillus Species ◽  
Support Vector ◽  
Spectral Features ◽  
Mid Infrared ◽  
Near Infrared Spectra ◽  
Infrared Spectral ◽  
Chemical Groups

The feasibility of rapid identification of Lactobacillus species using near-infrared spectral features coupled with chemometrics was investigated. First, bacterial colonies of 11 Lactobacillus strains covering four species ( Lactobacillus casei, Lactobacillus reuteri, Lactobacillus brevis, and Lactobacillus fermentum) were cultured using the spread-plate technique. Near-infrared spectra data of the Lactobacillus species were collected directly from the bacterial colonies. Second, 10 wavenumbers were selected from the near-infrared spectra data using uninformative variables elimination and genetic algorithm, and calibration models based on the 10 selected wavenumbers were built using least squares support vector machine. The identification rates for the prediction set and validation set were 89.04 and 85%, respectively. Third, chemical groups of the Lactobacillus cells contributing to the identification of the Lactobacillus strains were identified using mid infrared. The results of mid infrared data analysis indicated that 9 chemical groups could be considered characteristics for categorizing the 11 Lactobacillus strains. The relationship between the 10 selected wavenumbers and identified chemical groups was identified, which supported the satisfactory performance of the least squares support vector machine calibration model. This study demonstrated that near-infrared spectral features of bacterial colonies could be used for Lactobacillus typing at the strain level.

Near- versus Mid-Infrared Spectroscopy: Relationships between Spectral Changes Induced by Water and Relative Information Content of the Two Spectral Regions in Regard to High-Moisture Samples

1995 ◽  
Vol 49 (3) ◽  
pp. 295-303 ◽  
Author(s):  
James B. Reeves
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
High Moisture ◽  
Mid Infrared ◽  
Spectral Changes ◽  
Near Infrared Spectra ◽  
Infrared Spectral ◽  
Water Ph ◽  
Relative Information Content ◽  
Relationship Of

The objectives of this work were to examine similarities and differences in the near-infrared and mid-infrared spectral regions when one is working with high-moisture materials and to study spectral changes in these regions as a method to identify the relationship of spectral information in the near-IR to fundamental absorptions in the mid-IR. Near- and mid-infrared spectra were taken with a Digilab FTS-65 Fourier transform spectrometer. Liquids were examined by transmission and solids by reflectance. Results with solutions showed that less spectral distortion arises when one is subtracting water from mid- rather than from near-infrared spectra. It was also easier to produce high-quality spectra in the mid-infrared by using attenuated total reflectance than by using transmission in the near-infrared. While mid-infrared spectra showed changes (induced by water, pH, physical state, and ionic strength) similar to those found in the near-infrared, there appeared to be more information available in the mid-infrared, even in the presence of water.

Application of Support Vector Machine in Identifying inside Moldy Chestnut Using Near Infrared Spectroscopy

2014 ◽  
Vol 615 ◽  
pp. 169-172
Author(s):  
Jie Liu ◽  
Xiao Yu Li ◽  
Wei Wang ◽  
Jun Zhang
Keyword(s):  
Support Vector Machine ◽  
Near Infrared ◽  
Scatter Correction ◽  
Nir Spectroscopy ◽  
Support Vector ◽  
Training Set ◽  
Near Infrared Spectra ◽  
Standard Normal ◽  
Validation Set ◽  
Normal Transformation

NIR spectroscopy has been applied in detecting inside quality of chestnut successfully. In this work, Support Vector Machine Discriminant Analysis was utilized to identify the qualified chestnuts, the serious moldy chestnuts and the slight moldy chestnuts using their Near infrared spectra region from 833 nm to 2500 nm. 109 chestnut samples were involved and four different preprocessing methods were compared. The results showed that for all the models, the average correct rates of training set and validation set were higher than 90%. The performance of model based on raw spectra was not as good as other models, which indicated the necessity of preprocessing. The models based on the spectra preprocessed by first derivative and multiplicative scatter correction had the same performances, with 97% and 85% as the correct rate of training set and validation set. The models based on the spectra preprocessed by Standard normal transformation has 100% correct rate of training set while 88% of validation set. The second derivative model had the best result with 100% and 90% as the correct rate of training set and validation set. These results demonstrated that the NIR spectroscopy had capability to detect interior mildew of intact chestnut nondestructively.

DWT-SVM on Near-Infrared Spectra for Moisture and Volatile Determination of Coal

2013 ◽  
Vol 483 ◽  
pp. 71-74
Author(s):  
Xiao Li Yang ◽  
Fan Wang ◽  
Ji Shu Chen ◽  
Shui Hua Zhang
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Bituminous Coal ◽  
Near Infrared ◽  
Parameters Optimization ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Satisfactory Performance ◽  
Near Infrared Spectra

We studied moisture and volatile determination in bituminous coal samples using near-infrared (NIR) spectra. This research applied support vector machine (SVM) and discrete wavelet transform (DWT). Firstly, NIR spectra were pre-processed by DWT for fitting and compression. Then, DWT coefficients were used to build regression model with SVM. We used NIR spectra to determination moisture and volatile determination in coal samples separately. Through parameters optimization, the results show that DWT-SVM can obtain satisfactory performance for moisture and volatile determination in coal samples.

Rapid Determining Contents of the Rhubarb Anthraquinones Compounds by Support Vector Machines Modeling based on Near Infrared Spectra

2020 ◽  
Vol 16 ◽  
Author(s):  
Linqi Liu ◽  
JInhua Luo ◽  
Chenxi Zhao ◽  
Bingxue Zhang ◽  
Wei Fan ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Rapid Determination ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Near Infrared Spectra ◽  
Aloe Emodin ◽  
Relative Differences

BACKGROUND: Measuring medicinal compounds to evaluate their quality and efficacy has been recognized as a useful approach in treatment. Rhubarb anthraquinones compounds (mainly including aloe-emodin, rhein, emodin, chrysophanol and physcion) are its main effective components as purgating drug. In the current Chinese Pharmacopoeia, the total anthraquinones content is designated as its quantitative quality and control index while the content of each compound has not been specified. METHODS: On the basis of forty rhubarb samples, the correlation models between the near infrared spectra and UPLC analysis data were constructed using support vector machine (SVM) and partial least square (PLS) methods according to Kennard and Stone algorithm for dividing the calibration/prediction datasets. Good models mean they have high correlation coefficients (R2) and low root mean squared error of prediction (RMSEP) values. RESULTS: The models constructed by SVM have much better performance than those by PLS methods. The SVM models have high R2 of 0.8951, 0.9738, 0.9849, 0.9779, 0.9411 and 0.9862 that correspond to aloe-emodin, rhein, emodin, chrysophanol, physcion and total anthraquinones contents, respectively. The corresponding RMSEPs are 0.3592, 0.4182, 0.4508, 0.7121, 0.8365 and 1.7910, respectively. 75% of the predicted results have relative differences being lower than 10%. As for rhein and total anthraquinones, all of the predicted results have relative differences being lower than 10%. CONCLUSION: The nonlinear models constructed by SVM showed good performances with predicted values close to the experimental values. This can perform the rapid determination of the main medicinal ingredients in rhubarb medicinal materials.

scholarly journals Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 742
Author(s):  
Canh Nguyen ◽  
Vasit Sagan ◽  
Matthew Maimaitiyiming ◽  
Maitiniyazi Maimaitijiang ◽  
Sourav Bhadra ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Viral Diseases ◽  
Support Vector ◽  
Spectral Features ◽  
Feature Spaces

Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, −92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400–1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial–spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900–940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400–700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples.

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