Classification of foodborne pathogens using near infrared (NIR) laser scatter imaging system with multivariate calibration

Pan, Wenxiu; Zhao, Jiewen; Chen, Quansheng

doi:10.1038/srep09524

Cited by 29 publications

(14 citation statements)

References 41 publications

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“…We used the radial basis function (RBF) kernel as the kernel function of the LS-SVM and the grid-search algorithm to determine the regularization parameter c and the RBF kernel function parameter γ 2 . The optimum parameters are reached when the smallest root-mean-square error (RMSE) is produced 28 .…”

Section: Methodsmentioning

confidence: 99%

Early detection of germinated wheat grains using terahertz image and chemometrics

Jiang

Lian

et al. 2016

Sci Rep

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In this paper, we propose a feasible tool that uses a terahertz (THz) imaging system for identifying wheat grains at different stages of germination. The THz spectra of the main changed components of wheat grains, maltose and starch, which were obtained by THz time spectroscopy, were distinctly different. Used for original data compression and feature extraction, principal component analysis (PCA) revealed the changes that occurred in the inner chemical structure during germination. Two thresholds, one indicating the start of the release of α-amylase and the second when it reaches the steady state, were obtained through the first five score images. Thus, the first five PCs were input for the partial least-squares regression (PLSR), least-squares support vector machine (LS-SVM), and back-propagation neural network (BPNN) models, which were used to classify seven different germination times between 0 and 48 h, with a prediction accuracy of 92.85%, 93.57%, and 90.71%, respectively. The experimental results indicated that the combination of THz imaging technology and chemometrics could be a new effective way to discriminate wheat grains at the early germination stage of approximately 6 h.

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Section: Methodsmentioning

confidence: 99%

Early detection of germinated wheat grains using terahertz image and chemometrics

Jiang

Lian

et al. 2016

Sci Rep

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“…In this paper, two distinct kinds of spectral variable selection algorithms of RF 19 and SP 20 were conducted to extract the most important spectral wave bands to determine the collision strength index of EVAC. The RF algorithm is a dynamic process of simple random walk, which is considered as similar as the algorithm of reversible jump Markov chain Monte Carlo is implemented in an iterative way.…”

Section: Theory and Approachesmentioning

confidence: 99%

“…The paper is organized as follows: Section 2 describes the materials and devices. Section 3 presents theory and approaches of random frog (RF) 19 , successive projection (SP) 20 , support vector machine regression (SVMR) 21 and performance assessment. Section 4.1 and 4.2 explains the relationship between the absorption attributes of NIR spectroscopy and the inner chemical composition of EVAC materials.…”

mentioning

confidence: 99%

Fast quantifying collision strength index of ethylene-vinyl acetate copolymer coverings on the fields based on near infrared hyperspectral imaging techniques

Chen

Lin

et al. 2016

Sci Rep

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A novel strategy based on the near infrared hyperspectral imaging techniques and chemometrics were explored for fast quantifying the collision strength index of ethylene-vinyl acetate copolymer (EVAC) coverings on the fields. The reflectance spectral data of EVAC coverings was obtained by using the near infrared hyperspectral meter. The collision analysis equipment was employed to measure the collision intensity of EVAC materials. The preprocessing algorithms were firstly performed before the calibration. The algorithms of random frog and successive projection (SP) were applied to extracting the fingerprint wavebands. A correlation model between the significant spectral curves which reflected the cross-linking attributions of the inner organic molecules and the degree of collision strength was set up by taking advantage of the support vector machine regression (SVMR) approach. The SP-SVMR model attained the residual predictive deviation of 3.074, the square of percentage of correlation coefficient of 93.48% and 93.05% and the root mean square error of 1.963 and 2.091 for the calibration and validation sets, respectively, which exhibited the best forecast performance. The results indicated that the approaches of integrating the near infrared hyperspectral imaging techniques with the chemometrics could be utilized to rapidly determine the degree of collision strength of EVAC.

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“…Raman spectroscopy has been found to have increasing applications in the field of foodborne pathogen detection due to its rapid and ultrasensitive properties [9,10]. Pan et al reported a near infrared (NIR) laser scattering imaging system for rapid and noninvasive classification of foodborne pathogens [11]. The study results explored the potential of this system combined with multivariate calibrations for classifying three categories of common bacteria.…”

Section: Introductionmentioning

confidence: 99%

Rapid Classification of Single Bacterium Based on Backscattering Microscopic Spectrum—A Pilot Study

Wang

Liu

et al. 2020

Front. Phys.

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Rapid detection of foodborne pathogens is one of the most effective ways to solve food safety problems. To achieve rapid and noninvasive detection and classification of foodborne pathogens, we modified a fiber confocal backscattering micro-spectral system to suit an extremely small biological sample, that is, a bacterium. This system offers single-bacterium level, label-free, convenient, and environmentally friendly characterization. Three categories of common foodborne pathogens (Salmonella typhimurium, Escherichia coli, and Staphylococcus aureus) were measured. The scattering spectrum ranging from 450 to 900 nm was selected, and by the model of principal component analysis (PCA) and error back propagation algorithm of back propagation neural network (BPNN), the backscattering microscopic spectra of three categories of pathogens were dimensionally reduced, identified, and classified. The results showed that the identification accuracy of three categories of pathogens was above 90%, under neutral, acidic, and alkaline culturing conditions, respectively. The preliminary results demonstrated the feasibility of using confocal backscattering microscopic spectra combined with PCA and BPNN algorithm to identify and classify single bacterium in a rapid, noninvasive, and label-free manner.

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Classification of foodborne pathogens using near infrared (NIR) laser scatter imaging system with multivariate calibration

Cited by 29 publications

References 41 publications

Early detection of germinated wheat grains using terahertz image and chemometrics

Early detection of germinated wheat grains using terahertz image and chemometrics

Fast quantifying collision strength index of ethylene-vinyl acetate copolymer coverings on the fields based on near infrared hyperspectral imaging techniques

Rapid Classification of Single Bacterium Based on Backscattering Microscopic Spectrum—A Pilot Study

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