Dr. Yongliang Liu Profile

Dr. Yongliang Liu

Visiting Chemist at Agricultural Research Service

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Publications (9)

Proceedings Article | 15 May 2018 Paper

Rapid and non-destructive detection of aflatoxin contamination of peanut kernels using visible/near-infrared (Vis/NIR) spectroscopy

Haibo Yao, Zuzana Hruska, Yongliang Liu, Kanniah Rajasekaran, Deepak Bhatnagar, Feifei Tao

Proceedings Volume 10665, 106650K (2018) https://doi.org/10.1117/12.2304399

KEYWORDS: Contamination, Spectroscopy, Data modeling, Nondestructive evaluation, Statistical modeling, Agriculture

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Aflatoxin contamination can occur in a wide variety of agricultural products pre- and post-harvest, posing potential severe health hazards to human and livestock. However, current methods for detecting aflatoxins are generally based on wet chemical analyses, which are time-consuming, destructive to test samples and require skilled personnel to perform, making them impossible for large-scale non-destructive screening and on-site detection. In this study, we utilized the visible/near-infrared (Vis/NIR) spectroscopy over the spectral range of 400-2500 nm to detect contamination of shelled commercial peanut kernels with the predominant aflatoxin B₁ (AFB₁). Our results indicated the usefulness of Vis/NIR spectroscopy combined with the chemometric techniques of partial least squares discriminant analysis (PLS-DA) and least squares support vector machine (LS-SVM) in identifying the AFB₁ contamination of peanut kernels. Both PLS-DA and LS-SVM methods provided satisfactory classification results using the full spectral information over the ranges of 410-1070 (I), 1120-2470 nm (II) and I+II. Based on the classification threshold of 20 ppb, the best PLS-DA prediction results using the full spectra yielded the average accuracy of 87.9% and overall accuracy of 88.6%. With 100 ppb as the classification threshold, the best PLS-DA model using the full spectra achieved the average accuracy of 94.0% and overall accuracy of 91.4%. Using the full spectra, the best average accuracies recorded by LS-SVM were 90.9% and 98.0%, with the classification thresholds of 20 and 100 ppb, respectively. Correspondingly, the best overall accuracies by LS-SVM were 90.0% and 97.1%. In addition, the simplified models of CARS-PLS-DA and CARS-LS-SVM also demonstrated good prediction capability in identifying the AFB1 contamination from peanut surface. Based on both classification thresholds of 20 and 100 ppb, the best CARS-PLS-DA and CARS-LS-SVM prediction results were ≥ 90.0% in both average accuracy and overall accuracy. Most importantly, the computation complexity and the employed data dimensionality were significantly reduced by using the simplified models.

Proceedings Article | 18 May 2012 Paper

Simple XRD algorithm for direct determination of cotton crystallinity

Yongliang Liu, Devron Thibodeaux, Philip Bauer, Don VanDerveer, Gary Gamble

Proceedings Volume 8374, 83740A (2012) https://doi.org/10.1117/12.918628

KEYWORDS: Crystals, FT-IR spectroscopy, Algorithm development, Crystallography, Calibration, Spectroscopy, Standards development, Attenuated total reflectance, Solids, Deconvolution

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Proceedings Article | 12 May 2011 Paper

Development of simple algorithm for direct and rapid determination of cotton maturity from FT-IR spectroscopy

Devron Thibodeaux, Gary Gamble, Yongliang Liu

Proceedings Volume 8032, 803210 (2011) https://doi.org/10.1117/12.883025

KEYWORDS: FT-IR spectroscopy, Algorithm development, Structured optical fibers, Image analysis, Calibration, Attenuated total reflectance, Imaging systems, Crystals, Imaging spectroscopy, Infrared spectroscopy

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Proceedings Article | 20 April 2010 Paper

UV/visible/near-infrared reflectance spectroscopic determination of cotton fiber and trash content in lint cotton waste

Gary Gamble, Yongliang Liu, Devron Thibodeaux

Proceedings Volume 7676, 767606 (2010) https://doi.org/10.1117/12.852290

KEYWORDS: Near infrared, Visible radiation, Calibration, Reflectance spectroscopy, Reflectivity, Spectroscopy, Near infrared spectroscopy, Performance modeling, Ultraviolet radiation, Data modeling

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Proceedings Article | 18 April 2008 Paper

Rapid screening and species identification of E. coli, Listeria, and Salmonella by SERS technique

Moon Kim, Yongliang Liu, Xiangwu Nou, Kuanglin Chao

Proceedings Volume 6983, 698309 (2008) https://doi.org/10.1117/12.786881

KEYWORDS: Silver, Surface enhanced Raman spectroscopy, Nanoparticles, Raman spectroscopy, Bacteria, Signal processing, Glasses, Safety, Gold, Particles

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Proceedings Article | 22 October 2007 Paper

Surface-enhanced Raman scattering spectroscopy characterization and identification of foodborne bacteria

Xiangwu Nou, Yongliang Liu, Kaunglin Chao, Yud-Ren Chen

Proceedings Volume 6761, 676109 (2007) https://doi.org/10.1117/12.731275

KEYWORDS: Surface enhanced Raman spectroscopy, Silver, Bacteria, Raman scattering, Raman spectroscopy, Nanoparticles, Spectroscopy, Algorithm development, Metals, Glasses

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Proceedings Article | 20 October 2006 Paper

Detection of fecal/ingesta contaminants at slaughter plants from a number of characteristic visible and near-infrared bands

Diane Chan, Chun-Chieh Yang, Yud-Ren Chen, Moon Kim, Yongliang Liu, Xiangwu Nou, Kuanglin Chao

Proceedings Volume 6381, 63810U (2006) https://doi.org/10.1117/12.686225

KEYWORDS: Near infrared, Visible radiation, Forward error correction, Algorithm development, Principal component analysis, Inspection, Reflectivity, Agriculture, Pathogens, Contamination

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Proceedings Article | 19 November 2004 Paper

Development of hyperspectral imaging technique for the detection of chilling injury in cucumbers

Moon Kim, D. Chan, Yud-Ren Chen, Y. Liu, C. Wang

Proceedings Volume 5587, (2004) https://doi.org/10.1117/12.597550

KEYWORDS: Skin, Injuries, Principal component analysis, Hyperspectral imaging, Reflectivity, Image analysis, Calibration, Safety, Image processing, Information operations

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Proceedings Article | 28 March 2001 Paper

Advances in sensing technologies for poultry inspection

Yongliang Liu, Yud-Ren Chen, William Hruschka, Kuanglin Chao

Proceedings Volume 10301, 1030108 (2001) https://doi.org/10.1117/12.420102

KEYWORDS: Inspection, Agriculture, Safety, Food inspection

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Showing 5 of 9 publications

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