Prof. Yankun Peng Profile

Prof. Yankun Peng

Professor at China Agricultural University

SPIE Involvement:

Conference Program Committee | Author

Publications (42)

Proceedings Article | 13 June 2023 Poster + Paper

Raman enhancement effect of different Au @ Ag NPs on S. Typhi

Yankun Peng, Yahui Chen, Qinghui Guo, Jianwei Qin

Proceedings Volume 12545, 125450C (2023) https://doi.org/10.1117/12.2663272

KEYWORDS: Silver, Gold, Raman spectroscopy, Surface enhanced Raman spectroscopy, Bacteria, Nanoparticles, Pathogens, Colloids, Raman scattering, Gold nanoparticles

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Proceedings Article | 12 April 2021 Poster + Paper

Spatial scattering Raman spectral characteristics of clenbuterol

Yankun Peng, Qinghui Guo, Yongyu Li, Kuanglin Chao, Feifei Tao

Proceedings Volume 11754, 117540O (2021) https://doi.org/10.1117/12.2587831

KEYWORDS: Raman spectroscopy, Scattering, Raman scattering, Laser scattering, Line scan image sensors, Spectrographs, Shape memory alloys, Nondestructive evaluation, Food inspection, Detection and tracking algorithms

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Proceedings Article | 22 April 2020 Presentation + Paper

Online detection of tomato internal and external quality attributes by an optical sensing system

Yankun Peng, Kuanglin Chao, Sagar Dhakal, Long Li, Yongyu Li

Proceedings Volume 11421, 114210T (2020) https://doi.org/10.1117/12.2557058

KEYWORDS: Optical sensing, Sensing systems, Light scattering, Absorption, Image processing, Image quality, Calibration, Image filtering, Detector development, Machine vision

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Proceedings Article | 30 April 2019 Paper

Nondestructive rapid detection of benzoyl peroxide in flour based on Raman hyperspectral technique

Yan Li, Jianwei Qin, Sagar Dhakal, Yankun Peng, Kuanglin Chao

Proceedings Volume 11016, 110160G (2019) https://doi.org/10.1117/12.2517454

KEYWORDS: Raman spectroscopy, Quantitative analysis, Visualization, Nondestructive evaluation, Imaging systems, Statistical analysis, Image acquisition, Luminescence, Hyperspectral imaging, Light sources

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Proceedings Article | 1 May 2017 Paper

A portable nondestructive detection device of quality and nutritional parameters of meat using Vis/NIR spectroscopy

Yankun Peng, Wenxiu Wang, Fan Wang, Hongwei Sun

Proceedings Volume 10217, 102170R (2017) https://doi.org/10.1117/12.2262508

KEYWORDS: Proteins, Spectrometers, Nondestructive evaluation, Data modeling, Statistical modeling, Spectroscopes, Waveguides, Data acquisition, Monte Carlo methods, Near infrared

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The improvement of living standards has urged consumers to pay more attention to the quality and nutrition of meat, so the development of nondestructive detection device for quality and nutritional parameters is commercioganic undoubtedly. In this research, a portable device equipped with visible (Vis) and near-infrared (NIR) spectrometers, tungsten halogen lamp, optical fiber, ring light guide and embedded computer was developed to realize simultaneous and fast detection of color (L*, a*, b*), pH, total volatile basic nitrogen (TVB-N), intramuscular fat (IF), protein and water content in pork. The wavelengths of dual-band spectrometers were 400~1100 nm and 940~1650 nm respectively and the tungsten halogen lamp cooperated with ring light guide to form a ring light source and provide appropriate illumination intensity for sample. Software was self-developed to control the functionality of dual-band spectrometers, set spectrometer parameters, acquire and process Vis/NIR spectroscopy and display the prediction results in real time. In order to obtain a robust and accurate prediction model, fresh longissimus dorsi meat was bought and placed in the refrigerator for 12 days to get pork samples with different freshness degrees. Besides, pork meat from three different parts including longissimus dorsi, haunch and lean meat was collected for the determination of IF, protein and water to make the reference values have a wider distribution range. After acquisition of Vis/NIR spectra, data from 400~1100 nm were pretreated with Savitzky-Golay (S-G) filter and standard normal variables transform (SNVT) and spectrum data from 940~1650 nm were preprocessed with SNVT. The anomalous were eliminated by Monte Carlo method based on model cluster analysis and then partial least square regression (PLSR) models based on single band (400~1100 nm or 940~1650 nm) and dual-band were established and compared. The results showed the optimal models for each parameter were built with correlation coefficients in prediction set of 0.9101, 0.9121, 0.8873, 0.9094, 0.9378, 0.9348, 0.9342 and 0.8882, respectively. It indicated this innovative and practical device can be a promising technology for nondestructive, fast and accurate detection of nutritional parameters in meat.

Proceedings Article | 1 May 2017 Paper

Nondestructive detection of total viable count changes of chilled pork in high oxygen storage condition based on hyperspectral technology

Yankun Peng, Xiaochun Zheng, Yongyu Li, Kuanglin Chao, Jianwei Qin

Proceedings Volume 10217, 102170S (2017) https://doi.org/10.1117/12.2262515

KEYWORDS: Imaging systems, Safety, Visualization, Bacteria, Nondestructive evaluation, Hyperspectral imaging, Visible radiation

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Proceedings Article | 1 May 2017 Paper

A portable device for detecting fruit quality by diffuse reflectance Vis/NIR spectroscopy

Yankun Peng, Peng Li, Wenxiu Wang, Hongwei Sun

Proceedings Volume 10217, 102170T (2017) https://doi.org/10.1117/12.2262526

KEYWORDS: Calibration, Agriculture, Diffuse reflectance spectroscopy, Spectroscopes, LCDs, Halogens, Lamps, Spectroscopy

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Proceedings Article | 1 May 2017 Paper

Improvement of the Raman detection system for pesticide residues on/in fruits and vegetables

Yankun Peng, Yan Li, Chen Zhai, Kuanglin Chao, Jianwei Qin

Proceedings Volume 10217, 102170U (2017) https://doi.org/10.1117/12.2262534

KEYWORDS: Agriculture, Imaging systems, Statistical analysis, Chaos, Spherical lenses, Detector development, Control systems, Switches, Raman scattering, Surface enhanced Raman spectroscopy

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Proceedings Article | 17 May 2016 Paper

Rapid discrimination of main red meat species based on near-infrared hyperspectral imaging technology

Yankun Peng, Lu Qiao, Kuanglin Chao, Jianwei Qin

Proceedings Volume 9864, 98640U (2016) https://doi.org/10.1117/12.2223296

KEYWORDS: Hyperspectral imaging, Principal component analysis, Data modeling, Near infrared, Statistical modeling, Data acquisition, Statistical analysis, Imaging spectroscopy, Reflectivity, Imaging systems

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Proceedings Article | 17 May 2016 Paper

Nondestructive and rapid detection of potato black heart based on machine vision technology

Yankun Peng, Wensong Wei, Fang Tian

Proceedings Volume 9864, 98640T (2016) https://doi.org/10.1117/12.2223292

KEYWORDS: Heart, Image processing, Light sources, Nondestructive evaluation, Machine vision, Light emitting diodes, Statistical analysis, Skin, Analytical research, CCD cameras

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Proceedings Article | 17 May 2016 Paper

Development of hand-held nondestructive detection device for assessing meat freshness

Yankun Peng, Wensong Wei, Lu Qiao

Proceedings Volume 9864, 98640W (2016) https://doi.org/10.1117/12.2223300

KEYWORDS: Light emitting diodes, Data modeling, Light sources, Reflectivity, Statistical modeling, Calibration, Signal detection, Light, Nondestructive evaluation, Nitrogen

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Proceedings Article | 17 May 2016 Paper

A portable nondestructive real-time detection system for inspection of pork quality attributes using Vis/NIR spectral technique

Yankun Peng, Hongwei Sun

Proceedings Volume 9864, 98640V (2016) https://doi.org/10.1117/12.2223299

KEYWORDS: Nondestructive evaluation, Inspection, Calibration, Spectroscopy, Reflectivity, Light sources, Instrument modeling, Safety, Lamps, Statistical modeling

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Proceedings Article | 13 May 2015 Paper

A rapid detection method of Escherichia coli by surface enhanced Raman scattering

Yankun Peng, Feifei Tao, Tianfeng Xu

Proceedings Volume 9488, 94880Q (2015) https://doi.org/10.1117/12.2176826

KEYWORDS: Surface enhanced Raman spectroscopy, Raman spectroscopy, Silver, Bacteria, Smoothing, Raman scattering, Analytical research, Calibration, Luminescence, Safety

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Proceedings Article | 13 May 2015 Paper

Rapid detection of benzoyl peroxide in wheat flour by using Raman scattering spectroscopy

Yankun Peng, Juan Zhao, Kuanglin Chao, Jianwei Qin, Sagar Dhakal, Tianfeng Xu

Proceedings Volume 9488, 94880S (2015) https://doi.org/10.1117/12.2176830

KEYWORDS: Raman spectroscopy, Luminescence, Spectroscopy, Raman scattering, CCD cameras, Chromatography, Chemical analysis, Data acquisition, Nondestructive evaluation, Spectroscopes

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Benzoyl peroxide is a common flour additive that improves the whiteness of flour and the storage properties of flour products. However, benzoyl peroxide adversely affects the nutritional content of flour, and excess consumption causes nausea, dizziness, other poisoning, and serious liver damage. This study was focus on detection of the benzoyl peroxide added in wheat flour. A Raman scattering spectroscopy system was used to acquire spectral signal from sample data and identify benzoyl peroxide based on Raman spectral peak position. The optical devices consisted of Raman spectrometer and CCD camera, 785 nm laser module, optical fiber, prober, and a translation stage to develop a real-time, nondestructive detection system. Pure flour, pure benzoyl peroxide and different concentrations of benzoyl peroxide mixed with flour were prepared as three sets samples to measure the Raman spectrum. These samples were placed in the same type of petri dish to maintain a fixed distance between the Raman CCD and petri dish during spectral collection. The mixed samples were worked by pretreatment of homogenization and collected multiple sets of data of each mixture. The exposure time of this experiment was set at 0.5s. The Savitzky Golay (S-G) algorithm and polynomial curve-fitting method was applied to remove the fluorescence background from the Raman spectrum. The Raman spectral peaks at 619 cm^-1, 848 cm^-1, 890 cm^-1, 1001 cm^-1, 1234 cm^-1, 1603cm^-1, 1777cm^-1 were identified as the Raman fingerprint of benzoyl peroxide. Based on the relationship between the Raman intensity of the most prominent peak at around 1001 cm^-1 and log values of benzoyl peroxide concentrations, the chemical concentration prediction model was developed. This research demonstrated that Raman detection system could effectively and rapidly identify benzoyl peroxide adulteration in wheat flour. The experimental result is promising and the system with further modification can be applicable for more products in near future.

Proceedings Article | 13 May 2015 Paper

Nondestructive detection of pork quality based on dual-band VIS/NIR spectroscopy

Yankun Peng, Wenxiu Wang, Yuanyuan Liu, Yongyu Li, Xiuying Tang

Proceedings Volume 9488, 94880T (2015) https://doi.org/10.1117/12.2176831

KEYWORDS: Calibration, Spectrometers, Principal component analysis, Spectroscopy, Nondestructive evaluation, Reflectivity, Information fusion, Data modeling, Proteins, Standards development

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With the continuous development of living standards and the relative change of dietary structure, consumers’ rising and persistent demand for better quality of meat is emphasized. Colour, pH value, and cooking loss are important quality attributes when evaluating meat. To realize nondestructive detection of multi-parameter of meat quality simultaneously is popular in production and processing of meat and meat products. The objectives of this research were to compare the effectiveness of two bands for rapid nondestructive and simultaneous detection of pork quality attributes. Reflectance spectra of 60 chilled pork samples were collected from a dual-band visible/near-infrared spectroscopy system which covered 350-1100 nm and 1000-2600 nm. Then colour, pH value and cooking loss were determined by standard methods as reference values. Standard normal variables transform (SNVT) was employed to eliminate the spectral noise. A spectrum connection method was put forward for effective integration of the dual-band spectrum to make full use of the whole efficient information. Partial least squares regression (PLSR) and Principal component analysis (PCA) were applied to establish prediction models using based on single-band spectrum and dual-band spectrum, respectively. The experimental results showed that the PLSR model based on dual-band spectral information was superior to the models based on single band spectral information with lower root means quare error (RMSE) and higher accuracy. The PLSR model based on dual-band (use the overlapping part of first band) yielded the best prediction result with correlation coefficient of validation (Rv) of 0.9469, 0.9495, 0.9180, 0.9054 and 0.8789 for L*, a*, b*, pH value and cooking loss, respectively. This mainly because dual-band spectrum can provide sufficient and comprehensive information which reflected the quality attributes. Data fusion from dual-band spectrum could significantly improve pork quality parameters prediction performance. The research also indicated that multi-band spectral information fusion has potential to comprehensively evaluate other quality and safety attributes of pork.

Proceedings Article | 13 May 2015 Paper

Depth of penetration of a 785nm wavelength laser in food powders

Kuanglin Chao, Sagar Dhakal, Jianwei Qin, Moon Kim, Yankun Peng, Walter Schmidt

Proceedings Volume 9488, 94880U (2015) https://doi.org/10.1117/12.2177000

KEYWORDS: Raman spectroscopy, Particles, Chemical analysis, Quantitative analysis, Binary data, Lasers, Statistical analysis, Luminescence, Image processing, Mining

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Proceedings Article | 13 May 2015 Paper

Research on identification and determination of mixed pesticides in apples using surface enhanced Raman spectroscopy

Yankun Peng, Chen Zhai, Sagar Dhakal, Yongyu Li, Tianfeng Xu, Kuanglin Chao, Jianwei Qin

Proceedings Volume 9488, 94880R (2015) https://doi.org/10.1117/12.2176829

KEYWORDS: Surface enhanced Raman spectroscopy, Raman spectroscopy, Silver, Luminescence, Safety, Nondestructive evaluation, Signal detection, Solids, Interference (communication), Electronic filtering

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Residual pesticides in fruits and vegetables have become one of the major food safety concerns around the world. At present, routine analytical methods used for the determination of pesticide residue on the surface of fruits and vegetables are destructive, complex, time-consuming, high cost and not environmentally friendly. In this study, a novel Surface Enhanced Raman Spectroscopy (SERS) method with silver colloid was developed for fast and sensitive nondestructive detection of residual pesticides in fruits and vegetables by using a self-developed Raman system. SERS technology is a combination of Raman spectroscopy and nanotechnology. SERS can greatly enhance the Raman signal intensity, achieve single-molecule detection, and has a simple sample pre-treatment characteristic of high sensitivity and no damage; in recent years it has begun to be used in food safety testing research. In this study a rapid and sensitive method was developed to identify and analyze mixed pesticides of chlorpyrifos, deltamethrin and acetamiprid in apple samples by SERS. Silver colloid was used for SERS measurement by hydroxylamine hydrochloride reduced. The advantages of this method are seen in its fast preparation at room temperature, good reproducibility and immediate applicability. Raman spectrum is highly interfered by noise signals and fluorescence background, which make it too complex to get good result. In this study the noise signals and fluorescence background were removed by Savitzky-Golay filter and min-max signal adaptive zooming method. Under optimal conditions, pesticide residues in apple samples can be detected by SERS at 0.005 μg/cm² and 0.002 μg/cm² for individual acetamiprid and thiram, respectively. When mixing the two pesticides at low concentrations, their characteristic peaks can still be identified from the SERS spectrum of the mixture. Based on the synthesized material and its application in SERS operation, the method represents an ultrasensitive SERS performance in apple samples detection without sample pre-treatment, which indicates that it could be served as a useful means in monitoring pesticide residues.

Proceedings Article | 28 May 2014 Paper

Detection of the total viable counts in chicken based on visible/near-infrared spectroscopy

Yankun Peng, Xiuying Tang, Yuan Long, Linlin Zhao, Caiping Wang, Fachao Jiang

Proceedings Volume 9108, 91080S (2014) https://doi.org/10.1117/12.2053201

KEYWORDS: Calibration, Data modeling, Infrared radiation, Spectroscopy, Safety, Thermal modeling, Agriculture, Breast, Infrared detectors, Statistical modeling

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Proceedings Article | 28 May 2014 Paper

Nondestructive detection of pork comprehensive quality based on spectroscopy and support vector machine

Caiping Wang, Yankun Peng, Yuanyuan Liu, Leilei Zhang, Sagar Dhakal

Proceedings Volume 9108, 91080R (2014) https://doi.org/10.1117/12.2050143

KEYWORDS: Nondestructive evaluation, Calibration, Spectroscopy, Statistical modeling, Data modeling, Agriculture, Optical filters, Digital filtering, Lamps, Light sources

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Proceedings Article | 28 May 2014 Paper

A portable device for rapid nondestructive detection of fresh meat quality

Yankun Peng, Wan Lin

Proceedings Volume 9108, 91080Q (2014) https://doi.org/10.1117/12.2050142

KEYWORDS: LCDs, Lamps, Spectral data processing, Nondestructive evaluation, Light sources, Data acquisition, Halogens, Spectroscopy, Signal processing, Spectrographs

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Proceedings Article | 28 May 2014 Paper

A portable detection instrument based on DSP for beef marbling

Yankun Peng, Tong Zhou

Proceedings Volume 9108, 91080P (2014) https://doi.org/10.1117/12.2050141

KEYWORDS: Digital signal processing, Image processing, LCDs, Signal processing, Video, Process control, Switches, Video processing, CCD cameras, Charge-coupled devices

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Proceedings Article | 28 May 2014 Paper

Rapid detection of chlorpyrifos pesticide residue concentration in agro-product using Raman spectroscopy

Kuanglin Chao, Jianwei Qin, Yankun Peng, Yongyu Li, Sagar Dhakal, Leilei Zhang, Tianfeng Xu

Proceedings Volume 9108, 91080O (2014) https://doi.org/10.1117/12.2050137

KEYWORDS: Raman spectroscopy, Luminescence, Algorithm development, Signal processing, Agriculture, Nondestructive evaluation, Chromatography, Electronic filtering, Detection and tracking algorithms, Optical filters

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