Dr. Sagar Dhakal Profile

Dr. Sagar Dhakal

Scientist

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Author

Publications (22)

Proceedings Article | 22 April 2020 Presentation + Paper

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

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

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 | 22 April 2020 Paper

Continuous gradient temperature Raman spectroscopy of polyunsaturated fish lipids provides detailed vibrational analysis and rapid, nondestructive graphical product authentication

C. Leigh Broadhurst, Walter Schmidt, Sagar Dhakal, Jianwei Qin, Kuanglin Chao, Moon Kim

Proceedings Volume 11421, 1142106 (2020) https://doi.org/10.1117/12.2559227

KEYWORDS: Raman spectroscopy, Magnesium, Temperature metrology, Molecules, Liquids, Nondestructive evaluation, Calorimetry, Spectroscopy, Solid state physics, Absorption

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Proceedings Article | 14 May 2019 Presentation

Food safety and quality applications of line-scan Raman imaging and spectroscopy techniques (Conference Presentation)

Jianwei Qin, Moon Kim, Kuanglin Chao, Jeehwa Hong, Insuck Baek, Sagar Dhakal, Byoung-Kwan Cho

Proceedings Volume 11016, 1101604 (2019) https://doi.org/10.1117/12.2519083

KEYWORDS: Raman spectroscopy, Food inspection, Inspection, Safety, Laser safety, Laser systems engineering, Imaging systems, Laser scattering, Software development, Control systems

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

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 | 30 April 2019 Paper

Authentication of turmeric powder using hyperspectral Raman system

Sagar Dhakal, Kuanglin Chao, Moon Kim, Jianwei Qin

Proceedings Volume 11016, 1101602 (2019) https://doi.org/10.1117/12.2518940

KEYWORDS: Raman spectroscopy, Binary data, Contamination, Imaging systems, Hyperspectral imaging, Image analysis, Statistical analysis, Safety, Spectroscopy, Sensors

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Proceedings Article | 15 May 2018 Presentation + Paper

Detection of color dye contamination in spice powder using 1064 nm Raman chemical imaging system

Sagar Dhakal, Kuanglin Chao, Moon Kim, Jianwei Qin, Abigail Bae

Proceedings Volume 10665, 1066509 (2018) https://doi.org/10.1117/12.2303832

KEYWORDS: Raman spectroscopy, Imaging systems, Contamination, Hyperspectral imaging

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Proceedings Article | 15 May 2018 Presentation + Paper

Non-targeted and targeted Raman imaging detection of chemical contaminants in food powders

Jianwei Qin, Moon Kim, Kuanglin Chao, Sagar Dhakal, Byoung-Kwan Cho

Proceedings Volume 10665, 106650G (2018) https://doi.org/10.1117/12.2304384

KEYWORDS: Raman spectroscopy, Target detection, Imaging spectroscopy, Potassium, Chemical analysis, Safety, Line scan image sensors, Hyperspectral imaging, Imaging systems, Hyperspectral target detection

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Proceedings Article | 1 May 2017 Presentation + Paper

Raman spectroscopy method for subsurface detection of food powders through plastic layers

Sagar Dhakal, Kuanglin Chao, Jianwei Qin, Walter Schmidt, Moon Kim, Diane Chan, Abigail Bae

Proceedings Volume 10217, 1021706 (2017) https://doi.org/10.1117/12.2262102

KEYWORDS: Raman spectroscopy, Chemical analysis, Data modeling, Laser sources, Packaging, Imaging spectroscopy, Statistical analysis, Safety, Statistical modeling, Agriculture, Analytical research

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Proceedings Article | 17 May 2016 Paper

Detection of metanil yellow contamination in turmeric using FT-Raman and FT-IR spectroscopy

Sagar Dhakal, Kuanglin Chao, Jianwei Qin, Moon Kim, Walter Schmidt, Dian Chan

Proceedings Volume 9864, 98640A (2016) https://doi.org/10.1117/12.2223957

KEYWORDS: FT-IR spectroscopy, Contamination, Fourier transforms, Raman spectroscopy, Luminescence, Germanium, Statistical analysis, Chemical analysis, Crystals, Agriculture

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Proceedings Article | 17 May 2016 Paper

Raman spectroscopy-based detection of chemical contaminants in food powders

Kuanglin Chao, Sagar Dhakal, Jianwei Qin, Moon Kim, Abigail Bae

Proceedings Volume 9864, 98640Y (2016) https://doi.org/10.1117/12.2224470

KEYWORDS: Raman spectroscopy, Spatial resolution, Imaging spectroscopy, Spectroscopy, Particles, Imaging systems, Chemical analysis, Image resolution, Binary data, Image analysis

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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

Raman-spectroscopy-based chemical contaminant detection in milk powder

Sagar Dhakal, Kuanglin Chao, Jianwei Qin, Moon Kim

Proceedings Volume 9488, 94880E (2015) https://doi.org/10.1117/12.2176989

KEYWORDS: Raman spectroscopy, Particles, Spatial resolution, Chemical analysis, Quantitative analysis, Binary data, Statistical analysis, Spectral resolution, Chaos, Signal detection

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Proceedings Article | 13 May 2015 Paper

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

Juan Zhao, Yankun Peng, 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

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

Chen Zhai, Yongyu Li, Yankun Peng, Tianfeng Xu, Sagar Dhakal, 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

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

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

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

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

Sagar Dhakal, Yankun Peng, Yongyu Li, Kuanglin Chao, Jianwei Qin, 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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Proceedings Article | 29 May 2013 Paper

Rapid non-destructive assessment of pork edible quality by using VIS/NIR spectroscopic technique

Leilei Zhang, Yankun Peng, Sagar Dhakal, Yulin Song, Juan Zhao, Songwei Zhao

Proceedings Volume 8721, 872106 (2013) https://doi.org/10.1117/12.2015893

KEYWORDS: Spectroscopy, Nondestructive evaluation, Hyperspectral imaging, Calibration, Data modeling, Reflectivity, Near infrared spectroscopy, Biological research, Statistical analysis, Statistical modeling

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Proceedings Article | 29 May 2013 Paper

A noninvasive technique for real-time detection of bruises in apple surface based on machine vision

Juan Zhao, Yankun Peng, Sagar Dhakal, Leilei Zhang, Akira Sasao

Proceedings Volume 8721, 87210O (2013) https://doi.org/10.1117/12.2015897

KEYWORDS: Image processing, Machine vision, Cameras, Control systems, Agriculture, Binary data, Algorithm development, Image classification, Software development, Light emitting diodes

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Proceedings Article | 29 May 2013 Paper

Optical instrument development for detection of pesticide residue in apple surface

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

Proceedings Volume 8721, 87210M (2013) https://doi.org/10.1117/12.2015862

KEYWORDS: Prototyping, Data acquisition, Optical components, LabVIEW, Detector development, Luminescence, Algorithm development, Control systems, Software development, Raman spectroscopy

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Proceedings Article | 7 May 2012 Paper

Rapid detection of pesticide residue in apple based on Raman spectroscopy

Yongyu Li, Yunyun Sun, Yankun Peng, Sagar Dhakal, Kuanglin Chao, Qiaoqiao Liu

Proceedings Volume 8369, 83690I (2012) https://doi.org/10.1117/12.918527

KEYWORDS: Raman spectroscopy, Aluminum, Agriculture, Spectroscopy, Signal detection, Nondestructive evaluation, Analytical research, Denoising, Wavelets, Natural surfaces

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Proceedings Article | 3 June 2011 Paper

Hyperspectral imaging technique for determination of pork freshness attributes

Yongyu Li, Leilei Zhang, Yankun Peng, Xiuying Tang, Kuanglin Chao, Sagar Dhakal

Proceedings Volume 8027, 80270H (2011) https://doi.org/10.1117/12.883652

KEYWORDS: Hyperspectral imaging, Scattering, Reflectivity, Calibration, Imaging systems, Optical filters, Statistical modeling, Image filtering, Light scattering, Spectroscopy

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Proceedings Article | 21 April 2010 Paper

Rapid detection of total viable count of chilled pork using hyperspectral scattering technique

Yankun Peng, Feifei Tao, Yongyu Li, Wei Wang, Jingjing Chen, Jianhu Wu, Sagar Dhakal

Proceedings Volume 7676, 76760K (2010) https://doi.org/10.1117/12.849864

KEYWORDS: Scattering, Hyperspectral imaging, Light scattering, Safety, Calibration, Nondestructive evaluation, Data modeling, Laser scattering, Statistical modeling, Agriculture

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Showing 5 of 22 publications

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