Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review — Part I: Fundamentals

Wu, Di; Sun, Da‐Wen

doi:10.1016/j.ifset.2013.04.014

Cited by 448 publications

(297 citation statements)

References 56 publications

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“…In this research, the lighting system is configured to suit the most common sensing modes: reflectance and transmittance, where both are applicable since honey is neither 100% transparent nor 100% opaque substance. In the reflectance sensing mode, where the light source and the imager located on the same side, the light reflects from the sample to the imager, while in the transmittance sensing mode, where the light source and the imager located on opposite sides of the sample, the light travels through the sample to the imager [10]. The illustrations of the reflectance and transmittance sensing modes accompanied with their corresponding hypercubes in RGB version are shown in Fig.…”

Section: Hypercube Acquisitionmentioning

confidence: 99%

Honey dataset standard using hyperspectral imaging for machine learning problems

Noviyanto

Abdullah

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Hyperspectral imaging has been rarely investigated for honey analyses, on the contrary to the optical spectroscopy which is widely investigated. The essential missing component to kick start this research is a standard honey hyperspectral images, called hypercubes, dataset. This paper proposes a systematic procedure for the preparation of the first honey hypercube dataset using hyperspectral imaging. Moreover, a scalable and flexible dataset module is introduced to ease the interaction between raw hypercube data and machine learning software. The developed dataset greatly benefits researchers to progress on the research of honey analysis including constituents prediction and types classification using hyperspectral imaging and machine learning.

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Section: Hypercube Acquisitionmentioning

confidence: 99%

Honey dataset standard using hyperspectral imaging for machine learning problems

Noviyanto

Abdullah

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…Dozens or hundreds of images are usually obtained; hence, every pixel in a hyperspectral image has its own spectrum over a contiguous wavelength range (Wu and Sun, 2013). Hyperspectral imagery has the potential to extract more accurate and detailed information than that obtained in other cases involving remotely sensed data (Lugo.…”

Section: Introductionmentioning

confidence: 99%

Oil Spill Hyperspectral Data Analysis: Using Minimum Distance and Binary Encoding Algorithms

El_Rahman¹

2017

IJCNT

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Abstract:Oil spill calamities have increased, threating maritime ecosystems. This reinforces the need for accurate mapping of oilspill calamities. The use of hyperspectral classifiers to extract areas of oil spill in a test site was achieved in this work. The paper describes the effects of utilizing a set of hyperspectral image analysis algorithms such as Minimum Distance (MD) and Binary Encoding (BE) algorithms to classify hyperspectral images of oil-spill areas in the Gulf of Mexico using Environment for Visualizing Images software. Hyperspectral image subseting, region of interest and principal component analysis were performed in the preprocessing stage, which is used to reduce the vast amount of data and eliminate redundant data. The paper provides empirical insights on the classification accuracy of hyperspectral images. A confusion matrix is used to determine the accuracy of a classification by comparing a classification result with ground truth information. The overall accuracies were 94.6399% and 88.4422% for the MD and BE algorithms, respectively. Therefore, the two algorithms are accurate for classifying hyperspectral images of the Gulf of Mexico. However, the MD algorithm is more accurate than the BE algorithm.

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“…Optical sensing techniques including hyperspectral imaging (HSI), as research tools, have gained increasing attention as tools for the rapid detection of anomalies in food products in recent years [20][21][22][23][24]. Hyperspectral imaging, which combines imaging and spectroscopic technology, can rapidly acquire spectral information at each pixel in an image in a non-invasive and non-destructive manner, and thus has the potential for use in screening products for contaminants based on unique spectral features across the sample image [25].…”

Section: Introductionmentioning

confidence: 99%

Potential Application of Fluorescence Imaging for Assessing Fecal Contamination of Soil and Compost Maturity

Cho

Lee

Kim³

et al. 2016

Applied Sciences

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Pathogenic microorganisms can lead to serious outbreaks of foodborne illnesses, particularly if fresh produce becomes contaminated and then happens to be inappropriately handled in a manner that can incubate pathogens. Pathogenic microbial contamination of produce can occur through a variety of pathways, such as from the excrement of domesticated and wild animals, biological soil amendment, agricultural water, worker health and hygiene, and field tools used during growth and harvest. The use of mature manure compost and preventative control of fecal contamination from wildlife and livestock are subject to safety standards to minimize the risk of foodborne illness associated with produce. However, in a field production environment, neither traces of animal feces nor the degree of maturity of manure compost can be identified by the naked eye. In this study, we investigated hyperspectral fluorescence imaging techniques to characterize fecal samples from bovine, swine, poultry, and sheep species, and to determine feasibilities for both detecting the presence of animal feces as well as identifying the species origin of the feces in mixtures of soil and feces. In addition, the imaging techniques were evaluated for assessing the maturity of manure compost. The animal feces exhibited dynamic and unique fluorescence emission features that allowed for the detection of the presence of feces and showed that identification of the species origin of fecal matter present in soil-feces mixtures is feasible. Furthermore, the results indicate that using simple single-band fluorescence imaging at the fluorescence emission maximum for animal feces, simpler than full-spectrum hyperspectral fluorescence imaging, can be used to assess the maturity of manure compost.

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Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review — Part I: Fundamentals

Cited by 448 publications

References 56 publications

Honey dataset standard using hyperspectral imaging for machine learning problems

Honey dataset standard using hyperspectral imaging for machine learning problems

Oil Spill Hyperspectral Data Analysis: Using Minimum Distance and Binary Encoding Algorithms

Potential Application of Fluorescence Imaging for Assessing Fecal Contamination of Soil and Compost Maturity

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