Inspecting Species and Freshness of Fish Fillets Using Multimode Hyperspectral Imaging Techniques

This study developed multimode hyperspectral imaging techniques to detect substitution and mislabeling of fish fillets. Line-scan hyperspectral images were collected from fish fillets in four modes, including reflectance in visible and nearinfrared (VNIR) region, fluorescence by 365 nm UV excitation, reflectance in short-wave infrared (SWIR) region, and Raman by 785 nm laser excitation. Fish fillets of six species (i.e., red snapper, vermilion snapper, Malabar snapper, summer flounder, white bass, and tilapia) were used for species differentiation and frozen-thawed red snapper fillets were used for freshness evaluation. A total of 24 machine learning classifiers were used for fish species and freshness classifications using four types of spectral data in three different subsets (i.e., full spectra, first ten components of principal component analysis, and bands selected by a sequential feature selection method). The highest accuracies were achieved at 100% using full VNIR reflectance spectra for the species classification and 99.9% using full SWIR reflectance spectra for the freshness classification. The VNIR reflectance mode gave an overall best performance for both species and freshness inspection

Detection of Fish Fillet Substitution and Mislabeling Using Multimode Hyperspectral Imaging Techniques

Substitution of high-priced fish species with inexpensive alternatives and mislabeling frozen-thawed fish fillets as fresh are two important fraudulent practices of concern in the seafood industry. This study aimed to develop multimode hyperspectral imaging techniques to detect substitution and mislabeling of fish fillets. Line-scan hyperspectral images were acquired from fish fillets in four modes, including reflectance in visible and near-infrared (VNIR) region, fluorescence by 365 nm UV excitation, reflectance in short-wave infrared (SWIR) region, and Raman by 785 nm laser excitation. Fish fillets of six species (i.e., red snapper, vermilion snapper, Malabar snapper, summer flounder, white bass, and tilapia) were used for species differentiation and frozen-thawed red snapper fillets were used for freshness evaluation. All fillet samples were DNA tested to authenticate the species. A total of 24 machine learning classifiers in six categories (i.e., decision trees, discriminant analysis, Naive Bayes classifiers, support vector machines, k-nearest neighbor classifiers, and ensemble classifiers) were used for fish species and freshness classifications using four types of spectral data in three different datasets (i.e., full spectra, first ten components of principal component analysis, and bands selected by sequential feature selection method). The highest accuracies were achieved at 100% using full VNIR reflectance spectra for the species classification and 99.9% using full SWIR reflectance spectra for the freshness classification. The VNIR reflectance mode gave the overall best performance for both species and freshness inspection, and it will be further investigated as a rapid technique for detection of fish fillet substitution and mislabeling

What's wrong with the murals at the Mogao Grottoes : a near-infrared hyperspectral imaging method

Although a significant amount of work has been performed to preserve the ancient murals in the Mogao Grottoes by Dunhuang Cultural Research, non-contact methods need to be developed to effectively evaluate the degree of flaking of the murals. In this study, we propose to evaluate the flaking by automatically analyzing hyperspectral images that were scanned at the site. Murals with various degrees of flaking were scanned in the 126th cave using a near-infrared (NIR) hyperspectral camera with a spectral range of approximately 900 to 1700 nm. The regions of interest (ROIs) of the murals were manually labeled and grouped into four levels: normal, slight, moderate, and severe. The average spectral data from each ROI and its group label were used to train our classification model. To predict the degree of flaking, we adopted four algorithms: deep belief networks (DBNs), partial least squares regression (PLSR), principal component analysis with a support vector machine (PCA + SVM) and principal component analysis with an artificial neural network (PCA + ANN). The experimental results show the effectiveness of our method. In particular, better results are obtained using DBNs when the training data contain a significant amount of striping noise

Analysis of Particle Dispersion in Turbulent Mixed Convection of CuO-water Nanofluid

In the present paper, turbulent convection of CuO-Water Nanofluid in a vertical channel is investigated numerically. In order to simulate the flow, the fluid is considered as a continuous phase while the discrete nanoparticles are dispersed through it. The dispersion of CuO nanoparticles in different flow conditions are studied in order to find the effective mechanisms of particles dispersion in the channel. The results show that in the fully developed turbulent convection flow, thermophoresis is more dominant than Brownian motion of nanoparticles and therefore the nanoparticles aggregation are more in the central areas of the channel. While in entrance region, where the boundary layer is not fully formed, the particles dispersion are more uniform. Also, an increase in the nanoparticles concentration will increase the turbulent velocity fluctuations in regions near the wall and this two-sided effect will cause improvement in turbulent flow thermal transmitance than the laminar flow

Quantifying the optical properties and chromophore concentrations of turbid media using polarization sensitive hyperspectral imaging: Optical phantom studies

We present a polarization-sensitive hyperspectral imaging system (SkinSpect) that employs a spectrally-programmable light source in the visible and NIR domains. Multiple tissue-mimicking phantoms were fabricated to mimic the optical properties of normal skin as well as pigmented light and dark moles. The phantoms consist of titanium dioxide and a mixture of coffee, red food dye, and naphthol green as the scattering and the three absorptive agents in a polydimethylsiloxane (PDMS) base. Phantoms were produced with both smooth and rough textured surfaces and tested using Spatial Frequency Domain Imaging (SFDI) and Spatially Modulated Quantitative Spectroscopy (SMoQS) for homogeneity as well as determining absorption and scattering variance, respectively. The reflectance spectral images were also recorded using the SkinSpect research prototype; the spectral signatures of the phantoms were calculated using a two-flux single-layer Kubelka-Munk model and non-negative least square fitting routine was applied to extract the relative concentrations of the individual phantom components. © 2013 Copyright SPIE

Quantifying the optical properties and chromophore concentrations of turbid media using polarization sensitive hyperspectral imaging: Optical phantom studies

We present a polarization-sensitive hyperspectral imaging system (SkinSpect) that employs a spectrally-programmable light source in the visible and NIR domains. Multiple tissue-mimicking phantoms were fabricated to mimic the optical properties of normal skin as well as pigmented light and dark moles. The phantoms consist of titanium dioxide and a mixture of coffee, red food dye, and naphthol green as the scattering and the three absorptive agents in a polydimethylsiloxane (PDMS) base. Phantoms were produced with both smooth and rough textured surfaces and tested using Spatial Frequency Domain Imaging (SFDI) and Spatially Modulated Quantitative Spectroscopy (SMoQS) for homogeneity as well as determining absorption and scattering variance, respectively. The reflectance spectral images were also recorded using the SkinSpect research prototype; the spectral signatures of the phantoms were calculated using a two-flux single-layer Kubelka-Munk model and non-negative least square fitting routine was applied to extract the relative concentrations of the individual phantom components. © 2013 Copyright SPIE

Multimode optical dermoscopy (SkinSpect) analysis for skin with melanocytic nevus

We have developed a multimode dermoscope (SkinSpect™) capable of illuminating human skin samples in-vivo with spectrally-programmable linearly-polarized light at 33 wavelengths between 468nm and 857 nm. Diffusely reflected photons are separated into collinear and cross-polarized image paths and images captured for each illumination wavelength. In vivo human skin nevi (N = 20) were evaluated with the multimode dermoscope and melanin and hemoglobin concentrations were compared with Spatially Modulated Quantitative Spectroscopy (SMoQS) measurements. Both systems show low correlation between their melanin and hemoglobin concentrations, demonstrating the ability of the SkinSpectTM to separate these molecular signatures and thus act as a biologically plausible device capable of early onset melanoma detection