Effects of Single Wavelength Selection for Anisakid Roundworm Larvae Detection through Multispectral Imaging

2007 ◽  
Vol 70 (8) ◽  
pp. 1890-1895 ◽  
Author(s):  
SVEIN K. STORMO ◽  
AGNAR H. SIVERTSEN ◽  
KARSTEN HEIA ◽  
HEIDI NILSEN ◽  
EDEL ELVEVOLL
Keyword(s):  
Light Scattering ◽  
Multispectral Imaging ◽  
Spatial Information ◽  
Imaging Spectroscopy ◽  
Fish Muscle ◽  
Alternative Methods ◽  
Spectral Image ◽  
Nematode Parasites ◽  
Food Quality And Safety ◽  
Commercially Important

The occurrence of parasites in fillets of commercially important fish species affects both food quality and safety. Presently, the detection and removal of nematode parasites is done by inspection on a light table (candling) and manual trimming of the fillets. This operation is costly and time-consuming and is not effective for detecting and removing all the nematodes in the fillets. In the last decades, several alternative methods have been proposed, but these methods have failed to replace the candling method. A newly described method called imaging spectroscopy has produced promising results because the operator can record both spectral and spatial information from an object. In this work, we studied single-wavelength bands from a spectral image. Discrimination between nematodes and other objects in the fillets is dependent on the level of contrast. Quantification of the contrast in such images revealed that the level of contrast varied when different wavelengths were selected, and these variations are correlated with the absorption properties of the nematode. Visible light scatters greatly in fish muscle, generally complicating the detection of nematodes. In this study, light scattering was used in a way that reduces the background complexity in spectral images. When light scattering properties were used in a wavelength range different from the bulk of the nematode light absorption, spectral images with significantly higher contrast were produced.

scholarly journals Sparse Subspace Clustering in Hyperspectral Images using Incomplete Pixels

TecnoLógicas ◽  
2019 ◽  
Vol 22 (46) ◽  
pp. 1-14 ◽  
Author(s):  
Jorge Luis Bacca ◽  
Henry Arguello
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Subspace Clustering ◽  
Unsupervised Classification ◽  
Hyperspectral Images ◽  
Image Clustering ◽  
Spectral Signature ◽  
Spectral Image ◽  
Sparse Subspace Clustering

Spectral image clustering is an unsupervised classification method which identifies distributions of pixels using spectral information without requiring a previous training stage. The sparse subspace clustering-based methods (SSC) assume that hyperspectral images lie in the union of multiple low-dimensional subspaces.  Using this, SSC groups spectral signatures in different subspaces, expressing each spectral signature as a sparse linear combination of all pixels, ensuring that the non-zero elements belong to the same class. Although these methods have shown good accuracy for unsupervised classification of hyperspectral images, the computational complexity becomes intractable as the number of pixels increases, i.e. when the spatial dimension of the image is large. For this reason, this paper proposes to reduce the number of pixels to be classified in the hyperspectral image, and later, the clustering results for the missing pixels are obtained by exploiting the spatial information. Specifically, this work proposes two methodologies to remove the pixels, the first one is based on spatial blue noise distribution which reduces the probability to remove cluster of neighboring pixels, and the second is a sub-sampling procedure that eliminates every two contiguous pixels, preserving the spatial structure of the scene. The performance of the proposed spectral image clustering framework is evaluated in three datasets showing that a similar accuracy is obtained when up to 50% of the pixels are removed, in addition, it is up to 7.9 times faster compared to the classification of the data sets without incomplete pixels.

scholarly journals Comparison of spectral selection methods in the development of classification models from visible near infrared hyperspectral imaging data

2019 ◽  
Author(s):  
Aoife Gowen ◽  
Jun-Li Xu ◽  
Ana Herrero-Langreo
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Imaging Spectroscopy ◽  
Model Performance ◽  
Data Sampling ◽  
Classification Models ◽  
Imaging Data ◽  
Selection Of

Applications of hyperspectral imaging (HSI) to the quantitative and qualitative measurement of samples have grown widely in recent years, due mainly to the improved performance and lower cost of imaging spectroscopy instrumentation. Data sampling is a crucial yet often overlooked step in hyperspectral image analysis, which impacts the subsequent results and their interpretation. In the selection of pixel spectra for the calibration of classification models, the spatial information in HSI data can be exploited. In this paper, a variety of sampling strategies for selection of pixel spectra are presented, exemplified through five case studies. The strategies are compared in terms of the proportion of global variability captured, practicality and predictive model performance. The use of variographic analysis as a guide to the spatial segmentation prior to sampling leads to the selection of representative subsets while reducing the variation in model performance parameters over repeated random selection.

An update on the progress of microbial biotransformation of commercial monoterpenes

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ruchika Mittal ◽  
Gauri Srivastava ◽  
Deepak Ganjewala
Keyword(s):  
Web Of Science ◽  
Alternative Methods ◽  
Biosynthetic Pathways ◽  
Chemical Methods ◽  
Geranyl Diphosphate ◽  
The Past ◽  
Bioactive Properties ◽  
Pharmacological Significance ◽  
Commercially Important ◽  
Extract Information

Abstract Monoterpenes, a class of isoprenoid compounds, are extensively used in flavor, fragrance, perfumery, and cosmetics. They display many astonishing bioactive properties of biological and pharmacological significance. All monoterpenes are derived from universal precursor geranyl diphosphate. The demand for new monoterpenoids has been increasing in flavor, fragrances, perfumery, and pharmaceuticals. Chemical methods, which are harmful for human and the environment, synthesize most of these products. Over the years, researchers have developed alternative methods for the production of newer monoterpenoids. Microbial biotransformation is one of them, which relied on microbes and their enzymes. It has produced many new desirable commercially important monoterpenoids. A growing number of reports reflect an ever-expanding scope of microbial biotransformation in food and aroma industries. Simultaneously, our knowledge of the enzymology of monoterpene biosynthetic pathways has been increasing, which facilitated the biotransformation of monoterpenes. In this article, we have covered the progress made on microbial biotransformation of commercial monoterpenes with a brief introduction to their biosynthesis. We have collected several reports from authentic web sources, including Google Scholar, Pubmed, Web of Science, and Scopus published in the past few years to extract information on the topic.

Rapid Seafood Species Identification Using Chip-Based Capillary Electrophoresis and Protein Pattern Matching

2017 ◽  
Vol 100 (5) ◽  
pp. 1500-1510 ◽  
Author(s):  
Calvin C Walker ◽  
Cheryl L Lassitter ◽  
Shannara N Lynn ◽  
Courtney B Ford ◽  
Kevin R Rademacher ◽  
...  
Keyword(s):  
Protein Extraction ◽  
Protein Pattern ◽  
Screening Method ◽  
Fish Muscle ◽  
Protein Profiling ◽  
Rapid Screening ◽  
Important Species ◽  
Commercially Important ◽  
Species Specific ◽  
High Level

Abstract Authenticity is crucial to the seafood industry, as substitution and mislabeling have important economic, environmental, and food safety consequences. Toaddress this problem, protein profiling and softwarealgorithm techniques were developed to classify fishmuscle samples by species. The method uses water-based protein extraction, chip-based microfluidic electrophoresis (Agilent 2100 Bioanalyzer) for the analysis of high abundance fish muscle proteins, and a novel data analysis method for species-specific proteinpattern recognition. The method's performance in distinguishing commercially important fish from commonly reported substitutions was evaluated using sensitivity, specificity, and accuracy determinations with all three performance measures at >98% for commonsubstitutions. This study demonstrates that uncookedseafood products of commercially important species of catfish, snapper, and grouper can be rapidly distinguished from commonly substituted species with a high level of confidence. A tiered testing approach toseafood species verification by sequentially applying a rapid screening method and DNA testing is proposed to more effectively ensure accurate product labeling.

scholarly journals Imaging Spectroscopy on Very Large Telescopes

1984 ◽  
Vol 79 ◽  
pp. 515-517
Author(s):  
Paul Atherton
Keyword(s):  
Fourier Transform ◽  
Spatial Information ◽  
Broad Band ◽  
Imaging Spectroscopy ◽  
Wide Field ◽  
Two Dimensional ◽  
Imaging Spectrometer ◽  
Resolution Element ◽  
Fabry Perot ◽  
Large Telescopes

Imaging Spectroscopy is a technique in which a spectrum is obtained for each spatial resolution element across a wide field. The data is essentially 3-D, and may be viewed as a series of monochromatic images, or as a two dimensional array of spectra. A device generating such data may be called an imaging spectrometer. In a previous paper (Atherton, 1983 SPIE 445, 535) three different imaging spectrometers - based on grating, Fabry-Perot and Fourier Transform devices - were compared in terms of their ability to obtain spectral and spatial information over a wide field and broad band, to the same spectral resolution and S/N ratio, using the same detector array. From such a study it is clear that interferometer based devices are significantly faster than conventional grating spectrographs.

Self-training-based spectral image reconstruction for art paintings with multispectral imaging

2017 ◽  
Vol 56 (30) ◽  
pp. 8461 ◽  
Author(s):  
Peng Xu ◽  
Haisong Xu ◽  
Changyu Diao ◽  
Zhengnan Ye
Keyword(s):  
Image Reconstruction ◽  
Multispectral Imaging ◽  
Spectral Image

scholarly journals Color Accuracy-Guided Data Reduction for Practical LED-based Multispectral Imaging

2021 ◽  
Vol 2021 (1) ◽  
pp. 65-70
Author(s):  
Olivia Kuzio ◽  
Susan Farnand
Keyword(s):  
Multispectral Imaging ◽  
Imaging System ◽  
Optimal Combination ◽  
Light Sources ◽  
Spectral Image ◽  
Image Capture ◽  
Spectral Bands ◽  
Color Profile ◽  
Imaging Strategy ◽  
Optimal Set

The color accuracy of an LED-based multispectral imaging strategy has been evaluated with respect to the number of spectral bands used to build a color profile and render the final image. Images were captured under select illumination conditions provided by 10-channel LED light sources. First, the imaging system was characterized in its full 10-band capacity, in which an image was captured under illumination by each of the 10 LEDs in turn, and the full set used to derive a system profile. Then, the system was characterized in increasingly reduced capacities, obtained by reducing the number of bands in two ways. In one approach, image bands were systematically removed from the full 10-band set. In the other, images were captured under illumination by groups of several of the LEDs at once. For both approaches, the system was characterized using different combinations of image bands until the optimal set, giving the highest color accuracy, was determined when a total of only 9, 8, 7, or 6 bands was used to derive the profile. The results indicate that color accuracy is nearly equivalent when rendering images based on the optimal combination of anywhere from 6 to 10 spectral bands, and is maintained at a higher level than that of conventional RGB imaging. This information is a first step toward informing the development of practical LED-based multispectral imaging strategies that make spectral image capture simpler and more efficient for heritage digitization workflows.

scholarly journals Multispectral and X-ray Images for Characterization of Jatropha Curcas L. Seed Quality

2020 ◽  
Author(s):  
Vitor J Bianchini ◽  
Gabriel M Mascarin ◽  
Lúcia CAS Silva ◽  
Valter Arthur ◽  
Jean M Carstensen ◽  
...  
Keyword(s):  
Jatropha Curcas ◽  
Multispectral Imaging ◽  
Seed Quality ◽  
Strong Relationship ◽  
Biodiesel Production ◽  
Alternative Methods ◽  
Quality Analysis ◽  
X Ray ◽  
Jatropha Seed

Abstract Background: Jatropha curcas is an oilseed plant with great potential for biodiesel production. In agricultural industry, the seed quality is still estimated by manual inspection, using destructive, time-consuming and subjective tests that depend on the seed analyst experience. Recent advances in machine vision combined with artificial intelligence algorithms can provide spatial and spectral information for characterization of biological images, reducing subjectivity and optimizing the analysis process.Results: We present a new method for automatic characterization of jatropha seed quality, based on multispectral imaging (MSI) combined with X-ray imaging. We propose an approach along with X-ray images in order to investigate internal problems such as damages in the embryonic axis and endosperm, considering the fact that seed surface profiles can be negatively affected, but without reaching important internal regions of the seeds. Our studies included the application of a normalized canonical discriminant analyses (nCDA) algorithm as a supervised transformation building method to classify spatial and spectral patters according to the classes of seed quality. Spectral reflectance signatures in a range of 780 to 970 nm and the X-ray images can efficiently predict quality traits such as normal seedlings, abnormal seedlings and dead seeds.Conclusions: MSI and X-ray images have a strong relationship with physiological performance of Jatropha curcas L. These techniques can be alternative methods for rapid, efficient, sustainable and non-destructive characterization of jatropha seed quality in the future, overcoming the intrinsic subjectivity of the conventional seed quality analysis.

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