scholarly journals Effect of colony age on near infrared hyperspectral images of foodborne bacteria

2019 ◽  
Author(s):  
Paul Williams ◽  
Terri-Lee Kammies ◽  
Pieter Gouws ◽  
Marena Manley
Keyword(s):  
Salmonella Enteritidis ◽  
Pathogenic Bacteria ◽  
Near Infrared ◽  
Imaging System ◽  
Principal Component ◽  
Growth Period ◽  
Luria Bertani ◽  
Hyperspectral Images ◽  
Growth Time ◽  
Order Polynomial

Near infrared hyperspectral imaging (NIR-HSI) and multivariate image analysis were used to distinguish between foodborne pathogenic bacteria, Bacillus cereus, Escherichia coli, Salmonella Enteritidis, Staphylococcus aureus and a non- pathogenic bacterium, Staphylococcus epidermidis. Hyperspectral images of bacteria, streaked out on Luria—Bertani agar, were acquired after 20 h, 40 h and 60 h growth at 37 °C using a SisuCHEMA hyperspectral pushbroom imaging system with a spectral range of 920–2514 nm. Three different pre-processing methods: standard normal variate (SNV), Savitzky—Golay (1stderivative, 2nd order polynomial, 15-point smoothing) and Savitzky—Golay (2nd derivative, 3rd order polynomial, 15-point smoothing) were evaluated. SNV provided the most distinct clustering in the principal component score plots and was thus used as the sole pre-processing method. Partial least squares discriminant analysis (PLS-DA) models were developed for each growth period and was tested on a second set of plates, to determine the effect the age of the colony has on classification accuracies. The highest overall prediction accuracies where test plates required the least amount of growth time, was found with models built after 60 h growth and tested on plates after 20 h growth. Predictions for bacteria differentiation within these models ranged from 83.1 % to 98.8 % correctly predicted pixels.

scholarly journals Hyperspectral Imaging from a Multipurpose Floating Platform to Estimate Chlorophyll-a Concentrations in Irrigation Pond Water

2020 ◽  
Vol 12 (13) ◽  
pp. 2070
Author(s):  
Geonwoo Kim ◽  
Insuck Baek ◽  
Matthew D. Stocker ◽  
Jaclyn E. Smith ◽  
Andrew L. Van Tassell ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Principal Component ◽  
Hyperspectral Images ◽  
Water Sampling ◽  
Floating Platform ◽  
Irrigation Pond

This study provides detailed information about the use of a hyperspectral imaging system mounted on a motor-driven multipurpose floating platform (MFP) for water quality sensing and water sampling, including the spatial and spectral calibration for the camera, image acquisition and correction procedures. To evaluate chlorophyll-a concentrations in an irrigation pond, visible/near-infrared hyperspectral images of the water were acquired as the MFP traveled to ten water sampling locations along the length of the pond, and dimensionality reduction with correlation analysis was performed to relate the image data to the measured chlorophyll-a data. About 80,000 sample images were acquired by the line-scan method. Image processing was used to remove sun-glint areas present in the raw hyperspectral images before further analysis was conducted by principal component analysis (PCA) to extract three key wavelengths (662 nm, 702 nm, and 752 nm) for detecting chlorophyll-a in irrigation water. Spectral intensities at the key wavelengths were used as inputs to two near-infrared (NIR)-red models. The determination coefficients (R2) of the two models were found to be about 0.83 and 0.81. The results show that hyperspectral imagery from low heights can provide valuable information about water quality in a fresh water source.

scholarly journals Nondestructive Identification of Salmon Adulteration with Water Based on Hyperspectral Data

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Tao Zhang ◽  
Biyao Wang ◽  
Pengtao Yan ◽  
Kunlun Wang ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Near Infrared ◽  
Water Injection ◽  
Principal Component ◽  
Local Extremum ◽  
Hyperspectral Data ◽  
Identification Accuracy ◽  
Hyperspectral Images ◽  
Nondestructive Identification ◽  
Som Network ◽  
Weighting Coefficients

For the identification of salmon adulteration with water injection, a nondestructive identification method based on hyperspectral images was proposed. The hyperspectral images of salmon fillets in visible and near-infrared ranges (390–1050 nm) were obtained with a system. The original hyperspectral data were processed through the principal-component analysis (PCA). According to the image quality and PCA parameters, a second principal-component (PC2) image was selected as the feature image, and the wavelengths corresponding to the local extremum values of feature image weighting coefficients were extracted as feature wavelengths, which were 454.9, 512.3, and 569.1 nm. On this basis, the color combined with spectra at feature wavelengths, texture combined with spectra at feature wavelengths, and color-texture combined with spectra at feature wavelengths were independently set as the input, for the modeling of salmon adulteration identification based on the self-organizing feature map (SOM) network. The distances between neighboring neurons and feature weights of the models were analyzed to realize the visualization of identification results. The results showed that the SOM-based model, with texture-color combined with fusion features of spectra at feature wavelengths as the input, was evaluated to possess the best performance and identification accuracy is as high as 96.7%.

A Hyperspectral Imaging System for Identification of Faecal and Ingesta Contamination on Poultry Carcasses

2003 ◽  
Vol 11 (4) ◽  
pp. 269-281 ◽  
Author(s):  
Kurt C. Lawrence ◽  
William R. Windham ◽  
Bosoon Park ◽  
R. Jeff Buhr
Keyword(s):  
Real Time ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Principal Component ◽  
Ccd Camera ◽  
Sample Population ◽  
Multivariate System ◽  
Ratio Image ◽  
On Line

A method and system for detecting faecal and ingesta contaminants on poultry carcasses were demonstrated. A visible/near infrared monochromator, which measured reflectance and principal component analysis were first used to identify key wavelengths from faecal and uncontaminated skin samples. Measurements at 434, 517, 565 and 628 nm were identified and used for evaluation with a hyperspectral imaging system. The hyperspectral imaging system, which was a line-scan (pushbroom) imaging system, consisted of a hyperspectral camera, fibre-optic line lights, a computer and frame grabber. The hyperspectral imaging camera consisted of a high-resolution charge coupled device (CCD) camera, a prism-grating-prism spectrograph, focusing lens, associated optical hardware and a motorised controller. The imaging system operated from about 400 to 900 nm. The hyperspectral imaging system was calibrated for wavelength, distance and percent reflectance and analysis of calibrated images at the key wavelengths indicated that single-wavelength images were inadequate for detecting contaminants. However, a ratio of images at two of the key wavelengths was able to identify faecal and ingesta contaminants. Specifically, the ratio of the 565-nm image divided by the 517-nm image produced good results. The ratio image was then further processed by masking the background and either enhancing the image contrast with a non-linear histogram stretch, or applying a faecal threshold. The results indicated that, for the limited sample population, more than 96% of the contaminants were detected. Thus, the hyperspectral imaging system was able to detect contaminants and showed feasibility, but was too slow for real-time on-line processing. Therefore, a multivariate system operating at 565 and 517 nm, which should be capable of operating at real-time on-line processing speed, should be used. Further research with such a system needs to be conducted.

Identifying Turmeric Powder by Source and Metanil Yellow Adulteration Levels Using Near-Infrared Spectra and PCA-SIMCA Modeling

2020 ◽  
Vol 83 (6) ◽  
pp. 968-974
Author(s):  
ISAAC R. RUKUNDO ◽  
MARY-GRACE C. DANAO
Keyword(s):  
Near Infrared ◽  
Principal Component ◽  
Near Infrared Spectra ◽  
Order Polynomial ◽  
Powder Samples ◽  
Infrared Spectrometer ◽  
Metanil Yellow ◽  
Low Levels ◽  
Level Of Significance ◽  
Near Infrared Spectrometer

ABSTRACT Turmeric sourced from six retailers was processed into a powder and adulterated with metanil yellow (MY) at concentrations of 0.0 to 30% (w/w). A handheld near-infrared spectrometer was used to obtain spectral scans of the samples, which were preprocessed using Savitzky-Golay first-derivative (SG1) approximation using 61 smoothing points and second-order polynomial. The preprocessed spectra were analyzed using principal component analysis (PCA) followed by classification by soft independent modeling class analogy (SIMCA) and were used to group the adulterated turmeric powder samples according to the source (i.e., processor) of adulteration. Results showed the first principal component (PC1) of PCA models was sensitive to adulteration level, but when coupled with SIMCA, unadulterated and adulterated samples could be classified according to their source despite having high levels of MY. At 5% level of significance, all of the samples were correctly classed for origin during validation. Some samples were classified under two groups, indicating possible inherent similarities. When the PCA model was built using only unadulterated samples, the PCA-SIMCA model could not classify the adulterated samples but could classify those with very low levels (≤2%, w/w) of MY, allowing for segregation of adulterated samples but not identification of sources. The combination of near-infrared and PCA-SIMCA modeling is a great tool not only to detect adulterated turmeric powder but also, potentially, to deter it in the future because the source of adulterated food can be traced back to the source of adulteration. HIGHLIGHTS

scholarly journals Remote Sensing of Explosives-Induced Stress in Plants: Hyperspectral Imaging Analysis for Remote Detection of Unexploded Threats

2019 ◽  
Vol 11 (15) ◽  
pp. 1827 ◽  
Author(s):  
Paul V. Manley ◽  
Vasit Sagan ◽  
Felix B. Fritschi ◽  
Joel G. Burken
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
Vegetation Index ◽  
Near Infrared ◽  
Imaging System ◽  
Principal Component ◽  
Field Capacity ◽  
Remotely Sensed ◽  
Control Group ◽  
Red Edge

Explosives contaminate millions of hectares from various sources (partial detonations, improper storage, and release from production and transport) that can be life-threatening, e.g., landmines and unexploded ordnance. Exposure to and uptake of explosives can also negatively impact plant health, and these factors can be can be remotely sensed. Stress induction was remotely sensed via a whole-plant hyperspectral imaging system as two genotypes of Zea mays, a drought-susceptible hybrid and a drought-tolerant hybrid, and a forage Sorghum bicolor were grown in a greenhouse with one control group, one group maintained at 60% soil field capacity, and a third exposed to 250 mg kg−1 Royal Demolition Explosive (RDX). Green-Red Vegetation Index (GRVI), Photochemical Reflectance Index (PRI), Modified Red Edge Simple Ratio (MRESR), and Vogelmann Red Edge Index 1 (VREI1) were reduced due to presence of explosives. Principal component analyses of reflectance indices separated plants exposed to RDX from control and drought plants. Reflectance of Z. mays hybrids was increased from RDX in green and red wavelengths, while reduced in near-infrared wavelengths. Drought Z. mays reflectance was lower in green, red, and NIR regions. S. bicolor grown with RDX reflected more in green, red, and NIR wavelengths. The spectra and their derivatives will be beneficial for developing explosive-specific indices to accurately identify plants in contaminated soil. This study is the first to demonstrate potential to delineate subsurface explosives over large areas using remote sensing of vegetation with aerial-based hyperspectral systems.

scholarly journals Detection of Sulfite Dioxide Residue on the Surface of Fresh-Cut Potato Slices Using Near-Infrared Hyperspectral Imaging System and Portable Near-Infrared Spectrometer

Molecules ◽  
2020 ◽  
Vol 25 (7) ◽  
pp. 1651 ◽  
Author(s):  
Xiulin Bai ◽  
Qinlin Xiao ◽  
Lei Zhou ◽  
Yu Tang ◽  
Yong He
Keyword(s):  
Sulfur Dioxide ◽  
Hyperspectral Imaging ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Imaging System ◽  
Principal Component ◽  
Support Vector ◽  
Svm Model ◽  
Fresh Cut ◽  
Potato Slices

Sodium pyrosulfite is a browning inhibitor used for the storage of fresh-cut potato slices. Excessive use of sodium pyrosulfite can lead to sulfur dioxide residue, which is harmful for the human body. The sulfur dioxide residue on the surface of fresh-cut potato slices immersed in different concentrations of sodium pyrosulfite solution was classified by near-infrared hyperspectral imaging (NIR-HSI) system and portable near-infrared (NIR) spectrometer. Principal component analysis was used to analyze the object-wise spectra, and support vector machine (SVM) model was established. The classification accuracy of calibration set and prediction set were 98.75% and 95%, respectively. Savitzky–Golay algorithm was used to recognize the important wavelengths, and SVM model was established based on the recognized important wavelengths. The final classification accuracy was slightly less than that based on the full spectra. In addition, the pixel-wise spectra extracted from NIR-HSI system could realize the visualization of different samples, and intuitively reflect the differences among the samples. The results showed that it was feasible to classify the sulfur dioxide residue on the surface of fresh-cut potato slices immersed in different concentration of sodium pyrosulfite solution by NIR spectra. It provided an alternative method for the detection of sulfur dioxide residue on the surface of fresh-cut potato slices.

Detection of Defect on Navel Orange Using Hyperspectral Reflectance Image

2011 ◽  
Vol 320 ◽  
pp. 569-573
Author(s):  
Jing Li ◽  
Long Xue ◽  
Mu Hua Liu ◽  
Xiao Wang ◽  
Chun Sheng Luo
Keyword(s):  
Principal Component Analysis ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Principal Component ◽  
Multispectral Image ◽  
Hyperspectral Images ◽  
Navel Orange ◽  
Hyperspectral Reflectance ◽  
Combined Use ◽  
Translation Stage

A hyperspectral imaging system for detecting defect on navel orange was demonstrated. The hyperspectral imaging system, which was a line-scan imaging system, consisted of a hyperspectral camera, a halogen lighting unit, a computer and a translation stage. The imaging system operated from 400 to 1000nm. Principal component analysis (PCA) was performed using the hyperspectral images data (from 500 to 700nm); 2nd principal component (PC) image exhibited differential responses between normal and defect spots on the surface of navel orange. The combined use of the PC-2 images demonstrated the detection of defect spots with minimal false positives. Based on the PC-2 weighing coefficients, the dominant wavelengths were 528,529,530,673,674 and 675nm. This research demonstrated the potential of multispectral image for online applications for detection of defect on navel oranges.

Broadband near-infrared emission of K3ScF6:Cr3+ phosphors for night vision imaging system sources

2021 ◽  
Vol 417 ◽  
pp. 129271
Author(s):  
Haojun Yu ◽  
Jian Chen ◽  
Ruiyu Mi ◽  
Juyu Yang ◽  
Yan-gai Liu
Keyword(s):  
Near Infrared ◽  
Imaging System ◽  
Infrared Emission ◽  
Night Vision ◽  
Near Infrared Emission

scholarly journals Principal component analysis tomography in near-infrared integral field spectroscopy of young stellar objects – I. Revisiting the high-mass protostar W33A

2021 ◽  
Vol 503 (1) ◽  
pp. 270-291
Author(s):  
F Navarete ◽  
A Damineli ◽  
J E Steiner ◽  
R D Blum
Keyword(s):  
Large Scale ◽  
Near Infrared ◽  
Rotation Axis ◽  
Principal Component ◽  
Young Stellar Objects ◽  
High Mass ◽  
Continuum Emission ◽  
Rotating Disc ◽  
Stellar Objects ◽  
K Band

ABSTRACT W33A is a well-known example of a high-mass young stellar object showing evidence of a circumstellar disc. We revisited the K-band NIFS/Gemini North observations of the W33A protostar using principal components analysis tomography and additional post-processing routines. Our results indicate the presence of a compact rotating disc based on the kinematics of the CO absorption features. The position–velocity diagram shows that the disc exhibits a rotation curve with velocities that rapidly decrease for radii larger than 0.1 arcsec (∼250 au) from the central source, suggesting a structure about four times more compact than previously reported. We derived a dynamical mass of 10.0$^{+4.1}_{-2.2}$ $\rm {M}_\odot$ for the ‘disc + protostar’ system, about ∼33 per cent smaller than previously reported, but still compatible with high-mass protostar status. A relatively compact H2 wind was identified at the base of the large-scale outflow of W33A, with a mean visual extinction of ∼63 mag. By taking advantage of supplementary near-infrared maps, we identified at least two other point-like objects driving extended structures in the vicinity of W33A, suggesting that multiple active protostars are located within the cloud. The closest object (Source B) was also identified in the NIFS field of view as a faint point-like object at a projected distance of ∼7000 au from W33A, powering extended K-band continuum emission detected in the same field. Another source (Source C) is driving a bipolar $\rm {H}_2$ jet aligned perpendicular to the rotation axis of W33A.

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