B. Park, M. Eady, B. Oakley, S. Yoon, K. Lawrence, G. Gamble
Campylobacter is an emerging zoonotic bacterial threat in the poultry industry. The current methods for the isolation and detection of Campylobacter are culture-based techniques with several selective agars designed to isolate Campylobacter colonies, which is time-consuming, labour intensive and has low sensitivity. Several immunological and molecular techniques such as enzyme-linked immunosorbent assay (ELISA) and Latex agglutination are commercially available for the detection and identification of Campylobacter. However, these methods demand more advanced instruments as well as specially trained experts. A hyperspectral microscope imaging (HMI) technique with the fluorescence in situ hybridisation (FISH) technique has the potential for multiplex foodborne pathogen detection. Using Alexa488 and Cy3 fluorophores, the HMI (450–800 nm) technique was able to identify Campylobacter jejuni stains with high sensitivity and specificity. In addition, HMI was able to classify six bacteria using scattering intensity from their spectra without a FISH fluorophore. Overall classification accuracy of quadratic discriminant analysis (QDA) method for six bacteria including Bifidobacter longum, Campylobacter jejuni, Clostridium perfringens, Enterobacter cloacae, Lactobacillus salivarius and Shigella flexneri using the HMI technique without fluorescent markers was approximately 88.6 % with pixel-wise classification.
{"title":"Hyperspectral microscope imaging methods for multiplex detection of Campylobacter","authors":"B. Park, M. Eady, B. Oakley, S. Yoon, K. Lawrence, G. Gamble","doi":"10.1255/JSI.2019.A6","DOIUrl":"https://doi.org/10.1255/JSI.2019.A6","url":null,"abstract":"Campylobacter is an emerging zoonotic bacterial threat in the poultry industry. The current methods for the isolation\u0000 and detection of Campylobacter are culture-based techniques with several selective agars designed to isolate\u0000Campylobacter colonies, which is time-consuming, labour intensive and has low sensitivity. Several immunological and\u0000molecular techniques such as enzyme-linked immunosorbent assay (ELISA) and Latex agglutination are commercially\u0000available for the detection and identification of Campylobacter. However, these methods demand more advanced\u0000instruments as well as specially trained experts. A hyperspectral microscope imaging (HMI) technique with the\u0000fluorescence in situ hybridisation (FISH) technique has the potential for multiplex foodborne pathogen detection. Using\u0000Alexa488 and Cy3 fluorophores, the HMI (450–800 nm) technique was able to identify Campylobacter jejuni stains with\u0000high sensitivity and specificity. In addition, HMI was able to classify six bacteria using scattering intensity from their\u0000spectra without a FISH fluorophore. Overall classification accuracy of quadratic discriminant analysis (QDA) method for\u0000six bacteria including Bifidobacter longum, Campylobacter jejuni, Clostridium perfringens, Enterobacter cloacae,\u0000Lactobacillus salivarius and Shigella flexneri using the HMI technique without fluorescent markers was approximately 88.6\u0000% with pixel-wise classification.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46469423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fluorescent proteins are a crucial visualisation tool in a myriad of research fields including cell biology, microbiology and medicine. Fluorescence is a result of the absorption of electromagnetic radiation at one wavelength and its reemission at a longer wavelength. Coral communities exhibit a natural fluorescence which can be used to distinguish between diseased and healthy specimens, however, current methods, such as the underwater visual census, are expensive and time-consuming constituting many manned dive hours. We propose the use of a remotely operated vehicle mounted with a novel hyperspectral fluorescence imaging (HyFI) “payload” for more rapid surveying and data collection. We have tested our system in a laboratory environment on common coral species including Seriatopora spp., Montipora verrucosa, Montipora spp., Montipora capricornis, Echinopora lamellose, Euphyllia ancora, Pocillopora damicornis and Montipora confusa. With the aid of hyperspectral imaging, the coral specimens’ emission wavelengths can be accurately assessed by capturing the emission spectra of the corals when excited with light emitting diodes (395–405 and 440 nm). Fluorescence can also provide an indicator of coral bleaching as shown in our bleaching experiment where we observe fluorescence reduction alongside coral bleaching.
{"title":"Hyperspectral imaging as a tool for assessing coral health utilising natural fluorescence","authors":"J. Teague, J. Willans, M. Allen, T. Scott, J. Day","doi":"10.1255/JSI.2019.A7","DOIUrl":"https://doi.org/10.1255/JSI.2019.A7","url":null,"abstract":"Fluorescent proteins are a crucial visualisation tool in a myriad of research fields including cell biology,\u0000microbiology and medicine. Fluorescence is a result of the absorption of electromagnetic radiation at one wavelength and\u0000its reemission at a longer wavelength. Coral communities exhibit a natural fluorescence which can be used to distinguish\u0000between diseased and healthy specimens, however, current methods, such as the underwater visual census, are\u0000expensive and time-consuming constituting many manned dive hours. We propose the use of a remotely operated vehicle\u0000mounted with a novel hyperspectral fluorescence imaging (HyFI) “payload” for more rapid surveying and data collection.\u0000We have tested our system in a laboratory environment on common coral species including Seriatopora spp., Montipora\u0000verrucosa, Montipora spp., Montipora capricornis, Echinopora lamellose, Euphyllia ancora, Pocillopora damicornis and\u0000Montipora confusa. With the aid of hyperspectral imaging, the coral specimens’ emission wavelengths can be accurately\u0000assessed by capturing the emission spectra of the corals when excited with light emitting diodes (395–405 and 440 nm).\u0000Fluorescence can also provide an indicator of coral bleaching as shown in our bleaching experiment where we observe\u0000fluorescence reduction alongside coral bleaching.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45890045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Williams, Terri-Lee Kammies, P. Gouws, M. Manley
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.
{"title":"Effect of colony age on near infrared hyperspectral images of foodborne bacteria","authors":"P. Williams, Terri-Lee Kammies, P. Gouws, M. Manley","doi":"10.1255/JSI.2019.A5","DOIUrl":"https://doi.org/10.1255/JSI.2019.A5","url":null,"abstract":"Near infrared hyperspectral imaging (NIR-HSI) and multivariate image analysis were used to distinguish between\u0000foodborne pathogenic bacteria, Bacillus cereus, Escherichia coli, Salmonella Enteritidis, Staphylococcus aureus and a non-\u0000pathogenic bacterium, Staphylococcus epidermidis. Hyperspectral images of bacteria, streaked out on Luria—Bertani agar,\u0000 were acquired after 20 h, 40 h and 60 h growth at 37 °C using a SisuCHEMA hyperspectral pushbroom imaging system\u0000with a spectral range of 920–2514 nm. Three different pre-processing methods: standard normal variate (SNV),\u0000Savitzky—Golay (1stderivative, 2nd order polynomial, 15-point smoothing) and Savitzky—Golay (2nd derivative, 3rd order\u0000polynomial, 15-point smoothing) were evaluated. SNV provided the most distinct clustering in the principal component\u0000score plots and was thus used as the sole pre-processing method. Partial least squares discriminant analysis (PLS-DA)\u0000models were developed for each growth period and was tested on a second set of plates, to determine the effect the age\u0000 of the colony has on classification accuracies. The highest overall prediction accuracies where test plates required the\u0000least amount of growth time, was found with models built after 60 h growth and tested on plates after 20 h growth.\u0000Predictions for bacteria differentiation within these models ranged from 83.1 % to 98.8 % correctly predicted\u0000pixels.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44249662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Most plastics need to incorporate flame retardants to meet fire safety standards requirements. The amount and the type of flame retardants can differ, so that in waste plastics a large variety of polymers and flame retardants can be found. The recycling of plastics containing flame retardants is increasing. However, only plastics of the same polymer type and the same additive content can be recycled together. Three models based on different chemometrics techniques applied to hyperspectral imaging in the near infrared range were developed [partial least square-discriminant analysis, decision tree (DT) and hierarchical model (HM)]. Optimal results were obtained for all classification techniques. HM shows the highest error at all levels due to the noisy spectra of the black plastics. However, DT classification gave outstanding results, considering that the sensitivity was higher than 0.9 in all cases. Thus, the application of DT with hyperspectral imaging could be used to sort plastic samples with respect to the type of polymer and the flame retardant used with a high degree of accuracy in an automated way. These findings are highly valuable for the plastic and waste management industries.
{"title":"Application of hyperspectral imaging and chemometrics for classifying plastics with brominated flame\u0000retardants","authors":"D. Caballero, M. Bevilacqua, J. Amigo","doi":"10.1255/JSI.2019.A1","DOIUrl":"https://doi.org/10.1255/JSI.2019.A1","url":null,"abstract":"Most plastics need to incorporate flame retardants to meet fire safety standards requirements. The amount and the\u0000 type of flame retardants can differ, so that in waste plastics a large variety of polymers and flame retardants can be\u0000found. The recycling of plastics containing flame retardants is increasing. However, only plastics of the same polymer type\u0000 and the same additive content can be recycled together. Three models based on different chemometrics techniques\u0000applied to hyperspectral imaging in the near infrared range were developed [partial least square-discriminant analysis,\u0000decision tree (DT) and hierarchical model (HM)]. Optimal results were obtained for all classification techniques. HM shows\u0000the highest error at all levels due to the noisy spectra of the black plastics. However, DT classification gave outstanding\u0000results, considering that the sensitivity was higher than 0.9 in all cases. Thus, the application of DT with hyperspectral\u0000imaging could be used to sort plastic samples with respect to the type of polymer and the flame retardant used with a high\u0000degree of accuracy in an automated way. These findings are highly valuable for the plastic and waste management\u0000industries.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45493457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Comparison of spectral selection methods in the development of classification models from visible near infrared\u0000hyperspectral imaging data","authors":"A. Gowen, Jun‐Li Xu, A. Herrero-Langreo","doi":"10.1255/JSI.2019.A4","DOIUrl":"https://doi.org/10.1255/JSI.2019.A4","url":null,"abstract":"Applications of hyperspectral imaging (HSI) to the quantitative and qualitative measurement of samples have grown\u0000 widely in recent years, due mainly to the improved performance and lower cost of imaging spectroscopy instrumentation.\u0000Data sampling is a crucial yet often overlooked step in hyperspectral image analysis, which impacts the subsequent\u0000results and their interpretation. In the selection of pixel spectra for the calibration of classification models, the spatial\u0000information in HSI data can be exploited. In this paper, a variety of sampling strategies for selection of pixel spectra are\u0000presented, exemplified through five case studies. The strategies are compared in terms of the proportion of global\u0000variability captured, practicality and predictive model performance. The use of variographic analysis as a guide to the\u0000spatial segmentation prior to sampling leads to the selection of representative subsets while reducing the variation in model\u0000 performance parameters over repeated random selection.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48484021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. Caponigro, F. Marini, R. Dorrepaal, A. Herrero-Langreo, A. Scannell, A. Gowen
Milk is a complex emulsion of fat and water with proteins (such as caseins and whey), vitamins, minerals and lactose dissolved within. The purpose of this study is to automatically distinguish different dairy residues on substrates commonly used in the food industry using hyperspectral imaging. Fourier transform infrared (FT-IR) and Raman hyperspectral imaging were compared as candidate techniques to achieve this goal. Aluminium and stainless-steel, types 304-2B and 316-2B, were chosen as surfaces due to their widespread use in food production. Spectra of dried samples of whole, skimmed, protein, butter milk and butter were compared. The spectroscopic information collected was not only affected by the chemical signal of the milk composition, but also by surface signals, evident as baseline and multiplicative effects. In addition, the combination of the spectral information with spatial information can improve data interpretation in terms of characterising spatial variability of the selected surfaces.
{"title":"Raman and Fourier transform infrared hyperspectral imaging to study dairy residues on different surface","authors":"V. Caponigro, F. Marini, R. Dorrepaal, A. Herrero-Langreo, A. Scannell, A. Gowen","doi":"10.1255/JSI.2019.A3","DOIUrl":"https://doi.org/10.1255/JSI.2019.A3","url":null,"abstract":"Milk is a complex emulsion of fat and water with proteins (such as caseins and whey), vitamins, minerals and\u0000lactose dissolved within. The purpose of this study is to automatically distinguish different dairy residues on substrates\u0000commonly used in the food industry using hyperspectral imaging. Fourier transform infrared (FT-IR) and Raman\u0000hyperspectral imaging were compared as candidate techniques to achieve this goal. Aluminium and stainless-steel, types\u0000304-2B and 316-2B, were chosen as surfaces due to their widespread use in food production. Spectra of dried samples\u0000of whole, skimmed, protein, butter milk and butter were compared. The spectroscopic information collected was not only\u0000affected by the chemical signal of the milk composition, but also by surface signals, evident as baseline and multiplicative\u0000effects. In addition, the combination of the spectral information with spatial information can improve data interpretation in\u0000terms of characterising spatial variability of the selected surfaces.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47017439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Near infrared hyperspectral photoluminescence imaging of crystalline silicon wafers can reveal new knowledge on the spatial distribution and the spectral response of radiative recombination active defects in the material. The hyperspectral camera applied for this imaging technique is subject to background shot noise as well as to oscillating background noise caused by temperature fluctuations in the camera chip. Standard background noise subtraction methods do not compensate for this oscillation. Many of the defects in silicon wafers lead to photoluminescence emissions with intensities that are one order of magnitude lower than the oscillation in the background noise level. These weak signals are therefore not detected. In this work, we demonstrate an enhanced background noise subtraction scheme that accounts for temporal oscillations as well as spatial differences in the background noise. The enhanced scheme drastically increases the sensitivity of the camera and hence allows for detection of weaker signals. Thus, it may be useful to implement the method in all hyperspectral imaging applications studying weak signals.
{"title":"Increased sensitivity in near infrared hyperspectral imaging by enhanced background noise subtraction","authors":"T. Mehl, G. Wyller, I. Burud, E. Olsen","doi":"10.1255/JSI.2019.A2","DOIUrl":"https://doi.org/10.1255/JSI.2019.A2","url":null,"abstract":"Near infrared hyperspectral photoluminescence imaging of crystalline silicon wafers can reveal new knowledge on the spatial distribution and the spectral response of radiative recombination active defects in the material. The hyperspectral camera applied for this imaging technique is subject to background shot noise as well as to oscillating background noise caused by temperature fluctuations in the camera chip. Standard background noise subtraction methods do not compensate for this oscillation. Many of the defects in silicon wafers lead to photoluminescence emissions with intensities that are one order of magnitude lower than the oscillation in the background noise level. These weak signals are therefore not detected. In this work, we demonstrate an enhanced background noise subtraction scheme that accounts for temporal oscillations as well as spatial differences in the background noise. The enhanced scheme drastically increases the sensitivity of the camera and hence allows for detection of weaker signals. Thus, it may be useful to implement the method in all hyperspectral imaging applications studying weak signals.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42584181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When dealing with practical applications of hyperspectral imaging, the development of efficient, fast and flexible classification algorithms is of the utmost importance. Indeed, the optimal classification method should be able, in a reasonable time, to maximise the separation between the classes of interest and, at the same time, to correctly reject possible outlier samples. To this aim, a new extension of Partial Least Squares Discriminant Analysis (PLS-DA), namely Soft PLS-DA, has been implemented. The basic engine of Soft PLS-DA is the same as PLS-DA, but class assignment is subjected to some additional criteria which allow samples not belonging to the target classes to be identified and rejected. The proposed approach was tested on a real case study of plastic waste sorting based on near infrared hyperspectral imaging. Household plastic waste objects made of the six recyclable plastic polymers commonly used for packaging were collected and imaged using a hyperspectral camera mounted on an industrial sorting system. In addition, paper and not recyclable plastics were also considered as potential foreign materials that are commonly found in plastic waste. For classification purposes, the Soft PLS-DA algorithm was integrated into a hierarchical classification tree for the discrimination of the different plastic polymers. Furthermore, Soft PLS-DA was also coupled with sparse-based variable selection to identify the relevant variables involved in the classification and to speed up the sorting process. The tree- structured classification model was successfully validated both on a test set of representative spectra of each material for a quantitative evaluation, and at the pixel level on a set of hyperspectral images for a qualitative assessment.
{"title":"Development of a classification algorithm for efficient handling of multiple classes in sorting systems based on\u0000hyperspectral imaging","authors":"R. Calvini, Giorgia Orlandi, G. Foca, A. Ulrici","doi":"10.1255/JSI.2018.A13","DOIUrl":"https://doi.org/10.1255/JSI.2018.A13","url":null,"abstract":"When dealing with practical applications of hyperspectral imaging, the development of efficient, fast and flexible\u0000classification algorithms is of the utmost importance. Indeed, the optimal classification method should be able, in a\u0000reasonable time, to maximise the separation between the classes of interest and, at the same time, to correctly reject\u0000possible outlier samples. To this aim, a new extension of Partial Least Squares Discriminant Analysis (PLS-DA), namely\u0000Soft PLS-DA, has been implemented. The basic engine of Soft PLS-DA is the same as PLS-DA, but class assignment is\u0000subjected to some additional criteria which allow samples not belonging to the target classes to be identified and rejected.\u0000The proposed approach was tested on a real case study of plastic waste sorting based on near infrared hyperspectral\u0000imaging. Household plastic waste objects made of the six recyclable plastic polymers commonly used for packaging were\u0000collected and imaged using a hyperspectral camera mounted on an industrial sorting system. In addition, paper and not\u0000recyclable plastics were also considered as potential foreign materials that are commonly found in plastic waste. For\u0000classification purposes, the Soft PLS-DA algorithm was integrated into a hierarchical classification tree for the\u0000discrimination of the different plastic polymers. Furthermore, Soft PLS-DA was also coupled with sparse-based variable\u0000selection to identify the relevant variables involved in the classification and to speed up the sorting process. The tree-\u0000structured classification model was successfully validated both on a test set of representative spectra of each material\u0000for a quantitative evaluation, and at the pixel level on a set of hyperspectral images for a qualitative assessment.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42019463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hyperspectral imaging (HSI) can provide valuable information about the spatial distribution of ingredients in an object, therefore the technique has been widely adopted in numerous applications, ranging from remote sensing and land planning, food quality control, to biomedical applications. However, HSI instruments are expensive, which has limited the technique to some high-end applications. In this study, we developed a cost-effective HSI technique with an internal scene-scan mechanism, which enables rapid acquisitions of a scene without moving the instrument or the tested object. The apparatus was characterised, revealing an imaging resolution of 0.4 mm in a field of view (FoV) of 10 cm and a spectral resolution of 1.3 nm in the 40–800 nm visible light region. We succeeded in applying our apparatus to analyse the oxidation processes of apple and meat, which demonstrated our design and relevant data analysis to be of high value to visualise chemistry related to food quality and safety.
{"title":"Development of a hyperspectral imaging technique with internal scene scan for analysing the chemistry of food\u0000degradation","authors":"Guo Chen, Jung Huang","doi":"10.1255/JSI.2018.A12","DOIUrl":"https://doi.org/10.1255/JSI.2018.A12","url":null,"abstract":"Hyperspectral imaging (HSI) can provide valuable information about the spatial distribution of ingredients in an\u0000object, therefore the technique has been widely adopted in numerous applications, ranging from remote sensing and land\u0000planning, food quality control, to biomedical applications. However, HSI instruments are expensive, which has limited the\u0000technique to some high-end applications. In this study, we developed a cost-effective HSI technique with an internal\u0000scene-scan mechanism, which enables rapid acquisitions of a scene without moving the instrument or the tested object.\u0000The apparatus was characterised, revealing an imaging resolution of 0.4 mm in a field of view (FoV) of 10 cm and a\u0000spectral resolution of 1.3 nm in the 40–800 nm visible light region. We succeeded in applying our apparatus to analyse the\u0000oxidation processes of apple and meat, which demonstrated our design and relevant data analysis to be of high value to\u0000visualise chemistry related to food quality and safety.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44625833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Catelli, L. Randeberg, H. Strandberg, B. Alsberg, A. Maris, Lily Vikki
The application of hyperspectral imaging in the field of cultural heritage investigation is growing rapidly. In this study, short wavelength infrared hyperspectral imaging (960–2500 nm) has been explored as a potential non-invasive technique for in situ mapping of corrosion products on bronze sculptures. Two corrosion products, brochantite and antlerite, commonly found on the surfaces of outdoor bronze monuments, were considered. Their spatial distribution was investigated on the surface of the bronze sculpture The Man with the Key by Auguste Rodin in Oslo. The results demonstrate that hyperspectral imaging combined with image analysis algorithms can display the distribution of the two corrosion products in different areas (unsheltered and partially sheltered) of the sculpture.
{"title":"Can hyperspectral imaging be used to map corrosion products on outdoor bronze sculptures?","authors":"E. Catelli, L. Randeberg, H. Strandberg, B. Alsberg, A. Maris, Lily Vikki","doi":"10.1255/JSI.2018.A10","DOIUrl":"https://doi.org/10.1255/JSI.2018.A10","url":null,"abstract":"The application of hyperspectral imaging in the field of cultural heritage investigation is growing rapidly. In this\u0000study, short wavelength infrared hyperspectral imaging (960–2500 nm) has been explored as a potential non-invasive\u0000technique for in situ mapping of corrosion products on bronze sculptures. Two corrosion products, brochantite and\u0000antlerite, commonly found on the surfaces of outdoor bronze monuments, were considered. Their spatial distribution was\u0000investigated on the surface of the bronze sculpture The Man with the Key by Auguste Rodin in Oslo. The results\u0000demonstrate that hyperspectral imaging combined with image analysis algorithms can display the distribution of the two\u0000corrosion products in different areas (unsheltered and partially sheltered) of the sculpture.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46009987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：