scholarly journals Application of hyperspectral imaging and chemometrics for classifying plastics with brominated flame retardants

2019 ◽  
Author(s):  
Daniel Caballero ◽  
Marta Bevilacqua ◽  
José Amigo
Keyword(s):  
Hyperspectral Imaging ◽  
Flame Retardants ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Infrared Range ◽  
Safety Standards ◽  
Analysis Decision Tree ◽  
High Degree ◽  
Near Infrared Range

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.

scholarly journals Comparison of Hyperspectral Imaging and Near-Infrared Spectroscopy to Determine Nitrogen and Carbon Concentrations in Wheat

2021 ◽  
Vol 13 (6) ◽  
pp. 1128
Author(s):  
Iman Tahmasbian ◽  
Natalie K Morgan ◽  
Shahla Hosseini Bai ◽  
Mark W Dunlop ◽  
Amy F Moss
Keyword(s):  
Infrared Spectroscopy ◽  
Hyperspectral Imaging ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Economic Benefits ◽  
Partial Least Square ◽  
Least Square ◽  
Statistical Population ◽  
Processing Plants ◽  
C And N

Hyperspectral imaging (HSI) is an emerging rapid and non-destructive technology that has promising application within feed mills and processing plants in poultry and other intensive animal industries. HSI may be advantageous over near infrared spectroscopy (NIRS) as it scans entire samples, which enables compositional gradients and sample heterogenicity to be visualised and analysed. This study was a preliminary investigation to compare the performance of HSI with that of NIRS for quality measurements of ground samples of Australian wheat and to identify the most important spectral regions for predicting carbon (C) and nitrogen (N) concentrations. In total, 69 samples were scanned using an NIRS (400–2500 nm), and two HSI cameras operated in 400–1000 nm (VNIR) and 1000–2500 nm (SWIR) spectral regions. Partial least square regression (PLSR) models were used to correlate C and N concentrations of 63 calibration samples with their spectral reflectance, with 6 additional samples used for testing the models. The accuracy of the HSI predictions (full spectra) were similar or slightly higher than those of NIRS (NIRS Rc2 for C = 0.90 and N = 0.96 vs. HSI Rc2 for C (VNIR) = 0.97 and N (SWIR) = 0.97). The most important spectral region for C prediction identified using HSI reflectance was 400–550 nm with R2 of 0.93 and RMSE of 0.17% in the calibration set and R2 of 0.86, RMSE of 0.21% and ratio of performance to deviation (RPD) of 2.03 in the test set. The most important spectral regions for predicting N concentrations in the feed samples included 1451–1600 nm, 1901–2050 nm and 2051–2200 nm, providing prediction with R2 ranging from 0.91 to 0.93, RMSE ranging from 0.06% to 0.07% in the calibration sets, R2 from 0.96 to 0.99, RMSE of 0.06% and RPD from 3.47 to 3.92 in the test sets. The prediction accuracy of HSI and NIRS were comparable possibly due to the larger statistical population (larger number of pixels) that HSI provided, despite the fact that HSI had smaller spectral range compared with that of NIRS. In addition, HSI enabled visualising the variability of C and N in the samples. Therefore, HSI is advantageous compared to NIRS as it is a multifunctional tool that poses many potential applications in data collection and quality assurance within feed mills and poultry processing plants. The ability to more accurately measure and visualise the properties of feed ingredients has potential economic benefits and therefore additional investigation and development of HSI in this application is warranted.

3D Plant Modelling Using Spectral Data from Visible to Near Infrared Range

2015 ◽  
pp. 273-294
Author(s):  
Ali Zia ◽  
Jie Liang
Keyword(s):  
Hyperspectral Imaging ◽  
Material Properties ◽  
Near Infrared ◽  
Infrared Range ◽  
Plant Surface ◽  
Imaging Device ◽  
Plant Phenomics ◽  
Physical Traits ◽  
Plant Modelling ◽  
Near Infrared Range

Plant phenomics research requires different types of sensors employed to measure the physical traits of plant surface and to estimate the biomass. Of particular interests is the hyperspectral imaging device which captures wavelength indexed band images that characterize material properties of objects under study. This chapter introduces a proof of concept research that builds 3D plant model directly from hyperspectral images captured in a controlled lab environment. The method presented in this chapter allows fine structural-spectral information of an object be captured and integrated into the 3D model, which can be used to support further research and applications. The hyperspectral imaging has shown clear advantages in segmenting plant from its background and is very promising in generating comprehensive 3D plant models.

scholarly journals Hyperspectral Imaging and Hierarchical PLS-DA Applied to Asbestos Recognition in Construction and Demolition Waste

2019 ◽  
Vol 9 (21) ◽  
pp. 4587 ◽  
Author(s):  
Giuseppe Bonifazi ◽  
Giuseppe Capobianco ◽  
Silvia Serranti
Keyword(s):  
Hyperspectral Imaging ◽  
Spectral Response ◽  
Principal Component ◽  
Short Wave ◽  
Partial Least Square ◽  
Least Square ◽  
Construction And Demolition Waste ◽  
Infrared Range ◽  
Demolition Waste ◽  
Control Approach

Asbestos-Containing Materials (ACMs) are hazardous and prohibited to be sold or used as recycled materials. In the past, asbestos was widely used, together with cement, to produce “asbestos cement-based” products. During the recycling process of Construction and Demolition waste (C&DW), ACM must be collected and deposited separately from other wastes. One of the main aims of the recycling strategies applied to C&DW was thus to identify and separate ACM from C&DW (e.g., concrete and brick). However, to obtain a correct recovery of C&DW materials, control methodologies are necessary to evaluate the quality and the presence of harmful materials, such as ACM. HyperSpectral Imaging (HSI)-based sensing devices allow performing the full detection of materials constituting demolition waste. ACMs are, in fact, characterized by a spectral response that nakes them is different from the “simple” matrix of the material/s not embedding asbestos. The described HSI quality control approach is based on the utilization of a platform working in the short-wave infrared range (1000–2500 nm). The acquired hyperspectral images were analyzed by applying different chemometric methods: Principal Component Analysis for data exploration and hierarchical Partial Least-Square-Discriminant Analysis (PLS-DA) to build classification models. Following this approach, it was possible to set up a repeatable, reliable and efficient technique able to detect ACM presence inside a C&DW flow stream. Results showed that it is possible to discriminate and identify ACM inside C&DW. The recognition is potentially automatic, non-destructive and does not need any contact with the investigated products.

Hyperspectral Imaging for Predicting Soluble Solid Content of Starfruit

2015 ◽  
Vol 73 (1) ◽  
Author(s):  
Feri Candra ◽  
Syed Abd. Rahman Abu Bakar
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Solid Content ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Soluble Solid Content ◽  
Calibration Model ◽  
Soluble Solid

Hyperspectral imaging technology is a powerful tool for non-destructive quality assessment of fruits. The objective of this research was to develop novel calibration model based on hyperspectral imaging to estimate soluble solid content (SSC) of starfruits. A hyperspectral imaging system, which consists of a near infrared  camera, a spectrograph V10, a halogen lighting and a conveyor belt system, was used in this study to acquire hyperspectral  images of the samples in visible and near infrared (500-1000 nm) regions. Partial least square (PLS) was used to build the model and to find the optimal wavelength. Two different masks were applied for obtaining the spectral data. The optimal wavelengths were evaluated using multi linear regression (MLR). The coefficient of determination (R2) for validation using the model with first mask (M1) and second mask (M2) were 0.82 and 0.80, respectively.

scholarly journals Hyperspectral imaging of the degradation of meat and comparison with necrotic tissue in human wounds

2019 ◽  
Author(s):  
Amadeus Holmer ◽  
Christoph Homberger ◽  
Thomas Wild ◽  
Frank Siemers
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Objective Evaluation ◽  
Infrared Range ◽  
Spectral Features ◽  
Necrotic Tissue ◽  
Tissue Discrimination ◽  
Near Infrared Range ◽  
Imaging Setup

The objective evaluation of scattering tissue and the discrimination of tissue types is an issue that cannot be solved with colour cameras and image processing alone in many cases. Examples can be found in the determination of freshness and ageing of meat, and the discrimination of tissue types in food technology. In medical applications tissue discrimination is also an issue, e.g. in wound diagnostics. A novel hyperspectral imaging setup with powerful signal analysis algorithms is presented which is capable of addressing these topics. The spectral approach allows the chemical analysis of material and tissues and the measurement of their temporal change. We present a method of hyperspectral imaging in the visible-near infrared range which allows both the separation and spatial allocation of different tissue types in a sample, as well as the temporal changes of the tissue as an effect of ageing. To prove the capability of the method, the ageing of meat (slices of pork) was measured and, as a medical example, the application of the hyperspectral imaging setup for the recording of wound tissue is presented. The method shows the ability to discriminate the different tissue components of pork meat, and the ageing of the meat is observable as changes in spectral features. An additional result of our study is the fact that some spectral features, which seem to be typical for the ageing of the meat, are similar to those observed in the necrotic tissue from wound diagnostics in medicine.

scholarly journals Surface Investigation of Photo-Degraded Wood by Colour Monitoring, Infrared Spectroscopy, and Hyperspectral Imaging

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Giorgia Agresti ◽  
Giuseppe Bonifazi ◽  
Luca Calienno ◽  
Giuseppe Capobianco ◽  
Angela Lo Monaco ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Infrared Region ◽  
Short Wave ◽  
Infrared Range ◽  
Surface Chemical ◽  
Colour Changes ◽  
Short Wave Infrared ◽  
Near Infrared Range

The aim of this investigation is to study the changes occurring on the surface of poplar wood exposed to artificial irradiation in a Solar Box. Colour changes were monitored with a reflectance spectrophotometer. Surface chemical modifications were evaluated by measuring the infrared spectra. Hyperspectral imaging was also applied to study the surface wood changes in the visible-near infrared and the short wave infrared wavelength ranges. The data obtained from the different techniques were compared to find the possible correlations in order to evaluate the applicability of the Hyperspectral imaging to investigate wood modifications in a non-invasive modality. The study of colour changes showed an important variation due to photo-irradiation which is the greatest change occurring within the first 24 hours. Infrared spectroscopy revealed that lignin degrades mainly in the first 48 hours. Concerning Hyperspectral imaging, the spectral features in the visible-near infrared range are mainly linked to the spectral shape, whereas in the short wave infrared cellulose and lignin affect shape and reflectance levels. The proposed approach showed that a correlation can be established between colour variation and wood degradation in the visible-near infrared range; furthermore in the short wave infrared region surface chemical changes can be assessed.

scholarly journals Hyperspectral imaging for textile sorting in the visible–near infrared range

2019 ◽  
Author(s):  
Carolina Blanch-Perez-del-Notario ◽  
Wouter Saeys ◽  
Andy Lambrechts
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Hierarchical Classification ◽  
Infrared Range ◽  
Chemical Recycling ◽  
Textile Waste ◽  
Textile Materials ◽  
Material Discrimination ◽  
Sample Set ◽  
Near Infrared Range

Recycling of textile materials is becoming important due to the increasing amount of textile waste and its large environmental impact. The Resyntex project aims at dealing with this textile waste by enabling its chemical recycling. To do so, pure textile materials and blends need to be sorted first. In this paper we evaluate the suitability of hyperspectral imaging for pure and blend textile sorting. We also test the discrimination capacity between denim and non-denim textile, since this is required prior to the de-colouration processes. For this purpose, we use a line-scan sensor in the 450–950 nm range, since its cost, compactness and speed characteristics make it suitable for industrial deployment. To deal with the strong colour interference of the textile a hierarchical classification approach is proposed. The results on the available sample set show promising discrimination potential for material discrimination as well as for denim versus non-denim detection.

3D Plant Modelling Using Spectral Data From Visible to Near Infrared Range

2018 ◽  
pp. 1904-1925
Author(s):  
Ali Zia ◽  
Jie Liang
Keyword(s):  
Hyperspectral Imaging ◽  
Material Properties ◽  
Near Infrared ◽  
Infrared Range ◽  
Plant Surface ◽  
Imaging Device ◽  
Plant Phenomics ◽  
Physical Traits ◽  
Plant Modelling ◽  
Near Infrared Range

Plant phenomics research requires different types of sensors employed to measure the physical traits of plant surface and to estimate the biomass. Of particular interests is the hyperspectral imaging device which captures wavelength indexed band images that characterize material properties of objects under study. This chapter introduces a proof of concept research that builds 3D plant model directly from hyperspectral images captured in a controlled lab environment. The method presented in this chapter allows fine structural-spectral information of an object be captured and integrated into the 3D model, which can be used to support further research and applications. The hyperspectral imaging has shown clear advantages in segmenting plant from its background and is very promising in generating comprehensive 3D plant models.

scholarly journals Determination and Quantification of the Distribution of CN-NL Nanoparticles Encapsulating Glycyrrhetic Acid on Novel Textile Surfaces with Hyperspectral Imaging

2020 ◽  
Vol 11 (2) ◽  
pp. 32 ◽  
Author(s):  
Kudirat A. Obisesan ◽  
Simona Neri ◽  
Elodie Bugnicourt ◽  
Inmaculada Campos ◽  
Laura Rodriguez-Turienzo
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Cross Validation ◽  
Imaging Techniques ◽  
Antioxidant Properties ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Glycyrrhetic Acid ◽  
Chemometric Tools

Chitin Lignin nanoparticles (CN-NL), standalone and encapsulating glycyrrhetic acid (GA), were applied on novel substrates for textiles to obtain antibacterial, antioxidant properties. Their homogeneous application is an important parameter that can strongly influence the final performance of the investigated textiles for its cosmetic and medical use. In this paper, hyperspectral imaging techniques combined with chemometric tools were investigated to study the distribution and quantification of CN-NL/GA on chitosan and CN-NL on pullulan substrates. To do so, samples of chitosan and pullulan impregnated with CN-NL/GA and CN-NL were analysed through Short Wave Infrared (SWIR) and Visible-Near Infrared (VisNIR) hyperspectral cameras. Two different chemometric tools for qualitative and quantitative analysis have been applied, principal component analysis (PCA) and partial least square regression (PLSR) models. Promising results were obtained in the VisNIR range, which made it possible for us to visualize the CN-NL/GA compound on chitosan and CN-NL on pullulan substrates. Additionally, the PLSR model results had determination coefficient ( R C 2 ) for calibration and cross-validation ( R C V 2 )   values of 0.983 and 0.857, respectively. Minimum values of root-mean-square error for calibration (RMSEC) and cross-validation (RMSECV) of CN-NL/GA were 0.333 and 0.993 g, respectively. The results demonstrate that hyperspectral imaging combined with chemometrics offers a powerful tool for studying the distribution on chitosan and pullulan substrates and to quantify the content of CN-NL/GA compounds on chitosan substrates.

scholarly journals Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging

Foods ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 154 ◽  
Author(s):  
Hongzhe Jiang ◽  
Fengna Cheng ◽  
Minghong Shi
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Limit Of Detection ◽  
Principal Component ◽  
Rapid Identification ◽  
Performance Comparison ◽  
Correlation Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression

Minced pork jowl meat, also called the sticking-piece, is commonly used to be adulterated in minced pork, which influences the overall product quality and safety. In this study, hyperspectral imaging (HSI) methodology was proposed to identify and visualize this kind of meat adulteration. A total of 176 hyperspectral images were acquired from adulterated meat samples in the range of 0%–100% (w/w) at 10% increments using a visible and near-infrared (400–1000 nm) HSI system in reflectance mode. Mean spectra were extracted from the regions of interests (ROIs) and represented each sample accordingly. The performance comparison of established partial least square regression (PLSR) models showed that spectra pretreated by standard normal variate (SNV) performed best with Rp2 = 0.9549 and residual predictive deviation (RPD) = 4.54. Furthermore, functional wavelengths related to adulteration identification were individually selected using methods of principal component (PC) loadings, two-dimensional correlation spectroscopy (2D-COS), and regression coefficients (RC). After that, the multispectral RC-PLSR model exhibited the most satisfactory results in prediction set that Rp2 was 0.9063, RPD was 2.30, and the limit of detection (LOD) was 6.50%. Spatial distribution was visualized based on the preferred model, and adulteration levels were clearly discernible. Lastly, the visualization was further verified that prediction results well matched the known distribution in samples. Overall, HSI was tested to be a promising methodology for detecting and visualizing minced jowl meat in pork.

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