Water monitoring with hyperspectral techniques

1 - The poor of the world depends directly on water and other natural resources for their livelihoods. Water resources must therefore be managed in a sustainable manner in order to maintain the economic, social and environmental functions and to contribute to the livelihoods of people. 2 - Advancements in sensor technologies and processing algorithms have resulted in technical capabilities that can record and identify Earth surface materials based on the interaction of electromagnetic energy with the molecular structure of the material being sensed. 3 - Non-destructive and operative methodologies (NIR and Raman) will be tested through field surveys and laboratory analysis using Aquaphotomics approach. This approach requires precise measuring and mapping capabilities at field level of key data at a sufficient level of accuracy depending on the availability of equipment that must be also operated at a cost-effective way

Hyperspectral chemical imaging reveals spatially varied degradation of polycarbonate urethane (PCU) biomaterials

Hyperspectral chemical imaging (HCI) is an emerging technique which combines spectroscopy with imaging. Unlike traditional point spectroscopy, which is used in the majority of polymer biomaterial degradation studies, HCI enables the acquisition of spatially localised spectra across the surface of a material in an objective manner. Here, we demonstrate that attenuated total reflectance Fourier transform infra-red (ATR-FTIR) HCI reveals spatial variation in the degradation of implantable polycarbonate urethane (PCU) biomaterials. It is also shown that HCI can detect possible defects in biomaterial formulation or specimen production; these spatially resolved images reveal regional or scattered spatial heterogeneity. Further, we demonstrate a map sampling method, which can be used in time-sensitive scenarios, allowing for the investigation of degradation across a larger component or component area. Unlike imaging, mapping does not produce a contiguous image, yet grants an insight into the spatial heterogeneity of the biomaterial across a larger area. These novel applications of HCI demonstrate its ability to assist in the detection of defective manufacturing components and lead to a deeper understanding of how a biomaterial’s chemical structure changes due to implantation. Statement of Signifance The human body is an aggressive environment for implantable devices and their biomaterial components. Polycarbonate urethane (PCU) biomaterials in particular were investigated in this study. Traditionally one or a few points on the PCU surface are analysed using ATR-FTIR spectroscopy. However the selection of acquisition points is susceptible to operator bias and critical information can be lost. This study utilises hyperspectral chemical imaging (HCI) to demonstrate that the degradation of a biomaterial varies spatially. Further, HCI revealed spatial variations of biomaterials that were not subjected to oxidative degradation leading to the possibility of HCI being used in the assessment of biomaterial formulation and/or component production

Raman and Fourier transform infrared hyperspectral imaging to study dairy residues on different surface

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

Terahertz time domain spectroscopy and imaging: Emerging techniques for food process monitoring and quality control

The Terahertz (THz) region of the electromagnetic spectrum, spanning the range between 100 GHz and 30 THz, has recently enjoyed a renaissance due to technological developments in source and detector components. With the development of THz instrumentation, applications of THz spectroscopy and imaging for quality control of food products have expanded in scope and improved in performance. This article gives an overview of the fundamentals of THz technology and a comprehensive review of applications of THz time domain spectroscopy and imaging for food quality and control. Technical challenges and future outlook for these emerging techniques are also discussed

Use of spectral pre-processing methods to compensate for the presence of packaging film in visible–near infrared hyperspectral images of food products

The presence of polymeric packaging film in images of food products may modify spectra obtained in hyperspectral imaging (HSI) experiments, leading to undesirable image artefacts which may impede image classification. Some pre-processing of the image is typically required to reduce the presence of such artefacts. The objective of this research was to investigate the use of spectral pre-processing techniques to compensate for the presence of packaging film in hyperspectral images obtained in the visible–near infrared wavelength range (445–945 nm), with application in food quality assessment. A selection of commonly used pre-processing methods, used individually and in combination, were applied to hyperspectral images of flat homogeneous samples, imaged in the presence and absence of different packaging films (polyvinyl chloride and polyethylene terephthalate). Effects of the selected pre-treatments on variation due to the film’s presence were examined in principal components score space. The results show that the combination of first derivative Savitzky–Golay followed by standard normal variate transformation was useful in reducing variations in spectral response caused by the presence of packaging film. Compared to other methods examined, this combination has the benefits of being computationally fast and not requiring a priori knowledge about the sample or film used

Development of a polarized hyperspectral imaging system for investigation of absorption and scattering properties

International audienceThis study was carried out to investigate the feasibility of an original polarized hyperspectral imaging setup in the spectral range of 400'1100 nm for enhancement of absorbance signal measurement on highly scattering samples. Spatial response and spectral calibration have been verified, indicating the consistency of this system and reliability of the acquired data. Model samples consisting of layered sand were prepared and used to uncover the hidden spectral information. In the model matrix, sand worked as scattering particle and dye E141 as absorbing material. Cross (R') and parallel (RII) reflectance signals, along with the back-scattered reflectance, RBS(RII+R') and the weakly scattered reflectance RSS(RII-R') spectra were computed and compared. Results demonstrated that cross-polarized images showed more subsurface information from the second layer due to the rejection of the superficial reflectance, while weakly scattered reflectance ((Formula presented.)) preserved only the surface information from the first layer. In addition, polarized light spectroscopy absorbance based on Dahm's equation in the frame of the representative layer theory and standard normal variate preprocessing RBS spectra were also obtained from the prepared model matrix. The visual inspection of spectral curves revealed that RSS/RBS and PoLiS absorbance showed two narrow peaks at 405 nm and 630 nm that were less impacted by multi-scattering effects. Partial least squares regression models were developed to predict dye concentration in the mixture sample. Consistent with the spectral profiles, RSS/RBS and PoLiS absorbance presented the best model performances with determination of coefficients of prediction (r2 Pred) equal to 0.96 and 0.95, respectively. The resulting distribution maps of S1/S2 sand sample again confirmed the superior performance of RSS/RBS and PoLiS absorbance, manifesting their better ability to reveal chemically related information. The overall results obtained in this research showed that the developed polarized-hyperspectral imaging system coupled with scattering correction methods has great potential for the analysis of powdered or turbid samples

A polarized hyperspectral imaging system for in vivo detection: multiple applications in sunflower leaf analysis

International audienceThis study aims to investigate the potential of an original polarized hyperspectral imaging (HSI) setup in the spectral domain of 400-1000 nm for sunflower leaves in real-world. Dataset 1 includes hypercubes of sunflower leaves in two varieties with different life growth stages, while Dataset 2 is comprised of healthy and contaminated sunflower leaves suffering from powdery mildew (PM) and/or septoria leaf spot (SLS). Cross polarised (R⊥), parallel polarised (R||) reflectance signals, RBS (R|| + R⊥) and RSS (R||-R⊥) spectra were obtained and used to develop partial least squares-discriminant analysis (PLS-DA) models. Surface information played an important role in separating two varieties of leaves due to the fact that the best model performance was achieved by using RSS mean spectra, while both surface and subsurface were equally important in classifying leaves between two major growth stages because model of RBS mean spectra outperformed other models. The best classification model for disease detection was achieved by using pixel R⊥ spectra with the correct classification rate (CCR) of 0.963 for both cross validation and prediction, meaning that subsurface spectral features were the most important to detect infected leaves. The resulting classification maps were also displayed to visualize the distribution of the infected regions on the leaf samples. The overall results obtained in this research showed that the developed polarized-HSI system coupled with multivariate analysis has considerable promise in agricultural real-world applications