No abstract

Authentication of pasta is currently determined using molecular biology-based techniques focusing on DNA as the target analyte. Whilst proven to be effective, these approaches can be criticised as being destructive, time consuming, and requiring specialist instrument training. Advances in the field of multispectral imaging (MSI) and hyperspectral imaging (HSI) have facilitated the development of compact imaging platforms with the capability to rapidly differentiate a range of materials (inclusive of grains and seeds) based on surface colour, texture and chemical composition. This preliminary investigation evaluated the applicability of spectral imaging for identification and quantitation of durum wheat grain samples in relation to pasta authenticity. MSI and HSI were capable of rapidly distinguishing between durum wheat and adulterant common wheat cultivars and assigning percentage adulteration levels characterised by low biases and good repeatability estimates. The results demonstrated the potential for spectral imaging based seed/grain adulteration testing to augment existing standard molecular approaches for food authenticity testing.

Recent research has shown hyperspectral imaging to be a powerful tool to distinguish carbonate phases with slight compositional differences on quarry cliff faces. The traditional remote sensing setup uses an optimal short distance between the hyperspectral camera mounted on a tripod and a quarry wall characterized by a planar, mostly unweathered surface. Here we present results of a modified workflow geared to the application of ground-based hyperspectral imaging of rough and weathered cliff faces in order to map large scale dolomite bodies from a distance of up to several kilometres. The goal of the study was to determine unique spectral properties of fracture-controlled dolomite bodies in order to be able to distinguish them from a dolomitic host rock. In addition, the impact of weathering on carbonate phases and thus, the modification of the spectral signature between altered and unaltered carbonates is assessed. The spectral analysis is complemented by ICP-AES measurements of the spectrally measured powders. Furthermore, we examined the detection limits and characterisation potential of dolomite bodies from hyperspectral images captured at varying distances from cliff faces in the study area. Hyperspectral images of 10 natural cliffs distributed across the Central Oman Mountains were obtained with a Push broom scanner system. The high resolution of 5.45 nm (288 bands in total) enabled the visualization of small-scale changes in the near infrared continuous spectrum of all present lithofacies types. The determination of dolomite bodies of varying sizes (metre to hundreds of metres) on natural cliffs was achieved with the hyperspectral mapping approach and mapping results have been tested with the position of visually defined dolomite bodies on field panoramas. Spectra of natural cliffs contain a strong absorption peak indicative for iron which is absent in spectra of unweathered sample powders. However, ICP-AES analysis of powders revealed relatively low contents of iron of 12392 ppm. The strong peaks in field images are interpreted as linked to intensive weathering associated with the precepitation of goethite, hematite, specularite and manganese as well as intensive dedolomitization. Dedolomitization is indicated by calcitic spectra derived from the

Solar energy is one of the most important solutions to reduce the concerns of the severe climate change phenomenon. Granted, the main manner to harness solar energy to generate power electricity is implemented through arrays made up of PV solar panels. However, the accumulation of dust on PV surfaces nevertheless remains a serious issue that considerably reduces the efficient conversion of PV panels. Therefore, this research is aimed at automating both monitoring and cleaning of the PV panel’s surfaces through the design, manufacture, and operation and evaluating a dry-cleaning robot based on a color monitoring system. The preliminary results demonstrate that the color analysis of the PV panels can distinguish between the density of dust accumulated, where the total color differences between the clean PV panels and both the PV panels with simple, moderate, and intense dust were 43.69, 61.19, and 75.23. This raised the efficiency of the power produced for simple dust panels from 88.03 to 98.91% (one cleaning round), moderate dust panels from 70.72 to 92.96%, and intense dust panels from 39.05 to 62.11% (two cleaning rounds). These preliminary finds illustrate the possibility of using this approach to automatic monitoring the PV panel color and operate the cleaning robot.