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Radiochromic film has become an important tool to verify dose distributions for intensity‐modulated radiotherapy (IMRT) and quality assurance (QA) procedures. A new radiochromic film model, EBT3, has recently become available, whose composition and thickness of the sensitive layer are the same as those of previous EBT2 films. However, a matte polyester layer was added to EBT3 to prevent the formation of Newton's rings. Furthermore, the symmetrical design of EBT3 allows the user to eliminate side‐orientation dependence. This film and the flatbed scanner, Epson Perfection V750, form a dosimetry system whose intrinsic characteristics were studied in this work. In addition, uncertainties associated with these intrinsic characteristics and the total uncertainty of the dosimetry system were determined. The analysis of the response of the radiochromic film (net optical density) and the fitting of the experimental data to a potential function yielded an uncertainty of 2.6%, 4.3%, and 4.1% for the red, green, and blue channels, respectively. In this work, the dosimetry system presents an uncertainty in resolving the dose of 1.8% for doses greater than 0.8 Gy and less than 6 Gy for red channel. The films irradiated between 0 and 120 Gy show differences in the response when scanned in portrait or landscape mode; less uncertainty was found when using the portrait mode. The response of the film depended on the position on the bed of the scanner, contributing an uncertainty of 2% for the red, 3% for the green, and 4.5% for the blue when placing the film around the center of the bed of scanner. Furthermore, the uniformity and reproducibility radiochromic film and reproducibility of the response of the scanner contribute less than 1% to the overall uncertainty in dose. Finally, the total dose uncertainty was 3.2%, 4.9%, and 5.2% for red, green, and blue channels, respectively. The above uncertainty values were obtained by minimizing the contribution to the total dose uncertainty of the film orientation and film homogeneity. PACS number(s): 87.53.Bn

In this work, the response of peeled-off Gafchromic EBT2 film to argon plasma jet (APJ) was investigated. Peeled-off Gafchromic EBT2 films were exposed to APJ for different durations of varying steps from 0 to 70 s at ambient conditions. Thereafter, the films were scanned with a flatbed scanner of high spatial resolution and color depth of 16 bits per color channel. The optical properties were measured using a UV-Vis spectrophotometer. The induced chemical modification in the active layer was confirmed by ATR-FTIR spectroscopy. The pixel values correlation of each color channel of peeled-off Gafchromic EBT2 film and the exposure time by APJ at different durations were studied. The obtained results showed that the pixel values of the red and green color channels of the peeled-off Gafchromic EBT2 film exponentially decrease with increasing the exposure time to APJ, with decay constants of 0.010 ± 0.024 and 0.071 ± 0.011 for the red and green channels, respectively. The blue channel exhibits poor and anomalous responses compared to both red and green color channels. The two characteristic peaks in the UV-Vis absorption spectra at the wavelengths of 580 ± 4 nm and 632 ± 4 nm for the peeled-off Gafchromic EBT2 film go up exponentially as the exposure time increases. The ATR-FTIR spectra contain two characteristic new C = C bonds; the first is formed due to a solid-state 1,4-polymerization reaction as conjugated with neighboring C ≡ C, while the second is further stabilized by conjugation to allene structure, increasing the double bond character, while the latter is the major contributor. The obtained results of the peeled-off Gafchromic EBT2 film can be utilized in APJ diagnosis and measurements.

AbstractThe review systematizes the works of the researchers of the Division of Analytical Chemistry at the Moscow State University in the field of chemical colorimetry for the last 10 years. Various versions of the method, its possibilities, and prospects of use for solving many analytical problems are considered. The main ways of the development of chemical colorimetry are discussed.

This paper presents an analysis of colour change in concrete under the influence of heat. The colour change observed in concrete is primarily a result of the gradual dehydration of the cement paste, but also of transformations occurring within the aggregate. The colour change may be used to reveal the exposure temperature of concrete from which the corresponding fire damage of concrete can be estimated. The paper presents the results of tests carried out on ordinary and high performance concretes (OC and HPC) prepared with natural river-bed aggregates. In addition, mortars and cement pastes prepared with the same components were observed to change colour upon heating. The colour change was investigated using Scion Image v. 4.0.3, an image analysis software package (Scion Corporation ©, USA). In the proposed method the digital image is split into three RGB colour components: red, green and blue, which are then presented as a histogram using counts of pixel intensity. The histogram results show colour distributions in unheated cementitious material and in material heated to temperatures ranging from 100°C to 1000°C. The concrete colour changes as a result of heating are linked to the physical and chemical transformations taking place in the heated material.

Wheat infections caused by fungi of the genus Fusarium decrease yields and have serious economic consequences. The produced mycotoxins have harmful effects on human and animal health. The aim of this study was to develop classification models based on selected textural parameters to distinguish between infected and healthy wheat kernels. The classification accuracy of kernels positioned on the ventral side was determined at 78–100% in the model based on textural parameters from hyperspectral images, and at 95–100% based on images generated by a flatbed scanner. Kernels positioned on the dorsal side were correctly classified in 78–98% based on hyperspectral images, and in 92–100% based on colour images. In the models combining textural parameters from the ventral and dorsal sides of wheat kernels, classification accuracy reached 76–98% in hyperspectral images, and 94–100% in images generated by a flatbed scanner. The imaging technique—flatbed scanner and the ventral side of the kernels provided higher classification accuracy results. The results will contribute to further research aiming to develop models for the determination of fungal chemotypes and/or fungal species based on selected textural features of wheat kernels.

Industrial adoption of additive manufacturing (AM) processes demands improvement in the geometrical accuracy of manufactured parts. One key achievement would be to ensure that manufactured layer contours match the correspondent theoretical profiles, which would require integration of on-machine measurement devices capable of digitizing individual layers. Flatbed scanners should be considered as serious candidates, since they can achieve high scanning speeds at low prices. Nevertheless, image deformation phenomena reduce their suitability as two-dimensional verification devices. In this work, the possibilities of using flatbed scanners for AM contour verification are investigated. Image distortion errors are characterized and discussed and special attention is paid to the plication effect caused by contact imaging sensor (CIS) scanners. To compensate this phenomena, a new local distortion adjustment (LDA) method is proposed and its distortion correction capabilities are evaluated upon actual layer contours manufactured on a fused filament fabrication (FFF) machine. This proposed method is also compared to conventional global distortion adjustment (GDA). Results reveal quasi-systematic deformations of the images which could be minimized by means of distortion correction. Nevertheless, the irregular nature of such a distortion and the superposition of different errors penalize the use of GDA, to the point that it should not be used with CIS scanners. Conclusions indicate that LDA-based correction would enable the use of flatbed scanners in AM for on-machine verification tasks.

Apricot stones have high commercial value and can be used for manufacturing functional foods, cosmetic products, active carbon, and biodiesel. The optimal processing of the stones is dependent on the cultivar and there is a need for methods to sort among different cultivars (which are often mixed in processing facilities). This study investigates the effectiveness of two low-cost colour imaging systems coupled with supervised learning to develop classification models to determine the cultivar of different stones. Apricot stones of the cultivars ‘Bella’, ‘Early Orange’, ‘Harcot’, ‘Skierniewicka Słodka’, and ‘Taja’ were used. The RGB images were acquired using a flatbed scanner or a digital camera; and 2172 image texture features were extracted within the R, G, B; L, a, b; X, Y, Z; U, and V colour coordinates. The most influential features were determined and resulted in 103 and 89 selected features for the digital camera and the flatbed scanner, respectively. Linear and nonlinear classifiers were applied including Linear Discriminant Analysis (LDA), Decision Trees (DT), k-Nearest Neighbour (kNN), Support Vector Machines (SVM), and Naive Bayes (NB). The models resulting from the flatbed scanner and using selected features achieved an accuracy of 100% via either quadratic diagonal LDA or kNN classifiers. The models developed using images from the digital camera and all or selected features had an accuracy of up to 96.77% using the SVM classifier. This study presents novel and simple-to-implement at-line (flatbed scanner) and online (digital camera) methodologies for apricot stone sorting. The developed procedure combining colour imaging and machine learning may be used for the authentication of apricot stone cultivars and quality evaluation of apricot from sustainable production.

The study utilizes the colorimetric method (involving 1,5-diphenylcarbazide and potassium thiocyanate as complexing agents), computer vision, and machine learning (ML) regression algorithms to determine the content of Cr (VI) and Fe (III) in water samples. To process digital images of water samples, the integration technique utilized a flatbed scanner known as the CanoScan LiDE 100, operating as a digital image capture device, and its performance was compared to that of conventional instruments. The study reveals that PolyReg and SVR-Poly are the most reliable ML regression algorithms for processing color space data (G and B of RGB, c* of CIELch, and b* of CIELab) of digital images of water samples that contain Cr (VI) and Fe (III). The mean absolute percentage error (MAPE) of the ML regression algorithms PolyReg and SVR-Poly for determining the content of Cr (VI) and Fe (III) is < 10% (with 8.48% error for Cr (VI) determination using PolyReg G of RGB and 6.78% error for Fe (III) determination using PolyReg B of RGB) in the estimation algorithm model. The Mean Absolute Percentage Error (MAPE) indicates that the prediction method is highly accurate. The Limit of Detection (LOD) value of the flatbed scanner colorimetric method integrated with PolyReg G of Red–Green–Blue (RGB) for Chromium (VI) and Blue of RGB for Iron (III) is approximately 0.02 mg/L. The Limit of Detection (LOD) for Chromium (VI) and Iron (III) is 0.0209 mg/L and 0.0257 mg/L, respectively. The limit of detection (LOD) values from this technique are superior to those obtained from certain UV–vis spectrometric and colorimetric methods. The low LOD values demonstrate that this technique is suitable for estimating the concentration of Cr (VI) and Fe (III) in water samples for quality assessment purposes, as these values are below the maximum concentration levels established by various regulations, including US-EPA, ASEAN, and EECCA. Graphical abstract

The development of the first microfluidic paper-based analytical device (µPAD) for the speciation of inorganic arsenic in environmental aqueous samples as arsenite (As(III)) and arsenate (As(V)) which implements hydride generation on a paper platform is described. The newly developed µPAD has a 3D configuration and uses Au(III) chloride as the detection reagent. Sodium borohydride is used to generate arsine in the device’s sample zone by reducing As(III) in the presence of hydrochloric acid or both As(III) and As(V) (total inorganic As) in the presence of sulfuric acid. Arsine then diffuses across a hydrophobic porous polytetrafluoroethylene membrane into the device’s detection zone where it reduces Au(III) to Au nanoparticles. This results in a color change which can be related to the concentration of As(III) or total inorganic As (i.e., As(III) and As(V)) concentration. Under optimal conditions, the µPAD is characterized by a limit of detection of 0.43 mg L −1 for total inorganic As (As(III) + As(V)) and 0.41 mg L −1 for As(III) and a linear calibration range in both cases of 1.2–8.0 mg As L −1 . The newly developed µPAD-based method was validated by applying it to groundwater and freshwater samples and comparing the results with those obtained by conventional atomic spectrometric techniques. Graphical abstract

Population studies of diminutive species often present unique challenges associated with methods of identifying individuals in a manner that can be reliable and retained over long periods of time. Small snakes present even more difficulties, as many marking methods rely on the use of either invasive implantation of devices, such as passive integrated transponder (PIT) tags, or through mutilation such as branding or scale clipping. Here I present an alternative method by utilizing a flatbed scanner to obtain consistent images of sufficient quality for use in photographic identification of the Common Sharp-tailed Snake (Contia tenuis) using the unique individual pigmentation patterns on the venter of the head. Additionally, I show that HotSpotter© software consistently scored image pairs of known and assessed matches higher than between pairs of known and assessed mismatches using the flatbed scanner imagery. This method provides a repeatable, reliable, non-invasive, and non-mutilating alternative to more traditional methods of identifying individual snakes.