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Introduction: The consumption of alcohol-based hand sanitizers has increased significantly after the pandemic caused by SARS-CoV-2. Despite the conclusion of the health emergency declared by the WHO in 2023, the habit of sanitizing hands with ethanol-based gel sanitizers has been globally adopted by the population. Since general methods described in official compendia for ethanol determination such as gas chromatography or distillation are laborious and not-specific to carbomer-containing gels, this work proposes an alternative method based on digital image colorimetry. Method: The digital image (provided by ethanol-phenolphthalein reaction) was captured and transformed into an analytical signal based on the Red-Green-Blue system. The image acquisitions were performed using a Samsung Galaxy J6 smartphone, and the signals were generated using the Photometrix Pro® free program. The method was validated in accordance with ICH and applied in commercial samples. Additionally, the proposed method was evaluated for its environmental impact using the Green Analytical Process Index (GAPI) tool. Pictograms were generated using the ComplexGAPI® free program. Results: Green channel exhibited a linear response in the calibration curves for ethanol concentrations ranging from 5 to 40 % (w/w) in acidic medium. The method showed linearity, precision, accuracy, and robustness. Conclusions: The proposed method presented as main advantages the use of low-cost and easy-to-handle devices and reduced reagent consumption, in accordance with green analytical chemistry principles. [ABSTRACT FROM AUTHOR]

The current research aims to evaluate the positive and negative effects of electronic word of mouth communication on the digital image of travel agencies. In this context, research data were obtained from the five most-commented travel agencies in Nevşehir registered in the TripAdvisor system. In this study, which adopted the qualitative method, the research design was a case study and phenomenology. This research employing the document review technique and criterion sampling method, identified 7.259 online comments. This study subjected 1.724 online comments from 2022 to content analysis and descriptive analysis and obtained five main themes, twenty one positive sub-themes, eight negative sub-themes, and 4.534 codes. The main themes are tour, tourist guide, transport, eating and drinking, and accommodation. The tour main theme contains the sub-themes of types of tours, places included in the tour, all-inclusive package tours, price alternatives, organization and timing, transfer services, shopping opportunities, repreference and recommendations. The tourist guide main theme covers the sub-themes of accumulation of knowledge and detailed expression, language skill, personality traits, interest and relevance to tourists. The transport main theme includes the sub-themes of vehicle comfort and cleanliness, captain and safe driving, and in-vehicle catering. The eating and drinking main theme covers the sub-themes of restaurant location, food variety, and food taste. The accommodation main theme contains the sub-themes of referral to a travel agency, accommodation services, and food and beverage services. In the main themes, the most frequently mentioned topics were the tour and tourist guide who represent the travel agency. On the other hand, the most focused themes by the tourists were the tourist guide's knowledge and detailed explanation and the re-preference and recommendations. Overall, it has been concluded that travel agencies have a positive image, and the determined themes affect digital image formations. [ABSTRACT FROM AUTHOR]

In this work, the notion of digital fiber homotopy is defined and its properties are given. We present some new results on digital fibrations. Moreover, we introduce digital h-fibrations. We prove some of the properties of these digital h-fibrations. We show that a digital fibration and a digital map p are fiber homotopic equivalent if and only if p is a digital h-fibration. Finally, we explore a relation between digital fibrations and digital h-fibrations. [ABSTRACT FROM AUTHOR]

BACKGROUND RESULTS CONCLUSION Coffee roasting is one of the crucial steps in obtaining a high‐quality product as it forms the product's color and flavor characteristics. Roast control is made by visual inspection or traditional instruments such as the Agtron spectrophotometer, which can have high implementation costs. Therefore, the present study evaluated colorimetric approaches (a bench colorimeter, smartphone digital images, and a colorimetric sensor) to predict the Agtron roasting degrees of whole and ground coffee. Two calibration approaches were assessed, that is, multiple linear regression and least‐squares support vector machine. For that, 70 samples of whole and ground roasted coffees comprising the Agtron roasting range were prepared.The results showed that all three colorimetric acquisition types were efficient for the model building, but the bench colorimeter and the smartphone digital images generally performed with good determination coefficients and low errors as measured by external validation. For the whole bean coffee, the best model presented a determination coefficient (R2) of 0.99 and a root‐mean‐squared error (RMSE) of 1.91%, while R2 of 0.99 and RMSE of 0.87% was obtained for ground coffee, both using the colorimeter.The obtained models presented good prediction capability, as assessed by external validation and randomization tests. The obtained findings point to an alternative for coffee roasting monitoring that can lead to higher digitalization and local control of the process, even for smaller producers, due to its lower costs. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

This paper continues a series discussing flaws in published assertions concerning fixed points in digital images. [ABSTRACT FROM AUTHOR]

Introduction: Malignancies are prevalent affections that demand an early diagnosis for an opportune treatment. The making of an images gallery as teaching means with digital technology constitutes a learning tool of unquestionable value in the oncology specialty. Objective: To make a gallery of malignancies digital images as alternative teaching means for the residents and nursing professionals. Methods: A technological innovation was carried out in Conrado Benítez García Provincial Teaching Oncological Hospital in Santiago de Cuba, during the January-February two-month period, 2023. The study object and action field were the learning resources and the digital images on malignancies, respectively. The theoretical investigation methods (systematizing, experiential and analytic synthetic) and empiric (documental analysis and interviews in questionnaire form to key informants) were combined. Results: As alternative teaching means for medical and nursing teaching, a digital gallery was manually made with 164 malignancies images that included almost all the localizations. Computer science experts, specialists, residents and nurses valued as very appropriate the computer product, based on the scientificity and the technological didactics for the integral academic training. Conclusions: The gallery of digital images constitutes a didactic, motivational, attractive and interesting tool for learning of malignancies; it is an illustrative and utilitarian resource for medical and nursing professional teaching. [ABSTRACT FROM AUTHOR]

This paper proposes a new cover selection method for steganography. We focus on the scenario that the available images for selection contain diversified sources, i.e., nature images and metaverse images. For the scenario, we design a targeted strategy to evaluate the suitability for steganography of a candidate image, which selects images according to the undetectability against steganalytic tools symmetrically. Firstly, steganalytic features of the candidate images are extracted. Then, the features are fed on a steganalytic classifier, and the possibility of carrying secret data is calculated for cover selection. As a result, the selected images are the best candidates to resist steganalysis. Experimental results show that our method performs better than existing cover selection schemes when checked by steganalytic tools. [ABSTRACT FROM AUTHOR]

On 17 October 2019 in Beirut, a Lebanese state bodyguard confronted by demonstrators opened fire when a female protestor hit him with a side kick. Another protestor recorded this moment on his mobile phone. A single frame of the video footage was remediated shortly after as a still image and gained rapid traction across social media platforms, as did the original video. The circulation of the content online inspired a group of artists to remediate the digital image themselves into a series of artistic works, adding in the process additional layers of meaning and interpretation to its original context. Taking the digital image of the side kick as a case study, this qualitative research examines how five young Lebanese artists use ‘cyberartivism’ to participate in the rising wave of political and social transformation. Based on empirical material that consists of a series of interviews with five Lebanese artists, this study aims to understand the role of digital images in their transition from forms of evidence to remediated sites of transgression in the context of the October 2019 Revolution in Lebanon. Its purpose is to explore the way camera witnessing, connective witnessing, and the creative modification of an image and its circulation on social media can foster a sense of solidarity and dissent by empowering and strengthening protestors’ voices, thus generating new forms of leadership and agency. [ABSTRACT FROM AUTHOR]

In this work a dilute‐and‐shoot procedure using digital image colorimetry (DIC) was evaluated for the determination of aluminum in antiperspirants. Derivatization was performed by means of the reaction between Al(III) and Alizarin Red S (ARS) reagent. Digital images were acquired using a table scanner and the average values for red, green and blue channels (RGB) were obtained. Optimal conditions were set at pH=4.5, ARS concentrations 10‐fold higher than Al(III) and using the G channel. The method presented linearity in the 2.0–10.0 mg L−1 range, the limit of detection (LOD) was 1.1 mg L−1 and precision was better than 2.55 %. Accuracy was checked by the analysis of spiked samples at 2.0 and 4.0 mg L−1 Al(III), and good recoveries (82.6–117.5 %) were obtained. Eight samples of roll‐on antiperspirants were analyzed by the proposed colorimetric method and the well‐stablished flame atomic absorption spectrometry (FAAS) comparative method. The obtained results were not statistically different at 95 % confidence level. An analytical greenness assessment was performed for both methods and the procedure based on digital image colorimetry was proven to be a greener approach due to miniaturization, low sample consumption, least amount of waste and high sample throughput. [ABSTRACT FROM AUTHOR]

This essay argues that Sanjay Leela Bhansali's 2018 Bollywood historical film Padmaavat is part of a wider media-informational atmospherics of contemporary Hindu pride and Islamophobia, drawing on advertised energies of disaffection and ethnological stereotyping around the figure of the Muslim. In the process, it constructs a "double shift" Orientalist prism of race perception to view a splendid "Aryan" Hindu past as well as a dark interval of Islamic rule in India marked by a Semitic, Turko-Arabic pathology. The film is part of an overall Hindu nationalist project of constructing a moral memory (contra history) in the era of the digital image that can not only reinvent the past, but also re-texture and re-canvas it, making purported pictures of a glorious Hindu bygone appearing as not just nove, but also tactile and sensuous. [ABSTRACT FROM AUTHOR]

Objective: To investigate the effect of cross‐polarization filters on the colors of shade tabs obtained with a digital single‐lens reflex (DSLR) camera, macrolens, and a ring flash. Materials and Methods: Digital images of four shade tables (1M1, 3L2.5, 3R2.5, and 5M3) from the VITA Toothguide 3D‐Master shade guide were taken using a DSLR camera, 100 mm macrolens, and ring flash with two different cross‐polarizing filters (Polar_eyes and Filtropolar), and without (Nonpolarizer; n = 7). The CIE L*a*b* color coordinates of digital images were calculated and remeasured with a spectroradiometer (SR). The color differences (ΔE00) between the SR and digital images were calculated and analyzed with the two‐way analysis of variance (ANOVA) and Tukey HSD test (α = 0.05). Results: ΔE00 values of all test groups were higher than the clinically acceptable threshold (∆E00 > 1.80). While the ∆E00 values of Filtropolar (6.19 ± 0.44) and Polar_eyes (7.82 ± 0.23) groups were significantly higher than the Nonpolarizer (4.69 ± 0.32) for the 1M1 shade tab, ∆E00 value of Polar_eyes (6.23 ± 0.34) was significantly lower than Nonpolarizer (10.71 ± 0.48) group for 5M3 shade tab (p < 0.05). Conclusion: The color‐matching results of tested digital photography techniques with and without cross‐polarization were unacceptable, compared to a spectroradiometer. While digital photography with Polar_eyes cross‐polarizing filter has closer results to the reference device for the low‐in‐value shade table (5M3), for the high‐in‐value shade table (1M1), improved results were obtained without a cross‐polarizing filter. Clinical Significance: The cross‐polarization filters are increasingly used in dentistry for tooth color communication with digital photography techniques. However, the digital photography techniques with‐without cross‐polarization filterers should be improved to obtain clinically acceptable color‐matching results. [ABSTRACT FROM AUTHOR]

The application of unmanned aerial vehicle (UAV) remote sensing technology for high-throughput acquisition of phenotypic values of field plant height is of great significance in plant height monitoring of wheat varieties (lines). Based on the UAV, mounted with high-resolution digital cameras, a low-altitude remote sensing platform was built to obtain images of the Berkut/Worrakatta recombinant inbred lines (RIL) wheat population (297 lines). The optimal digital surface model (DSM) for plant height extraction was constructed by combining the actual wheat plant heights measured in the field. Using a wheat 50K SNP chip, quantitative trait locus (QTL) analysis was performed for field plant height of the RIL population using UAV plant height data to detect the locus related to plant height. We verified the effectiveness of QTL analysis using the UAV plant height data. The results of the DSM plant height extraction method showed that the plant height extraction models during the entire growth period had a highly significant correlation (p < 0.001); the measured plant height correlated with the predicted plant height, with prediction model accuracy R2 = 0.7527 and verification model accuracy R2 = 0.8214. QTL analysis was conducted on the manually and UAV-measured plant height traits. For the manually measured plant height phenotypes, one locus related to plant height was detected on wheat chromosome 6A and explained 13.12% of phenotypic variation. For the UAV-measured phenotypes, one locus related to plant height was also detected on wheat chromosome 6A and explained 9.62% of phenotypic variation. The QPH.xjau-6A locus on chromosome 6A, which featured in the results of the two measurement methods, is a stable locus, indicating that the mapping results obtained using the actual plant height values were in good agreement with those obtained using the UAV extracted values. Three candidate genes related to plant height were screened: they encode protein kinase, NAC domain protein, and cytochrome P450, respectively. Therefore, this study provided reference information for monitoring plant phenotype and growth of wheat and also for the extraction of plant height for wheat breeding in the future. [ABSTRACT FROM AUTHOR]

Head and neck tumor differential diagnosis and prognosis have always been a challenge for oral pathologists due to their similarities and complexity. Artificial intelligence novel applications can function as an auxiliary tool for the objective interpretation of histomorphological digital slides. In this review, we present digital histopathological image analysis applications in oral squamous cell carcinoma. A literature search was performed in PubMed MEDLINE with the following keywords: "artificial intelligence" OR "deep learning" OR "machine learning" AND "oral squamous cell carcinoma". Artificial intelligence has proven to be a helpful tool in histopathological image analysis of tumors and other lesions, even though it is necessary to continue researching in this area, mainly for clinical validation. [ABSTRACT FROM AUTHOR]

Today, the issue of protecting information from unauthorized access is one of the key issues in the field of information technology. As digital technologies continue to spread into all areas of human activity and methods of protecting information from unwanted access are improving, the development of methods and technologies for obtaining unauthorized access to confidential information for malicious purposes does not stop either. Therefore, it is important to continue to improve information security systems, thereby hindering intruders. An integral part of any modern information security system is a steganographic system that provides a hidden data transmission channel that allows you to transmit information without revealing the fact of its existence to an attacker. The task of choosing a steganographic container allows you to solve some of the requirements for a steganographic system when building it. One of the most important requirements is to ensure the steganographic system's resistance to attacks against the embedded message, since such attacks do not require extensive knowledge of steganography and steganalysis from the attacker and do not require special technical means, which makes this type of attack simple and widespread. The aim of the work is to improve the efficiency of the steganosystem by modifying the method of selecting a container from the given set, developed by the author earlier, which will ensure the maximum possible, or at least close to the maximum, resistance of the steganomessage to attack against the embedded message for the analyzed images. This goal was achieved by modifying the quantitative assessment of the amount of protected information, namely, using the relative value of the amount of protected information to the amount of all embedded information. The result of the work is the development of a modification of the method for selecting a steganographic container, which is ready for practical application. The effectiveness of the proposed method is higher than the prototype method, and remains high regardless of which steganomethod or perturbation was used. The significance of the result is to increase the overall resistance of the steganosystem to attacks against the embedded message by using this method to select the steganocontainer. [ABSTRACT FROM AUTHOR]

Freezing sets and cold sets have been introduced as part of the theory of fixed points in digital topology. In this paper, we introduce a generalization of these notions, the limiting set, and examine properties of limiting sets. [ABSTRACT FROM AUTHOR]

In environmental supervision, it is necessary to measure waste piles volume to determine whether the activities of the waste manager comply with the established requirements. The aim of this research is to determine whether the model, formed from images collected with low-priced unmanned aerial vehicle (UAV) - not with Real Time Kinematic Global Navigation Satellite System (RTK GNSS) capability - is sufficiently accurate to carry out waste-related surveying. Data collection took place in spring 2021 at the Aardlapalu transhipment station in Tartu County. The objects of the research were an unscreened composting pile and a covered composting pile. In the fieldwork, terrestrial laser scanning and photogrammetric flight were carried out. The reference value was the volume of the model formed from the data of laser scan. The volumes of all models formed by the photogrammetric method were within the permissible difference of 10% provided by law. The most accurate results were obtained from the covered composting pile with an overlap of 70% × 70% and 21 ground control points (GCPs). Using these parameters, the absolute error of the model was 1.48 m? and the relative error was 0.65%. The most inaccurate results were obtained from the unscreened composting pile with an overlap of 80% × 80% and 21 GCP-s. The research confirmed the hypothesis that sufficient accuracy to calculate waste piles volumes can also be achieved by using a cheaper UAV and camera and with software not specially designed for photogrammetry, design, and drawing. [ABSTRACT FROM AUTHOR]

Stenographic methods are closely related to the security and confidentiality of communications, which have always been essential domains of human life. Steganography itself is a science dedicated to the process of hiding information in public communication channels. Its main idea is to use digital files or even communication protocols as a medium inside which data are hidden. The present research aims to investigate the applicability of ant algorithms in steganography and evaluate the effectiveness of this approach. Ant systems could be employed both in spatial and frequency-based image steganography. The combination of frequency domain and optimization method to increase robustness is used, and an integer wavelet transform is performed on the host image. ACO optimization is used to find the optimal coefficients describing where to hide the data. The other method utilizes ACO to determine the optimal pixel locations for embedding secret data in the cover image. ACO is also used to detect complex regions of the cover image. Afterward, the least-significant-bits (LSB) substitution is used to hide secret information in the detected complex regions' pixels. Our study focuses on optimizing two mutually exclusive features of steganograms—high capacity and low distortion. An attempt was made to use ant systems to select areas of digital images that allow the greatest amount of information to be hidden with the least loss of image quality. The effect of variants of the ant system and its parameters on the quality of the results obtained was also investigated, and the final effectiveness of the proposed method was evaluated. The results of the experiments were compared with those published in related articles. The proposed procedures proved to be effective and allowed the embedding of large amounts of data with relatively little impact on image quality. [ABSTRACT FROM AUTHOR]

Relating image contours and regions and their attributes according to connectivity based on incidence or adjacency is a crucial task in numerous applications in the fields of image processing, computer vision and pattern recognition. In this paper, the crucial incidence topological information of 2-dimensional images is extracted in an efficient manner through the computation of a new structure called the HomDuRAG of an image; that is, the dual graph of the HomRAG (a topologically consistent extended version of the classical RAG). These representations are derived from the two traditional self-dual square grids (in which physical pixels play the role of 2-dimensional cells) and encapsulate the whole set of topological features and relations between the three types of objects embedded in a digital image: 2-dimensional (regions), 1-dimensional (contours) and 0-dimensional objects (crosses). Here, a first version of a fully parallel algorithm to compute this new representation is presented, whose timing complexity order (in the worst case and supposing one processing element per 0-cell) is O (l o g (M × N)) , M and N being the height and width of the image. Efficient implementations of this parallel algorithm would allow images to be processed in real time, as well as permit us to uncover fast algorithms for contour detection and segmentation, opening new perspectives within the image processing field. [ABSTRACT FROM AUTHOR]

The use of nondestructive techniques that can guarantee in situ monitoring of the quality of food products during drying processes can promote improvements in industrial processes. In this context, digital imaging and near infrared spectroscopy can play an important role. Red beet moisture content, betalain content, shrinkage, and color were measured during drying. The digital images showed changes in color, which intensified with increasing temperature and correlated with betalain content through multivariate linear regression (R2 of 0.90). In addition, sample shrinkage was identified by digital images. The volumetric shrinkage increased proportionally to the drying air temperature, with a shrinkage of 85.4 ± 0.3% at 70 °C. Spectral data were obtained by a low-cost portable near infrared spectrometer. Partial least square regression was used to predict the product moisture content ( R P 2 = 0.89 ; RMSEP = 0.79 %) and total betalain content ( R P 2 = 0.79 ; RMSEP = 0.30 m g g d. b.) during drying. These tools can be used for the in situ control of red beet drying. [ABSTRACT FROM AUTHOR]

The advancement of smartphones that produce high-resolution photos has made their use even more developed. One application that is currently developing is a digital image produced as colorimetry. Digital image-based colorimetry is an easy and simple determination method and can be an alternative for analyzing dyes quantitatively. In this method, the color response of the digital image is analyzed, namely red-green-blue (RGB) with values from 0 to 255. This data is extracted from digital images using ImageJ 1.52a software for quantitative determination. In this study, 2 standard methods were used, namely external standards and standard addition to evaluating the most suitable method to obtain accurate measurements. In addition, 2 methods were used to determine the relationship of response to methylene blue levels. The 1st method used simple linear regression of each color red, green, or blue (univariate). The 2nd method is used in multivariate linear regression of the 3 colors red, green, and blue. The accuracy of the method was validated using UV-Vis spectrophotometry as the reference method. The results showed that the multivariate models produced very good accuracy, namely precision of up to 99 % compared to the simple regression method with the univariate. [ABSTRACT FROM AUTHOR]

Objectives: Representative of a panel, an average face image could be used to analyse/display skin changes while alleviating image rights constraints. Therefore, we used landmark‐based deformation (warping) of individual skin images onto their panel's average face, evaluating this approach's relevance and possible limits. Methods: An average front face image was constructed from images of 71 Japanese women (50–60 years old). After warping individual skin images onto this average face, the resulting skin‐warped average faces were presented to three experts who graded: forehead wrinkles, nasolabial fold, wrinkle of the corner of the lips, pore visibility and skin pigmentation homogeneity. Two experts estimated subjects' age. Results were compared to gradings performed on original images. Results: Inter‐expert grading shows excellent to good correlation whatever image type: from 0.918 (forehead wrinkles) to 0.693 (visibility of pores). Correlations between scoring of both image types are almost always higher than inter‐expert correlations (maximum: 0.939 for forehead wrinkles—minimum: 0.677 for pore visibility). Frequencies of grades/ages are similar when scoring original and skin‐warped average face images. Experts scores are similar in 90.6%–99.3% of the cases. Average deviations upon scoring both image types are smaller than average inter‐expert deviations on original images. Conclusions: Scoring facial characteristics in original images and skin‐warped average face images show an excellent agreement, even for perceived age, a complex feature. This opens the possibility of using this approach to grade facial skin features, monitor changes over time, and to valorise results on a face deprived of image rights. [ABSTRACT FROM AUTHOR]

Sardinella lemuru, one of the important pelagic fish in Bali Strait waters, has been reported to be in the overfishing condition. Various studies have been conducted to understand its population condition. Length-weight data has been also widely used to understand the population dynamic of S. lemuru, although data collection generally using manual direct measurement methods, which has the disadvantages if used on the enormous number of samples. Therefore, this research aims to understand the significance of S. lemuru length measurement using digital image analysis (ImageJ) compared with the manual measurement. Moreover, this research also aims to understand the population condition of S. lemuru landed in PPI Kedonganan analyzed using its length-weight data. The result indicated no significance difference between length measurement using manual methods and digital image methods, thus conclude that digital image data can be used to measure the fish length and represent the accurate measurement. Population condition analysis showed that S. lemuru population is in isometric condition (b=3); with indication of declining in the length growth pattern compared with previous research (L8= 20.75 cm with K= 1.1); the mortality estimation showed that fishing mortality (F=1.27) was lower than natural mortality (M=2.22), with the survival rate higher on smaller size fish. This research result can be used as an addition to the S. lemuru fisheries data and can also be used as reference methods to help conduct sampling and data collection more efficiently. [ABSTRACT FROM AUTHOR]

In this paper the concept of soft continuity is focused on for digital images by using soft sets which is defined on κ - adjacent digital images. Also the definitions of digital soft isomorphism and digital soft retraction are given. Some theorems are obtained deal with soft isomorphism and soft retraction for digital images and some numerical examples are presented in dimension 2. Finally digital soft retraction is obtained as a soft topological invariant. [ABSTRACT FROM AUTHOR]

Image analysis is a powerful tool that can be applied in scientific research, industry, and everyday life, but still, there is more room to use it in materials science. The interdisciplinary cooperation between materials scientists and computer scientists can unlock the potential of digital image analysis. Traditional image analysis used in materials science, manual or computer-aided, permits for the quantitative assessment of the coexisting components at the cross-sections, based on stereological law. However, currently used cutting-edge tools for computer image analysis can greatly speed up the process of microstructure analysis, e.g., via simultaneous extraction of quantitative data of all phases in an SEM image. The dedicated digital image processing software Aphelion was applied to develop an algorithm for the automated image analysis of multi-phase high-temperature ceramic material. The algorithm recognizes each phase and simultaneously calculates its quantity. In this work, we compare the traditional stereology-based methods of image analysis (linear and planimetry) to the automated method using a developed algorithm. The analysis was performed on a digital SEM microstructural image of high-temperature ceramic material from the Cu-Al-Fe-O system, containing four different phase components. The results show the good agreement of data obtained by classical stereology-based methods and the developed automated method. This presents an opportunity for the fast extraction of both qualitative and quantitative from the SEM images. [ABSTRACT FROM AUTHOR]

Background: Several advanced methods for evaluating wrinkles are currently available, however, with limitations in their application because wrinkle structures change in response to facial expressions and surrounding environments. A Visual Illusion‐based image feature enhancement System (VIS) was used to develop a real‐time evaluation method. Objectives: This study expands the VIS application into the wrinkle evaluation method by adjusting VIS to evaluate facial wrinkles, evaluating the age‐dependent wrinkles, and validating it for real‐time wrinkle evaluation. Methods: Wrinkles in various Japanese men and women were evaluated using VIS and the current methods. Furthermore, the effectiveness of an eye cream containing niacinamide was evaluated before and after the 4‐week treatment. Results: VIS qualitatively detects even fine wrinkles and numerically records them without any special instrument. Moreover, VIS can be applied to moving images, revealing the effectiveness of the antiwrinkle formulation qualitatively and quantitatively even when the subjects are smiling. Conclusion: This paper presents an epoch‐making wrinkle evaluation method that is qualitative and quantitative, with high sensitivity in real‐time and relies solely on digital images without any difficulties. Therefore, these results imply that this method enables the wrinkle evaluation under real‐life conditions. [ABSTRACT FROM AUTHOR]

The relationship between the early ground cover and the grain yield in winter wheat is not yet fully understood. In a winter wheat breeding programme, selection for early ground cover is traditionally made using visual scoring. Although visual scoring is preferred as a phenotypic screening tool by wheat breeders, its output may not be reliable, as it requires experience. A smartphone camera-based digital image technique can be recommended as a feasible, reliable, repeatable, affordable, and fast selection tool for early ground cover in wheat as an alternative to visual scoring. For this purpose, two wheat trials were conducted in the 2017--2018 and 2019--2020 seasons. In both seasons, 215 wheat genotypes in total, together with three checks from spring wheat, were tested under rain-fed conditions in the spring wheat zone in Turkey. All the tested wheat genotypes were grouped into spring, facultative, and winter growth habit using visual scoring. Simultaneously, photos were taken from each plot with a smartphone camera, and the early ground cover (%) was estimated using the smartphone camera-based digital image technique. The relationships between grain yield, visual scoring, and early ground cover could so be estimated. In both seasons, significant negative correlation between grain yield and visual scoring (r = -0.679** and r = -0.704**, respectively) and significant positive correlation between the grain yield and the early ground cover (r = 0.745** and r = 0.747**, respectively) were observed. The correlation between visual scoring and early ground cover were negative (r = -0.862** and r = -0.926**, respectively). The broad sense heritability estimates in both seasons were 0.51 and 0.85, respectively, for early ground cover, 0.91 and 0.94 for visual scoring, and 0.86 and 0.69 for grain yield. In this study, we revealed that testing winter wheat genotypes in the spring wheat zone rather than in the winter wheat zone could be a more effective way to unveil the positive relationship between the early ground cover and the grain yield. We have shown that the smartphone-based digital image technique is a useful selection tool for early ground cover in winter wheat. [ABSTRACT FROM AUTHOR]

Carbon nanotubes are considered as a promising material for the development of high performance concrete. In order to study the effects of Multi walled carbon nanotubes (MWCNTs) content, water cement ratio and addition method on the mechanical properties and microstructure of cement-based composites, specimens with MWCNTs content of 0%, 0.05%, 0.08%, 0.10%, 0.15%, 0.20%, and 0.30% were prepared when the water cement ratio was 0.25, 0.3, 0.35, and 0.4, respectively. The physical properties, mechanical properties and microstructure were tested. The test results show that with the increase of MWCNTs content, the flexural strength and compressive strength of cement-based composites increase first and then decrease. The DIC test shows that the displacement and strain of the specimen with MWCNTs are smaller than the blank sample under the same concentrated load, while it is opposite before the failure of sample. The SEM test shows that MWCNTs play the role of bridging and filling in the matrix. The EDS analysis results show that the five main elements of carbon, oxygen, aluminum, silicon, and calcium in the composite are evenly distributed in the cement matrix. [ABSTRACT FROM AUTHOR]

To solve the problem of low recognition accuracy and long recognition time in conventional methods for digital moving image feature recognition, a method for digital image feature recognition on mobile platforms based on machine learning is proposed. The support vector machine method is used to cluster the dataset, and then the weight reconstruction method is used to reduce the dimension of digital moving image data. The reduced dimension of the digital moving image is embedded in the coordinates to obtain the reduced dimension of the image data information and complete the feature recognition of the digital moving image. The simulation results show that the recognition accuracy of this method in recognizing features of moving digital images is basically stable at more than 95% and the running time is 0.42s. The recognition effect of the proposed method is good, the recognition efficiency is high, and it has some practical application value. [ABSTRACT FROM AUTHOR]

In shale oil production using in-situ thermal recovery method, the temperature in the borehole is high or even ultra-high, and it is easy to result in failure of the sealing integrity of the cement sheath. It is thus important to study the changes of the mechanical characteristics and the porosity and permeability of a set cement under high or ultra-high temperatures. Using digital image technology, changes of the failure mode of a set cement after high temperature treatment is analyzed. Using low field NMR, the changes of the porosity and permeability of a set cement after high temperature treatment are studied, and the correlation of porosity and permeability is analyzed. The study shows that for a high temperature treated set cement sample, as the temperature increases, the monoaxial compressive strength of the set cement first increases and then decreases. The porosity and the permeability of the set cement increase continuously; at first, the increase goes slowly and then goes fast. This is because at lower temperatures, there are only micro and small pores produced inside the cement and these pores are not inter-connected with each other. As the temperature increases, some micro and small pores combine into medium and large pores, and pores of different sizes begin to connect with each other, thereby increasing the permeability of the set cement. The achievements of the study have important reference significance for protecting the sealing integrity of cement sheath and optimizing cement slurry formulation in in-situ thermal recovery of shale oil. [ABSTRACT FROM AUTHOR]

The development of automated systems for detecting defects in and damage to buildings is ongoing in the construction industry. Remaining aware of the surface conditions of buildings and making timely decisions regarding maintenance are crucial. In recent years, machine learning has emerged as a key technique in image classification methods. It can quickly handle large amounts of symmetry and asymmetry in images. In this study, three supervised machine learning models were trained and tested on images of efflorescence, and the performance of the models was compared. The results indicated that the support vector machine (SVM) model achieved the highest accuracy in classifying efflorescence (90.2%). The accuracy rates of the maximum likelihood (ML) and random forest (RF) models were 89.8% and 87.0%, respectively. This study examined the influence of different light sources and illumination intensity on classification models. The results indicated that light source conditions cause errors in image detection, and the machine learning field must prioritize resolving this problem. [ABSTRACT FROM AUTHOR]

Background/purpose: Skin color is used as an index for diagnosing and predicting skin irritation, dermatitis, and skin conditions because skin color changes based on various factors. Therefore, a new method for consistently and accurately evaluating skin color while overcoming the limitations of the existing skin color evaluation method was proposed, and its usefulness was demonstrated. Methods: Skin color was quantified using the RGB (Red, Green, Blue), HSV (Hue Saturation Value), CIELab, and YCbCr color spaces in the acquired Korean skin images, which were classified through clustering. In addition, the classification performances of the existing visual scoring method and the proposed skin color classification method were compared and analyzed using multinomial logistic regression, support vector machine, K‐nearest neighbor, and random forest. Results: After quantifying the skin color through the color space conversion of the skin image, the skin color classification performance according to the number of quantified features and the classifier was verified. In addition, the usefulness of the proposed classification method was confirmed by comparing its classification performance with that of the existing skin color classification method. Conclusion: In this study, a method was proposed to objectively classify skin color values quantified from skin images of Koreans acquired using a digital camera through clustering. To verify the proposed method, its classification performance was compared with that of the existing classification method, and an optimized classification method was presented for the classification of Korean skin color. Thus, the proposed method can objectively classify skin color and can be used as a cornerstone in research to quantify skin color and establish objective classification criteria. [ABSTRACT FROM AUTHOR]

Virus infection in chili plants may cause various symptoms. The complexity of the symptoms and human vision ability often become limiting factors during disease investigations. Digital image analysis is expected to become a method to assist in comprehensively describing the symptoms of plant viruses. A disease survey was conducted on cayenne pepper fields in Southeast Sulawesi Province to observe symptoms of virus infection virtually and to record the symptomatic plant using an RGB camera. The split-channel method is used to process images, followed by multidimensional scaling statistical analysis. Later on, viruses associated with plants were detected serologically. Single or mixed infection of Tobacco mosaic virus, Cucumber mosaic virus, Chili veinal mottle virus, and Pepper mottle virus was confirmed by plant leaves showing yellow-mosaic and mottle symptoms. The digital image analysis method could show variations in the characteristics of symptoms based on digital numbers in that cannot be recognized based on the observation of visual symptoms. A new approach to study the interactions between plant infecting viruses and their effects based on image analysis has also been developed during this research. This method needs to be further validated through testing under controlled conditions, such as inoculating plants with a predetermined type of virus. [ABSTRACT FROM AUTHOR]

The paper covers the problem of determination of defects and contamination in malting barley grains. The analysis of the problem indicated that although several attempts have been made, there are still no effective methods of identification of the quality of barley grains, such as the use of information technology, including intelligent sensors (currently, quality assessment of grain is performed manually). The aim of the study was the construction of a reduced set of the most important graphic descriptors from machine-collected digital images, important in the process of neural evaluation of the quality of BOJOS variety malting barley. Grains were sorted into three size fractions and seed images were collected. As a large number of graphic descriptors implied difficulties in the development and operation of neural classifiers, a PCA (Principal Component Analysis) statistical method of reducing empirical data contained in the analyzed set was applied. The grain quality expressed by an optimal set of transformed descriptors was modelled using artificial neural networks (ANN). The input layer consisted of eight neurons with a linear Postsynaptic Function (PSP) and a linear activation function. The one hidden layer was composed of sigmoid neurons having a linear PSP function and a logistic activation function. One sigmoid neuron was the output of the network. The results obtained show that neural identification of digital images with application of Principal Component Analysis (PCA) combined with neural classification is an effective tool supporting the process of rapid and reliable quality assessment of BOJOS malting barley grains. [ABSTRACT FROM AUTHOR]

This paper discusses a feasible software solution that enables automatic marking and grading of scripts. Technology keeps expanding, and more advanced innovations are being implemented with time. The marking and allocation of grades for examination scripts through human efforts are gradually becoming a thing of the past. Hence, machines and software applications are introduced to make the entire marking and grading of examination scripts more efficient, fast, and less tedious. Computer vision is an artificial intelligence (AI) knowledge domain that ensures devices obtain useful information from digital images, videos, and other visual inputs. Image processing and recognition, a unique part of computer vision alongside the python programming language and the OpenCV library was employed for this project. These are the most used in developing most recent applications that utilize, to some extent, artificial intelligence to attain specific desired results. The result of the project seeks to develop a maintainable android software application that uses image processing technology to scan patterns or images and grades results of multiple-choice question scripts based on a set marking scheme. This ensures that desired results are obtained while increasing efficiency and productivity. [ABSTRACT FROM AUTHOR]

Digital image analyses were used to measure livestock. In this study we determine the accuracy of beef carcass measurements obtained from digital images analysed using a mobile phone application. We photographed 100 cross-bred beef carcasses using a mobile phone camera and analysed the images using Adobe Photoshop® CS6 computer software and ImageMeter® mobile application. Both methods were used to measure ribeye area and backfat thickness and the results were compared using a paired t-test. Ribeye area and backfat thickness measurements were not significantly different (p > 0.05) between the two methods. The correlation between beef carcass images of the ribeye area and backfat thickness analysed using the two methods were very high (concordance correlation coefficients of 0.9997 and 0.9926 respectively). The results of this study suggest that measurements of the beef carcass's ribeye area and backfat thickness can be easily and accurately obtained in the field using ImageMeter® on a mobile device. [ABSTRACT FROM AUTHOR]

Background: Fractional vegetation coverage (FVC) is a crucial parameter in determining vegetation structure. Automatic measurement of FVC using digital images captured by mobile smart devices is a potential direction for future research on field survey methods in plant ecology, and this algorithm is crucial for accurate FVC measurement. However, there is a lack of insight into the influence of illumination on the accuracy of FVC measurements. Therefore, the main objective of this research is to assess the adaptiveness and performance of different algorithms under varying light conditions for FVC measurements and to deepen our understanding of the influence of illumination on FVC measurement. Methods and results: Based on a literature survey, we selected four algorithms that have been reported to have high accuracy in automatic FVC measurements. The first algorithm (Fun01) identifies green plants based on the combination of R / G , B / G , and ExG (R , G , and B are the actual pixel digital numbers from the images based on each RGB channel, ExG is the abbreviation of the Excess Green index), the second algorithm (Fun02) is a decision tree that uses color properties to discriminate plants from the background, the third algorithm (Fun03) uses E x G - E x R ( ExR is the abbreviation of the Excess Red index) to recognize plants in the image, and the fourth algorithm (Fun04) uses ExG and O tsu to separate the plants from the background. Otsu is an algorithm used to determine a threshold to transform the image into a binary image for the vegetation and background. We measured the FVC of several surveyed quadrats using these four algorithms under three scenarios, namely overcast sky, solar forenoon, and solar noon. FVC values obtained using the Photoshop-assisted manual identification method were used as a reference to assess the accuracy of the four algorithms selected. Results indicate that under the overcast sky scenario, Fun01 was more accurate than the other algorithms and the MAPE (mean absolute percentage error), BIAS, relBIAS (relative BIAS), RMSE (root mean square error), and relRMSE (relative RMSE) are 8.68%, 1.3, 3.97, 3.13, and 12.33%, respectively. Under the scenario of the solar forenoon, Fun02 (decision tree) was more accurate than other algorithms, and the MAPE, BIAS, relBIAS, RMSE, and relRMSE are 22.70%, − 2.86, − 7.70, 5.00, and 41.23%. Under the solar noon scenario, Fun02 was also more accurate than the other algorithms, and the MAPE, BIAS, relBIAS, RMSE, and relRMSE are 20.60%, − 6.39, − 20.67, 7.30, and 24.49%, respectively. Conclusions: Given that each algorithm has its own optimal application scenario, among the four algorithms selected, Fun01 (the combination of R / G , B / G , and ExG ) can be recommended for measuring FVC on cloudy days. Fun02 (decision tree) is more suitable for measuring the FVC on sunny days. However, it considerably underestimates the FVC in most cases. We expect the findings of this study to serve as a useful reference for automatic vegetation cover measurements. [ABSTRACT FROM AUTHOR]

Soil is the essential part for agricultural and environmental sciences, and soil salinity and soil water content are both the important influence factors for sustainable development of agriculture and ecological environment. Digital camera, as one of the most popular and convenient proximal sensing instruments, has its irreplaceable position for soil properties assessment. In this study, we collected 52 soil samples and photographs at the same time along the coast in Yancheng City of Jiangsu Province. We carefully analyzed the relationship between soil properties and image brightness, and found that soil salt content had higher correlation with average image brightness value than soil water content. From the brightness levels, the high correlation coefficients between soil salt content and brightness levels concentrated on the high brightness values, and the high correlation coefficients between soil water content and brightness levels focused on the low brightness values. Different significance levels (P) determined different brightness levels related to soil properties, hence P value setting can be an optional way to select brightness levels as the input variables for modeling soil properties. Given these information, random forest algorithm was applied to develop soil salt content and soil water content inversion models using randomly 70% of the dataset, and the rest data for testing models. The results showed that soil salt content model had high accuracy (Rv2 = 0.79, RMSEv = 12 g/kg, and RPDv = 2.18), and soil water content inversion model was barely satisfied (Rv2 = 0.47, RMSEv = 3.04%, and RPDv = 1.38). This study proposes a method of modeling soil properties with a digital camera. Combining unmanned aerial vehicle (UAV), it has potential popularization and application value for precise agriculture and land management. [ABSTRACT FROM AUTHOR]

Gas permeability, which is measured mainly through gas permeability experiments, is a critical technical index in many engineering fields. In this study, permeability is firstly calculated based on information from a digital image and an improved permeability prediction model. The calculated results are experimentally verified. Subsequently, a selfdeveloped image-processing program is used to extract feature parameters from a scanning electron microscopy image. Meanwhile, an extreme learning machine algorithm is used to input the image feature parameters obtained using the image-processing program into the extreme learning machine algorithm for machine learning. Additionally, we compare several typically used machine learning algorithms, which confirmed the reliability and accuracy of our algorithm. The best activation function can be obtained by comparing the predicted permeability using an appropriate number of neuron nodes. Experimental results show that the program can accurately identify the features of the microscopy image. Combining the program with an extreme learning machine neural network algorithmgas permeability results to be obtained with high accuracy. This method yields good predictions of permeability in certain cases and has been adapted to other geomaterials. [ABSTRACT FROM AUTHOR]

The emergence of the Internet brought about significant changes that altered the hypermodern life experience. Faced with this new scenario, how are subjectivities produced? This paper seeks to reflect on the current exposure of the digital image of the body and its relation to the processes of subjectivation. Based on studies on digital culture carried out in Latin America and their interlocution with Jacques Lacan's theory of the mirror stage, it delves into the dimension of virtuality and its impact on subjectivity, which is addressed regarding its aspect of extimacy and liquid plurality and is constructed from a digital body produced for the gaze. [ABSTRACT FROM AUTHOR]

Information is one of the most valuable objects of modern life. Accessing it has become incredibly easy with the development of global computer networks. At the same time, the ease and speed of such access significantly increased the threat of data security violations in the absence of means to protect them. The relevance of the topic lies in the fact that in today's realities messengers are an integral part of the life of almost every person in the world, therefore the security of personal information has a high priority. The purpose of the work is to develop a method of hiding messages in a set of sequential images, its software implementation and use in the messenger. To achieve the defined goal, the following tasks were formed: analysis of existing popular messengers and their methods of information protection, analysis of the relevance of steganography in messengers; development of a steganographic method of hiding messages in a set of consecutive images; development of software implementation of the proposed method; study of the resistance of the proposed hiding method to possible disturbed actions by the attacker; software implementation of the messenger that uses the proposed method. The object of the study is the process of ensuring the security of the user's personal information when using messengers. The subject of the research is methods of hiding information and algorithms for transporting messages in messengers. The novelty of the work lies in the development of a method for encoding messages using images, which was first used in a user messenger. The developed method of hiding messages is implemented in an independent software product for communication, in addition, its implementation can be used in other existing messengers. [ABSTRACT FROM AUTHOR]

The study of freezing sets is part of the theory of fixed points in digital topology. Most of the previous work on freezing sets is for digital images in the digital plane Z 2 . In this paper, we show how to obtain freezing sets for digital images in Z n for arbitrary n, using the c 1 and c n adjacencies. [ABSTRACT FROM AUTHOR]

Nowadays, neural networks are actively used for data hiding; however, there is currently no systematic knowledge regarding their utilization in this field. This is a significant gap, considering that neural network-based data hiding has already formed a large and quite independent area of research. This review aims to provide such systematization. It also provides a general framework of neural network usage for data hiding, comparisons of quantitative and qualitative indicators of the effectiveness of existing data hiding methods, and discussion and conclusions containing the most promising directions and tools for research in the field of data hiding using neural networks, as well as the main achievements and the persisting challenges. This work targets researchers looking for the most suitable data hiding method for their application, as well as the developers of data hiding methods. [ABSTRACT FROM AUTHOR]

It is difficult to realize real-time monitoring of the moisture content of construction waste recycled materials by conventional methods. Qualitative analysis was carried out on the macro-mesoscopic morphology and color characteristics of construction waste recycled materials. The correlation analysis between the digital image feature value and moisture content of materials based on MATLAB was carried out. The results show that, except for the color tone, the digital image characteristic parameters of construction waste recycled materials are significantly correlated with the moisture content. The linear regression analysis of the characteristic parameters and the moisture content was carried out by using the step-by-step method. The fitted model for calculating the moisture content of the recycled construction waste was tested and verified on site. The test results show that the water content calculation model of the construction waste recycled material has an error of -0. 2% ~0. 9% under laboratory conditions and -3. 0% ~2. 6% under field conditions. It can provide the basis for the construction and filling process control of automatic pulping system with recycled construction waste as raw material. [ABSTRACT FROM AUTHOR]

BACKGROUND Sugar is consumed worldwide and so the quality control of sugar cane is necessary. Solid impurities are an inherent part of industrial sugar processing. Dark particles and adulteration with sand must be controlled. Sixty‐four samples of white crystal cane sugar analysis in the presence of both kinds of impurities (dark particles and sand) were assessed using an affordable digital image system and a multivariate calibration strategy. RESULTS: The quality parameters for the multivariate calibration models obtained to estimate sugar content were remarkable. Color descriptors from digital images allowed identification of different levels of sugar content for the following three ranges: 0–49.99 wt%, 50.03–78.99 wt%, and 82.99–100 wt%. The multivariate model using red (R), green (G), Blue (B), and luminosity (L) color descriptors showed low standard errors of cross‐validation (SECV) and validation (SEV) of 7.63 and 6.01 wt%, respectively. CONCLUSIONS: The method is affordable and reliable, and might aid quick screening in situations where access to a laboratory or instrumentation is restricted. © 2021 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

This study provides a framework for the comparative assessment of the key industry aspects of competitiveness among logistics services and the logistics systems of enterprises in the informational environment. Frequently, the relationships between a consumer and a company created by means of the informational environment determine how the enterprise positions itself in the market. For instance, the evaluation of a company's representation in the information field is an essential aspect of determining the company's competitiveness. The study suggests a set of special metrics for measuring the representation of digital components and other aspects of an enterprise's digital image via data gathering and analysis of the most encountered tokens. The proposed automated analysis algorithm allows companies to examine their image in the digital environment and implement effective decisions. The functionality of the algorithm fosters data collection, helping to form the desired image of the company. Tokens of several thematic groups on social media are collected during the process, and the most significant of them that are valuable for the competitiveness of the enterprise are extracted. The outcome can be used for the tracking of the dynamics of key parameters of an enterprise's image and for conducting a comparative analysis of the digital image of its competitors. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new data-hiding framework: multisource data hiding, in which multiple senders (multiple sources) are able to transmit different secret data to a receiver via the same cover image symmetrically. We propose two multisource data-hiding schemes, i.e., separable and anonymous, according to different applications. In the separable scheme, the receiver can extract the secret data transmitted by all senders using the symmetrical data-hiding key. A sender is unable to know the content of the secret data that is not transmitted by them (non-source sender). In the anonymous scheme, it is unnecessary to extract all secret data on the receiver side. The content extracted by the receiver is a co-determined result of the secret data transmitted by all senders. Details of the secret data are unknown to the receiver and the non-source senders. In addition, the two proposed schemes achieve multisource data hiding without decreasing the undetectability of data hiding. [ABSTRACT FROM AUTHOR]

Geotechnical heterogeneous materials contain a large number of defects, such as granular structures of different types and spatial geometric distribution, cracks, and voids, and have a complex internal meso-structure. Moreover, the mechanical properties and defect structures of various meso-media have an important influence on the fracture mechanism, which makes the analysis of the fracture process of geotechnical materials a nonequilibrium and nonlinear three-dimensional mechanical problem in space. However, due to the complexity of mathematical theory, the limitation of laboratory test and field observation, test conditions, and technology, it is very difficult to study the three-dimensional fracture process of geotechnical materials, and numerical simulation has become one of the powerful tools to solve these problems. This paper first introduces the concept of digital image and the development of geotechnical engineering, and then analyses the types of engineering materials used in geotechnical engineering construction. A finite element method for mechanical analysis based on digital images is proposed. The method can be used to study the influence of the internal microstructure of geotechnical engineering materials on the mechanical response of materials. By analyzing the images, it can be concluded that the aggregate content is 36.1%, the matrix content is 48.9%, and the rest is the proportion of interface elements. The digital image numerical analysis method in this paper is based on two-dimensional space, but according to the principle of stereological logic transformation, it can be applied to three-dimensional finite element analysis. [ABSTRACT FROM AUTHOR]

A digital image is a finite set of integer lattice points in an ambient Euclidean space together with a suitable adjacency relation between points. Subdivision, which is a process of enlarging a digital image in the photographic sense, provides a basic tool for operating with digital images. But given a map of digital images, there is as yet no general way to define a map of their subdivisions that might reasonably be called a subdivision of the map. In this paper, we construct such maps of subdivisions when the map of digital images has a 1- or 2-dimensional domain. From our constructions we deduce path covering and homotopy covering results that play a role in our development of the digital fundamental group. [ABSTRACT FROM AUTHOR]

This study was aimed at developing a double-monitoring detection system for measuring the TAC (total antioxidant capacity) of apple juice samples. The conventional CUPRAC (cupric ion reducing antioxidant capacity) assay was coupled to high-performance liquid chromatography–ultraviolet (HPLC–UV) and digital image-based colorimetric detection systems. In the CUPRAC assay, a chromogenic copper(II)-neocuproine (Cu(II)-Nc) reagent was used to oxidize the antioxidants, which is further reduced by the antioxidants to a yellow-colored copper(I)-neocuproine (Cu(I)-Nc) chelate having maximum absorption at 450 nm. The yellow-colored samples were measured by HPLC–UV and digital image-based colorimetric detection systems. Parameters of the HPLC–UV system were optimized to obtain a sharp and transient peak at 450 nm analytical wavelength. Under the optimum experimental conditions, the detection limits calculated for ascorbic acid using the HPLC–UV and digital image-based colorimetric detection systems were 0.28 and 0.70 mg/L, respectively. Satisfactory results (95.6–102%) were calculated for apple juice samples spiked at different concentrations for both detection systems, and very low (< 5.0%) percent relative standard deviation values were recorded. The TAC value for ascorbic acid equivalent in apple juice sample was found as 131 ± 0.1 mg/L using the HPLC–UV system. [ABSTRACT FROM AUTHOR]