Color Space Research Articles

Rainbow-inspired multicolor lateral flow immunoassay using multibranched platinum–rhodium-cobalt nanoparticles

Traditional lateral flow immunoassay (LFIA) using single-color labels has limitations in indistinct color change and restriction to the single-mode readout, which can hardly meet the demands of high-performance detection. Inspired by the multiple colors of the rainbow, we outline a multicolor LFIA using multibranched trimetallic platinum–rhodium-cobalt nanoparticles (PRCNPs) as signal labels to initiate multicolor conversion for diverse and multicolor readouts. The intrinsic properties of PRCNPs including their trimetallic composition, multibranched morphology, and strong absorption in the visible spectrum, confer advantages in catalytic activity, antibody utilization, and signal readout. Through the synergistic integration of the catalytic PRCNPs with the etching of gold nanorods, PRCNPs-LFIA enables multicolor readouts in a rainbow-like manner. PRCNPs-LFIA allows for qualitative analysis of acetamiprid, a widely-used neonicotinoid insecticide, at 2 ng mL−1. It also provides semi-quantitative analysis based on the discrimination of at least 10 colors. Furthermore, quantitative analysis can be conducted across multiple color spaces within the range of 0.01-10 ng mL−1, achieving a lowest detection result of 0.006 ng mL−1. The recoveries of acetamiprid in food samples detected by PRCNPs-LFIA in grayscale and multiple color space modes are 87.35%-103.59%, 85.56%-114.33%, 86.14%-114.77%, and 87.81%-112.01%, respectively, with all coefficients of variation below 15%. These results demonstrate the feasibility and accuracy of PRCNPs-LFIA in practical applications. Overall, this study illustrates the potential of multifunctional PRCNPs in the development of multicolor LFIA and broadens the biosensing strategies for point-of-care testing.

Robust blind color deconvolution and blood detection on histological images using Bayesian K-SVD

Hematoxylin and Eosin (H&E) color variation among histological images from different laboratories can significantly degrade the performance of Computer-Aided Diagnosis systems. The staining procedure is the primary factor responsible for color variation, and consequently, the methods designed to reduce such variations are designed in concordance with this procedure.In particular, Blind Color Deconvolution (BCD) methods aim to identify the true underlying colors in the image and to separate the tissue structure from the color information. Unfortunately, BCD methods often assume that images are stained solely with pure staining colors (e.g., blue and pink for H&E). This assumption does not hold true when common artifacts such as blood are present, requiring an additional color component to represent them. This is a challenge for color standardization algorithms, which are unable to correctly identify the stains in the image, leading to unexpected results.In this work, we propose a Blood-Robust Bayesian K-Singular Value Decomposition model designed to simultaneously detect blood and extract color from histological images while preserving structural details. We evaluate our method using both synthetic and real images, which contain varying amounts of blood pixels.

Colorimetric detection of serum creatinine on a miniaturized platform using hue-saturation-value space analysis

Chronic kidney disease (CKD) is a widespread condition with considerable health and economic impacts globally. However, existing methodologies for serum creatinine assessment often involve prolonged wait times and sophisticated equipment, such as spectrometers, hindering real-time diagnosis and care. Innovative solutions like point-of-care (POC) devices are emerging to address these challenges. In this context, there is a recognized need for remote, regular, automated, and low-cost analysis of serum creatinine levels, given its role as a critical parameter for CKD diagnosis and management. This study introduces a miniaturized system with integrated heater elements designed for precise serum creatinine measurement. The system operates based on the Jaffe method and accurate serum creatinine measurement within a microreservoir chip. Smartphone-based image processing using the hue-saturation-value (HSV) color space was applied to captured images of microreservoirs. The creatinine analyses were conducted in serum with a limit of detection of ~ 0.4 mg/dL and limit of quantification of ~ 1.3 mg/dL. Smartphone-based image processing employing the HSV color space outperformed spectrometric analysis for creatinine measurement conducted in serum. This pioneering technology and smartphone-based processing offer the potential for decentralized renal function testing, which could significantly contribute to improved patient care. The miniaturized system offers a low-cost alternative ($87 per device), potentially reducing healthcare expenditures (~ $0.5 per test) associated with CKD diagnosis and management. This innovation could greatly improve access to diagnosis and monitoring of CKD, especially in regions where access to sophisticated laboratory equipment is limited.

A web-based mpox skin lesion detection system using state-of-the-art deep learning models considering racial diversity

The recent ‘Mpox’ outbreak, formerly known as ‘Monkeypox’, has been a significant public health concern and spread to over 110 countries globally. The challenge of clinically diagnosing mpox early on is due, in part, to its similarity to other types of rashes. Computer-aided screening tools have been proven valuable in cases where Polymerase Chain Reaction (PCR) based diagnosis is not immediately available. Deep learning methods excel at learning complex data representations, but their efficacy largely depends on adequate training data. To address this challenge, we present the “Mpox Skin Lesion Dataset Version 2.0 (MSLD v2.0)” as a follow-up to the previously released open-access dataset, one of the first to contain mpox lesion images. This dataset contains images of patients with mpox and five other non-mpox classes (chickenpox, measles, hand-foot-mouth disease, cowpox, and healthy). We benchmark the performance of several state-of-the-art deep learning models, including VGG16, ResNet50, DenseNet121, MobileNetV2, EfficientNetB3, InceptionV3, and Xception, to classify mpox and other infectious skin diseases. We leverage transfer learning implemented with pre-trained weights generated from the HAM10000 dataset, an extensive collection of pigmented skin lesion images. To reduce the impact of racial bias, we employ a color space data augmentation method to increase skin color variability during training and present a rigorous assessment of the technique’s effectiveness in skin lesion classification; we achieved the best overall accuracy of 83.59±2.11%. Finally, the developed models are incorporated within a prototype web application to analyze uploaded skin images by a user and determine whether a subject is a suspected mpox patient. We believe that the presented tool can be of significant impact in screening of mpox in case of a future outbreak.

Quantitative analysis of zearalenone in wheat leveraging support vector machine and olfactory visualization technology

Zearalenone (ZEN) is a contaminant found naturally in grains such as wheat that poses a potential threat to human and animal health. This study describes a colorimetric sensor array detection system based on chemically reactive dyes and chemometric algorithms for the determination of ZEN in wheat. Firstly, six chemically responsive dyes were screened to construct a colorimetric sensor array to obtain the response fingerprints of the volatile components of wheat. The RGB differences of the chemically reactive dyes before and after the response were then calculated to obtain the color component data of the image. The maximum information coefficient (MIC) and ReliefF methods were then used to select the best color features. Finally, the Support Vector Machine Regression (SVR) prediction model was developed and validated based on the optimized features on 132 sample sets. The results showed that the SVR model developed on the initial 132 wheat samples had good predictive ability. Its root mean square error (RMSE) on the prediction set was 38.1625 μg∙kg−1 with a correlation coefficient (R) 0.9516. In particular, the SVR model combining 13 color components reduced the RMSE on the prediction set to 30.4484 μg∙kg−1, with an improved R of 0.9695. Olfactory visualization techniques were shown to be effective for the determination of ZEN in wheat. This study is expected to provide a scientifically novel approach for quality stabilization during the storage of wheat or other cereals.

Estimation of Anthocyanins in Heterogeneous and Homogeneous Bean Landraces Using Probabilistic Colorimetric Representation with a Neuroevolutionary Approach

The concentration of anthocyanins in common beans indicates their nutritional value. Understanding this concentration makes it possible to identify the functional compounds present. Previous studies have presented color characterization as two-dimensional histograms, based on the probability mass function. In this work, we proposed a new type of color characterization represented by three two-dimensional histograms that consider chromaticity and luminosity channels in order to verify the robustness of the information. Using a neuroevolutionary approach, we also found a convolutional neural network (CNN) for the regression task. The results demonstrate that using three two-dimensional histograms increases the accuracy compared to the color characterization represented by one two-dimensional histogram. As a result, the precision was 93.00 ± 5.26 for the HSI color space and 94.30 ± 8.61 for CIE L*a*b*. Our procedure is suitable for estimating anthocyanins in homogeneous and heterogeneous colored bean landraces.

Biostimulant Effects of Algae Species, Arbuscular Mycorrhizal Fungi, and Their Combinations on Yield and Quality of Yellow Tomato Landrace Under Different Crop Cycles

Recent agricultural research has prioritized the development of environmentally friendly management strategies to ensure food security, among which the application of biostimulants such as brown algae extracts, arbuscular mycorrhizal fungi (AMF), and their combination are included. The experimental protocol was based on the factorial combination of two planting times (4 May and 1 June) and seven biostimulant treatments (three brown algae species, Cystoseria tamariscifolia—C.t.; Fucus vesiculosus—F.v.; Padina pavonica—P.p.; arbuscular mycorrhizal fungi—AMF; C.t. + AMF; F.v. + AMF; P.p. + AMF) plus an untreated control. The earlier transplant resulted in a higher yield, due to the higher number of fruits per plant, and a higher plant fresh and dry biomass. The treatments with P.p. and F.v. extracts and the combination P.p. + AMF led to the highest yields (56.7 t ha−1), mainly due to the highest fruit number per plant. The earlier planting time led to higher values of dry residue, soluble solids, firmness, and colour component ‘a’. The highest values of fruit dry residue were recorded under the F.v. and P.p. extracts, and the combinations F.v. + AMF and P.p. + AMF, the highest soluble solid content with P.p. treatment, and firmness under P.p. + AMF. The highest levels of ‘L’ and ‘a’ fruit colour components were obtained under the P.p. extract treatment, of ‘b’ upon the application of P.p. and F.v. extract, and AMF + P.p. and AMF + F.v. The later planting time led to significantly higher values of the antioxidant parameters, as did the application of the P.p. extract and P.p. + AMF. CAT activity was more intense corresponding to the later tomato crop cycle, P.p. extract, and AMF + P.p. Overall, our study highlights the potential of biostimulants, particularly brown algae extracts and their combination with AMF, to improve tomato yield, antioxidant properties, and biochemical activities.

Effect of texture and color enhancement imaging on the visibility of gastric tumors

Texture and color enhancement imaging (TXI) may improve the visibility of gastric tumors and allow their early detection. However, few reports have examined the utility of TXI. Between June 2021 and October 2022, 56 gastric tumors in 51 patients undergoing endoscopic submucosal dissection at Fukuchiyama City Hospital were evaluated preoperatively using conventional white light imaging (WLI), narrow-band imaging (NBI), and TXI modes 1 and 2. The color differences of the tumors and surrounding mucosae were evaluated using the CIE 1976 L*a*b color space, Additionally, the visibility scores were scaled. Of the 56 gastric tumors, 45 were early gastric cancers, and 11 were adenomas. Overall, the color difference in TXI mode 1 was considerably higher compared to WLI (16.36 ± 7.05 vs. 10.84 ± 4.05; p < 0.01). Moreover, the color difference in early gastric cancers was considerably higher in TXI mode 1 compared to WLI, whereas no significant difference was found in adenomas. The visibility score in TXI mode 1 was the highest, and it was significantly higher compared to WLI. Regarding adenomas, the visibility score in TXI mode 1 was also significantly higher compared to that in WLI. TXI may provide improved gastric tumor visibility.

Recent Advances in Electrochromic Devices: From Multicolor to Flexible Applications

With increasing demand for energy saving and environmental sustainability, electrochromic devices (ECDs) are considered as emerging display devices with low energy consumption. While various reflective‐type displays produce images with low energy, achieving full color displays often involves much device complexity and nonflexibility. Multicolor ECDs aim to realize full color reflective‐type displays, surpassing the current monochromic type or limited coloration capabilities in a 1D color space. Enhancing device flexibility is also highly desirable for use of ECDs in wearable and flexible electronics for health monitoring and advanced textiles with easy visualization. In this review, recent advances in multicolor and flexible ECDs are examined. Several primary strategies to achieve multicolor ECD are described, including material modifications, color overlay, and dye‐mediated colorations. In addition, recent developments in flexible ECDs are explored, emphasizing novel materials and fabrication processes that improve mechanical durability and reliability under deformation. It is expected that this review will provide a comprehensive understanding of multicolor and flexible ECDs for applications in smart windows, displays, and wearable electronics.

Design, synthesis, and characterization of a novel pH-responsive azo dye incorporating a 1,3,4-thiadiazole ring for advanced textile applications

This study presents the synthesis and comprehensive characterization of a novel azo dye derived from 2-(5-amino-1,3,4-thiadiazol-2-yl) phenol (1a). The synthesis was initiated by reacting salicylic acid with thiosemicarbazide in the presence of phosphorus oxychloride (POCl3), yielding an 89 % conversion to compound 1a. The subsequent diazotization and coupling of 1a under alkaline conditions led to the formation of the azo dye (1b). Structural elucidation was confirmed through Fourier Transform Infrared Spectroscopy (FTIR) and Nuclear Magnetic Resonance (NMR) spectroscopy. The incorporation of the 1,3,4-thiadiazole ring is significant due to its known influence on enhancing dye stability, reactivity, and potential biological activities, while the salicylic acid moiety contributes to the dye's pH responsiveness. A notable pH-dependent color transition from yellow (pH < 6.0) to red (pH > 8.0) was observed, driven by protonation-induced resonance changes. Computational studies, including Frontier Molecular Orbital (FMO) analysis, revealed a Highest Occupied Molecular Orbital (HOMO)-Lowest Unoccupied Molecular Orbital (LUMO) gap of 1.94 eV, indicating efficient electronic transitions critical for its color properties. Molecular Electrostatic Potential (MEP) and Fukui function analyses provided further insights into the dye's reactivity and potential applications. The dyeing properties were rigorously assessed through K/S values, CIELAB color space, and fastness properties on cotton fabric, demonstrating varied color depths and hues influenced by different mordants, with copper and manganese significantly enhancing color intensity. Colorfastness tests revealed good to fair durability, with untreated fabrics showing optimal performance. This thorough characterization underscores the promising potential of this newly synthesized azo dye for applications in pH-responsive textiles and functional materials, highlighting its relevance for advanced textile and material science applications.

A note of double-layer cloud detection method based on cloud base height and brightness

Double-layer cloud is a special cloud condition, and clouds in two layers have different effects on solar radiation reaching ground. Getting the shape of double-layer clouds is helpful to study the change of solar irradiance. The proposed double-layer clouds detection method is based on the cloud base height (CBH) and brightness of double-layer clouds. The CBH is calculated based on binocular parallax, which requires two images filmed synchronously by two cameras, and the brightness is offered by the brightness channel in HSV color space. The accuracy of this method is 98.5% for single-layer clouds and 89.4% for double-layer clouds. The reasons for misidentifications are (1) the brightness contrast decreases when the high-layer clouds are thick, (2) the distant low clouds have different brightness in top and bottom.

A novel approach to coral species classification using deep learning and unsupervised feature extraction

ABSTRACT This study presents a novel methodology to enhance coral species classification in underwater images by integrating Tri Convolutional Deep Autoencoders (TRI-CDAE) and Sparse Deep Autoencoders (SPDAE). TRI-CDAE employs three parallel CDAEs trained on distinct color spaces (RGB, HSV, LUV) to capture diverse features. These extracted features are fused and refined using SPDAE, promoting sparsity and enhancing discriminative power. The refined features are then classified using a softmax classifier. Evaluation on four coral image datasets shows exceptional performance, with recall (96.5%), F1 score (97.4%), precision (97.2%), accuracy (98.5%), Cohen’s J (0.959), Jaccard Index (0.971), Cohen’s Kappa (0.961), and Matthews Correlation Coefficient (0.982).

Revealing novel insights into the enhancement of quality in black tea processing through microbial intervention

Black tea is highly favored by consumers worldwide, with enzymatic reactions being recognized as a pivotal factor influencing tea quality. The role of microorganisms in shaping the composition of black tea has emerged as a focus of research due to their involvement in enzyme catalysis and metabolic processes. In this study, full-length amplicon sequencing combined with qPCR more accurately reflected microbial profile, and Pantoea, Pseudomonas, Paucibacter, and Cladosporium were identified as the main microbial genera. Moreover, by comprehensively analyzing color, aroma, and taste components over time in black tea samples, correlations were established between the dominant genus and various quality factors. Notably, peroxidase activity levels, total soluble sugar content, and tea pigments concentration exhibited significant associations with the dominant genus. Consequently, this microbiological perspective facilitated the exploration of driving factors for improving black tea quality while establishing a theoretical foundation for quality control in industrial production.

CIELAB Color Space as a Field for Tracking Color-Changing Chemical Reactions of Polymeric pH Indicators.

Two types of cross-linked polymeric dye films that exhibit reversible color changes in response to pH were prepared. Upon protonation, films with a nitrophenol dye moiety (NP) changed from yellow to colorless, whereas films with an azobenzene dye moiety (AB) changed from yellow to reddish-purple. The colored light transmitted through these films from a D65 white-light source was measured colorimetrically, and the transition of the chromaticity point in CIELAB upon protonation of the polymeric dyes was observed. At low dye concentrations ([NP] < 4.7 × 10-5 and [AB] < 2.7 × 10-5 mol L-1), linear loci of the chromaticity points were displayed for both polymeric dye films, indicating perceptual linearity. This linearity is caused by the avoidance of nonlinear perceptual phenomena such as the Abney effect and the Bezold-Brücke phenomenon. The perceptual linearity of space is a necessary condition for achieving stoichiometric linearity, which requires an additional linear relationship between the dye concentration and each component, a*, b*, and L*. A geometric equation that included the AB concentration and the protonation ratio of AB was used to successfully depict, on the basis of stoichiometric linearity, the protonation of the polymeric AB films three-dimensionally in the restricted area of CIELAB.

Substitutional effects of the anionic group systems [BO33−], [PO43− ], and [SO42−] on the down-conversion photoluminescence properties of Y2O3:Er3+ nanophosphors

A series of Y2O3, Y2O3: Er3+ (1 %) and Y2O3-AG: Er3+ (1 %) (where AG = BO33−, PO43−, and SO42−) nanophosphors were prepared via a chemical combustion technique. The primary X-ray powder diffraction results showed that the Y2O3:Er3+ phosphor materials crystallized into a cubic standard structure, while the Y2O3-AG: Er3+ phosphor materials transformed to hexagonal and tetragonal structures for the [PO43−] and [BO33−]-based phosphors, respectively. However, no changes were observed for the [SO42−]-based phosphor materials. The scanning electron microscope micrographs revealed that the particles were formed in the nanometre range with different sizes and shapes. The fourier-transformed infrared spectra showed the presence of various structural groups in the pure Y2O3 and Y2O3-AG: Er3+ phosphors. In addition to that, the optical bandgap energy values were obtained using the diffuse reflection spectra (DRS) spectra and Kubelka-Munk function theory. Under UV-379 nm excitation for the Y2O3-AG: Er3+ phosphors, the Y2O3–SO4: Er3+ emitted the most intense green light at 563 nm wavelength. The Commission Internationale de l'Elcairage colour coordinates and correlated color temperature values indicated that the Y2O3:Er3+, Y2O3-PO4:Er3+, and Y2O3–SO4:Er3+ phosphor materials are potential candidates for producing enhanced green color components in white light-emitting diode (w-LED) applications.

Exploring adversarial deep learning for fusion in multi-color channel skin detection applications

Deep learning, a robust framework for complex learning, outperforms previous machine learning approaches and finds widespread use. However, security vulnerabilities, especially in fusion in multi-color channel skin detection applications using adversarial machine learning (AML) and generative adversarial networks (GANs), lead to misclassifications. Researchers are actively exploring AML's and GANs' impact on misclassification, focusing on vulnerabilities in lighting conditions, skin-like patches in lesion segmentation, and insufficient data in facial emotion recognition. Yet, these areas only scratch the surface of potential AML vulnerabilities and GANs. To comprehensively address challenges, an in-depth investigation into AML and GANs components is crucial to uncover underlying reasons for misclassifying skin detection. This study addresses challenges of fusion in multi-color channel skin detection by creating a diverse dataset with 17M patches for enhanced feature fusion/training and meeting dataset criteria, investigating misclassifications using various deep learning models belonging to AML and GANs and color spaces (e.g., RGB, YCbCr, HSV, YUV), and exploring binary and multiclass scenarios. Notably, YCbCr outperformed RGB, achieving 98 % for binary skin classification, 84 % and 69 % for multiclass four and five-class scenarios. Binary classification for skin tones and their skin-like counterparts (e.g., black skin tone and black skin-like) yielded 97 %, 81 %, 60 %, and 51 % for black, brown, medium, and fair, respectively. Exploration of darker skin tones showed improved accuracy. Benchmarking with a CNN and RNN hybrid achieved 99 % accuracy, surpassing the initial 91 %, while SAE reached 97 %. The study explores implications of overlapping between skin and skin-tone recognition, offering insights for developing a generalized skin detector. The investigation demonstrates that improper color space selection can make lighting conditions exploitable in AML attacks and GANs, emphasizing the crucial role of color space choice in mitigating vulnerabilities.

Структурно-семантичні особливості ідіом з колірними компонентами в англомовному діловому дискурсі

In recent years, the study of language has evolved to explore the intricate nuances that shape our communication. One fascinating aspect of language is the use of idioms, which enable speakers to convey emotions indirectly through metaphorical expression. Although idioms enrich language and enhance its expressiveness and vividness, they often cause difficulties for people learning English. Among the myriad of idioms, those related to colours stand out as vibrant and captivating elements of linguistic diversity. Colour is an objective quality of the material world, encoded by language signs into a diverse array of information. Colour schemes and their designations are universal elements of the larger colour symbolic system, composed of multiple subsystems each with their own function and structure. As carriers of information about the surrounding world, colour designations are meaningful topics for cognitive, psycholinguistic, linguistic, and cultural studies. Understanding the structural and semantic features of colour idioms is essential for unravelling the intricate threads that make up effective communication, especially in the context of business discourse. The purpose of this paper is to delve into the semantic and structural features of colour idioms used in English business discourse. General methods (analysis, synthesis, induction, deduction, classification), theoretical methods (system analysis), graphical, as well as methods of logical, system-structural analysis and connections, comparative and dialectical approaches have been used. Results. Colour symbolism, like any other, is based on the peculiarities of the human psyche, on various associations, which are based on everyday human experience, fed by mythological, religious and aesthetic views. Examining business idioms with the colour component we can say that the semantics of this component often differ from the semantics of the same colour name in free use. The meanings of different colours in business idioms were scrutinized. A detailed analysis was also carried out and a certain proportion of idioms was determined by structures and colours, and the corresponding diagrams were given. The study shows the prevalence of the blue colour (17%), followed by black (15%), red and white (13% each). The yellow colour appears to be less utilized, constituting only 2% of the analysed idioms. The analysis of the syntactic characteristics of colour idioms has shown the dominance of nominal idioms (80%), followed by verbal idioms (10%) with adjectival making up 6% and the adverbial being represented by 1 lexical unit (4%). The originality of this article lies in the study of the symbolism of colours in terms of their use in business phraseological units, and a detailed structural and semantic analysis of colour idioms used in business discourse. Conclusion. The study underscores the significance of colour idioms within the business vocabulary, emphasizing their relevance. Proficiency in these expressions not only enriches one's linguistic repertoire but also enhances communication skills. Business idioms are an important segment in the business vocabulary. Colour idioms occupy a large share in business phraseology, the study of which requires further research.

Terminal sequence consistency verification method for small diameter abreast optical fibers based on computer vision

In a kind of precision industrial equipment, small diameter abreast optical fibers are used for high-speed communication among functional nodes. The arrangement order at both terminals of the abreast optical fibers need to comply with communication protocols. In this paper, we propose an automatic terminal sequence consistency verification method based on computer vision. The Hue Saturation Value (HSV) color space is used for improving the image feature extraction capability. An abreast optical fiber sequence dictionary which converts the protocol logic into an input-output mapping table is provided to follow protocol confidentiality and improve inspecting speed. A light control baffle position adaptive algorithm is designed for improving the accuracy of optical fiber incident light control. The experimental results show that the method can achieve the conductivity inspection of 1 optical fiber every 50 seconds, and the inspection accuracy is over 96.5%, which generally improves the inspection efficiency by 45% compared with manual inspection.

Enhancing Medical Image Security: A Deep Learning Approach with Cloud-based Color Space Scrambling

Enhancing Medical Image Security: A Deep Learning Approach with Cloud-based Color Space Scrambling

Assessment of a color measurement-based method for the characterization of polymer thermo-oxidation

Epoxy resins are used in various applications where environmental factors can interact and degrade the material. Thermo-oxidation is one of the degradation processes that can lead to both mechanical and chemical changes. This work aims to present a technique for characterizing thermo-oxidative degradation based on color analysis. The UV–vis spectroscopy reveals the direct link between the chemical modification and the color variation. The color difference ΔEab* between a virgin and an aged sample (in CIELAB color space) provides an excellent indicator of oxidation degree. Calibration correlations have been developed based on reference samples aged under known conditions of temperature and pressure, translating color differences into an oxidation equivalent duration, represented as an equivalent time t* or to directly estimate mechanical properties. The t* parameter is the time that the sample should be exposed to the reference conditions to undergo the same oxidation level (equivalent to the same color difference change and then, degradation). Nanoindentation measurements were performed to validate the color measurement method. Some limitations were identified, including the poor correlation under non-equivalent time-temperature-pressure conditions, poor relevance for assessing high oxidation levels, and the impact of light scattering in areas with strong color gradients. The spatial resolution of color measurement is ten times higher than nanoindentation. Furthermore, the color measurement is non-destructive, can be conducted in situ, and is suitable for monitoring the aging of industrial components.