Color Prediction Research Articles

Rapid nondestructive evaluation of duck meat pH and color using visible and near-infrared reflectance spectroscopy

ABSTRACTIn the present study, visible and near-infrared reflectance spectroscopy were applied to predict quality attributes of duck breast meat. The real color (expressed as lightness, redness, and yellowness) and pH of duck samples were recorded using traditional contact methods and then modeled with their corresponding spectral data by partial least square regression. Before the establishment of prediction models, three different spectral preprocessing methods including first derivative, standard normalized variate, and Savitzky–Golay smoothing were used. Compared to the models obtained from original spectral data set, the predictive capabilities of models based on the spectra after preprocessing were improved effectively. As a result, the determination coefficient of calibration set and prediction set of the best models for lightness, redness, yellowness, and pH parameters were 0.96 and 0.85, 0.94 and 0.94, 0.96 and 0.94, 0.81 and 0.76, respectively. Results demonstrated that visible and near-infrared spectroscopy can become a useful tool for rapid prediction of duck color and pH quality attributes.

Determination of the difference on color changes of watermelons by laser light backscattering imaging.

The potential of laser light backscattering imaging was investigated for monitoring color parameters of seeded and seedless watermelons during storage. Two watermelon cultivars were harvested and stored for 3 weeks with seven measuring storage days (0, 4, 8, 12, 15, 18, and 21). The color parameters of watermelons were monitored using the conventional colorimetric methods (L*, a*, b*, C*, H*, and ∆E*) and laser light backscattering imaging system. A laser diode emitting at 658nm and 30 mW power was used as a light source to obtain the backscattering image. The backscattering images were evaluated by the extraction of backscattering parameters based on the mean pixel values. The results showed that a good color prediction was achieved by the seedless watermelon with the R2 are all above 0.900. Thus, the application of the laser light backscattering imaging can be used for evaluating the color parameters of watermelons during the storage period.

Color prediction model for pre-colored fiber blends based on modified Stearns-Noechel function

The goal of this work is to propose a color prediction model for pre-colored fiber blends with high accuracy. The transfer function plays a vital role in an additive color mixing model. The better linearity between the transfer function and mass proportion, the higher accuracy of the model. However, the well-known Stearns-Noechel transfer function does not always hold good linearity, causing inaccurate color matching in many cases. Aiming at compensating the poor linearity, a new transfer function was established by minimizing the linear deviation. The proposed transfer function was applied to the additive model for color prediction of pre-colored fiber blends. The prediction accuracy of the proposed model was assessed by 44 samples. The average color difference was 0.63 CIEDE2000 unit, which was significantly better than the results of the Stearns-Noechel model (∼1.23) and the two-constant Kubelka-Munk model (∼1.11). These results indicate the proposed model has higher color prediction accuracy and can better satisfy the color requirement of practical production.

Three‐dimensional color prediction modeling of single‐ and double‐layered woven fabrics

AbstractIn this research, the three‐dimensional structural and colorimetric modeling of three‐dimensional woven fabrics was conducted for accurate color predictions. One‐hundred forty single‐ and double‐layered woven samples in a wide range of colors were produced. With the consideration of their three‐dimensional structural parameters, three‐dimensional color prediction models, K/S‐, R‐, and L*a*b*‐based models, were developed through the optimization of previous two‐dimensional models which have been reported to be the three most accurate models for single‐layered woven structures. The accuracy of the new three‐dimensional models was evaluated by calculating the color differences ΔL*, ΔC*, Δh°, and ΔECMC(2:1) between the measured and the predicted colors of the samples, and then the error values were compared to those of the two‐dimensional models. As a result, there has been an overall improvement in color predictions of all models with a decrease in ΔECMC(2:1) from 10.30 to 5.25 units on average after the three‐dimensional modeling.

Color contoning for 3D printing

Appearance reproduction is an important aspect of 3D printing. Current color reproduction systems use halftoning methods that create colors through a spatial combination of different inks at the object's surface. This introduces a variety of artifacts to the object, especially when viewed from a closer distance. In this work, we propose an alternative color reproduction method for 3D printing. Inspired by the inherent ability of 3D printers to layer different materials on top of each other, 3D color contoning creates colors by combining inks with various thicknesses inside the object's volume. Since inks are inside the volume, our technique results in a uniform color surface with virtually invisible spatial patterns on the surface. For color prediction, we introduce a simple and highly accurate spectral model that relies on a weighted regression of spectral absorptions. We fully characterize the proposed framework by addressing a number of problems, such as material arrangement, calculation of ink concentration, and 3D dot gain. We use a custom 3D printer to fabricate and validate our results.

A bi-layer model for nondestructive prediction of soluble solids content in apple based on reflectance spectra and peel pigments

Hyperspectral imaging technology was used to investigate the effect of various peel colors on soluble solids content (SSC) prediction model and build a SSC model insensitive to the color distribution of apple peel. The SSC and peel pigments were measured, effective wavelengths (EWs) of SSC and pigments were selected from the acquired hyperspectral images of the intact and peeled apple samples, respectively. The effect of pigments on the SSC prediction was studied and optimal SSC EWs were selected from the peel-flesh layers spectra after removing the chlorophyll and anthocyanin EWs. Then, the optimal bi-layer model for SSC prediction was built based on the finally selected optimal SSC EWs. Results showed that the correlation coefficient of prediction, root mean square error of prediction and selected bands of the bi-layer model were 0.9560, 0.2528 and 41, respectively, which will be more acceptable for future online SSC prediction of various colors of apple.

Depth-layer weighted prediction method for a full-color polygon-based holographic system with real objects.

We propose a full-color polygon-based holographic system for real three-dimensional (3D) objects using a depth-layer weighted prediction method. The proposed system is composed of four main stages: acquisition, preprocessing, hologram generation, and reconstruction. In the preprocessing stage, the point cloud model is separated into red, green, and blue channels with depth-layer weighted prediction. The color component values are characterized based on the depth information of the real object, then color prediction is derived from the measurement data. The computer-generated holograms reconstruct 3D full-color images with a strong sensation of depth resulting from the polygon approach. The feasibility of the proposed method was confirmed by numerical and optical reconstruction.

Erratum to: Global skin colour prediction from DNA.

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Global skin colour prediction from DNA

Human skin colour is highly heritable and externally visible with relevance in medical, forensic, and anthropological genetics. Although eye and hair colour can already be predicted with high accuracies from small sets of carefully selected DNA markers, knowledge about the genetic predictability of skin colour is limited. Here, we investigate the skin colour predictive value of 77 single-nucleotide polymorphisms (SNPs) from 37 genetic loci previously associated with human pigmentation using 2025 individuals from 31 global populations. We identified a minimal set of 36 highly informative skin colour predictive SNPs and developed a statistical prediction model capable of skin colour prediction on a global scale. Average cross-validated prediction accuracies expressed as area under the receiver-operating characteristic curve (AUC) ± standard deviation were 0.97 ± 0.02 for Light, 0.83 ± 0.11 for Dark, and 0.96 ± 0.03 for Dark-Black. When using a 5-category, this resulted in 0.74 ± 0.05 for Very Pale, 0.72 ± 0.03 for Pale, 0.73 ± 0.03 for Intermediate, 0.87±0.1 for Dark, and 0.97 ± 0.03 for Dark-Black. A comparative analysis in 194 independent samples from 17 populations demonstrated that our model outperformed a previously proposed 10-SNP-classifier approach with AUCs rising from 0.79 to 0.82 for White, comparable at the intermediate level of 0.63 and 0.62, respectively, and a large increase from 0.64 to 0.92 for Black. Overall, this study demonstrates that the chosen DNA markers and prediction model, particularly the 5-category level; allow skin colour predictions within and between continental regions for the first time, which will serve as a valuable resource for future applications in forensic and anthropologic genetics.

Novel quantitative pigmentation phenotyping enhances genetic association, epistasis, and prediction of human eye colour

Success of genetic association and the prediction of phenotypic traits from DNA are known to depend on the accuracy of phenotype characterization, amongst other parameters. To overcome limitations in the characterization of human iris pigmentation, we introduce a fully automated approach that specifies the areal proportions proposed to represent differing pigmentation types, such as pheomelanin, eumelanin, and non-pigmented areas within the iris. We demonstrate the utility of this approach using high-resolution digital eye imagery and genotype data from 12 selected SNPs from over 3000 European samples of seven populations that are part of the EUREYE study. In comparison to previous quantification approaches, (1) we achieved an overall improvement in eye colour phenotyping, which provides a better separation of manually defined eye colour categories. (2) Single nucleotide polymorphisms (SNPs) known to be involved in human eye colour variation showed stronger associations with our approach. (3) We found new and confirmed previously noted SNP-SNP interactions. (4) We increased SNP-based prediction accuracy of quantitative eye colour. Our findings exemplify that precise quantification using the perceived biological basis of pigmentation leads to enhanced genetic association and prediction of eye colour. We expect our approach to deliver new pigmentation genes when applied to genome-wide association testing.

Association of five SNPs with human hair colour in the Polish population

Twenty-two variants (single nucleotide polymorphisms – SNPs) of the genes involved in hair pigmentation (OCA2, HERC2, MC1R, SLC24A5, SLC45A2, TPCN2, TYR, TYRP1) were genotyped in a group of 186 Polish participants, representing a range of hair colours (45 red, 64 blond, 77 dark). A genotype-phenotype association analysis was performed.Using z-statistics we identified three variants highly associated with different hair colour categories (rs12913832:A>G in HERC2, rs1805007:T>C and rs1805008:C>T in MC1R). Two variants: rs1800401:C>T in OCA2 and rs16891982:C>G in SLC45A2 showed a high probability of a relation with hair colour, although that probability did not exceed the threshold of statistical significance after applying the Bonferroni correction. We created and validated mathematical logistic regression models in order to test the usefulness of the sets of polymorphisms for hair colour prediction in the Polish population. We subjected four models to stratified cross-validation. The first model consisted of three polymorphisms that proved to be important in the associative analysis. The second model included, apart from the mentioned polymorphisms, additionally rs16891982:C>G in SLC45A. The third model included, apart from the variants relevant in the associating analysis, rs1800401:C>T in OCA. The fourth model consisted of the set of polymorphisms from the first model supplemented with rs16891982:C>G in SLC45A and rs1800401:C>T in OCA. The validation of our models has shown that the inclusion of rs16891982:C>G in SLC45A and rs1800401:C>T in OCA increases the prediction of red hair in comparison with the algorithm including only rs12913832:A>G in HERC2, rs1805007:T>C and rs1805008:C>T in MC1R. The model consisting of all the five above-mentioned genetic variants has shown good prediction accuracies, expressed by the area under the curve (AUC) of the receiver operating characteristics: 0.84 for the red-haired, 0.82 for the dark-haired and 0.71 for the blond-haired.A genotype-phenotype association analysis brought results similar to those in other studies and confirmed the role of rs16891982:C>G, rs12913832:A>G, rs1805007:T>C and rs1805008:C>T in hair colour determination in the Polish population. Our study demonstrated for the first time the possibility of a share of the rs1800401:C>T SNP in the OCA2 gene in hair colour determination. Including this single nucleotide polymorphism in the actual hair colour predicting models would improve their predictive accuracy.

Evaluation of color prediction methods in terms of least-dissimilar asymmetric matching

The performance of Color Prediction Methods CAT02, KSM2, Waypoint, Best Linear, MMV center, and relit color signal are compared in terms of how well they explain Logvinenko & Tokunaga [1] asymmetric color matching results. In their experiment, given a Munsell paper under a test illuminant, 4 observers were asked to determine (3 repeats) which of 22 other Munsell papers made the least-dissimilar match under a match illuminant. Given this data, we address the following four questions. Question 1: Are observers choosing the original Munsell paper under the match illuminant? If they are, then the average (12 matches) color signal (cone response triple or XYZ) made under a given illuminant condition should correspond to that of the Munsell paper's color signal under the match illuminant. Computation shows that in 274 of the 400 cases, the relit color signal is close to the mean color signal of the matches. Question 2: How do algorithm predictions compare to the average observer prediction of the actual color signal of the relit paper? The Wilcoxon signed-rank test shows that KSM2, Waypoint, and Best Linear perform equally, and that both slightly outperform the observer average, which, in turn, significantly outperforms CAT02, and MMV (metamer mismatch volume) center. Question 3: Which method most closely predicts the observer average? We found that the color signal of the relit reflectance is a better predictor of the average observer than Best Linear, which in turn is marginally better than Wpt and KSM2, both of which outperform CATO2 and MMV center. Question 4: Do the observers agree with one another? Using a leave-one-observer-out comparison shows that individual observers predict the average matches of the remaining observers somewhat better than the relit color signal, which in turn slightly outperforms Best Linear, Wpt and KSM2, which then all significantly outperform CATO2 and MMV center.

Bringing colour back after 70 years: Predicting eye and hair colour from skeletal remains of World War II victims using the HIrisPlex system

Retrieving information about externally visible characteristics from DNA can provide investigative leads to find unknown perpetrators, and can also help in disaster victim and other missing person identification cases. Aiming for the application to both types of forensic casework, we previously developed and forensically validated the HIrisPlex test system enabling parallel DNA prediction of eye and hair colour. Although a recent proof-of-principle study demonstrated the general suitability of the HIrisPlex system for successfully analysing DNA from bones and teeth of various storage times and conditions, practical case applications to human remains are scarce. In this study, we applied the HIrisPlex system to 49 DNA samples obtained from bones or teeth of World War II victims excavated at six sites, mostly mass graves, in Slovenia. PCR-based DNA quantification ranged from 4pg/μl to 313pg/μl and on an average was 41pg/μl across all samples. All 49 samples generated complete HIrisPlex profiles with the exception of one MC1R DNA marker (N29insA) missing in 83.7% of the samples. In 44 of the 49 samples (89.8%) complete 15-loci autosomal STR (plus amelogenin) profiles were obtained. Of 5 pairs of skeletal remains for which STR profiling suggested an origin in the same individuals, respectively, 4 showed the same HIrisPlex profiles and predicted eye and hair colours, respectively, while discrepancies in one pair (sample 26 and 43) are likely to be explained by DNA quantity and quality issues observed in sample 43. Sample 43 had the lowest DNA concentration of only 4pg/μl, producing least reliable STR results and could be misleading in concluding that samples 43 and 26 originate from the same individual. The HIrisPlex-predicted eye and hair colours from two skeletal samples, suggested to derive from two brothers via STR profiling together with a living sister, were confirmed by the living sister's report. Overall, we demonstrate that after more than 70 years, HIrisPlex-based eye and hair colour prediction from skeletal remains is feasible with high success rate. Our results further encourage the use of the HIrisPlex system in missing person/disaster victim identification to aid the identification process in cases where ante-mortem samples or putative relatives are not directly available, and DNA predicted eye and hair colour information provides leads for locating them, allowing STRbased individual identification.

Implementation of the four-flux model for spectral and color prediction of 2.5D prints

Implementation of the four-flux model for spectral and color prediction of 2.5D prints

Implementation of the four-flux model for spectral and color prediction of 2.5D prints

Optical models to predict visual appearance of 2D prints are relatively well-known. Two-flux models, such as the Kubelka-Munk (KM) theory, are the most commonly used and offer good prediction rates. However, most two-flux models assume that the ink layer and the printing support have the same optical indices neglecting their wavelength dependency. An improvement of such constraint would be to include detailed optical indices of the inks in current models. In this paper we compute optical indices of our inks by printing ink stacks of different thicknesses on a transparent support for reflectance and transmittance measurements. Since KM-based models work under limited conditions, we input our computed indices into a more robust model. By taking additional fluxes into account, one can address the limitations of the two-flux approaches. For instance, the four-flux model considers two collimated and two diffuse fluxes propagating upward and downward the layer stack offering better reflectance and transmittance predictions especially when translucent materials are involved. Our four-flux theory including inks optical indices enables us to make spectral predictions of 2.5D prints without any preliminary measurements. The model is fairly accurate with primary colorants since the ΔE94 values do not exceed 1 unit.

Characterization of display pyrotechnic propellants: Colored light

Pyrotechnic flames utilized as a source of illumination are typically characterized by the color and the luminous intensity of the flame. In the current study, five display pyrotechnic formulations utilized to produce colored flames have been investigated experimentally and theoretically. The experimental study was focused on photographically recording the colored flame of fireworks propellants burning at ambient conditions in order to quantify the flame color and the luminous intensity. This study reports the application of a digital single reflex camera as a colorimeter as well as a luminance meter. Theoretical estimation of the flame color was carried out by equilibrium thermochemical analysis followed by additive color mixing of the spectral emitters deduced to be present in the combustion products. The experimentally observed chromaticity and theoretical predictions were found to be reasonably close validating the methodology for the color prediction.

Preliminary evaluation on self-developed dentin porcelain color prediction system

To apply the self-developed dentin porcelain color prediction system in the fabrication of porcelain-fused-to-metal-crown(PFMC), and to evaluate its accuracy in color-matching. Twenty upper central incisors were recruited according to preset criteria, and three PFMC were made for each tooth using three shade-matching techniques. Group A: PFMC were made according to the result of visual color selection; Group B: an spectrophotometer-based color-matching technique was used; Group C: PFMC were fabricated with dentin porcelain powder calculated by the prediction system according to the L(*), a(*), b(*) value measured by a spectrophotometer. Color differences(ΔE) (measured by spectrophotometer) of three groups of crowns were calculated in the cervical, middle, and incisal regions. The results were analyzed using one-way ANOVA. Mean color differences in body regions were: Group A: 3.53±1.80, Group B: 2.86±1.63, Group C: 3.77±1.40(P>0.05), and those in incisal regions were: Group A: 2.70 ± 1.13, Group B: 2.80 ± 0.90, Group C: 3.04 ± 1.03(P>0.05). In cervical region, Group C had greater color difference than Group B(2.78±1.14)(P<0.05). Group A(3.80±2.02) and Group B, Group A and Group C had similar color difference(P>0.05). PFMC fabricated using self-developed dentin porcelain color prediction system had similar color matching compared with conventional and instrument-based methods.

Spectrophotometric prediction of pre-colored fiber blends with a hybrid model based on artificial neural network and Stearns–Noechel model

The performance of the traditional color prediction model in the color prediction of colored fiber blends is usually not very satisfactory under a variety of conditions, due to the limitation of the trail data and the assumptions used for derivation. In contrast to the traditional model, artificial neural networks (ANN) have an excellent nonlinear mapping ability; however, they also have poor generalization ability if the training data are not sufficient. In this paper a hybrid model, called the Stearns–Noechel (S-N)–ANN model, is proposed, which combines the S-N model with the ANN model. This uses the S-N model first to build the approximate relationship between the recipe and spectrophotometric response of the color blends, followed by optimization with the ANN to achieve higher prediction accuracy and better practicability. Compared with the ANN model, the S-N–ANN model needs less training time with the same training data, yet achieves higher validation and correlation coefficients, indicating that the training of the S-N–ANN model is much easier. The average color difference of the predicted spectrum obtained with the S-N–ANN model was 0.86 CMC(2:1) unit, which was much lower than that obtained with either the ANN model (∼2.21) or the traditional S-N model (∼1.66), indicating that the S-N–ANN model is a more accurate method for the color prediction of colored fiber blends.

N -Ink Printer Characterization With Barycentric Subdivision.

Printing with a large number of inks, also called N -ink printing, is a challenging task. The challenges comprise spectral modeling of the printer, color separation, halftoning, and limitations of the amount of inks. Juxtaposed halftoning, a perfectly dot-off-dot halftoning method, has proved to be useful to address some of these challenges. However, for juxtaposed halftones, prediction of colors as a function of ink area coverages has not yet been fully investigated. The goal of this paper is to introduce a spectral prediction model for N -ink juxtaposed-halftone prints. As the area-coverage domain of juxtaposed inks forms a simplex, we propose a cellular subdivision of the area-coverage domain using the barycentric subdivision of simplexes. The barycentric subdivision provides algorithmically straightforward means to design and implement an N -ink color prediction model. Within the subdomain cells, the Yule-Nielsen spectral Neugebauer model is used for the spectral prediction. Our proposed model is highly accurate for prints with a large number of inks while requiring a relatively low number of calibration samples.

An intelligent system for fashion colour prediction based on fuzzy C‐means and gray theory

AbstractFor design and manufacturing industries, to be able to capture the fashion trend is an essential factor that leads to winning a sale. However, colour predicting process in many organizations is not visible to the public. In order to provide colour trend to industries in advance, a predicting method is proposed in this study. In the method, the fuzzy c‐means was used to separate the collected colour data, then the minimum mean‐square error was used to place the similar colour clusters within different time point together and the gray model was adopted for prediction. In order to verify the prognostication of the system, four data announced by Pantone from spring 2014 to fall, 2015 were taken as the predicted samples and the colour for spring 2016 was predicted to compare with that in Pantone spring, 2016. The results show that the system has a high accuracy for predicting colour. The residual modified model constructed with the colour samples rearranged with MMSE has the best‐predicted result that ranged from 83.3% to 99.4%. It indicates that the result obtained with the rearranged samples is higher than that without rearrangement. Besides, the accuracy of the gray predicted results with residual modification would be more precise than the one without residual modification. Moreover, the value of mean squared error is quite low, which was ranged from 0.000025 to 0.0277. Therefore, the current intelligent predicting system satisfies the criteria of capturing colour in trend for enterprises. Moreover, it enables industries to make decisions for selecting the colour trend. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 273–285, 2017