CIELAB Color Space Research Articles

No-reference stereoscopic image quality assessment on both complex contourlet and spatial domain via Kernel ELM

Stereoscopic imaging is widely used in many fields. To guarantee the best quality of experience, it is necessary to design a robust and accurate quality assessment model for stereoscopic content. In this paper, we proposed a no-reference stereoscopic image quality assessment (NR-SIQA) model using both complex contourlet and spatial domain features of monocular and binocular images. Monocular features extracted from the CIELAB color space are exploited to characterize quality degradation, including the across-scale and across-orientation correlation in complex contourlet domain and natural scene statistics-based (NSS) features in spatial domain. Then, the binocular features consist of energy along with energy difference and structural correlation in complex contourlet domain, and statistics distribution in spatial domain extracted from the synthesized cyclopean image and the 3D visual discomfort measure based on the statistics of disparity map. Finally, the above features are mapped into the predicted quality scores by the Kernel ELM (KELM) regression model. Experimental results on four public datasets show that the proposed model is highly consistent with human subjective perception in terms of accuracy and generalization.

The mathematics of erythema: Development of machine learning models for artificial intelligence assisted measurement and severity scoring of radiation induced dermatitis

Although significant advancements in computer-aided diagnostics using artificial intelligence (AI) have been made, to date, no viable method for radiation-induced skin reaction (RISR) analysis and classification is available. The objective of this single-center study was to develop machine learning and deep learning approaches using deep convolutional neural networks (CNNs) for automatic classification of RISRs according to the Common Terminology Criteria for Adverse Events (CTCAE) grading system. ScarletredⓇ Vision, a novel and state-of-the-art digital skin imaging method capable of remote monitoring and objective assessment of acute RISRs was used to convert 2D digital skin images using the CIELAB color space and conduct SEV* measurements. A set of different machine learning and deep convolutional neural network-based algorithms has been explored for the automatic classification of RISRs. A total of 2263 distinct images from 209 patients were analyzed for training and testing the machine learning and CNN algorithms. For a 2-class problem of healthy skin (grade 0) versus erythema (grade ≥ 1), all machine learning models produced an accuracy of above 70%, and the sensitivity and specificity of erythema recognition were 67–72% and 72–83%, respectively. The CNN produced a test accuracy of 74%, sensitivity of 66%, and specificity of 83% for predicting healthy and erythema cases. For the severity grade prediction of a 3-class problem (grade 0 versus 1 versus 2), the overall test accuracy was 60–67%, and the sensitivities were 56–82%, 35–59%, and 65–72%, respectively. For estimating the severity grade of each class, the CNN obtained an accuracy of 73%, 66%, and 82%, respectively. Ensemble learning combines several individual predictions to obtain a better generalization performance. Furthermore, we exploited ensemble learning by deploying a CNN model as a meta-learner. The ensemble CNN based on bagging and majority voting shows an accuracy, sensitivity and specificity of 87%, 90%, and 82% for a 2-class problem, respectively. For a 3-class problem, the ensemble CNN shows an overall accuracy of 66%, while for each grade (0, 1, and 2) accuracies were 76%, 69%, and 87%, sensitivities were 70%, 57%, and 71%, and specificities were 78%, 75%, and 95%, respectively. This study is the first to focus on erythema in radiation-dermatitis and produces benchmark results using machine learning models. The outcome of this study validates that the proposed system can act as a pre-screening and decision support tool for oncologists or patients to provide fast, reliable, and efficient assessment of erythema grading.

Influence of Firing Temperature on Phase Composition and Color Properties of Ceramic Tile Bodies.

This study is focused on the behavior of the cream-firing clays from Opoczno region (Poland). The ceramic masses on which tests were carried out consisted of cream-firingBorkowice clay and dolomite in two different grain sizes as an additive that changes the color of ceramic materials. Test samples were prepared by plastic method and fired at range of 1100–1240 °C. Phase composition of theinvestigated materials was characterized by XRD method with quantitive analysis of the amorphous phase determined by the Retvield technique. Color properties of the surface of the obtained ceramic materials were determined in CIE-Lab color space. The phase composition of the obtained ceramics depends on the firing temperature. The color of the surface of the ceramic materials also depends on the firing temperature. There was a tendency to decrease the brightness, decrease the blue shade, and increase the yellow shade of the surface of materials with increasing the temperature. The conducted tests allowed to conclude that the color of ceramic materials depends on their phase composition. The most important role in the formation of color correspond to the amorphous phase, formed during the process. The lower content of the amorphous phase in the material allows to obtain brighter products with a lower proportion of yellow, and therefore closer to white. Moreover, following tests were carried out: total water absorption, total open porosity, linear shrinkage, and flexural strength. With increasing the temperature, total water absorption and total open porosity decrease, and total linear shrinkage increases due to the progressive sintering process. Flexural strength increases with the increase of the firing temperature for materials consisting of Borkowice clay. The addition of dolomite introduced new pores into the material, which resulted in an increase in flexural strength at lower firing temperatures and a decrease in flexural strength at higher firing temperatures.

Detection of binocular chromatic fusion limit for opposite colors

When the input colors of the left and right eyes are different from one another, binocular rivalry may occur. According to Hering theory, opponent colors would have the most significant tendency for rivalry. However, binocular color fusion still occurs under the condition that each eye's opponent chromatic responses do not exceed a specific chromatic fusion limit (CFL). This paper detects the binocular chromatic fusion limit for opposite colors within a conventional 3D display color gamut. We conducted a psychophysical experiment to quantitatively measure the binocular chromatic fusion limit on four opposite color directions in the CIELAB color space. Due to color inconsistency between eyes may affect the binocular color fusion, the experiment was divided into two sessions by swapping stimulation colors of left and right eyes. There were 5 subjects and they each experienced 320 trials. By analyzing the results, we used ellipses to quantify the chromatic fusion limits for opposing colors. The average semi-major axis of the ellipses is 27.55 Δ E a b∗, and the average semi-minor axis is 16.98 Δ E a b∗. We observed that the chromatic fusion limit varies with the opposite color direction: the CFL on RedBlue-GreenYellow direction is greater than that on Red-Green direction, the latter being greater than that on Yellow-Blue direction and the CFL on RedYellow-GreenBlue direction is smallest. Furthermore, we suggested that the chromatic fusion limit is independent of the distribution of cells, and there is no significant change in the fusion ellipse boundaries after swapping left and right eye colors.

Vibrations Analysis of the Fruit-Pedicel System of Coffea arabica var. Castillo Using Time–Frequency and Wavelets Techniques

Colombian coffee production is well-known, and selective manual harvesting plays a vital task in guaranteeing high ripe coffee fruit rates in this process, leading to its known worldwide aroma and flavor. To maintain this quality approach, selective harvesting methods based on mechanical vibrations are a promising alternative for developing technologies that could accomplish the challenging Colombian coffee production context. In this study, a vibrations analysis in coffee fruits at three ripening stages was carried out to evaluate the dynamic behavior at two frequency windows: 10 to 100 Hz and 100 to 1000 Hz. Two groups of fruits previously classified in the CIELab color space were chosen for the vibration test study samples. Time and frequency signals were characterized via FFT (fast Fourier transform), and bump wavelets were determined to obtain the frequency–time magnitude scalograms. The measurements were obtained in three degrees of freedom over the fruits: one for measuring the input force (computed in voltage way) and the other two measured by the velocity. The results revealed frequency ranges with specific resonant peaks between 24 and 45 Hz, and close to 700 Hz, where the ripe fruits presented higher magnitudes in the calculated parameters. FFT of the velocity and scaled mechanical impedance were used to estimate these frequency ranges. This work is an important step to identify a “vibrational fingerprint” of each Coffea arabica var. Castillo fruit-ripening stage. However, we consider that more experiments should be performed to reconstruct the modal shape in each resonance. In future studies, fatigue analysis could show which are the most effective frequency ranges to detach the ripe fruits from the perspective of a real selective coffee-harvesting scenario.

Evaluation and consistency calibration of hyperspectral imaging system based on liquid crystal tunable filter for fabric color measurement

AbstractThe Hyperspectral Imaging System can obtain the spectral response value of each pixel in a fabric sample image while being applied to fabric color measurement. Compared with the common grating Hyperspectral Imaging System, the Hyperspectral Imaging System based on Liquid Crystal Tunable Filter (LCTF) has the advantages of small size, high spatial resolution, and easy integration, which has potential application prospects in textile industry. In this article, the performance of the LCTF Hyperspectral Imaging System for fabric color measurement was evaluated, and an improved model for calibrating the consistency between it and Datacolor 650 was proposed. First, the 210 Pantone cotton color cards were used as samples to study the color repeatability and reproducibility of the LCTF Hyperspectral Imaging System. For evaluation its color measurement ability, the measured reflectance values were converted to the CIELAB color space and compared with those of Datacolor 650. Then, a Radial Basis Function neural network model was proposed to calibrate the color consistency between LCTF Hyperspectral Imaging System and Datacolor 650 which can fully use the reflectance information of the whole band instead of only adjacent wavelengths. Experimental results show that the LCTF Hyperspectral Imaging System can accurately measure the fabric color with good repeatability and reproducibility, and the consistency between the two instruments was significantly improved based on the Radial Basis Function neural network model compared with the R model.

CMYK-CIELAB Color Space Transformation Using Machine Learning Techniques

In this work four different machine learning approaches have been implemented to perform the color space transformation between CMYK and CIELAB color spaces. We have explored the performance of Support-Vector Regression (SVR), Artificial Neural Networks (ANN), Deep Neural Networks (DNN), and Radial Basis Function (RBF) models to achieve this color space transformation, both AToB and BToA direction. The data set used for this work was FOGRA53 which is composed of 1617 color samples represented both in CMYK and CIELAB color space values. The accuracy of the transformation models was measured in terms of ΔE* color difference. Moreover, the proposed models were compared, in practical terms, with the performance of the standard ICC profile for this color space transformation. The results showed that, for the forward transformation (CMYK to CIELAB), the highest accuracy was obtained using RBF. While, for the backward transformation (CIELAB to CMYK), the highest accuracy was obtained with DNN.

Effects of tooth bleaching protocols assisted by Er,Cr:YSGG and diode (980 nm) lasers on color change of resin-based restoratives.

To evaluate color change of three resin-based restorative materials after two laser-assisted in-office tooth bleaching protocols using Er,Cr:YSGG and diode (980 nm) lasers. A nanohybrid composite resin (EP), a Bis-GMA free composite resin (EPBio) and a resin-matrix CAD-CAM ceramic (HC) were tested. Ninety specimens were prepared and stored in artificial saliva. Group 1 received an in-office bleaching treatment using 40% H2 O2. The same bleaching procedure was assisted by Er,Cr:YSGG laser in Group 2 and by a diode laser (980 nm) in Group 3. Color measurements were performed using a double-beam UV-Vis spectrophotometer at four time intervals (before, 24 h, 15 and 30 days), converted to L*, a*, and b* units of the CIELAB color space and assessed on the basis of 50:50% acceptability (ΔΕab *=2.7 and ΔΕ00 =1.77) and 50:50% perceptibility (ΔΕab *=1.2 and ΔΕ00 =0.81) thresholds. Three-way ANOVA with repeated measurements was used for statistical analysis of the data. Color and whiteness changes did not exceed the established 50:50% acceptability thresholds. EP presented the highest color change after the bleaching followed by EPBio and HC (p ≤ 0.05). Three-way ANOVA revealed that type of material and time interval significantly affected color change (p ≤ 0.001). The tested bleaching treatments assisted by Er,Cr:YSGG and diode (980 nm) lasers did not induce unacceptable color and whiteness changes in the resin-based restorative materials. The tested Er,Cr:YSGG and diode (980 nm) laser-assisted tooth bleaching protocols which may be recommended to accelerate the clinical procedures cannot affect the color of the existing resin-based restorations in case they are accidentally exposed on the bleaching gel and laser irradiation.

Biological parameters, discolouration and radiopacity of calcium silicate‐based materials in a simulated model of partial pulpotomy

To analyse the discolouration, radiopacity, pH and calcium ion release of Biodentine (BD), Bio-C repair (BCR) and Bio-C temp (BCT), as well as their biological effects on human dental pulp cells (hDPCs). Sixty-four extracted bovine incisors were prepared to simulate crown fractures with pulp exposure and open root apex. The roots were filled using a mixture of agar and blood (control), and BD, BCR or BCT were placed over this mixture. Colour assessment analyses of the samples were performed before and immediately after material insertion and repeated at 30 and 90days, using a spectrophotometer. The colour change of each specimen was evaluated at the crown and calculated based on the CIELab colour space. Digital radiographs were acquired for radiopacity analysis. hDPCs were placed in contact with different dilutions of culture media previously exposed to such materials and tested for cell viability using the MTT assay. The pH and calcium ion release of all materials were measured after 24h; the data were assessed using one-way analysis of variance (ANOVA). Cell viability was analysed by two-way ANOVA. Differences in colour parameters and wound-healing data were assessed by two-way repeated measures ANOVA (α=0.05). Tukey's and Dunnett's tests were used to compare the experimental groups with the control group. BCR had grater radiopacity and smaller colour alteration (ΔEab/ΔE00) than the other materials tested (p<.005; p<.001). No significant differences in pH were found amongst the tested materials (p>.05). BCT was associated with the largest release of calcium ions (p<.0001). BD had cell viability similar to that of the control at the lowest dilutions, and BCR was similar to that of the control, regardless of the dilution tested (p>.05). BCT had a lower percentage of viability than that of the control at all tested dilutions (p<.0001). Cell migration rates in BD and BCR were similar to those in the control group after 24h and 48h (p>.05), whilst BCT had larger voids than the control in both periods (p<.0001). BCR, BCT and BD were associated with tooth discolouration. BCR had the lowest staining values, the highest radiopacity and viability greater than 80% hDPCs.

Photodegradation of Unmodified and Thermally Modified Wood Due to Indoor Lighting

Considering interior applications, sunlight, both direct through open window and through window glass, and artificial lighting are the main sources of radiation possessing sufficient energy to trigger photodegradation processes in wood. LED lamps, which emit mostly visible light, are becoming the dominant artificial light source in various interiors. In the present study, photodegradation of thermally modified (TM) and unmodified (UM) ash (Fraxinus excelsior), aspen (Populus tremula), and pine (Pinus sylvestris) due to exposure to UV radiation and LED lamps was evaluated and compared by analysing wood discolouration (CIELAB colour space), changes in reflectance and FTIR spectra, and formation of water-soluble components. The results show that, apart from UV radiation, LED lamps may cause considerable photodegradation of both TM and UM wood resulting in visually perceptible colour change, alteration in chemical structure and formation of water-soluble components. Improved photo-stability was observed for TM wood exposed to UV radiation, while even more changes in FTIR spectra were detected for TM than UM wood in the experiment with LED lamps. Comparing TM and UM wood, the changes due to photodegradation were quite similar for TM wood of all species while significant differences were observed in the case of UM wood.

Color changes in beef meat during pan cooking: kinetics, modeling and application to predict turn over time

The kinetics of heat-induced color changes in beef meat was determined and implemented in a numerical model for double-sided pan cooking of steak. The CIELab color space was used to obtain the lightness (coordinate L^*) and the reddish tone (coordinate a^*) of the cooked meat. L^* was the CIELab coordinate that contributed the most to the change in the absolute color. Two response surfaces were found to describe the evolution with time and temperature of both color coordinates, L^* and a^*. The model results were successfully verified with experimental data of the two coordinates along the thickness of the meat for three degrees of cooking. The Root-Mean-Squared Errors (RMSE) for coordinate L^* were 5.17 (very rare), 2.02 (medium rare) and 3.83 (done), and for coordinate a^* 1.44 (very rare), 1.26 (medium rare) and 0.89 (done). The applicability of the model for practical cooking purposes was illustrated by determining the optimum turn over time to achieve a similar color profile on both sides of the meat. The turn over time depended on the desired degrees of cooking, and were comprised between one-half and two-thirds of the final cooking time, increasing from very rare cooking degree to done cooking degree.

No-reference stereoscopic image quality assessment using quaternion wavelet transform and heterogeneous ensemble learning

As the demand for high-quality stereo images has grown in recent years, stereoscopic image quality assessment (SIQA) has become an important research area in modern image processing technology.In this paper, we propose a no-reference stereoscopic image quality assessment (NR-SIQA) model using heterogeneous ensemble learning ‘quality-aware’ features from luminance image, chrominance image, disparity and cyclopean images via quaternion wavelet transform (QWT). Firstly, luminance image and chrominance image are generated by CIELAB color space as monocular perception, and the novel disparity and cyclopean images are utilized to complement with monocular information. Then, a number of ‘quality-aware’ features in the quaternion wavelet domain are discovered, including entropy, texture features, energy features, energy differences features and MSCN coefficients of high frequency sub-band. Finally, a heterogeneous ensemble model via support vector regression (SVR) & extreme learning machine (ELM) & random forest (RF) is proposed to predict quality score, and bootstrap sampling and rotated feature space are used to increase the diversity of data distribution. Comparing with the state-of-the-art NR-SIQA models, experimental results on four public databases prove the accuracy and robustness of the proposed model.

Effect of Partial Replacement of Soybean with Chickpea to the Nutritional and Textural Properties of Tofu

&#x0D; Abstract— Tofu is an oriental food that originated from China and later became a traditional cuisine in many South East Asia Countries, e.g. Indonesia, Singapore, and Thailand. The tofu was made by coagulating soy milk with salt to form curds, which further compress to form a solid block. Tofu usually uses soybeans as the main ingredients; however, in this experiment, the soybeans were partially replaced with chickpeas to increase the amount of dietary fibre and reduce fat levels in the final products. The tofu was made of soybean and chickpea at a ratio of 100:0 (control), 90:10, 80:20 and, 70:30. The products were evaluated based on their nutritional contents (crude protein, crude fat, and crude fibre), and textural properties using Texture Profile Analysis. Some analytical parameters, such as pH, moisture content, and colour were also studied. Partial replacement of soybean with chickpea decreased overall crude protein and fat content, whereas increased crude fibre. The presence of chickpea in the blends, up to 30% (w/w), had affected the tofu curd's protein stability, lowered their pH, and moisture content. A denser tofu curd was obtained by adding more chickpea to the product. The 70:30 blend has the highest hardness value compared to the other formulations. This low soy content mixture, however, can not produce a cohesive and springy tofu gel. The CIELAB colour space of the soybean/chickpea tofu cubes records a trend of increase in redness and yellowness values for tofu with chickpea.

Yellow Pigment Powders Based on Lead and Antimony: Particle Size and Colour Hue.

This paper reports the results of particle size analysis and colour measurements concerning yellow powders, synthesised in our laboratories according to ancient recipes aiming at producing pigments for paintings, ceramics, and glasses. These pigments are based on lead and antimony as chemical elements, that, combined in different proportions and fired at different temperatures, times, and with various additives, gave materials of yellow colours, changing in hues and particle size. Artificial yellow pigments, based on lead and antimony, have been widely studied, but no specific investigation on particle size distribution and its correlation to colour hue has been performed before. In order to evaluate the particle size distribution, segmentation of sample data has been performed using the MATLAB software environment. The extracted parameters were examined by principal component analysis (PCA) in order to detect differences and analogies between samples on the base of those parameters. Principal component analysis was also applied to colour data acquired by a reflectance spectrophotometer in the visible range according to the CIELAB colour space. Within the two examined groups, i.e., yellows containing NaCl and those containing K-tartrate, differences have been found between samples and also between different areas of the same powder indicating the inhomogeneity of the synthesised pigments. On the other hand, colour data showed homogeneity within each yellow sample and clear differences between the different powders. The comparison of results demonstrates the potentiality of the particle segmentation and analysis in the study of morphology and distribution of pigment powders produced artificially, allowing the characterisation of the lead and antimony-based pigments through micro-image analysis and colour measurements combined with a multivariate approach.

Spectral sensitivity of the discoloration of Historical rag paper

This paper discusses the spectral sensitivity of the discoloration of historical rag paper simultaneously affected by Relative Humidity (RH) and Oxygen concentration [O2] in the ambient environment. Sacrificial samples were degraded using narrowband radiation sources centred at 450 nm, 525 nm, and 625 nm in combinations of RH and [O2] at two levels: 0% [O2] and 70% RH, 21% [O2] and 70% RH, 0% [O2] and 20% RH, and 21% [O2] and 20% RH. Diffuse reflectance was measured before and during the degradation experimental runs. Consistent qualitative results were obtained for the change in reflectance and the change in tristimulus total color change in CIELAB color space. In both cases, the increase of discoloration was modelled logarithmically over time. Among the three factors investigated in this research, wavelength of the radiation (Λ) was found to have the strongest effect. The radiation at 450 nm induced the most and fastest discoloration whereas the radiation at 625 nm induced the least and slowest discoloration. This spectral dependence was likely to be related to the photo energies at different wavelengths, but other factors were found to have played a role. Further analyses revealed that the main effects and the effects of the interactions between [O2] and Λ and between RH and Λ on the discoloration of historical rag paper were statistically significant. It suggests that managing the spectral power distribution of the radiation source can be crucial in the collection management.

Comparative Analysis Of Efficient Image Segmentation Technique For Text Recognition And Human Skin Recognition

Computer Vision and Pattern Recognition is one of the most interesting research subject on computer science, especially in case of reading or recognition of objects in realtime from the camera device. Object detection has wide range of segments, in this study we will try to find where the better methodologies for detecting a text and human skin. This study aims to develop a computer vision technology that will be used to help people with disabilities, especially illiterate (tuna aksara) and deaf (penyandang tuli) to recognize and learn the letters of the alphabet (A-Z). Based on our research, it is found that the best method and technique used for text recognition is Convolutional Neural Network with achievement accuracy reaches 93%, the next best achievement obtained OCR method, which reached 98% on the reading plate number. And also OCR method are 88% with stable image reading and good lighting conditions as well as the standard font type of a book. Meanwhile, best method and technique to detect human skin is by using Skin Color Segmentation: CIELab color space with accuracy of 96.87%. While the algorithm for classification using Convolutional Neural Network (CNN), the accuracy rate of 98%&#x0D; Key word: Computer Vision, Segmentation, Object Recognition, Text Recognition, Skin Color Detection, Motion Detection, Disability Application

EFFICACY OF CIELAB AND CMYK COLOR SPACES IN LEUKEMIA IMAGE ANALYSIS: A COMPARISON BY STATISTICAL TECHNIQUES

There are several color models available and which color model to be used is a question to many researchers in the field of Medical Image Processing and Analysis. In this paper, the suitability of using the CIELAB and CMYK color spaces in processing and analyzing blood smear images of leukemia is assessed for comparison. Leukemia is commonly known as blood cancer and it usually leads to an abnormal proliferation of leukocytes (White Blood Cells) in the bone marrow and blood. The diagnosis of leukemia is primarily done by Pathologists by microscopically examining the blood and bone marrow smears of the suspected patient. Image processing-based methods can be used for automated detection and classification of leukemia to assist the Pathologists for a speedy diagnosis. The proposed study used [Formula: see text]-means clustering to segment the leukocytes and leukemic blast cells. The microscopic smear images in RGB color format were transformed and processed in CIELAB as well as CMYK color spaces for comparison. Statistical analysis was performed using Student’s [Formula: see text] test. The visual observation of the segmented images revealed that, processing the images in the CIELAB color space is comparatively better than the CMYK color space and it is reflected in the results of statistical comparison by Student’s [Formula: see text] test. The study assessed the suitability of using the CIELAB and CMYK color spaces in segmentation and analysis of microscopic smear images of Leukemic and Normal cases and found that processing the images in CIELAB color space is comparatively better than CMYK and such comparative analysis is not available in the literature.

Bison muscle discrimination and color stability prediction using near-infrared hyperspectral imaging

A novel photonics-based multivariate pattern recognition technique is presented to segregate bison meat samples based on muscle type, ageing, and retail display period. The technique uses color attributes obtained from visible to near-infrared hyperspectral images (400–1000 nm) to predict the stability of bison muscle samples. Unsupervised and supervised classification methods were implemented with an aim to discriminate muscle samples based on muscle type, ageing period, and retail display period. The wavelength region from 500 to 690 nm which is associated with the a ∗ value in the CIE Lab color space was found to be significantly important for the classification of muscle samples over the storage period. Partial least squares discriminant analysis (PLS-DA) demonstrated classification accuracies from 0.88 to 0.94 for the classification of muscle type, ageing period and retail display followed by development of classification maps. For the estimation of color changes in the muscle samples over the storage and retail display period, a ∗ value was predicted with an R 2 of calibration of 0.89, and R 2 of cross-validation of 0.88. Conclusively, the wavelength range from 550 to 690 nm can significantly contribute to sorting and predicting freshness of bison muscle samples based on muscle type, color stability and storage period. • Muscles were classified (>0.90 accuracy) at 2 d post-mortem using a classification map. • Classification accuracies were moderate (<0.90 accuracy) for samples based on ageing period. • Prediction of color values ( a ∗ value) was successful using selected wavelength range. • Hyperspectral imaging provided reliable segregation of muscles by color stability.

The Effect of High-Pressure Treatment and Skimming on Caprine Milk Proteins

Background: Proteins are susceptible to HP-treatment and there is a need to determine the applicability of HP-treatment in dairy production. The aim of this study was to determine the effect of HP-treatment at 200–500 MPa (tconst. = 10 min; Tconst. = 20 °C) and skimming of HP-treated milk on the content of nitrogen compounds and protein composition of caprine milk. Methods: The content of nitrogen (total, non-casein, non-protein) was determined using the Kjeldahl method. Casein fractions and whey proteins were separated using SDS-PAGE electrophoresis. Color parameters were measured in the CIELAB color space. Results: HP-treatment decreased (p &lt; 0.05) the content of non-casein nitrogen and soluble whey proteins. Skimming decreased the content of nitrogen compounds, and the noted decrease was more pronounced in HP-treated milk. Pressure and skimming had no influence on the proportions of α-, β-, κ-casein, β-lactoglobulin and α-lactalbumin. Total color difference (ΔE) increased with a rise in pressure, particularly in skim milk. Conclusion: HP-treatment led to a loss of protein solubility at pH 4.6 in caprine milk. In HP-treated milk, skimming did not induce changes in protein composition, despite a decrease in the content of nitrogen compounds after the separation of the cream layer. Higher values of ΔE in skim milk than in whole milk point to changes in colloidal phase components.

Prediction of Ratoon Sugarcane Family Yield and Selection Using Remote Imagery

Remote sensing techniques and the use of Unmanned Aerial Systems (UAS) have simplified the estimation of yield and plant health in many crops. Family selection in sugarcane breeding programs relies on weighed plots at harvest, which is a labor-intensive process. In this study, we utilized UAS-based remote sensing imagery of plant-cane and first ratoon crops to estimate family yields for a second ratoon crop. Multiple families from the commercial breeding program were planted in a randomized complete block design by family. Standard red, green, and blue imagery was acquired with a commercially available UAS equipped with a Red–Green–Blue (RGB) camera. Color indices using the CIELab color space model were estimated from the imagery for each plot. The cane was mechanically harvested with a sugarcane combine harvester and plot weights were obtained (kg) with a field wagon equipped with load cells. Stepwise regression, correlations, and variance inflation factors were used to identify the best multiple linear regression model to estimate the second ratoon cane yield (kg). A multiple regression model, which included family, and five different color indices produced a significant R2 of 0.88. This indicates that it is possible to make family selection predictions of cane weight without collecting plot weights. The adoption of this technology has the potential to decrease labor requirements and increase breeding efficiency.