Color Index Research Articles

Research on precise phenotype identification and growth prediction of lettuce based on deep learning

In recent years, precision agriculture, driven by scientific monitoring, precise management, and efficient use of agricultural resources, has become the direction for future agricultural development. The precise identification and assessment of phenotypes, which serve as external representations of a crop's growth, development, and genetic characteristics, are crucial for the realization of precision agriculture. Applications surrounding phenotypic indices also provide significant technical support for optimizing crop cultivation management and advancing smart agriculture, contributing to the efficient and high-quality development of precision agriculture.This paper focuses on lettuce and employs common nutritional stress conditions during growth as experimental settings. By collecting RGB images throughout the lettuce's complete growth cycle, we developed a deep learning-based computational model to tackle key issues in the lettuce's growth and precisely identify and assess phenotypic indices. We discovered that some phenotypic indices, including custom ones defined in this study, are representative of the lettuce's growth status. By dynamically monitoring the changes in phenotypic traits during growth, we quantitatively analyzed the accumulation and evolution of phenotypic indices across different growth stages. On this basis, a predictive model for lettuce growth and development was trained.The model incorporates MSE, SSIM, and perceptual loss, significantly enhancing the predictive accuracy of the lettuce growth images and phenotypic indices. The model trained with the reconstructed loss function outperforms the original model, with the SSIM and PSNR improving by 1.33% and 10.32%, respectively. The model also demonstrates high accuracy in predicting lettuce phenotypic indices, with an average error less than 0.55% for geometric indices and less than 1.7% for color and texture indices. Ultimately, it achieves intelligent monitoring and management throughout the lettuce's life cycle, providing technical support for high-quality and efficient lettuce production.

Identification of the optical emission detected by Gaia with radio structures in parsec-scale active galactic nucleus jets

Aims. We investigated the location of the optical emission with respect to the parsec-scale radio structure and attempted to identify whether the optical centroid is coincident with the radio core or a radio component of the jet. Methods. We used optical Gaia DR3 and 8 GHz ICRF3 positions and a model fitting of 15 GHz imaging by the MOJAVE VLBA survey for 422 sources, mostly blazars. We searched for possible associations between optical centroids and radio components. We also compared the astrometric and photometric properties of each category, looked for trends between the various source parameters, and discussed possible biases. Results. Sources can be separated into four categories based on whether their optical centroid (i) falls onto the radio core (category C), (ii) lies close to the base of the jet (category B), (iii) coincides with a radio component downstream in the jet (category J), or (iv) is not found to coincide with a detected radio component (category O). Due to a number of random and systematic errors, the number of sources falling into each category remains approximate but close to 32%, 36%, 22%, and 10%, respectively. The family of quasars, mostly flat spectrum radio quasars (FSRQs), clearly dominates the C and B categories, with their percentage decreasing from categories C to O. Conversely, the family of BL Lacs is spread over the four classes and dominates the category O. Radio galaxies are mainly in classes O and J, and the radio-loud narrow-line Seyfert 1 all belong to class C. An individual analysis of O sources shows that, despite the absence of a direct association with a specific radio component, the optical Gaia centroid is globally related to the radio VLBI jet structure. Conclusions. Our study shows that the spatial distribution of the optical counterparts seen by Gaia below the 0.2-arcsec scale around the radio cores appears in the vast majority of cases to be related to the AGN core or to its jet, with most of them located downstream in the jet. Those associated to the core also exhibit a bluer color index, suggesting a possible contribution of the accretion disk to the optical emission, while those associated to a radio knot in the jet appear redder and tend on average to be found in more polarized radio sources. Most BL Lacs have their optical emission coincident with the jet base or a knot in the jet, while sources with an optical emission on or close to the radio core are mostly FSRQs. Radio components associated with the optical centroid at the jet base or along the jet are mainly stationary or quasi-stationary features, with low apparent MOJAVE velocities. There are indications that the apparent proper motion of the Gaia centroids may be higher than the speed of the associated radio components, but the significance of this trend requires further investigation.

TADF deep-blue OLED with color index approaching Rec.2020 standard blue gamut achieved by a boron-based D–A type molecular skeleton

The ultra-high definition (UHD) displays in the market require organic light-emitting diodes (OLEDs) to meet the Rec.2020 color gamut standard, while it is still challenging to exploit ultra-deep-blue emitters that satisfy such blue standard. Herein, by substituting tCz (3,6-di(tert-butyl)carbazole) donor unit on the robust BOC (8,8,12,12-tetramethyl-8,12-dihydro-4-oxa-3a2-boradibenzo[cd,mn]pyrene) acceptor unit with high structural rigidity and appropriate energy levels, a novel boron-based D–A type thermally activated delayed fluorescence (TADF) ultra-deep-blue emitter, namely, tCz-BOC, was readily designed and synthesized. Inheriting from its rigid organoboron core, the emitter presents ultra-deep-blue emission with narrow spectral full width at half maximum (FWHM) of 54 nm and high photoluminescence quantum yield up to 73.7%. Additionally, tCz-BOC can effectively harvest T1 excitons through high-lying T2 mediated reverse intersystem crossing process. As a consequence, the fabricated OLED based on tCz-BOC exhibits a satisfactory ultra-deep-blue emission with λEL of 424 nm, narrow emission bandwidth with FWHM of 47 nm, excellent maximum external quantum efficiency (EQEmax) of 13.9%, as well as the CIEy coordinate of 0.047, which is fairly close to the Rec.2020 blue color gamut.

VLBI position variability of AGNs is inversely correlated with their photometric variability

Aims. The stability of the International Celestial Reference Frame (ICRF), realized through geodetic very long baseline interferometry (VLBI) positions of thousands of extragalactic objects, is dependent on the individual positional stability of these objects. It has been recently shown that the prevalence of offsets between the VLBI positions of ICRF objects and their Gaia optical positions, which limit the optical-radio reference frame tie, is inversely correlated with optical photometric variability, suggesting that photometrically variable objects may be more positionally stable. In this work, we determine the relationship between VLBI position stability of ICRF objects and optical-radio position offsets as well as optical photometric variability. Methods. We created multi-epoch geodetic VLBI solutions for a sample of 520 ICRF sources that have sufficient data to determine the variability in their VLBI positions over time. We compared this position variability with the fractional photometric variability provided by the Gaia extragalactic source catalog, the Gaia-ICRF optical-radio position offsets, the uncertainty-normalized position offsets, and optical BP-RP color as well as with possible confounders such as optical magnitude, VLBI/Gaia position error, and redshift. We determined the relationship between VLBI position stability and γ-ray detection by the Fermi Large Area Telescope (LAT), and we determined how the VLBI position and optical flux variabilities correlate with the spectral classification of our sample, considering flat spectrum radio quasars (FSRQs), quasi-stellar objects, BL Lacs, Seyfert, and gigahertz-peaked spectrum radio sources or compact-steep-spectrum radio sources. Results. We found that VLBI astrometric variability is (i) negatively correlated with optical flux variability, (ii) positively correlated with optical-radio offsets, (iii) negatively correlated with optical color index BP-RP, and (iv) negatively correlated with γ-ray detection. We also found that the most positionally stable sources are among the FSRQ and BL Lac classes. In other words, redder, photometrically variable sources have the most stable VLBI positions, the smallest optical-radio position offsets, and the highest rate of γ-ray detection, and these sources tend to be spectrally classified as blazars. Conclusions. Our results are consistent with the most positionally stable sources being blazars, a class of object in which the jet is oriented close to the line of sight and where relativistic beaming increases photometric variability and minimizes the projected offset between the optical and radio positions. Our study should therefore orient future geodetic VLBI observing programs preferentially toward sources with high photometric variability because these sources are predicted to have better VLBI position stabilities and smaller optical-radio position offsets, improving the stability of the celestial reference frame axes.

Design and application research of portable reversible thermochromic patches in grid power supply systems

This study aims to develop a novel portable reversible thermochromic patch and evaluate its potential applications in temperature monitoring and indication. The film is based on the concept of electronic charge transfer thermochromic coatings, enabling accurate detection of high temperatures. Experimental results demonstrate that when the temperature of the monitored device’s circuit reaches 70 °C or above, the color of the film changes from deep blue to colorless, indicating potential overheating in the power grid. Using salt spray corrosion testing and cycle performance testing, we validate the film’s excellent corrosion resistance in harsh natural environments and its provision of reliable color indication. The film possesses portability and ease of attachment to various electrical devices and circuits requiring temperature sensing. By connecting the color information of the film to a computer system, we can achieve digital temperature monitoring, providing potential applications in future smart grid development.

Comparative Analysis of RGB and CIELAB Colour Spaces in Quality Assessment of Brick Products

Methods for assessing the colour (quality) of manufactured products, as a rule, are aimed at analysing the data obtained by colorimeters or spectrometers for the objects under study. The problematic side of such measurements is the small area of the object affected by the measurements. The introduction to the practice of evaluating the colour of the produced image products makes it possible to evaluate the colour of the entire object, while leaving the traditional measurement methods for verifying the obtained colour characteristics from the image. The typical colour space for images is RGB, and for colorimeters LAB. Naturally, when applying the image method, it becomes necessary to convert from the RGB to LAB colour space in order to obtain colour indicators that can be compared with colorimeters. The article provides a description of the obtained colour characteristics when assessing the quality of brick products.

Effects of different concentrations of canthaxanthin microencapsulated with gelatin or lignosulfonate on laying performance, yolk color of hens

Gelatine (GL) is widely available as a microencapsulated material that prevents the decomposition of canthaxanthin (CX). However, there are still disadvantages such as instability, poor safety, and chromium overload. Our study aimed to determine the effects of using Lignosulfonate (LS) as a potential substitute to GL microencapsulation of CX on the productive performance, egg quality, yolk colour, and serum biochemical indicators of laying hens, as well as the optimal dosage. 1458 healthy ISA brown hens were divided into 9 treatments (162 birds in each group): basal diet containing 0 mg/kg CX (Control), basal diet containing 2 mg/kg GL-microencapsulated CX (GMC2), 4 mg/kg GL-microencapsulated CX (GMC4), 6 mg/kg GL-microencapsulated CX (GMC6), 8 mg/kg GL-microencapsulated CX (GMC8), basal diet containing 2 mg/kg LS-microencapsulated CX (LMC2), 4 mg/kg LS-microencapsulated CX (LMC4), 6 mg/kg LS-microencapsulated CX (LMC6), 8 mg/kg LS-microencapsulated CX (LMC8). The productive performance of each group was recorded daily for 4 weeks. Egg samples were collected and analysed weekly. Serum samples were taken at the end of the experimental period. The results showed that CX-containing diets had no significant effects on laying performance and egg quality (p > .05). The yolk colour level of the CX group was higher than the control group (p < .05), and the LMC group showed better colouring than the GMC group at the same CX concentration (p < .05). Meanwhile, with the increasing of CX concentration, the yolk colour increased by linear and quadratic (p < .001). CX-treatment improved serum total superoxide dismutase (T-SOD) level (p < .05). In conclusion, LMC was superior to GMC in improving egg yolk colour, and antioxidant function, and there was no adverse impact on productive performance, and 8 mg/kg was the optimal dosage of LMC. Therefore, LS could be used as a potential substitute for GL in the CX microencapsulation industry of laying hens.

A new method for accurate inversion of Forel-Ule index using MODIS images - revealing the water color evolution in China's large lakes and reservoirs over the past two decades

The Forel Ule water color index (FUI) based on satellite inversion can characterize the comprehensive characteristics of water quality on a large spatiotemporal scale. The high-frequency observations and rich historical data of the MODIS surface reflectance product (MODIS-500 m) provide important data support for monitoring the FUI of inland lakes. However, MODIS-500 m has only three bands in the visible light range, resulting in significant uncertainty in FUI inversion. To address this problem, this study developed an improved FUI inversion model using 500 synthetic spectra covering natural waters. The model, with a performance threshold set at 170° (FUI = 8), used a segmented algorithm across the entire color space. Validated with on-site measurement datasets (3500 samples), the model exhibited excellent performance, with mean relative error (MRE) and root mean square error (RMSE) of 1.71 % and 3.63°, respectively. Compared to existing models, it was more suitable for long-term FUI inversion in various types of lakes, particularly in eutrophic regions. Subsequently, the model was applied to MODIS-500 m observations from 2000 to 2022, revealing the spatiotemporal dynamics of FUI in 180 large lakes and reservoirs (hereinafter referred to as lakes) in China. The results indicated that the long-term mean FUI in the study area was 9, with 7 and 12 in the western and eastern regions, respectively, showing a distinct spatial distribution of "blue in the west and green in the east." The mean change rate of hue angle for all lakes was -0.085°/yr, showing an overall decreasing trend. Environmental factors' relative contributions to long-term water color changes in each lake region were quantified using the multiple general linear model (GLM). Although each lake region exhibited different driving forces, they were primarily influenced by vegetation, lake surface area, and anthropogenic factors. Additionally, the seasonal types of lake water color were analyzed, with the west and east showing opposite patterns, reflecting the significant influence of topographic features and seasonal changes in climate on water color. The research results provide techniques for accurate inversion of FUI using MODIS-500 m data, while deepening the understanding of long-term water color changes in inland lakes in China.

The Retrieval of Ground NDVI (Normalized Difference Vegetation Index) Data Consistent with Remote-Sensing Observations

The Normalized Difference Vegetation Index (NDVI) is widely used for monitoring vegetation status, as accurate and reliable NDVI time series are crucial for understanding the relationship between environmental conditions, vegetation health, and productivity. Ground digital cameras have been recognized as important potential data sources for validating remote-sensing NDVI products. However, differences in the spectral characteristics and imaging methods between sensors onboard satellites and ground digital cameras hinder direct consistency analyses, thereby limiting the quantitative application of camera-based observations. To address this limitation and meet the needs of vegetation monitoring research and remote-sensing NDVI validation, this study implements a novel NDVI camera. The proposed camera incorporates narrowband dual-pass filters designed to precisely separate red and near-infrared (NIR) spectral bands, which are aligned with the configuration of sensors onboard satellites. Through software-controlled imaging parameters, the camera captures the real radiance of vegetation reflection, ensuring the acquisition of accurate NDVI values while preserving the evolving trends of the vegetation status. The performance of this NDVI camera was evaluated using a hyperspectral spectrometer in the Hulunbuir Grassland over a period of 93 days. The results demonstrate distinct seasonal characteristics in the camera-derived NDVI time series using the Green Chromatic Coordinate (GCC) index. Moreover, in comparison to the GCC index, the camera’s NDVI values exhibit greater consistency with those obtained from the hyperspectral spectrometer, with a mean deviation of 0.04, and a relative root mean square error of 9.68%. This indicates that the narrowband NDVI, compared to traditional color indices like the GCC index, has a stronger ability to accurately capture vegetation changes. Cross-validation using the NDVI results from the camera and the PlanetScope satellite further confirms the potential of the camera-derived NDVI data for consistency analyses with remote sensing-based NDVI products, thus highlighting the potential of camera observations for quantitative applications The research findings emphasize that the novel NDVI camera, based on a narrowband spectral design, not only enables the acquisition of real vegetation index (VI) values but also facilitates the direct validation of vegetation remote-sensing NDVI products.

Pembuatan Tortilla Chips Komposit Kulit Melinjo Kerikil dengan Jagung Terhadap Kualitas Inderawi, Tingkat Kesukaan Masyarakat, Kandungan Protein dan Kapasitas Antioksidan

The purpose of this research is to find out 1) Differences in the quality of sensory quality in terms of taste, color, aroma and texture. 2) The level of community preference. 3) Protein content and antioxidant capacity of tortilla chips different kerikil melinjo peel composites. The research method used is the prerequisite test, One Way Analysis of Variance and continued to Tukey's test if there are differences in sensory quality, the preference test uses descriptive analysis of means and Laboratory test. The results showed that there were differences in sensory quality on the color and aroma indicators of tortilla chips. Preferred test results from composite tortilla chips with kerikil melinjo peel (0%:100%, 50%:50%, 60%:40% and 70%:30%) with values (3,98, 3,86, 4,16 dan 4,03). Laboratory test results of protein content and antioxidant capacity of sample 0%:100% (6.9719%, 16.2665%), sample 50%:50% (6.6989%, 18.3361%), sample 60%:40% (7.1234%, 19.8261%), and sample (184) 70%:30% (7.3761%, 21.6887%). The conclusion is that in terms of the quality of the senses there is a significant difference in the color and aroma indicators of different composite tortilla chips of kerikil melinjo peel. The highest average preference test result is 60%: 40% sample. The highest protein content and the highest antioxidant capacity is a sample 70%:30%.

Ultrasound Technology in Carpet Cleaning

Carpets are widely used in living spaces for beautiful appearance, heat, and sound insulation. Cleaning carpets is essential for both their lifespan and health and hygiene. Washing operations are carried out at regular intervals in carpet-washing factories. Industrial carpet washing operations must be done consciously because a significant amount of water and time is consumed during these processes. In addition, problems such as discoloration and strength losses may be encountered in delicate carpets. In this study, stained carpets were washed at mediums with and without ultrasound at room temperature at different times. Mud stain was used as an example of particle dirt. The original unwashed carpet without stain was taken as a reference and compared with the washed carpet by evaluating the color differences and changes in the yellowness index to check the amount of stain removal. The color differences (Da*, Db*, DL*, DC*, Dh*, DE*) were close to zero, and the differences in the yellowness index were low, so it was concluded that the stained carpet was cleaned and turned to the original form after washing. The washing process was evaluated as successful because the color and yellowness index of the washed carpet were close to the unwashed carpet. Effects of washing type (with and without ultrasound), washing agent (with and without detergent), and washing time (30, 60, and 90 minutes) are significant for α=0,05 in washed carpets according to the original carpet. Interactions between parameters are also significant. If the washing conditions are consciously adjusted, ultrasonic washing can be an alternative to the soaking process in carpet washing. With ultrasonic washing, it can be possible to shorten the duration of the wetting step and effectively clean sensitive carpets in softer conditions without damaging them. It would be useful to carry out studies that reduce water usage by optimizing the amount of washing baths in the future.

Exploring deep eutectic solvent extraction's impact on anthocyanin degradation kinetics in various conditions

This study evaluated the stability of anthocyanins in red cabbage extracts using deep eutectic solvents (DESs) and acidified methanol under thermal and storage conditions. DESs included combinations of choline chloride with malic acid (Ma), xylitol (Xy), lactic acid (La), and glycerol (Gl). Extracts were heat-treated at 40 °C–100 °C and stored at temperatures of 25 °C, 4 °C, and −18 °C for 35 days. Analysis involved pH differential methodology, browning and polymeric color indexes, antioxidant assays (DPPH• and ABTS•+), HPLC, and FT-IR spectroscopy. Results showed that anthocyanin content in all solvents decreased with increasing temperature. Acidified methanol samples experienced a notable reduction from 304.2 mg/L at 40 °C to 246.5 mg/L at 100 °C. Malic acid, lactic acid, xylitol, and glycerol samples also showed significant declines at higher temperatures. Cold storage effectively preserved anthocyanin levels. Kinetic analysis indicated that ChCl-Xy was the most effective solvent in preserving anthocyanins and minimizing browning, while ChCl-Ma led to the highest anthocyanin degradation and color changes. At 25 °C, Ac-MetOH demonstrated the highest antioxidant activity (DPPH• scavenging rate k of 5.66 ± 0.15), but ChCl-Ma exhibited the highest browning index (k of 3.4 ± 0.13) and polymeric color index (k of 5.54 ± 0.12), suggesting lower stability compared to other solvents.

Extension of Quality and Shelf Life of Tomatoes Using Chitosan Coating Incorporated with Cinnamon Oil.

This study examined the effects of 2% chitosan (CS) coatings incorporated with varying concentrations of cinnamon oil (CO) (0%, 0.5%, 1.0%, and 1.5%) on the extension of the quality and shelf-life of tomatoes stored under ambient conditions. Control samples were untreated and coated with distilled water. All samples were stored for 14 days at 25 ± 1 °C, with quality assessments conducted every two days. The application of CS-CO treatments was notably effective in controlling weight loss (3.91-5.26%) and firmness loss (10.81-16.51 N), sustaining the color index score (11.98-16.78), and stabilizing the total soluble solids (4.64-4.71 brix), titratable acidity (0.374-0.383%), total phenolic content (75.89-81.54 mg/100 g), ascorbic acid concentration (21.64-33.69 mg/100 g), total antioxidant capacity (85.89-91.54%) and pigment levels, particularly chlorophyll (52.80-63.18 mg/100 g), compared to control samples (p < 0.05). Higher CO concentrations (1.0% and 1.5%) in the CS coating maintained a significant level of phytochemicals in the samples compared to the control group, while CS-CO at 0.5% performed similarly in preserving the other physicochemical qualities. Both CS and CS-CO treatments extended the shelf life of the tomatoes up to 14 days (<6.78 log10 CFU/mL), whereas control samples were only viable for storage for 6 days due to higher microbial growth (>7.8 log10 CFU/mL) (p < 0.05). Overall, CS-CO-treated tomatoes demonstrated superior quality preservation and shelf-life enhancement, with a notable improvement in overall qualities as compared to the CS and control samples.

Incorporation of spray-dried encapsulated bioactive peptides from coconut (Cocos nucifera L.) meal by-product in bread formulation.

This study aimed to stabilize and mask the bitterness of peptides obtained from the enzymatic hydrolysis of coconut-meal protein with maltodextrin (MD) and maltodextrin-pectin (MD-P) as carriers via spray-drying. Essential (~35%), hydrophobic (~32%), antioxidant (~15%), and bitter (~45%) amino acids comprised a significant fraction of the peptide composition (with a degree of hydrolysis of 33%). The results indicated that the peptide's production efficiency, physical and functional properties, and hygroscopicity improved after spray-drying. Morphological features of free peptides (fragile and porous structures), spray-dried with MD (wrinkled with indented structures), and MD-P combination (relatively spherical particles with smooth surfaces) were influenced by the process type and feed composition. Adding free and microencapsulated peptides to the bread formula (2% W/W) caused changes in moisture content (35%-43%), water activity (0.89-0.94), textural properties (1-1.6 N), specific volume (5.5-6 cm3/g), porosity (18%-27%), and color indices of the fortified product. MD-P encapsulated peptides in bread fortification resulted in thermal stability and increased antioxidant activity (DPPH and ABTS+ radical scavenging: 4.5%-39.4% and 31.6%-46.8%, respectively). MD-P (as a carrier) could maintain sensory characteristics and mask the bitterness of peptides in the fortified bread. The results of this research can be used to produce functional food and diet formulations.

Estimation of blight and spot diseases severity in Ciherang and Ciliwung rice varieties based on vegetation index algorithms

Abstract. Bande LOS, Hasan A. 2024. Estimation of blight and spot diseases severity in Ciherang and Ciliwung rice varieties based on vegetation index algorithms. Biodiversitas 25: 1015-1021. Blight and spot diseases are often associated with rice plant, causing high disease severity and potentially reducing crop production. One effective disease management strategy is using resistant varieties and intensive disease monitoring through camera sensor technology and vegetation index-based image processing. Therefore, this study aimed to assess the severity of blight and spot diseases on two rice plant varieties based on vegetation indexes. Image recording was carried out on rice field and leaf samples of Ciherang and Ciliwung varieties. This was followed by image processing based on normalized difference vegetation index (NDVI) to determine the proportion of sick/healthy plants in the field and dark green color index (DGCI) to quantify disease severity. The results showed that the proportion of healthy plant in Ciliwung rice field was greater than in Ciherang as shown by NDVI. Based on DGCI, Ciliwung also had a relatively lower level of disease severity compared to Ciherang, although the difference was not statistically significant. Blight disease caused more severe damage to rice leaf than spot disease based on image processing results. Furthermore, a positive correlation was observed between the increase in DGCI and NDVI.

Estimation of wheat protein content and wet gluten content based on fusion of hyperspectral and RGB sensors using machine learning algorithms

The protein content (PC) and wet gluten content (WGC) are crucial indicators determining the quality of wheat, playing a pivotal role in evaluating processing and baking performance. Original reflectance (OR), wavelet feature (WF), and color index (CI) were extracted from hyperspectral and RGB sensors. Combining Pearson-competitive adaptive reweighted sampling (CARs)-variance inflation factor (VIF) with four machine learning (ML) algorithms were used to model accuracy of PC and WGC. As a result, three CIs, six ORs, and twelve WFs were selected for PC and WGC datasets. For single-modal data, the back–propagation neural network exhibited superior accuracy, with estimation accuracies (WF > OR > CI). For multi-modal data, the random forest regression paired with OR + WF + CI showed the highest validation accuracy. Utilizing the Gini impurity, WF outweighed OR and CI in the PC and WGC models. The amalgamation of MLs with multimodal data harnessed the synergies among various remote sensing sources, substantially augmenting model precision and stability.

Quantifying Abdominal Coloration of Worker Honey Bees.

The main drawback in using coloration to identify honey bee subspecies is the lack of knowledge regarding genetic background, subjectivity of coloration grading, and the effect of the environment. The aim of our study was to evaluate the effect of environmental temperature on the abdominal coloration of honey bee workers and to develop a tool for quantifying abdominal coloration. We obtained four frames of honey bee brood from two colonies and incubated them at two different temperatures (30 and 34 °C). One colony had workers exhibiting yellow marks on the abdomen, while the other did not. We collected hatched workers and photographed abdomens. Images were analyzed using custom-written R script to obtain vectors that summarize the coloration over the abdomen length in a single value-coloration index. We used UMAP to reduce the dimensions of the vectors and to develop a classification procedure with the support vector machine method. We tested the effect of brood origin and temperature on coloration index with ANOVA. UMAP did not distinguish individual abdomens according to experimental group. The trained classifier sufficiently separated abdomens incubated at different temperatures. We improved the performance by preprocessing data with UMAP. The differences among the mean coloration index values were not significant between the gray groups incubated at different temperatures nor between the yellow groups. However, the differences between the gray and yellow groups were significant, permitting options for application of our tool and the newly developed coloration index. Our results indicate that the environmental temperature in the selected range during development does not seem to impact honey bee coloration significantly. The developed color-recording protocol and statistical analysis provide useful tools for quantifying abdominal coloration in honey bees.

Ensemble machine learning as a mathematical approach to predict high-voltage electric field-assisted removal of color and oxidative indices from soybean oil using the activated porous electrodes

Porous electrode materials provide a large surface area for electrochemical reactions and facilitate ion transport efficiently. By optimizing the design and properties of porous electrodes, it is possible to enhance the bleaching performance. In this research, three machine learning (ML) algorithms, namely Support Vector Regression, Ridge Regression, and Decision Tree Regression were utilized for predicting the bleaching efficiency under different conditions of HVEF-assisted bleaching (HVEFAB). Subsequently, the outcomes of these models were compared to ensemble models created by combining the algorithms as mentioned earlier using three techniques including bagging, boosting, and stacking. The levels of chlorophyll and carotenoids were reduced as a result of increased voltage, modification in the electrode surface, number of cathode-anode, and the presence of an inert electrolyte. Meanwhile, the reduction of red color and chlorophyll pigments was more significant than yellow color and carotenoid pigments. Moreover, the application of HVEFAB can potentially decrease the levels of polar compounds, such as oxidative compounds on the electrode surface. Based on the MSE, MAE, and R2 metrics, the algorithms' efficacy in forecasting outputs 1 (color index) and 2 (oxidative index) can be ordered as follows: Stacking > Boosting > Bagging > Decision Tree (DT) > Ridge > Support Vector Regression (SVR). The findings demonstrate that integrating these three models, mainly through the stacking technique, substantially improves the accuracy of the predicting model. Particle swarm optimization (PSO) and polynomial regression rather than RSM represented a superior model to predict red color removal.

Influence of ultrasonic-assisted osmotic dehydration pretreatment on hot air-assisted radio frequency drying of bitter gourd

Bitter gourd is a versatile vegetable that brings health-promoting benefits to consumers. To maintain its high nutrition values and ensure year-round availability, proper post-harvest processes are essential for preserving its original properties. However, there is limited knowledge regarding how pretreatment methods impact the physicochemical properties of pretreated bitter gourd and the performance of subsequent hot air-assisted radio frequency drying (HARFD) process. In the present study, bitter gourd underwent osmotic dehydration (OD) and ultrasonic-assisted osmotic dehydration (UAOD) pretreatment with different ultrasound power levels (400, 800, and 1200 W). The physicochemical properties of the pretreated samples, such as mass transfer behavior, water migration status, dielectric loss factor, and others, were compared. The drying duration, energy consumption, and various quality attributes (e.g., color index, rehydration, nutritional compositions, antioxidant activity, etc.) of the HARFD dried products were also examined. The microstructure observations demonstrated that acoustic waves induced the structural changes and created microchannels. Additionally, physicochemical properties of the pretreated samples showed that OD and UAOD methods significantly facilitated water migration status and increased the dielectric loss factor. Compared to control and OD methods, three UAOD methods successfully reduced the drying duration by 7.9%–30.3% and total energy consumption by 5.8%–28.3% for the entire processing steps. Furthermore, UAOD methods improved the total phenolic content, total chlorophyll retention, DPPH and ABTS values of dried bitter gourds by 32.3%–36.7%, 12.6%–20.3%, 23.4%–31.6%, and 20.7%–26.3%, respectively, compared to the control methods. Overall, UAOD-400 W samples after drying exhibited improved quality characteristics, including reduced color difference, increased retention of bioactive compounds, higher rehydration ratio, and enhanced antioxidant capacity. This study underscores the effectiveness of UAOD application in improving both processing efficiency and quality of bitter gourds produced by HARFD.

Color and Firmness Quality Changes of Java Apple During Postharvest Transportation and Storage

Transportation vibration adversely affects fresh produce during transportation. In addition, storage temperature affects the quality of fresh commodities. The physical changes in Java apples during transportation and storage were evaluated in this study. Java apples were transported from local farms to wholesale markets (180 km). Java apples were stored at room temperature (28°C) for six days. Physical qualities such as weight loss and firmness of the Java apple samples were evaluated. The RGB image acquisition system was used to assess changes in the color of the Java apple. The vibration showed that more than 70% of the acceleration occurred between 220-290 cm/s2 in the vertical and horizontal directions during transportation. Analysis showed that physical qualities, such as weight loss and firmness, were strongly affected by the packaging used, vibration during transportation, and storage temperature. The weight loss and reduction in firmness was highest in Java apples transported using wholesaler packaging (packaging A). The lightness, yellowness, and hue values decreased significantly because transportation vibration was relatively high, and the Java apples were stored at room temperature. Redness, total color difference, and color index increased significantly in Java apples that were transported using package A and stored at room temperature. The results showed that the use of transportation packaging affected changes in the physical quality of Java apples. Packaging A generally increase in weight loss, hardness, and changes in fruit color than other packaging types.