CIELAB Color Space Research Articles

Virgin cork colour and porosity as predictors for secondary cork industrial quality

Currently, the selection of the trees to be removed in a thinning, carried out in a young cork oak plantation, is determined by a systematic rule to reach a predefined tree density value, in combination with simple criteria regarding individual tree sanitary conditions and stem shape. Since no criteria regarding the cork are available, thinnings can result in the removal of individuals that would later produce high quality secondary cork, leaving tree producers with lower quality cork. This study aims to test the feasibility of using CIELAB colour space coordinates and porosity values, measured in virgin cork samples, as predictors of the industrial cork quality and porosity obtained in secondary cork, thereby providing additional information usable for the classification of trees prior to the first thinning implementation. The L* and b* colour coordinates, measured in the transverse direction, depicted three main cork quality groups. Other L* and b* colour parameters, measured in the radial direction or in the cork back surface, have also shown statistically different average values while measured in different quality cork samples. Cork porosity from virgin and secondary cork showed a positive moderate monotonic relationship. A hyperbolic model for the prediction of secondary cork porosity was developed based on the virgin cork porosity, with one parameter expressed as a function of virgin cork colour coordinates (R2 =0.445). Results were successful in confirming the hypothesis raised. They may be used for the classification of trees prior to thinning.

Illuminance Color Independent in Remote Photoplethysmography for Pulse Rate Variability and Respiration Rate Measurement

Remote photoplethysmography (rPPG) is now becoming a new trend method to measure human physiological parameters. Especially due to it noncontact measurement which safe dan suitable to use in this new era condition. Pulse rate variability (PRV) and respiration rate (RR) included as parameters can be measured by using rPPG. PRV and RR are used to measure both physical and psychological wellness of the subject. However, current performance challenges in rPPG algorithm in measuring PRV and RR are illuminance invariant and motion. Especially in different light condition which represent real-life environment, signal-to-noise ratio (SNR) will be affected and directly reduce the measurement accuracy. Therefore in this study, we develop rPPG algorithm and then investigate the performance rPPG in different illuminance scenarios. We perform PRV and RR measurement under each scenario. On this study, for the pulse signal extraction, we were using algorithm is based on the modification of plane orthogonal-to-skin (POS) algorithm. While, for respiration signal extraction is done in CIE Lab color space. Our experimental results show the mean absolute error (MAE) of each measured parameters are 3.25 BPM and 2 BPM for PRV and RR respectively compared with clinical apparatus. The proposed method proved to be more reliable to use in real environments measurement. However, limitation of our proposed algorithm is still running in offline mode, hence for the future we want try to make our algorithm run in real time.

Lightweight real-time hand segmentation leveraging MediaPipe landmark detection

Real-time hand segmentation is a key process in applications that require human–computer interaction, such as gesture recognition or augmented reality systems. However, the infinite shapes and orientations that hands can adopt, their variability in skin pigmentation and the self-occlusions that continuously appear in images make hand segmentation a truly complex problem, especially with uncontrolled lighting conditions and backgrounds. The development of robust, real-time hand segmentation algorithms is essential to achieve immersive augmented reality and mixed reality experiences by correctly interpreting collisions and occlusions. In this paper, we present a simple but powerful algorithm based on the MediaPipe Hands solution, a highly optimized neural network. The algorithm processes the landmarks provided by MediaPipe using morphological and logical operators to obtain the masks that allow dynamic updating of the skin color model. Different experiments were carried out comparing the influence of the color space on skin segmentation, with the CIELab color space chosen as the best option. An average intersection over union of 0.869 was achieved on the demanding Ego2Hands dataset running at 90 frames per second on a conventional computer without any hardware acceleration. Finally, the proposed segmentation procedure was implemented in an augmented reality application to add hand occlusion for improved user immersion. An open-source implementation of the algorithm is publicly available at https://github.com/itap-robotica-medica/lightweight-hand-segmentation.

UNDERGROUND CRUDE OIL PIPELINE LEAKAGE DETECTION USING DEXINED DEEP LEARNING TECHNIQUES AND LAB COLOR SPACE

Computer vision plays a big role in pipeline leakage detection systems and is one of the latest techniques. Still, it requires a powerful image-processing algorithm to detect objects. The purpose of this work is to develop and implement spill detection in oil pipes caused by leakage using images taken by a drone equipped with a Raspberry Pi 4. The acquired images are sent to the base station along with the global positioning system (GPS) location of the captured images via the message queuing telemetry transport Internet of Things (MQTT IoT) protocol. At the base station, images are processed to identify contours by dense extreme inception networks for edge detection(DexiNed) deep learning techniques based on holistically-nested edge detection(HED) and extreme inception (Xception) networks. This algorithm is capable of finding many contours in images. To find a contour with black color, the CIELAB color space (LAB) has been used. The proposed algorithm removes small contours and computes the area of the remaining contours. If the contour is above the threshold value, it is considered a spill; otherwise, it will be saved in a database for further inspection. For testing purposes, three different spill areas were implemented with spill sizes of (1 m^2,2 m^2 ,and 3 m^2). Images have been captured at three different heights (5 m, 10 m, and 15 m) by the drone used to capture the images. The result shows that effective detection has been obtained at 10 meters high. To monitor the entire system, a web application has been integrated into the base station.

Effectiveness of Ozonation for Improving the Microbiological Safety of Fresh-Cut Parsley (Petroselinum crispum) Leaves

In the last decade, fresh-cut plants have become a more popular flavoring additive in food. It is important to find an effective method for ensuring the safety and quality of plant materials used as food additives. Ozonated water is being considered by the EFSA for approval as a cidal agent for plant protection. The objective of this study was to evaluate the effectiveness of ozonated water in improving the microbial safety of fresh-cut parsley leaves, with a particular focus on mesophilic and psychrotrophic bacteria and fungi. The yeasts and bacteria were identified with the MALDI-TOF MS system. Color changes on the surface of the parsley samples were measured in the CIE L*a*b trichromatic color model. The chemical composition of the essential oil was evaluated via gas chromatography with mass spectrometry (GCMS). The microbial level of the fresh leaves varied and depended on the season. The highest microbial levels were found in the leaves picked in the summer and autumn, at 104 to 106 CFU/g for fungi and 106 to 108 CFU/g for bacteria. Among the isolates with the highest isolation frequency, bacteria belonging to Pseudomonas fluorescens, Staphylococcus warneri, and Bacillus megaterium dominated. The dominant yeasts and molds were Candida sp., Rhodotorula sp., Cladosporium sp., and Fusarium sp. The conditions for water ozonation (ozone dose and time) were established for both mono- and mixed cultures. Time of 3 min, ozone content of 0.5, O3 mg/L or 1 min, and 1.5 mg of O3 mg/L were sufficient for a 90% reduction in the number of living microorganisms. Yeasts and bacteria were sensitive to ozone treatment, excluding P. fluorescens rods. The tested mold strains were the most resistant. However, it was noted that organic matter might reduce the decontamination effect. The effectiveness of ozonation was negatively influenced by organic compound content above 1%. Spectrophotometric measurements of parsley leaves after ozonation, especially after 3 min treatment at 1.5 O3 mg/L, revealed morphological changes. The CIELAB color space (L*a*b*) changed in the direction of lightness and yellowness; however, ΔE showed no statistically significant differences in comparison with the untreated leaves. In preliminary studies, no differences were noted in GLC-MS chromatograms for essential oils of parsley leaves before and after ozonation. The aroma of parsley treated with ozonated water was more intensely herbal than the control sample, probably due to the higher content of α and β phellandrene. The results of this study show that decontamination of parsley leaves by ozonated water containing 1.5 O3 mg/L in a closed 5 min process can effectively ensure the microbiological quality of fresh-cut parsley leaves. It can be concluded that ozone treatments in aqueous form appear to provide promising qualitative and quantitative results for the decontamination of this fresh-cut plant material. However, more work is necessary to study chemical and volatilome changes. Especially the sensory analyses should be conducted before and after ozone treatment.

Navigation Line Extraction Method for Broad-Leaved Plants in the Multi-Period Environments of the High-Ridge Cultivation Mode

Navigation line extraction is critical for precision agriculture and automatic navigation. A novel method for extracting navigation lines based on machine vision is proposed herein using a straight line detected based on a high-ridge crop row. Aiming at the low-level automation of machines in field environments of a high-ridge cultivation mode for broad-leaved plants, a navigation line extraction method suitable for multiple periods and with high timeliness is designed. The method comprises four sequentially linked phases: image segmentation, feature point extraction, navigation line calculation, and dynamic segmentation horizontal strip number feedback. The a* component of the CIE-Lab colour space is extracted to preliminarily extract the crop row features. The OTSU algorithm is combined with morphological processing to completely separate the crop rows and backgrounds. The crop row feature points are extracted using an improved isometric segmented vertical projection method. While calculating the navigation lines, an adaptive clustering method is used to cluster the adjacent feature points. A dynamic segmentation point clustering method is used to determine the final clustering feature point sets, and the feature point sets are optimised using lateral distance and point line distance methods. In the optimisation process, a linear regression method based on the Huber loss function is used to fit the optimised feature point set to obtain the crop row centreline, and the navigation line is calculated according to the two crop lines. Finally, before entering the next frame processing process, a feedback mechanism to calculate a number of horizontal strips for the next frame is introduced to improve the ability of the algorithm to adapt to multiple periods. The experimental results show that the proposed method can meet the efficiency requirements for visual navigation. The average time for the image processing of four samples is 38.53 ms. Compared with the least squares method, the proposed method can adapt to a longer growth period of crops.

Reversible halochromic PLA film for naked-eye detection at the extreme base condition

ABSTRACT Exposure to extreme base conditions in indoor environments may lead to adverse health effects on living beings. Proper ventilation and real-time detection are very important upon exposure. A simple and highly sensitive reversible base sensor is successfully prepared for real-time detection of the extreme base condition of aqueous base analytes. Halochromic poly lactid acid (PLA) pH film sensor containing thymolphthalein (THY) dye and polyethylene glycol (PEG) was developed by solution casting method. To increase the effectiveness of PLA pH film capabilities as a base sensor, the impact of altering the concentration of THY and PEG substrate was explored. The interactions between PLA, THY, and PEG were observed by Fourier transform infrared (FTIR), and the intensity of color changes was measured using UV-VIS spectroscopy. It was found that the PLA pH film has a clear color response from colorless to blue at pH 11–13 with a rapid response time within a minute. The results also revealed that varying PEG content showed a significant effect on response time compared to varying the THY content. Real-time visual color changes of the PLA pH film with different THY content and PEG content on total color difference, (∆E) values over pH and time were observed. Color changes after immersion in buffer solutions with different pH values were evaluated spectrophotometrically using CIELAB color space. The results indicated that varying the PEG content in the film leads to a faster and more distinct reversible color change. This outcome can be attributed to PEG ability to augment the free volume within the PLA material and enabling PEG and the dye and become trapped between PLA chain. The addition of PEG and THY has improved the flexibility of PLA film, which enables great potential in many applications such as medical, textile, and packaging in base environments.

Detection of Early Blight Tomato Leaf Using k-Means Clustering

Early blight is one of the major diseases of tomatoes that affects the leaves and fruit quality. Detection and estimation of the disease severity are performed using the visual observation method. Visual detection requires significant time for visual inspection of a large cultivated area. Thus, image processing techniques have proven to be an effective method as compared to visual analysis. In this study, digital image processing methods and techniques were used to detect early blight of tomato (EBT), estimate the disease severity, and classify tomato leaves. Totally, 198 infected plants were randomly taken from the Haramaya University research site "Rare" at four different times. Diseased potato leaf images were captured, resized, and stored for experimentation. The stored images were processed using median filtering to remove noise while preserving useful features in an image and image enhancement. The RGB images were transformed to gray scale and CIELAB color space, and the k-means clustering was applied to estimate the disease severity of the potato leaves, and Otsu’s thresholding algorithm was applied to estimate the disease severity of both the detached and live leaves. MATLAB algorithms will be developed to determine the total area and infected lesion area of the leaf samples.

Colour as an indicator for stabilization of polyacrylonitrile fibres

The conversion of polyacrylonitrile (PAN)-based precursors into high-performance carbon fibres requires careful control of treatment conditions. The stabilization process is a crucial step in this conversion process as the precursor undergoes chemical modifications that will substantially influence the properties of the final product. As the PAN fibre undergoes stabilization, its colour changes from white through various shades of orange and brown until an overall black appearance is achieved as a result of the imine-based chromophores generated from the underlining chemical reactions. In this work, we quantified the colour changes caused by a wide range of stabilization conditions on a reflectance spectrophotometer. Both CIELAB colour space and RGB colour space measurements correlated well with widely adopted stabilization indicators for oxidized PAN fibre (OPF), including fibre density and relative cyclization index. In particular, RGB colour component B showed very good linear and quadratic relationships with OPF density and relative cyclization index, respectively. The method used in this investigation may be further developed into a convenient tool for monitoring the progression of PAN fibre stabilization.

Predicción del tiempo de almacenamiento de carne congelada usando modelado de redes neuronales artificiales con valores de color

Among the various methods available to determine the storage time of frozen meat, including analyses based on physical and chemical properties, sensory analysis, particularly color changes, is an important aspect of meat acceptability for consumers. In this study, an artificial neural network (ANN) was employed to predict the storage time of the meat based on the CIELAB color space, represented by the Lab* (L*), (a*), and (b*) values measured by a computer vision system at two–month intervals over a period of up to one year. The ANN topology was optimized based on changes in correlation coefficients (R2) and mean square errors (MSE), resulting in a network of 60 neurons in a hidden layer (R2 = 0.9762 and MSE = 0.0047). The ANN model's performance was evaluated using criteria such as mean absolute deviation (MAD), MSE, root mean square error (RMSE), R2, and mean absolute error (MAE), which were found to be 0.0344, 0.0047, 0.0687, 0.9762, and 0.0078, respectively. Overall, these results suggest that using a computer vision–based system combined with artificial intelligence could be a reliable and nondestructive technique for evaluating meat quality throughout its storage time.

Automated digital color restitution of mural paintings using minimal art historian input

Digital color restitution aims to digitally restore the original colors of a painting. Existing image editing applications can be used for this purpose, but they require a select-and-edit workflow and thus they do not scale well to large collections of paintings or different regions of the same painting. To address this issue, we propose an automated workflow that requires only a few representative source colors and associated target colors as input from art historians. The system then creates a control grid to model a deformation of the CIELAB color space. Such deformation can be applied to arbitrary images of the same painting. The proposed approach is suitable for restituting the color of images from a large photographic campaign, as well as for the textures of 3D reconstructions of a monument. We demonstrate the benefits of our method on a collection of mural paintings from a medieval monument.

An enhanced 2-D color space diversity using RGB-LED overlapping for optical camera communication

In this paper, an enhanced two-dimension (2-D) color space diversity technique using red, green, and blue light emitting diode (RGB LED) overlapping is proposed and investigated experimentally to improve the system reliability for optical camera communication (OCC). The transmitter adopts an 8-color shift keying (8-CSK) modulation scheme with two RGB LED overlapping. The receiver uses a camera to form a multi-input single-output system to improve capacity. Different mapping methods are proposed and compared in 2-D YCbCr and CIElab color space to avoid the influence of brightness in conventional 3-D RGB space. Besides, clustering-based demapping is applied in the OCC system to transfer the color information into a data stream. The experimental results show that the mapping scheme with the maximum Euclidean distance in 2-D YCbCr color space has the best bit error rate (BER) performance. Compared with the conventional 3-D RGB color space, the proposed enhanced 2-D YCbCr color space diversity has a higher resolution to signal, which can realize 1.46 dB Q-factor improvement at an illumination of 660 lux. In addition, it can achieve a net data rate of 11.52 kb/s and maintain a 7% forward error correction (FEC) requirement even under low illumination. It is verified that the proposed scheme is a potential application for capacity and performance improvement.

StainView: A Fast and Reliable Method for Mapping Stains in Facades Using Image Classification in HSV and CIELab Colour Space

The new Construction 4.0 paradigm takes advantage of existing technologies. In this scope, the development and application of image-based methods for evaluating and monitoring the state of conservation of buildings has shown significant growth, including support for maintenance plans. Recently, powerful algorithms have been applied to automatically evaluate the state of conservation of buildings using deep learning frameworks, which are utilised as a black-box approach. The large amount of data required for training, the difficulty in generalising, and the lack of parameters to assess the quality of the results often make it difficult for non-experts to evaluate them. For several applications and scenarios, simple and more intuitive image-based approaches can be applied to support building inspections. This paper presents the StainView, which is a fast and reliable method. The method is based on the classification of the mosaic image, computed from a systematic acquisition, and allows one to (i) map stains in facades; (ii) locate critical areas; (iii) identify materials; (iv) characterise colours; and (v) produce detailed and comprehensive maps of results. The method was validated in three identical buildings in Bairro de Alvalade, in Lisbon, Portugal, that present different levels of degradation. The comparison with visual inspection demonstrates that StainView enables the automatic location and mapping of critical areas with high efficiency, proving to be a useful tool for building inspection: differences were of approximately 5% for the facade with the worst and average state of conservation, however, the values deteriorate for the facade under good conditions, reaching the double of percentage. In terms of processing speed, StainView allows a facade mapping that is 8–12 times faster, and this difference tends to grow with the number of evaluated façades.

Mayonnaises from fresh or frozen egg yolk with rapeseed and sesame oils -the influence of egg yolk freezing and storage time and oils ratio

The rheological behavior of some mayonnaise varieties prepared from liquid egg yolks, fresh or frozen, whose oily phase is made up of mixtures of sesame oil and rapeseed oil was studied in the current paper. The study examined the influence of the freezing time of the egg yolk, of the ratio between the two types of oil, as well as of the storage time, on the rheological parameters (apparent viscosity, consistency coefficient, flow behavior index). It has been shown that all types of mayonnaise present non-Newtonian behavior (pseudoplastic), because the values of the flow behavior index established from the specific mathematical models (Ostwald de Waele and Herschel-Bulkley) are sub-unit, and the apparent viscosity decreases with the increase of the shear rate. Mayonnaises prepared from frozen egg yolks and those with an oily phase in which sesame oil is predominant, have a higher consistency. The oxidation stability was evaluated by determining the acid values, which increase slightly with the freezing time of the egg yolk and with the amount of sesame oil in the oily phase. From the color tests carried out in the CIELAB color space, the characteristic parameters L*, a* and b* were determined and the lightness of the mayonnaise was assessed.

Rotational Invariance Using Gabor Convolution Neural Network and Color Space for Image Processing

Convolutional neural networks (CNNs) are deep learning methods that are utilized in image processing such as image classification and recognition. It has achieved excellent results in various sectors; however, it still lacks rotation invariant and spatial information. To establish whether two images are rotational versions of one other, one can rotate them exhaustively to see if they compare favorably at some angle. Due to the failure of current algorithms to rotate images and provide spatial information, the study proposes to transform color spaces and use the Gabor filter to address the issue. To gather spatial information, the HSV and CieLab color spaces are used, and Gabor is used to orient images at various orientation. The experiments show that HSV and CieLab color spaces and Gabor convolutional neural network (GCNN) improves image retrieval with an accuracy of 98.72% and 98.67% on the CIFAR-10 dataset.

Effects of using composite flour containing wheat flour with different levels of green banana pulp flour on the quality of saj flatbread

There is an increasing trend in formulating food to contain dietary fibers and particularly resistant starch. Saj bread (a type of flatbread baked on a plate placed directly on fire) is a potential candidate to act as a vehicle for delivering resistant starch. This study aimed to investigate the effects of using composite flour containing wheat flour substituted with different levels (0, 5, 10, 15, and 20%) of green banana pulp flour "GBPF" on some physicochemical properties of flour (moisture, ash, wet and dry gluten content, gluten index, falling number, and farinograph parameters) and the quality of saj bread as measured by CIELAB color space, texture (stretchability and texture profile analysis "TPA"), and sensory properties. The texture of the saj bread was monitored during three days of storage. Composite flour moisture content and falling number were unaffected by wheat flour substitution with GBPF, while dry gluten content decreased significantly for composite flour containing 15% or more GBPF. With increasing wheat flour substitution level with GBPF, dough stability decreased. For saj bread color, the L* and b* values decreased with increasing substitution levels, while a* and ∆E*ab values increased. With increasing substitution levels, the stretchability of bread decreased, and all tested TPA parameters increased. With increasing saj bread storage time, the stretchability of bread decreased, and all TPA parameters increased except cohesiveness which decreased. Using composite flour improved bread taste and odor scores and decreased color acceptability scores. Texture and overall acceptability scores were not affected. In conclusion, GBPF can potentially substitute up to 20% wheat flour without negatively affecting saj bread quality.

Improved Preprocessing Strategy under Different Obscure Weather Conditions for Augmenting Automatic License Plate Recognition

Automatic license plate recognition (ALPR) systems are widely used for various applications, including traffic control, law enforcement, and toll collection. However, the performance of ALPR systems is often compromised in challenging weather and lighting conditions. This research aims to improve the effectiveness of ALPR systems in foggy, low-light, and rainy weather conditions using a hybrid preprocessing methodology. The research proposes the combination of dark channel prior (DCP), non-local means denoising (NMD) technique, and adaptive histogram equalization (AHE) algorithms in CIELAB color space. And used the Python programming language comparisons for SSIM and PSNR performance. The results showed that this hybrid approach is not merely robust to a variety of challenging conditions, including challenging weather and lighting conditions but significantly more accurate for existing ALPR systems.

Quality of White-Brined Nabulsi Cheese Made with Different Proportions of Sheep’s and Cows’ Milk

Nabulsi cheese is the dominant white-brined cheese in Jordan and neighboring countries. Although this cheese is typically prepared using sheep’s milk, achieving large-scale production or attaining a specific quality involves the use of cows’ milk. Nabulsi cheese is kept in >18% brine for up to 1 year at room temperature (25 °C) and then soaked in water for desalting before eating. In this study, the effects of the milk type/combination used for production (100% sheep’s milk; 100% cows’ milk; 1:1, 3:1, or 1:3 sheep’s:cows’ milk mixture), desalting (salted vs. desalted cheese), and storage time (0, 6, and 12 months) on the cheese’s chemical composition directly after processing (in the salted form), texture (according to texture profile analysis “TPA”), and color (according to CIE lab color space) were assessed. The type or the combination of milk used does not affect the chemical composition of Nabulsi cheese, which was 43.35% ± 1.50 for moisture, 20.24% ± 0.59 for protein, 24.70% ± 1.53 for fat, 11.52% ± 0.60 for ash, 9.96% ± 0.44 for salt, and pH 6.43 ± 0.16. However, cheeses prepared using 100% sheep’s and 100% cows’ milk, respectively showed the highest (26.85%) and lowest (14.77%) yields. Desalting and milk type had the highest effects on cheese texture and color, respectively. Considering all tested parameters, mixing equal proportions of sheep’s and cows’ milk ensures that the Nabulsi cheese produced is similar to that produced from 100% sheep’s milk, albeit with increased cohesiveness (0.52 ± 0.07for cheese made from sheep’s milk and 0.59 ± 0.05 for 1:1 sheep’s: cows’ milk mixture).

Color and Chemical Composition of Timber Woods (Daniellia oliveri, Isoberlinia doka, Khaya senegalensis, and Pterocarpus erinaceus) from Different Locations in Southern Mali

Wood characteristics and properties are related to various factors connected to the biochemical processes that occur in the tree during wood formation, but also, to the interactions with the environmental conditions at the tree growing location. In addition to climatic factors, several investigations drew attention to the significance of the influence of other environmental parameters at the tree growing location. In this perspective, this work aimed to characterize the variation in color and chemical composition of timber wood from different locations in southern Mali, of trees growing under the same climatic conditions. To do so, a total of 68 grounded wood samples, from 4 timber wood species (Daniellia oliveri, Isoberlinia doka, Khaya senegalensis, and Pterocarpus erinaceus), were analyzed using CIELab color space and FTIR-ATR. Overall, the results indicated that the variation in wood color and chemical properties can be related to the local environmental conditions. Pterocarpus erinaceus presented significant differences between samples from the three areas according to the highest number of variables (color parameters, molecular composition determined by FTIR-ATR spectroscopy, and FTIR-ATR ratios). Daniellia oliveri and Khaya senegalensis, however, showed significant differences between areas of provenance for a lower number of variables. Isoberlinia doka, for its part, showed no significant differences and seems to be less sensitive to environmental factors. Furthermore, the results revealed that important differences exist between wood samples from Kati and Kéniéba.

Color change and thermochromism of sodalite, Na8[Al6Si6O24](I,S)2, synthesized by hydrothermal reaction

A sodalite material with a composition of Na8[Al6Si6O24](I,S)2 was synthesized from zeolite A via a hydrothermal reaction of a mixed solution of sodium iodide and sodium sulfide. The stoichiometric amount of NaI required to synthesize iodide sodalite (Na8[Al6Si6O24]I2) from zeolite A (Na12[Al12Si12O48]) was defined as a NaI content of 1.0, and synthesis was carried out using a NaI content of 0–25. The color of the obtained samples was almost white; however, the samples became blue or yellow following calcination. The blue color, which was attributed to the formation of S3− color centers similar to those in ultramarine, was obtained after calcination at 700–800 °C. However, samples calcined at 900 and 1000 °C were yellow. Raman spectroscopic analysis revealed that the formation of I2 molecules induced the color change to yellow. Among the prepared specimens, the yellow specimen prepared at 7.5 times the stoichiometric ratio of NaI exhibited the largest b* value in the CIELAB color space, which represents the blue–yellow component, with blue in the negative direction and yellow in the positive direction. No proportional relation was observed between the b* values and the amount of NaI added, suggesting that an appropriate I/S ratio existed for yellow coloration in sodalite. The yellow sample exhibited thermochromism with the absorption band broadening toward longer wavelengths and reddening at higher temperatures. This material has not been previously reported and is expected to have applications as a new host–guest material.