Matrix Analysis Research Articles

Catalytic properties of TiO2 nanofibers in CO2 conversion: a comparative analysis of polymer matrices

Catalytic properties of TiO2 nanofibers in CO2 conversion: a comparative analysis of polymer matrices

Relations Between Amylose Content and Physicochemical Properties of Starches from White and Red Sorghum Varieties

AbstractThis research aims to determine the relationship between amylose content and physicochemical properties in white and red sorghum starches. Five varieties of red sorghum and four varieties of white sorghum were evaluated. The chemical properties, the amylose content, the resistant starch content, scanning electron microscopy, the absorption and solubility index in water, the viscosity profile, the thermal properties, and the correlation between the amylose content were determined. Sorghum starches had an amylose content between 24.51% and 34.34%, but did not show any relationship with color. Regarding the microstructure, the starches that appear larger granules (20–30 µm) have lower amylose content. Sorghum red varieties shown higher values in gelatinization enthalpy, pasting temperature and water absortion index, this effect was attributed to the formation of complexes with phenolic compounds. Starches with a higher proportion of amylose shown lower viscosity profiles and a high thermal stability. Correlation matrix analysis reveals that amylose content had a negative correlation with water absorption index, water solubility index, maximum viscosity, minimum viscosity, and positive correlation with the resistant starch content. The amylose content can define the potential application of the sorghum starches.

THE IMPACT OF ICT EDUCATION ON GDP EVOLUTION AT LOCAL LEVEL. A ROMANIAN CASE STUDY

Purpose – This article focusses on the impact generated on GDP growth by the number of graduates in the ICT domain of Romanian counties in the period 2014–2020. Research methodology – We have collected the number of ICT graduates, the GDP per inhabitant in EUR per Romanian county, the number of ICT employees, the number of information service activities employees from the National Institute of Statistics (Romania) and Eurostat. Additionally, we consolidate the correlation matrix and regression analysis for previous statistics indicators. Findings – We have revealed a strong correlation (85,77%) between the number of ICT graduates and the GDP per inhabitant in EUR per Romanian county, and very strong correlations between the number of ICT graduates and the number of ICT employees (94,80%) and the number of employees of information service activities (97,38%). Research limitations – The data set from the National Institute of Statistics (Romania) does not contain entries for every county, due to its statistical methodology, and other socioeconomic phenomena which contribute to the development of the ICT field were just enumerated, such as natality, immigration, and emigration. Practical implications – The study revealed that investment in ICT education (number of graduates) contributes to the local prosperity of communities, counties, and, in general, at the national level as GDP growth. Romanian government can utilize the outcomes of this paper to engage additional resources, funds from Ministry of Education, to develop a long-lived learning culture of ICT skills for the entire population. Originality/Value – The article methodologically highlights the most important ICT hubs in Romania, which are correlated with the existence of an ICT educational infrastructure (high schools and universities) and the measure of impact on GDP growth for the number of highly skilled graduates in the ICT domain. The selected national and European indicators contributed to the novelty of this study.

Distribution, local use, and bio-prospecting opportunity of Ocimum americanum L. in Northwestern part of the Amhara Region, Ethiopia

Ocimum americanum (Basil) encompasses various uses. The plant is valued both as a food additive for its aromatic qualities and in folk medicine for treating various ailments. However, its uses, distribution, and the associated community knowledge have not been properly and systematically documented. Therefore, a survey was conducted to explore the bio-prospecting potential of Ocimum americanum and gather baseline information on its community knowledge. Purposive and proportional samplings were used to select districts, villages, users, and growers who participated in the study. The selected districts - Dembiya, Gonder Zuria, Takusa, Alefa, Bahir Dar city, and Bahir Dar Zuria Districts - were selected based on the plant’s growth potential and user availability. Data collection methods included semi-structured interviews, group discussions, and field observations. The findings revealed that basil is used for various purposes: ailment treatment (32.67%), food flavoring (26.6%), food preservative (7.7%), and to impart aroma to different foods (8.33%). Specifically, 32.67 % of the respondents used Ocimum americanum for traditional medicine purposes, primarily to treat depression/headaches, stomach aches, and ward off evil spirits. The methods for preparation and administration included concoction (21.05 %), direct application of plant parts (15.79%), grinding (15.79%), melting (5.26%), Fumigation (10.53%), and inhalation (31.58%). Medicinal dosage varied among users depending on the intensity, type, experience, and the severity of the disease. Nine traditional foods spiced, with basil were identified, with the highest fidelity level for basil use being food flavoring (18.33%) followed by preservation (18.33%). The direct matrix analysis indicated that chilli pepper spice was the most preferred local food sweetened by basil, followed by hot-spiced pepper. This study highlights the traditional foods spiced with basil and providesvaluable insights for bio-prospecting companies in the food and pharmacological industries.

Impact of Emotional Intelligence on Organizational Performance in Commercial Banks, Itahari, Sunsari, Nepal

The ongoing debate surrounding Emotional Intelligence (EI) and Organizational Performance (OP) has been a consistent topic of discussion across various cultures worldwide. This article aims to assess the current status of EI and OP in Nepalese Commercial Banks, analyze the relationship between EI and OP, and measure the impact of EI on OP. Employing a deductive research approach, the study utilizes a descriptive and cross-sectional research design, with the population comprising all employees in commercial banks in Itahari, Sunsari, Nepal. The sample size is 167, chosen through convenience sampling, employing a structured questionnaire with a five-point Likert scale to collect primary data. Mean and Standard Deviations assess the status of EI and OP, while Pearson correlation matrix and linear regression analysis measure the impact of EI on OP. Chronbatch alpha is used to test the reliability of constructs. The results indicate a positive presentation of both EI and OP in commercial banks, with statistically significant relationships within the model. The analysis suggests that employees with high EI exhibit higher OP compared to those with lower emotional intelligence. Notably, social awareness emerges as a key construct significantly influencing OP, followed by relationship management and self-awareness. The findings propose potential benefits for banks in enhancing EI levels during recruitment, training, and development programs for their personnel.

Kinematic Age of the β-Pictoris Moving Group

Accurate age estimation of nearby young moving groups (NYMGs) is important as they serve as crucial testbeds in various fields of astrophysics, including the formation and evolution of stars and planets, as well as loose stellar associations. The β-Pictoris moving group (BPMG), being one of the closest and youngest NYMGs, has been extensively investigated, and its estimated ages have a wide range from ∼10–25 Myr, depending on the age estimation methods and data used. Unlike other age dating methods, kinematic traceback analysis offers a model-independent age assessment hence the merit in comparing many seemingly discordant age estimates. In this study, we determine the kinematic ages of the BPMG using three methods: probabilistic volume calculation, mean pairwise distance calculation, and covariance matrix analysis. These methods yield consistent results, with estimated ages in the range of 14–20 Myr. Implementing corrections to radial velocities due to gravitational redshift and convectional blueshift increases the ages by ∼2–4 Myr. Conversely, considering data uncertainties decreases the estimated ages by 1–2 Myr. Taken together, our analysis determined the kinematic age of the BPMG to be 16.3−2.1+3.4 Myr. This age is significantly younger than the commonly accepted age of the BPMG (∼24 Myr) determined primarily from the lithium depletion boundary analysis. This younger kinematic age may point to the discrepancy between the luminosity evolution and lithium depletion models or the presence of unaccounted systematic error in the method. This result underscores the necessity for systematic reevaluations of age-dating methods for nearby, young moving groups.

Leveraging growth of savings and credit cooperatives through innovative practices-cases from Uganda

PurposeThis paper explores how the growth of selected Savings and Credit Cooperatives (SACCOs) in an African setting was nurtured through innovative practices that enabled them to fulfill their roles.Design/methodology/approachThe paper applies an exploratory qualitative approach using face-to-face interviews and observation to obtain in-depth primary data. Data were then examined using thematic and matrix analyses to understand the key resources, innovative practices and growth strides in the cases studied. FindingsThe SACCOs undertook a variety of innovative practices based on resources, which included: creating teams, focusing on community needs, involving top management and all SACCOS’ members and having visionary entrepreneurial leaders. This led to an increase in outreach and savings.Originality/valueWhereas the concept of innovative practices has been widely studied, there is scanty literature on how such practices within SACCOs are developed based on the resources of SACCOs in Africa. This paper further provides new insights based on empirical data from SACCOs that applied innovative practices and were able to grow.

Entanglement in photo-ionization process.

We report a study of the entanglement between the quantized photon field and an atom arising in the photo-ionization process. Our approach is based on an ab initio solution of the time-dependent Schrödinger equation (TDSE) describing the quantum evolution of a bipartite system consisting of the atom and the quantized electromagnetic field. Using the solution of the TDSE, we calculate the reduced photon density matrix, which we subsequently use to compute entanglement entropy. We explain some properties of the entanglement entropy and propose an approximate formula for the entanglement entropy based on the analysis of the density matrix and its eigenvalues. We present the results of a comparative study of the entanglement in the photo-ionization process for various ionization regimes, including the tunneling and the multiphoton ionization regimes.

A journey in the world of craniofacial development: From 1968 to the future.

This article is based on my talk at the meeting "3rd Advances in Craniosynostosis: Basic Science to Clinical Practice", held at University College, London, on 25 August 2023. It describes my contribution, together with that of my research team and external collaborators, to the field of craniofacial development. This began with my PhD research on the effects of excess vitamin A in rat embryos, which led to a study of normal as well as abnormal formation of the cranial neural tube. Many techniques for analysing morphogenetic change became available to me over the years: whole embryo culture, scanning and transmission electron microscopy, cell division analysis, immunohistochemistry and biochemical analysis of the extracellular matrix. The molecular revolution of the 1980s, and key collaborations with international research teams, enabled functional interpretation of some of the earlier morphological observations and required a change of experimental species to the mouse. Interactions between the molecular and experimental analysis of craniofacial morphogenesis in my laboratory with specialists in molecular genetics and clinicians brought my research journey near to my original aim: to contribute to a better understanding of the causes of human congenital anomalies.

Automatic extraction of facial median sagittal plane for patients with asymmetry based on the EDMA alignment algorithm.

We aimed to establish a novel method for automatically constructing three-dimensional (3D) median sagittal plane (MSP) for mandibular deviation patients, which can increase the efficiency of aesthetic evaluating treatment progress. We developed a Euclidean weighted Procrustes analysis (EWPA) algorithm for extracting 3D facial MSP based on the Euclidean distance matrix analysis, automatically assigning weight to facial anatomical landmarks. Forty patients with mandibular deviation were recruited, and the Procrustes analysis (PA) algorithm based on the original mirror alignment and EWPA algorithm developed in this study were used to construct the MSP of each facial model of the patient as experimental groups 1 and 2, respectively. The expert-defined regional iterative closest point algorithm was used to construct the MSP as the reference group. The angle errors of the two experimental groups were compared to those of the reference group to evaluate their clinical suitability. The angle errors of the MSP constructed by the two EWPA and PA algorithms for the 40 patients were 1.39 ± 0.85°, 1.39 ± 0.78°, and 1.91 ± 0.80°, respectively. The two EWPA algorithms performed best in patients with moderate facial asymmetry, and in patients with severe facial asymmetry, the angle error was below 2°, which was a significant improvement over the PA algorithm. The clinical application of the EWPA algorithm based on 3D facial morphological analysis for constructing a 3D facial MSP for patients with mandibular deviated facial asymmetry deformity showed a significant improvement over the conventional PA algorithm and achieved the effect of a dental clinical expert-level diagnostic strategy.

P28 Data-independent acquisition mass spectrometry improves spatially resolved analysis of the human skin proteome

Abstract Introduction and aims Proteomic analysis of the extracellular matrix (ECM) presents challenges because of the highly crosslinked and low-solubility nature of ECM proteins. Traditional homogenization and protein digestion approaches result in the loss of crucial information regarding protein localization and spatial relationships. To address this, spatially resolved proteomics emerges as a powerful tool for exploring heterogeneity within bulk tissues. This study aims to determine the minimum tissue volume required for comprehensive proteome coverage using data-independent acquisition mass spectrometry (DIA-MS) on skin tissue. The study focused on optimizing spatially resolved proteomic techniques to enhance depth-of-analysis while preserving spatial specificity. Methods Human abdominal skin biopsies were obtained from a single individual and subsequently cryosectioned. Histological assessment was performed through haematoxylin and eosin staining for visualization purposes. Laser-capture microdissection coupled with mass spectrometry facilitated the precise isolation of target regions. Comparative analyses were performed between data-dependent acquisition mass spectrometry (DDA-MS) and DIA-MS, with a particular emphasis on ECM proteins within the dermis. Results Our findings revealed an improvement in proteome coverage with DIA-MS compared with DDA-MS, in addition to clear scaling relationships between the depth-of-analysis and sample concentration. The Results demonstrated the superiority of DIA-MS in achieving robust and comprehensive proteomic profiles, even with minimal tissue volumes. Preliminary findings suggest the capability of DIA-MS in elucidating the complexities of the skin proteome with spatial resolution. Conclusions In conclusion, our study highlights the efficacy of DIA-MS in spatially resolved proteomics on skin tissue. The optimized approach presented here offers a reliable and efficient method for obtaining in-depth proteome information with minimal tissue requirements. These Results form the foundation for ongoing experiments, utilizing DIA-MS to advance spatially resolved proteomic analyses of human skin.

Driving towards sustainability: A review of natural fiber reinforced polymer composites for eco-friendly automotive light-weighting

The automotive industry stands at a critical juncture, compelled by the imperative of sustainability to seek innovative materials for eco-friendly light-weighting. Natural fiber reinforced polymer composites (NFRPCs) have gained popularity due to their environmentally friendly nature and excellent engineering capabilities. This review paper comprehensively examines the landscape of NFRPCs in the context of automotive applications. Beginning with an overview of the ecological urgency and regulatory framework driving sustainable automotive materials, the review navigates through key advancements in NFRPC technology. The paper delineates the diverse array of natural fibers employed as reinforcements, elucidating their intrinsic properties, sources, and processing considerations. Concurrently, an in-depth analysis of various polymer matrices showcases their compatibility with different fiber types, emphasizing the critical interplay between fiber and matrix for optimal composite performance. A pivotal facet of this review manuscript lies in the rigorous evaluation of NFRPC performance across an array of metrics, including mechanical, thermal, and environmental considerations. Studies examining the interfacial interactions between natural fibers and polymers, as well as enhancements through additives and treatments, are critically assessed. Environmental and economic considerations are paramount in the quest for sustainable automotive materials. While economic evaluations delve into the viability and cost-effectiveness of widespread adoption, life cycle assessments and environmental impact analyses are evaluated to estimate the ecological footprint of NFRPCs. The paper also surveys current trends and prospects, offering insights into forthcoming innovations and directions for research. Therefore, this review article consolidates a comprehensive body of knowledge on NFRPCs for eco-friendly automotive light-weighting. By synthesizing findings from diverse studies, it provides a holistic perspective on the potential, challenges, and future trajectory of NFRPCs in the automotive sector.

Geochemical evaluation and the mechanism controlling groundwater chemistry using chemometric approach and groundwater pollution index (GPI) in the Kishangarh city of Rajasthan, India.

This study is primarily focused on delving into the geochemistry of groundwater in the Kishangarh area, located in the Ajmer district of Rajasthan, India. In pursuit of this research goal, the sampling locations were divided into three parts within the Kishangarh region: Badgaon Rural (KSGR), Kishangarh Urban (KSGU), and the Kishangarh RIICO marble industrial area (KSGI). Various analytical methods have been executed to assess the suitability of groundwater for various purposes based on pH, electric conductivity, total dissolved solids, hardness, salinity, major anions, and cations. The ionic trend of anions and cations was found as HCO3- > Cl- > SO42- > NO3- > Br- > NO2- > F- and Na+ > Ca2+ > Mg2+ > K+, respectively. Applying statistical techniques such as principal component analysis (PCA) and Pearson correlation matrix analysis (PCMA) makes it evident that the physicochemical attributes of water sourced from the aquifers in the study area result from a blend of diverse origins. In addition, Gibbs, Piper, Durov, and scatter plots were used to assess groundwater's geochemical evolution. Piper plot demonstrated the two types of groundwater facies, Na-HCO3- and Na-Cl, implying significant contributions from evaporitic dissolution and silicate weathering. Also, the scatter plots have evaluated the impression of mine acid leachate, evaporitic dissolution, and silicate weathering to upsurge salt formation in the groundwater. The pollution risk evaluation within the study area was conducted using the groundwater pollution index (GPI). This index revealed a prominent concern for pollution, particularly in the northern segment of the study region. As a result, it can be inferred that the fine aeolian sand and silt formations in the northern part are relatively more vulnerable to contamination.

Left and Right‐Handed Light Reflection and Emission in Ultrathin Cellulose Nanocrystals Films with Printed Helicity

AbstractNatural polymers, particularly plant‐derived nanocelluloses, self‐organize into hierarchical structures, enabling mechanical robustness, bright iridescence, emission, and polarized light reflection. These biophotonic properties are facilitated by the assembly of individual components during evaporation, such as cellulose nanocrystals (CNCs), which exhibit a left‐handed helical pitch in a chiral nematic state. This work demonstrates how optically active films with pre‐programmed opposite handedness (left or right) can be constructed via shear‐induced twisted printing with clockwise and counter‐clockwise shearing vectors. The resulting large‐area thin films are transparent yet exhibit pre‐determined mirror‐symmetrical optical activity, enabling the distinction of absorbed and emitted circularly polarized light. This processing method allows for sequential printing of thin and ultrathin films with twisted layered organization and on‐demand helicity. The complex light polarization behavior is due to step‐like changes in linear birefringence within each deposited layer and circular birefringence, different from that of conventional CNC films as revealed with Muller matrix analysis. Furthermore, intercalating an achiral organic dye into printed structures induces circularly polarized luminescence while preserving high transmittance and controlled handedness. These results suggest that twisted sequential printing can facilitate the construction of chiroptical metamaterials with tunable circular polarization, absorption, and emission for optical filters, encryption, photonic coatings, and chiral sensors.

A Comprehensive Review on Advantages of Ultra Performance Liquid Chromatography (UPLC) for Analytical Method Development, Validation, and Stability Studies

Ultra Performance Liquid Chromatography (UPLC) represents a paradigm shift in analytical separation science, offering enhanced speed, sensitivity, and resolution while retaining the foundational principles of traditional liquid chromatography. This comprehensive review explores the principles, instrumentation, advantages, and applications of UPLC in analytical method development, validation, and stability studies, with a focus on pharmaceutical analysis. UPLC leverages finer particles and intensified flow rates to achieve unprecedented performance gains, facilitating rapid and precise separations without compromising analytical rigor. The discussion delves into the intricate instrumentation involved, including sample injection, column selection, and detector operation, highlighting the critical role of each component in ensuring optimal analytical performance. Advantages of UPLC include drastically reduced run times, streamlined method optimization, and increased sample throughput, while limitations such as higher back pressures and limited column regenerability are also discussed. Furthermore, the review outlines the diverse applications of UPLC across pharmaceutical, environmental, food and beverage, clinical, forensic, bioanalytical, and polymer analysis domains, underscoring its versatility and significance in advancing research, development, and quality control in various industries. In summary, UPLC represents a transformative tool in analytical chemistry, offering unparalleled capabilities for expedited and accurate analysis of complex sample matrices, ultimately driving innovation and scientific progress. Key Words: Ultra Performance Liquid Chromatography (UPLC), analytical method development, validation, stability studies, pharmaceutical analysis, instrumentation, advantages, applications, sample injection, column chemistry, detector technology, back pressures, method optimization, sample throughput, environmental analysis, food and beverage analysis, clinical diagnostics, forensic analysis, bioanalytical research, polymer analysis, analytical chemistry.

Microstructural analysis of nitinol-based self-healing metals matrix composite of A356 alloy produced by high-pressure die casting

Abstract A self-healing non-ferrous metal-matrix composite is prepared by the high-pressure die-casting process. It includes casting set-up, sample preparation of metal matrix composite (MMC), microstructural characterization, and analysis of its ability to close the crack. Aluminum alloy (A356) is deployed as a matrix material in the MMC. Nitinol is a smart alloy produced by a combination of Nickel and Titanium in equal mass proportion. Apart from excellent mechanical properties it also exhibits super-elasticity and shape-memory effect. The wire of the Nitinol is integrated as reinforcement within the matrix of A356 alloy through a high-pressure die-casting process. The recovery percentage of the metal matrix composite and microstructural evaluation are reported. The deployment of shape memory wire provides the ability to recover the matrix material even from plastic strain by just heating the sample slightly above the activation temperature of the Nitinol wire. Microstructural evaluation indicates fair integration of the reinforcement within the matrix material. Gaining the ability for 30.27% angular restoration and 19.37% crack closer is a very positive sign for designing self-healing metallic materials.

Breaking barriers, building faculty: A qualitative analysis to exploring faculty development in academic hospital medicine.

Hospital medicine (HM) continues to be primarily composed of junior hospitalists and research has highlighted a paucity of mentors and academic output. Faculty advancement programs have been identified as a means to support junior hospitalists in their career trajectories and to advance the field. The optimal approach to supporting faculty development (FD) efforts is not known. To understand hospitalist groups' approaches to FD, including efforts that were perceived to be effective, and to identify barriers as well as potential future directions for FD. Rapid qualitative methods were utilized including templated summaries and matrix analysis to identify major themes. Virtual focus groups with hospitalists in the Hospital Medicine Reengineering Network (HOMERuN). Qualitative themes RESULTS: Nineteen individuals from 17 unique institutions from across the United States in May 2022 participated in seven focus groups. Four key themes emerged from the study and included (1) academic hospitalist programs face multifaceted challenges and barriers to FD in HM, (2) groups have embraced a diversity of structures and frameworks, (3) due to clinical volumes, FD programs have had to adapt and evolve to meet FD needs, and (4) participants identified multiple areas for improvement, including defining tangible outcomes of FD programs and creating a repository of FD material which can be shared widely.

Development of a machine learning-based model to predict prognosis of alpha-fetoprotein-positive hepatocellular carcinoma

BackgroundPatients with alpha-fetoprotein (AFP)-positive hepatocellular carcinoma (HCC) have aggressive biological behavior and poor prognosis. Therefore, survival time is one of the greatest concerns for patients with AFP-positive HCC. This study aimed to demonstrate the utilization of six machine learning (ML)-based prognostic models to predict overall survival of patients with AFP-positive HCC.MethodsData on patients with AFP-positive HCC were extracted from the Surveillance, Epidemiology, and End Results database. Six ML algorithms (extreme gradient boosting [XGBoost], logistic regression [LR], support vector machine [SVM], random forest [RF], K-nearest neighbor [KNN], and decision tree [ID3]) were used to develop the prognostic models of patients with AFP-positive HCC at one year, three years, and five years. Area under the receiver operating characteristic curve (AUC), confusion matrix, calibration curves, and decision curve analysis (DCA) were used to evaluate the model.ResultsA total of 2,038 patients with AFP-positive HCC were included for analysis. The 1-, 3-, and 5-year overall survival rates were 60.7%, 28.9%, and 14.3%, respectively. Seventeen features regarding demographics and clinicopathology were included in six ML algorithms to generate a prognostic model. The XGBoost model showed the best performance in predicting survival at 1-year (train set: AUC = 0.771; test set: AUC = 0.782), 3-year (train set: AUC = 0.763; test set: AUC = 0.749) and 5-year (train set: AUC = 0.807; test set: AUC = 0.740). Furthermore, for 1-, 3-, and 5-year survival prediction, the accuracy in the training and test sets was 0.709 and 0.726, 0.721 and 0.726, and 0.778 and 0.784 for the XGBoost model, respectively. Calibration curves and DCA exhibited good predictive performance as well.ConclusionsThe XGBoost model exhibited good predictive performance, which may provide physicians with an effective tool for early medical intervention and improve the survival of patients.

Identifying and analysing the phonetics of low resources Nyishi language using artificial neural network technique

Phonemic identification is an analysis and classification based on sounds. Phonetics can be categorized into three various types based on how it is articulated, perceived, and produced. The study introduces new Niyshi phonetic identification approaches based on artificial neural networks to improve the detectability of the system. This work has the goal of coming up with an effective technique that can be used to detect both non-nasalized and nasalized phonemes; by classifying phonemes into those belonging or not originating from the Nyishi dialect. The model that can be used to train, test, and validate can include a multilayer-based perceptron classifier. The proposed model identifies a complex old language with an accuracy as high as 87% through Multi-Layer Perceptron (MLP). To end, a confusion matrix analysis for ten trials involving an average of 89% accuracy for 20% trained dataset validation purposes confirms the efficiency and efficacy of the methodologies employed.

Using satellite image data to identify rice varieties through linear spectral unmixing method (case study: Karangjati Sub District, Ngawi Regency)

<p>Remote sensing technology has increasingly emerged as a potent tool for precision agriculture, particularly in facilitating the mapping and monitoring of crops on a large scale. An application of this technology is the identification of different types of rice by analyzing the pixels acquired in satellite images. Regrettably, the pixels in the image have been mixed from different recorded items. Therefore, they have the potential to influence the outcome of the identification. An effective approach to address this problem is to employ the linear spectral unmixing (LSU) technique. The LSU approach quantifies the ratio of pure objects in every pixel of an image by utilizing the spectral value associated with the endmember of the rice variety. The investigation was carried out in the Karangjati District during the generative stage (70 ± DAP) of the rice planting season. The data indicates that the dominant variety is Inpari 32 HDB. The data validation tests, which involved the use of a confusion matrix and Kappa analysis, resulted in an overall accuracy rate of 85.48% and a Kappa analysis score of 70.6%.<strong></strong></p>