Identification Applications Research Articles

Diversity, characterization, and biotechnological potential of plant growth-promoting bacteria from Bryophyllum pinnatum (Lam.) (Crassulaceae) roots and rhizosphere soil.

Cultivable microbial communities associated with plants inhabiting extreme environments have great potential in biotechnological applications. However, there is a lack of knowledge about these microorganisms from Bryophyllum pinnatum (which survives in severely barren soil) and their ability to promote plant growth. The present study focused on the isolation, identification, biochemical characterization, and potential applications of root endophytic bacteria and rhizosphere bacteria. A total of 73 bacterial isolates were obtained, with 50 derived from rhizospheric soil and 23 from root tissue. The identified strains were categorized into 16 genera, with Bacillus, Priestia, Pseudarthrobacter, Neobacillus, Mesobacillus, and Arthrobacter being the most species-rich genera. Heat stress experiments indicated that almost half (50.7%) of the selected isolates were tolerant to heat stress. Furthermore, most strains present diverse capabilities for biotechnological applications, including the potential for indole-3-acetic acid (IAA) production, organic phosphorus solubilization, inorganic phosphorus solubilization, and nitrogen fixation. Some isolates (21.92%) exhibited broad-spectrum antagonistic activity against various phytopathogenic fungi, including Fusarium spp. Agar plate assays revealed that the Cellulomonas hominis strain LS43 and Bacillus inaquosorum strain LS77 significantly increased the total fresh weight of Arabidopsis (P < 0.05), yet these strains did not significantly affect the primary root length or the number of leaves. Notably, a subset of the strains tested did not significantly increase the growth of Arabidopsis and, in fact, had inhibitory effects on certain growth parameters. This is the first investigation highlighting the potential of root endophytic bacteria and rhizosphere bacteria in association with B. pinnatum in barren soils. Thus, these isolated strains positively influence plant nutrient uptake, stress resilience, and biocontrol to reduce chemical inputs in conventional agricultural practices, highlighting the importance of their development as biofertilizers for improving the quality of barren soil.

EEG-based functional and effective connectivity patterns during emotional episodes using graph theoretical analysis

Understanding the neural mechanisms underlying emotional processing is critical for advancing neuroscience and mental health interventions. This study examined these mechanisms by analyzing EEG connectivity patterns across different brain regions while participants evoked various emotions. After applying independent component analysis (ICA) to eliminate non-cortical activity, we assessed frequency-specific connectivity patterns using coherence, Granger causality, and graph theoretical measures to evaluate both functional and effective connectivity. Graph theoretical analysis revealed significant differences in connectivity between emotions across multiple frequency bands, particularly in the delta and beta bands. These results indicated modulations in key brain regions, such as the precentral, superior frontal, and temporal areas, suggesting that these regions play a crucial role in emotional processing. Coherence analysis demonstrated predominant alpha band activity across all emotions, with specific emotional states, such as fear, grief, and jealousy, exhibiting enhanced beta band activity. In terms of coherence strength, we observed that the gamma band was largely inactive, except for the emotion of sadness, which displayed significant activity in the right lobe, particularly in regions such as the supplementary motor area and the lingual gyrus. Additionally, Granger causality analysis highlighted that the beta and gamma bands were dominant across all emotional states, with minimal modulation observed in the theta band. Clustering coefficients from the graph analysis further revealed distinct patterns of connectivity in the delta and beta bands, with significant variations across different emotions, particularly in the temporal and frontal regions. These findings enhance our understanding of emotional processing and have practical applications in mental health, biomarker identification, and human-computer interaction.

A xylenol orange-based pH-sensitive sensor array for identification of bacteria and differentiation of probiotic drinks.

The development of straightforward and cost-efficient methods for bacterial identification is very important. In this study, we utilized xylenol orange, metal ions, and diverse bacterial carbon sources to construct a sensor array, achieving precise bacterial identification. Initially, we examined the absorbance variations of xylenol orange with five metal ions (Mn2+, Co2+, Ni2+, Cu2+, and Zn2+) at pH levels ranging from 4 to 7, observing significant changes at 570 nm or 580 nm. Given that bacteria can generate varying amounts of acid in different carbon sources, we employed a blend of xylenol orange and the five metal ions as pH-sensitive probes to characterize bacterial metabolism in three carbon sources, resulting in the development of a five-sensor array that effectively differentiated seven bacteria. Additionally, by utilizing xylenol orange with Co2+, we successfully identified six different bacterial mixtures and five types of probiotic drink products. These findings highlight the potential of our methods for broader practical application in bacterial identification in practical use.

The Role and Research Progress of ACPA in the Diagnosis and Pathological Mechanism of Rheumatoid Arthritis.

An autoimmune condition known as rheumatoid arthritis (RA) is characterized by persistent polyarticular inflammation. Within two years of the disease's onset, irreparable bone and joint deterioration can occur as a result of the inflammatory course of the illness, leading to joint deformity and loss of function. In most patients diagnosed with RA, a range of autoantibodies, particularly anti-citrullinated protein antibodies (ACPA), can be detected months to years before the onset of clinically recognizable disease. Additionally, an increasing number of studies suggest that ACPA is involved in the pathogenesis of RA and may play a direct pathogenic role in the disease. This paper focuses on the role of ACPA in the pathomechanism of RA and discusses its unique clinical applications for the early identification and prediction of RA, as well as the influencing factors. Moreover, this article outlines the association of ACPA-positive (ACPA+) RA with other autoimmune diseases.

Some practical regards on the application of the physics-informed sparse identification for strongly NESs

Some practical regards on the application of the physics-informed sparse identification for strongly NESs

APPLICATION OF GLOBAL SENSITIVITY ANALYSIS FOR IDENTIFICATION OF PROBABILISTIC DESIGN SPACES

The design space (DS) is defined as the combination of materials and process conditions that provides assurance of quality for a pharmaceutical product. A model-based approach to identify a probability-based DS requires costly simulations across the entire process parameter space (certain) and the uncertain model parameter space and material properties space. We demonstrate that application of global sensitivity analysis (GSA) can significantly reduce model complexity and reduce computational time for identifying and quantifying a probabilistic DS by screening out nonimportant uncertain parameters. The novelty of this approach is that the use of an indicator function, which takes only binary values as a model function, allows application of a straightforward GSA based on Sobol' sensitivity indices and avoids using more costly Monte Carlo filtering or GSA for constrained problems. We consider a chemical reaction example to illustrate how this formulation results in a model reduction and a significant decrease of model runs.

Insights into Q-marker of Shensong Yangxin capsule in treating cardiac arrhythmias based on a linear substitution strategy in quantification of multiple components.

The concept of a Quality marker (Q-marker) has emerged as a crucial tool for ensuring the safety and efficacy of Traditional Chinese Medicine (TCM) formulas. However, significant challenges remained in the identification and practical application of Q-marker, particularly due to the scarcity of reference standards. This study aimed to achieve a multidimensional integration of chemical profiling, target tissue distribution and in vivo high-throughput screening model to effectively identify the Q-marker of Shensong Yangxin Capsule (SSYX) and propose a linear substitution strategy for the quantification of multiple components. First, the chemical constituents of SSYX were detected and systematically characterized using UHPLC/Q-TOF MS. Next, through heart distribution study, high-exposure components in vivo were identified after the oral administration of SSYX. Third, a high-throughput arrhythmia zebrafish model was employed to further screen for key constituents. Finally, potential Q-marker were selected by integrating the aforementioned studies, and a quantification method for the Q-marker was developed using UHPLC-TQ-MS. The results of chemical profiling, heart tissue distribution and anti-arrhythmic activities were integrated into four properties: specificity, traceability from prescription to in vivo, effectiveness and prescription compatibility, which led to the identification of 30 ingredients as potential Q-marker of SSYX. Subsequently, an external standard method (ESM) was developed for these 30 components and applied to the analysis of 10 commercial batches of SSYX. In addition, the feasibility of multi-marker detection via a linear substitution method (LSM) was explored for the first time using SSYX as a case study for method development, based on the stability of linear equations of the compounds in single standard solutions and multi-component mixed standard solutions. The simultaneous quantification of 30 components in SSYX was achieved by employing two linearly stable substances, greatly reducing the amount of standard substances used while maintaining measurability and convenience. A comparison of LSM and ESM revealed no significant difference in the component contents calculated by the two methods, with relative errors within ± 4 %. Our results suggested that 30 ingredients, including six key elements, could be considered as Q-marker of SSYX. Moreover, the LSM strategy offered a novel approach for developing environmentally friendly and convenient methods for the quality control of multi-index components in TCM formulas.

Serum Exosome SERS Assay Based on TiN‐Ag@Ag Sol Composite Substrate and Its Application in the Diagnosis of Gastric Cancer

ABSTRACTGastric cancer (GC) is a highly lethal malignancy, seriously threatening people's physical health. Accurate screening of gastric cancer could improve the survival rate of patients. Therefore, exploring noninvasive and efficient cancer screening methods for gastric cancer is of great significance. In the past few years, exosomes have received much attention for their potential in disease diagnosis and treatment. Here, the aim of this study was to explore the detection of serum exosomes via surface‐enhanced Raman spectroscopy (SERS) technique based on TiN‐Ag@Ag sol composite substrate, and its potential application in gastric cancer diagnosis is evaluated. Exosomes were extracted from the serum of 31 GC patients and 31 healthy controls (HC) using an exosome kit. This study used various machine learning algorithms such as principal component analysis linear discriminant analysis (PCA‐LDA), partial least squares discriminant analysis (PLS‐DA), support vector machine (SVM), and k‐nearest neighbor (KNN) algorithm to analyze SERS spectra, in order to distinguish between HC and GC. The results show that the k‐nearest neighbor algorithm performs the best in HC and GC classification. These results indicate that the combination of SERS and machine learning methods provides a new technological approach for gastric cancer screening. This study offers a new proposal for the universal applicability of analysis and identification with SERS of serum exosomes samples in clinical diagnosis.

Application of Automated Machine Learning and Bagging Techniques to Classify Rice Varieties

We have witnessed increased research investigating digitalisation in the agricultural sector in recent years. In particular, machine learning and artificial intelligence find applications in agricultural product classification, quality control and species identification. The fast-processing times, high accuracy levels and cost-effectiveness offered by digital solutions for quality control and classification accelerate these studies. This study proposes a collaborative learning model utilising Automated Machine Learning and Bagging techniques for rice species detection and classification. The model uses a dataset from the UCI Irvine Machine Learning Repository, which contains characteristics specific to the Osmancık and Cammeo rice varieties grown in Turkey. The data set consists of 3810 data points, 2180 of which belong to Osmancık rice and 1630 to Cammeo rice. During the analysis, MLBox, an Automated Machine Learning library, was used to determine the optimal algorithm (Light Gradient Boosting Machine - LGBM) and its hyperparameters. Later, by applying the Bagging technique within the developed learning model, an accuracy rate of 93.54% was achieved in rice-type classification.

Reliability of ameloglyphics in forensic identification: a systematic review.

Teeth are considered as hard tissue analogue to fingerprints, being unique to an individual. The enamel which forms the outer layer of the tooth is formed through a highly dynamic process in which ameloblasts lay down enamel rods in an undulating and intertwining path, which is reflected as a series of enamel rod pattern. The study of these patterns is termed as "Ameloglyphics". These patterns formed on the surface of enamel are called tooth prints each of which comprises the combination of different sub patterns, and are unique for every single tooth of an individual. This characteristic of uniqueness of the tooth print can serve as a significant biometric tool in forensic identification. To assess if ameloglyphics is an accurate and reliable method for forensic identification of individuals. Registration number- CRD42022338138. Data sources- Google Scholar, PubMed, Proquest, Europe PMC, Scopus. Study appraisal and synthesis methods- Studies were assessed for quality with the help of predetermined criteria which categorized the studies into high, medium and low quality with the help of JBI critical appraisal checklist for analytical cross-sectional studies using the key words such as Ameloglyphics, tooth prints and enamel rod end patterns. A total of 475 articles were obtained in the primary search from PubMed, Scopus, Europe PMC, ProQuest, and Google Scholar including gray literature for thorough search of related publications on Ameloglyphics. Following initial search of titles, 433 articles were excluded because they were not related to the objectives of the systematic review and 42 articles were further excluded after abstract reading and removal of duplicates. Finally,18 out of 22 articles which were fulfilling the inclusion criteria of the study were selected for qualitative synthesis after full-text screening and exclusion of review articles. The data obtained from the eighteen included studies were systematically reviewed but a meta-analysis could not be performed due to heterogeneity of the data. Ameloglyphics is a relatively newer technique and a fair number of research are done on its application in identification of individuals. The results of the eighteen studies included in this review suggest that Ameloglyphicsis an useful adjunct for forensic identification. The study protocol can be accessed through the International Prospective Register of Systematic Reviews, the PROSPERO database with the following register number: CRD42022338138.

DOG BREED CLASSIFICATION USING TRANSFER LEARNING

“Dog Breed Classification using Transfer Learning “ the project focus on accurately classifying dog breeds is a challenging computer vision task due to the subtle differences between breeds and the high variability within breeds. This project addresses these complexities using transfer learning with the Inception V3 model, a state-of-the-art convolutional neural network (CNN). Traditional machine learning approaches often struggle with subtle inter-breed differences and require large labeled datasets and significant computational resources. Training deep learning models from scratch is typically infeasible, which makes transfer learning an ideal solution. By using a pre-trained Inception V3 model, this approach leverages learned features from large-scale datasets like ImageNet and fine-tunes them for dog breed classification, reducing trainingtime while enhancing accuracy. The fine- tuned Inception V3 model undergoes transfer learningwhere initial layers remain frozen to retain general features, and fine-tuning optimizes the later layers for specific breed classification. To make the solution accessible and user-friendly, a Flask web application is developed. Users can upload dog images and receive breed predictions in real time, with the backend integrating the trained model to process the images, extract features, and output predictions. The application is scalable and practical for diverse deployment scenarios, such as pet identification, educational purposes, or veterinary applications. This project not only highlights the efficiency of transfer learning in reducing training time but also demonstrates how AI can be applied effectively in real-world challenges. Combining advanced deep learning with a simple web interface bridges the gap between complex machine learning models and practical applications, emphasizing accuracy, efficiency, and accessibility in pet care and animal research. Key Words: Inception v3, fine-tuning , CNN , flask

Alapa-APP: Developing an Innovative Android Application for Cloud Type Identification

This study aims to develop "Alapa-APP," an Augmented Reality (AR) Cloud Identifier application designed to help users identify cloud types and predict rainfall. The app provides an easy-to-use tool that delivers real-time weather updates, allowing users to plan their activities effectively while promoting awareness of weather preparedness to enhance safety and reduce inconvenience during rainfall. Leveraging AR technology, which overlays digital information on real-world visuals, the app offers a modern and engaging approach to weather forecasting. Using a quantitative quasi-experimental design, the researchers collected data from 15 IT professionals through adaptive questionnaires distributed via Google Forms. The data was analyzed using descriptive statistics with Jamovi software, highlighting the app’s accuracy and reliability in cloud classification and weather prediction. Powered by advanced machine learning algorithms and an updated cloud database, the app provides precise information on temperature, humidity, wind speed, and precipitation. Its user-friendly interface, low energy consumption, and robust performance make it suitable for various users, including students, teachers, and outdoor workers. Stability tests confirmed minimal crashes and seamless functionality, while its unique AR feature distinguishes it from conventional weather apps. By offering timely and accurate updates, Alapa-APP ensures users are well-prepared for sudden weather changes, promoting safety and reducing risks. This innovative tool enhances daily planning while making weather forecasting accessible, practical, and engaging for its users.

Biting behavior against Varroa mites in honey bees is associated with changes in mandibles, with tracking by a new mobile application for mite damage identification

Certain populations of feral or wild European Honey Bees (EHB), Apis mellifera, have developed a tolerance of or persistence against the parasitic mite Varroa destructor in the USA. Although the grooming or mite-biting behavior is a trait bees use to defend against parasitic Varroa mites, little information is available concerning the grooming or mite-biting behavior of feral EHBs. Accordingly, we observed damaged mites of feral bees in Ohio (Apis mellifera ligustica) and commercial bees in four field seasons, 2020 to 2023, as well as feral bees in Kentucky (A. mellifera mellifera) in 2023, for a total of 7494 mites. When the mite-biting behavior was compared among these three populations, feral bees of Ohio (A. m. ligustica) and Kentucky (A. m. mellifera) displayed a significantly higher percentage (22% and 27% more) than the commercial bees. To investigate the mechanism of the mite-biting behavior, we examined bee mandibles. Mandibles are the primary mouthparts and tools used to remove or amputate the Varroa mites. Gentle Africanized honey bees (gAHB, A. mellifera scutellata-hybrid) in Puerto Rican exhibit mite resistance through microevolution on the island. Asian honey bees (A. cerana) are the original natural host of V. destructor. However, little has been reported on the morphometric analysis of mandibles between the two species A. cerana and A. mellifera and between the subspecies A. m. scutellata-hybrid and A. m. ligustica. Utilizing X-ray micro-computed tomography (microCT), we then examined the three-dimensional morphology of mandibles in (1) A. cerana, (2) gAHB A. m. scutellata-hybrid, (3) Ohio feral A. m. ligustica, and (4) package or commercial colonies A. m. ligustica. Our findings revealed distinctive three-dimensional shapes of mandibles, indicating substantial variations among these populations. These variations suggest that mandible morphology has an integral role in the bee’s defensive mechanisms against parasitic mites. We also developed the first smartphone application to quantify the mite damage by applying artificial intelligence to image analysis. This research contributes valuable insights into the prospective selection and breeding of honey bees with enhanced mite resistance, thus promoting the sustainability of apiculture.

DANNET: deep attention neural network for efficient ear identification in biometrics

Biometric identification, particularly ear biometrics, has gained prominence amidst the global prevalence of mask-wearing, exacerbated by the COVID-19 outbreak. This shift has highlighted the need for reliable biometric systems that can function effectively even when facial features are partially obscured. Despite numerous proposed convolutional neural network (CNN) based deep learning techniques for ear detection, achieving the expected efficiency and accuracy remains a challenge. In this manuscript, we propose a sophisticated method for ear biometric identification, named the encoder-decoder deep learning ensemble technique incorporating attention blocks. This innovative approach leverages the strengths of encoder-decoder architectures and attention mechanisms to enhance the precision and reliability of ear detection and segmentation. Specifically, our method employs an ensemble of two YSegNets, which significantly improves the performance over a single YSegNet. The use of an ensemble method is crucial in ear biometrics due to the variability and complexity of ear shapes and the potential for partial occlusions. By combining the outputs of two YSegNets, our approach can capture a wider range of features and reduce the risk of false positives and negatives, leading to more robust and accurate segmentation results. Experimental validation of the proposed method was conducted using a combination of data from the EarVN1.0, AMI, and Human Face datasets. The results demonstrate the effectiveness of our approach, achieving a segmentation framework accuracy of 98.93%. This high level of accuracy underscores the potential of our method for practical applications in biometric identification. The proposed innovative method demonstrates significant potential for individual recognition, particularly in scenarios involving large gatherings. When complemented by an effective surveillance system, our method can contribute to improved security and identification processes in public spaces. This research not only advances the field of ear biometrics but also provides a viable solution for biometric identification in the context of mask-wearing and other facial obstructions.

Engineered Strategies for Lipid Droplets-Targeted AIEgens Based on Tetraphenylethene.

Lipid droplets (LDs), once regarded as inert fat particles, have been ignored by scientific researchers for a long time. Now, studies have shown that LDs are dynamic organelles used to store neutral lipids in cells and maintain cell stability. The abnormality of intracellular LDs usually causes metabolic disorders in the body, such as obesity, atherosclerosis, diabetes, and cancer, so the LDs have attracted wide attention. The traditional small molecules used for LDs recognition seriously affect the imaging effect due to their poor photo-stability, low signal-to-noise ratios, and aggregation-induced quenching (ACQ). In contrast to ACQ, aggregation-induced emission (AIE) materials, with structural modifiability, can make up for the aforementioned deficiencies in the field of fluorescence imaging and have attracted much attention. In this review, the importance of LDs in vivo, the design principles for LDs recognition, and the recent research progress of AIE compounds with tetraphenylethene (TPE) structure in LDs targets are reviewed. We expect this review to further provide researchers with feasible methods and protocols for expanding LDs identification, imaging, and other applications.

Mueller matrix polarimetry for quantitative evaluation of the Achilles tendon injury recovery

Achilles tendon injuries, as a widely existing disease, have attracted a lot of research interest. Mueller matrix polarimetry, as a novel label-free quantitative imaging method, has been widely used in various applications of lesion identification and pathological diagnosis. However, focusing on the recovery process of Achilles tendon injuries, current optical imaging methods have not yet achieved the label-free precise identification and quantitative evaluation. In this study, using Mueller matrix polarimetry, various Achilles tendon injury samples were characterized specifically, and the efficacy of different recovery schemes was evaluated accordingly. Experiments indicate that injured Achilles tendons show less phase retardance, larger diattenuation, and relatively disordered orientation. The combination of experiments with Monte Carlo simulation results illustrate the microscopic mechanism of the Achilles tendon recovery process from three aspects, that is, the increased fiber diameter, a more consistent fiber orientation, and greater birefringence induced by more collagen protein. Finally, based on the statistical distribution of polarization measurements, a polarization specific characterization parameter was extracted to construct a label-free image, which cannot only intuitively show the injury and recovery of Achilles tendon samples, but also give a quantitative evaluation of the treatment.Graphical

Hybrid 3D Convolutional–Transformer Model for Detecting Stereotypical Motor Movements in Autistic Children During Pre-Meltdown Crisis

Computer vision using deep learning algorithms has served numerous human activity identification applications, particularly those linked to safety and security. However, even though autistic children are frequently exposed to danger as a result of their activities, many computer vision experts have shown little interest in their safety. Several autistic children show severe challenging behaviors such as the Meltdown Crisis which is characterized by hostile behaviors and loss of control. This study aims to introduce a monitoring system capable of predicting the Meltdown Crisis condition early and alerting the children’s parents or caregivers before entering more difficult settings. For this endeavor, the suggested system was constructed using a combination of a pre-trained Vision Transformer (ViT) model (Swin-3D-b) and a Residual Network (ResNet) architecture to extract robust features from video sequences to extract and learn the spatial and temporal features of the Stereotyped Motor Movements (SMMs) made by autistic children at the beginning of the Meltdown Crisis state, which is referred to as the Pre-Meltdown Crisis state. The evaluation was conducted using the MeltdownCrisis dataset, which contains realistic scenarios of autistic children’s behaviors in the Pre-Meltdown Crisis state, with data from the Normal state serving as the negative class. Our proposed model achieved great classification accuracy, at 92%.

Intelligent Fingerprint Recognition System Using Machine Learning

Secure authentication relies heavily on fingerprint recognition systems; however, conventional techniques are hindered by differences in image quality and possible security breaches. This study suggests a novel method for improving fingerprint recognition accuracy and robustness that makes use of machine learning techniques. By utilizing convolutional neural networks' (CNNs') capacity to deduce intricate patterns and representations from fingerprint photos, we investigate the use of CNNs for feature extraction and matching. Comparing our experimental results against conventional approaches, we find considerable improvements in accuracy and false acceptance rates on benchmark datasets. By advancing biometric authentication technologies, this research helps to improve security and dependability for a range of applications in law enforcement, access control, and personal identification.

The role of motivation in selection processes-comparing sports and business.

Talent identification and selection in sports pose significant challenges, necessitating a nuanced understanding of factors influencing athletes' elite-level potential. While physical and physiological aspects have conventionally played roles in the selection process, also other constructs of talent development have to be considered. Various talent models have included psychological aspects, especially motivation, as either moderators or catalysts. Based on empirical evidence of the relationship between motivation and performance, different views are hold in which form motivation should be used for talent selection. Considering the hierarchical model of achievement motivation and self-determination theory, the importance of different motivational dimensions in talent selection is assessed. This study extends its focus beyond sports, exploring whether differences and similarities between sports and business in their selection processes exist. The objective is to discern whether scouts, coaches and recruiters prioritize motivational dimensions differently. Along with the statistical analyses [conjoint analysis, analytical hierarchy processes (AHP) and constant-sum procedures], this research aims to provide insights into the weighted importance of diverse motivational dimensions and their influence on the decision-intention of decision-makers. The study aims to provide exploratory insights into how motivational dimensions could inform talent selection processes by comparing different contexts. This research may offer a first step to further investigate practical applications for talent identification and selection processes with insights from other contexts.

Determination of Biogenic Amines in Foods by Capillary Electrophoresis

This review article intends to provide comprehensive analysis on biogenic amines, their source, type and classification. Biogenic amines are nitrogen containing organic compound that naturally exits in nature. They have low molecular weight and are basic in nature. They are active components that act as precursors for synthesis of alkaloids, hormones, protein, nucleotides and aromatic compounds. They occur in plants, animal and microorganism and play an essential role in metabolic and physiological function. This paper focuses on determination of biogenic amines in food by capillary electrophoresis as it causes several health problems such as histamine causes headache, digestive disorders and neurotoxicity, tyramine can cause hypertension and cadaverine can cause cytotoxixity. Biogenic amines are determined by several techniques such as high-performance liquid chromatography (HPLC), gas chromatography (GC), capillary electrophoresis (CE), Ion exchange chromatography. This paper further focuses on advantages of capillary electrophoresis in determination of biogenic amines in food. Capillary electrophoresis is a liquid phase separation method. The technique of capillary electrophoresis is used to identify, separate and quantify large range of components. There are several types of CE. Instrumentation of CE includes fused silica capillary which has length of 20 - 50cm. The reservoir is filled with separation buffer and the two ends of capillary tube extend between two reservoirs. CE has wide variety of application in identification, separation and quantification. In conclusion, biogenic amines cause many health hazard and is important to be identified and this paper deals with their presence and identifican in food.