Three-dimensional porous calcium alginate fluorescence bead-based immunoassay for highly sensitive early diagnosis of breast cancer.

A sensitive biosensor capable of detecting trace concentrations of several cancer biomarkers in clinical samples is critical for early detection of cancer because different cancer biomarkers may be expressed at different stages of cancer. Previous multiplex studies using microarrays or color-coded beads had limited multiplex detection in a single well, and difficulty in optimizing and unifying the incubation parameters for all tests made in different wells had posed challenges to small sample size and lengthened assay time. Herein, we proposed a novel approach to achieve multiplex analysis on a single three-dimensional porous calcium alginate bead. Because of the high surface area to volume ratio of the calcium alginate immuno-bead, the sensitivity and linear dynamic range of the as-proposed multiplex analysis method are significantly improved. Based on the direct sandwich immunoassay principle, dual-capturing antibodies were encapsulated into a single 3D porous calcium alginate bead as a proof-of-concept for multiplexity detection of serum-HER2 and serum-CA125 breast cancer biomarkers. High sensitivity was attained, with LODs of 0.004 ng mL-1 for serum HER2, and 0.005 U mL-1 for serum CA125, both of which are below the clinical cutoff values, enabling for early breast cancer diagnosis. Stability tests revealed that the 3D immuno-beads were stable at 4 °C and room temperature (25 °C) for at least 14 days. Most importantly, the results obtained using the developed system were in good agreement with those obtained using standard methods while analyzing real clinical samples. In addition, the analysis required only approximately 30 min, which was much less time than typical ELISA techniques. When endogenous interferences were introduced, no cross-reactivity was observed. We anticipate this approach to be potentially used in the multiplex assays and biosensors.

Inexpensive Portable Infrared Device to Detect and Quantify Alcohols in Hand Sanitizers for Public Health and Safety.

The onset of Covid-19 pandemic has resulted in the exponential growth of alcohol-based hand rub (ABHR)/hand sanitizer use. Reports have emerged of ABHR products containing methanol, a highly toxic compound to humans, exposing users to acute and chronic medical illnesses. While gas chromatography-mass spectrometry (GC-MS) remains the gold-standard method for the detection and identification of impurities in ABHRs, there exist limitations at widespread volume testing. This paper demonstrates the capability of an inexpensive portable pyroelectric linear array infrared spectrometer to rapidly test ABHR and compare the performance with a benchtop Fourier transform infrared spectrometer and HS-GC-MS. Multicomponent partial least square quantification models were built with performance found to be comparable between the two spectrometers and with the HS-GC-MS. Furthermore, the portable spectrometer was field-tested with real-world samples in Malaysia on both retail products (Group A) and freely deployed public dispensers (Group B) between May and November 2020. A total of 386 samples were tested. Only 75.2% of Group A met the criteria of safe and effective ABHR [no detectable methanol and alcohol concentration above 60% (v/v)], while <50% of Group B did. In addition, 7.4 and 18.8% of Group A and Group B, respectively, were found to contain methanol above permissible limits. The high percentage of sub-standard and methanol-containing samples combined with the frequent use of ABHR by the public highlights the need for and importance of a portable and rapid testing device for widespread screening of ABHR against falsified products and protects the general public.

Protein-Protein Interactions: Insight from Molecular Dynamics Simulations and Nanoparticle Tracking Analysis.

Protein-protein interaction plays an essential role in almost all cellular processes and biological functions. Coupling molecular dynamics (MD) simulations and nanoparticle tracking analysis (NTA) assay offered a simple, rapid, and direct approach in monitoring the protein-protein binding process and predicting the binding affinity. Our case study of designed ankyrin repeats proteins (DARPins)-AnkGAG1D4 and the single point mutated AnkGAG1D4-Y56A for HIV-1 capsid protein (CA) were investigated. As reported, AnkGAG1D4 bound with CA for inhibitory activity; however, it lost its inhibitory strength when tyrosine at residue 56 AnkGAG1D4, the most key residue was replaced by alanine (AnkGAG1D4-Y56A). Through NTA, the binding of DARPins and CA was measured by monitoring the increment of the hydrodynamic radius of the AnkGAG1D4-gold conjugated nanoparticles (AnkGAG1D4-GNP) and AnkGAG1D4-Y56A-GNP upon interaction with CA in buffer solution. The size of the AnkGAG1D4-GNP increased when it interacted with CA but not AnkGAG1D4-Y56A-GNP. In addition, a much higher binding free energy (∆GB) of AnkGAG1D4-Y56A (-31 kcal/mol) obtained from MD further suggested affinity for CA completely reduced compared to AnkGAG1D4 (-60 kcal/mol). The possible mechanism of the protein-protein binding was explored in detail by decomposing the binding free energy for crucial residues identification and hydrogen bond analysis.

Biosensing based on surface-enhanced Raman spectroscopy as an emerging/next-generation point-of-care approach for acute myocardial infarction diagnosis.

Cardiovascular disease is a major global health issue. In particular, acute myocardial infarction (AMI) requires urgent attention and early diagnosis. The use of point-of-care diagnostics has resulted in the improved management of cardiovascular disease, but a major drawback is that the performance of POC devices does not rival that of central laboratory tests. Recently, many studies and advances have been made in the field of surface-enhanced Raman scattering (SERS), including the development of POC biosensors that utilize this detection method. Here, we present a review of the strengths and limitations of these emerging SERS-based biosensors for AMI diagnosis. The ability of SERS to multiplex sensing against existing POC detection methods are compared and discussed. Furthermore, SERS calibration-free methods that have recently been explored to minimize the inconvenience and eliminate the limitations caused by the limited linear range and interassay differences found in the calibration curves are outlined. In addition, the incorporation of artificial intelligence (AI) in SERS techniques to promote multivariate analysis and enhance diagnostic accuracy are discussed. The future prospects for SERS-based POC devices that include wearable POC SERS devices toward predictive, personalized medicine following the Fourth Industrial Revolution are proposed.

Graphite-Based Nanocomposite Electrochemical Sensor for Multiplex Detection of Adenine, Guanine, Thymine, and Cytosine: A Biomedical Prospect for Studying DNA Damage.

Guanine (G), adenine (A), thymine (T), and cytosine (C) are the four basic constituents of DNA. Studies on DNA composition have focused especially on DNA damage and genotoxicity. However, the development of a rapid, simple, and multiplex method for the simultaneous measurement of the four DNA bases remains a challenge. In this study, we describe a graphite-based nanocomposite electrode (Au-rGO/MWCNT/graphite) that uses a simple electro-co-deposition approach. We successfully applied the developed sensor for multiplex detection of G, A, T, and C, using square-wave voltammetry. The sensor was tested using real animal and plant DNA samples in which the hydrolysis of T and C could be achieved with 8 mol L-1 of acid. The electrochemical sensor exhibited excellent sensitivity (G = 178.8 nA/µg mL-1, A = 92.9 nA/µg mL-1, T = 1.4 nA/µg mL-1, and C = 15.1 9 nA/µg mL-1), low limit of detection (G, A = 0.5 µg mL-1; T, C = 1.0 µg mL-1), and high selectivity in the presence of common interfering factors from biological matrixes. The reliability of the established method was assessed by method validation and comparison with the ultraperformance liquid chromatography technique, and a correlation of 103.7% was achieved.

3-Chloropropane-1,2-diol (3-MCPD) in Soy Sauce: A Review on the Formation, Reduction, and Detection of This Potential Carcinogen.

Soy sauce, a dark-colored seasoning, is added to enhance the sensory properties of foods. Soy sauce can be consumed as a condiment or added during the preparation of food. There are 3 types of soy sauce: fermented, acid-hydrolyzed vegetable protein (acid- HVP), and mixtures of these. 3-Chloropropane-1,2-diol (3-MCPD) is a heat-produced contaminants formed during the preparation of soy sauce and was found to be a by-product of acid-HVP-produced soy sauce in 1978. 3-MCPD has been reported to be carcinogenic, nephrotoxic, and reproductively toxic in laboratory animal testing and has been registered as a chemosterilant for rodent control. 3-MCPD is classified as a possible carcinogenic compound, and the maximum tolerated limit in food has been established at both national and international levels. The purpose of this review is to provide an overview on the detection of 3-MCPD in soy sauce, its toxic effects, and the potential methods to reduce its concentration, especially during the production of acid-HVP soy sauce. The methods of quantification are also critically reviewed with a focus on efficiency, suitability, and challenges encountered in analysis.

The impact of antibody/epitope affinity strength on the sensitivity of electrochemical immunosensors for detecting small molecules.

A displacement immunoassay involves having a labelled analogue of the analyte (the epitope) already bound to the antibody. The presence of the analyte causes a competition for antibodies, and some of the antibodies dissociates from the epitope so that it can bind with the analyte. Herein, the influence of the affinity of the surface-bound epitope for the antibody on the sensitivity and selectivity of a displacement immunosensor is explored both theoretically and experimentally. An electrochemical immunosensor described previously, where the dissociation of antibodies from an electrode surface causes an increase in current from surface-bound ferrocene species, is used for this purpose. As expected, the ease and effectiveness of the bound antibody being displaced is inversely related to the affinity of the antibody to the surface-bound epitope relative to the analyte in solution as expected. However, if the affinity constant is too low, selectivity and/or sensitivity are compromised. Experimental results are qualitatively compared with a simple mass-action model.

Modern analytical and bioanalytical technologies and concepts for smart and precision farming.

Unpredictable natural disasters, disease outbreaks, climate change, pollution, and war constantly threaten food crop production. Smart and precision farming encourages using information or data obtained by using advanced technology (sensors, AI, and IoT) to improve decision-making in agriculture and achieve high productivity. For instance, weather prediction, nutrient information, pollutant assessment, and pathogen determination can be made with the help of new analytical and bioanalytical methods, demonstrating the potential for societal impact such as environmental, agricultural, and food science. As a rising technology, biosensors can be a potential tool to promote smart and precision farming in developing and underdeveloped countries. This review emphasizes the role of on-field, in vivo, and wearable biosensors in smart and precision farming, especially those biosensing systems that have proven with suitably complex and analytically challenging samples. The development of various agricultural biosensors in the past five years that fulfill market requirements such as portability, low cost, long-term stability, user-friendliness, rapidity, and on-site monitoring will be reviewed. The challenges and prospects for developing IoT and AI-integrated biosensors to increase crop yield and advance sustainable agriculture will be discussed. Using biosensors in smart and precision farming would ensure food security and revenue for farming communities.

Emerging trends in wearable glove-based sensors: A review.

Glove-based wearable chemical sensors are universal analytical tools that provide surface analysis for various samples in dry or liquid form by swiping glove sensors on the sample surface. They are useful in crime scene investigation, airport security, and disease control for detecting illicit drugs, hazardous chemicals, flammables, and pathogens on various surfaces, such as foods and furniture. It overcomes the inability of most portable sensors to monitor solid samples. It outperforms most wearable sensors (e.g., contact lenses and mouthguard sensors) for healthcare monitoring by providing comfort that does not interfere with daily activities and reducing the risk of infection or other adverse health effects caused by prolonged usage. Detailed information is provided regarding the challenges and selection criteria for the desired glove materials and conducting nanomaterials for developing glove-based wearable sensors. Focusing on nanomaterials, various transducer modification techniques for various real-world applications are discussed. The steps taken by each study platform to address the existing issues are revealed, as are their benefits and drawbacks. The Sustainable Development Goals (SDGs) and strategies for properly disposing of used glove-based wearable sensors are critically evaluated. A glance at all the provided tables provides insight into the features of each glove-based wearable sensor and enables a quick comparison of their functionalities.

A biomarker discovery of acute myocardial infarction using feature selection and machine learning.

Acute myocardial infarction (AMI) or heart attack is a significant global health threat and one of the leading causes of death. The evolution of machine learning has greatly revamped the risk stratification and death prediction of AMI. In this study, an integrated feature selection and machine learning approach was used to identify potential biomarkers for early detection and treatment of AMI. First, feature selection was conducted and evaluated before all classification tasks with machine learning. Full classification models (using all 62 features) and reduced classification models (using various feature selection methods ranging from 5 to 30 features) were built and evaluated using six machine learning classification algorithms. The results showed that the reduced models performed generally better (mean AUPRC via random forest (RF) algorithm for recursive feature elimination (RFE) method ranges from 0.8048 to 0.8260, while for random forest importance (RFI) method, it ranges from 0.8301 to 0.8505) than the full models (mean AUPRC via RF: 0.8044). The most notable finding of this study was the identification of a five-feature model that included cardiac troponin I, HDL cholesterol, HbA1c, anion gap, and albumin, which had achieved comparable results (mean AUPRC via RF: 0.8462) as to the models that containing more features. These five features were proven by the previous studies as significant risk factors for AMI or cardiovascular disease and could be used as potential biomarkers to predict the prognosis of AMI patients. From the medical point of view, fewer features for diagnosis or prognosis could reduce the cost and time of a patient as lesser clinical and pathological tests are needed.

Development of an electrochemical immunosensor for the detection of HbA1c in serum.

An electrochemical immuno-biosensor for detecting glycosylated haemoglobin (HbA1c) is reported based on glassy carbon (GC) electrodes with a mixed layer of an oligo(phenylethynylene) molecular wire (MW) and an oligo(ethylene glycol) (OEG). The mixed layer is formed from in situ-generated aryl diazonium cations. To the distal end of the MW, a redox probe 1,1'-di(aminomethyl)ferrocene (FDMA) was attached followed by the covalent attachment of an epitope N-glycosylated pentapeptide (GPP), an analogon to HbA1c, to which an anti-HbA1c monocolonal antibody IgG can selectively bind. HbA1c was detected by a competitive inhibition assay based on the competition for binding to anti-HbA1c IgG antibodies between the analyte in solution, HbA1c, and the surface bound epitope GPP. Exposure of the GPP modified sensing interface to the mixture of anti-HbA1c IgG antibody and HbA1c results in the attenuation of ferrocene electrochemistry due to free antibody binding to the interface. Higher concentrations of analyte led to higher Faradaic currents as less anti-HbA1c IgG is available to bind to the electrode surface. It was observed that there is a good linear relationship between the relative Faradaic current of FDMA and the concentration of HbA1c from 4.5% to 15.1% of total haemoglobin in serum without the need for washing or rinsing steps.

Current state and future prospects of sensors for evaluating polymer biodegradability and sensors made from biodegradable polymers: A review.

Since the invention of fully synthetic plastic in the 1900s, plastics have been extensively applied in various fields and represent a significant market due to their satisfactory properties. However, the non-biodegradable nature of most plastics has contributed to the accumulation of plastic waste, which poses a threat to both the environment and living beings. Given this, biodegradable polymers have emerged as eco-friendly substitutes for non-biodegradable polymers, and standard test methods have been established to evaluate polymer biodegradability. Technological advancement and the weaknesses of conventional test methods drive the invention of sensors that enable real-time monitoring of biodegradability. Besides, biodegradable polymers have been utilized to make sensors with different functionalities. Given this, the current paper is the first to compare and contrast sensors capable of identifying biodegradable polymers. The detection using sensors represents an innovative perspective for real-time monitoring of biodegradability. Besides, sensors made from biodegradable polymers are included, and these sensors are of different types and show various applications. Finally, the challenges associated with developing these sensors are described to advance future research.

The state-of-the-art in bioluminescent whole-cell biosensor technology for detecting various organic compounds in oil and grease content in wastewater: From the lab to the field.

Oil and grease content in wastewater is used as an environmental monitoring parameter in the oil and gas industry to prevent serious pollution. Conventional oil and grease laboratory testing is time-consuming and necessitates the use of a hazardous chemical solvent, resulting in non-real-time test data and unnecessary chemical waste. On-site or real-time analysis can enable monitoring of oil and grease in wastewater before discharge to the environment from an operating plant, allowing immediate action to be taken to mitigate environmental impact before contamination spirals out of control. Bioluminescent whole-cell biosensors have been reported to have high sensitivity and selectivity in environmental samples, but only for a few traces of organic compounds such as polycyclic aromatics and naphthalene, allowing for faster analysis times. However, no evaluation of biosensor application for oil and grease (a mixture of hydrocarbons) detection in wastewater, which is critical in the oil and gas industry, has been published to date. Herein, the advantages, disadvantages, challenges, and limitations of using a whole-cell bioluminescent biosensor technology to measure oil and grease content in wastewater are carefully reviewed. This review attempts to bridge the knowledge gap between conventional laboratory methods and biosensor technology in terms of analytical challenges, identifying areas for improvement as well as real-world applications for oil and grease content detection in wastewater.

Challenges and Strategies in Developing an Enzymatic Wearable Sweat Glucose Biosensor as a Practical Point-Of-Care Monitoring Tool for Type II Diabetes.

Recently, several studies have been conducted on wearable biosensors. Despite being skin-adhesive and mountable diagnostic devices, flexible biosensor patches cannot truly be considered wearable biosensors if they need to be connected to external instruments/processors to provide meaningful data/readings. A realistic and usable wearable biosensor should be self-contained, with a fully integrated device framework carefully designed and configured to provide reliable and intelligent diagnostics. There are several major challenges to achieving continuous sweat monitoring in real time for the systematic and effective management of type II diabetes (e.g., prevention, screening, monitoring, and treatment) through wearable sweat glucose biosensors. Consequently, further in-depth research regarding the exact interrelationship between active or passive sweat glucose and blood glucose is required to assess the applicability of wearable glucose biosensors in functional health monitoring. This review provides some useful insights that can enable effective critical studies of these unresolved issues. In this review, we first classify wearable glucose biosensors based on their signal transduction, their respective challenges, and the advanced strategies required to overcome them. Subsequently, the challenges and limitations of enzymatic and non-enzymatic wearable glucose biosensors are discussed and compared. Ten basic criteria to be considered and fulfilled in the development of a suitable, workable, and wearable sweat-based glucose biosensor are listed, based on scientific reports from the last five years. We conclude with our outlook for the controllable, well-defined, and non-invasive monitoring of epidermal glucose for maximum diagnostic potential in the effective management of type II diabetes.

An immunodetection assay developed using cobra cytotoxin-specific antibodies: Potential diagnostics for cobra envenoming.

Cobra (Naja spp.) envenoming is a life-threatening medical emergency, and a correct diagnosis is crucial to initiating timely and appropriate antivenom treatment. However, snakebite diagnostics remain unavailable in Southeast Asia. This study, therefore, developed an immunodetection assay with a potential diagnostic application for cobra envenoming. The cytotoxin of Naja kaouthia (Thai Monocled Cobra) (Nk-CTX) was purified from its venom to produce CTX-specific antibodies in rabbits and chickens. A double-antibody sandwich enzyme-linked immunosorbent assay was developed using the purified anti-Nk-CTX antibodies (immunoglobulin G and immunoglobulin Y), and its selectivity, specificity, and sensitivity for the venoms of five major cobra species in Southeast Asia (N. kaouthia, Naja sumatrana, Naja sputatrix, Naja siamensis, and Naja philippinensis) were studied. The results showed the immunoassay discriminates cobra venoms from other species commonly implicated in snakebites in Southeast Asia, i.e., the Malayan Krait, Many-banded Krait, King Cobra, Eastern Russell's Viper, Malayan Pit Viper and White-lipped Pit Viper. The immunoassay has a high sensitivity for the five cobra venoms, with detection limits (LoD) ranging from 0.6 to 2.6 ng/ml. Together, the findings suggest the potential diagnostic application of the cytotoxin immunoassay for cobra envenoming. The immunoassay was found to exhibit high immunoreactivity toward ten Asiatic cobra venoms (absorbance > 1.5), in contrast to African cobra venoms with low immunoreactivity (absorbance < 0.9). Considering the varying CTX antigenicity between Asiatic and African cobras, the immunoassay for African cobras should utilize antibodies produced specifically from the cytotoxins of African cobra venoms.

Recent advances in halal food authentication: Challenges and strategies.

Increasing public awareness of food quality and safety has prompted a rapid increase in food authentication of halal food, which covers the production method, technical processing, identification of undeclared components, and species substitution in halal food products. This urges for extensive research into analytical methods to obtain accurate and reliable results for monitoring and controlling the authenticity of halal food. Nonetheless, authentication of halal food is often challenging because of the complex nature of food and the increasing number of food adulterants that cause detection difficulties. This review provides a comprehensive and impartial overview of recent studies on the analytical techniques used in the analysis of halal food authenticity (from 1980 to the present, but there has been no significant trend in the choice of techniques for authentication of halal food during this period). Additionally, this review highlights the classification of different methodologies based on validity measures that provide valuable information for future developments in advanced technology. In addition, methodological developments, and novel emerging techniques as well as their implementations have been explored in the evaluation of halal food authentication. This includes food categories that require halal authentication, illustrating the advantages and disadvantages as well as shortcomings during the use of all approaches in the halal food industry.

A review of risk prediction models in cardiovascular disease: conventional approach vs. artificial intelligent approach.

Cardiovascular disease (CVD) is the leading cause of death worldwide and is a global health issue. Traditionally, statistical models are used commonly in the risk prediction and assessment of CVD. However, the adoption of artificial intelligent (AI) approach is rapidly taking hold in the current era of technology to evaluate patient risks and predict the outcome of CVD. In this review, we outline various conventional risk scores and prediction models and do a comparison with the AI approach. The strengths and limitations of both conventional and AI approaches are discussed. Besides that, biomarker discovery related to CVD are also elucidated as the biomarkers can be used in the risk stratification as well as early detection of the disease. Moreover, problems and challenges involved in current CVD studies are explored. Lastly, future prospects of CVD risk prediction and assessment in the multi-modality of big data integrative approaches are proposed.

Differential-based biosensor array for fluorescence-chemometric discrimination and the quantification of subtle chloropropanols by cross-reactive serum albumin scaffolding.

Food contamination is a serious concern because of a high level of chemicals in food causes severe health issues. Safeguarding the public from the risk of adulterated foods has become a challenging mission. Chloropropanols are of importance to food safety and food security because they are common chemical food contaminants and believed to be carcinogenic to humans. In chemical sensing, chloropropanols are challenging analytes owing to the lacking diversity of functional groups and difficulty in targeting the hydroxyl group in aqueous environments. Moreover, because of their small molecular size, the compositions of chloropropanols remain challenging for achieving chromatographic determination. Herein, to simulate human smell and taste sensations, serum albumins, which are protein-based receptors, were introduced as low-selective receptors for differential sensing. Utilizing serum albumins, a fluorophore (PRODAN), and an additive (ascorbic acid), a differential-based optical biosensor array was developed to detect and differentiate chloropropanols. By integrating the sensor array with linear discriminant analysis (LDA), four chloropropanols were effectively differentiated based on their isomerism properties and the number of the hydroxyl groups, even at ultra-low concentration (5 nM). This concentration is far below the maximum tolerable level of 0.18 µM for chloropropanols. The sensing array was then employed for chloropropanols differentiation and quantification in the complex mixtures (e.g., synthetic soy and dark soy sauces). Leave-one-out cross-validation (LOOCV) analysis demonstrated 100% accurate classification for all tests. These results signify our differential sensing array as a practical and powerful tool to speedily identify, differentiate, and even quantify chloropropanols in food matrices.

Using SERS-based microfluidic paper-based device (µPAD) for calibration-free quantitative measurement of AMI cardiac biomarkers.

Recently, surface enhanced Raman scattering (SERS) has attracted much attention in medical diagnosis applications owing to better detection sensitivity and lower limit of detection (LOD) than colorimetric detection. In this paper, a novel calibration-free SERS-based µPAD with multi-reaction zones for simultaneous quantitative detection of multiple cardiac biomarkers - GPBB, CK-MB and cTnT for early diagnosis and prognosis of acute myocardial infarction (AMI) are presented. Three distinct Raman probes were synthesised, subsequently conjugated with respective detecting antibodies and used as SERS nanotags for cardiac biomarker detection. Using a conventional calibration curve, quantitative simultaneous measurement of multiple cardiac biomarkers on SERS-based µPAD was performed based on the characteristic Raman spectral features of each reporter used in different nanotags. However, a calibration free point-of-care testing device is required for fast screening to rule-in and rule-out AMI patients. Partial least squares predictive models were developed and incorporated into the immunosensing system, to accurately quantify the three unknown cardiac biomarkers levels in serum based on the previously obtained Raman spectral data. This method allows absolute quantitative measurement when conventional calibration curve fails to provide accurate estimation of cardiac biomarkers, especially at low and high concentration ranges. Under an optimised condition, the LOD of our SERS-based µPAD was identified at 8, 10, and 1 pg mL-1, for GPBB, CK-MB and cTnT, respectively, which is well below the clinical cutoff values. Therefore, this proof-of-concept technique shows significant potential for highly sensitive quantitative detection of multiplex cardiac biomarkers in human serum to expedite medical decisions for enhanced patient care.

Estimation of the dietary intake and risk assessment of food carcinogens (3-MCPD and 1,3-DCP) in soy sauces by Monte Carlo simulation.

Quantifiable levels of 3-chloropropane-1,2-diol (3-MCPD) and 1,3-dichloro-2-propanol (1,3-DCP) were found in domestically manufactured soy-based sauces. Selected commercial foods in the Malaysian market (n = 43) were analyzed for their 3-MCPD and 1,3-DCP contents using a validated gas chromatography-mass spectrometry technique. The 3-MCPD and 1,3-DCP contents of the analyzed food samples varied from not detectable levels to 0.1223 ± 0.0419 mg kg-1 and not detectable levels to 0.025 ± 0.0041 mg kg-1, respectively. High concentrations of 3-MCPD, exceeding Malaysia's maximum tolerable limit of 0.02 mg kg-1, were found in chicken seasoning cubes (mean = 0.0898 ± 0.0378 mg kg-1). Monte Carlo simulation-based health risk assessment revealed that 3-MCPD and 1,3-DCP intakes in the 50th, 95th, and 99th percentiles were lower than 4 µg kg-1 bw day-1, the limit recommended by JECFA in 2016. Hence, it was concluded that the exposure of Malaysian citizens to chloropropanols through soy sauce consumption does not present a health risk.