Machine learning combined with electrochemical sensor for rapid detection of Sudan Red I in food

Sun, Xi; Liu, Feng; Xue, Xiao

doi:10.1007/s11694-023-02150-w

Cited by 4 publications

(2 citation statements)

References 50 publications

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“…This method primarily focuses on validating the truthfulness of information provided on food packaging, which includes a detailed breakdown of ingredients and nutritional content. 6 Species authentication.-In contrast, species authentication targets a different aspect of food integrity, focusing on preventing the substitution or mislabeling of meat, fish, and other products at the species level. Electrochemical sensors excel in species authentication by detecting species-specific biomarkers, such as DNA sequences or proteins.…”

Section: Applications Of Electrochemical Sensors In Ensuring Food Saf...mentioning

confidence: 99%

“…The AgNPs/SSN electrode demonstrated increased electrochemical reactivity towards Sudan Red I, achieving a linear detection range from 0.1 to 20 μM and a remarkable detection limit of 10 nM. 6 Thus, developing and implementing effective, reliable, and rapid detection methods are crucial for monitoring the integrity of the food supply and for the proactive management of potential risks. Within this context, electrochemical sensors have emerged as a highly promising class of analytical tools, offering unique advantages in terms of sensitivity, specificity, portability, and the capability for realtime analysis.…”

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confidence: 99%

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Editors’ Choice—Review—Advances in Electrochemical Sensors: Improving Food Safety, Quality, and Traceability

Sakthivel,

Balasubramanian,

Chang-Chien

et al. 2024

ECS Sens. Plus

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Electrochemical sensors have become a pivotal tool in ensuring the safety and security of the global food supply chain, which is crucial for public health, economic stability, and environmental sustainability. Modern food systems, with their complex global distribution and varied processing methods, require advanced solutions for detecting contaminants and maintaining food quality. This review delves into recent advancements in electrochemical food sensor technology, highlighting their operating principles, types, cutting-edge materials, and methods enhancing their effectiveness. These sensors are adept at identifying a broad range of foodborne pathogens, chemical contaminants, and adulterants while monitoring food freshness and quality. Innovations include using nanomaterials and conductive polymers and shifting towards miniaturized, portable devices for on-site and real-time analysis. The review also addresses challenges such as sensitivity, selectivity, and matrix effects, pointing out emerging trends and future research avenues to overcome these hurdles. Regulatory and standardization issues relevant to adopting these technologies in food safety protocols are also considered. Highlighting the last three years, this review emphasizes the indispensable role of electrochemical sensors in boosting food safety and security and the need for ongoing innovation and cross-disciplinary cooperation to advance this area.

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Section: Applications Of Electrochemical Sensors In Ensuring Food Saf...mentioning

confidence: 99%

mentioning

confidence: 99%

Editors’ Choice—Review—Advances in Electrochemical Sensors: Improving Food Safety, Quality, and Traceability

Sakthivel,

Balasubramanian,

Chang-Chien

et al. 2024

ECS Sens. Plus

View full text Add to dashboard Cite

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Aptamer-modified paper-based analytical devices for the detection of food hazards: Emerging applications and future perspective

Qin,

Khan,

Ding

et al. 2024

Biotechnology Advances

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Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions

Bhaiyya,

Panigrahi,

Rewatkar

et al. 2024

ACS Sens.

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Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration of Machine Learning (ML) into biosensors has ushered in a new era of innovation in the field of PoCT. This article investigates the numerous uses and transformational possibilities of ML in improving biosensors for PoCT. ML algorithms, which are capable of processing and interpreting complicated biological data, have transformed the accuracy, sensitivity, and speed of diagnostic procedures in a variety of healthcare contexts. This review explores the multifaceted applications of ML models, including classification and regression, displaying how they contribute to improving the diagnostic capabilities of biosensors. The roles of ML-assisted electrochemical sensors, lab-on-a-chip sensors, electrochemiluminescence/chemiluminescence sensors, colorimetric sensors, and wearable sensors in diagnosis are explained in detail. Given the increasingly important role of ML in biosensors for PoCT, this study serves as a valuable reference for researchers, clinicians, and policymakers interested in understanding the emerging landscape of ML in point-of-care diagnostics.

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Machine learning combined with electrochemical sensor for rapid detection of Sudan Red I in food

Cited by 4 publications

References 50 publications

Editors’ Choice—Review—Advances in Electrochemical Sensors: Improving Food Safety, Quality, and Traceability

Editors’ Choice—Review—Advances in Electrochemical Sensors: Improving Food Safety, Quality, and Traceability

Aptamer-modified paper-based analytical devices for the detection of food hazards: Emerging applications and future perspective

Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions

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