Pattern-based colorimetric sensor array to monitor food spoilage using automated high-throughput analysis

Singh, Harupjit; Singh, Gagandeep; Kaur, Navneet; Singh, Narinder

doi:10.1016/j.bios.2021.113687

Cited by 44 publications

(16 citation statements)

References 27 publications

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“…Despite the existence of quick and accurate analytical techniques for detecting biogenic amines that are present in food products, extensive attempts have been made to develop portable and affordable instruments for discriminating biogenic amines in food items to accomplish on-site detection of food spoilage. Singh et al developed a field-deployable cross-reactive array-based sensor and mobile plate reader for the identification of biogenic amines as shown in Figure A . The sensor array comprises metal complexes of azo-dye-based ligand that produced effective results upon interaction with distinct target analytes such as spermine, histamine, spermidine, tryptamine, creatine, histidine, cysteamine, etc.…”

Section: Machine Learning-inspired Devices In Food Forensicsmentioning

confidence: 99%

“…After that, color-responsive results and the discrimination viability of the developed array-based sensor were assessed utilizing pattern-based recognition approaches such as PCA and LDA as shown in Figure 18B. 88 In the field of portable devices for food diagnostics, where the goal is to avoid the need of expensive instrumentationbased investigations, sample preparation is still a barrier. Sampling carried out by an untrained user might result in undesired contamination and defiled outcomes.…”

Section: Machine Learning-inspired Devices In Food Forensicsmentioning

confidence: 99%

“…Singh et al developed a field-deployable cross-reactive array-based sensor and mobile plate reader for the identification of biogenic amines as shown in Figure 18 A. 88 The sensor array comprises metal complexes of azo-dye-based ligand that produced effective results upon interaction with distinct target analytes such as spermine, histamine, spermidine, tryptamine, creatine, histidine, cysteamine, etc. After that, color-responsive results and the discrimination viability of the developed array-based sensor were assessed utilizing pattern-based recognition approaches such as PCA and LDA as shown in Figure 18 B.…”

Section: Machine Learning-inspired Devices In Food Forensicsmentioning

confidence: 99%

Section: Machine Learning-inspired Devices In Food Forensicsmentioning

confidence: 99%

Section: Machine Learning-inspired Devices In Food Forensicsmentioning

confidence: 99%

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Machine Learning-Based Analytical Systems: Food Forensics

et al. 2022

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Despite a large amount of money being spent on both food analyses and control measures, various food-borne illnesses associated with pathogens, toxins, pesticides, adulterants, colorants, and other contaminants pose a serious threat to human health, and thus food safety draws considerable attention in the modern pace of the world. The presence of various biogenic amines in processed food have been frequently considered as the primary quality parameter in order to check food freshness and spoilage of protein-rich food. Various conventional detection methods for detecting hazardous analytes including microscopy, nucleic acid, and immunoassay-based techniques have been employed; however, recently, array-based sensing strategies are becoming popular for the development of a highly accurate and precise analytical method. Array-based sensing is majorly facilitated by the advancements in multivariate analytical techniques as well as machine learning-based approaches. These techniques allow one to solve the typical problem associated with the interpretation of the complex response patterns generated in array-based strategies. Consequently, the machine learning-based neural networks enable the fast, robust, and accurate detection of analytes using sensor arrays. Thus, for commercial applications, most of the focus has shifted toward the development of analytical methods based on electrical and chemical sensor arrays. Therefore, herein, we briefly highlight and review the recently reported array-based sensor systems supported by machine learning and multivariate analytics to monitor food safety and quality in the field of food forensics.

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Section: Machine Learning-inspired Devices In Food Forensicsmentioning

confidence: 99%

Section: Machine Learning-inspired Devices In Food Forensicsmentioning

confidence: 99%

Section: Machine Learning-inspired Devices In Food Forensicsmentioning

confidence: 99%

Section: Machine Learning-inspired Devices In Food Forensicsmentioning

confidence: 99%

Section: Machine Learning-inspired Devices In Food Forensicsmentioning

confidence: 99%

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Machine Learning-Based Analytical Systems: Food Forensics

et al. 2022

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Organic Transistor–Based Chemical Sensors for Real‐Sample Analysis

Sasaki

Minami

2023

Physica Status Solidi (a)

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An organic field‐effect transistor (OFET) is the representative amplification device showing a switching profile by applying a gate voltage, which indicates the potential as a chemical sensor device in combination with appropriate molecular recognition materials. On the other hand, the realization of OFET‐based chemical sensors for real‐sample analysis has been limited owing to the instability of organic semiconductive materials under ambient conditions and the difficulty of the designs of molecular recognition materials. Herein, this Review describes methodologies and actual approaches to maximize the potential of the OFETs as chemical sensor platforms based on fusion technologies between organic electronics and molecular recognition chemistry.This article is protected by copyright. All rights reserved.

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Current Techniques and Technologies of Meat Quality Evaluation

Ismail,

Huda

2024

Hand Book of Processed Functional Meat Products

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Pattern-based colorimetric sensor array to monitor food spoilage using automated high-throughput analysis

Cited by 44 publications

References 27 publications

Machine Learning-Based Analytical Systems: Food Forensics

Machine Learning-Based Analytical Systems: Food Forensics

Organic Transistor–Based Chemical Sensors for Real‐Sample Analysis

Current Techniques and Technologies of Meat Quality Evaluation

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