Non-destructive determination of beef freshness based on colorimetric sensor array and multivariate analysis

Xu, Weidong; He, Yingchao; Zhang, Yingbo; Deng, Yao; Xu, Enbo; Feng, Jian; Ding, Tian; Liu, Donghong; Wang, Wenjun

doi:10.1016/j.snb.2022.132282

Cited by 43 publications

(9 citation statements)

References 42 publications

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“…For example, commercially available porphyrin derivatives (1-10) and pH dyes (11)(12) have been employed to fabricate dye arrays for discriminating volatile organic compounds (VOCs) present in beef samples, and various colorimetric responses have been observed during beef storage (Figure 2(a)). [35] Furthermore, off-the-shelf colorimetric dyes such as bromopyrogallol red (13), pyrogallol red (14), pyrocatechol violet (15), 4-(2-pyridylazo)resorcinol ( 16), chrome azurol S (17), and 2-[(5bromo-2-pyridinyl)azo]-5-(diethylamino)phenol) (18) have been utilized to a dye array to discriminate Chinese liquors (Figure 2(b)). [36] These demonstrations revealed that a combination of off-the-shelf dyes contributed to various optical responses, providing fingerprint-like profiles.…”

Section: Power Of Off-the-shelf Reagents For Chemical Sensingmentioning

confidence: 99%

Section: Molecular Self-assemblies Using Off-the-shelf Building Block...mentioning

confidence: 99%

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confidence: 99%

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Methodologies for Spontaneous Preparation of Chemosensors and Their Arrays Using Off‐the‐Shelf Reagents

Sasaki,

Minami

2023

ChemNanoMat

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Chemosensors are promising candidates to visualize molecular recognition information through colorimetric or fluorescence responses. In chemosensor designs, the following requirements should be considered; 1) molecular geometries with analytes, 2) mechanisms to cause optical changes upon analyte capture, and 3) solubility for sensing applications. On the other hand, the designs and realization of chemosensors covering the abovementioned requirements are still at the frontiers. In the conventional strategy, molecular geometries between receptors and analytes have been mainly considered. However, this approach confronts issues of synthetic efforts to obtain elaborate designs of chemosensors, which leads to a decrease in the water solubility of chemosensors derived from the complicated and aromatic molecular structures. Herein, this Review summarizes methodologies for self‐assembled chemosensors only using off‐the‐shelf reagents to easily obtain various chemosensors without organic synthesis. The concept of self‐assembled chemosensors comprising off‐the‐shelf reagents with water‐solubility realizes not only the easy tuning of optical sensing properties but also chemical sensing in real samples. Through the comprehensive sensing applications using the facile self‐assembled chemosensors and their arrays, the usability of off‐the‐shelf reagents in analytical chemistry will be clarified.

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Section: Power Of Off-the-shelf Reagents For Chemical Sensingmentioning

confidence: 99%

Section: Molecular Self-assemblies Using Off-the-shelf Building Block...mentioning

confidence: 99%

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Methodologies for Spontaneous Preparation of Chemosensors and Their Arrays Using Off‐the‐Shelf Reagents

Sasaki,

Minami

2023

ChemNanoMat

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“…60 According to the volatile basic nitrogen (TVB-N) and total viable counts (TVC) levels, Xu et al developed the color responsive sensor array (CSA) coupled with whale optimization algorithm (WOA) and backpropagation neural network (BPNN) for the monitoring or identification of beef freshness. 61 3.2. For Beverages and Sweeteners.…”

Section: Applications Of Machine Learning In Food Forensicsmentioning

confidence: 99%

“…Upon exposure to 0–50 ppm, each sensor label showed different properties on the basis of optical signals, high reactivity of NH 3 , and fluorescent shifting paths due to intramolecular charge transfer . According to the volatile basic nitrogen (TVB-N) and total viable counts (TVC) levels, Xu et al developed the color responsive sensor array (CSA) coupled with whale optimization algorithm (WOA) and back-propagation neural network (BPNN) for the monitoring or identification of beef freshness …”

Section: Applications Of Machine Learning In Food Forensicsmentioning

confidence: 99%

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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Current Techniques and Technologies of Meat Quality Evaluation

Ismail,

Huda

2024

Hand Book of Processed Functional Meat Products

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Non-destructive determination of beef freshness based on colorimetric sensor array and multivariate analysis

Cited by 43 publications

References 42 publications

Methodologies for Spontaneous Preparation of Chemosensors and Their Arrays Using Off‐the‐Shelf Reagents

Methodologies for Spontaneous Preparation of Chemosensors and Their Arrays Using Off‐the‐Shelf Reagents

Machine Learning-Based Analytical Systems: Food Forensics

Current Techniques and Technologies of Meat Quality Evaluation

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