Optical sensor arrays for the detection and discrimination of natural products

Yang, Maohua; Zhang, Mei; Jia, Mingyan

doi:10.1039/d2np00065b

Cited by 23 publications

(6 citation statements)

References 126 publications

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“…Areas where assurance might be useful (alongside anticounterfeiting, vide supra), include rapidly assessing the nutrient content of raw ingredients (vitamins, minerals, antioxidants etc. ); 50 monitoring batch to batch variation in production inside sealed containers (e.g., inside a cask or barrel); or assuring a product has met legal minima, such as minimum aging requirements. 51 Returning to the example of whisk(e)y, this is a product where the major processing step is long aging in a closed wooden cask for a legally mandated minimum period (3 years in the case of Scotch Whisky, and often much longer) before blending (or vatting) of multiple casks together into a batch for retail.…”

Section: Food and Beveragesmentioning

confidence: 99%

Section: Optical Sensor Arrays For Challenges In Food and Beveragesmentioning

confidence: 99%

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Food for Thought: Optical Sensor Arrays and Machine Learning for the Food and Beverage Industry

Peveler

2024

ACS Sens.

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Arrays of cross-reactive sensors, combined with statistical or machine learning analysis of their multivariate outputs, have enabled the holistic analysis of complex samples in biomedicine, environmental science, and consumer products. Comparisons are frequently made to the mammalian nose or tongue and this perspective examines the role of sensing arrays in analyzing food and beverages for quality, veracity, and safety. I focus on optical sensor arrays as low-cost, easy-to-measure tools for use in the field, on the factory floor, or even by the consumer. Novel materials and approaches are highlighted and challenges in the research field are discussed, including sample processing/handling and access to significant sample sets to train and test arrays to tackle real issues in the industry. Finally, I examine whether the comparison of sensing arrays to noses and tongues is helpful in an industry defined by human taste.

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Section: Food and Beveragesmentioning

confidence: 99%

Section: Optical Sensor Arrays For Challenges In Food and Beveragesmentioning

confidence: 99%

Food for Thought: Optical Sensor Arrays and Machine Learning for the Food and Beverage Industry

Peveler

2024

ACS Sens.

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“…Inspired by the olfactory or gustatory system, the sensor array has become a powerful tool in biosensing, especially in rapid discrimination of multiple targets in complex samples. − Typically, the identification of various analytes can be realized by analyzing the characteristic fingerprints originating from the responses of probes to analytes. ,− Nevertheless, the development of sensor arrays for surfactant identification is rarely reported. , Wherein, Luo et al synthesized two fluorescent nanomaterials as signal receptors to establish a fluorescent sensor array for multiple surfactant detection, whereas the preparation of multiple sensing units will increase the synthetic cost, analytical operation, and detection time, as well as disturb the repeatability. , To simplify the array, an individual sensing unit with multiple signal channels could provide an effective strategy to avoid the above issues, , highlighting the variations between individual analytes based on the completely different responses . For instance, Yu et al prepared an Au–Ag nanocomposite with three signal channels (the fluorescence, light scattering, and absorption) as sensing units against sulfur species .…”

Section: Introductionmentioning

confidence: 99%

Dual-Emission Single Sensing Element-Assembled Fluorescent Sensor Arrays for the Rapid Discrimination of Multiple Surfactants in Environments

Wei,

Zhang,

Tao

et al. 2024

Anal. Chem.

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Surfactants are considered as typical emerging pollutants, their extensive use of in disinfectants has hugely threatened the ecosystem and human health, particularly during the pandemic of coronavirus disease-19 (COVID-19), whereas the rapid discrimination of multiple surfactants in environments is still a great challenge. Herein, we designed a fluorescent sensor array based on luminescent metal−organic frameworks (UiO-66-NH 2 @Au NCs) for the specific discrimination of six surfactants (AOS, SDS, SDSO, MES, SDBS, and Tween-20). Wherein, UiO-66-NH 2 @Au NCs were fabricated by integrating UiO-66-NH 2 (2aminoterephthalic acid-anchored-MOFs based on zirconium ions) with gold nanoclusters (Au NCs), which exhibited a dualemission features, showing good luminescence. Interestingly, due to the interactions of surfactants and UiO-66-NH 2 @Au NCs, the surfactants can differentially regulate the fluorescence property of UiO-66-NH 2 @Au NCs, producing diverse fluorescent "fingerprints", which were further identified by pattern recognition methods. The proposed fluorescence sensor array achieved 100% accuracy in identifying various surfactants and multicomponent mixtures, with the detection limit in the range of 0.0032 to 0.0315 mM for six pollutants, which was successfully employed in the discrimination of surfactants in real environmental waters. More importantly, our findings provided a new avenue in rapid detection of surfactants, rendering a promising technique for environmental monitoring against trace multicontaminants.

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“…By mimicking mammals’ gustatory or olfactory systems, array-based sensing methods are powerful approaches for the simultaneous determination and discrimination of multiple analytes with similar structure or chemical characteristics. − The sensor array is composed of cross-responsive sensing elements, which is different from ‘‘key-and-lock” sensors consisting of specific receptors, to produce composite responses for detection and identification of various targets. − Many kinds of quantum dot based sensor arrays have been devised for the identification and detection of biothiols. − However, complicated synthesis and low sensitivity limit their practical applicability. Hence, the further development of highly sensitive and cost-effective sensor arrays for biothiol monitoring is still desirable.…”

Section: Introductionmentioning

confidence: 99%

Nanozyme Inhibited Sensor Array for Biothiol Detection and Disease Discrimination Based on Metal Ion-Doped Carbon Dots

Zhou,

Li,

Wang

et al. 2023

Anal. Chem.

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Developing highly active and sensitive nanozymes for biothiol analysis is of vital significance due to their essential roles in disease diagnosis. Herein, two metal ion-doped carbon dots (M-CDs) with high peroxidase-like activity were designed and prepared for biothiol detection and identification through the colorimetric sensor array technique. The two M-CDs can strongly catalyze the decomposition of H 2 O 2 , accompanied by color changes of 3,3′,5,5′tetramethylbenzidine (TMB) from colorless to blue, indicating peroxidase-mimicking activities of M-CDs. Compared with pure carbon dots (CDs), M-CDs exhibited enhanced peroxidase-like activity owing to the synergistic effect between metal ions and CDs. However, due to the strong binding affinity between biothiols and metal ions, the catalytic activities of M-CDs could be inhibited by different biothiols to diverse degrees. Therefore, using TMB as a chromogenic substrate in the presence of H 2 O 2 , the developed colorimetric sensor array can form differential fingerprints for the three most important biothiols (i.e., cysteine (Cys), homocysteine (Hcy), and glutathione (GSH)), which can be accurately discriminated through pattern recognition methods (i.e., hierarchical clustering analysis (HCA) and principal component analysis (PCA)) with a detection limit of 5 nM. Moreover, the recognition of a single biothiol with various concentrations and biothiol mixtures was also realized. Furthermore, actual samples such as cells and sera can also be well distinguished by the as-fabricated sensor array, demonstrating its potential in disease diagnosis.

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Optical sensor arrays for the detection and discrimination of natural products

Abstract: This review provides a brief introduction to the optical sensor array and focuses on its progress toward the detection and discrimination of natural products.

Cited by 23 publications

References 126 publications

Food for Thought: Optical Sensor Arrays and Machine Learning for the Food and Beverage Industry

Food for Thought: Optical Sensor Arrays and Machine Learning for the Food and Beverage Industry

Dual-Emission Single Sensing Element-Assembled Fluorescent Sensor Arrays for the Rapid Discrimination of Multiple Surfactants in Environments

Nanozyme Inhibited Sensor Array for Biothiol Detection and Disease Discrimination Based on Metal Ion-Doped Carbon Dots

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