Providing Multicolor Plasmonic Patterns with Au@Ag Core–Shell Nanostructures for Visual Discrimination of Biogenic Amines

Orouji, Afsaneh; Ghasemi, Forough; Bigdeli, Arafeh; Hormozi-Nezhad, M. Reza

doi:10.1021/acsami.1c03183

Cited by 47 publications

(43 citation statements)

References 60 publications

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“…These responses were probably due to the high number of recognition sites, resulting in high sensitivity compared to conventional colorimetric chemosensors . 56 As shown in Fig. 3b and S38, † the cross-reactivity of S1–S3 for A1–A10 is suitable to fabricate the chemosensor array for simultaneous recognition of multi-amines.…”

Section: Resultsmentioning

confidence: 99%

Freshness monitoring of raw fish by detecting biogenic amines using a gold nanoparticle-based colorimetric sensor array

Lao

Sasaki

et al. 2022

RSC Adv.

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Section: Resultsmentioning

confidence: 99%

Freshness monitoring of raw fish by detecting biogenic amines using a gold nanoparticle-based colorimetric sensor array

Lao

Sasaki

et al. 2022

RSC Adv.

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“…Various data visualization and pattern recognition techniques (LDA, PLS) were used to visually evaluate and statistically analyze the data. 56 Ma et al developed a color responsive sensor array, which is made up of ice-templated Dye@chitosan/UiO-66-Br and optimized by Density Functional Theory (DFT) and Grand Canonical Monte Carlo (GCMC) simulation, which successfully detected the gases released from shrimp (ammonia, methylamine, and trimethylamine with limit of detection of 37.17, 25.90, and 40.65 ppm, respectively) and formed a unique fingerprint. 57 On the other hand, by synthesizing complexes of pH-sensitive dyes and nanomaterials, Zhong et al examined the discrimination of eight biogenic amines with more than 92% accuracy using pattern recognition approaches and measurement of trimethylamine with a detection limit up to 1.3 ppb.…”

Section: Applications Of Machine Learning In Food Forensicsmentioning

confidence: 99%

“…Depending upon the fluorescence shifts of the developed sensor, the detection value for hydrazine is 1.22 × 10 –5 mol/L in tetrahydrofuran (THF), which is used as a solvent . Orouji et al designed multicolor sensor arrays based on gold nanorods and gold nanospheres in order to discriminate and identify biogenic amines such as spermine, tryptamine, ethylenediamine, tyramine, spermidine, histamine, etc., in meat and fish samples . Furthermore, the detection limit of each analyte has also been calculated and found to be 24.6, 4.79, 8.58, 14.26, 10.03, and 27.29 mmol L –1 for spermidine, spermine, tryptamine, histamine, and tyramine, respectively.…”

Section: Applications Of Machine Learning In Food Forensicsmentioning

confidence: 99%

“…Furthermore, the detection limit of each analyte has also been calculated and found to be 24.6, 4.79, 8.58, 14.26, 10.03, and 27.29 mmol L –1 for spermidine, spermine, tryptamine, histamine, and tyramine, respectively. Various data visualization and pattern recognition techniques (LDA, PLS) were used to visually evaluate and statistically analyze the data …”

Section: Applications Of Machine Learning 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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“…Specific methods using ion chromatography, electrochemical measurements, and chemical modification have been studied for their detection. [49][50][51][52][53] Rapid on-site detection using a small sample volume is required in the medical and food industries. In the present work, our thin-film device based on nanosheets showed color changes with the casting of 20 mm 3 (=20 µL) of the sample solution within 5 min.…”

Section: Introductionmentioning

confidence: 99%

Nanosheet‐Based Uneven Thin Film Exhibiting Angle‐Independent Structural Color and Its Application to Detection of Biogenic Amines

Haraguchi

Imai

Oaki

2022

Adv Materials Inter

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processes in the liquid phase. In general, monolayer nanosheets are extracted only from exfoliated materials through purification. The unique properties of monolayers have been extensively studied in previous works. [1,2,4,6,[8][9][10] In contrast, other exfoliated nanosheets thicker than monolayers, such as few-layers or multiple-layers, were not widely used for applications, such as sensors and catalysts. At the same time, such materials also have potential applications. [16][17][18][19][20] Therefore, the size, surface chemistry, and assembly state of nanosheets, including monolayers and few-layers, are important for device fabrication. Our group has previously studied the exfoliation of layered transition metal oxides in organic dispersion media through the formation of layered composites with the intercalation of organic guests. [21,22] Surface-modified nanosheets were obtained by exfoliation in an organic medium. The prediction models for the yield, size, and size distribution were constructed through the extraction of the descriptors by a combination of machine learning and chemical insight on small experimental data. [23][24][25][26][27][28] These predictors contributed to the efficient exploration of appropriate experimental conditions, such as host-guest-medium combinations that yield the monodispersed and polydispersed nanosheets. [26] In the present work, surface-modified nanosheets with polydispersed lateral sizes and thicknesses were used to prepare an uneven thin film exhibiting an angle-independent structural color (Figure 1). The corresponding host-guest-medium combinations were selected based on the prediction model.Exfoliated nanosheets have been used as building blocks for the production of composites, [29][30][31][32][33][34][35] such as layer-by-layer assemblies, gels, and organic-inorganic composites. However, nanosheets are not easy to organize into the aligned states, such as thin films and liquid crystals. Specific methods and optimized conditions are required to align the nanosheets because the evaporation of the dispersion media causes random aggregation. Thin films and liquid-crystalline assemblies exhibiting structural colors have been previously prepared by homogeneous stacking of monolayered nanosheets. [35][36][37][38][39][40][41] Structural colors have been applied in the detection of chemical vapors and mechanical stresses. [37][38][39]41] However, self-assembled thin films with homogeneous thickness exhibit angle-dependent structural colors. In previous works, the angle-independent structural color was

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Providing Multicolor Plasmonic Patterns with Au@Ag Core–Shell Nanostructures for Visual Discrimination of Biogenic Amines

Cited by 47 publications

References 60 publications

Freshness monitoring of raw fish by detecting biogenic amines using a gold nanoparticle-based colorimetric sensor array

Freshness monitoring of raw fish by detecting biogenic amines using a gold nanoparticle-based colorimetric sensor array

Machine Learning-Based Analytical Systems: Food Forensics

Nanosheet‐Based Uneven Thin Film Exhibiting Angle‐Independent Structural Color and Its Application to Detection of Biogenic Amines

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