A laser-induced fluorescent detector for pesticide residue detection based on the spectral recognition method

Zhao, Shixian; Lei, Jincan; Huo, Danqun; Hou, Changjun; Yang, Ping; Huang, Jing; Luo, Xiaogang

doi:10.1039/c8ay02067a

Cited by 15 publications

(9 citation statements)

References 39 publications

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“…Further, the practicability of this device was also demonstrated by investigating a spiked cabbage sample, and the recovery rate was found to be 95.17−105.76%. 71 3.4. For Dairy Products.…”

Section: Applications Of Machine Learning In Food Forensicsmentioning

confidence: 99%

“…The device showed good response toward concentrations below 10 ppb. Further, the practicability of this device was also demonstrated by investigating a spiked cabbage sample, and the recovery rate was found to be 95.17–105.76% …”

Section: Applications Of Machine Learning In Food Forensicsmentioning

confidence: 99%

“…70 Zhao et al constructed a laser-induced fluorescent device for the identification of pesticide residue utilizing a sensor array approach. 71 Four pesticide residues (carbendazim, diazine, fenvalerate, and pentachloronitrobenzene) were easily identified by their ability to produce distinctive fingerprint-like response patterns and can also be distinguished by using an unsupervised pattern recognition technique, i.e., Spectral Recognition Method (SRM). The device showed good response toward concentrations below 10 ppb.…”

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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Section: Applications Of Machine Learning In Food Forensicsmentioning

confidence: 99%

Section: Applications Of Machine Learning In Food Forensicsmentioning

confidence: 99%

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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“…A cheap, fast, and reusable microfluidic-based fluorescent sensor array was suggested to detect pesticide residues (carbendazim, diazine, fenvalerate, and pentachloronitrobenzene) with high sensitivity [50]. The system can give new opportunities for MSS devices, as a fingerprint-like response can be obtained from colorimetric patterns.…”

Section: Agricultural Analysismentioning

confidence: 99%

Electronic Tongues for Inedible Media

Kirsanov

Corrêa

Gaál

et al. 2019

Sensors

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“Electronic tongues”, “taste sensors”, and similar devices (further named as “multisensor systems”, or MSS) have been studied and applied mostly for the analysis of edible analytes. This is not surprising, since the MSS development was sometimes inspired by the mainstream idea that they could substitute human gustatory tests. However, the basic principle behind multisensor systems—a combination of an array of cross-sensitive chemical sensors for liquid analysis and a machine learning engine for multivariate data processing—does not imply any limitations on the application of such systems for the analysis of inedible media. This review deals with the numerous MSS applications for the analysis of inedible analytes, among other things, for agricultural and medical purposes.

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“…1,2 The concentration of pesticides needs to be determined accurately through robust, costeffective, and quickly validated analytical protocols and strategies. [3][4][5][6][7][8][9][10][11][12][13] In the early 1950s, phenyl urea was rst employed to prevent weeds from growing in the cereal and poppy crops. Chlortoluron belonging to the class of phenyl urea derivatives is one of the excessively used herbicides with higher efficiency to control the broad-leaved weeds and annual grasses in the agricultural crops.…”

Section: Introductionmentioning

confidence: 99%