Resolution of opiate illicit drugs signals in the presence of some cutting agents with use of a voltammetric sensor array and machine learning strategies

Ortiz-Aguayo, Dionisia; Cetó, Xavier; Wael, Karolien De; Valle, Manel del

doi:10.1016/j.snb.2021.131345

Cited by 15 publications

(3 citation statements)

References 41 publications

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“…Dionisia Ortiz-Aguayo et al used a voltammetric sensor array composed of three different screen-printed electrodes modified by graphite, cobalt(II) phthalocyanine and palladium. Characteristic voltammograms for heroin, morphine, codeine, caffeine and paracetamol were extracted using SWV [ 88 ]. Next, a quantitative model was established to quantify each analyte individually by genetic algorithm and PLS.…”

Section: Chemometric Tools In Biosensingmentioning

confidence: 99%

Application and Progress of Chemometrics in Voltammetric Biosensing

Liu

et al. 2022

Biosensors

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The voltammetric electrochemical sensing method combined with biosensors and multi-sensor systems can quickly, accurately, and reliably analyze the concentration of the main analyte and the overall characteristics of complex samples. Simultaneously, the high-dimensional voltammogram contains the rich electrochemical features of the detected substances. Chemometric methods are important tools for mining valuable information from voltammetric data. Chemometrics can aid voltammetric biosensor calibration and multi-element detection in complex matrix conditions. This review introduces the voltammetric analysis techniques commonly used in the research of voltammetric biosensor and electronic tongues. Then, the research on optimizing voltammetric biosensor results using classical chemometrics is summarized. At the same time, the incorporation of machine learning and deep learning has brought new opportunities to further improve the detection performance of biosensors in complex samples. Finally, smartphones connected with miniaturized voltammetric biosensors and chemometric methods provide a high-quality portable analysis platform that shows great potential in point-of-care testing.

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Section: Chemometric Tools In Biosensingmentioning

confidence: 99%

Application and Progress of Chemometrics in Voltammetric Biosensing

Liu

et al. 2022

Biosensors

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“…The analysis of scientific data, from sensors and other devices, for monitoring and diagnosis has long relied on ML algorithms, particularly for those cases in which pattern recognition methods are the most suitable. Prototypical examples are in the use of electronic tongues to assess the quality of wine and coffee ( Riul et al, 2004 ), to identify opiate illicit drugs ( Ortiz-Aguayo et al, 2022 ), and to diagnose oral cancer ( Braz et al, 2022 ). In the latter paper, impedance spectroscopy data obtained with an electronic tongue applied to saliva samples were combined with clinical information to enhance diagnosis accuracy.…”

Section: Machine Learning Applied To Chemistry Of Materialsmentioning

confidence: 99%

Materials Discovery With Machine Learning and Knowledge Discovery

Oliveira¹,

Oliveira

2022

Front. Chem.

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Machine learning and other artificial intelligence methods are gaining increasing prominence in chemistry and materials sciences, especially for materials design and discovery, and in data analysis of results generated by sensors and biosensors. In this paper, we present a perspective on this current use of machine learning, and discuss the prospects of the future impact of extending the use of machine learning to encompass knowledge discovery as an essential step towards a new paradigm of machine-generated knowledge. The reasons why results so far have been limited are given with a discussion of the limitations of machine learning in tasks requiring interpretation. Also discussed is the need to adapt the training of students and scientists in chemistry and materials sciences, to better explore the potential of artificial intelligence capabilities.

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“…Cocaine, “crack,” amphetamines (amphetamine, dextroamphetamine, and mixed amphetamine salts), methylphenidate, etc., are the most common stimulants abused (Faraone, 2018). In contrast, opiates are chemical compounds extracted or purified from natural plant material (poppy) (Ortiz‐Aguayo et al, 2022). Opiates attach to opioid receptors to serve as strong pain relievers in addition to euphoria, leading to drowsiness and sedation (Dafny & Rosenfeld, 2017).…”

Section: Introductionmentioning

confidence: 99%

A comparison of risk factors for relapse in opiate‐related and stimulant‐related substance use disorders: A cross‐sectional multicenter study

Amini

Long

Varjoshani

et al. 2023

J of Nursing Scholarship

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Introduction Recognizing the specific factors in relapse disorders related to each substance can help improve treatment methods and adopt more effective preventive strategies. This study aimed to compare the situational factors associated with relapse in opiate‐related disorders with stimulant‐related disorders (SRDs) of those referred to substance misuse treatment centers. Design This study was a cross‐section type. Methods The study participants were 150 clients with SRDs and 150 with opiate‐related disorders. Samples were selected using two stages random sampling method. Data were collected through a demographic questionnaire and the Inventory of Drug‐Taking Situations (IDTS). Results The mean score of IDTS in the two groups was significantly different (X̄1 = 45.93 ± 11.12 vs. X̄2 = 48.34 ± 15.07; t = 3.32, p < 0.01). The mean scores of ‘unpleasant emotions,’ ‘physical discomfort,’ ‘conflict with others,’ and ‘social pressure to use and urge/temptations’ subscales were significantly higher in the stimulant group than in the opiate group (p < 0.05). However, the mean of the testing’ personal control’ subscale was higher in the opiate group than in the stimulant group (p < 0.05). Conclusion This study reveals that despite some similarities, relapse‐related situational factors in opiates and stimulants differ. Some situational factors, such as social pressure and coping with unpleasant emotions, play a more critical role in relapse to both stimulant and opiate groups.

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Resolution of opiate illicit drugs signals in the presence of some cutting agents with use of a voltammetric sensor array and machine learning strategies

Cited by 15 publications

References 41 publications

Application and Progress of Chemometrics in Voltammetric Biosensing

Application and Progress of Chemometrics in Voltammetric Biosensing

Materials Discovery With Machine Learning and Knowledge Discovery

A comparison of risk factors for relapse in opiate‐related and stimulant‐related substance use disorders: A cross‐sectional multicenter study

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