Enhanced artificial intelligence for electrochemical sensors in monitoring and removing of azo dyes and food colorant substances.

Journal: Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
PMID:

Abstract

It is necessary to determine whether synthetic dyes are present in food since their excessive use has detrimental effects on human health. For the simultaneous assessment of tartrazine and Patent Blue V, a novel electrochemical sensing platform was developed. As a result, two artificial azo colorants (Tartrazine and Patent Blue V) with toxic azo groups (-NN-) and other carcinogenic aromatic ring structures were examined. With a low limit of detection of 0.06 μM, a broad linear concentration range 0.09μM to 950μM, and a respectable recovery, scanning electron microscopy (SEM) was able to reveal the excellent sensing performance of the suggested electrode for patent blue V. The electrochemical performance of an electrode can be characterized using cyclic and differential pulse voltammetry, and electrochemical impedance spectroscopy. Moreover, the classification model was created by applying binary classification assessment using enhanced artificial intelligence comprises of support vector machine (SVM) and Genetic Algorithm (GA), respectively, a support vector machine and a genetic algorithm, which was then validated using the 50 dyes test set. The best binary logistic regression model has an accuracy of 83.2% and 81.1%, respectively, while the best SVM model has an accuracy of 90.3% for the training group of samples and 81.1% for the test group (RMSE = 0.644, R2 = 0.873, C = 205.41, and = 5.992). According to the findings, Cu-BTC MOF (copper (II)-benzene-1,3,5-tricarboxylate) has a crystal structure and is tightly packed with hierarchically porous nanomaterials, with each particle's edge measuring between 20 and 37 nm. The suggested electrochemical sensor's analytical performance is suitable for foods like jellies, condiments, soft drinks and candies.

Authors

  • Yujia Wu
    School of Mechatronic Engineering, Xi'an Technological University, Xi'an, 710021, China.
  • Arwa Al-Huqail
    Department of Biology, College of Science, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia. Electronic address: aaalhuqail@pnu.edu.sa.
  • Zainab A Farhan
    Air Conditioning and Refrigeration Techniques Engineering Department, Al-Mustaqbal University College, Babylon, Iraq.
  • Tamim Alkhalifah
    Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Buraydah 52571, Saudi Arabia.
  • Fahad Alturise
    Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass 58892, Qassim, Saudi Arabia.
  • H Elhosiny Ali
    Advanced Functional Materials & Optoelectronic Laboratory (AFMOL), Department of Physics, Faculty of Science, King Khalid University, P.O. Box 9004, Abha, Saudi Arabia; Research Center for Advanced Materials Science (RCAMS), King Khalid University, Abha, 61413, P.O. Box 9004, Saudi Arabia; Physics Department, Faculty of Science, Zagazig University, 44519, Zagazig, Egypt.