Limits of Detection Determination of Aflatoxin B1 using the Optical Waveguide Lightmode Spectroscopy via the Four-Parameter Logistic Model

Authors

  • Garba Uba Department of Science Laboratory Technology, College of Science and Technology, Jigawa State Polytechnic, Dutse, PMB 7040, Nigeria.
  • Hafeez Muhammad Yakasai Department of Biochemistry, Faculty of Basic Medical Sciences, College of Health Sciences, Bayero University Kano, Nigeria.
  • Aisami Abubakar Department of Biochemistry, Faculty of Science, Gombe State University, P.M.B 127, Tudun Wada, Gombe, Gombe State, Nigeria.

DOI:

https://doi.org/10.54987/bstr.v10i2.780

Keywords:

Aflatoxin B1, Sigmoidal calibration curve, Four-parameter logistic equation, Five-parameter logistic equation, Error functions analysis

Abstract

Mycotoxins are harmful secondary metabolites generated by a variety of fungi, and they may be found in a vast array of food and feed commodities and processed meats from animals fed infected meal. Numerous mycotoxins are extremely resistant and survive food processing, entering the food chain and posing a concern to human health. The "optical waveguide lightmode spectroscopy" (OWLS) method was used to detect aflatoxin B1 in plant sample matrices. The calibration curve for the detection of aflatoxin B1 utilizing "optical waveguide lightmode spectroscopy" (OWLS) displayed a sigmoidal shape; hence, the 5-PL or 4-PL model should be used to fit the data rather than a linear model. Using error function analysis with functions such as AICc, HQC, BIC, RMSE, adjR2, Bias Factor, and Accuracy Factor, the 5-PL and 4-Pl models are distinguished inconsistently. The overlapping confidence intervals of the LogEC50 values suggested that the two techniques did not differ much, and the 4-PL model was selected due to its smaller number of parameters. The Limits of Detection for aflatoxin B1 value based on the 4-PL equation was 8.787 ng/mL with the 95% confidence interval from 5.728 to 13.100. In this study, the use of the 4-PL model was successful and was able to represent the entire date curve, not only the linear section. The linear component is crucial as a handy and swift approach for assessing the sensitivity of a developed biosensor technology and is often a more beneficial method for field applications when a quick and straightforward evaluation is required.

References

Abbas HK, Cartwright RD, Xie W, Thomas Shier W. Aflatoxin and fumonisin contamination of corn (maize, Zea mays) hybrids in Arkansas. Crop Prot. 2006 Jan 1;25(1):1-9.

Warburton M, Williams P. Aflatoxin resistance in maize: what have we learned lately? Adv Bot. 2014 Jan 1;2014:1-10.

Adetunji MC, Aroyeun SO, Osho MB, Sulyok M, Krska R, Mwanza M. Fungal metabolite and mycotoxins profile of cashew nut from selected locations in two African countries. Food Addit Contam Part A. 2019 Dec 2;36(12):1847-59.

Munkvold GP, Arias S, Taschl I, Gruber-Dorninger C. Chapter 9 - Mycotoxins in Corn: Occurrence, Impacts, and Management. In: Serna-Saldivar SO, editor. Corn (Third Edition) [Internet]. Oxford: AACC International Press; 2019 [cited 2022 Nov 20]. p. 235-87. Available from: https://www.sciencedirect.com/science/article/pii/B9780128119716000097

Xue Z, Zhang Y, Yu W, Zhang J, Wang J, Wan F, et al. Recent advances in aflatoxin B1 detection based on nanotechnology and nanomaterials-A review. Anal Chim Acta. 2019;1069:1-27.

Liu D, Li W, Zhu C, Li Y, Shen X, Li L, et al. Recent progress on electrochemical biosensing of aflatoxins: A review. TrAC - Trends Anal Chem [Internet]. 2020;133. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096359360&doi=10.1016%2fj.trac.2020.115966&partnerID=40&md5=1a20bcfe8b93ddbbc252437b0c60c345

Goko ML, Murimwa JC, Gasura E, Rugare JT, Ngadze E. Identification and Characterisation of Seed-Borne Fungal Pathogens Associated with Maize (Zea mays L.). Int J Microbiol. 2021 Sep 30;2021:e6702856.

Nasaruddin N, Jinap S, Samsudin NI, Kamarulzaman NH, Sanny M. Prevalence of mycotoxigenic fungi and assessment of aflatoxin contamination: a multiple case study along the integrated corn-based poultry feed supply chain in Malaysia. J Sci Food Agric. 2021;101(5):1812-21.

Padrilah SN, Masdor NA. Limits of Detection Based on the Four-Parameter Logistic Model for E. coli Determined using a Fluorescent-based Sensor. J Environ Microbiol Toxicol. 2021 Jul 31;9(1):1-2.

Masdor NA. Detection Limit of the Four-Parameter Logistic Model for the Quantitative Detection of Serum Squamous Cell Carcinoma Antigenin Cervical Cancer Based on Surface Plasmon Resonance Biosensor. J Environ Microbiol Toxicol. 2021 Dec 31;9(2):30-2.

Masdor NA. Determination of the Detection Limit of the Detection of GMO in Food Using the Isothermal Solid-Phase Recombinase Polymerase Amplification on Microfluidic DVDs. Asian J Plant Biol. 2021 Dec 31;3(2):17-9.

Masdor NA. Determination of the detection limit using the four-parameter logistic model for the double-antibody sandwich ELISA for the rapid detection of Bacillus cereus in food. J Environ Microbiol Toxicol. 2017 Jul 31;5(1):12-3.

Holstein CA, Griffin M, Hong J, Sampson PD. Statistical method for determining and comparing limits of detection of bioassays. Anal Chem. 2015 Oct 6;87(19):9795-801.

Adányi N, Levkovets IA, Rodriguez-Gil S, Ronald A, Váradi M, Szendr? I. Development of immunosensor based on OWLS technique for determining Aflatoxin B1 and Ochratoxin A. Biosens Bioelectron. 2007 Jan 15;22(6):797-802.

Rohatgi A. WebPlotDigitizer. http://arohatgi.info/WebPlotDigitizer/app/ Accessed June 2 2014.; 2015.

Halmi MIE, Shukor MS, Johari WLW, Shukor MY. Mathematical modelling of the degradation kinetics of Bacillus cereus grown on phenol. J Environ Bioremediation Toxicol. 2014;2(1):1-5.

Khare KS, Phelan Jr FR. Quantitative comparison of atomistic simulations with experiment for a cross-linked epoxy: A specific volume-cooling rate analysis. Macromolecules. 2018;51(2):564-75.

Motulsky HJ, Ransnas LA. Fitting curves to data using nonlinear regression: a practical and nonmathematical review. FASEB J Off Publ Fed Am Soc Exp Biol. 1987;1(5):365-74.

Wayman M, Tseng MC. Inhibition?threshold substrate concentrations. Biotechnol Bioeng. 1976;18(3):383-7.

G?uszcz P, Petera J, Ledakowicz S. Mathematical modeling of the integrated process of mercury bioremediation in the industrial bioreactor. Bioprocess Biosyst Eng. 2011;34(3):275-85.

Kass RE, Raftery AE. Bayes Factors. J Am Stat Assoc. 1995 Jun 1;90(430):773-95.

Burnham KP, Anderson DR. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Springer Science & Business Media; 2002. 528 p.

Masdor NA, Altintas Z, Tothill IE. Sensitive detection of Campylobacter jejuni using nanoparticles enhanced QCM sensor. Biosens Bioelectron. 2016;78:328-36.

Schenker N, Gentleman JF. On judging the significance of differences by examining the overlap between confidence intervals. Am Stat. 2001;55(3):182-6.

Schroeder LD, Sjoquist DL, Stephan PE. Understanding Regression Analysis: An Introductory Guide. Second edition. Los Angeles: SAGE Publications, Inc; 2016. 120 p.

Hair J, Money A, Page M, Samouel P. Research methods for business. Chichester: John Wiley & sons Ltd; 2007. 448 p.

Figueiredo D, Júnior S, Rocha E. What is R2 all about? Leviathan-Cad Pesqui Polútica. 2011 Nov 16;3:60-8.

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Published

2022-12-31

How to Cite

Uba, G., Yakasai, H. M., & Abubakar, A. (2022). Limits of Detection Determination of Aflatoxin B1 using the Optical Waveguide Lightmode Spectroscopy via the Four-Parameter Logistic Model. Bioremediation Science and Technology Research (e-ISSN 2289-5892), 10(2), 40–44. https://doi.org/10.54987/bstr.v10i2.780

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