METHOD DEVELOPMENT FOR A NON INVASIVE DEDICATED NIR CALIBRATION MODEL OF AFLATOXIN B1 FOR REAL TIME ANALYSIS OF SIFTED MAIZE FLOUR

METHOD DEVELOPMENT FOR A NON INVASIVE DEDICATED NIR CALIBRATION MODEL OF AFLATOXIN B1 FOR REAL TIME ANALYSIS OF SIFTED MAIZE FLOUR

Registration ID: IJNRD_200948

Published ID: IJNRD2307342

DOI: Click Here to Get

Joseph Skudi , Daniel Oyoo , Mildred Nawiri , Ruth Wanjau

Aflatoxin B1,Dedicated Calibration, NIR spectroscopy, Multiple Linear Regression, Successive Projection Algorithm, Validation, Prediction, Optimization Techniques, Noninvasive, Quantitative Determination

The high demand for sifted flour and association of Aflatoxin B1 with cancer raises the need for extensive mitigation measures .Therefore; herein a method was developed exclusively for real time quantification and detection of aflatoxin B1 in all ranges of sifted flour. This method presents a fast reliable dedicated model for noninvasive real time detection and quantification of the dreaded aflatoxin B1 in all ranges of sifted maize flour. The NIR diffused reflectance Spectra of AFB1 at different levels of concentration were generated from branded and non-branded sifted maize flour with all variability captured in the model development. Aflatoxin standards of B1 was used in contamination at different levels and parallel confirmed by HPLC methods as reference primary method. Signals generated from diffused reflectance NIR were preprocessed by selected applications of signal enhancement techniques. Multiplicative signal correction, 1st and 2nd derivative and standard Normale Variate Detrending (SNVD) were all applied to minimize interferences on the signals. Quantitative model building involved multiple linear Regression (MLR) methods built on Beer’s law and qualitative method for authentication of B1 involved Partial Least Square-Discriminant Analysis (PLS-DA) with Successive Projection Algorithm (SPA) used in the spectral selection. The resultant model was tested for all ranges of sifted flour which were not included in the calibration set and a good percentage of accuracy of 97% in prediction and 98.5% in validation of the model obtained. This showed the potential of the model to be used selectively in the determinations of B1 on-line, in-line and at-line in sifted flour

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"METHOD DEVELOPMENT FOR A NON INVASIVE DEDICATED NIR CALIBRATION MODEL OF AFLATOXIN B1 FOR REAL TIME ANALYSIS OF SIFTED MAIZE FLOUR", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 7, page no.d355-d363, July-2023, Available :https://ijnrd.org/papers/IJNRD2307342.pdf

Volume 8 Issue 7, July-2023

Pages : d355-d363

Paper Reg. ID: IJNRD_200948

Published Paper Id: IJNRD2307342

Downloads: 000121128

Research Area: Chemistry

Country: Mombasa, Nairobi, Kenya

Published Paper PDF: https://ijnrd.org/papers/IJNRD2307342.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2307342

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

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