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Prediction of maize flour adulteration in chickpea flour (besan) using near infrared spectroscopy

The present study was performed to develop Near-infrared spectroscopy based prediction method for the quantification of the maize flour adulteration in chickpea flour. Adulterated samples of Chickpea flour (besan) were prepared by spiking different concentrations of maize flour with pure Chickpea fl...

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Detalles Bibliográficos
Autores principales: Bala, Manju, Sethi, Swati, Sharma, Sanjula, Mridula, D., Kaur, Gurpreet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer India 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9051818/
https://www.ncbi.nlm.nih.gov/pubmed/35505664
http://dx.doi.org/10.1007/s13197-022-05456-7
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author Bala, Manju
Sethi, Swati
Sharma, Sanjula
Mridula, D.
Kaur, Gurpreet
author_facet Bala, Manju
Sethi, Swati
Sharma, Sanjula
Mridula, D.
Kaur, Gurpreet
author_sort Bala, Manju
collection PubMed
description The present study was performed to develop Near-infrared spectroscopy based prediction method for the quantification of the maize flour adulteration in chickpea flour. Adulterated samples of Chickpea flour (besan) were prepared by spiking different concentrations of maize flour with pure Chickpea flour in the range of 1–90% (w/w). The spectra of pure Chickpea flour, pure maize flour, and adulterated samples of Chickpea flour with maize flour were acquired as the logarithm of reciprocal of reflectance (log 1/R) in the entire Visible-NIR wavelength range of 400–2498 nm. The acquired spectra were pre-processed by Ist derivative, standard normal variate, and detrending. The calibration models were developed using modified partial least square regression (MPLSR), partial least square regression and principal component regression. The optimal model was selected on the basis of highest values of the coefficient of determination (RSQ), one minus variance ratio (1-VR) and lowest values of standard errors of calibration (SEC), and standard error of cross-validation (SECV). MPLSR model having RSQ and 1-VR value of 0.999 and 0.996 having SEC and SECV value of 1.092 and 2.042 was developed for quantification of maize flour adulteration in chickpea flour. Cross validation and external validation of the developed models resulted in RSQ of 0.999, 0.997 and standard error of prediction of 1.117, and 2.075, respectively. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13197-022-05456-7.
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spelling pubmed-90518182022-04-29 Prediction of maize flour adulteration in chickpea flour (besan) using near infrared spectroscopy Bala, Manju Sethi, Swati Sharma, Sanjula Mridula, D. Kaur, Gurpreet J Food Sci Technol Original Article The present study was performed to develop Near-infrared spectroscopy based prediction method for the quantification of the maize flour adulteration in chickpea flour. Adulterated samples of Chickpea flour (besan) were prepared by spiking different concentrations of maize flour with pure Chickpea flour in the range of 1–90% (w/w). The spectra of pure Chickpea flour, pure maize flour, and adulterated samples of Chickpea flour with maize flour were acquired as the logarithm of reciprocal of reflectance (log 1/R) in the entire Visible-NIR wavelength range of 400–2498 nm. The acquired spectra were pre-processed by Ist derivative, standard normal variate, and detrending. The calibration models were developed using modified partial least square regression (MPLSR), partial least square regression and principal component regression. The optimal model was selected on the basis of highest values of the coefficient of determination (RSQ), one minus variance ratio (1-VR) and lowest values of standard errors of calibration (SEC), and standard error of cross-validation (SECV). MPLSR model having RSQ and 1-VR value of 0.999 and 0.996 having SEC and SECV value of 1.092 and 2.042 was developed for quantification of maize flour adulteration in chickpea flour. Cross validation and external validation of the developed models resulted in RSQ of 0.999, 0.997 and standard error of prediction of 1.117, and 2.075, respectively. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13197-022-05456-7. Springer India 2022-04-29 2022-08 /pmc/articles/PMC9051818/ /pubmed/35505664 http://dx.doi.org/10.1007/s13197-022-05456-7 Text en © Association of Food Scientists & Technologists (India) 2022
spellingShingle Original Article
Bala, Manju
Sethi, Swati
Sharma, Sanjula
Mridula, D.
Kaur, Gurpreet
Prediction of maize flour adulteration in chickpea flour (besan) using near infrared spectroscopy
title Prediction of maize flour adulteration in chickpea flour (besan) using near infrared spectroscopy
title_full Prediction of maize flour adulteration in chickpea flour (besan) using near infrared spectroscopy
title_fullStr Prediction of maize flour adulteration in chickpea flour (besan) using near infrared spectroscopy
title_full_unstemmed Prediction of maize flour adulteration in chickpea flour (besan) using near infrared spectroscopy
title_short Prediction of maize flour adulteration in chickpea flour (besan) using near infrared spectroscopy
title_sort prediction of maize flour adulteration in chickpea flour (besan) using near infrared spectroscopy
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9051818/
https://www.ncbi.nlm.nih.gov/pubmed/35505664
http://dx.doi.org/10.1007/s13197-022-05456-7
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