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

Creative Commons License

Bala M., Sethi S., Sharma S., Mridula D., Kaur G.

JOURNAL OF FOOD SCIENCE AND TECHNOLOGY-MYSORE, vol.59, no.8, pp.3130-3138, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 59 Issue: 8
  • Publication Date: 2022
  • Doi Number: 10.1007/s13197-022-05456-7
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Agricultural & Environmental Science Database, Analytical Abstracts, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Food Science & Technology Abstracts, INSPEC, Veterinary Science Database
  • Page Numbers: pp.3130-3138
  • Keywords: Adulteration, Besan, Chickpea flour, Maize flour, Modified partial least square regression, Near infrared spectroscopy, REFLECTANCE SPECTROSCOPY, STARCH ADULTERATION, FT-NIR, POWDER
  • Hacettepe University Affiliated: No


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.