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BibTeX
Non‐destructive determination of grass pea and pea flour adulteration in chickpea flour using near‐infrared reflectance spectroscopy and chemometrics
Journal of the science of food and agriculture, 2023-02, Vol.103 (3), p.1294-1302
Bala, Manju
Sethi, Swati
Sharma, Sanjula
Mridula, D
Kaur, Gurpreet
2023
Details
Autor(en) / Beteiligte
Bala, Manju
Sethi, Swati
Sharma, Sanjula
Mridula, D
Kaur, Gurpreet
Titel
Non‐destructive determination of grass pea and pea flour adulteration in chickpea flour using near‐infrared reflectance spectroscopy and chemometrics
Ist Teil von
Journal of the science of food and agriculture, 2023-02, Vol.103 (3), p.1294-1302
Ort / Verlag
Chichester, UK: John Wiley & Sons, Ltd
Erscheinungsjahr
2023
Link zum Volltext
Quelle
Wiley Online Library Journals Frontfile Complete
Beschreibungen/Notizen
Background In order to obtain more economic gains, some food products are adulterated with low‐cost substances, if they are toxic, they may pose public health risks. This has called forth the development of quick and non‐destructive methods for detection of adulterants in food. Near‐infrared reflectance spectroscopy (NIRS) has become a promising tool to detect adulteration in various commodities. We have developed rapid NIRS based analytical methods for quantification of two cheap adulterants (grass pea and pea flour) in a popular Indian food material, chickpea flour. Results The NIRS spectra of pure chickpea, pure grass pea, pure pea flour and adulterated samples of chickpea flour with grass pea and pea flour (1–90%) (w/w) were acquired and preprocessed. Calibration models were built based on modified partial least squares regression (MPLSR), partial least squares (PLS), principal component regression (PCR) methods. Based on lowest values of standard error of calibration (SEC) and standard error of cross‐validation (SECV), MPLSR‐NIRS models were selected. These models exhibited coefficient of determination (R2) of 0.999, 0.999, SEC of 0.905, 0.827 and SECV of 1.473, 1.491 for grass pea and pea, respectively. External validation revealed R2 and standard error of prediction (SEP) of 0.999 and 1.184, 0.997 and 1.893 for grass pea and pea flour, respectively. Conclusion The statistics confirmed that our MPLSR‐NIRS based methods are quite robust and applicable to detect grass pea and pea flour adulterants in chickpea flour samples and have potential for use in detecting food fraud. © 2022 Society of Chemical Industry.
Sprache
Englisch
Identifikatoren
ISSN: 0022-5142
eISSN: 1097-0010
DOI: 10.1002/jsfa.12223
Titel-ID: cdi_crossref_primary_10_1002_jsfa_12223
Format
–
Schlagworte
Adulterants
,
Analytical methods
,
Calibration
,
Chemometrics
,
chickpea flour
,
Chickpeas
,
Cicer
,
Flour
,
Flour - analysis
,
Food
,
Food Contamination - analysis
,
Fraud
,
grass pea flour
,
Grass peas
,
Health risks
,
Infrared reflection
,
Infrared spectroscopy
,
Least squares method
,
Least-Squares Analysis
,
modified partial least squares regression
,
Near infrared radiation
,
near‐infrared reflectance spectroscopy
,
pea flour
,
Peas
,
Pisum sativum
,
Public health
,
Reflectance
,
Spectroscopy
,
Spectroscopy, Near-Infrared - methods
,
Spectrum analysis
,
Standard error
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