Non-destructive detection of soybean oil addition in babassu oil by MIR spectroscopy and chemometrics

This study aimed to develop a method to detect adulteration in babassu oil, through the addition of soybean oil using mid-infrared spectroscopy associated with chemometric techniques. For this, five different brands of soybean oil were used for adulteration (5%–50%) of babassu oil. A variation in pe...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Food science & technology 2022-01, Vol.154, p.112857, Article 112857
Hauptverfasser: Pereira, Sthefany Nicolle Gomes, Lima, Amanda Beatriz Sales De, Oliveira, Thinara De Freitas, Batista, Acsa Santos, Jesus, Josane Cardim De, Ferrão, Sibelli Passini Barbosa, Santos, Leandro Soares
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This study aimed to develop a method to detect adulteration in babassu oil, through the addition of soybean oil using mid-infrared spectroscopy associated with chemometric techniques. For this, five different brands of soybean oil were used for adulteration (5%–50%) of babassu oil. A variation in peak intensities was observed in three regions of the spectrum as the percentage of oil addition increased. Principal component analysis differentiated the samples validating the spectral observations. A partial least squares model allowed the correct classification for the class with high level of adulteration. FTIR-ATR spectroscopy associated with chemometrics showed good performance in the detection and quantification of babassu oil adulteration by the addition of other lipid matrices such as soybean oil. This technique showed to be a good substitute for expensive, time consuming and complex traditional analytical techniques. •Soybean oil adulterations in babassu oil (0–50%) were detected.•MIR spectroscopy and chemometric methods were used to detect the adulteration.•PCA, LDA and PLS-DA models were developed to discriminate the samples.•PCA was able to discriminate the differences in the spectra.•The PLS-DA model showed high classification capability.
ISSN:0023-6438
1096-1127
DOI:10.1016/j.lwt.2021.112857