[Quality analysis of olive oil and quantification detection of adulteration in olive oil by near-infrared spectrometry and chemometrics].

Xiao-Li Zhuang, Yu-Hong Xiang, Hong Qiang, Zhuo-Yong Zhang, Ming-Qiang Zou, Xiao-Fang Zhang
Author Information
  1. Xiao-Li Zhuang: Department of Chemistry, Capital Normal University, Beijing 100048, China. hxzxl2005@126.com

Abstract

Discriminant analysis was used to classify 20 olive oil samples based on their near-infrared (NIR) spectra. The samples were successfully classified into two categories which are consistent with extra virgin olive oil and ordinary olive oil defined in the products. The NIR spectra of olive-oil mixtures containing colza oil, corn oil, peanut oil, camellia oil, sunflower oil, and poppy seed oil were collected, respectively. The volume percent of adulterants ranged from 0 to 100%. The best spectrum bands for analysis were selected before developing partial least-squares (PLS) calibration models. The relative errors of prediction ranged from -5.67% to 5.61%. Results showed that the method combined with chemometrics methods and near-infrared spectrometry is simple, fast and credible for qualitative and quantitative analyses of olive oil samples.

MeSH Term

Food Contamination
Olive Oil
Plant Oils
Spectroscopy, Near-Infrared

Chemicals

Olive Oil
Plant Oils

Word Cloud

Created with Highcharts 10.0.0oiloliveanalysissamplesnear-infraredNIRspectrarangedspectrometryDiscriminantusedclassify20basedsuccessfullyclassifiedtwocategoriesconsistentextravirginordinarydefinedproductsolive-oilmixturescontainingcolzacornpeanutcamelliasunflowerpoppyseedcollectedrespectivelyvolumepercentadulterants0100%bestspectrumbandsselecteddevelopingpartialleast-squaresPLScalibrationmodelsrelativeerrorsprediction-567%561%Resultsshowedmethodcombinedchemometricsmethodssimplefastcrediblequalitativequantitativeanalyses[Qualityquantificationdetectionadulterationchemometrics]

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