Your browser doesn't support javascript.
loading
Detection and quantification of groundnut oil adulteration with machine learning using a comparative approach with NIRS and UV-VIS.
Zaukuu, John-Lewis Zinia; Adam, Manal Napari; Nkansah, Abena Amoakoa; Mensah, Eric Tetteh.
Afiliación
  • Zaukuu JZ; Department of Food Science and Technology, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. zaukuu.jz@knust.edu.gh.
  • Adam MN; Department of Food Science and Technology, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
  • Nkansah AA; Department of Food Science and Technology, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
  • Mensah ET; Department of Food Science and Technology, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
Sci Rep ; 14(1): 20931, 2024 09 09.
Article en En | MEDLINE | ID: mdl-39251628
ABSTRACT
Groundnut oil is known as a good source of essential fatty acids which are significant in the physiological development of the human body. It has a distinctive fragrant making it ideal for cooking which contribute to its demand on the market. However, some groundnut oil producers have been suspected to produce groundnut oil by blending it with cheaper oils especially palm olein at different concentrations or by adding groundnut flavor to palm olein. Over the years, there have been several methods to detect adulteration in oils which are time-consuming and expensive. Near infrared (NIR) and ultraviolet-visible (UV-Vis) spectroscopies are cheap and rapid methods for oil adulteration. This present study aimed to apply NIR and UV-Vis in combination with chemometrics to develop models for prediction and quantification of groundnut oil adulteration. Using principal component analysis (PCA) scores, pure and prepared adulterated samples showed overlapping showing similarities between them. Linear discriminant analysis (LDA) models developed from NIR and UV-Vis gave an average cross-validation accuracy of 92.61% and 62.14% respectively for pure groundnut oil and adulterated samples with palm olein at 0, 1, 3, 5, 10, 20, 30, 40 and 50% v/v. With partial least squares regression free fatty acid, color parameters, peroxide and iodine values could be predicted with R2CV's up to 0.8799 and RMSECV's lower than 3 ml/100 ml for NIR spectra and R2CV's up to 0.81 and RMSECV's lower than 4 ml/100 ml for UV-Vis spectra. NIR spectra produced better models as compared to UV-Vis spectra.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Espectrofotometría Ultravioleta / Contaminación de Alimentos / Espectroscopía Infrarroja Corta / Aprendizaje Automático Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Ghana Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Espectrofotometría Ultravioleta / Contaminación de Alimentos / Espectroscopía Infrarroja Corta / Aprendizaje Automático Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Ghana Pais de publicación: Reino Unido