Detection and quantification of peanut traces in wheat flour by near infrared hyperspectral imaging spectroscopy using principal-component analysis

Puneet Mishra*, Ana Herrero-Langreo, Pilar Barreiro, Jean Michel Roger, Belén Diezma, Nathalie Gorretta, Lourdes Lleó

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

34 Citations (Scopus)

Abstract

The use of a common environment for processing different powder foods in the industry has increased the risk of finding peanut traces in powder foods. The analytical methods commonly used for detection of peanut such as enzyme-linked immunosorbent assay (ELISA) and real-time polymerase chain reaction (RT-PCR) represent high specificity and sensitivity but are destructive and time-consuming, and require highly skilled experimenters. The feasibility of NIR hyperspectral imaging (HSI) is studied for the detection of peanut traces down to 0.01% by weight. A principal-component analysis (PCA) was carried out on a dataset of peanut and flour spectra. The obtained loadings were applied to the HSI images of adulterated wheat flour samples with peanut traces. As a result, HSI images were reduced to score images with enhanced contrast between peanut and flour particles. Finally, a threshold was fixed in score images to obtain a binary classification image, and the percentage of peanut adulteration was compared with the percentage of pixels identified as peanut particles. This study allowed the detection of traces of peanut down to 0.01% and quantification of peanut adulteration from 10% to 0.1% with a coefficient of determination (r2) of 0.946. These results show the feasibility of using HSI systems for the detection of peanut traces in conjunction with chemical procedures, such as RT-PCR and ELISA to facilitate enhanced quality-control surveillance on food-product processing lines.

Original languageEnglish
Pages (from-to)15-22
Number of pages8
JournalJournal of Near Infrared Spectroscopy
Volume23
Issue number1
DOIs
Publication statusPublished - 1 Jan 2015
Externally publishedYes

Keywords

  • Adulteration
  • Classification
  • Food
  • Hyperspectral
  • NIR
  • Peanut
  • Principal-component analysis

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