Abstract

Oil content estimation of individual kernels of Quercus ilex subsp. rotundifolia [(Lam) O. Schwarz] acorns by Fourier transform near infrared spectroscopy and partial least squares regression

Cristina Sousa-Correia,^a Ana Alves,^b José C. Rodrigues,^b,* Suzana Ferreira-Dias,^c José M. Abreu,^d Nigel Maxted,^a Brian Ford- Lloyd^a and Manfred Schwanninger^e
aSchool of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT UK
^bTropical Research Institute of Portugal (IICT), Forest and Forest Products Centre, Tapada da Ajuda, 1349-017 Lisboa, Portugal. E-mail: jocarod@isa.utl.pt
^cInstituto Superior de Agronomia, DAIAT, Centro de Estudos Agro-Alimentares, Universidade Técnica de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portugal
^dSecção Autónoma de Ciências Agrárias, Faculdade de Ciências, UP, Campus Agrário de Vairão 4485-661 Vairão, Portugal
^eBOKU - University of Natural Resources and Applied Life Sciences, Vienna, Department of Chemistry, Muthgasse 18, A-1190 Vienna, Austria

The aim of this work was to use Fourier transform near infrared (FT-NIR) spectroscopy and partial least squares regression (PLSR) to estimate the oil content of individual Holm oak (Quercus sp.) acorn kernels from different trees, sites and years that should be used in the future for molecular marker association studies. Sampling of acorns in two consecutive years (2003 and 2004) and from different sites in Portugal provided independent sample sets. A total of 89 samples (acorn kernels) representative of the natural oil content range were extracted. The results of the analyses performed by three people revealed accuracy of the oil extraction procedure (n-hexane) and the precision (repeatability) of this method, assessed during a four-day period, gave a standard deviation of 0.1%. Careful wavenumber selection and several steps of validation of the PLSR models led to a final robust model that allowed the precise prediction of the oil content of individual acorns. By using the wavenumber ranges from 5995 to 5323 cm^–1 and from 4478 to 4177 cm^–1 of the vector normalised spectra, a PLSR model with a coefficient of determination (r²) of 0.992 and a root mean square error of cross-validation (RMSECV) of 0.37% was achieved. The RPD value of about 10 and a bias of almost zero showed that the developed models are good for process control, development, and applied research. Oil content estimation of individual Quercus sp. acorns by FT-NIR and PLSR was shown to be possible. The varying water content detected in the spectra of the milled kernels after drying in similar conditions, within and especially between years, could be handled.

Keywords: Fourier transform near infrared (FT-NIR) spectroscopy, Holm oak acorn, kernel, oil content, partial least squares regression (PLSR)

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