Non-destructive Evaluation of the Quality Characteristics of Pomegranate Kernel Oil by Fourier Transform Near-Infrared and Mid-Infrared Spectroscopy

Emmanuel E Okere; Ebrahiema Arendse; Helene Nieuwoudt; Willem J Perold; Umezuruike Linus Opara

doi:10.3389/fpls.2022.867555

Non-destructive Evaluation of the Quality Characteristics of Pomegranate Kernel Oil by Fourier Transform Near-Infrared and Mid-Infrared Spectroscopy

Front Plant Sci. 2022 Jul 7:13:867555. doi: 10.3389/fpls.2022.867555. eCollection 2022.

Authors

Emmanuel E Okere^{1

2}, Ebrahiema Arendse¹, Helene Nieuwoudt³, Willem J Perold², Umezuruike Linus Opara^{1

4}

Affiliations

¹ SARChI Postharvest Technology Research Laboratory, Africa Institute for Postharvest Technology, Faculty of AgriSciences, Stellenbosch University, Stellenbosch, South Africa.
² Department of Electrical and Electronic Engineering, Stellenbosch University, Stellenbosch, South Africa.
³ Department of Viticulture and Oenology, Institute for Wine Biotechnology, Stellenbosch University, Stellenbosch, South Africa.
⁴ UNESCO International Centre for Biotechnology, Nsukka, Enugu State, Nigeria.

Abstract

The pomegranate kernel oil has gained global awareness due to the health benefits associated with its consumption; these benefits have been attributed to its unique fatty acid composition. For quality control of edible fats and oils, various analytical and calorimetric methods are often used, however, these methods are expensive, labor-intensive, and often require specialized sample preparation making them impractical on a commercial scale. Therefore, objective, rapid, accurate, and cost-effective methods are required. In this study, Fourier transformed near-infrared (FT-NIR) and mid-infrared (FT-MIR) spectroscopy as a fast non-destructive technique was investigated and compared to qualitatively and quantitatively predict the quality attributes of pomegranate kernel oil (cv. Wonderful, Acco, Herskawitz). For qualitative analysis, principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) was applied. Based on OPLS-DA, FT-MIR spectroscopy resulted in 100% discrimination between oil samples extracted from different cultivars. For quantitative analysis, partial least squares regression was used for model development over the NIR region of 7,498-940 and 6,102-5,774 cm^-1 and provided the best prediction statistics for total carotenoid content (R ², coefficient of determination; RMSEP, root mean square error of prediction; RPD, residual prediction deviation; R ² = 0.843, RMSEP = 0.019 g β-carotene/kg, RPD = 2.28). In the MIR region of 3,996-1,118 cm^-1, models developed using FT-MIR spectroscopy gave the best prediction statistics for peroxide value (R ² = 0.919, RMSEP = 1.05 meq, RPD = 3.54) and refractive index (R ² = 0.912, RMSEP = 0.0002, RPD = 3.43). These results demonstrate the potential of infrared spectroscopy combined with chemometric analysis for rapid screening of pomegranate oil quality attributes.

Keywords: Punica granatum L.; discriminant analysis; infrared spectroscopy; oil quality; partial least squares regression.