Non-destructive detection of flawed hazelnut kernels and lipid oxidation assessment using NIR spectroscopy. (September 2015)

• Record Type: Journal Article
• Authors: Pannico, A.; Schouten, R.E.; Basile, B.; Romano, R.; Woltering, E.J.; Cirillo, C.
• Is Part Of: Journal of food engineering. Volume 160(2015:Sep.)
• Page Start: 42
• Page End: 48
• Publication Date: 2015-09
• Subjects: Corylus avellana L. -- In-shell nut -- Shelled nut -- Kernel defect -- K232 extinction coefficient; Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Analyse -- Périodiques; Aliments -- Recherche -- Périodiques; 664.005
• DOI: 10.1016/j.jfoodeng.2015.03.015
• Languages: English
• ISSNs: 0260-8774

Abstract

Highlights: In-shell and shelled 'Mortarella' hazelnuts were evaluated by NIR spectroscopy. PLS models were obtained for the detection of flawed kernels. A satisfactory PLS model was created to predict fat primary oxidation of hazelnuts. Abstract: Microbial contamination, seed browning, bad taste and lipid oxidation are primary causes of quality deterioration in stored hazelnuts, affecting their marketability. The feasibility of NIR spectroscopy to detect flawed kernels and estimate lipid oxidation in in-shell and shelled hazelnuts was investigated. 'Mortarella' hazelnuts were measured twice by NIR spectroscopy, first in-shell, and then as kernels. Afterwards, the kernels were evaluated visually, externally and internally, and by sensory evaluation with a subsequent measurement of fat oxidation. A satisfactory PLS model was created for the detection of flawed kernels. For lipid oxidation estimation the best performance of PLS models was obtained by first removing the flawed kernels from the calibration set. The PLS model for the K 232 extinction coefficient, that is indicative of lipid primary oxidation, was able to predict K 232 for both in-shell ( R 2 = 0.79) and shelled ( R 2 = 0.85) hazelnuts. Our results suggest, for shelled hazelnuts, a two-step NIR procedure: a first PLS model to detect and separate flawed kernels and then a second PLS model to grade healthy kernels by lipid oxidation levels.