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首页> 外文期刊>Journal of food engineering >Non-destructive detection of flawed hazelnut kernels and lipid oxidation assessment using NIR spectroscopy
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Non-destructive detection of flawed hazelnut kernels and lipid oxidation assessment using NIR spectroscopy

机译：无损检测榛子仁的无损检测和脂质氧化的近红外光谱分析

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摘要

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 PIS 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. (C) 2015 Elsevier Ltd. All rights reserved.
机译：微生物污染，种子褐变，不良口感和脂质氧化是导致榛子品质下降的主要原因，从而影响了榛子的适销性。研究了近红外光谱技术检测有缺陷的仁并评估带壳和带壳榛子中脂质氧化的可行性。通过近红外光谱法对“ Mortarella”榛子进行了两次测量，首先是在壳中，然后作为果仁。之后，通过肉眼，外部和内部以及通过感官评估以及随后的脂肪氧化测量对谷粒进行评估。创建了令人满意的PLS模型以检测有缺陷的内核。对于脂质氧化估计，首先从校准集中去除有缺陷的籽粒，即可获得PIS模型的最佳性能。 K-232消光系数的PLS模型可以预测脂质初级氧化，可以预测带壳榛子（R-2 = 0.79）和带壳榛子（R-2 = 0.85）的K-232。对于带壳榛子，我们的结果表明，分两步进行NIR程序：第一个PLS模型用于检测和分离有缺陷的籽粒，然后第二个PLS模型通过脂质氧化水平对健康的籽粒进行分级。 （C）2015 Elsevier Ltd.保留所有权利。

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