Detection of Invisible Damages in ‘Rojo Brillante’ Persimmon Fruit at Different Stages Using Hyperspectral Imaging and Chemometrics

高光谱成像 化学计量学 主成分分析 园艺 肉体 数学 线性判别分析 褐变 人工智能 模式识别(心理学) 植物 化学 计算机科学 生物 色谱法
作者
Sandra Munera,Alejandro Rodríguez,Nuria Aleixos,Sergio Cubero,Juan Gómez‐Sanchís,J. Blasco
出处
期刊:Foods [Multidisciplinary Digital Publishing Institute]
卷期号:10 (9): 2170-2170 被引量:24
标识
DOI:10.3390/foods10092170
摘要

The main cause of flesh browning in ‘Rojo Brillante’ persimmon fruit is mechanical damage caused during harvesting and packing. Innovation and research on nondestructive techniques to detect this phenomenon in the packing lines are necessary because this type of alteration is often only seen when the final consumer peels the fruit. In this work, we have studied the application of hyperspectral imaging in the range of 450–1040 nm to detect mechanical damage without any external symptoms. The fruit was damaged in a controlled manner. Later, images were acquired before and at 0, 1, 2 and 3 days after damage induction. First, the spectral data captured from the images were analysed through an algorithm based on principal component analysis (PCA). The aim was to automatically separate intact and damaged fruit, and to detect the damage in the PC images when present. With this algorithm, 90.0% of intact fruit and 90.8% of damaged fruit were correctly detected. A model based on partial least squares—discriminant analysis (PLS-DA), was later calibrated using the mean spectrum of the pixels detected as damaged, to determine the moment when the fruit was damaged. The model differentiated fruit corresponding correctly to 0, 1, 2 and 3 days after damage induction, achieving a total accuracy of 99.4%.

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