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

Munera, Sandra; Rodríguez-Ortega, Alejandro; Aleixos, Nuria; Cubero, Sergio; Gómez-Sanchis, Juan; Blasco, José

doi:10.3390/foods10092170

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

Sandra Munera

¹,

Alejandro Rodríguez-Ortega

¹,

³ and

^1,*

Centro de Agroingeniería, Instituto Valenciano de Investigaciones Agrarias (IVIA), Carretera CV-315, Km 10.7, 46113 Moncada, Spain

Departamento de Ingeniería Gráfica, Universitat Politècnica de València, Camino de Vera, s/n, 46022 Valencia, Spain

Intelligent Data Analysis Laboratory, Department of Electronic Engineering, ETSE (Engineering School), Universitat de València (UV), Av. Universitat, s/n, Bujassot, 46100 Valencia, Spain

Author to whom correspondence should be addressed.

Academic Editor: Maria Cecilia do Nascimento Nunes

Foods 2021, 10(9), 2170; https://doi.org/10.3390/foods10092170

Received: 4 August 2021 / Revised: 5 September 2021 / Accepted: 10 September 2021 / Published: 13 September 2021

(This article belongs to the Special Issue Applications of Non-destructive Optical Techniques for Quality and Authentication of Vegetal Crops)

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Abstract

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%. View Full-Text

Keywords: Diospyros kaki; fruit quality; browning; nondestructive; chemometrics; computer vision

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MDPI and ACS Style

Munera, S.; Rodríguez-Ortega, A.; Aleixos, N.; Cubero, S.; Gómez-Sanchis, J.; Blasco, J. Detection of Invisible Damages in ‘Rojo Brillante’ Persimmon Fruit at Different Stages Using Hyperspectral Imaging and Chemometrics. Foods 2021, 10, 2170. https://doi.org/10.3390/foods10092170

AMA Style

Munera S, Rodríguez-Ortega A, Aleixos N, Cubero S, Gómez-Sanchis J, Blasco J. Detection of Invisible Damages in ‘Rojo Brillante’ Persimmon Fruit at Different Stages Using Hyperspectral Imaging and Chemometrics. Foods. 2021; 10(9):2170. https://doi.org/10.3390/foods10092170

Chicago/Turabian Style

Munera, Sandra, Alejandro Rodríguez-Ortega, Nuria Aleixos, Sergio Cubero, Juan Gómez-Sanchis, and José Blasco. 2021. "Detection of Invisible Damages in ‘Rojo Brillante’ Persimmon Fruit at Different Stages Using Hyperspectral Imaging and Chemometrics" Foods 10, no. 9: 2170. https://doi.org/10.3390/foods10092170

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

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