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

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%.

Study on Qualitative Impact Damage of Loquats Using Hyperspectral Technology Coupled with Texture Features

“…Due to the interference of dark current in the CCD camera, and the uneven distribution of light source intensity under each band, some bands with weaker light intensity contained more noise [ 24 ], so the acquired loquat hyperspectral images needed to be corrected by black and white reference image [ 25 ]. The relative reflectance was calculated by Formula (1): where R xy ( λ ), T xy ( λ ), T d ( λ ), and T w ( λ ) are the corrected hyperspectral image, the acquired original hyperspectral image, and the dark and white reference images, respectively.…”

Feasibility Study on the Classification of Persimmon Trees’ Components Based on Hyperspectral LiDAR

“…Varga et al established an evaluation model of hyperspectral images combined with deep neural networks to discriminate and visualize different fruit ripeness levels [ 6 ]. The discrepancy in reflectance at specific wavelengths was used to detect potentially damaged fruit [ 7 , 8 ]. The capabilities of hyperspectral imaging have also been explored for applications such as fruit identification and detection [ 9 ], nondestructive testing of dry matter [ 10 ], moisture content estimation [ 11 ], etc.…”

Knowledge mapping of research on spectral technology in the fruit field using CiteSpace (1981-2021)

“…Up to now, spectral technology has become one of the most important analytical methods in modern analytical chemistry. The research in the field of fruit has also made great progress, especially in fruit variety identification (Gaikwad & Tidke, 2022), sugar and acidity detection (Li et al, 2022), external and internal defect detection (Munera et al, 2021;Tian et al, 2022a). These studies are helpful to realize the rapid and accurate detection of fruits, which is of great significance to the improvement of fruit quality and the income of fruit farmers.…”