Use of hyperspectral transmittance imaging to evaluate the internal quality of nectarines

Abstract

The internal quality of nectarines (Prunus persica L. Batsch var. nucipersica) cv. 'Big Top' (yellow flesh) and 'Magique' (white flesh) has been inspected using hyperspectral transmittance imaging. Hyperspectral images of intact fruits were acquired in the spectral range of 630−900 nm using transmittance mode during their ripening under controlled conditions. The detection of split pit disorder and classification according to an established firmness threshold were performed using PLS-DA. The prediction of the Internal Quality Index (IQI) related to ripeness was performed using PLS-R. The most important variables were selected using interval-PLS. As a result, an accuracy of 94.7% was obtained in the detection of fruits with split 1 pit of the 'Big Top' cultivar. Accuracies of 95.7 % and 94.6 % were achieved in the classification of the 'Big Top' and 'Magique' cultivars, respectively, according to the firmness threshold. The internal quality was predicted through the IQI with R 2 values of 0.88 and 0.86 for the two cultivars. The results obtained indicate the great potential of hyperspectral transmittance imaging for the assessment of the internal quality of intact nectarines.

A Novel Hyperspectral Method to Detect Moldy Core in Apple Fruits

“…In addition, round 5 shows an increase of infected population's transmittance from 825 to 880 nm. Similar studies were conducted on Fuji apples by Tian et al, and Zhaoyong et al [34,36]. Tian et al measured in moldy samples an increase of TR in the spectral region from 775 to 830 nm and a decrease from 830 to 880 nm.…”

“…In addition, round 5 shows an increase of infected population's transmittance from 825 to 880 nm. Similar studies were conducted on Fuji apples by Tian et al, and Zhaoyong et al [34,36]. Tian et al measured in moldy sam- In round 1 and 2, the TR curve does not show any significant difference between healthy and moldy samples.…”

“…Nevertheless, such methods were applied sporadically on apples while they were already successfully tested in different commercial fruits; e.g., TR techniques and convolutional neural network (CNN) were successfully applied in blueberry internal damage detection with a classification accuracy over 80% [33]. Moreover, TR techniques represent an important analysis tool in high-income fruits, e.g., the grade of ripeness in nectarine (Prunus Persica) was evaluated with an accuracy of 88% by TR analysis in combination with Partial Last Square Regression (PLS-R) [34].…”

Quality inspection of nectarine based on hyperspectral imaging technology

“…Wavelength selection reduced the dimensionality of the hyperspectral images using the regression coefficients of the PLS-DA model. An accuracy of 96% is obtained by using 14 optimal wavelengths (Munera et al, 2018). Munera et al (2019) inspect the internal quality of nectarines by means of hyperspectral transmittance imaging(Prunuspersica L. Batsch var.…”

Near-Infrared Transmittance Spectral Imaging for Nondestructive Measurement of Internal Disorder in Korean Ginseng

“…There are two modes of spectral imaging: reflectance, which is only suitable for the surface identification of objects, and the transmittance mode, which can be used to identify objects' internal attributes by collecting much inside information. Near-infrared transmittance spectral imaging has previously been used for internal quality detection in various kinds of agro-food products [12][13][14]. However, no study has reported the use of the near-infrared hyperspectral transmittance imaging technique for detecting internal defects in ginseng roots.…”