Non-Destructive Identification and Estimation of Granulation in Honey Pomelo Using Visible and Near-Infrared Transmittance Spectroscopy Combined with Machine Vision Technology

Abstract

Granulation is a physiological disorder of juice sacs in citrus fruit, causing juice sacs to become hard and dry and resulting in decreased internal quality of citrus fruit. Honey pomelo is a thick-skinned citrus fruit, and it is difficult to identify the extent of granulation by observation of the outer peel and fruit shape. In this study, a rapid and non-destructive testing method using visible and near-infrared transmittance spectroscopy combined with machine vision technology was applied to identify and estimate granulation inside fruit. A total of 600 samples in different growth periods was harvested, and fruit were divided into five classes according to five granulation levels. Spectral data were obtained for two ranges of 400–1100 nm and 900–1700 nm by visible and near-infrared transmittance spectroscopy. In addition, chemometrics were used to measure the chemical changes of soluble solid content (SSC), titratable acidity (TA), and moisture content (MC) caused by different granulation levels. Machine vision technology can rapidly estimate the external characteristics of samples and measure the physical changes in mass and volume caused by different granulation levels. Compared with using a single or traditional methods, the predictive performances of multi-category classification models (PCA-SVM and PCA-GRNN) (Table A1) were significantly enhanced. In particular, the model accuracy rate (ARM) was 99% for PCA-GRNN, with classification accuracy (CA), classification sensitivity (CS), and classification specificity (CSP) of 0.9950, 0.9750, and 0.9934, respectively. The results showed that this method has great potential for the identification and estimation of granulation. Multi-source data fusion and application of a multi-category classification model with the smallest number of input layers and acceptable high predictive performances are proposed for on-line applications. This method can be effectively used on-line for the non-destructive detection of fruits with granulation.

Exploring the nutraceutical potential: Evaluating the nutritional and bioactive functions of five pomelo fruit varieties in Bangladesh

“…The obtained TA value from selected pomelo samples ranged from 1.10 ± 0.01 to 0.76 ± 0.01 %. The obtained results corroborate those that have been previously reported in Indian pomelo 1.24 to 0.2 % [ 40 ], and in Chinese pomelo 0.86 ± 0.09 to 0.54 ± 0.05 % [ 41 ]. The percentage of TA was found to be higher in pink fruit pulp including LOCAL, BARI-2, BARI-3, and BARI-6, while lower TA was found in white pulp mentioned as BARI-4 ( Fig.…”

Nondestructive Assessment of Citrus Fruit Quality and Ripening by Visible–Near Infrared Reflectance Spectroscopy

“…The LOCAL algorithm has also shown to produce better models than MPLS for firmness and juice mass in 'Powell Summer Navel' orange [67], and in 'Clemenvilla' mandarin [68]. SIMCA, SVM and particularly PCA-ANN, also allowed to assess with a total accuracy > 98% the freeze damage in sweet lemon [13] and PCA-GRNN allowed the assessment of the granulation in 'Honey' pomelo at a classification accuracy (CA) > 95% [14].…”

“…[13], and 'Honey' pomelo (Citrus maxima Merr.) [14], respectively. These disorders are difficult to sort out by visual inspection at harvest, but lead to posterior fruit deterioration, limiting their quality, shelf-life, price and acceptance by consumers.…”

A Model of Diameter Measurement Based on the Machine Vision

“…Machine visual detection can realize noncontact and online detection [1][2][3], so application of the machine vision has been widely studied [4][5][6]. With the attention of industry being paid to the development of digital manufacturing and metrological support manufacturing, machine vision has also been widely used in industrial measurement [7][8][9][10].…”