###### 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), titutable 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) 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. Keywords:visible and near-infrared transmittance spectroscopy; machine vision technology; granulation; honey pomelo; multi-source data fusion; multi-category classification +
Footnote †: journal: Applied  
Raby [2], and subsequently studied by researchers from different citrus fruit-growing countries, including China [3]. Granulation is a physiological disorder, where the juice sacs in citrus fruit become hard, dry, and enlarged, become grayish in appearance, and have little extractable juice [4; 5; 6]. The severity of the disorder increases during storage, as the stored fruit loses additional moisture from the juice vesicles. With continued granulation, there is rapid deterioration of the organoleptic property, reducing the edible value of the fruit [7]. Granulation of citrus fruit has been found to correlate with secondary wall formation and cell wall thickening in granulated juice vesicles [5; 8]. The harvest and post-harvest storage times can affect the degree of granulation, as late harvest and prolonged storage enhance granulation levels in pomelo fruit, which affects the overall internal fruit quality [9; 10]. Visible and near-infrared transmittance spectroscopy has been widely applied to identify and estimate the internal quality attributes of fruit, by assessing soluble solids content (SSC) [11], maturity [12], and early decay [13]. However, there have been few studies of granulation of citrus fruit. Chemometrics is the application of mathematics, statistics, and computer science to the extraction of useful information from multivariate data. It includes exploratory data analysis, machine learning, data mining, and classification [14]. In this research, visible and near-infrared transmittance spectra data combined with chemometrics were first used to assess granulation using multi-category models (LDA, PCA-SVM, and PCA-GRNN) (Table A1 in Appendix A).