Non-destructive identification and estimation of granulation in ‘Sai Num Pung’ tangerine fruit using near infrared spectroscopy and chemometrics

Theanjumpol, Parichat; Wongzeewasakun, Kumpon; Muenmanee, Nadthawat; Wongsaipun, Sakunna; Krongchai, Chanida; Changrue, Viboon; Boonyakiat, Danai; Kittiwachana, Sila

doi:10.1016/j.postharvbio.2019.03.009

Cited by 47 publications

(19 citation statements)

References 34 publications

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“…TA was measured using an automatic titrator (SCHOTTGERATE, ConsortC831, Belgium), and expressed as the mass percentage of citric acid [31]. MC was measured according to the Theanjumpol method [32]. Approximately 60 g of fruit pulp was randomly selected from three different parts of each sample and dried in a hot air oven (FD53/E2, Binder, Germany) at The parameters of the QE-pro and NIR-QUEST 512 spectrometers were set to 400 ms and 8 s, respectively.…”

Section: Physical and Chemical Measurementmentioning

confidence: 99%

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

Sun

2020

Applied Sciences

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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.

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Section: Physical and Chemical Measurementmentioning

confidence: 99%

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

Sun

2020

Applied Sciences

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“…Granulation of citrus fruit is associated with the loss of water in the juice sacs, leaving behind hard, dry, shrunken, and grey-white juice sacs without extractable juice [9,11,12], which severely affects the internal structure, flavor, sugar, and acid quality of the fruit [11]. Previous studies have demonstrated that citrus juice sac granulation is often correlated with cell wall thickening and lignin deposition [6,13], and changes in cell wall structure of juice vesicles were suggested to be associated with the increasing contents of cell wall components including pectin, lignin, hemicellulose, and cellulose, and the increase in lignin content of juice sacs plays a significant role in citrus fruit granulation process [14][15][16][17]. Some studies have also reported an association of particular enzymes with citrus granulation.…”

Section: Introductionmentioning

confidence: 99%

Transcriptome Analysis Unravels Metabolic and Molecular Pathways Related to Fruit Sac Granulation in a Late-Ripening Navel Orange (Citrus sinensis Osbeck)

Wang

et al. 2020

Plants

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Lanelate navel orange (Citrus sinensis Osbeck) is a late-ripening citrus cultivar increasingly planted in China. The physiological disorder juice sac granulation often occurs in the fruit before harvest, but the physiological and molecular mechanisms underlying this disorder remain elusive. In this study, we found that fruit granulation of the late-ripening navel orange in the Three Gorges area is mainly caused by the low winter temperature in high altitude areas. Besides, dynamic changes of water content in the fruit after freezing were clarified. The granulation of fruit juice sacs resulted in increases in cell wall cellulose and decreases in soluble solid content, and the cells gradually became shrivelled and hollow. Meanwhile, the contents of pectin, cellulose, and lignin in juice sac increased with increasing degrees of fruit granulation. The activities of pectin methylesterase (PME) and the antioxidant enzymes peroxidase (POD), superoxide dismutase, and catalase increased, while those of polygalacturonase (PG) and cellulose (CL) decreased. Furthermore, a total of 903 differentially expressed genes were identified in the granulated fruit as compared with non-disordered fruit using RNA-sequencing, most of which were enriched in nine metabolic pathways, and qRT-PCR results suggested that the juice sac granulation is closely related to cell wall metabolism. In addition, the expression of PME involved in pectin decomposition was up-regulated, while that of PG was down-regulated. Phenylalanine ammonia lyase (PAL), cinnamol dehydrogenase (CAD), and POD related to lignin synthesis were up-regulated, while CL involved in cellulose decomposition was down-regulated. The expression patterns of these genes were in line with those observed in low-temperature treatment as revealed by qRT-PCR, further confirming that low winter temperature is associated with the fruit granulation of late-ripening citrus. Accordingly, low temperature would aggravate the granulation by affecting cell wall metabolism of late-ripening citrus fruit.

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“…Compared with traditional methods, the NIR spectroscopy has the advantages of fast detection speed, no damage to the samples and high efficiency. As a matter of fact, NIR spectroscopy has been widely studied to test the quality of fruits and vegetables (Theanjumpol et al, 2019). NIR spectroscopy mainly includes two optical alternatives: "reflectance" and "transmittance."…”

Section: Introductionmentioning

confidence: 99%

Nondestructive monitoring storage quality of apples at different temperatures by near‐infrared transmittance spectroscopy

Guo

Wang

Ali

et al. 2020

Food Science & Nutrition

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Apple is the most widely planted fruit in the world and is popular in consumers because of its rich nutritional value. In this study, the portable near‐infrared (NIR) transmittance spectroscopy coupled with temperature compensation and chemometric algorithms was applied to detect the storage quality of apples. The postharvest quality of apples including soluble solids content (SSC), vitamin C (VC), titratable acid (TA), and firmness was evaluated, and the portable spectrometer was used to obtain near‐infrared transmittance spectra of apples in the wavelength range of 590–1,200 nm. Mixed temperature compensation method (MTC) was used to reduce the influence of temperature on the models and to improve the adaptability of the models. Then, variable selection methods, such as uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA), were developed to improve the performance of the models by determining characteristic variables and reducing redundancy. Comparing the full spectral models with the models established on variables selected by different variable selection methods, the CARS combined with partial least squares (PLS) showed the best performance with prediction correlation coefficient ( R p ) and residual predictive deviation (RPD) values of 0.9236, 2.604 for SSC; 0.8684, 2.002 for TA; 0.8922, 2.087 for VC; and 0.8207, 1.992 for firmness, respectively. Results showed that NIR transmittance spectroscopy was feasible to detect postharvest quality of apples during storage.

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Non-destructive identification and estimation of granulation in ‘Sai Num Pung’ tangerine fruit using near infrared spectroscopy and chemometrics

Cited by 47 publications

References 34 publications

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

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

Transcriptome Analysis Unravels Metabolic and Molecular Pathways Related to Fruit Sac Granulation in a Late-Ripening Navel Orange (Citrus sinensis Osbeck)

Nondestructive monitoring storage quality of apples at different temperatures by near‐infrared transmittance spectroscopy

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