2014
DOI: 10.1016/j.postharvbio.2013.11.009
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Using visible and near infrared diffuse transmittance technique to predict soluble solids content of watermelon in an on-line detection system

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Cited by 81 publications
(32 citation statements)
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“…Recently, much attention has been paid on online detection of SSC using Vis/NIR spectroscopy. Several studies about online SSC determination using diffuse transmittance mode were reported for fruits such as pear (Sun et al 2009;Xu et al 2012) and watermelon with thick skin (Jie et al 2014), which indicated that diffused transmittance mode was suitable for internal quality determination and was a viable option for high-speed fruit measurement. Therefore, a prototype of diffused transmittance system was realized in our laboratory to provide some reference for the online detection of apple SSC.…”
Section: Introductionmentioning
confidence: 99%
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“…Recently, much attention has been paid on online detection of SSC using Vis/NIR spectroscopy. Several studies about online SSC determination using diffuse transmittance mode were reported for fruits such as pear (Sun et al 2009;Xu et al 2012) and watermelon with thick skin (Jie et al 2014), which indicated that diffused transmittance mode was suitable for internal quality determination and was a viable option for high-speed fruit measurement. Therefore, a prototype of diffused transmittance system was realized in our laboratory to provide some reference for the online detection of apple SSC.…”
Section: Introductionmentioning
confidence: 99%
“…It was found that the MLR calibration model built using GA-SPA on 18 selected wavelengths exhibited coefficient of determination r 2 pre =0.880 and root mean square error of prediction (RMSEP)=0.459°Brix for the prediction set. Jie et al (2014) investigated Vis/NIR diffuse transmission spectrum of 687-920 nm region for online determination of SSC of watermelon. They found that the MC-UVE-SMLR calibration model with baseline offset correction pretreatment was the best with r pre of 0.70 and RMSEP of 0.33°Brix for the prediction set.…”
Section: Introductionmentioning
confidence: 99%
“…Sugar content is an important determinant of fruit quality, for example it is a key factor that determines the eating quality of watermelons [26]. We detected a rapid decrease in soluble solids appeared in all samples during the storage period (Fig.…”
Section: Fruit Firmness and Soluble Solidsmentioning
confidence: 68%
“…Analyzing the material based on specific physical and chemical reactions and metrological relationships has the characteristics such as high precision, cumbersome operation, time-consuming, destructive and high reagents cost [2,3] . Presently, many fast non-destructive testing technologies are gradually replacing the traditional chemical testing in some fields, especially, image processing technology based on machine vision has been widely used in the determination of moisture and total sugar content [4][5][6][7] . Wattanavicheanand et al [8] analyzed maturity of 'Kao Nampheung' pummelo by measuring both size and density of oil glands on the fruit surface with a developed image processing program.…”
Section: Introductionmentioning
confidence: 99%