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Nondestructive evaluation and high-throughput edible rate prediction method for durian based on X-ray CT and machine learning.

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Title: Nondestructive evaluation and high-throughput edible rate prediction method for durian based on X-ray CT and machine learning.
Authors: Lu, Yuwei1,2,3 (AUTHOR), Yu, Li1,2,3 (AUTHOR), Kong, Xiaolong2,3 (AUTHOR), Zhao, Qing2,3 (AUTHOR), Yu, Lejun1,2,3 (AUTHOR) yulj@hainanu.edu.cn, Liu, Qian1,2,3 (AUTHOR) qliu@hainanu.edu.cn
Source: Food Control. Sep2025, Vol. 175, pN.PAG-N.PAG. 1p.
Subjects: Computed tomography, X-ray imaging, Tomography, Nondestructive testing, Durian
Abstract: The sarcocarp content and volume are key factors for assessing the quality of durian fruit. Non-destructive and efficient detection techniques can provide critical data support for durian breeding, postharvest technology research, and grading during industrialization processes. This study primarily investigated the potential of X-ray computed tomography (CT) in non-destructive analysis of internal durian traits and high-throughput evaluation of edible rate. The X-ray CT system was used for durian imaging. The U-Net model was utilized to segment tomographic images into background, sarcocarp, pericarp, kernels, and cavity regions. A total of 18 phenotyping traits, such as fruit volume and sarcocarp volume, were automatically calculated. To overcome the impact of tomographic image quantity on detection efficiency, a rapid prediction model for edible rate was developed using a single image. Experimental results indicated that the method based on X-ray CT system achieved mean absolute percentage error (MAPE) of 1.14 % for fruit volume and 3.09 % for sarcocarp volume when compared to manual measurements, with coefficient of determination (R2) values of 0.989 and 0.955, respectively. When predicting the edible rate based on all tomographic images, the R2 and MAPE were 0.923 and 3.39 %. Further results indicated that the edible rate prediction model based on a single image achieved R2 and MAPE values of 0.917 and 4.03 %. Overall, X-ray CT imaging technology facilitates comprehensive and accurate extraction of internal durian traits while also demonstrating its potential for high-throughput prediction of the edible rate. • An innovative evaluation method of durian internal quality based on CT imaging. • Rapid extraction and correlation analysis of eighteen durian phenotypic traits. • Fast prediction of edible rate based on single tomography image. [ABSTRACT FROM AUTHOR]
Copyright of Food Control is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 184522002
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
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  Data: Nondestructive evaluation and high-throughput edible rate prediction method for durian based on X-ray CT and machine learning.
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  Data: <searchLink fieldCode="AR" term="%22Lu%2C+Yuwei%22">Lu, Yuwei</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Li%22">Yu, Li</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kong%2C+Xiaolong%22">Kong, Xiaolong</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Qing%22">Zhao, Qing</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Lejun%22">Yu, Lejun</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> yulj@hainanu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Qian%22">Liu, Qian</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> qliu@hainanu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Food+Control%22">Food Control</searchLink>. Sep2025, Vol. 175, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Computed+tomography%22">Computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22X-ray+imaging%22">X-ray imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Tomography%22">Tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Nondestructive+testing%22">Nondestructive testing</searchLink><br /><searchLink fieldCode="DE" term="%22Durian%22">Durian</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The sarcocarp content and volume are key factors for assessing the quality of durian fruit. Non-destructive and efficient detection techniques can provide critical data support for durian breeding, postharvest technology research, and grading during industrialization processes. This study primarily investigated the potential of X-ray computed tomography (CT) in non-destructive analysis of internal durian traits and high-throughput evaluation of edible rate. The X-ray CT system was used for durian imaging. The U-Net model was utilized to segment tomographic images into background, sarcocarp, pericarp, kernels, and cavity regions. A total of 18 phenotyping traits, such as fruit volume and sarcocarp volume, were automatically calculated. To overcome the impact of tomographic image quantity on detection efficiency, a rapid prediction model for edible rate was developed using a single image. Experimental results indicated that the method based on X-ray CT system achieved mean absolute percentage error (MAPE) of 1.14 % for fruit volume and 3.09 % for sarcocarp volume when compared to manual measurements, with coefficient of determination (R2) values of 0.989 and 0.955, respectively. When predicting the edible rate based on all tomographic images, the R2 and MAPE were 0.923 and 3.39 %. Further results indicated that the edible rate prediction model based on a single image achieved R2 and MAPE values of 0.917 and 4.03 %. Overall, X-ray CT imaging technology facilitates comprehensive and accurate extraction of internal durian traits while also demonstrating its potential for high-throughput prediction of the edible rate. • An innovative evaluation method of durian internal quality based on CT imaging. • Rapid extraction and correlation analysis of eighteen durian phenotypic traits. • Fast prediction of edible rate based on single tomography image. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Food Control is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.foodcont.2025.111314
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: X-ray imaging
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      – SubjectFull: Tomography
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      – SubjectFull: Nondestructive testing
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      – SubjectFull: Durian
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    Titles:
      – TitleFull: Nondestructive evaluation and high-throughput edible rate prediction method for durian based on X-ray CT and machine learning.
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            NameFull: Lu, Yuwei
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            NameFull: Yu, Li
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            – D: 01
              M: 09
              Text: Sep2025
              Type: published
              Y: 2025
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