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Research on non-destructive detection method of Citrullus lanatus seed phenotypic traits based on terahertz spectral imaging
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Jia YU, Yu-Tong LIU, Yang LI, Yuan-Yuan CHEN*, Jing-Zhu WU
Journal of Food Safety & Quality | 2026, 17(15) : 100 - 107
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Journal of Food Safety & Quality | 2026, 17(15): 100-107
• Special Topic: Application of Modern Analysis Instrument in Food Detection •
Research on non-destructive detection method of Citrullus lanatus seed phenotypic traits based on terahertz spectral imaging
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Jia YU, Yu-Tong LIU, Yang LI, Yuan-Yuan CHEN*, Jing-Zhu WU
Affiliations
  • Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
doi: 10.19812/j.cnki.jfsq11-5956/ts.20260304003
Outline
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Objective

To explore the effective extraction of internal and external morphological features of Citrullus lanatus seeds and the identification of their varieties by applying terahertz (THz) mapping technology.

Methods

A total of 120 seeds from 3 common Citrullus lanatus varieties, “Ruixin”, “Langchao 1” and “Xiangxiu”, were selected as experimental samples, and THz transmission spectral images were collected. First, a method based on Sym6 wavelet transform was used for spectral denoising and image correction. Subsequently, correlation coefficient imaging was employed to reconstruct THz pseudo-color images of the seed coat and kernel, and the DeepLab V3+semantic segmentation model was applied to achieve precise segmentation of tissue regions. From the segmented images, 9 morphological parameters were extracted from each of the seed coat and kernel, resulting in a total of 18 phenotypic features, which were used to construct a seed coat feature set, a kernel feature set and an integrated feature set. Variety identification was performed using 3 kinds of machine learning models: extreme learning machine (ELM), random forest (RF), and support vector machine (SVM).

Results

The coefficients of determination between THz imaging measurements and manual measurements for the seed major axis and minor axis reached 0.830 and 0.916, respectively, verifying the high accuracy and reliability of THz technology. In terms of variety identification, the integrated feature set combined with the RF model achieved the best identification performance on the test set, with an average accuracy of 88.0%, showed significant improvement compared to models based on single-tissue features.

Conclusion

This study demonstrates that the THz spectral-based method enables non-destructive and accurate measurement of phenotypic traits in Citrullus lanatusv seeds and effective variety identification, providing a new technical approach for the evaluation of Citrullus lanatus germplasm resources and breeding research.

terahertz imaging  /  Citrullus lanatus seeds  /  phenotypic traits  /  non-destructive detection  /  variety identification
俞佳, 刘禹彤, 李阳, 陈媛媛, 吴静珠. 基于太赫兹图谱的西瓜种子表型性状无损检测方法研究. 食品安全质量检测学报, 2026 , 17 (15) : 100 -107 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20260304003
Jia YU, Yu-Tong LIU, Yang LI, Yuan-Yuan CHEN, Jing-Zhu WU. Research on non-destructive detection method of Citrullus lanatus seed phenotypic traits based on terahertz spectral imaging[J]. Journal of Food Safety & Quality, 2026 , 17 (15) : 100 -107 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20260304003
Year 2026 volume 17 Issue 15
9
2
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Article Info
doi: 10.19812/j.cnki.jfsq11-5956/ts.20260304003
  • Receive Date:2026-03-04
  • Online Date:2026-11-05
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  • Received:2026-03-04
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    Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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