Abstract
[Objective] To address issues such as the difficulty in accurate internal indicator quantification, the missed detection of external defects, and fragmented testing methods in the quality inspection of pickled products (taking duck eggs as an example), thereby enhancing the accuracy, efficiency, and intelligence of comprehensive food quality inspection in automatic production. [Methods] A comprehensive quality inspection method for pickled products is constructed based on an automatic food quality inspection system, which integrates deep learning, machine learning, and multiple detection technologies (hyperspectral technology + machine vision technology). Specifically, hyperspectral imaging technology is used to collect internal optical characteristic data of pickled duck eggs. After preprocessing, an improved partial least squares regression model is input to achieve synchronous detection of four core internal quality indicators: sodium chloride concentrations and moisture content in both egg yolk and egg white. Simultaneously, a machine vision system is employed to collect external image data of duck eggs. After preprocessing, the improved PP-YOLOE model is input to complete the recognition of surface crack defects and the rapid detection of yolk area ratio. The superiority of the proposed method is verified through comparative experiments. [Results] The experimental method achieves determination coefficients (R2) of greater than 0.975, and a root mean square error (RMSE) of less than 0.5 % in internal indicator detection. The experimental method has an accuracy rate of 98.50 % in crack defect identification, with a miss rate of only 0.40 %. Additionally, the detection error of egg yolk area ratio is less than 1 %, and the detection speed is increased by more than 20 % compared to conventional methods. [Conclusion] This comprehensive quality inspection method for pickled products integrates multiple technologies, enabling integrated and high-precision detection of internal quality indicators and external defects/morphological parameters. It effectively overcomes the limitations of a single detection technology, consequently possessing strong engineering practicality and promotional value.
Publication Date
5-15-2026
First Page
94
Last Page
102
DOI
10.13652/j.spjx.1003.5788.2025.60166
Recommended Citation
Min, PANG; Shuwan, LIU; Yanbo, MA; and Chao, ZHENG
(2026)
"Intelligent quality detection method for salted duck eggs based on improved PLSR and PP-YOLOE,"
Food and Machinery: Vol. 42:
Iss.
4, Article 12.
DOI: 10.13652/j.spjx.1003.5788.2025.60166
Available at:
https://www.ifoodmm.cn/journal/vol42/iss4/12
References
[1] 丛军,李星.基于电子鼻、电子舌技术的荣昌猪肉及其制品贮藏过程新鲜度检测研究 [J].食品安全质量检测学报,2024,15(7):192-201.CONG J,LI X.Detection of freshness of Rongchang pork and its products during storage based on electronic nose and electronic tongue technology [J].Journal of Food Safety & Quality,2024,15(7):192-201.
[2] 孙 俊 洋,符 运 来,吕 晶,等.基 于 改 进 YOLOv 7模 型 的 海 参 苗计数方法研究 [J].计算机技术与发展,2024,34(11):166-171.SUN J Y,FU Y L,LYU J,et al.Study on counting method of sea cucumber seedlings based on improved YOLOv 7 model [J].Computer Technology and Development,2024,34(11):166-171.
[3] ERNA K H,ROVINA K,MANTIHAL S.Current detection techniques for monitoring the freshness of meat-based products:a review [J].Journal of Packaging Technology and Research,2021,5(3):127-141.
[4] HUANG J,REN L F,ZHOU X K,et al.An improved neural network based on SENet for sleep stage classification [J].IEEEJournal of Biomedical and Health Informatics,2022,26(10):4 948-4 956.
[5] 顾文娟,魏金,阴艳超,等.基于改进 DeepLabv 3+的番茄图像多类别分割方法 [J].农业机械学报,2023,54(12):261-271.GU W J,WEI J,YIN Y C,et al.Multi-category segmentation method of tomato image based on improved DeepLabv 3+[J].Transactions of the Chinese Society for Agricultural Machinery,2023,54(12):261-271.
[6] 康明月,王成,孙鸿雁,等.基于改进的 WOA-LSSVM 樱桃番茄 内 部 品 质 检 测 方 法 研 究 [J].光 谱 学 与 光 谱 分 析,2023,43(11):3 541-3 550.KANG M Y,WANG C,SUN H Y,et al.Research on internal quality detection method of cherry tomatoes based on improved WOA-LSSVM [J].Spectroscopy and Spectral Analysis,2023,43(11):3 541-3 550.
[7] 朱婷婷,滕广,张亚军,等.基于改进 YOLO v 11的番茄表面缺陷检测方法 [J].农业机械学报,2025,56(6):546-553.ZHU T T,TENG G,ZHANG Y J,et al.Improved YOLO v 11 method for surface defect detection of tomato [J].Transactions of the Chinese Society for Agricultural Machinery,2025,56(6):546-553.
[8] 施 利 春,边 可 可,王 松 伟,等.基 于 改 进 U-Net 和IWOA-LSSVM 的番茄综合品质检测方法研究 [J].食品与机械,2025,41(8):109-117.SHI L C,BIAN K K,WANG W S,et al.Research on tomato comprehensive quality detection method based on improved U-Net and IWOA-LSSVM [J].Food & Machinery,2025,41(8):109-117.
[9] 野晶菀,周一鸣,王明龙,等.多源感知技术融合机器学习在食品品质评价中的研究进展 [J].食品工业科技,2025,46(23):466-475.YE J W,ZHOU Y M,WANG M L,et al.Advances in multi-source perception technology integrated with machine learning in food quality evaluation [J].Science and Technology of Food Industry,2025,46(23):466-475.
[10] 赵泽华,王庆艳,陈俊杰,等.基于机器视觉与高光谱成像的西瓜考种系统研究 [J].农业机械学报,2025,56(12):450-459.ZHAO Z H,WANG Q Y,CHEN J J,et al.Watermelon breeding system based on machine vision and hyperspectral imaging [J].Transactions of the Chinese Society for Agricultural Machinery,2025,56(12):450-459.
[11] 文韬,代兴勇,李浪,等.基于机器视觉与光谱融合的柑橘品质无损检测分级系统设计与试验 [J].江苏大学学报 (自然科学版),2024,45(1):38-45.WEN T,DAI X Y,LI L,et al.Design and experiment of non-destructive testing and grading system for citrus quality based on machine vision and spectral fusion [J].Journal of Jiangsu University (Natural Science Edition),2024,45(1):38-45.[12] 郭德超,饶远立,张豪,等.结合机器视觉和光谱技术的番茄综合品质检测方法 [J].食品与机械,2024,40(9):123-130.GUO D C,RAO Y L,ZHANG H,et al.Comprehensive quality detection method for tomatoes combining machine vision and spectral techniques [J].Food & Machinery,2024,40(9):123-130.
[13] 王康生,林峰,谢忠鑫,等.用于多光谱相机的机器视觉系统设计 [J].应用光学,2025,46(2):233-241.WANG K S,LIN F,XIE Z X,et al.Machine vision system design for multispectral cameras [J].Journal of Applied Optics,2025,46(2):233-241.
[14] 吕金锐,付燕,倪美玉,等.基于改进 YOLOv 4模型的番茄成熟度检测方法 [J].食品与机械,2023,39(9):134-139.LU J R,FU Y,NI M Y,et al.Research on tomato maturity detection method based on improved YOLOv 4 model [J].Food & Machinery,2023,39(9):134-139.
[15] 刘浩,任宏,赵丁选,等.基于亚像素定位的图像边缘检测策略研究 [J].农业机械学报,2024,55(2):242-248,294.LIU H,REN H,ZHAO D X,et al.Image edge detection strategy based on sub-pixel location [J].Transactions of the Chinese Society for Agricultural Machinery,2024,55(2):242-248,294.
[16] KAUKAB S,KOMAL,GHODKI B M,et al.Improving real-time apple fruit detection:multi-modal data and depth fusion with non-targeted background removal [J].Ecological Informatics,2024,82:102691.
[17] 张伟进,王福顺,孙小华,等.传统图像分割算法在农作物籽粒考种应用中的研究进展 [J].中国农机化学报,2024,45(2):280-287.ZHANG W J,WANG F S,SUN X H,et al.Research progress of traditional image segmentation algorithm in seed testing of crops [J].Journal of Chinese Agricultural Mechanization,2024,45(2):280-287.
[18] DONG P,FENG W H,WANG R,et al.Automatic classification and detection of faulty packaging using deep learning algorithms: a study for industrial applications [J].Intelligent Methods in Engineering Sciences,2024,3(1):13-21.
[19] 戴海宸,韦鑫宇,徐一新,等.基于相位和高光谱的番茄果实多模态融合检测方法 [J].光子学报,2024,53(7):268-282.DAI H C,WEI X Y,XU Y X,et al.Multimodal fusion detection method of tomato fruit based on phase and hyperspectral [J]. Acta Photonica Sinica,2024,53(7):268-282.
[20] 杨泽青,李志蒙.水果外观品质视觉检测及自动分级控制系统设计 [J].制造业自动化,2024,46(2):42-46.YANG Z Q,LI Z M.Design of visual inspection and automatic grading control system for fruit appearance quality[J].Manufacturing Automation,2024,46(2):42-46.
