Evidence map›Paper›PMID 42185587›Full record

ArticleScientific reports2026

Development of a portable online nondestructive detection device for apple watercore based on visible/near-infrared spectroscopy.

Yong Lin, Haijian Wu, Chunlin Zhao, Wenbin Zhang, Zhipeng Yin, Zikang Cao, Yaxing Ma, Yue Lu, Liju Liu, Din Hu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Yong LinZhijiang College of Zhejiang University of Technology, Shaoxing, 310024, Zhejiang, China.
Haijian WuFaculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, 650500, China.
Chunlin ZhaoFaculty of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150001, China.
Wenbin ZhangCollege of Mechanical and Electrical Engineering, Kunming University, Kunming, 650214, China. zhangwenbin@kmu.edu.cn.
Zhipeng YinFaculty of Mechanical and Electrical Engineering, Xian Jiaotong University, Xi'an, 710049, China.
Zikang CaoFaculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, 650500, China.
Yaxing MaFaculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, 650500, China.
Yue LuFaculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, 650500, China.
Liju LiuFaculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, 650500, China.
Din HuFaculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, 650500, China.

Funding

Department of Science and Technology of Shandong Province 2025TSGCCZZB0708People's Government of Yunnan Province 202449CE340008
6 · The paper itself

Abstract

Visible and near-infrared (Vis/NIR) spectroscopy has been widely applied in fruit quality detection due to its advantages of rapid efficiency, non-invasiveness, and suitability for detecting opaque samples. To address the issue of whether apple watercore occurs during the growth and maturation of apples, a portable on-line nondestructive detection device based on Vis/NIR spectroscopy was designed to achieve accurate detection of apple watercore. The device employs the AIOX2000-13 spectrometer as the detection unit, with an STM32F103VET6 ARM-based processor as the main control chip, and integrates a 4G wireless communication module to establish a stable data transmission channel between the processor and the computer. This structure ensures the efficient and stable transmission of apple spectral data and detection results, thereby meeting the need for in-field nondestructive detection of apple watercore on apple trees. The system is based on a self-designed spectral data acquisition mechanism and uses a transmission detection method to collect spectral data from 500 'Fuji' apple samples in two directions. The spectral data were preprocessed using Standard Normal Variate (SNV), and the dataset was divided using the Spectral Projection based on X-Y distances (SPXY) algorithm. Important feature wavelengths related to apple watercore were extracted by combining the Uninformative Variable Elimination method with the Successive Projections Algorithm (UVE-SPA). Subsequently, a detection model, SNV-UVE-SPA-SVM, was constructed using a Support Vector Machine (SVM) optimized by the Honey Badger Algorithm (HBA), achieving a test set accuracy of 96%. After research and analysis, Direction 1 was identified as the optimal acquisition direction, and field verification was conducted on 50 apple samples, with a detection accuracy of 94%. The results show that the detection device has the advantages of portability, high efficiency, and suitability for in-field detection, making it suitable for the rapid in-field detection of apple watercore.

Indexed as

FruitMalusAlgorithmsSpectroscopy, Near-InfraredApple watercoreEmbedded systemOnline nondestructive detectionPortableVisible/near-infrared spectroscopy

Identifiers

PMID42185587
PMCPMC13434719

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