Qualitative and Quantitative Assessments of Apple Quality Using Vis Spectroscopy Combined with Improved Particle-Swarm-Optimized Neural Networks.

Wenping Peng, Zhong Ren, Junli Wu, Chengxin Xiong, Longjuan Liu, Bingheng Sun, Gaoqiang Liang, Mingbin Zhou

Abstract read

In one paragraph

Article in Foods (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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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.

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3 · Its place in the literature

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1 citing paper in PubMed.

Automated Mango Variety Classification Using Deep Feature Extraction and Machine Learning Classifier Integration.Foods (Basel, Switzerland) · 2026
Article

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5 · Who and what money

Authors and funding

8 authors.

Wenping PengKey Laboratory of Optic-Electronics and Communication, Jiangxi Science and Technology Normal University, Nanchang 330038, China.

Zhong RenKey Laboratory of Optic-Electronics and Communication, Jiangxi Science and Technology Normal University, Nanchang 330038, China.

Junli WuKey Laboratory of Optic-Electronics and Communication, Jiangxi Science and Technology Normal University, Nanchang 330038, China.

Chengxin XiongKey Laboratory of Optic-Electronics and Communication, Jiangxi Science and Technology Normal University, Nanchang 330038, China.

Longjuan LiuKey Laboratory of Optic-Electronics and Communication, Jiangxi Science and Technology Normal University, Nanchang 330038, China.

Bingheng SunKey Laboratory of Optic-Electronics and Communication, Jiangxi Science and Technology Normal University, Nanchang 330038, China.

Gaoqiang LiangKey Laboratory of Optic-Electronics and Communication, Jiangxi Science and Technology Normal University, Nanchang 330038, China.

Mingbin ZhouKey Laboratory of Optic-Electronics and Communication, Jiangxi Science and Technology Normal University, Nanchang 330038, China.

Funding

Key Project of Natural and Science Fund of Jiangxi Province 20224ACB202004the Chinese National Natural Science Fund 62165006the project of Key Laboratory of Optic-electronic Detection and Information Processing of Nanchang City 2019-NCZDSY-008Young top-notch personnel fund project of JXSTNU 2014QNBJRC004

6 · The paper itself

Abstract

Exploring a cost-effective and high-accuracy optical detection method is of great significance in promoting fruit quality evaluation and grading sales. Apples are one of the most widely economic fruits, and a qualitative and quantitative assessment of apple quality based on soluble solid content (SSC) was investigated via visible (Vis) spectroscopy in this study. Six pretreatment methods and principal component analysis (PCA) were utilized to enhance the collected spectra. The qualitative assessment of apple SSC was performed using a back-propagation neural network (BPNN) combined with second-order derivative (SD) and Savitzky-Golay (SG) smoothing. The SD-SG-PCA-BPNN model's classification accuracy was 87.88%. To improve accuracy and convergence speed, a dynamic learning rate nonlinear decay (DLRND) strategy was coupled with the model. After that, particle swarm optimization (PSO) was employed to optimize the model. The classification accuracy was 100% for testing apples via the SD-SG-PCA-PSO-BPNN model combined with a Gaussian DLRND strategy. Then, quantitative assessments of apple SSC values were performed. The correlation coefficient (

Indexed as

dynamic learning rate nonlinear decay strategyparticle swarm optimizationquality assessmentsoluble solid contentVis spectra

Identifiers

PMID37238810

DOI10.3390/foods12101991

PMCPMC10217276

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.