Detection of sub-healthy apples with watercore based on visible/near-infrared transmission spectroscopy

Authors

WANG Chenchen, School of Mechanical and Electrical Engineering, Shihezi University, Shihezi, Xinjiang 832003, China
ZHAI Mingcan, School of Mechanical and Electrical Engineering, Shihezi University, Shihezi, Xinjiang 832003, China
LI He, School of Mechanical and Electrical Engineering, Shihezi University, Shihezi, Xinjiang 832003, China
MO Xiaoming, School of Mechanical and Electrical Engineering, Shihezi University, Shihezi, Xinjiang 832003, China
ZHA Zhihua, School of Mechanical and Electrical Engineering, Shihezi University, Shihezi, Xinjiang 832003, China;Northwest Key Laboratory of Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, Xinjiang 832003, China;Engineering Research Center of Ministry of Education for Mechanization of Oasis Specialty Cash Crop Production, Shihezi, Xinjiang 832003, China
WU Jie, School of Mechanical and Electrical Engineering, Shihezi University, Shihezi, Xinjiang 832003, China;Northwest Key Laboratory of Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, Xinjiang 832003, China;Engineering Research Center of Ministry of Education for Mechanization of Oasis Specialty Cash Crop Production, Shihezi, Xinjiang 832003, ChinaFollow

Corresponding Author(s)

吴杰（1972—），男，石河子大学教授，博士生导师，博士。E-mail: wjshz@126.com

Abstract

[Objective] Achieving non-destructive testing of sub-healthy watercore apples. [Methods] First, the logarithmic function method and the power function method proposed by this study were used to correct the sample spectra. Subsequently, the corrected data were converted into different images of gramian angular difference field (GADF), gramian angular summation field (GASF), markov transition field (MTF), recurrence plot (RP), symmetric dot pattern (SDP). Then, the ResNet50 network model with the convolutional block attention module (CBAM) was used to mine the deep image features related to the degree of watercore, which were downscaled by the t-distributed stochastic neighbor embedding (t-SNE) method and analyzed by clustering to determine the most suitable image transformation method. Finally, the most suitable image features were inputted into the improved particle swarm algorithm (IPSO) optimized support vector machine (SVM), extreme learning machine (ELM), k-nearest neighbour (KNN) and random forest (RF) classifier for the three-class classification of watercore apple. [Results] The results showed that the power function method was better than the logarithmic function method in eliminating the effect of diameter on the transmission spectrum. The silhouette coefficient (SC), the calinski harabasz score (CHS), and the davies- bouldin index (DBI) were 0.93, 0.88 and 0.24 after visualization of the image features in the GADF, better than the rest of the image transformation methods. ResNet50-IPSO-ELM achieved the highest classification accuracy of 96.8%. The overall discrimination accuracy of the three watercore classes apples in the test set reached 96.3%, and the stable precision (SP), stable recall (SR), and stable F₁-score (SF) were 87.2%, 95.8%, and 92.3%, respectively. [Conclusion] The model maintains a high classification accuracy for the majority class of apples without watercore and healthy apples with watercore and a high discriminatory ability for the minority class of sub-healthy apples with watercore.

Publication Date

9-11-2024

First Page

117

Last Page

125,182

DOI

10.13652/j.spjx.1003.5788.2024.80368

Recommended Citation

Chenchen, WANG; Mingcan, ZHAI; He, LI; Xiaoming, MO; Zhihua, ZHA; and Jie, WU (2024) "Detection of sub-healthy apples with watercore based on visible/near-infrared transmission spectroscopy," Food and Machinery: Vol. 40: Iss. 7, Article 18.
DOI: 10.13652/j.spjx.1003.5788.2024.80368
Available at: https://www.ifoodmm.cn/journal/vol40/iss7/18

References

[1] BARANOWSKI P, LIPECKI J, MAZUREK W, et al. Detection of watercore in 'Gloster' apples using thermography[J]. Postharvest Biology & Technology, 2008, 47（3）: 358-366.
[2] NEUWALD D A, KITTEMANN D, STREIF J, et al. Watercore dissipation in 'Fuji' apples by postharvest temperature conditioning treatments[J]. Acta Horticulturae, 2012, 934: 1 097-1 102.
[3] 张海辉, 田世杰, 马敏娟, 等. 考虑直径影响的苹果霉心病透射光谱修正及检测[J]. 农业机械学报, 2019, 50（1）: 313-320. ZHANG H H, TIAN S J, MA M J, et al. Detection method of moldy core in apples using modified transmission spectrum based on size of fruit[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50（1）: 313-320.
[4] CHANG H, WU Q F, TIAN H, et al. Non-destructive identification of internal watercore in apples based on online Vis/NIR spectroscopy[J]. Transactions of the ASABE, 2020, 63（6）: 1 711-1 721.
[5] TIAN S J, ZHANG J H, ZHANG Z X, et al. Effective modification through transmission Vis/NIR spectra affected by fruit size to improve the prediction of moldy apple core[J]. Infrared Physics & Technology, 2019, 100: 117-124.
[6] XIA Y, FAN S X, LI J B, et al. Optimization and comparison of models for prediction of soluble solids content in apple by online Vis/NIR transmission coupled with diameter correction method[J]. Chemometrics and Intelligent Laboratory Systems, 2020, 201: 104017.
[7] 田世杰. 基于透射光谱的苹果霉心病判别影响因素及其修正方法研究[D]. 咸阳: 西北农林科技大学, 2021. TIAN S J. Research on influencing factors and correction methods for discrimination of moldy core in apples based on transmission spectrum[D]. Xianyang: Northwest Agriculture and Forestry University, 2021.
[8] 喻国威, 马本学, 陈金成, 等. 基于GADF变换和多尺度CNN的哈密瓜表面农药残留可见—近红外光谱判别方法[J]. 光谱学与光谱分析, 2021, 41（12）: 3 701-3 707. YU G W, MA B X, CHEN J C, et al. VIS-NIR spectra discriminant of pesticide residues on the Hami melon surface by GADF and multi-scale CNN[J]. Spectroscopy and Spectral Analysis, 2021, 41（12）: 3 701-3 707.
[9] WANG B, DENG J H, JIANG H. Markov transition field combined with convolutional neural network improved the predictive performance of near-infrared spectroscopy models for determination of aflatoxin B₁ in maize[J]. Foods, 2022, 11（15）: 2 210-2 210.
[10] TAMURA K, ICHIMURA T. Time series classification using MACD-histogram-based recurrence plot[J]. International Journal of Computational Intelligence Studies, 2018, 7（3/4）: 192-213.
[11] PICKOVER C A. On the use of symmetrized dot patterns for the visual characterization of speech waveforms and other sampled data[J]. The Journal of the Acoustical Society of America, 1986, 80（3）: 955-960.
[12] 周兆永, 何东健, 张海辉, 等. 基于深度信念网络的苹果霉心病病害程度无损检测[J]. 食品科学, 2017, 38（14）: 297-303. ZHOU Z Y, HE D J, ZHANG H H, et al. Non-destructive detection of moldy core in apple fruit based on deep belief network[J]. Food Science, 2017, 38（14）: 297-303.
[13] ZHANG Y F, YANG X H, CAI Z L, et al. Online detection of watercore apples by Vis/NIR full-transmittance spectroscopy coupled with ANOVA method[J]. Foods, 2021, 10（12）: 2 983-2 983.
[14] 郭俊先, 马永杰, 郭志明, 等. 流形学习方法及近红外透射光谱的新疆冰糖心红富士水心鉴别[J]. 光谱学与光谱分析, 2020, 40（8）: 2 415-2 420. GUO J X, MA Y J, GUO Z M, et al. Watercore identification of Xinjiang Fuji apple based on manifold learning algorithm and near infrared transmission spectroscopy[J]. Spectroscopy and Spectral Analysis, 2020, 40（8）: 2 415-2 420.
[15] GUO Z M, WANG M M, AGYEKUM A A, et al. Quantitative detection of apple watercore and soluble solids content by near infrared transmittance spectroscopy[J]. Journal of Food Engineering, 2020, 279: 109955.
[16] 胡澳, 裴峥. K-Medoids和FCM融合聚类法语音信号分类的应用[J]. 济南大学学报（自然科学版）, 2016, 30（1）: 17-22. HU A, PEI Z. K-Medoids and FCM fusion clustering application research on broadcast and aviation speech signal classification[J]. Journal of University of Jinan （Science and Technology）, 2016, 30（1）: 17-22.
[17] 徐国权, 罗倩, 郭鹏飞. 多特征评估筛选的滚动轴承故障诊断算法[J]. 计算机仿真, 2018, 35（12）: 446-450, 455. XU G Q, LUO Q, GUO P F, et al. Rolling bearing fault diagnosis algorithm based on multi feature evaluation and selection[J]. Computer Simulation, 2018, 35（12）: 446-450, 455.
[18] FRASER A M, SWINNEY H L. Independent coordinates for strange attractors from mutual information[J]. Physical Review A, 1986, 33（2）: 1 134.
[19] CAO L Y. Practical method for determining the minimum embedding dimension of a scalar time series[J]. Physica D: Nonlinear Phenomena, 1997, 110（1/2）: 43-50.
[20] WANG H, XU J W, YAN R Q, et al. A new intelligent bearing fault diagnosis method using SDP representation and SE-CNN[J]. IEEE Transactions on Instrumentation and Measurement, 2019, 69（5）: 2 377-2 389.
[21] GU Y K, ZENG L, QIU G Q. Bearing fault diagnosis with varying conditions using angular domain resampling technology, SDP and DCNN[J]. Measurement, 2020, 156: 107616.
[22] MEN H, YUAN H C, SHI Y, et al. A residual network with attention module for hyperspectral information of recognition to trace the origin of rice[J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2021, 263: 120155.
[23] 孙林, 刘梦含, 徐久成. 基于优化初始聚类中心和轮廓系数的K-means聚类算法[J]. 模糊系统与数学, 2022, 36（1）: 47-65. SUN L, LIU M H, XU J C, et al. K-means clustering algorithm using optimal initial clustering center and contour coefficient[J]. Fuzzy Systems and Mathematics, 2022, 36（1）: 47-65.
[24] BALAVAND A, KASHAN A H, SAGHAEI A. Automatic clustering based on crow search algorithm-Kmeans （CSA-Kmeans） and data envelopment analysis （DEA） [J]. International Journal of Computational Intelligence Systems, 2018, 11（1）: 1 322-1 337.
[25] XIAO J W, LU J F, LI X Y. Davies bouldin index based hierarchical initialization K-means[J]. Intelligent Data Analysis, 2017, 21（6）: 1 327-1 338.
[26] 谭章禄, 陈孝慈. 改进的分类器分类性能评价指标研究[J]. 统计与信息论坛, 2020, 35（9）: 3-8. TAN Z L, CHEN X C. Study on improved classifier's performance evaluation indicators[J]. Statistic & Information Forum, 2020, 35（9）: 3-8.
[27] CHANG H, YIN J F, TIAN H, et al. Evaluation of the optical layout and sample size on online detection of apple watercore and SSC using Vis/NIR spectroscopy[J]. Journal of Food Composition and Analysis, 2023, 123: 105528.
[28] LI J B, ZHANG Y F, ZHANG Q, et al. Establishment of a multi-position general model for evaluation of watercore and soluble solid content in 'Fuji' apples using on-line full-transmittance visible and near infrared spectroscopy[J]. Journal of Food Composition and Analysis, 2023, 117: 105150.
[29] ZHANG Y F, WANG Z, TIAN X, et al. Online analysis of watercore apples by considering different speeds and orientations based on Vis/NIR full-transmittance spectroscopy[J]. Infrared Physics & Technology, 2022, 122: 104090.