|
|
|
|
|
|
Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3* |
1. College of Mathematics and Physics,Xinjiang Agricultural University,Urumqi 830052,China
2. Comprehensive Testing Ground,Xinjiang Academy of Agricultural Sciences, Urumqi 830013,China
3. Mechanical and Traffic College,Xinjiang Agricultural University,Urumqi 830052,China
|
|
|
Abstract Soluble solids content (SSC) is an important physiological indicator of apple quality and maturation, and can be used for predicting the quality and maturity of apples. In this paper, 552 samples were collected at equal intervals of 3 d from the fruit swelling and setting stage to the complete mature stage, and the SSC was determined by collecting visible/near-infrared spectra from 380 to 1100 nm, and fused with fractional differential (FD) technique and replacement importance-random forest (Permutation Importance-Random Forest, PIMP-RF) algorithm to construct an ensemble learning model for SSC prediction in apple during maturing period. The results showed that the fractional differential orders of the PLS model were 0, 0.4, 1.1, and 1.6, and the results of feature importance and interpretability analysis by the PIMP-RF algorithm showed that the key wavelengths for predicting the SSC of maturity apples using visible/near-infrared spectroscopy were mainly in the visible band, which provided a theoretical basis for the future development of a rapid nondestructive detection device for Xinjiang Red Fuji apples. The ensemble learning model of apple ripening SSC constructed based on fractional differential technique and PIMP-RF algorithm has good prediction ability, with the correlation coefficient r equal to 0.989 2, mean absolute error MAE equal to 0.241 2, root mean square error RMSE equal to 0.309 1 and mean absolute percentage error equal to 0.018 3 in the training set. The correlation coefficient r of the test set is equal to 0.903 8, the mean absolute error MAE is equal to 0.549 9, the root mean square error RMSE is equal to 0.740 8, and the mean absolute percentage error is equal to 0.043 4, compared to the FD0-PIMP-RF, FD0.4-PIMP-RF, FD1.1-PIMP-RF, and FD1.6-PIMP-RF models, the ensemble learning model is optimal. Therefore, the integrated fractional order differentiation technique and PIMP-RF algorithm, combined with visible/near-infrared spectroscopy, can successfully and effectively predict the soluble solids content of apples during maturing period.
|
Received: 2022-03-09
Accepted: 2022-11-28
|
|
Corresponding Authors:
GUO Jun-xian
E-mail: junxianguo@163.com
|
|
[1] Pavlina D D, Georgios P. Scientia Horticulturae,2011, 129(4): 752.
[2] Wang H, Peng J, Xie B, et al. Sensors, 2015, 15(5): 11889.
[3] Wang L, Sun D W, Pu H B, et al. Food Science and Nutrition, 2017, 57(7): 1524.
[4] ZHAO Jie-wen, ZHANG Hai-dong, LIU Mu-hua(赵杰文, 张海东, 刘木华). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2005, 21(3): 162.
[5] YUAN Lei-ming, GAO Hai-ning, LÜ Song, et al(袁雷明, 高海宁, 吕 松, 等). Journal of Food Safety and Quality(食品安全质量检测报), 2012, 3(5): 448.
[6] Fan G, Zha J, Du R, et al. Journal of Food Engineering, 2009, 93(4): 416.
[7] LIU Yan-de, WANG Jun-zheng, JIANG Xiao-gang, et al(刘燕德, 王军政, 姜小刚, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(7): 2064.
[8] Tian X, Li J, Wang Q, et al. Food Chemistry, 2018, 239(15): 1055.
[9] Fan S, Huang W, Li J, et al. Biosystem Engineering,2016, 43(45): 9.
[10] Guo Z, Huang W, Peng Y, et al. Postharvest Biology & Technology, 2016, 115: 81.
[11] MENG Qing-long, SHANG Jing, HUANG Ren-shuai, et al(孟庆龙, 尚 静, 黄人帅, 等). Storage and Process(保鲜与加工), 2020, 20(5): 185.
[12] Bai Y, Xiong Y, Huang J, et al. Postharvest Biology and Technology, 2019, 156: 110943.
[13] ZHANG He-dong, WU Jing-zhu, HAN Ping, et al(张鹤冬, 吴静珠, 韩 平, 等). Journal of Food Safety and Quality(食品安全质量检测学报), 2019, 10(1): 209.
[14] Zhang Dongyan, Xu Yunfei, Huang Wenqian, et al. Infrared Physics & Technology, 2019, 98: 297.
[15] RAO Li-bo, CHEN Xiao-yan, PANG Tao(饶利波, 陈晓燕, 庞 涛). Chinese Journal of Luminescence(发光学报), 2019, 40(3): 389.
[16] Huang Y, Wang J, Li N, et al. Pattern Recognition Letters, 2021, 151: 76.
[17] Xia Y, Fan S, Li J, et al. Chemometrics and Intelligent Laboratory Systems, 2020, 201(15): 104017.
[18] Tian X, Fan S, Li J, et al. Biosystems Engineering, 2020, 197: 64.
[19] Ahyeong L, Jaeseung S, Balgeum K, et al. Journal of Food Engineering, 2022, 321: 110945.
[20] Lengauer T. Bioinformatics, 2010, 26(10): 1340.
|
[1] |
CAI Jian-rong1, 2, HUANG Chu-jun1, MA Li-xin1, ZHAI Li-xiang1, GUO Zhi-ming1, 3*. Hand-Held Visible/Near Infrared Nondestructive Detection System for Soluble Solid Content in Mandarin by 1D-CNN Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2792-2798. |
[2] |
LI Xiong1, 2, LIU Yan-de1, 2*, WANG Guan-tian1, JIANG Xiao-gang1, 2. Grapefruit Light Energy Decay Law and Analysis of the Effect of
Transmission Depth on Model Accuracy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2574-2580. |
[3] |
ZHANG Mei-zhi1, ZHANG Ning1, 2, QIAO Cong1, XU Huang-rong2, GAO Bo2, MENG Qing-yang2, YU Wei-xing2*. High-Efficient and Accurate Testing of Egg Freshness Based on
IPLS-XGBoost Algorithm and VIS-NIR Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1711-1718. |
[4] |
YAN Zhong-wei1, 2, 3, TIAN Xi2, 3, ZHANG Yi-fei2, 3, LI Lian-jie2, 3, LIU San-qing1, 2, 3, HUANG Wen-qian2, 3*. Online Detection of Soluble Solids Content in Different Parts of
Watermelons Based on Full Transmission Near Infrared
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1800-1808. |
[5] |
WANG Dong1, 2, FENG Hai-zhi3, LI Long3, HAN Ping1, 2*. Compare of the Quantitative Models of SSC in Tomato by Two Types of NIR Spectrometers[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1351-1357. |
[6] |
ZHANG Fu1, 2, 3, CAO Wei-hua1, CUI Xia-hua1, WANG Xin-yue1, FU San-ling4*, ZHANG Ya-kun1. Non-Destructive Detection of Soluble Solids in Cherry Tomatoes by
Visible/Near Infrared Spectroscopy Based on SG-CARS-IBP[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 737-743. |
[7] |
WANG Jin-jie1, 2, 3, 4, 5, DING Jian-li1, 4, 5*, GE Xiang-yu1, 4, 5, ZHANG Zhe1, 4, 5, HAN Li-jing1, 4, 5. Application of Fractional Order Differential Technology in the Estimation of Soil Moisture Content Using UAV-Based Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3559-3567. |
[8] |
HUANG Hua1, NAN Meng-di1, LI Zheng-hao1, CHEN Qiu-ying1, LI Ting-jie1, GUO Jun-xian2*. Multi-Model Fusion Based on Fractional Differential Preprocessing and PCA-SRDA for the Origin Traceability of Red Fuji Apples[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3249-3255. |
[9] |
HE Nian, SHAN Peng*, HE Zhong-hai, WANG Qiao-yun, LI Zhi-gang, WU Zhui. Study on the Fractional Baseline Correction Method of ATR-FTIR
Spectral Signal in the Fermentation Process of Sodium Glutamate[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1848-1854. |
[10] |
ZHANG Fu1, 2, 3, CUI Xia-hua1, DING Ke4*, ZHANG Ya-kun1, WANG Yong-xian1, PAN Xiao-qing5. Study on the Influence of Different Pretreatment Methods on Gender Determination of Multiposition[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 434-439. |
[11] |
YANG Bao-hua, GAO Zhi-wei, QI Lin, ZHU Yue, GAO Yuan. Prediction Model of Soluble Solid Content in Peaches Based on Hyperspectral Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3559-3564. |
[12] |
ZHANG Fu1, 2, 3, CUI Xia-hua1, ZHANG Ya-kun1, WANG Yong-xian1. Relationship Between Visible/Near Infrared Spectral Data and Fertilization Information at Different Positions of Hatching Eggs[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3064-3068. |
[13] |
LI Qing-xu1, WANG Qiao-hua1, 2*, MA Mei-hu3, XIAO Shi-jie1, SHI Hang1. Non-Destructive Detection of Male and Female Information of Early Duck Embryos Based on Visible/Near Infrared Spectroscopy and Deep Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1800-1805. |
[14] |
ZHANG Xu1, ZHANG Tian-gang2, MU Wei-song1, FU Ze-tian2,3, ZHANG Xiao-shuan2,3*. Prediction of Soluble Solids Content for Wine Grapes During Maturing Based on Visible and Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(01): 229-235. |
[15] |
LIU Yan-de, ZHANG Yu, JIANG Xiao-gang, SUN Xu-dong, XU Hai, LIU Hao-chen. Detection on Firmness and Soluble Solid Content of Peach During Different Storage Days[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(01): 243-249. |
|
|
|
|