|
|
|
|
|
|
Effects of Prediction Model of Kolar Pear Based on NIR Diffuse Transmission under Different Moving Speed on Online |
CHEN Dong-jie1, 2, JIANG Pei-hong1, 2, GUO Feng-jun1, 2, ZHANG Yu-hua1, 2*, ZHANG Chang-feng1, 2 |
1. National Engineering Research Center for Agricultural Products Logistics, Ji’nan 250103, China
2. Shandong Key Laboratory of Storage and Transportation Technology of Agricultural Products,Ji’nan 250103, China |
|
|
Abstract With the aim of solving problems related to cost, and the complicated structure of the online grading and inspection system for detecting the quality of pears, the online non-destructive system was designed for inspecting and classification of the internal quality of pears. Based on the system, the effects of prediction models on the Soluble Solids Content (SSC) and firmness of pears were researched under the different moving speeds (0.3 and 0.5 m·s-1) . Collected spectra from the same position of the pear were discrepancy at different moving speeds. Due to the discrepancy in the collected spectra, adapting spectral pre-processing methods, as SG-smoothing, SG-convolution derivative, multiple scattering correction (MSC), standard normal energy transformation (SNV), Normalization, was to eliminate differences. Adopt Partial Least Squares (PLS), prediction models of SSC and hardness for Korla Pears were established at moving speeds of 0.3 m·s-1 (S1) and 0.5 m·s-1 (S2). The results showed that the established SSC prediction model at 0.5 m·s-1 was more effective than 0.3 m·s-1 by using SG-DER (Savitzky-Golay Derivative) processing spectrogram. The correlation coefficient of the prediction set, and the root mean square errors of prediction were to be 0.880 2 and 0.391 5°Brix respectively. However, when the moving speed was 0.3 m·s-1, established the SSC model, by adapting SGS (Savitzky-Golay Smooth) processing spectrogram, was more robust than at 0.5 m·s-1. Its correlation coefficient of the prediction set, and the root mean square errors of prediction were to be 0.820 2 and 0.470 8 N respectively . Afterwards two speed hybrid prediction models were established. Competitive adaptive re-weighted sampling (CARS) and Successive projections algorithm (SPA) were used to select the characteristic variables, and PLS was used to establish hardness and SSC prediction models at mixed speeds. In view of the perspective of the model effect, SPA and CARS effectively reduced the number of variables, improving the online prediction ability and processing data speed, and enhancing the robustness of the model. Using CARS to select 24 variables from a total of 501, then which established the CARS-PLS model. Establishing the SSC prediction model was more efficient, and its correlation coefficient of the prediction set and root mean square errors of prediction were calculated as 0.915 0 and 0.371 9°Brix respectively. Using SPA to select, 32 variables were selected from a set of 501, and a firmness model was established. The correlation coefficient of the prediction set and the root mean square errors of prediction were ascertained as 0.821 0 and 0.492 0 N respectively. Establishing predictive quality model at the mixing speed is more robust than at the single speed. The research showed that the different moving speeds have different effects on the fruit quality prediction models. The research provides technical support for on-line classification of fruit quality.
|
Received: 2019-05-23
Accepted: 2019-09-02
|
|
Corresponding Authors:
ZHANG Yu-hua
E-mail: zllf@163.com
|
|
[1] Xu X, Xu H, Xie L, et al. Journal of Food Measurement and Characterization, 2018, 13(5):506.
[2] Chen H, Liu Z, Cai K, et al. Vibrational Spectroscopy, 2017, 94: 7.
[3] Lee H, Kim M S, Lim H S, et al. Biosystems Engineering, 2016, 148: 138.
[4] SUN Tong,MO Xin-xin,LIU Mu-hua(孙 通, 莫欣欣, 刘木华). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(5): 1406.
[5] LI Long, PENG Yan-kun,LI Yong-yu,et al(李 龙, 彭彦昆, 李永玉,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2018, 34(9): 275.
[6] McGlone V A, Martinsen P J. Journal of Nea Infrared Spectroscopy, 2004, 12(1): 37.
[7] Sun T, Lin H, Xu H, et al. Postharvest Biology and Technology, 2009, 51(1): 86.
[8] Xie C, Xu N, Shao Y, et al. Spectrochimica Acta Part A: Molecular & Biomolecular Spectroscopy, 2015, 149: 971.
[9] Fan S, Guo Z, Zhang B, et al. Food Analytical Methods, 2016, 9(5): 1333.
[10] Yu X, Lu H, Di W. Postharvest Biology & Technology, 2018, 141: 39.
[11] Huang Y P, Lu R F, Chen K J. Journal of Food Engineering, 2018, (222):185.
[12] GUO Zhi-ming,HUANG Wen-qian,CHEN Quan-sheng,et al(郭志明, 黄文倩, 陈全胜,等). Modern Food Science & Technology (现代食品科技), 2016(9): 147.
[13] LIU Yan-de, SHI Yu,CAI Li-jun, et al(刘燕德, 施 宇, 蔡丽君,等). Transactions of the Chinese Society of Agricultural Engineering(农业机械学报), 2013, 44(9): 138. |
[1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[2] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[3] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[4] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[5] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[6] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[7] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[8] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[9] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[10] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[11] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[12] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
[13] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
[14] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[15] |
GUO Ge1, 3, 4, ZHANG Meng-ling3, 4, GONG Zhi-jie3, 4, ZHANG Shi-zhuang3, 4, WANG Xiao-yu2, 5, 6*, ZHOU Zhong-hua1*, YANG Yu2, 5, 6, XIE Guang-hui3, 4. Construction of Biomass Ash Content Model Based on Near-Infrared
Spectroscopy and Complex Sample Set Partitioning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3143-3149. |
|
|
|
|