|
|
|
|
|
|
Research on Parameter Optimization of Apple Sugar Model Based on Near-Infrared On-Line Device |
JIANG Xiao-gang1, ZHU Ming-wang1, YAO Jin-liang1, LI Bin1, LIAO Jun1, LIU Yan-de1*, ZHANG Jian-yi2, JING Han-song2 |
1. School of Intelligent Electromechanical Equipment Innovation Research Institute, East China Jiaotong University, Nanchang
330013, China
2. Zhejiang Dekfeller Intelligent Machinery Manufacturing Co., Ltd., Jinhua 321000, China
|
|
|
Abstract Soluble solids content is one of the leading evaluation indicators for internal apple quality. NIR spectroscopy is the first choice for predicting apple soluble solids. Optimizing the parameters of near-infrared spectroscopy collection devices can improve the model’s performance. In this paper, the near-infrared spectrum (350~1 150 nm) of apples was collected by the dynamic online equipment independently developed by our research group, and the effects of different parameters (movement speed, integration time, and light intensity) on the apple quality prediction model by near-infrared spectrum were studied, the parameters of the dynamic online equipment were optimized. The 210 Fuji apples were divided into two batches. The first batch of 90 apple samples was divided into a modeling set and a prediction set by the K-S algorithm, which was used to study the effect of the online prediction model on the solid soluble content of apples with different movement speeds and different integration times. At two moving speeds of 0.3 and 0.5 m·s-1, multiple scattering correction (MSC) and wavelet transform (WT) are used to preprocess the collected spectra, and the SSC model is built for the spectra with different moving speeds. The results show that the prediction model built with amoving speed of 0.5 m·s-1 performs better. Among the four different integration times, the best performance of the model built by SNV preprocessing was achieved at an integration time of 120 ms. The second batch of 120 apples was divided into modeling and prediction sets by the K-S algorithm. The influence of different light intensities on the apple’s SSC prediction model was studied using device parameters with a moving speed of 0.5 m·s-1 and integration time of 120ms. The results showed that when the light intensity was 4.5 A, the collected spectrum changed significantly compared with other light intensity groups, and the peaks at 640 and 800 nm of the spectrum disappeared. When the light intensity is 6.5A, the model after SNV pretreatment has the best performance. Competitive Adaptive Reweighting Algorithm (CARS) and Successive Projections Algorithm (SPA) were used to screen the wavelength of the collected spectral data to establish the apple SSC model. The results show that the model-based on CARS-PLS has good performance and the correlation coefficient and root mean square error of its prediction set are 0.991 and 0.149, respectively. At the same time, the model is simplified, and the stability of the model is improved. The research shows that parameter optimization of dynamic online equipment is helpful in improving the prediction accuracy of the apple model. This research is beneficial in providing technical support for online apple quality sorting.
|
Received: 2021-09-05
Accepted: 2022-04-02
|
|
Corresponding Authors:
LIU Yan-de
E-mail: jxliuyd@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] GUO Zhi-ming, HUANG Wen-qian, CHEN Quan-sheng, et al(郭志明,黄文倩,陈全胜,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2016, 32(6): 283.
[6] Liu Y, Sun X, Ouyang A. LWT—Food Science and Technology, 2010, 43(4): 602.
[7] 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.
[8] HAN Dong-hai, LIU Xin-xin, LU Chao, et al(韩东海,刘新鑫,鲁 超,等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2006, 37(6): 86.
[9] LIU Xin-xin, HAN Dong-hai, TU Run-lin, et al(刘新鑫,韩东海,涂润林,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2004, 20(1): 211.
[10] GUO Cheng, LIANG Meng-xing, JIANG Ming-zhu, et al(郭 成,梁梦醒,江明珠,等). Journal of Jiangsu University of Science and Technology(江苏科技大学学报), 2018, 32(2): 285.
[11] Xu Xiao, Mo Jiancan, Xie Lijuan, et al. Food Analytical Methods, 2019, 12(9): 2078.
[12] CUI Feng-juan, ZHA Jian-wen(崔丰娟,闸建文). Journal of Agricultural Mechanization(农机化研究), 2010, (11): 170.
[13] McGlone V A, Martinsen P J, Clark C J, et al. Postharvest Biology and Technology, 2005, 37(2): 142.
[14] Sun T, Lin H, Xu H, et al. Postharvest Biology and Technology,2009, 51(1): 86.
|
[1] |
CHENG Gang1, CAO Ya-nan1, TIAN Xing1, CAO Yuan2, LIU Kun2. Simulation of Airflow Performance and Parameter Optimization of
Photoacoustic Cell Based on Orthogonal Test[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3899-3905. |
[2] |
WANG Yu-qi, LI Bin, ZHU Ming-wang, LIU Yan-de*. Optimizations of Sample and Wavelength for Apple Brix Prediction Model Based on LASSOLars Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1419-1425. |
[3] |
CHEN Rui1, WANG Xue1, 2*, WANG Zi-wen1, QU Hao1, MA Tie-min1, CHEN Zheng-guang1, GAO Rui3. Wavelength Selection Method of Near-Infrared Spectrum Based on
Random Forest Feature Importance and Interval Partial
Least Square Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1043-1050. |
[4] |
HAO Jie, DONG Fu-jia, WANG Song-lei*, LI Ya-lei, CUI Jia-rui, LIU Si-jia, LÜ Yu. Rapid Detection of Pesticide Residues on Navel Oranges by Fluorescence Hyperspectral Imaging Technology Combined With Characteristic Wavelength Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3789-3796. |
[5] |
ZHANG Jun-yi1, 2, GAO De-hua1, SONG Di1, QIAO Lang1, SUN Hong1, LI Min-zan1*, LI Li1. Wavelengths Optimization and Chlorophyll Content Detection Based on PROSPECT Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1514-1521. |
[6] |
QIAO Lu, WANG Song-lei*, GUO Jian-hong, HE Xiao-guang. Combination of Spectral and Textural Informations of Hyperspectral Imaging for Predictions of Soluble Protein and GSH Contents in Mutton[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 176-183. |
[7] |
LU Hao-xiang1, ZHANG Jing2, LI Ling-qiao1*, LIU Zhen-bing1, YANG Hui-hua1,3, FENG Yan-chun4, YIN Li-hui4. Least Angle Regression Combined With Competitive Adaptive Re-Weighted Sampling for NIR Spectral Wavelength Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1782-1788. |
[8] |
SUN Zong-bao, LIANG Li-ming, LI Jun-kui, ZOU Xiao-bo*, LIU Xiao-yu, WANG Tian-zhen. Identification of Chilled and Frozen-Thawed Salmon Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(11): 3530-3536. |
[9] |
LI Kai1, 2, CHEN Yun-zhi1, 2*, XU Zhang-hua1, 2, 3, HUANG Xu-ying4, HU Xin-yu3, WANG Xiao-qin1, 2. Hyperspectral Estimation Method of Chlorophyll Content in MOSO Bamboo under Pests Stress[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(08): 2578-2583. |
[10] |
MA Ben-xue1,2*, YU Guo-wei1,2, WANG Wen-xia1,2, LUO Xiu-zhi1,2, LI Yu-jie1,2, LI Xiao-zhan1,2, LEI Sheng-yuan1,2. Recent Advances in Spectral Analysis Techniques for Non-Destructive Detection of Internal Quality in Watermelon and Muskmelon: A Review[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(07): 2035-2041. |
[11] |
DIWU Peng-yao, BIAN Xi-hui*, WANG Zi-fang, LIU Wei. Study on the Selection of Spectral Preprocessing Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(09): 2800-2806. |
[12] |
LI Pao1, 2, SHEN Ru-jia1, LI Shang-ke1, SHAN Yang2, DING Sheng-hua2, JIANG Li-wen1, LIU Xia1, DU Guo-rong1, 3*. Nondestructive Identification of Green Tea Based on Near Infrared Spectroscopy and Chemometric Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(08): 2584-2589. |
[13] |
SUN Hong1, LIU Ning1, XING Zi-zheng1, ZHANG Zhi-yong1, LI Min-zan1*, WU Jing-zhu2. Parameter Optimization of Potato Spectral Response Characteristics and Growth Stage Identification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(06): 1870-1877. |
[14] |
YUAN Jing-ze1, 2, LU Qi-peng1*, WU Chun-yang1, 2, DING Hai-quan1, GAO Hong-zhi1, LI Wan-xia1, 2, WANG Yang3. Noninvasive Human Triglyceride Detecting with Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(01): 42-48. |
[15] |
HE Wen-qin1, 2, YAN Wen-juan3, HE Guo-quan3, YANG Zeng-bao3, TAN Yong3, LI Gang1, 2, LIN Ling1, 2*. Study on the Wavelength Selection Based on VIP Analysis in Noninvasive Measurement of Blood Components[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(04): 1080-1084. |
|
|
|
|