|
|
|
|
|
|
Determination of Soluble Solid Content in Peach Based on Hyperspectral Combination With BPSO |
ZHANG Li-xiu, ZHANG Shu-juan*, SUN Hai-xia, XUE Jian-xin, JING Jian-ping, CUI Tian-yu |
College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
|
|
|
Abstract Soluble solids (SSC) are an important index to evaluate the internal quality of Kubo peach. Traditional SSC content detection is destructive, time-consuming and laborious. Rapid and nondestructive detection of the SSC content of Kubo peach is of great importance for its quality classification. Binary particle swarm optimization (BPSO) is obtained by updating the speed formula based on standard particle swarm optimization (PSO). BPSO has the characteristics of high accuracy and fast convergence and is mostly used in optimization problems in separate spaces. Taking Kubo peach as the research object. Basedon hyperspectral technology combined with BPSO and based on BPSO combined characteristic wavelength selection algorithm to study the SSC content of Kubo peach. Firstly,hyperspectral information of 198 Kubo peaches was collected to obtain the spectral curve of Kubo peaches in the range of 900~1 700 nm. Meanwhile, theSSC value of Kubo peaches was. Used (Kennard-stone) algorithm to divide samples into a correction set (147) and a prediction set (51). The BPSO feature wavelength selection algorithm is used to extract the feature wavelength from Kubo's original spectral data. It is compared with the Competitive Adaptive Reweighting algorithm (CARS), Successive projections algorithm (SPA), and Uninformative variable selection algorithm (UVE). A method of extracting characteristic wavelength based on BPSO is proposed for primary combination (BPS0+CARS, BPSO+SPA, BPSO+UVE) and secondary combination ((BPSO+ CARS)-SPA), (BPSO+SPA)-SPA), (BPSO+UVE)-SPA). Based on the10 characteristic wavelength extraction methods above. Established support vector machine (LS-SVM) model and the genetic algorithm (GA) optimized support vector machine (GA-SVM) model of Kubo peach SSC content. The results show that the prediction performance of the model based on the BPSO algorithm is higher than that of other single characteristic wavelength algorithm, and the coefficient of determination R2p of the prediction set of the two models is above 0.97. Among the combination algorithms based on BPSO, the LS-SVM based on the quadratic combination (BPSO+SPA)-SPA algorithm has the highest prediction performance for Kubo peach SSC content when the number of characteristic wavelengths is small. The coefficient of determination between the correction set and the prediction set are 0.982 and 0.955, respectively. The root mean square errors RMSEC and RMSEP were 0.108 and 0.139, respectively. The prediction performance of the proposed model is slightly lower than that of the BPSO algorithm, but only 22 characteristic wavelengths are used for modeling, which greatly simplifies the model. These results show that (BPSO+SPA)-SPA is an effective method for extracting characteristic wavelength, which provides a new method for nondestructive detection of fruit SSC content.
|
Received: 2022-09-16
Accepted: 2022-11-17
|
|
Corresponding Authors:
ZHANG Shu-juan
E-mail: zsujuan1@163.com
|
|
[1] WANG You-sheng, WANG Sheng-jie, CHEN Xiao-yan, et al(王友升, 王胜杰, 陈小燕, 等). Journal of Chinese Institute of Food Science and Technology(中国食品学报), 2016, 16(10): 214.
[2] Li X L, Wei Y Z, , Xu J, et al. Postharvest Biology and Technology, 2018, 143: 112.
[3] Li B, Yin Ha, Liu Y D, et al. Journal of Molecular Structure, 2022, 1265: 133407.
[4] Li Y J, Ma B X, Li C, et al. Computers and Electronics in Agriculture, 2022, 193:106655.
[5] Li B C, Hou B L, Zhang D W, et al. Optik, 2016, 127(5): 2624.
[6] Fan S X, Zhang B H, Li J B, et al. Postharvest Biology and Technology, 2016, 121: 51.
[7] Wang T T, Li G H, Dai C L. Infrared Physics & Technology, 2022, 123: 104119.
[8] CAO Yin, YE Yun-tao, ZHAO Hong-li, et al(曹 引, 冶运涛, 赵红莉, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2018, 49(1): 173.
[9] ZHANG Jue, TIAN Hai-qing, ZHAO Zhi-yu, et al(张 珏, 田海清, 赵志宇,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2019, 35(1): 285.
[10] GUO Lu-bin, ZHAO Xiong-wen, GENG Sui-yan, et al(郭鲁斌, 赵雄文, 耿绥燕, 等). Electric Power Information and Communication Technology(电力信息与通信技术), 2021, 19(9): 8.
[11] LIU Jian-hua, YANG Rong-hua, SUN Shui-hua, et al(刘建华, 杨荣华, 孙水华, 等). Journal of Nanjing University (Natural Sciences Edition)[南京大学学报(自然科学版)], 2011, 47(5): 504.
[12] TANG Fei, HE Yong-yi(汤 飞, 何永义). Industrial Control Computer(工业控制计算机), 2021, 34(5): 83.
[13] SHEN Jia-jie, JIANG Hong, WANG Su(沈佳杰, 江 红, 王 肃). Computer Science(计算机科学), 2013, 40(S2): 125.
[14] XU Bao-ding, QIN Yu-hua, YANG Ning, et al(徐宝鼎, 秦玉华, 杨 宁,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2019, 39(3): 717.
[15] Zhang X, Sun J L, Li P P, et al. LWT, 2021, 152:112295.
[16] HUANG Feng-hua, ZHANG Shu-juan, YANG Yi, et al(黄锋华, 张淑娟, 杨 一, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2015, 46(11): 252.
[17] Bonah E, Huang X Y, Yi R, et al. Infrared Physics & Technology, 2020, 105: 103220.
[18] YU Feng-hua, XING Si-min, GUO Zhong-hui, et al(于丰华, 邢思敏, 郭忠辉,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2022, 38(2): 175.
[19] Wang Z L, Chen J X, Fan Y F, et al. Computers and Electronics in Agriculture, 2020, 169: 105160.
[20] Chen X W, Dong Z Y, Liu J B et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2020, 243: 118786.
[21] Zhao H T, Feng Y Z, Chen W et al. Meat Science, 2019, 151: 75.
[22] WANG Ming-hai, GUO Wen-chuan, SHANG Liang, et al(王铭海,郭文川,商 亮,等). Journal of Northwest A&F University (Natural Science Edition)[西北农林科技大学学报(自然科学版)], 2014, 42(2): 142.
[23] MENG Qing-long, SHANG Jing, HUANG Ren-shuai, et al(孟庆龙, 尚 静, 黄人帅, 等). Packaging Engineering(包装工程), 2021, 42(3): 19.
|
[1] |
LI Hui1, LIU Xu-sheng2, JIANG Jin-bao3*, CHEN Xu-hui4, ZHANG Shuai5, TANG Ke1, ZHAO Xin-wei1, DU Xing-qiang1, YU LONG Fei-xue1. Extraction of Natural Gas Microleakage Stress Regions Based on Hyperspectral Images of Winter Wheat[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 770-776. |
[2] |
HU Cheng-hao1, WU Wen-yuan1, 2*, MIAO Ying1, XU Lin-xia1, FU Xian-hao1, LANG Xia-yi1, HE Bo-wen1, QIAN Jun-feng3, 4. Study on Hyperspectral Rock Classification Based on Initial Rock
Classification System[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 784-792. |
[3] |
WANG Juan1, 2, 3, ZHANG Ai-wu1, 2, 3*, ZHANG Xi-zhen1, 2, 3, CHEN Yun-sheng1, 2, 3. Residual Quantization of Radiation Depth in Hyperspectral Image and Its Influence on Terrain Classification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 872-882. |
[4] |
LI Guo-hou1, LI Ze-xu1, JIN Song-lin1, ZHAO Wen-yi2, PAN Xi-peng3, LIANG Zheng4, QIN Li5, ZHANG Wei-dong1*. Mix Convolutional Neural Networks for Hyperspectral Wheat Variety
Discrimination[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 807-813. |
[5] |
DUAN Dan-dan1, 2, LIU Zhong-hua1*, ZHAO Chun-jiang2, 3, ZHAO Yu2, 3, WANG Fan2, 3. Estimation of Leaf and Canopy Scale Tea Polyphenol Content Based on Characteristic Spectral Parameters[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 814-820. |
[6] |
ZHANG Fu1, 2, ZHANG Fang-yuan1, CUI Xia-hua1, WANG Xin-yue1, CAO Wei-hua1, ZHANG Ya-kun1, FU San-ling3*. Identification of Ginkgo Fruit Species by Hyperspectral Image Combined With PSO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 859-864. |
[7] |
ZHANG Mei-ling, CHEN Yong-jie, WANG Min-juan, LI Min-zan, ZHENG Li-hua*. A Hyperspectral Deep Learning Model for Predicting Anthocyanin
Content in Purple Leaf Lettuce[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 865-871. |
[8] |
ZHANG Hai-liang1, ZHOU Xiao-wen1, LIU Xue-mei2*, LUO Wei2, ZHAN Bai-shao2, PAN Fan3. Freshness Identification of Turbot Based on Convolutional Neural
Network and Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 367-371. |
[9] |
ZHAO Jian-gui1, WANG Guo-liang1, 2, ZHANG Yu1, ZHAO Li-jie3, CHEN Ning1, WANG Wen-jun1, DU Hui-ling3, LI Zhi-wei1*. Hyperspectral Detection and Visualization of Pigment Content in
Different Positions of Tomato Leaf at Seadling Stage[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 386-391. |
[10] |
KANG Rui1, 2, CHENG Ya-wen1, 2, ZHOU Ling-li1, 2, REN Ni1, 2*. A Novel Classification Method of Foodborne Bacterial Species Based on Hyperspectral Microscopy Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 392-397. |
[11] |
ZHANG Fan1, WANG Wen-xiu1, WANG Chun-shan2, ZHOU Ji2, PAN Yang3, SUN Jian-feng1*. Study on Hyperspectral Detection of Potato Dry Rot in Gley Stage Based on Convolutional Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 480-489. |
[12] |
WANG Kai, XUE Jian-xin*, LI Yao-di, ZHANG Ming-yue. Hyperspectral Study on Polyphenol Oxidase Content of Cauliflower at the Early Stages of Gray Mold Infection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 534-541. |
[13] |
LI Yin-na1, 2, LI Zheng-qiang1, 2*, ZHENG Yang1, HOU Wei-zhen1, 2, XU Wen-bin1, 3, MA Yan1, FAN Cheng1, GE Bang-yu1, YAO Qian1, 2, SHI Zheng1, 2. Spectral Reconstruction Method of Mid-Infrared Surface Characteristics Based on Non-Negative Matrix Factorization[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 563-570. |
[14] |
XIAO Bin1, 2, HE Hong-chang1, DOU Shi-qing1*, FAN Dong-lin1, FU Bo-lin1, ZHANG Jie1, XIONG Yuan-kang1, SHI Jin-ke1. A Fine Classification Method of Citrus Fruit Trees Based on UAV
Hyperspectral Images and SULOV_XGBoost Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 548-557. |
[15] |
XU Zi-yang1, 2, JIANG Xin-hua1, 2*, BAI Jie1, 2, ZHANG Wen-jing1, 2, LI Jing1, 2. A Nondestructive Method for Freshness Detection of Chilled Mutton With Multiple Indicators and Improved Deep Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 580-587. |
|
|
|
|