光谱学与光谱分析 |
|
|
|
|
|
Online Detection of Soluble Solids Content of Pear by Near Infrared Transmission Spectrum |
SUN Tong1, YING Yi-bin1*, LIU Kui-wu1, HU Lei-xiu2 |
1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China 2. College of Adult Education,Zhejiang University, Hangzhou 310029, China |
|
|
Abstract The research was to detect soluble solids content (SSC) of pear online by near infrared transmission spectrum. The movement speed of pear was 0.5 m·s-1 the power of light source was 300 W, and semi-transmission was used to collect the spectrum of pears. The total experiment samples were 187 pears, with a calibration set of 147 pears and a validation set of 40 pears. Partial least squares (PLS) and principal component regression (PCR) technique were used to develop the calibration model for online detection. Spectral ranges of 550-700 nm, 700-850 nm, 550-850 nm were used to establish the calibration models, and it was found that the model with 550-850 nm was better than others whether for PLS or for PCR. Also, the models based on different pretreatment methods such as Savitzky-Golay smooth, first derivative, second derivative and so on were compared, and the result showed that the five point Savitzky-Golay smooth could increase S/N ratio and improve performance of the model, whereas first derivative and second derivative could do little to improve performance of the model. The best model had the satisfactory calibration and prediction abilities, with the correlation coefficient (RC)=0.948 8, root mean square error of calibration (RMSEC)=0.236 and root mean square error of validation (RMSEP)=0.548. The result in this study shows that the detection of SSC of pear online is feasible.
|
Received: 2007-07-02
Accepted: 2007-10-08
|
|
Corresponding Authors:
YING Yi-bin
E-mail: ybying@zju.edu.cn
|
|
[1] Kawano S, Sato T, Iwamoto M. Determination of Sugars in Satsuma Orange Using NIR Transmittance. In Proceedings of the Fourth International Conference on NIR Spectroscopy, by Ed. Murray I, School of Agriculture, Aberdeen, Scotland, 1992. [2] Peiris K H S, Dull G G, Leffler R G, et al. Hort Science, 1999, 34(1): 114. [3] Peirs Ann, Scheerlinck Nico, Nicolai Bart M. Postharvest Biology and Technology, 2003, 30: 233. [4] LIU Yan-de, YING Yi-bin, JIANG Huan-yu(刘燕德, 应义斌,蒋焕煜). Chinese Journal of Sensors and Actuators(传感技术学报), 2003, 16(3): 328. [5] LIU Yan-de, YING Yi-bin, FU Xia-ping(刘燕德,应义斌,傅霞萍). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2005, 25(11): 1793. [6] Liu Yande, Ying Yibin. Postharvest Biology and Technology, 2005, 37:65. [7] FU Xia-ping, YING Yi-bin, LIU Yan-de, et al(傅霞萍,应义斌,刘燕德,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(6): 1038. [8] Gomez Antihus Hernandez, He Yong, Pereira Annia Garcia. Journal of Food Engineering, 2006, 77:319. [9] LI Jian-ping, FU Xia-ping, ZHOU Ying, et al(李建平,傅霞萍,周 莹,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(9): 1605. [10] LIU Yande, YING Yibin, YU Haiyan, et al. Agricultural and Food Chemistry, 2006, 54: 2810. [11] Zou Xiaobo, Zhao Jiewen, Huang Xingyi, et al. Chemometrics and Intelligent Laboratory Systems, 2007, 87: 143. [12] Liu Yande, Ying Yibin, Fu Xiaping, et al. Journal of Food Engineering, 2007, 80: 986. [13] LU Hui-shan, YING Yi-bin, FU Xia-ping, et al(陆辉山,应义斌,傅霞萍,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(3): 494. [14] MA Guang, FU Xia-ping, ZHOU Ying, et al(马 广, 傅霞萍, 周 莹, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(5): 907. [15] LIU Yan-de, LUO Ji, OUYANG Ai-guo(刘燕德, 罗 吉, 欧阳爱国). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(3): 569. [16] XU Lu(许 禄). Method of Chemometrics(化学计量学方法). Beijing: Science Press(北京:科学出版社), 1995. 143. [17] WANG Hui-wen(王惠文). Application and Method of Partial Least-Squares (PLS) Regression (偏最小二乘回归方法及其应用). Beijing: Defense Industry Press(国防工业出版社), 1999. 4. |
[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. |
|
|
|
|