|
|
|
|
|
|
An Updating Method of NIR Model Based on Characteristic Wavelength for Yellow Rice Wine Detection |
CHEN Ling-yi, ZHAO Zhong-gai, LIU Fei* |
Key Laboratory of Advanced Control for Light Industry Processes of Ministry of Education,Jiangnan University,Wuxi 214122,China |
|
|
Abstract NIR (near-infrared) spectroscopy is a fast, non-destructive quantitative analysis tool. In order to improve the detection of yellow rice wine, NIR is employed for the quantitative analysis. In the detection, due to the varying factors (e. g. environment, raw material, instrument aging), the performance of model developed by the old samples may deteriorate over time. To guarantee the prediction accuracy, the recursive partial least square (RPLS) method is introduced to update the prediction model. However, the whole spectrum used to be involved in the model update, and the number of spectral variables in a whole spectrum is very large, which may result in intensive computation and no obvious improvement in prediction accuracy due to interference information included. Considering the insignificant change of characteristic wavelengths in yellow rice wine production, a model updating method is proposed in this paper based on characteristic wavelength. The correlation coefficient method is employed to extract the characteristic wavelength, and then the RPLS model is developed by incorporating the new sample information in the method. This method is applied to update the NIR detection model of total acid in yellow rice wine. The correlation coefficient r, root mean square errors of prediction (RMSEP) and residual predictive deviation (RPD) are employed to evaluate model performance. These three indices reach 0.965 7, 0.184 3 and 3.736 2 by using the proposed method. Therefore, the proposed method may optimize the model stability, improve the computational efficiency and provide an useful practical reference.
|
Received: 2016-10-26
Accepted: 2017-02-24
|
|
Corresponding Authors:
LIU Fei
E-mail: fliu@jiangnan.edu.cn
|
|
[1] YAN Yan-lu(严衍禄). Basic and Application of Near Infrared Spectroscopy Analysis(近红外光谱分析基础与应用). Beijing: Chinese Light Industry Press(北京:中国轻工业出版社), 2005.
[2] Wu Y, Jin Y, Li Y, et al. Vibrational Spectroscopy, 2012, 58(1): 109.
[3] William S. Cleveland. Journal of the American Statistical Association, 1979, 74(368): 829.
[4] Jiang J H, Berry R J, Siesler H W, et al. Analytical Chemistry, 2002, 74(14): 3555.
[5] Qin S J. Computers & Chemical Engineering, 1998, 22(4-5): 503.
[6] Haavisto O, Kaartinen, et al. International Journal of Mineral Processing, 2008, 88(3): 80.
[7] Haavisto O, Hytyniemi H. Analytica Chimica Acta, 2009, 642(1-2): 102.
[8] Haavisto O, Hytyniemi H. Journal of Process Control, 2011, 21(2): 14.
[9] Jia S, Li H, Wang Y, et al. Geoderma, 2016, 268: 92.
[10] Chen M, Khare S, Huang B, et al. Industrial & Engineering Chemistry Research, 2013, 52(23): 7886.
[11] Norgaard L, Saudland A, Wagner J, et al. Applied Spectroscopy, 2000, 54(3): 413.
[12] Centner V, Massart D L, de Noord O E, et al. Analytical Chemistry, 1996, 68(21): 3851.
[13] Leardi R, Gonzalez A L. Chemometrics & Intelligent Laboratory Systems, 1998, 41(2): 195.
[14] Halsey S A. Journal of the Institute of Brewing, 1985, 91(5): 306.
[15] Garcia-Jares C M, Medina B. Fresenius Journal of Analytical Chemistry, 1997, 357(1): 86.
[16] Tipparat P, Lapanantnoppakhun S, Jakmunee J, et al. Talanta, 2001, 53(6): 1199.
[17] Urbano-Cuadrado M, Castro L D, Perez-Juan P M, et al. Analytica Chimica Acta, 2004, 527(1): 81.
[18] HU Zhou-xiang, XIE Guang-fa(胡周祥,谢广发). China Brewing(中国酿造), 2007,(11): 73.
[19] General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China(中华人民共和国质量监督检验检疫总局). GB/T 13662—2008 Chinese Rice Wine(GB/T 13662—2008黄酒),2008.
[20] Dayal B S, Macgregor J F. Journal of Chemometrics, 1997, 11(1): 73.
[21] Dayal B S, Macgregor J F. Journal of Process Control, 2010, 7(3): 169.
[22] Malley D F, Rnicke H, Findlay D L, et al. Journal of Paleolimnology, 1999, 21(3): 295. |
[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] |
LI Xin-ting, ZHANG Feng, FENG Jie*. Convolutional Neural Network Combined With Improved Spectral
Processing Method for Potato Disease Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 215-224. |
[3] |
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. |
[4] |
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. |
[5] |
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. |
[6] |
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. |
[7] |
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. |
[8] |
MENG Shan1, 2, LI Xin-guo1, 2*. Estimation of Surface Soil Organic Carbon Content in Lakeside Oasis Based on Hyperspectral Wavelet Energy Feature Vector[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3853-3861. |
[9] |
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. |
[10] |
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. |
[11] |
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. |
[12] |
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. |
[13] |
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. |
[14] |
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. |
[15] |
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. |
|
|
|
|