|
|
|
|
|
|
| Application of Hyperspectral Technology for the Determination of the Solid Concentration of the Anaerobic Digestion |
| YE Hui, LI Xiao-li, YU Ke-qiang, XIA Yi-hua, ZHANG Chu, HE Yong* |
| College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China |
|
|
|
|
Abstract Timely monitoring variations of the solid concentration plays a significant role in the stability control of the anaerobic digestion process. In this study, infrared hyperspectral technology coupled with chemometrics methods is applied to detect the amount of solid concentration in which process water hyacinth and rice straw are co-digested. Compared to the traditional way (2540G APHA, 1990) , it is faster and non-destructive. Firstly, the hyperspectral information of fermentation broth is obtained by application of infrared hyperspectral and the spectroscopy data is pretreated by utilizing moving average smoothing (MAS), and then adaptive weighted sampling competition (CARS), random frog (RF) and successive projections algorithm (SPA) are applied to extract characteristic wavelengths. Finally the calibration models of total solid (TS) and volatile solid (VS) are established based on the extracted characteristic wavelengths, partial least square (PLS) and least square-support vector machine (LS-SVM), Which are taken to predict the solid concentration of fermentation broth. The study indicates that SPA-LS-SVM model achieves optimal result, among which the root mean square error prediction (RMSEP) and correlation efficient (R) of the total solid concentration are respectively 0.005 8 and 0.841; the root mean square error prediction and correlation efficient of the volatile solid concentration are respectively 0.004 1 and 0.874. The study shows that it is feasible to utilize infrared hyperspectral combined with chemometrics methods for prediction of the solid concentration of the fermentation broth, and it can provide a theoretic and practical basis for setting up a spectral sensor to detect the solid concentration of anaerobic digestion process.
|
|
Received: 2015-10-17
Accepted: 2016-04-20
|
|
|
|
Corresponding Authors:
HE Yong
E-mail: yhe@zju.edu.cn
|
|
[1] Madsen M, Holm-Nielsen J B, Esbensen K H. Renewable and Sustainable Energy Reviews, 2011, 15: 3141.
[2] Reed J P, Devlin D, Esteves S R R. Water Research, 2011, 45: 2463.
[3] ANPA. Standard Methods for the Examination of Water and Wastewater. Washington DC, 1990.
[4] Serranti S, Fabbri A, Bonifazi G. Optical Engineering, 2012, 51(11): 111708.
[5] Reed J P, Devlin D, Esteves S R R, et al. Bioresource Technology, 2013, 133: 398.
[6] Krapf L C, Gronauer A, Schmidhalter U. Near Infrared Spectrosc, 2012, 20: 465.
[7] Everard C D, Kim M S, Lee H. Proc. SPIE, Sensing for Agriculter and Food Quality and Safety (Ⅵ), 2014, 9108: 91080A.
[8] Liu F, He Y. Food Chemistry，2009, 115: 1430.
[9] Li H D, Xu Q S, Liang Y Z. Analytica Chimica Acta, 2012, 740: 20.
[10] Wei F, Yang S, Gao Y. Food Analysis Methods, 2012, 5: 585.
[11] Kennard R W, Stone L A. Technometrics, 1969, 11(1): 137.
[12] Wu D, Yang H Q, Chen X J, et al. Journal of Food Engineering, 2008, 88: 474.
[13] Jacobi H F, Moschner C R, Hartung E. Bioresource Technology, 2011, 102: 4688.
[14] Madsen M, Ihungebo F N, Holm-Nielsen J B. Journal of Near Infrared Spectroscopy, 2012, 20: 635.
[15] Cozzolino D, Parker M, Dambergs R G. Biotechnology and Bioengineering, 2006, 95(6): 1101.
[16] Liu D, Sun D W, Zeng X A. Food and Bioprocess Technology，2014, 7: 307. |
| [1] |
YE Wen-chao1, LUO Shui-yang1, LI Jin-hao1, LI Zhao-rong1, FAN Zhi-wen1, XU Hai-tao1, ZHAO Jing1, LAN Yu-bin1, 2, DENG Hai-dong1*, LONG Yong-bing1, 2, 3*. Research on Classification Method of Hybrid Rice Seeds Based on the Fusion of Near-Infrared Spectra and Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2935-2941. |
| [2] |
ZHANG Fu-jie, SHI Lei, LI Li-xia*, ZHAO Hao-ran, ZHU Yin-long. Study on Nondestructive Identification of Panax Notoginseng Powder Quality Grade Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2255-2261. |
| [3] |
LOU Meng-han1, 2, JIN Hong-mei2, 3, 4*, LIANG Dong2, 3, ZHU Yan-yun2, 3, ZHU Ning2, 3, 4, LI Dan-yang2, 3. Fluorescence Spectra Characteristics of Dissolved Organic Matter in Mesophilic Anaerobic Digestion of Pig and Dairy Manure Slurries[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 141-146. |
| [4] |
LI Peng-cheng1, 2, LIU Han1, 2, ZHAO Long-lian1, 2, LI Jun-hui1, 2*. Key Parameters for Maize Leaf Moisture Measurement Using NIR Camera With Filters Based on Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3184-3188. |
| [5] |
WANG Guo-liang1, 2, YU Ke-qiang3, CHENG Kai2, LIU Xin2, WANG Wen-jun1, LI Hong2, GUO Er-hu2, LI Zhi-wei1*. Hyperspectral Technique Coupled With Chemometrics Methods for Predicting Alkali Spreading Value of Millet Flour[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3189-3193. |
| [6] |
FAN Nai-yun, LIU Gui-shan*, ZHANG Jing-jing, ZHANG Chong, YUAN Rui-rui, BAN Jing-jing. Hyperspectral Model Optimization for Protein of Tan Mutton Based on Box-Behnken[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(03): 918-923. |
| [7] |
WANG Cheng1, YU Hang1, YAO Wei-rong1, CHENG Yu-liang1, GUO Ya-hui1, QIAN He1, TAN Zhi-qiang2, XIE Yun-fei1*. A Study on the Screening of Anti-Inflammatory Drug Diclofenac Sodium in Dietary Supplements by Near Infrared Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 592-598. |
| [8] |
LIU Qun-wei, SHAO Yong-ni, CHEN Xiao-wan, JIANG Lin-hua*. Study on the Changes of Material in Scenedesmus Obliquus Based on Fourier Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(12): 3890-3894. |
| [9] |
LÜ Meng-qi1, SONG Yu-jie4, WENG Hai-yong1, 3, SUN Da-wei1, 3, DONG Xiao-ya2, FANG Hui1, 3, CEN Hai-yan1, 3*. Effect of Near Infrared Hyperspectral Imaging Scanning Speed on Prediction of Water Content in Arabidopsis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(11): 3508-3514. |
| [10] |
SHAO Yuan-yuan1, 2, WANG Yong-xian1, XUAN Guan-tao1, 3*, GAO Zong-mei4, LIU Yi1, HAN Xiang1, HU Zhi-chao2*. Hyperspectral Imaging Technique for Estimating the Shelf-Life of Kiwifruits[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(06): 1940-1946. |
| [11] |
ZHANG Jing-jing1, LIU Gui-shan1*, REN Ying-chun1, SU Wen-hao1, KANG Ning-bo2, MA Chao3. Rapid Detection of Freshness in Tan-Lamb Mutton Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(06): 1909-1914. |
| [12] |
YANG Yu-qing1, 2, ZHANG Tian-tian1,2, LI Jun-hui1, 2*, LU Meng-yao1, 2, LIU Hui1, 2, ZHAO Long-lian1, 2, ZHANG Ye-hui1, 2. Water Imaging of Living Corn Leaves Based on Near-Infrared Hysperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(12): 3743-3747. |
| [13] |
WANG Ning1, 2, WANG Chi1, BIAN Hai-yi2, WANG Jun3, WANG Peng2, BAI Peng-li3, YIN Huan-cai3, TIAN Yu-bing2, GAO Jing2*. The Identification Method of Blood by Applying Hilbert Transform to Extract Phase Information of Raman Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(08): 2412-2418. |
| [14] |
CAO Xiao-feng, REN Hui-ru, LI Xing-zhi, YU Ke-qiang*, SU Bao-feng*. Discrimination of Winter Jujube’s Maturity Using Hyperspectral Technique Combined with Characteristic Wavelength and Spectral Indices[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2175-2182. |
| [15] |
XIANG Li-rong, MA Zhi-hong, ZHAO Xin-yu, LIU Fei, HE Yong, FENG Lei*. Comparative Analysis of Chemometrics Method on Heavy Metal Detection in Soil with Laser-Induced Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(12): 3871-3876. |
|
|
|
|
