|
|
|
|
|
|
| Transmittance Vis-NIR Spectroscopy for Detecting Fibre Content of
Living Sugarcane |
| TANG Ruo-han1, 2, LI Xiu-hua1, 2*, LÜ Xue-gang1, 2, ZHANG Mu-qing2, 3, YAO Wei2, 3 |
1. School of Electrical Engineering, Guangxi University, Nanning 530004, China
2. Guangxi Key Laboratory of Sugarcane Biology, Guangxi University, Nanning 530004, China
3. College of Agriculture, Guangxi University, Nanning 530004, China
|
|
|
|
|
Abstract The fibre content of sugarcane is a non-negligible factor in the breeding process of sugarcane and the production of sugar, paper and other industries. It is of great significance to detect the fibre content of living sugarcane non-destructively by using visible-near infrared spectroscopy in transmission form. One hundred and twenty-three sugarcane samples of six varieties at different growth stages were collected and divided into a calibration set (82 samples) and prediction set (41 samples) in the ratio of 2∶1 using the Duplex sample set division method. The transmission spectra of sugarcane cane stem in the original state and dewaxing state were acquired at a measurement angle of 120°, and the band from 670 to 950 nm with less noise and obvious amplitude fluctuations was chosen as the actual modeling band. The waveforms were observed to find a significant increase in transmittance after dewaxing, and a PLS (Partial least squares) regression model was established to analyze the effect of wax coverage on the predictive model ability. The sugarcane samples were modeled more effectively after dewaxing. The 9 preprocessing methods, including first derivation (FD), continuous wavelet transform (CWT), and standard normal transform (SNV), are divided into four steps: baseline correction, scattering correction, smoothing, and scale scaling. The order of the steps was permuted to produce 108 combined preprocessing methods, and the PLS modeling analysis was performed for the spectra after each combined preprocessing separately, finally, the preprocessing method FD+SG with the best comprehensive modeling effect was acquired. To screen for the wavelengths carrying the most effective information, effective variables screening algorithms such as uninformative variable elimination (UVE), genetic algorithm (GA), competitive adaptive reweighted sampling (CARS), and random frog algorithm (RF) were taken to select important wavelengths for the transmission spectra after optimal pretreatment. The important wavelengths extracted by each algorithm were analyzed for PLS modeling separately, in which the important wavelengths extracted by the UVE method were modeled the best, with the number of selected wavelengths being 40, accounting for 14.3% of the full band. The R2p is 0.73, which improves 14.1% over the full-band modeling results with the same preprocessing method, and the RMSEP is 0.88, which decreases 14.6% over the full-band modeling results. The results showed that the transmittance visible-NIR spectrum could effectively predict the fibre content of living sugarcane. This study can provide a theoretical basis for developing corresponding portable sensors, and provide technical support for sugarcane breeding and production efficiency in various industries.
|
|
Received: 2022-07-12
Accepted: 2022-10-19
|
|
|
|
Corresponding Authors:
LI Xiu-hua
E-mail: lixh@gxu.edu.cn
|
|
[1] Otieno J O, Okumu N, Adalla M, et al. Asian Journal of Chemical Sciences, 2021, 10(1): 22.
[2] White W H, Tew T L, Richard E P. Journal American Society Sugar Cane Technologists, 2006, 26: 87.
[3] Waclawovsky A J, Sato P M, Lembke C G, et al. Plant Biotechnology Journal, 2010, 8(3): 263.
[4] Zhang M, Zhang B, Li H, et al. Infrared Physics & Technology, 2020, 111: 103529.
[5] Huang Z, Sanaeifar A, Tian Y, et al. Journal of Food Engineering, 2021, 293: 110374.
[6] Magwaza L S, Opara U L, Nieuwoudt H, et al. Food & Bioprocess Technology, 2012, 5(2): 425.
[7] Guthrie J A, Walsh K B, Reid D J, et al. Australian Journal of Agricultural Research, 2005, 56(4): 405.
[8] Asagekar S D, Joshi V K. Indian Journal of Fibre & Textile Research, 2014, 39(2): 180.
[9] LÜ Xue-gang, LI Xiu-hua, ZHANG Shi-min, et al（吕雪刚，李修华，张诗敏，等）. Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(12): 3747.
[10] Nawi N M, Jensen T, Chen G. Journal American Society of Sugar Cane Technologists, 2012, 32: 16.
[11] Mat N N, Rowshon K M, Guangnan C, et al. Agriculture & Agricultural Science Procedia, 2014, 2: 136.
[12] Phuphaphud A, Saengprachatanarug K, Posom J, et al. Vibrational Spectroscopy, 2019, 101: 71.
[13] Centner V, Massart D L, Denoord O E, et al. Analytical Chemistry, 1996, 68(21): 3851.
[14] Taylor C E. The Quarterly Review of Biology, 1994, 69(1): 88.
[15] Li H, Liang Y, Xu Q, et al. Analytica Chimica Acta, 2009, 648(1): 77.
[16] Li H D, Xu Q S, Liang Y Z. Analytica Chimica Acta, 2012, 740: 20.
[17] Purcell D E, Leonard G J, O'Shea M G, et al. Chemometrics & Intelligent Laboratory Systems, 2005, 76(2): 135.
|
| [1] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
| [2] |
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. |
| [3] |
MU Da1, 2, WANG Qi-shu1, 2*, CUI Zong-yu1, 2, REN Jiao-jiao1, 2, ZHANG Dan-dan1, 2, LI Li-juan1, 2, XIN Yin-jie1, 2, ZHOU Tong-yu3. Study on Interference Phenomenon in Terahertz Time Domain
Spectroscopy Nondestructive Testing of Glass Fiber Composites[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3031-3040. |
| [4] |
CAI Jian-rong1, 2, HUANG Chu-jun1, MA Li-xin1, ZHAI Li-xiang1, GUO Zhi-ming1, 3*. Hand-Held Visible/Near Infrared Nondestructive Detection System for Soluble Solid Content in Mandarin by 1D-CNN Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2792-2798. |
| [5] |
WANG Jing-yong1, XIE Sa-sa2, 3, GAI Jing-yao1*, WANG Zi-ting2, 3*. Hyperspectral Prediction Model of Chlorophyll Content in Sugarcane Leaves Under Stress of Mosaic[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2885-2893. |
| [6] |
LUO Zheng-fei1, GONG Zheng-li1, 2, YANG Jian1, 2*, YANG Chong-shan2, 3, DONG Chun-wang3*. Rapid Non-Destructive Detection Method for Black Tea With Exogenous Sucrose Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2649-2656. |
| [7] |
ZHANG Yue1, 2, LI Yang1, 2, SONG Yue-peng1, 2*. Nondestructive Detection of Slight Mechanical Damage of Apple by Hyperspectral Spectroscopy Based on Stacking Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2272-2277. |
| [8] |
LIN Jing-tao, XIN Chen-xing, LI Yan*. Spectral Characteristics of “Trapiche-Like Sapphire” From ChangLe, Shandong Province[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1199-1204. |
| [9] |
MAO Xiao-tian1, CHEN Chang2, YIN Zuo-wei1*, WANG Zi-min1. Spectra Characterization of Cr-Grossular (Tsavorite) With “Frogspawn” Color Zoning From Canada[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 520-525. |
| [10] |
SHENG Qiang1, 2, ZHENG Jian-ming1*, LIU Jiang-shan2, SHI Wei-chao1, LI Hai-tao2. Advances and Prospects in Inner Surface Defect Detection Based on Cite Space[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 9-15. |
| [11] |
BAO Pei-jin1, CHEN Quan-li1, 2*, WU Yan-han1, LI Xuan1, ZHAO An-di1. Spectroscopy Characteristics of Emerald From Swat Valley, Pakistan[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 213-219. |
| [12] |
ZHU Jin-yan, ZHU Yu-jie*, FENG Guo-hong*, ZENG Ming-fei, LIU Si-qi. Optimization of Near-Infrared Detection Model of Blueberry Sugar Content Based on Deep Belief Network and Hybrid Wavelength Selection Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3775-3782. |
| [13] |
WANG Wei, LI Yong-yu*, PENG Yan-kun, YANG Yan-ming, YAN Shuai, MA Shao-jin. Design and Experiment of a Handheld Multi-Channel Discrete Spectrum Detection Device for Potato Processing Quality[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3889-3895. |
| [14] |
LI Ming1*, HAN Dong-hai2*, LU Ding-qiang1, LU Xiao-xiang1, CHAI Chun-xiang1, LIU Wen3, SUN Ke-xuan1. Research Progress of Universal Model of Near-Infrared Spectroscopy in Agricultural Products and Foods Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3355-3360. |
| [15] |
JIN Cheng-qian1, 2, GUO Zhen1, ZHANG Jing1, MA Cheng-ye1, TANG Xiao-han1, ZHAO Nan1, YIN Xiang1. Non-Destructive Detection and Visualization of Soybean Moisture Content Using Hyperspectral Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3052-3057. |
|
|
|
|
