|
|
|
|
|
|
| Near Infrared Spectroscopy for Determination of the Geographical Origin of Huajiao |
| WU Xi-yu1,2, ZHU Shi-ping1*, HUANG Hua1, XU Dan2, GUO Qi-gao3 |
1. College of Engineering and Technology, Southwest University, Chongqing 400716, China
2. College of Food Science, Southwest University, Chongqing 400716, China
3. College of Horticulture and Landscape, Southwest University, Chongqing 400716, China |
|
|
|
|
Abstract Identification method of 205 Huajiao samples from 8 different geographical origins by near infrared spectroscopy coupled with principal component analysis (PCA) and pattern recognition based on discriminant partial least squares (DPLS) was proposed in this paper. In the spectra region between 12 500～3 800 cm-1, predictive models with different pretreatments of calibration set were built separately, and robust models indicating these geographic origins of Huajiao samples could be achieved using DPLS pattern recognition method. The correct identification rates of the independent validation set were between 85.37%~97.56%, in which DPLS discriminant model with standard normal variate (SNV) or multiplicative scatter correction (MSC) preprocessing was best. The method was effective in Huajiao origin recognition.
|
|
Received: 2017-02-21
Accepted: 2017-06-20
|
|
|
|
Corresponding Authors:
ZHU Shi-ping
E-mail: zspswu@126.com
|
|
[1] HUANG Cheng-jiu(黄成就). Rutaceae, Dicotyledoneae, Angiospermae. Flora Reipublicae Popularis Sinicae. Vol. 43(中国植物志，第43卷). Beijing: Science Press(北京：科学出版社), 1997. 13.
[2] ZHENG Dian-sheng, LIU Xu, LI Yu(郑殿升，刘 旭，黎 裕). Journal of Plant Genetic Resources(植物遗传资源学报), 2012, 13(1): 1.
[3] LI Li-xin, YANG Tu-xi, WEI An-zhi, et al(李立新，杨途熙，魏安智，等). Acta Agriculturae Boreali-Sinica(华北农学报), 2016, 31(5): 122.
[4] Negi J S, Bisht V K, Bhandari A K, et al. African Journal of Pure and Applied Chemistry, 2011, 5(12): 412.
[5] YU Xiao-qin, ZHENG Xian-yi, KAN Jian-quan, et al(余晓琴，郑显义，阚健全，等). Food Science(食品科学), 2009, 30(15): 45.
[6] ZHANG Jing-wen, ZHAO Lei, SHI Bo-lin, et al(张敬文，赵 镭，史波林，等). West China Journal of Pharmaceutical Sciences(华西药学杂志), 2016, 31(2): 109.
[7] Huang S, Zhao L, Zhou X L, et al. Chinese Chemical Letters, 2012, 23(11): 1247.
[8] Ito C, Katagiri H, Sato A, et al. Natural Medicines, 1997, 51(3): 249.
[9] YANG Zheng, GONG Jing-xin, ZHANG Ling, et al(杨 峥，公敬欣，张 玲，等). Journal of Chinese Institute of Food Science and Technology(中国食品学报), 2014, 14(5): 226.
[10] DENG Zhen-yi, SUN Bing-yin, KANG Ke-gong, et al(邓振义，孙丙寅，康克功，等). Acta Agriculturae Boreali-Occidentalis Sinica(西北农业学报), 2005, 14(3): 107.
[11] Alander J T, Bochko V, Martinkauppi B, et al. International Journal of Spectroscopy, 2013. 1.
[12] WANG Yuan-zhong, ZHAO Yan-li, ZHANG Ji, et al(王元忠，赵艳丽，张 霁，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(2): 394.
[13] WEN Tao, ZHENG Li-zhang, GONG Zhong-liang, et al(文 韬，郑立章，龚中良，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(16): 293.
[14] Marquettia I, Link J V, Lemes A L G, et al. Computers and Electronics in Agriculture, 2016, 121: 313.
[15] Luna A S, Da Silva A P, Pinho J S A, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2013, 100(12): 115.
[16] Teye E, Huang X Y, Lei W, et al. Food Research International, 2014, 55(12): 288.
[17] ZHU Shi-ping, LIANG Jing, TU Da-wei, et al(祝诗平，梁 晶，屠大伟，等). Journal of Southwest University·Natural Science Edition(西南大学学报·自然科学版), 2012, 34(5): 1. |
| [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] |
WANG Cai-ling1，ZHANG Jing1，WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
| [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] |
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. |
| [9] |
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. |
| [10] |
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. |
| [11] |
FANG Zheng, WANG Han-bo. Measurement of Plastic Film Thickness Based on X-Ray Absorption
Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3461-3468. |
| [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. |
|
|
|
|
