|
|
|
|
|
|
A Nondestructive Identification Method of Producing Regions of Citrus Based on Near Infrared Spectroscopy |
ZHANG Xin-xin1, LI Shang-ke1, LI Pao1, 2*, SHAN Yang2, JIANG Li-wen1, LIU Xia1 |
1. College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha 410128, China
2. Hunan Agricultural Product Processing Institute, Hunan Academy of Agricultural Sciences, Changsha 410125, China |
|
|
Abstract Citrus is one of the popular fruits in the world. There are differences in internal quality and price of different-regions citrus. However, the appearance differences are small, and it is difficult for laypeople to identify by appearance. Methods such as DNA labeling and instrumental analysis are complex in operation and destructive to samples, which cannot achieve rapid and non-destructive analysis, affecting the secondary sales of products. Near-infrared spectroscopy is a fast and nondestructive detection method that can be used to identify different-regions agricultural products. Due to the large interference of citrus peel on the spectra, there is a lack of nondestructive identification of citrus origin. Besides, citrus is large. Therefore, it is necessary to optimize the spectral collection points. This paper proposed a new method for nondestructive identification of different-regions citrus based on near infrared spectroscopy and chemometrics. The diffuse reflectance spectra of 120 fertile oranges from Yunnan, Hunan, Wuming and Laibin of Guangxi were obtained by near-infrared spectroscopy. Single and combined spectral pretreatment was used to eliminate multiple interferences in the spectra. The principal component analysis method was used to reduce the data dimension, which was used as the input value. Combining with Fisher linear discriminant analysis method, the citrus origin identification model was obtained and compared with the principal component analysis model. In addition, the effects of different spectral collection locations (4 collection points along the equator, top and bottom) on the results were investigated. The results showed that the principal component analysis method combined with the optimized spectral pretreatment method could not accurately identify different-regions citrus, and the best identification accuracy was 5%. When the principal component analysis-Fisher linear discriminant analysis was used, the average spectra of 4 collection points along the equator combined with De-bias correction or multivariate scattering correction pretreatment method could achieve 100% identification analysis of different-regions citrus. Furthermore, the average spectra of 6 collection points combined with raw data could achieve 100% identification analysis of different-regions citrus. Therefore, by optimizing the spectral pretreatment methods and spectral collection points, the accurate identification model of different-regions citrus can be established by using principal component analysis-Fisher linear discriminant analysis method, which provides a new method for rapid identification of different-regions citrus.
|
Received: 2020-11-15
Accepted: 2021-02-19
|
|
Corresponding Authors:
LI Pao
E-mail: lipao@mail.nankai.edu.cn
|
|
[1] Farag M A, Abib B, Ayad L, et al. Food Chemistry, 2020, 331: 127306.
[2] Xiao Z B, Ma S T, Niu Y W, et al. Flavour and Fragrance Journal, 2016, 31(1): 41.
[3] Nicolosi E, Deng Z N, Gentile A, et al. Theoretical and Applied Genetics, 2000, 100(8): 1155.
[4] Liu P, Wang J, Li Q, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2019, 206:23.
[5] Li P, Zhang X X, Li S K, et al. Sensors, 2020, 20(6): 1586.
[6] LI Shang-ke, DONG Yi-qing, LI Pao, et al(李尚科, 董怡青, 李 跑, 等). Journal of Chinese Institute of Food Science and Technology(中国食品学报), 2020, 20(4): 240.
[7] Han X, Huang Z X, Chen X D, et al. Fuel, 2017, 207: 146.
[8] Han X, Tan Z, Huang Z X, et al. Analytical Methods, 2017, 9: 3720.
[9] Li J Y, Yu M, Li S K, et al. Food Science & Nutrition, 2021, 9(8): 4176.
[10] Bian X H, Wang K Y, Tan E X, et al. Chemometrics and Intelligent Laboratory Systems, 2020, 197(2): 103916.
[11] Barnes R J, Dhanoa M S, Lister J S. Applied Spectroscopy, 1989, 43(5): 772.
[12] 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.
[13] Helland S I, Naes T, Isaksson T. Chemometrics and Intelligent Laboratory Systems, 1995, 29(2): 233.
[14] Shao X G, Leung K M, Chau F T. Accounts of Chemical Research, 2003, 36(4): 276.
[15] Liu Y, Cai W S, Shao X G. Chemometrics and Intelligent Laboratory Systems, 2013, 125:11.
[16] Liu Y, Sun X, Ouyang A, et al. Food Science and Technology, 2010, 43(4): 602.
[17] Li P, Li S K, Du G R, et al. Food Science & Nutrition, 2020, 8(5): 2543. |
[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. |
|
|
|
|