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.
Key words：Near infrared spectroscopy; Fertile orange; Non-destructive analysis; Principal component analysis; Fisher linear discriminant analysis
张欣欣，李尚科，李 跑，单 杨，蒋立文，刘 霞. 近红外光谱的不同产地柑橘无损鉴别方法[J]. 光谱学与光谱分析, 2021, 41(12): 3695-3700.
ZHANG Xin-xin, LI Shang-ke, LI Pao, SHAN Yang, JIANG Li-wen, LIU Xia. A Nondestructive Identification Method of Producing Regions of Citrus Based on Near Infrared Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3695-3700.
[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.
