光谱学与光谱分析 |
|
|
|
|
|
Detection of Syrup Adulterants in Prepackaged Pure Pineapple Juice by Fourier-Transform Infrared Spectroscopy and Chemometric Analysis |
ZHOU Mi1, KE Jian1, LI Bao-li1, TANG Cui-e1, TAN Jun1, LIU Rui1*, WANG Hong2, LI Tao2, ZHOU Sheng-yin2 |
1. Key Laboratory of Environment Correlative Dietology, College of Food Science and Technology, Huazhong Agricultural University,Wuhan 430070,China 2. Hubei Research Institute of Products Quality Supervision and Inspection, Wuhan 430061, China |
|
|
Abstract This study was performed to establish a method that can quickly and accurately identify adulterated syrup in the pure pineapple juice. A attenuated total internal refraction-fourier transform infrared spectroscopy was used to collect the range of 900~1 500 cm-1 infrared spectra of 234 samples pure pineapple juice and adulterated syrup by beet syrup, rice syrup and cassava syrup. By using linear discriminant analysis and support vector machine for the identification model, comparing the full spectral and selected wavelengths based on principal component analysis loading plots of the two models to identify adulteration. Studies showed that the correct rate of validation set by linear discriminant analysis and support vector machine model on full spectral were both higher than 88%, variables were significantly reduced from 312 to 8 after selecting the eight characteristic wavelengths, the correct rate of validation set by linear discriminant analysis model was up to 96.15% and support vector machine was increase to 94.87%. The results demonstrated that the model built using a attenuated total internal refraction-fourier transform infrared spectroscopy in combination with chemometric methods after selected characteristic wavelengths could be used for the identification of the adulterated syrup in the pure pineapple juice.
|
Received: 2015-02-04
Accepted: 2015-05-16
|
|
Corresponding Authors:
LIU Rui
E-mail: liurui@mail.hzau.edu.cn
|
|
[1] YAN Cheng-ming, ZHANG Jiang-zhou, DU Xiao-yuan, et al(严程明, 张江周, 杜晓远,等). Chinese Journal of Tropical Agriculture(热带农业科学), 2013, 33(9): 16. [2] TANG Cui-e, LIU Rui, ZHANG Li, et al(唐翠娥, 刘 睿, 张 莉,等). Food Science(食品科学), 2014, 35(9): 306. [3] Sivakesava S, Irudayaraj J M K, Korach R L. Applied Engineering in Agriculture, 2001, 17(6): 815. [4] Tzayhri Gallardo-Velázquez, Guillermo Osorio-Revilla, Yadira Rivera-Espinoza, et al. Food Research International, 2009, 42: 313. [5] Nayelli Quiones-Islas, Ofelia Gabriela Meza-Márquez, Guillermo Osorio-Revilla, et al. Food Research International, 2013, 51: 148. [6] Jha S N, Gunasekaran S. Journal of Food Engineering, 2010, 101: 337. [7] Loredana F Leopold, Nicolae Leopold, Horst-A Diehl, et al. Spectroscopy, 2011, 26: 93. [8] Sylvie Bureau, David Ruiz, Maryse Reich, et al. Food Chemistry, 2009, 115: 1133. [9] LI Ti, LI Qian, XU Jin-long, et al(李 俶, 李 倩, 徐金龙,等). Chinese Journal of High Pressure Physics(高压物理学报),2013, 27(6): 936. [10] ZHANG Xin-xin, LI Yu, JI Yu-jia,et al(张新新, 李 雨, 季玉佳,等). Journal of Shandong University·Health Sciences Edition(山东大学学报·医学版), 2012, 50(1): 143. [11] CHENG Shu-xi, KONG Wen-wen, ZHANG Chu, et al(程术希, 孔汶汶, 张 初,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(9): 2519. [12] WU Wei-hua(武维华). Plant Physiology(植物生理学). Beijing: Science Press(北京: 科学出版社), 2008. [13] MA Hui, WANG Ya-chao, MA Yong-kun, et al(马 辉, 王亚超, 马永昆,等). Modern Food Science and Technology(现代食品科技), 2014, 30(3): 220. [14] Gamal F Mohamed, Mohamed S Shaheen, Safaa K H Khalil, et al. Nature and Science, 2011, 9(11): 21. |
[1] |
YANG Cheng-en1, 2, LI Meng3, LU Qiu-yu2, WANG Jin-ling4, LI Yu-ting2*, SU Ling1*. Fast Prediction of Flavone and Polysaccharide Contents in
Aronia Melanocarpa by FTIR and ELM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 62-68. |
[2] |
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. |
[3] |
LI Yu1, ZHANG Ke-can1, PENG Li-juan2*, ZHU Zheng-liang1, HE Liang1*. Simultaneous Detection of Glucose and Xylose in Tobacco by Using Partial Least Squares Assisted UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 103-110. |
[4] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
[5] |
GUO Jing-fang, LIU Li-li*, CHENG Wei-wei, XU Bao-cheng, ZHANG Xiao-dan, YU Ying. Effect of Interaction Between Catechin and Glycosylated Porcine
Hemoglobin on Its Structural and Functional Properties[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3615-3621. |
[6] |
SUN Cheng-yu1, JIAO Long1*, YAN Na-ying1, YAN Chun-hua1, QU Le2, ZHANG Sheng-rui3, MA Ling1. Identification of Salvia Miltiorrhiza From Different Origins by Laser
Induced Breakdown Spectroscopy Combined with Artificial Neural
Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3098-3104. |
[7] |
ZHAO Ling-yi1, 2, YANG Xi3, WEI Yi4, YANG Rui-qin1, 2*, ZHAO Qian4, ZHANG Hong-wen4, CAI Wei-ping4. SERS Detection and Efficient Identification of Heroin and Its Metabolites Based on Au/SiO2 Composite Nanosphere Array[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3150-3157. |
[8] |
ZHANG Peng1, 3, YANG Yi-fan1, WANG Hui1, TU Zong-cai1, 2, SHA Xiao-mei2, HU Yue-ming1*. A Review of Structural Characterization and Detection Methods of Glycated Proteins in Food Systems[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2667-2673. |
[9] |
LIU Wen-bo, LIU Jin, HAN Tong-shuai*, GE Qing, LIU Rong. Simulation of the Effect of Dermal Thickness on Non-Invasive Blood Glucose Measurement by Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2699-2704. |
[10] |
LUAN Xin-xin1, ZHAI Chen2, AN Huan-jiong3, QIAN Cheng-jing2, SHI Xiao-mei2, WANG Wen-xiu3, HU Li-ming1*. Applications of Molecular Spectral Information Fusion to Distinguish the Rice From Different Growing Regions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2818-2824. |
[11] |
ZHANG Fu1, 2, WANG Xin-yue1, CUI Xia-hua1, YU Huang1, CAO Wei-hua1, ZHANG Ya-kun1, XIONG Ying3, FU San-ling4*. Identification of Maize Varieties by Hyperspectral Combined With Extreme Learning Machine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2928-2934. |
[12] |
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. |
[13] |
ZHAO Yu-wen1, ZHANG Ze-shuai1, ZHU Xiao-ying1, WANG Hai-xia1, 2*, LI Zheng1, 2, LU Hong-wei3, XI Meng3. Application Strategies of Surface-Enhanced Raman Spectroscopy in Simultaneous Detection of Multiple Pathogens[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2012-2018. |
[14] |
JIN Cheng-liang1, WANG Yong-jun2*, HUANG He2, LIU Jun-min3. Application of High-Dimensional Infrared Spectral Data Preprocessing in the Origin Identification of Traditional Chinese Medicinal Materials[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2238-2245. |
[15] |
HU Hui-qiang1, WEI Yun-peng1, XU Hua-xing1, ZHANG Lei2, MAO Xiao-bo1*, ZHAO Yun-ping2*. Identification of the Age of Puerariae Thomsonii Radix Based on Hyperspectral Imaging and Principal Component Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1953-1960. |
|
|
|
|