光谱学与光谱分析 |
|
|
|
|
|
Study Physicochemical Characteristics of Spring Honeys from Yunnan with the Application of HPLC-RI and FAAS |
ZHANG Zheng1,2,3, CHEN Chao2, CAO Hong-gang2, CAI Sheng-bao2, ZHAO Feng-yun1,2* |
1. College of Food Science and Engineering, Gansu Agricultural University, Lanzhou 730070, China 2. Food Safety Institute, Kunming University of Science and Technology, Kunming 650500, China 3. College of Food Science, South China Agricultural University, Guangzhou 510641, China |
|
|
Abstract Many special honeys are produced in Yunnan province due to abundant nectar plants and minerals resources provided by the unique natural environment in this area. In this work, the physicochemical property of three honeys (Viciacracca honey, Hevea brasiliensis honey and Punica granatum honey) from Yunnan was studied. The results showed that in different honeys the moisture content, electrical conductivity and dynamic viscosity were different. The sugar contents of each honey were determined with HPLC-RI. The results showed that P. granatum honey had the most abundant glucose [35.62 g·(100 g)-1], and H. brasiliensis honey had the most abundant fructose [41.03 g·(100 g)-1]. Thirteen different mineral elements in three honey species were determined with FAAS. It was found that the mineral level was from 167.24 mg·kg-1 in P. granatum honey to 437.34 mg·kg-1 in H. brasiliensis. Based on the mineral content the three honey species were classified following the principal component analysis (PCA) method. The result showed that Cu, Zn and Na could act as the elemental markers for V. cracca honey, while Mg, K, Ca, As and Cd act as the elemental markers for H. brasiliensis honey, and Fe, Mn, Ni, and Cr act as the elemental markers for P. granatum honey. This study reported the physicochemical property of three special Yunnan honeys, which could help the further study and utilization of these honeys.
|
Received: 2015-07-18
Accepted: 2015-11-22
|
|
Corresponding Authors:
ZHAO Feng-yun
E-mail: zhaofy@kmust.edu.cn
|
|
[1] White J W. Composition of Honey, in Honey: A Comprehensive Survey London. Heinemann Press, 1979. 157. [2] Alvarez-Suarez J, Gasparrini M, Forbes-Hernández T Y, et al. Foods, 2014, 3(3): 420. [3] Alvarez-Suarez J M, Giampieri F, Battino M. Curr. Med. Chem., 2013, 20(5): 621. [4] YU Ya-li(于亚丽). Apiculture of China(中国蜂业), 2012, 63(9): 47. [5] DONG Xia,LIN Zun-cheng(董 霞,林尊诚). Journal of Bee(蜜蜂杂志), 2002, (8): 31. [6] Moar N T. New Zeal J. Agr Res., 1985, 28(1): 39. [7] Silva L R, Videira R, Monteiro A P, et al. Microchem J., 2009, 93(1): 73. [8] Terrab A, Recamales A F, Hernanz D, et al. Food Chem., 2004, 88(4): 537. [9] ZHAO Ya-zhou, TIAN Wen-li, GUO Zhan-bao, et al(赵亚周,田文礼,国占宝,等). Journal of Agricultural Scienceand Technology(中国农业科技导报), 2010, 12(3): 50. [10] de Alda-Garcilope C, Gallego-Picó A, Bravo-Yagüe J C, et al. Food Chem., 2012, 135(3): 1785. [11] GU Yun,LI Hai-sheng(顾 云,李海生). Tianjin Pharmacy(天津药学), 2001, 13(2): 49. [12] Chen H, Fan C, Chang Q, et al. J. Agr. Food Chem., 2014, 62(11): 2443. [13] Terrab A, Díez M J, Heredia F. J. Food Chem., 2002, 79(3): 373. [14] Bonvehí J S, Tarrés E G. Apidologie, 1993, 24(6): 586. [15] Downey G, Hussey K, Kelly J D, et al. Food Chem., 2005, 91(2): 347. [16] Anklam E. Food Chem., 1998, 63(4): 549. |
[1] |
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. |
[2] |
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. |
[3] |
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. |
[4] |
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. |
[5] |
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. |
[6] |
JIA Zong-chao1, WANG Zi-jian1, LI Xue-ying1, 2*, QIU Hui-min1, HOU Guang-li1, FAN Ping-ping1*. Marine Sediment Particle Size Classification Based on the Fusion of
Principal Component Analysis and Continuous Projection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3075-3080. |
[7] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[8] |
XUE Fang-jia, YU Jie*, YIN Hang, XIA Qi-yu, SHI Jie-gen, HOU Di-bo, HUANG Ping-jie, ZHANG Guang-xin. A Time Series Double Threshold Method for Pollution Events Detection in Drinking Water Using Three-Dimensional Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3081-3088. |
[9] |
JIA Hao1, 3, 4, ZHANG Wei-fang1, 3, LEI Jing-wei1, 3*, LI Ying-ying1, 3, YANG Chun-jing2, 3*, XIE Cai-xia1, 3, GONG Hai-yan1, 3, DING Xin-yu1, YAO Tian-yi1. Study on Infrared Fingerprint of the Classical Famous
Prescription Yiguanjian[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3202-3210. |
[10] |
CAO Qian, MA Xiang-cai, BAI Chun-yan, SU Na, CUI Qing-bin. Research on Multispectral Dimension Reduction Method Based on Weight Function Composed of Spectral Color Difference[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2679-2686. |
[11] |
ZHANG Zi-hao1, GUO Fei3, 4, WU Kun-ze1, YANG Xin-yu2, XU Zhen1*. Performance Evaluation of the Deep Forest 2021 (DF21) Model in
Retrieving Soil Cadmium Concentration Using Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2638-2643. |
[12] |
CHEN Wan-jun1, XU Yuan-jie2, LU Zhi-yun3, QI Jin-hua3, WANG Yi-zhi1*. Discriminating Leaf Litters of Six Dominant Tree Species in the Mts. Ailaoshan Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2119-2123. |
[13] |
WANG Yu-hao1, 2, LIU Jian-guo1, 2, XU Liang2*, DENG Ya-song2, SHEN Xian-chun2, SUN Yong-feng2, XU Han-yang2. Application of Principal Component Analysis in Processing of Time-Resolved Infrared Spectra of Greenhouse Gases[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2313-2318. |
[14] |
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. |
[15] |
LIU Yu-juan1, 2, 3 , LIU Yan-da1, 2, 3, SONG Ying1, 2, 3*, ZHU Yang1, 2, 3, MENG Zhao-ling1, 2, 3. Near Infrared Spectroscopic Quantitative Detection and Analysis Method of Methanol Gasoline[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1489-1494. |
|
|
|
|