光谱学与光谱分析 |
|
|
|
|
|
A Novel Spectral Fingerprint Analysis to Discriminate Dry Red Wines |
WEN Yan1, TAO Yong-sheng1,2*, HOU Xiao-fan1, Marta Dizy3 |
1. College of Enology, Northwest A&F University, Yangling 712100,China 2. Shaanxi Engineering Research Center for Viti-Viniculture, Yangling 712100,China 3. Agriculture and Alimentation Department, La Rioja University, Logroo 26006,Spain |
|
|
Abstract A novel spectral fingerprint to discriminate different dry red wines was built using data visualization method. Twelve red wines with different vintages, cultivars and ageing methods from Changli and Shacheng were sampled. Nine fractions of each wine were collected with a reversed-phase C18 column, and then they were lyophilized. The residue of each fraction was resolved with synthetic wine of the same volume with the fraction sample. The transmittance spectra of wines and their fractions were recorded from 190 to 1 100 nm. And the spectral data were visualized to show their visual differences directly. Mono-phenols in wine and fractions were analyzed by HPLC-DAD at wavelengths in the range where located the obvious differences of the spectral fingerprints. The results showed that the spectral differences of wine samples lied in the range of 190 to 600 nm. There were obvious differences in visual maps among wines with different vintages, mainly around 520 nm. The visualization differences among wines with distinct geographical origins lay in the F8 maps, and the differences from the aging methods almost cover the whole wavelength range visualized. However, wines from different grape cultivars had the similar visual characteristics. HPLC-DAD identified the possible mono-phenol groups for the spectral differences at 280, 313, 365 and 520 nm. It was concluded that the visualization of spectral data from 190 to 600 nm could be used to build red wine spectral fingerprint to distinguish dry red wines with different vintages, origins, and ageing methods.
|
Received: 2013-04-25
Accepted: 2013-07-18
|
|
Corresponding Authors:
TAO Yong-sheng
E-mail: taoyongsheng@nwsuaf.edu.cn
|
|
[1] Luykx D M, Ruth S M. Food Chemistry, 2008, 107: 897. [2] Reid L M, O’Donnell C P, Downey G. Trends in Food Science and Technology, 2006, 17: 344. [3] Arvanitoyannis I S, Katsota M N, Psarra E P, et al. Trends in Food Science and Technology, 1999, 10: 321. [4] Cayot N. Food Chemistry, 2007, 101: 154. [5] Lopez-Feria S, Cárdenas S, Valcárcel M. Trends in Analytical Chemistry,2008, 27: 794. [6] Plutowska B, Wardencki W. Food Chemistry, 2007, 101: 845. [7] Kozak M, Scaman C H. Journal of the Science of Food and Agriculture, 2008, 88: 1115. [8] Sáenz M P, Vicente F, Dizy M, et al. Analytica Chimica Acta, 2010, 673: 151. [9] Sáenz M P, Tao Y S, Dizy M, et al. Journal of Agricultural and Food Chemistry. 2010, 58: 12407. [10] Kennedy J A. Ciencia E Investigacion Agraria, 2008, 35: 107. [11] Sarneckis C J, Dambergs R G, Jones P, et al. Australian Journal of Grape and Wine Research, 2006, 12: 39. [12] Gutiérrez I H, Sánchez-Palomo L E, Espinosa A V. Food Chemistry, 2005, 92: 269. [13] Meléndez M E, Sánchez M S, Iiguez M, et al. Analytica Chimica Acta, 2001, 446: 157. [14] Gómez-Plaza E, Gil-Muoz R, López-Roca J M, et al. Journal of Agricultural and Food Chemistry, 2000, 48: 736. [15] Alcalde-Eon C, Escribano-Bailon M T, Santos-Buelga C, et al. Analytica Chimica Acta, 2006, 563: 238. [16] Monagas M, Bartolome B, Gomez-Cordoves C. Critical Reviews in Food Science and Nutrition, 2005, 45: 85. [17] Tao Y S, Zhang L. Science Agricultura Sinica, 2010, 43: 4271.
|
[1] |
FAN Ping-ping,LI Xue-ying,QIU Hui-min,HOU Guang-li,LIU Yan*. Spectral Analysis of Organic Carbon in Sediments of the Yellow Sea and Bohai Sea by Different Spectrometers[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 52-55. |
[2] |
YANG Chao-pu1, 2, FANG Wen-qing3*, WU Qing-feng3, LI Chun1, LI Xiao-long1. Study on Changes of Blue Light Hazard and Circadian Effect of AMOLED With Age Based on Spectral Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 36-43. |
[3] |
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. |
[4] |
LI Qi-chen1, 2, LI Min-zan1, 2*, YANG Wei2, 3, SUN Hong2, 3, ZHANG Yao1, 3. Quantitative Analysis of Water-Soluble Phosphorous Based on Raman
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3871-3876. |
[5] |
LIANG Jin-xing1, 2, 3, XIN Lei1, CHENG Jing-yao1, ZHOU Jing1, LUO Hang1, 3*. Adaptive Weighted Spectral Reconstruction Method Against
Exposure Variation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3330-3338. |
[6] |
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. |
[7] |
MA Qian1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, CHENG Hui-zhu1, 2, ZHAO Yan-chun1, 2. Research on Classification of Heavy Metal Pb in Honeysuckle Based on XRF and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2729-2733. |
[8] |
HUANG Chao1, 2, ZHAO Yu-hong1, ZHANG Hong-ming2*, LÜ Bo2, 3, YIN Xiang-hui1, SHEN Yong-cai4, 5, FU Jia2, LI Jian-kang2, 6. Development and Test of On-Line Spectroscopic System Based on Thermostatic Control Using STM32 Single-Chip Microcomputer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2734-2739. |
[9] |
ZHENG Yi-xuan1, PAN Xiao-xuan2, GUO Hong1*, CHEN Kun-long1, LUO Ao-te-gen3. Application of Spectroscopic Techniques in Investigation of the Mural in Lam Rim Hall of Wudang Lamasery, China[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2849-2854. |
[10] |
WANG Jun-jie1, YUAN Xi-ping2, 3, GAN Shu1, 2*, HU Lin1, ZHAO Hai-long1. Hyperspectral Identification Method of Typical Sedimentary Rocks in Lufeng Dinosaur Valley[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2855-2861. |
[11] |
WANG Jing-yong1, XIE Sa-sa2, 3, GAI Jing-yao1*, WANG Zi-ting2, 3*. Hyperspectral Prediction Model of Chlorophyll Content in Sugarcane Leaves Under Stress of Mosaic[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2885-2893. |
[12] |
ZHANG Hai-liang1, XIE Chao-yong1, LUO Wei1, WANG Chen2, NIE Xun1, TIAN Peng1, LIU Xue-mei3, ZHAN Bai-shao1*. Salmon Fat Visualization Based on MCR-ALS Hyperspectral
Reconstruction[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2601-2607. |
[13] |
WANG Yu-qi, LI Bin, ZHU Ming-wang, LIU Yan-de*. Optimizations of Sample and Wavelength for Apple Brix Prediction Model Based on LASSOLars Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1419-1425. |
[14] |
LI Shuai-wei1, WEI Qi1, QIU Xuan-bing1*, LI Chuan-liang1, LI Jie2, CHEN Ting-ting2. Research on Low-Cost Multi-Spectral Quantum Dots SARS-Cov-2 IgM and IgG Antibody Quantitative Device[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1012-1016. |
[15] |
DENG Xiao-jun1, 2, MA Jin-ge1, YANG Qiao-ling3, SHI Yi-yin1, HUO Yi-hui1, GU Shu-qing1, GUO De-hua1, DING Tao4, YU Yong-ai5, ZHANG Feng6. Visualized Fast Identification Method of Imported Olive Oil Quality Grade Based on Raman-UV-Visible Fusion Spectroscopy Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1117-1125. |
|
|
|
|