光谱学与光谱分析 |
|
|
|
|
|
High Throughput Screening Analysis of Preservatives and Sweeteners in Carbonated Beverages Based on Improved Standard Addition Method |
WANG Su-fang1, 2, LIU Yun1, GONG Li-hua1, DONG Chun-hong3, FU De-xue3, WANG Guo-qing1* |
1. School of Materials and Chemical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China 2. Inspection & Quarantine Technology Centre, Henan Entry-Exit Inspection & Quarantine Bureau of China, Zhengzhou 450003, China 3. Research Center of Huaiqing Chinese Medicine, Jiaozuo University, Jiaozuo 454003, China |
|
|
Abstract Simulated water samples of 3 kinds of preservatives and 4 kinds of sweeteners were formulated by using orthogonal design. Kernel independent component analysis (KICA) was used to process the UV spectra of the simulated water samples and the beverages added different amounts of the additive standards, then the independent components (ICs), i.e. the UV spectral profiles of the additives, and the ICs’ coefficient matrices were used to establish UV-KICA-SVR prediction model of the simulated preservatives and sweeteners solutions using support vector regression (SVR) analysis. The standards added beverages samples were obtained by adding different amounts level of additives in carbonated beverages, their UV spectra were processed by KICA, then IC information represented to the additives and other sample matrix were obtained, and the sample background can be deducted by removing the corresponding IC, other ICs’ coefficient matrices were used to estimate the amounts of the additives in the standard added beverage samples based on the UV-KICA-SVR model, while the intercept of linear regression equation of predicted amounts and the added amounts in the standard added samples is the additive content in the raw beverage sample. By utilization of chemometric “blind source separation” method for extracting IC information of the tested additives in the beverage and other sample matrix, and using SVR regression modeling to improve the traditional standard addition method, a new method was proposed for the screening of the preservatives and sweeteners in carbonated beverages. The proposed UV-KICA-SVR method can be used to determine 3 kinds of preservatives and 4 kinds of sweetener in the carbonate beverages with the limit of detection (LOD) are located with the range 0.2~1.0 mg·L-1, which are comparable to that of the traditional high performance liquid chromatographic (HPLC) method.
|
Received: 2014-08-31
Accepted: 2014-12-16
|
|
Corresponding Authors:
WANG Guo-qing
E-mail: gqwang@zzuli.edu.cn
|
|
[1] Standards for Use of Food Additives. National Standards of the People’s Republic of China(食品添加剂使用标准. 中华人民共和国国家标准). GB 2760-2011. [2] HUANG Yong-hui, YU Qing, LIN Qin, et al(黄永辉,余 清,林 钦,等). J. Instrum. Anal.(分析测试学报), 2011, 30: 877. [3] Andreia P do Nascimento, Marcello G Trevisan, Erika R M, et al. Poppi. Anal. Lett., 2007, 40: 975. [4] Yan J, Huang J H, He M, et al. J. Sep. Sci., 2013, 36: 2464. [5] Xin N, Gu X F, Wu H, et al. J. Chemometrics, 2012, 26: 353. [6] HOU Zhen-yu, CAI Wen-sheng, SHAO Xue-guang(侯振雨,蔡文生,邵学广). Chinese J. Anal. Chem.(分析化学), 2006, 34: 617. [7] Mamián-López M B, Poppi R J. Anal. Chimic. Acta, 2013, 760: 53. [8] Shariati-Rad M, Irandoust M, Amini T, et al. J. Chemometrics, 2013, 27: 63. [9] Reza H J, Esmat M, Nafiseh S. Food Chem., 2013, 138: 745. [10] WANG Guo-qing, GONG Li-hua, WANG Su-fang, et al(王国庆,弓丽华,王素方,等). J. Henan Normal Uni.·Sci. Ed.(河南师范大学学报·自然科学版), 2014, 42(1): 67. [11] Hyvariene A, Oja E. Neur. Netw., 2000, 13: 411. [12] Wang G Q, Ding Q Z, Hou Z Y. TrAC-Trend. Anal. Chem., 2008, 27: 368. [13] Rutledge D N, Bouveresse D J-R. TrAC-Trend. Anal. Chem., 2013, 50: 22. [14] Shao X G, Wang G Q, Wang S F, et al. Anal. Chem., 2004, 76: 5143. [15] Wang G Q, Hou Z Y, Tang Y X, et al. Anal. Chimic. Acta, 2010, 679: 43. [16] Wang G Q, Hou Z Y, Peng Y, et al. Analyst, 2011, 136: 4552. [17] Shao X G, Li Z C, Cai W S. Analyst, 2009, 134: 2095. [18] Andrade J M, Teran-Baamonde J, Soto-Ferreiro R M, et al. Anal. Chim. Acta, 2013, 780: 13. [19] Dong C H, Liu Y F, Yang W F, et al. Anal. Method., 2013, 5: 4513. |
[1] |
LI Xin1, LIU Jiang-ping1, 2*, HUANG Qing1, HU Peng-wei1, 2. Optimization of Prediction Model for Milk Fat Content Based on Improved Whale Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2779-2784. |
[2] |
JING Xia1, ZHANG Jie1, 2, WANG Jiao-jiao2, MING Shi-kang2, FU You-qiang3, FENG Hai-kuan2, SONG Xiao-yu2*. Comparison of Machine Learning Algorithms for Remote Sensing
Monitoring of Rice Yields[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1620-1627. |
[3] |
SHENG Hui1, CHI Hai-xu1, XU Ming-ming1*, LIU Shan-wei1, WAN Jian-hua1, WANG Jin-jin2. Inland Water Chemical Oxygen Demand Estimation Based on Improved SVR for Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3565-3571. |
[4] |
YANG Bao-hua, GAO Zhi-wei, QI Lin, ZHU Yue, GAO Yuan. Prediction Model of Soluble Solid Content in Peaches Based on Hyperspectral Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3559-3564. |
[5] |
ZHANG Yan-jun, KANG Cheng-long, LIU Ya-qian, FU Xing-hu*, ZHANG Jin-xiao, WANG Ming-xue, YANG Liu-zhen. Rapidly Detection of Total Nitrogen and Phosphorus Content in Water by Surface Enhanced Raman Spectroscopy and GWO-SVR Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3147-3152. |
[6] |
DAI Yuan1, XIE Ji-zheng1, YUAN Jing1, SHEN Wei1, GUO Hong-da1, SUN Xiao-ping1, WANG Zhi-gang2*. Application of Excitation-Emission Matrix (EEM) Fluorescence Combined With Linear SVM in Organic Pollution Monitoring of Water[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2839-2845. |
[7] |
GUO Mei1, 2, ZHANG Ruo-yu2, 3, ZHU Rong-guang2, 3, DUAN Hong-wei1, 2*. Quantitative Determination of Water-Soluble P in Biochar Based on NELIBS Technology and EN-SVR Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(07): 2301-2306. |
[8] |
WANG Jiao-jiao1, 2, SONG Xiao-yu1*, MEI Xin2, YANG Gui-jun1*, LI Zhen-hai1, LI He-li1, MENG Yang1. Sensitive Bands Selection and Nitrogen Content Monitoring of Rice Based on Gaussian Regression Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1722-1729. |
[9] |
LEI Hui-ping1, 2, HU Bing-liang1, YU Tao1*, LIU Jia-cheng1, LI Wei1, 2, WANG Xue-ji1, ZOU Yan3, SHI Qian3. Research on the Quantitative Analysis Method of Nitrate in Complex Water by Full Scale Spectrum With GS-SVR[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 372-378. |
[10] |
ZHENG Pei-chao1, HE Miao1, WANG Jin-mei1*, WANG Ning-shen1, LI Wei-qi1, LUO Yuan-jiang1, DONG Da-ming2, ZHENG Kun-peng1, YAN Bo-wen1. Ca and Mg Analysis in Solution by Solution Cathode Glow Discharge Combined with Standard Addition Method and Background Removal[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(01): 271-276. |
[11] |
ZHU Xiao-lin1, 2, LI Guang-hui1, 2*, ZHANG Meng1, 2. Prediction of Soluble Solid Content of Korla Pears Based on CARS-MIV[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(11): 3547-3552. |
[12] |
ZHANG Yan-jun1, 2, ZHANG Fang-cao1, FU Xing-hu1*, JIN Pei-jun1, HOU Jiao-ru1. Detection of Fatty Acid Content in Mixed Oil by Raman Spectroscopy Based on ABC-SVR Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(07): 2147-2152. |
[13] |
WANG Yu-tian1, LIU Ling-fei1*, ZHANG Li-juan1,2, ZHANG Zheng-shuai1, LIU Ting-ting1, WANG Shu-tao1, SHANG Feng-kai1. Determination of 1-Naphthol and 2-Naphthol Based on Fluorescence Spectrometry Combined with Improved FastICA-SVR[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(01): 142-149. |
[14] |
GUO Zhi-wei1, 2, SUN Lan-xiang1*, ZHANG Peng1, 3, QI Li-feng1, YU Hai-bin1, ZENG Peng1, ZHOU Zhong-han1, 3, WANG Wei1, 3, SHI You-zhen1, 4. On-Line Component Analysis of Cement Powder Using LIBS Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(01): 278-285. |
[15] |
YIN Xian-hua1, 2, WANG Qiang1, 2, MO Wei1, CHEN Tao1, 2*, SONG Ai-guo3. A Quantitative Analysis Method for GCB as Rubber Additive by Terahertz Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(11): 3385-3389. |
|
|
|
|