光谱学与光谱分析 |
|
|
|
|
|
Effect of Pathlength Variation on the NIR Spectra for Quality Evaluation of Chinese Rice Wine |
LIN Tao, YU Hai-yan, XU Hui-rong*,YING Yi-bin |
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China |
|
|
Abstract Near infrared (NIR) spectroscopy and chemometrics were applied to determine the effect of pathlength variation on the spectra of the Chinese rice wine and the consequences of the prediction precision of calibration models developed for measuring alcoholic degree, sugar content, and pH was investigated in the present research. Samples were scanned in transmission mode using a commercial FT-NIR spectrometer and a demountable liquid cell for versatile path length liquid sampling. By comparing the results of performance between models based on different optical pathlength (0.5, 1, 1.5, 2, 2.5, and 3 mm), the best indicators of optical pathlength were identified. Based on the optimum pathlength, the models for alcoholic degree, sugar content and pH were established. The best optical pathlength for the alcoholic degree was 2 mm, using partial least squares regression (PLSR) model with the original spectra, correlation coefficient (r) was 0.942, root mean standard error of calibration (RMSEC) and root mean standard error of cross-validation (RMESCV) were 0.256 (%,(φ)) and 0.292 (%,(φ)), respectively; the best optical pathlength for the sugar content was 1 mm, using PLSR model with the original spectra, r was 0.945, and RMSEC and RMESCV were 0.125% and 0.149%, respectively; the best optical pathlength for the pH was 2 mm, using PLSR model with the original spectra, r was 0.947, and RMSEC and RMESCV were 0.018 and 0.039, respectively. This study showed that pathlength variation had influence on the performance of calibration models for Chinese rice wine, and a suitable pathlength could effectively improve detection accuracy.
|
Received: 2007-10-26
Accepted: 2008-02-02
|
|
Corresponding Authors:
XU Hui-rong
E-mail: hrxu@zju.edu.cn
|
|
[1] XU Guang-tong, YUAN Hong-fu, LU Wan-zhen(徐广通, 袁洪福, 陆婉珍). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2000, 20(2): 134. [2] WANG Duo-jia, ZHOU Xiang-yang, JIN Tong-ming, et al(王多加, 周向阳, 金同铭, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2004, 24(4): 447. [3] XIAO Pu, SUN Su-qin, ZHOU Qun, et al(肖 璞, 孙素琴, 周 群, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2004, 24(11): 1352. [4] LIU Rong, CHEN Wen-liang, XU Ke-xin, et al(刘 蓉, 陈文亮, 徐可欣, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2005, 25(2): 207. [5] YAO Jia-biao, ZHAO Ying(姚家彪,赵 颖). Modern Instruments(现代仪器), 2006, (2):20. [6] LU Hui-shan, YING Yi-bin, FU Xia-ping, et al(陆辉山, 应义斌, 傅霞萍, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(3): 494. [7] Dambergs R G, Kambouris A, Gishen M J, et al. Journal of Agricultural and Food Chem., 2002, 50: 3079. [8] Sauvage L, Frank D, Stearne J, et al. Anal. Chim. Acta, 2002, 458: 223. [9] Yu H Y, Xu H R, Ying Y B, et al. Trans. ASAE, 2006, 49(5): 1463. [10] Mendes L S, Oliveira F C C, Suarez P A Z, et al. Anal. Chim. Acta, 2003, 493: 219. [11] Cozzolino D, Kwiatkowski M J, Parker M, et al. Anal. Chim. Acta, 2004, 513: 73. [12] LI Dai-xi, WU Zhi-yong, XU Duan-jun, et al(李代禧, 吴智勇, 徐端钧, 等). Chinese Journal of Analytical Chemistry(分析化学), 2004, 32(8): 1070. [13] Inon F A, Garrigues S, Guardia M. Anal. Chim. Acta, 2006, 571: 167. [14] Urbano-Cuadrado M, Luque de Castro M D, Perez-Juan P M, et al. Anal. Chim. Acta, 2004, 527: 81. [15] Urbano-Cuadrado M, Luque de Castro M D, Perez-Juan P M, et al. Talanta, 2005, 66: 218. [16] Yu H Y, Ying Y B, Fu X P, et al. J. Near Infrared Spectrosc., 2006, 14: 37. [17] Cozzolino D, Smyth H E, Gishen M. J. Agricultural and Food Chem., 2003, 51: 7703. [18] Liu L, Cozzolino D, Cynkar W U, et al. Journal of Agricultural and Food Chem., 2006, 54: 6754. [19] Yu H Y, Zhou Y, Ying Y B. European Food Research and Technology, 2007, 225: 313. [20] Yu H Y, Ying Y B, Fu X P, et al. Journal of Food Quality, 2006, 29: 339. |
[1] |
WANG Wen-xiu, PENG Yan-kun*, FANG Xiao-qian, BU Xiao-pu. Characteristic Variables Optimization for TVB-N in Pork Based on Two-Dimensional Correlation Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2094-2100. |
[2] |
LE Ba Tuan1, 3, XIAO Dong1*, MAO Ya-chun2, SONG Liang2, HE Da-kuo1, LIU Shan-jun2. Coal Classification Based on Visible, Near-Infrared Spectroscopy and CNN-ELM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2107-2112. |
[3] |
WANG Jie-jun1, CHEN Jia1,2, YE Song1, DONG Da-ming2*. Monitoring of Grape Decay via Its Volatiles Based on Open-Path Fourier Transform Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2132-2135. |
[4] |
LIU Jin, LUAN Xiao-li*, LIU Fei. Near Infrared Spectroscopic Modelling of Sodium Content in Oil Sands Based on Lasso Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2274-2278. |
[5] |
YU Hui-ling1, MEN Hong-sheng2, LIANG Hao2, ZHANG Yi-zhuo2*. Near Infrared Spectroscopy Identification Method of Wood Surface Defects Based on SA-PBT-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1724-1728. |
[6] |
XU Wei-jie1, WU Zhong-chen1, 2*, ZHU Xiang-ping2, ZHANG Jiang1, LING Zong-cheng1, NI Yu-heng1, GUO Kai-chen1. Classification and Discrimination of Martian-Related Minerals Using Spectral Fusion Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1926-1932. |
[7] |
LI Ying1, LI Yao-xiang1*, LI Wen-bin2, JIANG Li-chun3. Model Optimization of Wood Property and Quality Tracing Based on Wavelet Transform and NIR Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1384-1392. |
[8] |
DU Jian1, 2, HU Bing-liang1*, LIU Yong-zheng1, WEI Cui-yu1, ZHANG Geng1, TANG Xing-jia1. Study on Quality Identification of Macadamia nut Based on Convolutional Neural Networks and Spectral Features[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1514-1519. |
[9] |
HAN Guang, LIU Rong*, XU Ke-xin. Extraction of Effective Signal in Non-Invasive Blood Glucose Sensing with Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1599-1604. |
[10] |
WANG Li-shuang, ZHANG Wen-bo*, TONG Li. Studies on Dimensional Stability of Wood under Different Moisture Conditions by Near Infrared Spectroscopy Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1066-1069. |
[11] |
HUANG Hua1, WU Xi-yu2, ZHU Shi-ping1*. Feature Wavelength Selection and Efficiency Analysis for Paddy Moisture Content Prediction by Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1070-1075. |
[12] |
LI Hao-guang1,2, YU Yun-hua1,2, PANG Yan1, SHEN Xue-feng1,2. Study of Maize Haploid Identification Based on Oil Content Detection with Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1089-1094. |
[13] |
PENG Cheng1, FENG Xu-ping2*, HE Yong2, ZHANG Chu2, ZHAO Yi-ying2, XU Jun-feng1. Discrimination of Transgenic Maize Containing the Cry1Ab/Cry2Aj and G10evo Genes Using Near Infrared Spectroscopy (NIR)[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1095-1100. |
[14] |
XIA Ji-an1, YANG Yu-wang1*, CAO Hong-xin2, HAN Chen1, GE Dao-kuo2, ZHANG Wen-yu2. Classification of Broad Bean Pest of Visible-Near Infrared Spectroscopy Based on Cloud Computing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(03): 756-760. |
[15] |
MAO Ya-chun, WANG Dong, WANG Yue, LIU Shan-jun*. A FeO/TFe Determination Method of BIF Based on the Visible and Near-Infrared Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(03): 765-770. |
|
|
|
|