近红外光谱的结球甘蓝可溶性糖含量测定

doi:10.3964/j.issn.1000-0593(2018)10-3058-06

Abstract
References
Related Citation (15)

Download: PDF (2195 KB)
Export: BibTeX | EndNote (RIS)

Abstract Soluble sugar is an effective regulator of the taste of vegetables and fruits. It is also a necessary carbohydrate absorbed and used by human beings. The head cabbage is a common vegetable rich in carbohydrates. Soluble sugar content is an important parameter in determining the nutrient quality of head cabbage. Carbohydrates are made up of carbon, hydrogen and oxygen, and the molecular absorption spectra are mainly composed of the combination bands and overtone bands of C—H, O—H and CO groups, and contain abundant organic matter information. The experiment was conducted to study the rapid detection method of soluble sugar content in head cabbage by near infrared spectroscopy and chemometrics. The experiment collected a total of 161 samples of head cabbage. The spectral data were measured by the MATRIX-I FT-NIR spectrometer made in Bruker Company, Germany, and the soluble sugar was measured by the anthrone colorimetric method. Mahalanobis Distance (MD) method and Monte Carlo cross validation (MCCV) method were used to eliminate the abnormal samples. And then the Kennard-Stone (K-S) method was used to divide all samples into a calibration set and a validation set according to the given ratio. All 12 spectral pretreatment methods including Savitzky-Golay convolution smoothing (S-G), first derivative (FD), second derivative (SD), multiple scatter correction (MSC), variable Standardization (SNV), and their combinations, were used to improve the S/N ratio to find the best pretreatment method from them. The competitive adaptive reweighted sampling (CARS) algorithm was used to select and screen out the optimal wavenumbers with the greater absolute values of the regression coefficients in the PLS model, and to remove the wavenumbers with the small regression coefficients. Thus, the best wave number combination related to the nature of the measurement can be selected to get a good calibration model with good robustness and prediction ability. The coefficient of determination (R²), root mean squared error of cross validation (RMSECV), and root mean squared error of prediction (RMSEP) were used to evaluate models. According to the principles of Monte Carlo cross validation method and Mahalanobis distance method, 10 abnormal samples were eliminated, and finally 151 samples were used in modelling. The samples were divided into calibration set (110 samples) and validation set (41 samples) according to 3∶1 by K-S method. Three PLS models were established by using the original spectral data, the preprocessed spectral data, and the spectral data with optimal wavenumbers, respectively. The modeling results showed that the spectral preprocessing method using MSC combined with FD could well improve modeling accuracy, and the R² of the calibration model increased from 0.68 to 0.93 and was thought to be the best spectral data preprocessing method in this experiment. The CARS method was applied to select optimal wave numbers for modelling. From 12 000 to 10 000 cm^-1, there exist O—H str. second overtone and C—H third overtone, and the main background information in this area is water and other groups containing hydrogen. In this region 36 optimal wavenumbers were selected. From 8 500 to 10 000 cm^-1, there exist sugar’s and water’s O—H str. first overtone and glucosamine O—H str. first overtone. This region is the main spectral region containing soluble sugar information and is less affected by the background. 15 optimal wavenumbers were selected in this region. The region of 5 800 to 4 000 cm^-1 is similar to the region of 12 000 to 10 000 cm^-1, and contains 36 selected optimal wavenumbers. Based on the results of the CARS wave number optimization, a full spectrum PLS model and a CARS-PLS model to estimate the head cabbage soluble sugar content were established. The R², RMSECV, and RMSEP of the full spectrum PLS model were 0.93, 0.157 2%, and 0.132 8%, respectively. While the R², RMSECV, and RMSEP of the CARS-PLS model were 0.96, 0.076 8%, and 0.059 4%, respectively. The experimental results showed that both CARS-PLS model and full spectrum PLS model had the similar R², but the RMSECV of the CARS-PLS model was the 1/2 of that of the full spectrum PLS model, and the RMSEP of the CARS-PLS model was also close to 1/2 of that of the full spectrum PLS model. The CARS algorithm reduced the modeling variables so that the complexity of the model was reduced, and the accuracy of CARS-PLS model was improved. The CARS-PLS model is used to predict 41 samples of the validation set. The R² of the prediction set is 0.86 and the prediction standard error is 0.059 4%, which meant that the prediction model of soluble sugar content in head cabbage was practical. CARS algorithm can reduce the unrelated information and the complexity of the model, and the wavenumbers selected can introduce both the spectra related components information and the spectra related the background information to improve the adaptivity of the calibration model. It provides a new approach for quality evaluation of head cabbage.

Key words：Cabbage; Soluble sugar; Near infrared spectroscopy; Partial least squares regression

Received: 2018-04-12 Accepted: 2018-07-29

ZTFLH:

O433.4

Corresponding Authors: LI Min-zan E-mail: limz@cau.edu.cn

Cite this article:

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2018)10-3058-06 OR https://www.gpxygpfx.com/EN/Y2018/V38/I10/3058

[1] QIANG Feng, WANG Qin-zhi, HE Jian-guo, et al(强锋, 王芹志, 何建国, 等). Modern Food Science & Technology(现代食品科技), 2017, 212(4): 283.
[2] Sanchez M T, Dela Haba M J, Perez-Marin D. Computers and Electronics in Agriculture, 2013, 92: 66.
[3] Xu H R, Qi B, Sun T. Journal of Food Engineering, 2012, 109: 142.
[4] Wedding B B, White R D, Grauf S. Journal of the Science of Food and Agriculture, 2011, 91(2): 233.
[5] Liu R, Qi S, Han D. Journal of Near Infrared Spectroscopy, 2015, 23(5): 301.
[6] Fox G. Journal of the Institute of Brewing, 2016, 122(3): 437.
[7] HAO Yong, YU Yong-jian, ZHU Sheng-hu(郝勇, 余永建, 朱胜虎). Journal of Northwest A&F University·Natural Science Edition(西北农林科技大学学报·自然科学版) , 2013, 41(5): 102.
[8] DENG Liang, LENG Hong-qiong, DUAN Yuan-xing, et al(邓亮, 冷红琼, 段沅杏, 等). Journal of Yunnan Agricultural University·Nature Science(云南农业大学学报·自然科学), 2013, 28(6): 814.
[9] LI Lu, HUANG Han-ying, ZHAO Si-ming, et al(李路, 黄汉英, 赵思明, 等). Journal of the Chinese Cereals and Oils Association(中国粮油学报), 2017, 32(7): 121.
[10] Marrubini G, Papetti A, Genorini E, et al. Food Analytical Methods, 2017，10(5): 1556.
[11] Lee Y, Han S. Bulletin of the Korean Chemical Society, 2016, 37(2): 166.
[12] National Standards of the People’s Repbulic of China(中华人民共和国国家标准). GB/T 29858—2013. Standard Guidelines for Molecular Spectroscopy Multivariate Calibration Quantitative Analysis(分子光谱多元校正定量分析通则).
[13] LIU Hai-ying, WANG Hua-hua, CUI Chang-hai(刘海英, 王华华, 崔长海). Laboratory Science(实验室科学), 2013, 16(2): 19.
[14] LIANG Yi-ceng, XU Qing-song (梁逸曾, 徐青松). Instrumental Analysis of Complex Systems-White，Gray and Black Analytical Systems and Their Multivariate Methods(复杂体系仪器分析-白、灰、黑分析体系及其多变量解析方法). Beijing: Chemical Industry Press(北京: 化学工业出版社), 2012. 489.
[15] CHEN Yi-yun, ZHAO Rui-ying, QI Tian-ci，et al(陈奕云, 赵瑞瑛, 齐天赐, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(7): 2133.
[16] WANG Fu-teng, SUN Xiao-rong, LIU Cui-ling，et al(王赋腾, 孙晓荣, 刘翠玲, 等). Science and Technology of Food Industry(食品工业科技), 2017, 38(10): 58.
[17] LI Shui-fang, SHAN Yang, FAN Wei，et al(李水芳, 单杨, 范伟, 等). Food Science(食品科学), 2011, 32(8): 182.
[18] YUAN Shuai, ZHANG Juan, LIU Mei-juan，et al(原帅, 张娟, 刘美娟, 等). Journal of Analytical Science(分析科学学报), 2016, 32(4): 553.
[19] LIU Xiu-ying, WANG Li, CHANG Qing-rui，et al(刘秀英, 王力, 常庆瑞, 等). Chinese Journal of Applied Ecology(应用生态学报), 2015, 26(7): 2107.
[20] ZHANG Sen, SHI Wei-ren, SHI Xin, et al(张森, 石为人, 石欣, 等). Computer Engineering and Applications(计算机工程与应用), 2015, 51(15): 249.