|
|
|
|
|
|
| Rapid Determination of Protein Components and Their Subunits in Peanut Based on Near Infrared Technology |
| ZHAO Si-meng1, YU Hong-wei1, GAO Guan-yong2, CHEN Ning2, WANG Bo-yan3, WANG Qiang1*, LIU Hong-zhi1* |
1. Institute of Agro-Food Science and Technology, Chinese Academy of Agricultural Sciences，Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing 100193, China
2. Jinsheng Grain and Oil Group Co., Ltd., Lünan 276600, China
3. Beijing University of Agriculture, Beijing 102206, China |
|
|
|
|
Abstract The contents of arachin, conarachin and subunits significantly affect the gel properties and solubility of peanut proteins, and then affect its application in meat products and beverage. In this study, we collected 178 peanut varieties, measured arachin, conarachin, 23.5 and 37.5 kDa subunits contents by chemical methods. On the basis of peanut sample spectrum scan by near-infrared spectrum technology, we used Partial Least Squares Regression (PLSR) stoichiometry to build a mathematical model with the chemical data. By comparing single and composite spectral pretreatments, model correlation coefficient and errors to value the performance of the models. The best pretreatment method for arachin model was determined as 2nd-der with Detrend, the correlation coefficient of correction (Rc) set was 0.92, and the standard error of calibration (SEC) was 1.41; the best pretreatment method of conarachin model was detrended with 1st-der, the Rc and SEC were 0.85 and 1.46; the best pretreatment method for the 23.5 kDa subunit model was Normalization with 2nd-der, the Rc and SEC were 0.91 and 0.53; Detrend with Baseline was the best pretreatment method for the 37.5 kDa model, the Rc and SEC was 0.91 and 0.53. External validation results showed the Square Errors of Prediction (SEP) of arachin and conarachin were 1.25 and 0.73, respectively. The SEP of 23.5 kDa model and 37.5 kDa model were 0.47 and 0.75 separately. In this study, the contents of arachin, conarachin, 23.5 and 37.5kDa subunits in the whole peanut were detected simultaneously, rapidly and non-destructively based on NIRS. It’s important for the breeding specialist to select special varieties and raw materials for the protein processing industry.
|
|
Received: 2020-02-25
Accepted: 2020-06-13
|
|
|
|
Corresponding Authors:
WANG Qiang, LIU Hong-zhi
E-mail: wangqiang06@caas.cn; lhz0416@126.com
|
|
[1] Cheng Junhu, Jin Huali, Liu Zhiwei. Infrared Physics and Technology, 2018, 88: 92.
[2] GUO Zhen, CHEN Fu-sheng, LI Run-jie, et al(郭 珍, 陈复生, 李润洁，等). Journal of the Chinese Cereals and Oils Association(中国粮油学报), 2014, 29(4): 82.
[3] WANG Qiang(王 强). Peanut Processing Quality(花生加工品质学). Beijing: China Agricultural Press(北京：中国农业出版社), 2013.
[4] DU Yin, WANG Qiang, LIU Hong-zhi, et al(杜 寅, 王 强, 刘红芝，等). Food Science(食品科学), 2013, 34(9): 42.
[5] Wang L, Liu H, Liu L, et al. International Journal of Food Properties, 2014, 17(7)：1560.
[6] Liu Yan, Zhao Guanli, Su Xinguo. Modern Food Science and Technology. 2013, 29(9): 2095.
[7] YANG Xiao-quan, CHEN Zhong, ZHAO Mou-ming(杨晓泉, 陈 中, 赵谋明）. Journal of The Chinese Cereals and Oils Association(中国粮油学报), 2001, 16(5): 25.
[8] Kandala C V, Sundaram J. Journal of Food Measurement and Characterization, 2014, 8(2): 132.
[9] YUAN Qiu-yan, LIU Hong-zhi, ZHANG Jian, et al(袁秋岩，刘红芝，张 健，等). Food Science(食品科学)，2020，41（7）.
[10] Chiou Y Y. Journal of Food Biochemistry, 1990, 14: 219.
[11] Laemmli U K. Nature, 1970, 227: 680.
[12] Ronald A Holser. American Journal of Analytical Chemistry, 2014, 5：378.
[13] Bai Tie-cheng, Wang Tao, Chen You-qi, et al. Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(4): 1323.
[14] Yu H W, Liu H Z, Wang N, et al. Analytical Methods, 2016, 8(41): 7482. |
| [1] |
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. |
| [2] |
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. |
| [3] |
JIANG Xiao-gang1, ZHU Ming-wang1, YAO Jin-liang1, LI Bin1, LIAO Jun1, LIU Yan-de1*, ZHANG Jian-yi2, JING Han-song2. Research on Parameter Optimization of Apple Sugar Model Based on Near-Infrared On-Line Device[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 116-121. |
| [4] |
LIU Meng-xuan1, 2, 3, 4, WU Qiong5, WANG Xu-quan1, 2, 4, CHEN Qi5, ZHANG Yong-gang1, 2, HUANG Song-lei1, 2*, FANG Jia-xiong1, 2*. Validity and Redundancy of Spectral Data in the Detection Algorithm of Sucrose-Doped Content in Tea[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3647-3652. |
| [5] |
CHEN Miao, HOU Ming-yu, CUI Shun-li, LI Zhen, MU Guo-jun, LIU Ying-ru, LI Xiu-kun, LIU Li-feng*. Construction of Near-Infrared Model of Peanut Sugar Content in
Different Seed Coat Colors[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2896-2902. |
| [6] |
MA Ben-xue1,2*, YU Guo-wei1,2, WANG Wen-xia1,2, LUO Xiu-zhi1,2, LI Yu-jie1,2, LI Xiao-zhan1,2, LEI Sheng-yuan1,2. Recent Advances in Spectral Analysis Techniques for Non-Destructive Detection of Internal Quality in Watermelon and Muskmelon: A Review[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(07): 2035-2041. |
| [7] |
YU Hui-ling1, ZHANG Miao2, HOU Hong-yi2, ZHANG Yi-zhuo2*. The Inversion of Knots in Solid Wood Plates Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(08): 2618-2623. |
| [8] |
YUAN Jing-ze1, 2, LU Qi-peng1*, WU Chun-yang1, 2, DING Hai-quan1, GAO Hong-zhi1, LI Wan-xia1, 2, WANG Yang3. Noninvasive Human Triglyceride Detecting with Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(01): 42-48. |
| [9] |
ZHENG Yu1, CHEN Xiong1, ZHOU Mei2, WANG Meng-jun3, WANG Jin-hai1*, LI Gang2, CUI Jun1 . The Influence of Different Ionic Concentration in Cell Physiological Solution on Temperature Measurement by Near Infrared[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(10): 2718-2722. |
| [10] |
ZHANG Zhuo-yong . Studies on Cancer Diagnosis by Using Spectroscopy Combined with Chemometrics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(09): 2388-2392. |
| [11] |
WANG Dong-min1, JI Jun-min1, GAO Hong-zhi2 . The Effect of MSC Spectral Pretreatment Regions on Near Infrared Spectroscopy Calibration Results[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(09): 2387-2390. |
| [12] |
LEI Meng, LI Ming*, MA Xiao-ping, MIAO Yan-zi, WANG Jian-sheng . Spectral Scatter Correction of Coal Samples Based on Quasi-Linear Local Weighted Method [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(07): 1816-1820. |
| [13] |
HUANG Hua-jun1, YAN Yan-lu1, SHEN Bing-hui1, LIU Zhe1, GU Jian-cheng2, LI Shao-ming1, ZHU De-hai1, ZHANG Xiao-dong1, MA Qin1, LI Lin1, AN Dong1*. Near Infrared Spectroscopy Analysis Method of Maize Hybrid Seed Purity Discrimination [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(05): 1253-1258. |
| [14] |
WANG Kai-long1, 3, XIONG Hei-gang2, 3*, ZHANG Fang1, 3 . Study of Using Digital Photography to Measure Soil pH [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(03): 771-776. |
| [15] |
LI Qing-bo, JIA Zhao-hui . A Dimension Reduction Method Applied in Spectrum Analysis [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2013, 33(03): 780-784. |
|
|
|
|
