基于傅里叶变换红外光谱的平菇蛋白质、多糖含量预测方法研究

doi:10.3964/j.issn.1000-0593(2023)04-1262-06

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Abstract Pleurotus ostreatus is one of the wide varieties of edible fungus, ranking third for its yield in China. Except for its delicious taste, appreciated by consumers, it is known to be rich in high-quality protein and polysaccharides with various biological activities. However, there are different kinds of P. ostreatus following their quality, and the existing nutrient composition analysis methods are time-consuming and high in composition. It is difficult to meet the requirements of the detection of their nutrient composition, as well as for other edible fungi. Fourier Translation Infrared Spectroscopy (FTIR) technology, characterized by high-speed detection, convenient technique, simultaneous analysis of multiple compounds, and safe and environmental protection, was thus used combined with stoichiometry to develop mathematical models, to assess those nutrient compounds. Therefore,the infrared spectra of 85 samples from P. ostreatusas fruiting bodies collected nationwide were determined. 5 kinds of spectral data pretreatment methods, multiple scatter correction (MSC), standard normal transformation (SNV), orthogonal signal correction (OSC), smooth plus first derivative (F-GD), and smooth plus second derivative (S-GD) were used. Following the model of the validation set regression coefficients, OSC combined with S-GD, and OSC combined with F-GD were the best pretreatment methods for the fruiting body protein and polysaccharide models. Under the optimal spectral pretreatment, 7 458 spectral bands were extracted by the LASSO algorithm, and 93 characteristic wavenumbers of protein and 92 for polysaccharides were obtained, with a compression rate of 98%. PLS model was established by fitting the characteristic wavenumbers with the protein and polysaccharide contents of P. ostreatus fruiting bodies detected by chemical method. The results showed that, for the protein model, the R² regression coefficient of the calibration set was 0.999 8, RMSECV was 0.047 7, the R² regression coefficientof the validation set was 0.987 2, RMSEP was 0.506 8, and RPD was 8.840 6 greater than 3, while for polysaccharides model, The R² regression coefficient of calibration set was 0.999 9, RMSECV was 0.020 1, the R² regression coefficient of validation set was 0.980 3, RMSEP was 0.292 9, and RPD was 7.119 8 greater than 3. The models thus had good predictive ability and robustness. This research provides a practical reference to determine a high-speed detection method for the nutrient content ofedible fungi by FTIR, a foundation to establish a nutritional quality evaluation for P.ostreatus and the promotion of their high-quality development, even for other edible fungi.

Key words：Pleurotus ostreatus; Fourier transform infrared spectroscopy; Stoichiometry; Content prediction

Received: 2022-02-11 Accepted: 2022-06-08

ZTFLH:

TP391.41

Corresponding Authors: WANG Qi E-mail: q_wang2006@126.com

Cite this article:

SU Ling,BU Ya-ping,LI Yuan-yuan, et al. Study on the Prediction Method of Pleurotus Ostreatus Protein and Polysaccharide Content Based on Fourier Transform Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1262-1267.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2023)04-1262-06 OR https://www.gpxygpfx.com/EN/Y2023/V43/I04/1262

[1] HE Wang-xing, LI Yan-sheng, SHI Xu-ping, et al(贺望兴, 李延升, 石旭平, 等). Edible Fungi of China(中国食用菌), 2021, 40(1): 153.
[2] RAO Yi-ping, CHEN Jie-hui, ZHANG Bing-na, et al(饶毅萍, 陈洁辉, 张冰娜, 等). Journal of Biology(生物学杂志), 2011, 28(3): 94.
[3] Cauli O, Rodrigo R, Lansola M, et al. Metabolic Brain Disease, 2009, 24(1): 69.
[4] LAI Shan-shan, CHEN Yu-qing, LIU Yuan-yuan, et al(赖姗姗, 陈玉青, 刘媛媛, 等). Journal of Food Safety & Quality(食品安全质量检测学报), 2018, 9(7): 1619.
[5] LONG Rui, SU Ling, WANG Qi(龙瑞, 苏玲, 王琦). Edible Fungi of China(中国食用菌)，2020, 39(5): 43.
[6] MIAO Chen, XU Shuang, ZHANG Jin-xin, et al(苗晨, 徐爽, 张金鑫, 等). Chinese Journal of Ecology(生态学杂志), 2019, 38(12): 3864.
[7] ZHAO Run, YANG Ren-jie, MOU Mei-rui, et al(赵润, 杨仁杰, 牟美睿, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2019, 35(15): 217.
[8] HE Yun-xiao, ZHANG Xiao-qing, ZHANG Yang, et al(何云啸, 张晓青, 张阳, 等). Food & Machinery(食品与机械), 2017, 33(10): 56.
[9] ZHAO Si-meng, YU Hong-wei, GAO Guan-yong, et al(赵思梦, 于宏威, 高冠勇, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(3): 912.
[10] MEN Chang-qian, MENG Xiao-chao, JIANG Gao-xia, et al(门昌骞, 孟晓超, 姜高霞, 等). Journal of Chinese Computer Systems(小型微型计算机系统)，2021, 42(9): 1865.
[11] LI Yi, ZHANG Ben-hui, GUO Yu-yan, etal(李翼, 张本慧, 郭宇燕, 等). Statistics & Decision(统计与决策), 2021, 37(13): 45.
[12] XU Yun-juan, LUO You-xi(许赟娟, 罗幼喜). Statistics & Decision(统计与决策), 2021, 37(4): 31.
[13] HE Gang, ZHU Shu-zhen, GU Hai-feng(贺刚, 朱淑珍, 顾海峰). Statistics & Decision(统计与决策), 2018, 34(17): 149.
[14] WENG Shi-fu, XU Yi-zhuang(翁诗甫, 徐怡庄). Fourier Transform Infrared Spectroscopy(傅里叶变换红外光谱分析). Beijing: Chemical Industry Press(北京: 化学工业出版社), 2021, 287.
[15] GUO Song, CHANG Qing-rui, CUI Xiao-tao, et al(郭松, 常庆瑞, 崔小涛, 等). Journal of Northeast Agricultural University(东北农业大学学报), 2021, 52(8): 79.