基于近红外光谱法对五倍子主成分含量的快速检测

doi:10.3969/j.issn.0253-2417.2024.01.012

林产化学与工业 ›› 2024, Vol. 44 ›› Issue (1): 89-96.doi: 10.3969/j.issn.0253-2417.2024.01.012

基于近红外光谱法对五倍子主成分含量的快速检测

唐保山¹, 刘义稳², 和锐¹, 邹勇³, 谢山宇², 张弘¹^,*()

1. 中国林业科学研究院高原林业研究所; 国家林业和草原局资源昆虫培育与利用重点实验室, 云南昆明 650233
2. 五峰赤诚生物科技股份有限公司, 湖北宜昌 443413
3. 五峰土家族自治县公共检验检测中心, 湖北宜昌 443413

收稿日期:2023-01-19 出版日期:2024-02-28 发布日期:2024-02-23
通讯作者: 张弘 E-mail:kmzhhong@163.com
作者简介:张弘, 研究员, 博士生导师, 研究领域为林业生物资源化学与工程; E-mail: kmzhhong@163.com
唐保山(1995—)，男，云南师宗人，博士生，主要从事昆虫资源化学与利用的研究工作
基金资助:
国家重点研发计划资助项目(2022YFD2200803);企业委托项目(ZKSHX20210531)

Rapid Determination of Main Component Content of Chinese Gallnut Based on Near Infrared Spectroscopy

Baoshan TANG¹, Yiwen LIU², Rui HE¹, Yong ZOU³, Shanyu XIE², Hong ZHANG¹^,*()

1. Institute of Highland Forest Science, Chinese Academy of Forestry; Key Laboratory of Breeding and Utilization of Resource Insects, National Forestry and Grassland Administration, Kunming 650233, China
2. Wufeng Chicheng Biotechnology Co., Ltd., Yichang 443413, China
3. Wufeng Tujia Autonomous County Public Inspection and Testing Center, Yichang 443413, China

Received:2023-01-19 Online:2024-02-28 Published:2024-02-23
Contact: Hong ZHANG E-mail:kmzhhong@163.com

摘要/Abstract

摘要：

水分、单宁酸和没食子酸含量是评价五倍子质量的重要指标，目前多采用中华人民共和国林业行业标准的方法获得，繁琐且时效性得不到保证。为了探讨快速测量这些指标的可行性，通过采集五倍子样本130份，采用化学法测量化学值，利用近红外光谱技术进行五倍子样本的光谱扫描，将其与传统方法测定的化学值进行拟合，采用偏最小二乘法(PLS)构建数学模型，对比模型相关系数和误差评估模型预测性能。最终得到五倍子中水分、单宁酸和没食子酸的最佳预处理方法分别为FD+MSC、FD和FD+ML，水分的特征波段为9 403.7~7 498.2、6 012.0~5 774.1、4 601.5~4 246.7 cm^-1，单宁酸的特征波段为9 403.7~7 498.2、6 102.0~4 597.7 cm^-1，没食子酸的特征波段为6 102.0~5 446.3，4 601.5~4 246.7 cm^-1；主因子数分别为9、10和5；水分、单宁酸和没食子酸校正集相关系数(R_c²)分别为0.968、0.915和0.926，交叉验证均方根误差(RMSECV)分别为0.72%、2.55%和1.47%，相对分析误差(RPD)分别为5.57%、3.42%和3.69%；验证集相关系数(R_p²)分别为0.951、0.891和0.891，预测均方根误差(RMSEP)分别为0.563%、1.28%和0.414%。分析结果表明利用近红外光谱法结合化学计量学方法对五倍子中水分、单宁酸和没食子酸含量的检测具有可行性并达到很好的效果。

关键词: 五倍子, 快速检测, 近红外光谱, 质量评价

Abstract:

The content of moisture, tannic acid and gallic acid was an important index to evaluate the quality of Chinese gallnut. Generally, it was determined according to the forestry industry standard of the People's Republic of China, which was cumbersome and its timeliness was not guaranteed. In order to study the feasibility of rapid detection of these indicators, 130 Chinese gallnut samples were collected, and chemical values were determined by chemical method. NIR spectroscopy was used to scan Chinese gallnut samples, which was fitted with chemical values measured by traditional methods. The partial least squares(PLS) was used to build a mathematical model, and the correlation coefficient and error of the model were compared to evaluate the prediction performance of the model. The optimal pretreatment methods for moisture, tannic acid and gallic acid in gallnut were FD+MSC, FD and FD+ML, respectively. The characteristic bands of moisture were 9 403.7-7 498.2, 6 012.0-5 774.1, 4 601.5-4 246.7 cm^-1, the characteristic bands of tannic acid were 9 403.7-7 498.2, 6 102.0-4 597.7 cm^-1, and the characteristic bands of gallic acid were 6 102.0-5 446.3, 4 601.5-4 246.7 cm^-1, respectively. The number of principal factors were 9, 10 and 5, respectively. The correlation coefficients of corrected sets(R_c²) for moisture, tannic acid and gallic acid were 0.968, 0.915 and 0.926, the root mean square error of cross validation(RMSECV) were 0.72%, 2.55% and 1.47%, and the relative percent deviation(RPD) were 5.57%, 3.42% and 3.69%, respectively. The correlation coefficients of validation sets(R_p²) were 0.951, 0.891 and 0.891, and root mean square error of prediction(RMSEP) were 0.563%, 1.28% and 0.414%, respectively. The results showed that it was feasible and effective to detect the content of moisture, tannic acid and gallic acid in Chinese gallnut by NIR combined with chemometrics.

Key words: Chinese gallnut, rapid detection, near infrared spectrum, quality evaluation

中图分类号:

TQ35
R282

唐保山, 刘义稳, 和锐, 邹勇, 谢山宇, 张弘. 基于近红外光谱法对五倍子主成分含量的快速检测[J]. 林产化学与工业, 2024, 44(1): 89-96.

Baoshan TANG, Yiwen LIU, Rui HE, Yong ZOU, Shanyu XIE, Hong ZHANG. Rapid Determination of Main Component Content of Chinese Gallnut Based on Near Infrared Spectroscopy[J]. Chemistry and Industry of Forest Products, 2024, 44(1): 89-96.

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参考文献 25

1	XI Q P, HOTH-HANNIG W, DENG S L, et al. The effect of polyphenol-containing solutions on in situ biofilm formation on enamel and dentin[J/OL]. Journal of Dentistry, 2020, 102: 103482[2022-12-10]. https://doi.org/10.1016/j.jdent.2020.103482.
2	张亮亮. 五倍子资源加工利用产业发展现状[J]. 生物质化学工程, 2020, 54 (6): 1- 5.
	ZHANG L L . Development status of Chinese gallnut industry in China[J]. Biomass Chemical Engineering, 2020, 54 (6): 1- 5.
3	WU Q Y, JIANG Y, CHEN E Z, et al. Chinese gallnut(Galla chinensis) against Vibrio parahaemolyticus: In vitro activity and the use of medicated bath method to treat infected mud crab Scylla paramamosain[J/OL]. Aquaculture, 2021, 539: 736632[2022-12-10]. https://doi.org/10.1016/j.aquaculture.2021.736632.
4	WANG J, HU X F, YANG C L, et al. Growth restriction of Rhizoctonia solani via breakage of intracellular organelles using crude extracts of gallnut and clove[J/OL]. Molecules, 2021, 26(6): 1667[2022-12-10]. https://doi.org/10.3390/molecules26061667.
5	中华人民共和国国家卫生和计划生育委员会. GB 2760—2014食品安全国家标准食品添加剂使用标准[S]. 北京: 中国标准出版社, 2014: 1-5.
	National Health and Family Planning Commission of the People's Republic of China. GB 2760—2014 National food safety standard Standard for the use of food additives[S]. Beijing: Standards Press of China, 2014: 1-5.
6	刘兰香, 査玉平, 陈京元, 等. 肚倍在各生长期的主要成分含量变化分析[J]. 生物资源, 2017, 39 (5): 366- 372.
	LIU L X , ZHA Y P , CHEN J Y , et al. Analysis of the major components of bellied gallnut during different growth period[J]. Biotic Resources, 2017, 39 (5): 366- 372.
7	WATRELOT A , LE GUERNEVÉ C , HALLÉ H , et al. Multimethod approach for extensive characterization of gallnut tannin extracts[J]. Journal of Agricultural and Food Chemistry, 2020, 68 (47): 13426- 13438. doi: 10.1021/acs.jafc.9b08221
8	HE J, LIU Z. Water vapor retrieval from MERSI NIR channels of Fengyun-3B satellite using ground-based GPS data[J/OL]. Remote Sensing of Environment, 2021, 258: 112384[2022-12-10]. https://doi.org/10.1016/j.rse.2021.112384.
9	唐保山, 李坤, 张弘, 等. 基于近红外法的紫胶原胶主组分快速预测模型构建[J]. 林产化学与工业, 2020, 40 (4): 47- 56.
	TANG B S , LI K , ZHANG H , et al. Rapid prediction model of primary components of sticklac based on near infrared method[J]. Chemistry and Industry of Forest Products, 2020, 40 (4): 47- 56.
10	MU X L, WU F P, WANG R, et al. A cyanine-derived NIR molecular rotor for ratiometric imaging of amyloid-β aggregates[J/OL]. Sensors and Actuators B: Chemical, 2021, 338: 129842[2022-12-10]. https://doi.org/10.1016/j.snb.2021.129842.
11	WANG X C , BAI T X , CHU T S , et al. A molecular design for a turn-off NIR fluoride chemosensor[J]. Journal of Molecular Modeling, 2021, 27 (4): 1- 12.
12	YU G, WANG W, JIANG C. A new direction for transition metal ion doped cubic spinel-type oxides with broadband NIR emission[J/OL]. Journal of Luminescence, 2021, 235: 118061[2021-2-10]. https://doi.org/10.1016/j.jlumin.2021.118061.
13	高瑞琳, 杨鹏硕, 许刚, 等. 基于系统建模思想的脑心通胶囊中丹酚酸B近红外定量建模[J]. 光谱学与光谱分析, 2020, 40 (11): 3573- 3578.
	GAO R L , YANG P S , XU G , et al. Study on establishment of near-infrared quantitative model for Salvianolic acid B in Naoxintong Capsule based on the system modeling idea[J]. Spectroscopy and Spectral Analysis, 2020, 40 (11): 3573- 3578.
14	雷晓晴, 王秀丽, 李耿, 等. 近红外光谱法快速测定当归中7种成分的含量[J]. 中草药, 2019, 50 (16): 3947- 3954.
	LEI X Q , WANG X L , LI G , et al. Rapid determination of seven components in Angelica sinensis based on near infrared spectroscopy[J]. Chinese Traditional and Herbal Drugs, 2019, 50 (16): 3947- 3954.
15	国家林业局. LY/T1302—2016五倍子[S]. 北京: 中国标准出版社, 2016: 1-6.
	National Forestry Administration. LY/T1302—2016 Gallnuts[S]. Beijing: Standards Press of China, 2016: 1-6.
16	国家林业局. LY/T 1642—2005单宁酸分析试验方法[S]. 北京: 中国标准出版社, 2005: 1-9.
	National Forestry Administration. LY/T 1642—2005 Test method of analysis of tannic acid[S]. Beijing: Standards Press of China, 2005: 1-9.
17	刘建学, 张卫卫, 韩四海, 等. 白酒基酒中己酸、乙酸的近红外快速检测[J]. 食品科学, 2016, 37 (4): 181- 185.
	LIU J X , ZHANG W W , HAN S H , et al. Rapid detection of caproic acid and acetic acid in liquor base based on fourier transform near-infrared spectroscopy[J]. Food Science, 2016, 37 (4): 181- 185.
18	唐保山, 李坤, 张弘, 等. 基于近红外法的紫胶原胶主组分快速预测模型构建[J]. 林产化学与工业, 2020, 40 (4): 47- 56.
	TANG B S , LI K , ZHANG H , et al. Rapid prediction model of primary components of sticklac based on near infrared method[J]. Chemistry and Industry of Forest Products, 2020, 40 (4): 47- 56.
19	MALEGORI C, OLIVERI P, MUSTORGI E, et al. An in-depth study of cheese ripening by means of NIR hyperspectral imaging: Spatial mapping of dehydration, proteolysis and lipolysis[J/OL]. Food Chemistry, 2021, 343: 128547[2022-12-10]. https://doi.org/10.1016/j.foodchem.2020.128547.
20	CHIRSTY A A , SIVARUKSHY P . Comparison of adsorption properties of commercial silica and rice husk ash(RHA) silica: A study by NIR spectroscopy[J]. Open Chemistry, 2021, 19 (1): 426- 431. doi: 10.1515/chem-2021-0044
21	XU Y J, SHEN H L, XU B B, et al. High-performance MoOx/n-Si heterojunction NIR photodetector with aluminum oxide as a tunneling passivation interlayer[J/OL]. Nanotechnology, 2021, 32(27): 275502[2022-12-10]. https://doi.org/10.1088/1361-6528/abf37c.
22	洛曲, 于修烛, 张建新, 等. 基于近红外光谱的藏区酥油脂肪和蛋白质含量快速检测分析[J]. 中国油脂, 2018, 43 (3): 136- 140.
	LUO Q , YU X Z , ZHANG J X , et al. Rapid determination of fat and protein contents in ghee using near-infrared spectroscopy[J]. China Oils and Fats, 2018, 43 (3): 136- 140.
23	李化, 柯华香, 贺敬霞, 等. 近红外漫反射光谱结合偏最小二乘法快速测定厚朴中酚类成分和水分含量[J]. 中国实验方剂学杂志, 2015, 21 (22): 72- 76.
	LI H , KE H X , HE J X , et al. Rapid determination of phenolic compounds and water contents in Magnoliae officinalis Cortex using near infrared diffuse reflectance spectroscopy combined with PLS algorithm[J]. Chinese Journal of Experimental Traditional Medical Formulae, 2015, 21 (22): 72- 76.
24	刘红梅, 肖正午, 申涛, 等. 稻米直链淀粉含量近红外检测模型的建立[J]. 湖南农业大学学报(自然科学版), 2019, 45 (2): 189- 193.
	KIU H M , XIAO Z W , SHEN T , et al. Establishment of near infrared analysis model for amylose content of rice powder[J]. Journal of Hunan Agricultural University(Natural Science), 2019, 45 (2): 189- 193.
25	朱华, 吴珽, 房桂干, 等. 近红外技术的广西速生桉抽出物含量测定与模型优化[J]. 光谱学与光谱分析, 2020, 40 (3): 793- 798.
	ZHU H , WU T , FANG G G , et al. Analysis of extractives content of Guangxi fast-growing eucalyptus and models optimization based on near-infrared technique[J]. Spectroscopy and Spectral Analysis, 2020, 40 (3): 793- 798.

组分 component	预处理方法¹⁾ pretreatment method	主因子数 principal factor number	R_c²	RMSECV/%	RPD/%
水分moisture	FD+MSC	9	0.968	0.72	5.57
	MSC	6	0.954	0.86	4.64
	FD+VN	8	0.952	0.88	4.55
	FD+ML	8	0.937	1.00	3.99
	None	10	0.937	1.01	3.97
	ECO	8	0.935	1.02	3.91
单宁酸tannic acid	FD	10	0.915	2.55	3.42
	ECO	9	0.901	2.74	3.18
	ML	10	0.900	2.75	3.16
	None	9	0.896	2.76	3.16
	MSC	9	0.870	3.14	2.78
	M-MN	10	0.851	3.37	2.59
没食子酸gallic acid	FD+ML	5	0.926	1.47	3.69
	SD	7	0.923	1.51	3.59
	None	9	0.919	1.55	3.51
	ECO	8	0.918	1.55	3.49
	FD	9	0.916	1.58	3.44
	MSC	6	0.864	2.00	2.71

组分 component	波数范围/cm^-1 wavenumber range	主因子数 principal factor number	R_c²	RMSECV/%	RPD/%
水分 moisture	9 403.7~7 498.2, 6 012.0~5 774.1, 4 601.5~4 246.7	9	0.968	0.72	5.57
	9 403.7~7 498.2, 6 102.0~5 446.3, 4 601.5~4 246.7	10	0.967	0.73	5.47
	9 403.7~7 498.2, 6 102.0~5 446.3, 4 601.5~4 424.1	6	0.965	0.75	5.31
	9 403.7~7 498.2, 6 012.0~5 774.1, 4 601.5~4 424.1	7	0.964	0.76	5.29
	9 403.7~7 498.2, 6 012.0~5 774.1	7	0.963	0.77	5.20
	8 451~7 498.2, 6 102.0~5 774.1, 4 601.5~4 424.1	8	0.963	0.77	5.19
单宁酸 tannic acid	9 403.7~7 498.2, 6 102.0~4 597.7	10	0.915	2.55	3.42
	9 403.7~8 451, 6 102.0~4 597.7	10	0.906	2.67	3.27
	9 403.7~7 498.2, 6 102.0~4 246.7	10	0.905	2.68	3.25
	8 451~7 498.2, 6 102.0~4 597.7	10	0.905	2.68	3.25
	9 403.7~4 597.7	10	0.898	2.78	3.13
	6 102.0~5 349.8	7	0.895	2.82	3.09
没食子酸 gallic acid	6 102.0~5 446.3, 4 601.5~4 246.7	5	0.926	1.47	3.69
	9 403.7~7 498.2, 4 601.5~4 246.7	8	0.923	1.50	3.61
	9 403.7~6 098.1, 4 601.5~4 246.7	8	0.922	1.51	3.58
	9 403.7~4 597.7	7	0.917	1.56	3.47
	7 502.1~4 597.7	7	0.914	1.59	3.41
	7 502.1~6 098.1, 4 601.5~4 246.7	8	0.911	1.62	3.35

参数 parameter		样本数 sample size	质量分数范围/% mass fraction range	平均值/% average value	标准差/% standard deviation
水分moisture	校正集calibration set	100	9.30~35.00	16.33	3.99
水分moisture	验证集validation set	24	10.90~20.60	15.28	2.56
单宁酸tannic acid	校正集calibration set	100	8.35~67.30	56.72	8.72
单宁酸tannic acid	验证集validation set	30	51.14~66.83	62.22	3.86
没食子酸gallic acid	校正集calibration set	100	1.47~38.53	4.79	5.42
没食子酸gallic acid	验证集validation set	30	2.82~7.94	4.14	1.26

组分 component	回归方程 equation of regression	R_c²	F值 F value
水分moisture	y=0.947 1x+0.845	0.968	2 986.8
单宁酸tannic acid	y=0.946 0x+2.973	0.915	796.5
没食子酸gallic acid	y=0.828 0x+0.825	0.926	1 079.0

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[2]	张雯雯, 李坤, 唐保山, 张品德, 刘义稳, 张弘. 采后处理方式对五倍子品质的影响[J]. 林产化学与工业, 2021, 41(1): 29-37.
[3]	吴珽,梁龙,朱北平,邓拥军,房桂干. 基于LASSO法的桉木-相思混合制浆原料近红外分析模型的建立[J]. 林产化学与工业, 2020, 40(5): 83-90.
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[7]	吴珽, 房桂干, 梁龙, 林艳, 熊智新. 四种算法用于近红外测定制浆材材性的对比研究[J]. 林产化学与工业, 2016, 36(6): 63-70.
[8]	梁龙, 房桂干, 崔宏辉, 吴珽, 张新民, 赵振义. 近红外光谱结合支持向量机快速识别树种[J]. 林产化学与工业, 2016, 36(1): 55-60.
[9]	崔宏辉, 房桂干. 一种基于近红外光谱判别木材属种的方法[J]. 林产化学与工业, 2015, 35(6): 96-100.
[10]	刁松锋, 邵文豪, 栾启福, 董汝湘, 姜景民. 无患子果皮皂苷含量近红外光谱检测研究[J]. 林产化学与工业, 2014, 34(5): 91-96.
[11]	秦清;闵凡芹;徐浩;张宗和;李文君;王成章;. 响应面法优化微波辅助提取五倍子单宁工艺研究[J]. 林产化学与工业, 2012, 32(6): 84-88.
[12]	张宗和;仲崇茂;黄嘉玲;秦清;徐浩;陶林. 五倍子单宁与啤酒酿造[J]. 林产化学与工业, 2009, 29(S1): 7-11.
[13]	黄安民;焦淑菲;任海青;相玉红;张卓勇. 支持向量机结合近红外光谱法测定杉木木质素的含量[J]. 林产化学与工业, 2009, 29(5): 1-5.
[14]	徐浩;张宗和;仲崇茂;秦清;陶林;王光辉. 物理吸附法提纯五倍子单宁的研究[J]. 林产化学与工业, 2008, 28(6): 79-82.
[15]	逯家辉;郭伟良;越柏玲;王迪;孟庆繁;滕利荣. 基于偏最小二乘法的近红外光谱定量分析模型测定八角茴香中莽草酸含量[J]. 林产化学与工业, 2008, 28(5): 26-30.

基于近红外光谱法对五倍子主成分含量的快速检测

Rapid Determination of Main Component Content of Chinese Gallnut Based on Near Infrared Spectroscopy

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