Detection for Different Adulterants of Ziziphi Spinosae Semen Based on Near Infrared Spectroscopy

ZHAO Xin; LIU Xin; WANG Yunpeng; ZHAO Zhilei; WANG Xianyou; WANG Tingxin; LIU Mengchen

doi:10.13386/j.issn1002-0306.2022010028

Volume 43 Issue 21

Oct. 2022

Turn off MathJax

Article Contents

Abstract

References

Science and Technology of Food Industry > 2022 > 43(21): 294-301. > DOI: 10.13386/j.issn1002-0306.2022010028

ZHAO Xin, LIU Xin, WANG Yunpeng, et al. Detection for Different Adulterants of Ziziphi Spinosae Semen Based on Near Infrared Spectroscopy[J]. Science and Technology of Food Industry, 2022, 43(21): 294−301. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2022010028.

Citation:

PDF (1600 KB)

Detection for Different Adulterants of Ziziphi Spinosae Semen Based on Near Infrared Spectroscopy

ZHAO Xin^{1, 2,},
LIU Xin^{1, 3},
WANG Yunpeng¹,
ZHAO Zhilei^{1, 4, ,},
WANG Xianyou^{1, 2, ,},
WANG Tingxin^{1, 3},
LIU Mengchen¹

1.
College of Quality and Technical Supervision, Hebei University, Baoding 071002, China
2.
National & Local Joint Engineering Research Center of Metrology Instrument and System, Baoding 071002, China
3.
Hebei Key Laboratory of Energy Metering and Safety Testing Technology, Baoding 071002, China
4.
Institute of Geographical Indications of Hebei University, Baoding 071002, China

More Information

Received Date: January 05, 2022
Available Online: August 25, 2022

Graphical Abstract

Abstract

Abstract

In this paper, near infrared spectroscopy technology was used to study the qualitative and quantitative detection of Ziziphi Spinosae Semen and its three kinds of counterfeits, Ziziphus mauritiana lam, Hovenia dulcis Thunb. and Lens culinaris. Different single-adulterant samples were prepared with adulterant concentration in range of 1%~90%. Multiple-adulterants samples were also prepared by adding the three kinds of counterfeits simultaneously. Near-infrared spectroscopy data in the range of 800~2500 nm were acquired. Principal component analysis (PCA) was used firstly to qualitatively identify Ziziphi Spinosae Semen and three kinds of counterfeits. For single-adulterant samples, five different pretreatment methods were applied to denoising. Partial least squares regression was used to establish PLS1 models to quantitatively predict concentrations of the adulterants. Successive projection algorithm (SPA) was used to select the optimal wavelength to optimize the PLS1 models. Three-wavelength prediction model for the adulteration detection of Ziziphus mauritiana Lam was established, with determination coefficient R²_p for prediction set of 0.9659 and root mean square error (RMSEP) of 6.1910%. Eight-wavelength prediction model for the adulteration detection of Hovenia dulcis Thunb. was established, with R²_p of 0.9491 and RMSEP of 7.6232%. Five-wavelength prediction model for the Lens culinaris adulteration detection was established, with R²_p of 0.9666 and RMSEP of 6.1437%. For the multiple-adulterants samples, PLS2 models were established to determine concentrations of the different counterfeits simultaneously. The prediction results for Ziziphi Spinosae Semen were the best with R²_p≥0.7115, while the results for Hovenia dulcis Thunb. were the worst with R²_p≥0.2007. The results showed that the near infrared spectroscopy could be used to inspect different counterfeits adulterated in Ziziphi Spinosae Semen. The established method provided a theoretical basis for the subsequent development of portable detection equipment for authenticity of Ziziphi Spinosae Semen. It was also as a reference for other studies on quality inspection for seed Chinese medicinal materials. The method was of an important social significance for ensuring quality and safety of Chinese medicinal materials.
- Ziziphi Spinosae Semen,
- adulteration detection,
- near infrared spectroscopy,
- partial least squares regression

FullText(HTML)

References (35)

References

[1]	国家药典委员会. 中华人民共和国药典, (一部)[S]. 2020, 382 State Pharmacopoeia Committee. Pharmacopoeia of the People's Republic of China (Part I)[S]. 2020, 382.
[2]	HONG L, SHENG J, YAN W, et al. Revealing the sedative-hypnotic effect of the extracts of herb pair Semen Ziziphis Pinosae and Radix Polygalae and related mechanisms through experiments and metabolomics approach[J]. BMC Complementary Medicine and Therapies,2020,20(1):206. doi: 10.1186/s12906-020-03000-8
[3]	YANG Y, QIAO W, LIU J S, et al. Study on compatibility of antidepressant active components in Semen Ziziphi Spinosae[J]. Lishizhen Med Mater Med Res,2012,23(1):7−8.
[4]	SI Q, NIU X, LI J, et al. Effect of jujuboside A on glomerular cell apoptosis in diabetic model rats[J]. Chinese Journal of Pharmacology and Toxicology,2017,31(5):399−404.
[5]	YOU J, PENG M, SHI J, et al. Evaluation of anxiolytic activity of compound Valeriana jatamansi Jones in mice[J]. BMC Complementary Altern Med,2012,12(1):223. doi: 10.1186/1472-6882-12-223
[6]	王瑞忠, 鲁静. 薄层色谱和高效液相色谱法鉴别真伪蜂蜜的方法研究[J]. 药物分析杂志,2014,34(4):737−741. [WANG R Z, LU J. Method study for the determination of adulteration honey by TLC and HPLC[J]. Chinese Journal of Pharmaceutical Analysis,2014,34(4):737−741.
[7]	胡丹, 尹明, 曹红, 等. 中药成方制剂参苏丸挥发性成分的气相色谱指纹图谱[J]. 国际药学研究杂志,2019,46(4):311−314. [HU D, YIN M, CAO H, et al. GC fingerprint of volatile components of Shensu Wan, a formula preparation of traditional Chinese medicine[J]. Journal of International Pharmaceutical Research,2019,46(4):311−314.
[8]	田芳, 何小芳, 谭梓君, 等. 枳壳HPLC特征图谱及6个黄酮苷类成分含量测定[J]. 药物分析杂志,2020,40(6):1090−1096. [TIAN F, HE X F, TAN Z J, et al. Study on characteristic chromatogram and quantitation method of six components in Aurantii fructus[J]. Chinese Journal of Pharmaceutical Analysis,2020,40(6):1090−1096.
[9]	JOEL B J. An overview of near-infrared spectroscopy (NIRS) for the detection of insect pests in stored grains[J]. Journal of Stored Products Research,2020,86:101558. doi: 10.1016/j.jspr.2019.101558
[10]	吕慧, 张正竹, 王胜鹏, 等. 基于近红外光谱技术的大米品质分析与种类鉴别[J]. 食品工业科技,2012,33(3):322−325. [LÜ H, ZHANG Z Z, WANG S P, et al. Quality analysis and category identification of rice based on the near infrared spectroscopy (NIRS)[J]. Science and Technology of Food Industry,2012,33(3):322−325.
[11]	PANMANAS S. NIR spectroscopy for quality evaluation of fruits and vegetables[J]. Materials Today: Proceedings,2018,5(10):22481−22486. doi: 10.1016/j.matpr.2018.06.619
[12]	肖慧, 孙柯, 屠康, 等. 便携式葡萄专用可见-近红外光谱检测仪器开发与实验[J]. 食品科学,2019,40(8):300−305. [XIAO H, SUN K, TU K, et al. Development and application of a specialized portable visible and near-infrared instrument for grape qua-lity detection[J]. Food Science,2019,40(8):300−305. doi: 10.7506/spkx1002-6630-20171124-305
[13]	王彩霞, 王松磊, 贺晓光, 等. 基于可见/近红外高光谱成像技术的牛肉品种鉴别[J]. 食品工业科技,2019,40(12):241−247. [WANG C X, WANG S L, HE X G, et al. Identification of beef breeds based on the Vis/NIR hyperspectral imaging technique[J]. Science and Technology of Food Industry,2019,40(12):241−247.
[14]	BALAGE J M, E SILVA S L, GOMIDE C A, et al. Predicting pork quality using Vis/NIR spectroscopy[J]. Meat Science,2015,108:37−43. doi: 10.1016/j.meatsci.2015.04.018
[15]	乔春艳, 张雪茹, 郑卫民, 等. 基于近红外光谱技术对市售羊奶粉品质指标独立模型与通用模型探究[J]. 食品工业科技,2021,42(22):297−305. [QIAO C Y, ZHANG X R, ZHENG W M, et al. Research on the independent model and general quantitative model of commercial goat milk powder quality index based on near infrared spectroscopy technology[J]. Science and Technology of Food Industry,2021,42(22):297−305.
[16]	DOS SANTOS PEREIRA E V, DE SOUSA FERNANDES D D, DE ARAUJO M C U, et al. Simultaneous determination of goat milk adulteration with cow milk and their fat and protein contents using NIR spectroscopy and PLS algorithms[J]. LWT,2020,127:109427. doi: 10.1016/j.lwt.2020.109427
[17]	LI S L, XING B C, LIN D, et al. Rapid detection of saffron (Crocus sativus L.) adulterated with lotus stamens and corn stigmas by near-infrared spectroscopy and chemometrics[J]. Industrial Crops and Products,2020,152(5):112539.
[18]	CHEN H, TAN C, LI H J. Untargeted identification of adulterated San Qi powder by near-infrared spectroscopy and one-class model[J]. Journal of Food Composition and Analysis,2020,88:103450. doi: 10.1016/j.jfca.2020.103450
[19]	申晨曦, 杜晨晖, 李震宇, 等. 基于氢核磁共振与偏最小二乘法对酸枣仁及其掺伪品的鉴别[J]. 食品科学,2020,41(8):275−281. [SHEN C X, DU C H, LI Z Y, et al. Differentiation between authentic and adulterated Ziziphi Spinosae Semen by ¹H NMR spectroscopy combined with partial least squares[J]. Food Science,2020,41(8):275−281. doi: 10.7506/spkx1002-6630-20190314-180
[20]	刘红玉, 张悦. 酸枣仁及其伪品滇枣仁的鉴别研究[J]. 首都食品与医药,2016,23(6):72−73. [LIU H Y, ZHANG Y. Study on the identification of Suanzaoren and its adulterant Dianzaoren[J]. Capital Food Medicine,2016,23(6):72−73.
[21]	ZHAO X, WANG W, CHU X, et al. Early detection of Aspergillus parasiticus infection in maize kernels using near-infrared hyperspectral imaging and multivariate data analysis[J]. Applied Sciences,2017,7(1):90. doi: 10.3390/app7010090
[22]	BARNES R J, DHANOA M S, LISTER S J. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra[J]. Applied Spectroscopy,1989,43(5):772−777. doi: 10.1366/0003702894202201
[23]	第五鹏瑶, 卞希慧, 王姿方, 等. 光谱预处理方法选择研究[J]. 光谱学与光谱分析,2019,39(9):2800−2806. [DIWU P Y, BIAN X H, WANG Z F, et al. Study on the selection of spectral preprocessing methods[J]. Spectroscopy and Spectral Analysis,2019,39(9):2800−2806.
[24]	WANG C, SUN Y Y, ZHOU Y Y, et al. Dynamic monitoring oxidation process of nut oils through Raman technology combined with PLSR and RF-PLSR model[J]. LWT-Food Science and Technology,2021,146(5):111290.
[25]	PEDROD A M, FERREIRA M M. Simultaneously calibrating solids, sugars and acidity of tomato products using PLS2 and NIR spectroscopy[J]. Anal Chim Acta,2007,595:221−227.
[26]	何娟, 赵艳茹, 何勇. 近红外光谱结合偏最小二乘判别对硫熏浙贝母的无损鉴别[J]. 光谱学与光谱分析,2017,37(10):3070−3073. [HE J, ZHAO Y R, HE Y. Discrimination of Fritillariae thunbergii Bulbus treated by sulfur fumigation based on NIR coupled with PLS-DA[J]. Spectroscopy and Spectral Analysis,2017,37(10):3070−3073.
[27]	OSBORNE BG, FEARN T, HINDLE P H. Practical NIR spectroscopy: With applications in food and beverage analysis[M]. 2nd Ed. Harlow, UK: Longman Scientific and Technical, 1993: 227.
[28]	WOODCOCK T, DOWNEY G, CP O’DONNELL. Confirmation of declared provenance of European extra virgin olive oil samples by NIR spectroscopy[J]. J Agric Food Chem,2008,56(23):11520−11525. doi: 10.1021/jf802792d
[29]	TIGABU M, ODÉN P C. Discrimination of viable and empty seeds of Pinus patula Schiede & Deppe with near-infrared spectroscopy[J]. New Forests,2003,25(3):163−176. doi: 10.1023/A:1022916615477
[30]	YANG X H, GUANG P W, XU G Z, et al. Manuka honey adulteration detection based on near-infrared spectroscopy combined with aquaphotomics[J]. LWT-Food Science and Technology,2020,132:109837. doi: 10.1016/j.lwt.2020.109837
[31]	SHENK J S, WORKMAN J J, WESTERHAUS M O. Application of NIR spectroscopy to agricultural products[J]. Research Gate,1992:419−474.
[32]	OSBORNE BG, FEARN T. Near infrared spectroscopy in food analysis[M]. New York: Wiley, 1986.
[33]	马东来, 李新蕊, 司明东, 等. GC-MS测定不同采收期酸枣仁中脂肪油成分[J]. 食品研究与开发,2021,42(4):161−164. [MA D L, LI X R, SI M D, et al. Analysis of fatty oil of different harvesting stage in Ziziphi Spinosae Semen by GC-MS[J]. Food Research and Development,2021,42(4):161−164.
[34]	王彦平, 袁贵英, 陈月英, 等. 枳椇子营养成分和总黄酮含量分析及评价[J]. 食品研究与开发,2016,37(24):21−24. [WANG Y P, YUAN G Y, CHEN Y Y, et al. Analysis and evaluation of nutrition composition and total flavonoids on Hovenia seed[J]. Food Research and Development,2016,37(24):21−24. doi: 10.3969/j.issn.1005-6521.2016.24.005
[35]	申晨曦. 酸枣仁和理枣仁化学成分系统表征及对比研究[D]. 太原: 山西大学, 2020 SHEN C X. Comprehensive chemical characterization and comparison of Ziziphi Spinosae Semen and Ziziphi Mauritianae Semen[D]. Taiyuan: Shanxi University, 2020.

Supplements (0)

Cited By

Cited by

Periodical cited type(5)

1.	张瑞娟，苏艳群，夏菲，刘金刚，肖贵华，孙德文，杨小博，黄举. 不同种类研磨淀粉用于纸质食品包装的防油性能研究. 中国造纸. 2025(01): 62-68+84 .
2.	李晶晶，张甜甜，佟岳，刘培玲. 高静压协同酸水解促淀粉颗粒纳米晶体化. 中国食品学报. 2024(12): 57-68 .
3.	张芮娟. 固体制剂制药工艺及质量控制研究. 粘接. 2023(04): 149-152 .
4.	高琦，张首央，唐子程，彭雪，王宁，薛友林. 蛋白质纳米颗粒的制备及其在食品领域的应用研究进展. 食品工业科技. 2023(11): 30-37 . 本站查看
5.	段智颖，王申宛，艾斌凌，郑丽丽，郑晓燕，杨旸，校导，杨劲松，盛占武. 表没食子儿茶素没食子酸酯-香蕉脱支淀粉纳米颗粒的绿色制备及其性质. 食品科学. 2023(12): 74-83 .