
浏览全部资源
扫码关注微信
1.成都中医药大学 药学院,成都 611137
2.四川大学 计算机学院,视觉合成图形图像技术国家级重点实验室,成都 610065
3.成都中医药大学 附属医院,成都 610072
范林宏,在读硕士,从事中药炮制与制剂研究,E-mail:fanlinhong1996@163.com
吴纯洁,博士,研究员,从事中药炮制与制剂研究,Tel:028-61801001,E-mail:wucjcdtcm@163.com; *
黄永亮,博士,主管药师,从事中药炮制与制剂研究,E-mail:ld10000@126.com
收稿日期:2021-04-05,
网络出版日期:2021-06-02,
纸质出版日期:2022-02-05
移动端阅览
范林宏,何林,谭超群等.基于便携式近红外光谱技术快速检测川贝母掺伪问题[J].中国实验方剂学杂志,2022,28(03):131-138.
FAN Lin-hong,HE Lin,TAN Chao-qun,et al.Rapid Detection of Adulteration of Fritillariae Cirrhosae Bulbus Based on Portable Near Infrared Spectroscopy[J].Chinese Journal of Experimental Traditional Medical Formulae,2022,28(03):131-138.
范林宏,何林,谭超群等.基于便携式近红外光谱技术快速检测川贝母掺伪问题[J].中国实验方剂学杂志,2022,28(03):131-138. DOI: 10.13422/j.cnki.syfjx.20211757.
FAN Lin-hong,HE Lin,TAN Chao-qun,et al.Rapid Detection of Adulteration of Fritillariae Cirrhosae Bulbus Based on Portable Near Infrared Spectroscopy[J].Chinese Journal of Experimental Traditional Medical Formulae,2022,28(03):131-138. DOI: 10.13422/j.cnki.syfjx.20211757.
目的
2
利用便携式近红外光谱技术对川贝母及其掺伪品进行快速鉴别及掺伪量快速检测,以建立系统的川贝母掺伪质量评价体系。
方法
2
采集川贝母样品72批并制作不同类别掺伪品(浙贝母、平贝母、伊贝母、湖北贝母、光慈菇、面粉)570批;利用便携式近红外光谱仪采集样品近红外光谱数据;利用线性判别分析(LDA)及偏最小二乘法(PLS)分别建立川贝母-掺伪品、不同类别掺伪品定性校正模型及不同类别掺伪品掺伪量定量校正模型。
结果
2
川贝母及其掺伪品的定性分析模型对川贝母及其掺伪品的识别率分别为99.49%(校正集),100.00%(验证集);在不同类别的掺伪品模型中,校正集和验证集的识别率分别为70.47%和73.68%;6个掺伪量比例的定量模型的验证集相关系数分别为0.840 2(川贝母掺伪浙贝母),0.960 2(川贝母掺伪平贝母),0.765 7(川贝母掺伪伊贝母),0.902 5(川贝母掺伪湖北贝母),0.957 4(川贝母掺伪光慈菇),0.976 1(川贝母掺伪面粉),预测均方根误差(RMSEP)分别为10.948 5,5.463 9,13.256 4,8.549 2,5.655 3,4.235 6;2个定性模型及6个定量模型的预测性能良好。
结论
2
采用便携式近红外光谱技术可实时对川贝母及其掺伪品的快速鉴别和掺伪量快速测定,该方法快速准确,可满足川贝母现场无损真伪鉴别需求。
Objective
2
In order to establish a systematic quality evaluation system for Fritillariae Cirrhosae Bulbus adulteration, portable near-infrared (NIR) spectroscopy was used to identify Fritillariae Cirrhosae Bulbus and its adulterants and detect their adulteration quantity.
Method
2
A total of 72 batches of Fritillariae Cirrhosae Bulbus samples were collected and 570 batches of adulterated products (dry bulbs of
Fritillaria thunbergii
,
F. ussuriensis
,
F. pallidiflora
and
F. hupehensis
, Bulbus Tulipae, flour) were prepared, NIR spectral data of samples were collected by the portable NIR spectrometer. Linear discriminant analysis (LDA) was used to establish the qualitative correction models of Fritillariae Cirrhosae Bulbus-adulterants and adulterants of different categories, partial least squares (PLS) was used to establish the quantitative correction models of adulteration quantity of different kinds of adulterants.
Result
2
The recognition rates of qualitative analysis model of Fritillariae Cirrhosae Bulbus and its adulterants were 99.49% (calibration set) and 100.00% (validation set), respectively. In different adulterant models, the recognition rates of calibration set and validation set were 70.47% and 73.68%, respectively. Moreover, the correlation coefficients
of validation set (
R
2
P
) of the six quantitative models of adulteration ratio were 0.840 2 (Fritillariae Cirrhosae Bulbus adulterated with
F. thunbergii
dry bulbs), 0.960 2 (Fritillariae Cirrhosae Bulbus adulterated with
F. ussuriensis
dry bulbs), 0.765 7 (Fritillariae Cirrhosae Bulbus adulterated with
F. pallidiflora
dry bulbs), 0.902 5 (Fritillariae Cirrhosae Bulbus adulterated with
F. hupehensis
dry bulbs), 0.957 4 (Fritillariae Cirrhosae Bulbus adulterated with Bulbus Tulipae), 0.976 1 (Fritillariae Cirrhosae Bulbus adulterated with flour), the root mean square error of prediction (RMSEP) were 10.948 5, 5.463 9, 13.256 4, 8.549 2, 5.655 3, 4.235 6, respectively. The two qualitative models and six quantitative models showed good prediction performance.
Conclusion
2
The portable NIR spectroscopy can be used to identify Fritillariae Cirrhosae Bulbus and its adulterants in real time, the method is rapid and accurate, which can meet the requirements of nondestructive identification of Fritillariae Cirrhosae Bulbus on site.
国家药典委员会 . 中华人民共和国药典:一部 [M]. 北京 : 中国医药科技出版社 , 2020 , 38 - 39 .
王天志 , 杜蕾蕾 , 王曙 . 川贝母的研究进展 [J]. 华西药学杂志 , 2001 , 16 ( 3 ): 200 - 203 .
王曙 , 徐小平 , 谭昌勇 , 等 . 川贝母与其它贝母的薄层色谱鉴别 [J]. 华西药学杂志 , 2002 , 17 ( 3 ): 219 - 221 .
仰铁锤 , 谢慧敏 , 谢慧淦 , 等 . 聚合酶链式反应-限制性内切酶多态法检查川贝母的掺伪情况 [J]. 华西药学杂志 , 2020 , 35 ( 3 ): 265 - 269 .
汪波 , 周豫新 , 覃桂 , 等 . 多重连接探针扩增技术检测川贝母掺伪的研究 [J]. 药物分析杂志 , 2018 , 38 ( 12 ): 2104 - 2109 .
杨健 , 李靖 , 薛维娜 , 等 . 实时荧光定量PCR法鉴别川贝母掺伪 [J]. 中成药 , 2020 , 42 ( 5 ): 1262 - 1268 .
冯文豪 , 田亮玉 , 施钧瀚 , 等 . 电子鼻技术应用于川贝母真伪及规格辨识的可行性分析 [J]. 中国实验方剂学杂志 , 2021 , 27 ( 13 ): 108 - 118 .
胡钢亮 , 陈瑞珍 , 程柯 , 等 . 近红外漫反射光谱快速检测川贝母中浙贝母的掺入量 [J]. 药物分析杂志 , 2005 , 25 ( 2 ): 150 - 152 .
杜文俊 , 刘雪松 , 陶玲艳 , 等 . 热毒宁注射液金银花和青蒿(金青)醇沉过程中多指标的近红外快速检测 [J]. 中草药 , 2015 , 46 ( 1 ): 61 - 66 .
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 .
陈龙 , 张晓冬 , 孙扬波 , 等 . 基于近红外漫反射光谱和PCA-SVM算法快速鉴别炉甘石 [J]. 中国实验方剂学杂志 , 2019 , 25 ( 18 ): 116 - 123 .
解育静 , 张家楠 , 朱冬宁 , 等 . 肉桂中4种成分近红外定量分析模型的建立 [J]. 中国实验方剂学杂志 , 2020 , 26 ( 2 ): 119 - 123 .
SAVITZKY A , GOLAY M . Smoothing and differentiation of data by simplified least squares procedures [J]. Anal Chem , 1964 , 36 ( 8 ): 1627 - 1639 .
GORRY P A . General least-squares smoothing and differentiation by the convolution (Savitzky-Golay) method [J]. Anal Chem , 1990 , 62 ( 6 ): 570 - 573 .
RINNAN S , BERG F , ENGELSEN S B . Review of the most common pre-processing techniques for near-infrared spectra [J]. TrAC-Trend Anal Chem , 2009 , 28 ( 10 ): 1201 - 1222 .
MAUER L J , CHERNYSHOVA A A , HIATT A , et al . Melamine detection in infant formula powder using near- and mid-infrared spectroscopy [J]. J Agric Food Chem , 2009 , 57 ( 10 ): 3974 - 3980 .
CHONG X M , HU C Q , FENG Y C , et al . Construction of a universal model for non-invasive identification of penicillins for injection using near-infrared diffuse reflectance spectroscopy [J]. Vib Spectrosc , 2009 , 51 ( 2 ): 313 - 317 .
BARNES R J , DHANOA M S , LISTER S J . Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra [J]. Appl Spectrosc , 2016 , 43 ( 5 ): 772 - 777 .
ISAKSSON T , NAES T . The effect of multiplicative scatter correction (MSC) and linearity improvement in NIR spectroscopy [J]. Appl Spectrosc , 1988 , 42 ( 7 ): 1273 - 1284 .
王冬 , 吴静珠 , 韩平 , 等 . 光谱关键变量筛选在农产品及食品品质无损检测中的应用进展 [J]. 光谱学与光谱分析 , 2021 , 41 ( 5 ): 1593 - 1601 .
褚小立 . 近红外光谱分析技术实用手册 [M]. 北京 : 机械工业出版社 , 2016 , 150 .
任顺成 , 曹悦 , 常云彩 . 近红外光谱技术对马铃薯掺假淀粉的检测 [J]. 粮食与油脂 , 2021 , 34 ( 5 ): 156 - 159 .
0
浏览量
27
下载量
9
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621