Potential of Visible and Near Infrared Spectroscopy and  Pattern Recognition for Rapid Quantification of Notoginseng Powder with Adulterants

Nie, Ping; Wu, Di; Sun, Da‐Wen; Cao, Feilong; Bao, Yukun; He, Yong-yong

doi:10.3390/s131013820

Cited by 30 publications

(10 citation statements)

References 29 publications

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“…Nie et al. 20 used it for detecting Panax notoginseng powder and its dopants, also obtaining high accuracy. However, to the best of our knowledge, no research has been reported on the use of NIR spectroscopy to detect and quantify maca adulteration.…”

Section: Introductionmentioning

confidence: 99%

Near infrared spectroscopy combined with chemometrics to detect and quantify adulteration of maca powder

Zeng

Zheng

2020

Journal of Near Infrared Spectroscopy

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Maca ( Lepidium meyenii Walp.) is a cruciferous edible and medicinal plant rich in nutrients. As maca demand in the international market is gradually increasing, dishonest people have been using low-priced alternatives to either adulterate or falsify maca and increase their profit. Existing methods to identify and quantify adulterated maca are laborious, expensive, destructive, time-consuming, and environmentally unfriendly. Thus, it is imperative to develop a method to overcome these problems to effectively authenticate maca products. We combine near infrared spectroscopy with chemometrics to classify and quantify maca powder adulteration by turnip and radish powder. Different maca samples were adulterated with turnip and radish powder individually at different percentages (5–95%). Specifically, discriminant analysis based on a support vector machine provides a classification accuracy of 100%, allowing near infrared spectroscopy to be used to distinguish maca powder adulteration. Furthermore, to calibrate a regression model, we evaluated the partial least squares (PLS), interval PLS, and synergy interval PLS (siPLS). The siPLS models were determined as the best models for the quantification of maca powder adulterated with turnip and radish powder, in which the correlation coefficients were both 0.97 with root-mean-square error of prediction values of 5.79% and 5.85% for the two models, respectively. The combination of near infrared spectroscopy and chemometrics can provide a fast, simple, and environment-friendly analytical method for identifying and quantifying the properties of maca.

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Section: Introductionmentioning

confidence: 99%

Near infrared spectroscopy combined with chemometrics to detect and quantify adulteration of maca powder

Zeng

Zheng

2020

Journal of Near Infrared Spectroscopy

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“…The bands located around 5168 cm -1 correspond to the combination of O-H stretching and the first overtone of C-O deformation, and the 4756 cm -1 are from the combination of O-H deformation and the C-O stretching. In addition, the absorption band of 4300 cm -1 is assigned the combination overtone of C-H and C-C stretching ( Nie et al., 2013 ; Fu et al., 2017 ; Li et al., 2018 ; Yang et al., 2019 ). However, from the original and average NIR spectra, there were less significant differences between the four districts and towns.…”

Section: Resultsmentioning

confidence: 99%

Study of the suitable climate factors and geographical origins traceability of Panax notoginseng based on correlation analysis and spectral images combined with machine learning

Liu

Zuo

et al. 2023

Front. Plant Sci.

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IntroductionThe cultivation and sale of medicinal plants are some of the main ways to meet the increased market demand for plant-based drugs. Panax notoginseng is a widely used Chinese medicinal material. The growth and accumulation of bioactive constituents mainly depend on a satisfactory growing environment. Additionally, the occurrence of market fraud means that care should be taken when purchasing.MethodsIn this study, we report the correlation between saponins and climate factors based on high performance liquid chromatography (HPLC), and evaluate the influence of climate factors on the quality of P. notoginseng. In addition, the synchronous two-dimensional correlation spectroscopy (2D-COS) images of near infrared (NIR) data combined with the deep learning model were applied to traceability of geographic origins of P. notoginseng at two different levels (district and town levels).ResultsThe results indicated that the contents of saponins in P. notoginseng are negatively related to the annual mean temperature and the temperature annual range. A lower annual mean temperature and temperature annual range are favorable for the content accumulation of saponins. Additionally, high annual precipitation and high humidity are conducive to the content accumulation of Notoginsenoside R1 (NG-R1), Ginsenosides Rg1 (G-Rg1), and Ginsenosides Rb1 (G-Rb1), while Ginsenosides Rd (G-Rd), this is not the case. Regarding geographic origins, classifications at two different levels could be successfully distinguished through synchronous 2D-COS images combined with the residual convolutional neural network (ResNet) model. The model accuracy of the training set, test set, and external validation is achieved at 100%, and the cross-entropy loss function curves are lower. This demonstrated the potential feasibility of the proposed method for P. notoginseng geographic origin traceability, even if the distance between sampling points is small.DiscussionThe findings of this study could improve the quality of P. notoginseng, provide a reference for cultivating P. notoginseng in the future and alleviate the occurrence of market fraud.

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“…Near 480 nm, all the spectral curves had strong absorption peaks, which was why the color of all Panax notoginseng powder was gray and it was difficult to identify Panax notoginseng powder grades from appearance. The weak absorption peak around 980 nm was associated with the OH stretching second overtone of water, which can be explained by the fact that there was little water in Panax notoginseng powder (Nie et al, 2013).…”

Section: Resultsmentioning

confidence: 99%

Nondestructive detection for Panax notoginseng powder grades based on hyperspectral imaging technology combined with CARS‐PCA and MPA‐LSSVM

Yao

Sun

Tang

et al. 2021

J Food Process Engineering

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This study investigated the feasibility of using hyperspectral imaging (HSI) technology to detect Panax notoginseng powder grades. The hyperspectral images of 240 Panax notoginseng powder samples were collected in the spectral range of 450–1,000 nm. Savitzky–golay (SG) and multiplicative scatter correction (MSC) were used to preprocess the original spectra. A method of combing competitive adaptive reweighted sampling (CARS) and principal component analysis (PCA) was used to analyze the spectral data and eliminate the influence caused by the randomness of Monte Carlo (MC) sampling. The least‐squares support vector machine (LSSVM) modeling results showed that CARS‐PCA had better spectral information extraction performance than PCA and CARS, and two principal components were extracted for modeling. The average classification accuracy of PCA‐LSSVM, CARS‐LSSVM, and PCA‐CARS‐LSSVM was 88.33, 90.93, and 92.5%, respectively. To further improve the modeling accuracy, a marine predators algorithm least squares support vector machine (MPA‐LSSVM) model was proposed to identify the grades of Panax notoginseng powder. The result indicated that MPA‐LSSVM had higher modeling accuracy and stronger robustness than the other compared models, and the classification accuracy of the training set and test set was 96.67 and 95%, respectively. The results illustrated the potential of HSI technology as an effective tool in Panax notoginseng powder grades detection. Practical Application This study verified the feasibility of using HSI technology to detect the grades of Panax notoginseng powder. Two principal components were extracted, which were used for simplifying the LSSVM model based on full wavelengths. The simplified model achieved high classification accuracy (95%), which provides a basis for the design of a multispectral imaging system.

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Potential of Visible and Near Infrared Spectroscopy and Pattern Recognition for Rapid Quantification of Notoginseng Powder with Adulterants

Cited by 30 publications

References 29 publications

Near infrared spectroscopy combined with chemometrics to detect and quantify adulteration of maca powder

Near infrared spectroscopy combined with chemometrics to detect and quantify adulteration of maca powder

Study of the suitable climate factors and geographical origins traceability of Panax notoginseng based on correlation analysis and spectral images combined with machine learning

Nondestructive detection for Panax notoginseng powder grades based on hyperspectral imaging technology combined with CARS‐PCA and MPA‐LSSVM

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