1
|
Li Y, Chen B, Ye S, Wu Q, Zhu L, Ding Y. Discrimination of untreated and sodium sulphite treated bean sprouts by Fourier transform infrared spectroscopy and chemometrics. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2024; 41:587-600. [PMID: 38648105 DOI: 10.1080/19440049.2024.2341104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 04/05/2024] [Indexed: 04/25/2024]
Abstract
Sprouts of black beans (Phaseolus vulgaris L.), soybeans (Glycine max L.) and mung beans (Vigna radiata L.) are widely consumed foods containing abundant nutrients with biological activities. They are commonly treated with sulphites for the preservation and extension of shelf-life. However, our previous investigation found that immersing the bean sprouts in sulphite might convert the active components into sulphur-containing derivatives, which can affect both the quality and safety of the sprouts. This study explores the use of FTIR in conjunction with chemometric techniques to differentiate between non-immersed (NI) and sodium sulphite immersed (SI) black bean, soybean and mung bean sprouts. A total of 168 batches of raw spectra were obtained from NI and SI-bean sprouts using FTIR spectroscopy. Four pre-processing techniques, three modelling assessment techniques and four model evaluation indices were examined for differences in performance. The results show that the multiplicative scatter correction is the most effective pre-processing method. Among the models, the accuracy rate of the three models was as follows: radial basis function neural network (95%) > convolutional neural network (91%) > random forest (82%). The overall findings indicate that FTIR spectroscopy, in conjunction with appropriate chemometric approaches, has a high potential for rapidly determining the difference between NI and SI-bean sprouts.
Collapse
Affiliation(s)
- Yaxin Li
- SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian, China
| | - Baoguo Chen
- SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian, China
| | - Shuhong Ye
- SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian, China
| | - Qi Wu
- China National Institute of Standardization, Beijing, China
| | - Lin Zhu
- SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian, China
| | - Yan Ding
- SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian, China
| |
Collapse
|
2
|
Liu B, Wang J, Li C. Application of PLS-NN model based on mid-infrared spectroscopy in the origin identification of Cornus officinalis. RSC Adv 2024; 14:15209-15219. [PMID: 38737973 PMCID: PMC11082643 DOI: 10.1039/d4ra00953c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 05/03/2024] [Indexed: 05/14/2024] Open
Abstract
Mid-infrared spectroscopy has been increasingly used as a nondestructive analytical technique in Chinese herbal medicine identification in recent years. In this study, a new chemometric model named as PLS-NN model was proposed based on the mid-infrared spectral data of Cornus officinalis samples from 11 origins. It was realized by combining the partial least squares and neural networks for the identification of the origin of Chinese herbal medicines. First, we extracted features from the spectral data in 3448 bands using the partial least squares method, and extracted 122 components that contained more than 95% of the information. Then, we trained the PLS-NN model by neural network using the extracted components as inputs and the corresponding origin classes as outputs. Finally, based on an external test set, we evaluated the generalization ability of the PLS-NN model using metrics such as accuracy, F1-Score and Kappa coefficient. The results show that the PLS-NN model performs well in all three metrics when compared to models such as Decision trees, Support vector machine, Partial least squares Discriminant analysis, and Naive bayes. The model not only realizes the dimensionality reduction of full-spectrum data and improves the training efficiency of the model, but also has higher accuracy compared with the full-spectrum data model. The PLS-NN model was applied to identify the origin of Cornus officinalis with an accuracy of 91.9%.
Collapse
Affiliation(s)
- Bing Liu
- Public Foundational Courses Department, Nanjing Vocational University of Industry Technology Nanjing 210023 China
| | - Junqi Wang
- School of Electrical Engineering, Nanjing Vocational University of Industry Technology Nanjing 210023 China
| | - Chaoning Li
- Research and Development Department, Jiangsu Changxingyang Intelligent Home Company Limited Suzhou 215009 China
| |
Collapse
|
3
|
Yang X, Zeng P, Wen J, Wang C, Yao L, He M. Gain deeper insights into traditional Chinese medicines using multidimensional chromatography combined with chemometric approaches. CHINESE HERBAL MEDICINES 2024; 16:27-41. [PMID: 38375051 PMCID: PMC10874776 DOI: 10.1016/j.chmed.2023.07.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 05/30/2023] [Accepted: 07/12/2023] [Indexed: 02/21/2024] Open
Abstract
Traditional Chinese medicines (TCMs) possess a rich historical background, unique theoretical framework, remarkable therapeutic efficacy, and abundant resources. However, the modernization and internationalization of TCMs have faced significant obstacles due to their diverse ingredients and unknown mechanisms. To gain deeper insights into the phytochemicals and ensure the quality control of TCMs, there is an urgent need to enhance analytical techniques. Currently, two-dimensional (2D) chromatography, which incorporates two independent separation mechanisms, demonstrates superior separation capabilities compared to the traditional one-dimensional (1D) separation system when analyzing TCMs samples. Over the past decade, new techniques have been continuously developed to gain actionable insights from complex samples. This review presents the recent advancements in the application of multidimensional chromatography for the quality evaluation of TCMs, encompassing 2D-gas chromatography (GC), 2D-liquid chromatography (LC), as well as emerging three-dimensional (3D)-GC, 3D-LC, and their associated data-processing approaches. These studies highlight the promising potential of multidimensional chromatographic separation for future phytochemical analysis. Nevertheless, the increased separation capability has resulted in higher-order data sets and greater demands for data-processing tools. Considering that multidimensional chromatography is still a relatively nascent research field, further hardware enhancements and the implementation of chemometric methods are necessary to foster its robust development.
Collapse
Affiliation(s)
- Xinyue Yang
- Department of Pharmaceutical Engineering, School of Chemical Engineering, Xiangtan University, Xiangtan 411105, China
| | - Pingping Zeng
- Department of Pharmaceutical Engineering, School of Chemical Engineering, Xiangtan University, Xiangtan 411105, China
| | - Jin Wen
- Department of Pharmaceutical Engineering, School of Chemical Engineering, Xiangtan University, Xiangtan 411105, China
| | - Chuanlin Wang
- Department of Pharmaceutical Engineering, School of Chemical Engineering, Xiangtan University, Xiangtan 411105, China
| | - Liangyuan Yao
- Hunan Qianjin Xiangjiang Pharmaceutical Joint Stock Co., Ltd., Zhuzhou 412000, China
| | - Min He
- Department of Pharmaceutical Engineering, School of Chemical Engineering, Xiangtan University, Xiangtan 411105, China
| |
Collapse
|
4
|
Nouman Khan M, Wang Q, Idrees BS, Waheed R, Haq AU, Abrar M, Jamil Y. Evaluation of medicinal plants using laser-induced breakdown spectroscopy (LIBS) combined with chemometric techniques. Lasers Med Sci 2023; 38:149. [PMID: 37365431 DOI: 10.1007/s10103-023-03805-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 06/12/2023] [Indexed: 06/28/2023]
Abstract
Medicinal plants play a vital role in herbal medical field and allopathic medicine field industry. Chemical and spectroscopic studies of Taraxacum officinale, Hyoscyamus niger, Ajuga bracteosa, Elaeagnus angustifolia, Camellia sinensis, and Berberis lyceum are conducted in this paper by using a 532-nm Nd:YAG laser in an open air environment. These medicinal plant's leaves, roots, seed, and flowers are used to treat a range of diseases by the locals. It is crucial to be able to distinguish between beneficial and detrimental metal elements in these plants. We demonstrated how various elements are categorized and how roots, leaves, seeds and flowers of same plants differ from each other on the basis of elemental analysis. Furthermore, for classification purpose, different classification models, partial least square discriminant analysis (PLS-DA), k-nearest neighbors (kNN), and principal component analysis (PCA) are used. We found silicon (Si), aluminum (Al), iron (Fe), copper (Cu), calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), manganese (Mn), phosphorous (P), and vanadium (V) in all of the medicinal plant samples with a molecular form of carbon and nitrogen band. We detected Ca, Mg, Si, and P as primary components in all of the plant samples, as well as V, Fe, Mn, Al, and Ti as essential medicinal metals, and additional trace elements like Si, Sr, and Al. The result's findings show that the PLS-DA classification model with single normal variate (SNV) preprocessing method is the most effective classification model for different types of plant samples. The average correct classification rate obtained for PLS-DA with SNV is 95%. Moreover, laser-induced breakdown spectroscopy (LIBS) was successfully employed to perform rapid, sensitive, and quantitative trace element analysis on medicinal herbs and plant samples.
Collapse
Affiliation(s)
- Muhammad Nouman Khan
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, China.
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Qianqian Wang
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, 100081, China
- Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing, 314033, China
| | - Bushra Sana Idrees
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Rijah Waheed
- Department of Physics, Hazara University, Mansehra, Pakistan
| | - Ajaz Ul Haq
- Department of Physics, Hazara University, Mansehra, Pakistan
| | - Muhammad Abrar
- Department of Physics, Hazara University, Mansehra, Pakistan
| | - Yasir Jamil
- Department of Physics, University of Agriculture Faisalabad, Faisalabad, Pakistan
| |
Collapse
|
5
|
Jin Y, Liu B, Li C, Shi S. Origin identification of Cornus officinalis based on PCA-SVM combined model. PLoS One 2023; 18:e0282429. [PMID: 36854014 PMCID: PMC9974136 DOI: 10.1371/journal.pone.0282429] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/14/2023] [Indexed: 03/02/2023] Open
Abstract
Infrared spectroscopy can quickly and non-destructively extract analytical information from samples. It can be applied to the authenticity identification of various Chinese herbal medicines, the prediction of the mixing amount of defective products, and the analysis of the origin. In this paper, the spectral information of Cornus officinalis from 11 origins was used as the research object, and the origin identification model of Cornus officinalis based on mid-infrared spectroscopy was established. First, principal component analysis was used to extract the absorbance data of Cornus officinalis in the wavenumber range of 551~3998 cm-1. The extracted principal components contain more than 99.8% of the information of the original data. Second, the extracted principal component information was used as input, and the origin category was used as output, and the origin identification model was trained with the help of support vector machine. In this paper, this combined model is called PCA-SVM combined model. Finally, the generalization ability of the PCA-SVM model is evaluated through an external test set. The three indicators of Accuracy, F1-Score, and Kappa coefficient are used to compare this model with other commonly used classification models such as naive Bayes model, decision trees, linear discriminant analysis, radial basis function neural network and partial least square discriminant analysis. The results show that PCA-SVM model is superior to other commonly used models in accuracy, F1 score and Kappa coefficient. In addition, compared with the SVM model with full spectrum data, the PCA-SVM model not only reduces the redundant variables in the model, but also has higher accuracy. Using this model to identify the origin of Cornus officinalis, the accuracy rate is 84.8%.
Collapse
Affiliation(s)
- Yueqiang Jin
- Public Foundational Courses Department, Nanjing Vocational University of Industry Technology, Nanjing, China
- * E-mail:
| | - Bing Liu
- Public Foundational Courses Department, Nanjing Vocational University of Industry Technology, Nanjing, China
| | - Chaoning Li
- Research and Development Department, Nanjing Changxingyang Intelligent Home Company Limited, Nanjing, China
| | - Shasha Shi
- School of Science, Jiangsu Ocean University, Lianyungang, China
| |
Collapse
|
6
|
Discrimination of raw and sulfur-fumigated ginseng based on Fourier transform infrared spectroscopy coupled with chemometrics. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
7
|
Aspromonte J, Wolfs K, Adams E. Current application and potential use of GC × GC in the pharmaceutical and biomedical field. J Pharm Biomed Anal 2019; 176:112817. [DOI: 10.1016/j.jpba.2019.112817] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Revised: 08/14/2019] [Accepted: 08/17/2019] [Indexed: 01/25/2023]
|
8
|
Multidimensional Gas Chromatography in Essential Oil Analysis. Part 1: Technical Developments. Chromatographia 2018. [DOI: 10.1007/s10337-018-3649-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
|
9
|
Wang J, Liao X, Zheng P, Xue S, Peng R. Classification of Chinese Herbal Medicine by Laser-Induced Breakdown Spectroscopy with Principal Component Analysis and Artificial Neural Network. ANAL LETT 2017. [DOI: 10.1080/00032719.2017.1340949] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Jinmei Wang
- Chongqing Municipal Level Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, College of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Xiangyu Liao
- Chongqing Municipal Level Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, College of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Peichao Zheng
- Chongqing Municipal Level Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, College of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Shuwen Xue
- Chongqing Municipal Level Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, College of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Rui Peng
- Chongqing Academy of Chinese Medicine, Chongqing, China
| |
Collapse
|