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Huang H, Fang Z, Xu Y, Lu G, Feng C, Zeng M, Tian J, Ping Y, Han Z, Zhao Z. Stacking and ridge regression-based spectral ensemble preprocessing method and its application in near-infrared spectral analysis. Talanta 2024; 276:126242. [PMID: 38761656 DOI: 10.1016/j.talanta.2024.126242] [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: 02/23/2024] [Revised: 05/08/2024] [Accepted: 05/09/2024] [Indexed: 05/20/2024]
Abstract
Spectral preprocessing techniques can, to a certain extent, eliminate irrelevant information, such as current noise and stray light from spectral data, thereby enhancing the performance of prediction models. However, current preprocessing techniques mostly attempt to find the best single preprocessing method or their combination, overlooking the complementary information among different preprocessing methods. These preprocessing techniques fail to maximize the utilization of useful information in spectral data and restrict the performance of prediction models. This study proposed a spectral ensemble preprocessing method based on the rapidly developing ensemble learning methods in recent years and the ridge regression (RR) model, named stacking preprocessing ridge regression (SPRR), to address the aforementioned issues. Different from conventional ensemble learning methods, the proposed SPRR method applied multiple different preprocessing techniques to the original spectral data, generating multiple preprocessed datasets. These datasets were then individually inputted into RR base models for training. Ultimately, RR still served as the meta-model, integrating the output results of each RR base model through stacking. This approach not only produced diversity in base models but also achieved higher accuracy and lower computational complexity by using a single type of base model. On the apple spectral dataset collected by our team, correlation analysis showed significant complementary information among the data produced by different preprocessing techniques. This provided robust theoretical support for the proposed SPRR method. By introducing the currently popular averaging ensemble preprocessing method in a comparative experiment, the results of applying the proposed SPRR method to six datasets (apple, meat, wheat, olive oil, tablet, and corn) demonstrated that compared to the single preprocessing method and averaging ensemble preprocessing method, SPRR yielded the best accuracy and reliability for all six datasets. Furthermore, under the same conditions of the training and test datasets, the proposed SPRR method demonstrated better performance than the four commonly used ensemble preprocessing methods.
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Affiliation(s)
- Haowen Huang
- College of New Materials and New Energies, Shenzhen Technology University, Shenzhen, 518118, PR China
| | - Zile Fang
- College of New Materials and New Energies, Shenzhen Technology University, Shenzhen, 518118, PR China
| | - Yuelong Xu
- College of New Materials and New Energies, Shenzhen Technology University, Shenzhen, 518118, PR China
| | - Guosheng Lu
- College of New Materials and New Energies, Shenzhen Technology University, Shenzhen, 518118, PR China
| | - Can Feng
- College of New Materials and New Energies, Shenzhen Technology University, Shenzhen, 518118, PR China
| | - Min Zeng
- College of New Materials and New Energies, Shenzhen Technology University, Shenzhen, 518118, PR China
| | - Jiaju Tian
- College of New Materials and New Energies, Shenzhen Technology University, Shenzhen, 518118, PR China
| | - Yongfu Ping
- College of New Materials and New Energies, Shenzhen Technology University, Shenzhen, 518118, PR China
| | - Zhuolin Han
- College of New Materials and New Energies, Shenzhen Technology University, Shenzhen, 518118, PR China
| | - Zhigang Zhao
- College of New Materials and New Energies, Shenzhen Technology University, Shenzhen, 518118, PR China.
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Giussani B, Gorla G, Riu J. Analytical Chemistry Strategies in the Use of Miniaturised NIR Instruments: An Overview. Crit Rev Anal Chem 2024; 54:11-43. [PMID: 35286178 DOI: 10.1080/10408347.2022.2047607] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Miniaturized NIR instruments have been increasingly used in the last years, and they have become useful tools for many applications on a broad variety of samples. This review focuses on miniaturized NIR instruments from an analytical point of view, to give an overview of the analytical strategies used in order to help the reader to set up their own analytical methods, from the sampling to the data analysis. It highlights the uses of these instruments, providing a critical discussion including current and future trends.
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Affiliation(s)
- Barbara Giussani
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, Como, Italy
| | - Giulia Gorla
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, Como, Italy
| | - Jordi Riu
- Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Tarragona, Spain
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3
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Wang Y, Jin C, Ma L, Liu X. A Robust TabNet-Based Multi-Classification Algorithm for Infrared Spectral Data of Chinese Herbal Medicine with High-Dimensional Small Samples. J Pharm Biomed Anal 2024; 242:116031. [PMID: 38382317 DOI: 10.1016/j.jpba.2024.116031] [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: 12/10/2023] [Revised: 02/08/2024] [Accepted: 02/09/2024] [Indexed: 02/23/2024]
Abstract
Robust classification algorithms for high-dimensional, small-sample datasets are valuable in practical applications. Faced with the infrared spectroscopic dataset with 568 samples and 3448 wavelengths (features) to identify the origins of Chinese medicinal materials, this paper proposed a novel embedded multiclassification algorithm, ITabNet, derived from the framework of TabNet. Firstly, a refined data pre-processing (DP) mechanism was designed to efficiently find the best adaptive one among 50 DP methods with the help of Support Vector Machine (SVM). Following this, an innovative focal loss function was designed and joined with a cross-validation experiment strategy to mitigate the impact of sample imbalance on algorithm. Detailed investigations on ITabNet were conducted, including comparisons of ITabNet with SVM for the conditions of DP and Non-DP, GPU and CPU computer settings, as well as ITabNet against XGBT (Extreme Gradient Boosting). The numerical results demonstrate that ITabNet can significantly improve the effectiveness of prediction. The best accuracy score is 1.0000, and the best Area Under the Curve (AUC) score is 1.0000. Suggestions on how to use models effectively were given. Furthermore, ITabNet shows the potential to apply the analysis of medicinal efficacy and chemical composition of medicinal materials. The paper also provides ideas for multi-classification modeling data with small sample size and high-dimensional feature.
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Affiliation(s)
- Yongjun Wang
- School of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou, 325035, China
| | - Chengliang Jin
- School of Information and Engineering, Wenzhou Business College, Wenzhou, 325035, China.
| | - Li Ma
- College of Information Technology, Shanghai JianQiao University, Shanghai 201306, China
| | - Xiao Liu
- Wenzhou Hospital of Traditional Chinese Medicine, Wenzhou, 325000, China
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4
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Guo M, Wang K, Lin H, Wang L, Cao L, Sui J. Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives. Compr Rev Food Sci Food Saf 2024; 23:e13301. [PMID: 38284587 DOI: 10.1111/1541-4337.13301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 11/27/2023] [Accepted: 12/31/2023] [Indexed: 01/30/2024]
Abstract
In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low-level, mid-level, and high-level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra-to-spectra, spectra-to-machine vision, spectra-to-electronic nose, and spectra-to-nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real-world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone-based multi-sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.
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Affiliation(s)
- Minqiang Guo
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
- College of Food Science and Engineering, Xinjiang Institute of Technology, Aksu, Xinjiang, China
| | - Kaiqiang Wang
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Hong Lin
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Lei Wang
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Limin Cao
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Jianxin Sui
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
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5
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Mattoli L, Pelucchini C, Fiordelli V, Burico M, Gianni M, Zambaldi I. Natural complex substances: From molecules to the molecular complexes. Analytical and technological advances for their definition and differentiation from the corresponding synthetic substances. PHYTOCHEMISTRY 2023; 215:113790. [PMID: 37487919 DOI: 10.1016/j.phytochem.2023.113790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 07/06/2023] [Accepted: 07/11/2023] [Indexed: 07/26/2023]
Abstract
Natural complex substances (NCSs) are a heterogeneous family of substances that are notably used as ingredients in several products classified as food supplements, medical devices, cosmetics and traditional medicines, according to the correspondent regulatory framework. The compositions of NCSs vary widely and hundreds to thousands of compounds can be present at the same time. A key concept is that NCSs are much more than the simple sum of the compounds that constitute them, in fact some emerging phenomena are the result of the supramolecular interaction of the constituents of the system. Therefore, close attention should be paid to produce and characterize these systems. Today many natural compounds are produced by chemical synthesis and are intentionally added to NCSs, or to formulated natural products, to enhance their properties, lowering their production costs. Market analysis shows a tendency of people to use products made with NCSs and, currently, products made with ingredients of natural origin only are not conveniently distinguishable from those containing compounds of synthetic origin. Furthermore, the uncertainty of the current European regulatory framework does not allow consumers to correctly differentiate and identify products containing only ingredients of natural origin. The high demand for specific and effective NCSs and their high-cost offer on the market, create the conditions to economically motivated sophistications, characterized by the addition of a cheap material to a more expensive one, just to increase profit. This type of practice can concern both the addition of less valuable natural materials and the addition of pure artificial compounds with the same structure as those naturally present. In this scenario, it becomes essential for producers of natural products to have advanced analytical techniques to evaluate the effective naturalness of NCSs. In fact, synthetically obtained compounds are not identical to their naturally occurring counterparts, due to the isotopic composition or chirality, as well as the presence of different trace metabolites (since pure substances in nature do not exist). For this reason, in this review, the main analytical tests that can be performed to differentiate natural compounds from their synthetic counterparts will be highlighted and the main analytical technologies will be described. At the same time, the main fingerprint techniques useful for characterizing the complexity of the NCSs, also allowing their identification and quali-quantitative evaluation, will be described. Furthermore, NCSs can be produced through different manufacturing processes, not all of which are on the same level of quality. In this review the most suitable technologies for green processes that operate according to physical extraction principles will be presented, as according to the authors they are the ones that come closest to creating more life-cycle compatible NCSs and that are well suited to the European green deal, a strategy with the aim of transforming the EU into a sustainable and resource-efficient society by 2050.
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Affiliation(s)
- Luisa Mattoli
- Innovation & Medical Science, Aboca SpA, Sansepolcro, AR, Italy.
| | | | | | - Michela Burico
- Innovation & Medical Science, Aboca SpA, Sansepolcro, AR, Italy
| | - Mattia Gianni
- Innovation & Medical Science, Aboca SpA, Sansepolcro, AR, Italy
| | - Ilaria Zambaldi
- Innovation & Medical Science, Aboca SpA, Sansepolcro, AR, Italy
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6
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Aznan A, Gonzalez Viejo C, Pang A, Fuentes S. Review of technology advances to assess rice quality traits and consumer perception. Food Res Int 2023; 172:113105. [PMID: 37689840 DOI: 10.1016/j.foodres.2023.113105] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 06/02/2023] [Accepted: 06/09/2023] [Indexed: 09/11/2023]
Abstract
The increase in rice consumption and demand for high-quality rice is impacted by the growth of socioeconomic status in developing countries and consumer awareness of the health benefits of rice consumption. The latter aspects drive the need for rapid, low-cost, and reliable quality assessment methods to produce high-quality rice according to consumer preference. This is important to ensure the sustainability of the rice value chain and, therefore, accelerate the rice industry toward digital agriculture. This review article focuses on the measurements of the physicochemical and sensory quality of rice, including new and emerging technology advances, particularly in the development of low-cost, non-destructive, and rapid digital sensing techniques to assess rice quality traits and consumer perceptions. In addition, the prospects for potential applications of emerging technologies (i.e., sensors, computer vision, machine learning, and artificial intelligence) to assess rice quality and consumer preferences are discussed. The integration of these technologies shows promising potential in the forthcoming to be adopted by the rice industry to assess rice quality traits and consumer preferences at a lower cost, shorter time, and more objectively compared to the traditional approaches.
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Affiliation(s)
- Aimi Aznan
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia; Department of Agrotechnology, Faculty of Mechanical Engineering and Technology, Universiti Malaysia Perlis, 02600 Perlis, Malaysia
| | - Claudia Gonzalez Viejo
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia
| | - Alexis Pang
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia
| | - Sigfredo Fuentes
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia; Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey, N.L., México 64849, Mexico.
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7
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Lanjewar MG, Morajkar PP, Parab JS. Hybrid method for accurate starch estimation in adulterated turmeric using Vis-NIR spectroscopy. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2023; 40:1131-1146. [PMID: 37589473 DOI: 10.1080/19440049.2023.2241557] [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: 05/24/2023] [Revised: 07/15/2023] [Accepted: 07/15/2023] [Indexed: 08/18/2023]
Abstract
Turmeric is widely used as a health supplement and foodstuff in South East Asian countries because of its medicinal benefits. Like several other plants and peppers, turmeric is prone to exploitation because of its economic value, rising consumer need, and essential food element that adds colour and flavour. Due to this, quick and comprehensive testing processes are needed to detect adulterants in turmeric. In this study, pure turmeric powders were mixed with starch in proportions ranging from 0 to 50% with a 1% variation to obtain different combinations. Reflectance spectra of pure turmeric and starch mixed samples were recorded using a JASCO-V770 spectrometer from 400 to 2050 nm. The recorded spectra were pre-processed using a Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV). The Savitzky-Golay (SG) filter was initially applied to these original (X), MSC, and SNV-corrected spectra. Secondly, the Extra Tree Regressor (ETR) feature selection method was employed to select the best features. Finally, principal component analysis (PCA) was used to reduce the dimension of the selected features. The stacked generalization method was applied to improve the performance of this work. Both regressors and classifier stacking techniques have been tested with different classification and regression methods. The K-Nearest Neighbours (KNN), Decision Tree (DT), and Random Forest (RF) models were used as base learners, and Logistic Regression (LRC) was used as a meta-model for classification and Linear Regression (LR) for regression analysis. The proposed method achieved the best regression performance with r2 of 0.999, Root Mean Square Error (RMSE) of 0.206, Ratio of Performance to Deviation (RPD) of 73.73, and Range Error Ratio (RER) of 480.58, whereas 100% F1 score and Matthew's Correlation Coefficient (MCC) classification performance.
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Affiliation(s)
| | - Pranay P Morajkar
- School of Chemical Sciences, Goa University, Taleigao Plateau, India
| | - Jivan S Parab
- School of Physical and Applied Sciences, Goa University, Taleigao Plateau, India
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8
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Khan S, Rai AK, Singh A, Singh S, Dubey BK, Lal RK, Negi AS, Birse N, Trivedi PK, Elliott CT, Ch R. Rapid metabolic fingerprinting with the aid of chemometric models to identify authenticity of natural medicines: Turmeric, Ocimum, and Withania somnifera study. J Pharm Anal 2023; 13:1041-1057. [PMID: 37842663 PMCID: PMC10568180 DOI: 10.1016/j.jpha.2023.04.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 04/02/2023] [Accepted: 04/27/2023] [Indexed: 10/17/2023] Open
Abstract
Herbal medicines are popular natural medicines that have been used for decades. The use of alternative medicines continues to expand rapidly across the world. The World Health Organization suggests that quality assessment of natural medicines is essential for any therapeutic or health care applications, as their therapeutic potential varies between different geographic origins, plant species, and varieties. Classification of herbal medicines based on a limited number of secondary metabolites is not an ideal approach. Their quality should be considered based on a complete metabolic profile, as their pharmacological activity is not due to a few specific secondary metabolites but rather a larger group of bioactive compounds. A holistic and integrative approach using rapid and nondestructive analytical strategies for the screening of herbal medicines is required for robust characterization. In this study, a rapid and effective quality assessment system for geographical traceability, species, and variety-specific authenticity of the widely used natural medicines turmeric, Ocimum, and Withania somnifera was investigated using Fourier transform near-infrared (FT-NIR) spectroscopy-based metabolic fingerprinting. Four different geographical origins of turmeric, five different Ocimum species, and three different varieties of roots and leaves of Withania somnifera were studied with the aid of machine learning approaches. Extremely good discrimination (R2 > 0.98, Q2 > 0.97, and accuracy = 1.0) with sensitivity and specificity of 100% was achieved using this metabolic fingerprinting strategy. Our study demonstrated that FT-NIR-based rapid metabolic fingerprinting can be used as a robust analytical method to authenticate several important medicinal herbs.
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Affiliation(s)
- Samreen Khan
- Metabolomics Lab, Council of Scientific and Industrial Research (CSIR)-Central Institute of Medicinal and Aromatic Plants (CIMAP), Lucknow, 226015, India
| | - Abhishek Kumar Rai
- Metabolomics Lab, Council of Scientific and Industrial Research (CSIR)-Central Institute of Medicinal and Aromatic Plants (CIMAP), Lucknow, 226015, India
| | - Anjali Singh
- Department of Crop Production & Protection, Council of Scientific and Industrial Research (CSIR)-Central Institute of Medicinal and Aromatic Plants (CIMAP), Lucknow, 226015, India
| | - Saudan Singh
- Department of Crop Production & Protection, Council of Scientific and Industrial Research (CSIR)-Central Institute of Medicinal and Aromatic Plants (CIMAP), Lucknow, 226015, India
- Academy of Council of Scientific and Industrial Research (ACSIR), Ghaziabad, 201002, India
| | - Basant Kumar Dubey
- Department of Biotechnology, Council of Scientific and Industrial Research (CSIR)-Central Institute of Medicinal and Aromatic Plants (CIMAP), Lucknow, 226015, India
| | - Raj Kishori Lal
- Genetics and Plant Breeding Department, Council of Scientific and Industrial Research (CSIR)-Central Institute of Medicinal and Aromatic Plants (CIMAP), Lucknow, 226015, India
| | - Arvind Singh Negi
- Department of Phytochemistry, Council of Scientific and Industrial Research (CSIR)-Central Institute of Medicinal and Aromatic Plants (CIMAP), Lucknow, 226015, India
| | - Nicholas Birse
- School of Biological Sciences, Queen's University, Belfast, BT9 5DL, UK
| | - Prabodh Kumar Trivedi
- Academy of Council of Scientific and Industrial Research (ACSIR), Ghaziabad, 201002, India
- Department of Biotechnology, Council of Scientific and Industrial Research (CSIR)-Central Institute of Medicinal and Aromatic Plants (CIMAP), Lucknow, 226015, India
| | | | - Ratnasekhar Ch
- Metabolomics Lab, Council of Scientific and Industrial Research (CSIR)-Central Institute of Medicinal and Aromatic Plants (CIMAP), Lucknow, 226015, India
- Academy of Council of Scientific and Industrial Research (ACSIR), Ghaziabad, 201002, India
- Department of Phytochemistry, Council of Scientific and Industrial Research (CSIR)-Central Institute of Medicinal and Aromatic Plants (CIMAP), Lucknow, 226015, India
- School of Biological Sciences, Queen's University, Belfast, BT9 5DL, UK
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9
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Johnson JB, Walsh KB, Naiker M, Ameer K. The Use of Infrared Spectroscopy for the Quantification of Bioactive Compounds in Food: A Review. Molecules 2023; 28:molecules28073215. [PMID: 37049978 PMCID: PMC10096661 DOI: 10.3390/molecules28073215] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/29/2023] [Accepted: 04/03/2023] [Indexed: 04/14/2023] Open
Abstract
Infrared spectroscopy (wavelengths ranging from 750-25,000 nm) offers a rapid means of assessing the chemical composition of a wide range of sample types, both for qualitative and quantitative analyses. Its use in the food industry has increased significantly over the past five decades and it is now an accepted analytical technique for the routine analysis of certain analytes. Furthermore, it is commonly used for routine screening and quality control purposes in numerous industry settings, albeit not typically for the analysis of bioactive compounds. Using the Scopus database, a systematic search of literature of the five years between 2016 and 2020 identified 45 studies using near-infrared and 17 studies using mid-infrared spectroscopy for the quantification of bioactive compounds in food products. The most common bioactive compounds assessed were polyphenols, anthocyanins, carotenoids and ascorbic acid. Numerous factors affect the accuracy of the developed model, including the analyte class and concentration, matrix type, instrument geometry, wavelength selection and spectral processing/pre-processing methods. Additionally, only a few studies were validated on independently sourced samples. Nevertheless, the results demonstrate some promise of infrared spectroscopy for the rapid estimation of a wide range of bioactive compounds in food matrices.
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Affiliation(s)
- Joel B Johnson
- School of Health, Medical & Applied Science, Central Queensland University, North Rockhampton, QLD 4701, Australia
| | - Kerry B Walsh
- School of Health, Medical & Applied Science, Central Queensland University, North Rockhampton, QLD 4701, Australia
| | - Mani Naiker
- School of Health, Medical & Applied Science, Central Queensland University, North Rockhampton, QLD 4701, Australia
| | - Kashif Ameer
- Institute of Food Science and Nutrition, University of Sargodha, Sargodha 40100, Pakistan
- Department of Integrative Food, Bioscience and Biotechnology, Chonnam National University, Gwangju 61186, Republic of Korea
- School of Food Science and Biotechnology, Kyungpook National University, Daegu 41566, Republic of Korea
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10
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Comprehensive Metabolomic Fingerprinting Combined with Chemometrics Identifies Species- and Variety-Specific Variation of Medicinal Herbs: An Ocimum Study. Metabolites 2023; 13:metabo13010122. [PMID: 36677046 PMCID: PMC9862730 DOI: 10.3390/metabo13010122] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 12/27/2022] [Accepted: 01/04/2023] [Indexed: 01/14/2023] Open
Abstract
Identification of plant species is a crucial process in natural products. Ocimum, often referred to as the queen of herbs, is one of the most versatile and globally used medicinal herbs for various health benefits due to it having a wide variety of pharmacological activities. Despite there being significant global demand for this medicinal herb, rapid and comprehensive metabolomic fingerprinting approaches for species- and variety-specific classification are limited. In this study, metabolomic fingerprinting of five Ocimum species (Ocimum basilicum L., Ocimum sanctum L., Ocimum africanum Lour., Ocimum kilimandscharicum Gurke., and Hybrid Tulsi) and their varieties was performed using LC-MS, GC-MS, and the rapid fingerprinting approach FT-NIR combined with chemometrics. The aim was to distinguish the species- and variety-specific variation with a view toward developing a quality assessment of Ocimum species. Discrimination of species and varieties was achieved using principal component analysis (PCA), partial least squares discriminate analysis (PLS-DA), data-driven soft independent modelling of class analogy (DD-SIMCA), random forest, and K-nearest neighbours with specificity of 98% and sensitivity of 99%. Phenolics and flavonoids were found to be major contributing markers for species-specific variation. The present study established comprehensive metabolomic fingerprinting consisting of rapid screening and confirmatory approaches as a highly efficient means to identify the species and variety of Ocimum, being able to be applied for the quality assessment of other natural medicinal herbs.
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Park Y, Jin S, Noda I, Jung YM. Continuing progress in the field of two-dimensional correlation spectroscopy (2D-COS), part II. Recent noteworthy developments. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 284:121750. [PMID: 36030669 DOI: 10.1016/j.saa.2022.121750] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 06/30/2022] [Accepted: 08/09/2022] [Indexed: 06/15/2023]
Abstract
This comprehensive survey review compiles noteworthy developments and new concepts of two-dimensional correlation spectroscopy (2D-COS) for the last two years. It covers review articles, books, proceedings, and numerous research papers published on 2D-COS, as well as patent and publication trends. 2D-COS continues to evolve and grow with new significant developments and versatile applications in diverse scientific fields. The healthy, vigorous, and diverse progress of 2D-COS studies in many fields strongly confirms that it is well accepted as a powerful analytical technique to provide an in-depth understanding of systems of interest.
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Affiliation(s)
- Yeonju Park
- Kangwon Radiation Convergence Research Support Center, Kangwon National University, Chuncheon 24341, South Korea
| | - Sila Jin
- Kangwon Radiation Convergence Research Support Center, Kangwon National University, Chuncheon 24341, South Korea
| | - Isao Noda
- Department of Materials Science and Engineering, University of Delaware, Newark, DE 19716, USA.
| | - Young Mee Jung
- Kangwon Radiation Convergence Research Support Center, Kangwon National University, Chuncheon 24341, South Korea; Department of Chemistry, and Institute for Molecular Science and Fusion Technology, Kangwon National University, Chuncheon 24341, South Korea.
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12
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Jin Y, Tian H, Gao Z, Yang G, Dong D. Oil content analysis of corn seeds using a hand-held Raman spectrometer and spectral peak decomposition algorithm. FRONTIERS IN PLANT SCIENCE 2023; 14:1174747. [PMID: 37077627 PMCID: PMC10106593 DOI: 10.3389/fpls.2023.1174747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 03/22/2023] [Indexed: 05/03/2023]
Abstract
Rapid, non-destructive and reliable detection of the oil content of corn seeds is important for development of high-oil corn. However, determination of the oil content is difficult using traditional methods for seed composition analysis. In this study, a hand-held Raman spectrometer was used with a spectral peak decomposition algorithm to determine the oil contents of corn seeds. Mature and waxy Zhengdan 958 corn seeds and mature Jingke 968 corn seeds were analyzed. Raman spectra were obtained in four regions of interest in the embryo of the seed. After analysis of the spectra, a characteristic spectral peak for the oil content was identified. A Gaussian curve fitting spectral peak decomposition algorithm was used to decompose the characteristic spectral peak of oil at 1657 cm-1. This peak was used to determine the Raman spectral peak intensity for the oil content in the embryo and differences in the oil contents among seeds of varying maturity and different varieties. This method is feasible and effective for detection of corn seed oil.
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Affiliation(s)
- Yuan Jin
- School of Computer, Electronics and Information, Guangxi University, Nanning, China
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing, China
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Hongwu Tian
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing, China
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Zhen Gao
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing, China
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Guiyan Yang
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing, China
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
- *Correspondence: Guiyan Yang,
| | - Daming Dong
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing, China
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
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13
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W. Huck C. Present and Future of Miniaturized NIR Spectrometers Combined with Challenging Data Management Strategies. MAKEDONSKO FARMACEVTSKI BILTEN 2022. [DOI: 10.33320/maced.pharm.bull.2022.68.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Affiliation(s)
- Christian W. Huck
- 1 Institute of Analytical Chemistry and Radiochemistry, CCB – Center for Chemistry and Radiochemistry, Leopold-Franzens University, Innsbruck, Austria
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14
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Post-Mortem Interval of Human Skeletal Remains Estimated with Handheld NIR Spectrometry. BIOLOGY 2022; 11:biology11071020. [PMID: 36101401 PMCID: PMC9312135 DOI: 10.3390/biology11071020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/30/2022] [Accepted: 07/04/2022] [Indexed: 11/17/2022]
Abstract
Estimating the post-mortem interval (PMI) of human skeletal remains is a critical issue of forensic analysis, with important limitations such as sample preparation and practicability. In this work, NIR spectroscopy (NIRONE® Sensor X; Spectral Engines, 61449, Germany) was applied to estimate the PMI of 104 human bone samples between 1 day and 2000 years. Reflectance data were repeatedly collected from eight independent spectrometers between 1950 and 1550 nm with a spectral resolution of 14 nm and a step size of 2 nm, each from the external and internal bone. An Artificial Neural Network was used to analyze the 66,560 distinct diagnostic spectra, and clearly distinguished between forensic and archaeological bone material: the classification accuracies for PMIs of 0−2 weeks, 2 weeks−6 months, 6 months−1 year, 1 year−10 years, and >100 years were 0.90, 0.94, 0.94, 0.93, and 1.00, respectively. PMI of archaeological bones could be determined with an accuracy of 100%, demonstrating the adequate predictive performance of the model. Applying a handheld NIR spectrometer to estimate the PMI of human skeletal remains is rapid and extends the repertoire of forensic analyses as a distinct, novel approach.
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15
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Determination of quality markers for quality control of Zanthoxylum nitidum using ultra-performance liquid chromatography coupled with near infrared spectroscopy. PLoS One 2022; 17:e0270315. [PMID: 35749476 PMCID: PMC9231700 DOI: 10.1371/journal.pone.0270315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 06/07/2022] [Indexed: 11/18/2022] Open
Abstract
With the increasing demand for quality control in the traditional Chinese medicine industry, there is a need for the development of quality markers and a quick, non-destructive technique for the discrimination of related species. In our previous study, ultra-performance liquid chromatography (UPLC) was used for the simultaneous determination of five compounds, including three alkaloids (nitidine chloride, chelerythrine, and magnoflorine), one flavonoid (aurantiamarin), and one lignan (sesamin). In this study, the simultaneous quantification of the above-mentioned compounds could be used to discriminate the powders of roots from those of stems. To further test the reliability of the five compounds, seventy-two batches of wild and seventy-five batches of cultivated Zanthoxylum nitidum samples collected from Guangdong, Guangxi, and Fujian provinces in China were analyzed by UPLC and near-infrared spectroscopy (NIRS). In general, the quantitative results of UPLC were consistent with those of NIRS, and cultivated Z. nitidum has similar major bioactive compounds as the wild one, as supported by principal component analysis. Consequently, these five major bioactive compounds are suggested as potential quality markers. In addition, the NIRS method with discriminant analysis successfully differentiated Z. nitidum from three related species (Z. avicennae, Z. scandens and Toddalia asiatica) of the Rutaceae family. In summary, this study provides a method for the rapid identification of Z. nitidum and discrimination of root and stem powders, and suggests five compounds as quality markers for the evaluation of Z. nitidum.
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16
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Raypah ME, Faris AN, Mohd Azlan M, Yusof NY, Suhailin FH, Shueb RH, Ismail I, Mustafa FH. Near-Infrared Spectroscopy as a Potential COVID-19 Early Detection Method: A Review and Future Perspective. SENSORS 2022; 22:s22124391. [PMID: 35746172 PMCID: PMC9229781 DOI: 10.3390/s22124391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 05/16/2022] [Accepted: 05/23/2022] [Indexed: 02/05/2023]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic is a worldwide health anxiety. The rapid dispersion of the infection globally results in unparalleled economic, social, and health impacts. The pathogen that causes COVID-19 is known as a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A fast and low-cost diagnosis method for COVID-19 disease can play an important role in controlling its proliferation. Near-infrared spectroscopy (NIRS) is a quick, non-destructive, non-invasive, and inexpensive technique for profiling the chemical and physical structures of a wide range of samples. Furthermore, the NIRS has the advantage of incorporating the internet of things (IoT) application for the effective control and treatment of the disease. In recent years, a significant advancement in instrumentation and spectral analysis methods has resulted in a remarkable impact on the NIRS applications, especially in the medical discipline. To date, NIRS has been applied as a technique for detecting various viruses including zika (ZIKV), chikungunya (CHIKV), influenza, hepatitis C, dengue (DENV), and human immunodeficiency (HIV). This review aims to outline some historical and contemporary applications of NIRS in virology and its merit as a novel diagnostic technique for SARS-CoV-2.
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Affiliation(s)
- Muna E. Raypah
- School of Physics, Universiti Sains Malaysia, George Town 11800, Pulau Pinang, Malaysia;
| | - Asma Nadia Faris
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia Health Campus, Kubang Kerian 16150, Kelantan, Malaysia; (A.N.F.); (M.M.A.); (N.Y.Y.); (R.H.S.)
| | - Mawaddah Mohd Azlan
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia Health Campus, Kubang Kerian 16150, Kelantan, Malaysia; (A.N.F.); (M.M.A.); (N.Y.Y.); (R.H.S.)
| | - Nik Yusnoraini Yusof
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia Health Campus, Kubang Kerian 16150, Kelantan, Malaysia; (A.N.F.); (M.M.A.); (N.Y.Y.); (R.H.S.)
| | - Fariza Hanim Suhailin
- Department of Physics, Faculty of Science, Universiti Teknologi Malaysia, Skudai 81310, Johor, Malaysia;
| | - Rafidah Hanim Shueb
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia Health Campus, Kubang Kerian 16150, Kelantan, Malaysia; (A.N.F.); (M.M.A.); (N.Y.Y.); (R.H.S.)
- Department of Medical Microbiology and Parasitology, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
| | - Irneza Ismail
- Advanced Devices & System (ADS) Research Group, Department of Electrical & Electronic Engineering, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Bandar Baru Nilai, Nilai 71800, Negeri Sembilan, Malaysia
- Correspondence: (I.I.); (F.H.M.); Tel.: +60-7986569 (I.I.); +60-9-7672432 (F.H.M.)
| | - Fatin Hamimi Mustafa
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia Health Campus, Kubang Kerian 16150, Kelantan, Malaysia; (A.N.F.); (M.M.A.); (N.Y.Y.); (R.H.S.)
- Correspondence: (I.I.); (F.H.M.); Tel.: +60-7986569 (I.I.); +60-9-7672432 (F.H.M.)
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17
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The Application of Visible and Near-Infrared Spectroscopy Combined with Chemometrics in Classification of Dried Herbs. SUSTAINABILITY 2022. [DOI: 10.3390/su14116416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The fast differentiation and classification of herb samples are complicated processes due to the presence of many various chemical compounds. Traditionally, separation techniques have been employed for the identification and quantification of compounds present in different plant matrices, but they are tedious, time-consuming and destructive. Thus, a non-targeted approach would be specifically advantageous for this purpose. In the present study, spectroscopy in the visible and near-infrared range and pattern recognition techniques, including the principal component analysis (PCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), regularized discriminant analysis (RDA), super k-nearest neighbor (SKNN) and support vector machine (SVM) techniques, were applied to develop classification models that enabled the discrimination of various commercial dried herbs, including mint, linden, nettle, sage and chamomile. The classification error rates in the validation data were below 10% for all the classification methods, except for SKNN. The results obtained confirm that spectroscopy and pattern recognition methods constitute a good non-destructive tool for the rapid identification of herb species that can be used in routine quality control by the pharmaceutical industry, as well as herbal suppliers, to avoid mislabeling.
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18
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Beć KB, Grabska J, Huck CW. Miniaturized NIR Spectroscopy in Food Analysis and Quality Control: Promises, Challenges, and Perspectives. Foods 2022; 11:foods11101465. [PMID: 35627034 PMCID: PMC9140213 DOI: 10.3390/foods11101465] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/05/2022] [Accepted: 05/13/2022] [Indexed: 01/27/2023] Open
Abstract
The ongoing miniaturization of spectrometers creates a perfect synergy with the common advantages of near-infrared (NIR) spectroscopy, which together provide particularly significant benefits in the field of food analysis. The combination of portability and direct onsite application with high throughput and a noninvasive way of analysis is a decisive advantage in the food industry, which features a diverse production and supply chain. A miniaturized NIR analytical framework is readily applicable to combat various food safety risks, where compromised quality may result from an accidental or intentional (i.e., food fraud) origin. In this review, the characteristics of miniaturized NIR sensors are discussed in comparison to benchtop laboratory spectrometers regarding their performance, applicability, and optimization of methodology. Miniaturized NIR spectrometers remarkably increase the flexibility of analysis; however, various factors affect the performance of these devices in different analytical scenarios. Currently, it is a focused research direction to perform systematic evaluation studies of the accuracy and reliability of various miniaturized spectrometers that are based on different technologies; e.g., Fourier transform (FT)-NIR, micro-optoelectro-mechanical system (MOEMS)-based Hadamard mask, or linear variable filter (LVF) coupled with an array detector, among others. Progressing technology has been accompanied by innovative data-analysis methods integrated into the package of a micro-NIR analytical framework to improve its accuracy, reliability, and applicability. Advanced calibration methods (e.g., artificial neural networks (ANN) and nonlinear regression) directly improve the performance of miniaturized instruments in challenging analyses, and balance the accuracy of these instruments toward laboratory spectrometers. The quantum-mechanical simulation of NIR spectra reveals the wavenumber regions where the best-correlated spectral information resides and unveils the interactions of the target analyte with the surrounding matrix, ultimately enhancing the information gathered from the NIR spectra. A data-fusion framework offers a combination of spectral information from sensors that operate in different wavelength regions and enables parallelization of spectral pretreatments. This set of methods enables the intelligent design of future NIR analyses using miniaturized instruments, which is critically important for samples with a complex matrix typical of food raw material and shelf products.
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19
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Nagy MM, Wang S, Farag MA. Quality analysis and authentication of nutraceuticals using near IR (NIR) spectroscopy: A comprehensive review of novel trends and applications. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2022.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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20
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Comprehensive Study of Traditional Plant Ground Ivy ( Glechoma hederacea L.) Grown in Croatia in Terms of Nutritional and Bioactive Composition. Foods 2022; 11:foods11050658. [PMID: 35267291 PMCID: PMC8909519 DOI: 10.3390/foods11050658] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 02/16/2022] [Accepted: 02/22/2022] [Indexed: 01/27/2023] Open
Abstract
In the present study, ground ivy was harvested from different natural habitats in Croatia and subjected to screening analysis for nutritional and bioactive composition. To achieve maximum recovery of phenolic compounds, different extraction techniques were investigated—heat-assisted (HAE), microwave-assisted (MAE) and subcritical water (SWE) extraction. Prepared extracts were analysed by spectrophotometric methods, LC-MS/MS and HPLC-PAD methodologies. Results regarding nutritive analyses, conducted using standard AOAC methods, showed the abundance of samples in terms of insoluble dietary fibre, protein, calcium and potassium, while rutin, chlorogenic, cryptochlorogenic, caffeic and rosmarinic acid were the most dominant phenolic compounds. In addition, LC-MS/MS analysis revealed the presence of apigenin and luteolin in glycosylated form. Maximum recovery of target phenolic compounds was achieved with MAE, while SWE led to the formation of new antioxidants, which is commonly known as neoformation. Moreover, efficient prediction of phenolic composition of prepared extracts was achieved using NIR spectroscopy combined with ANN modelling.
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21
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Nabgan W, Jalil AA, Nabgan B, Ikram M, Ali MW, Lakshminarayana P. A state of the art overview of carbon-based composites applications for detecting and eliminating pharmaceuticals containing wastewater. CHEMOSPHERE 2022; 288:132535. [PMID: 34648794 DOI: 10.1016/j.chemosphere.2021.132535] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/16/2021] [Accepted: 10/08/2021] [Indexed: 06/13/2023]
Abstract
The growing prevalence of new toxins in the environment continues to cause widespread concerns. Pharmaceuticals, organic pollutants, heavy metal ions, endocrine-disrupting substances, microorganisms, and others are examples of persistent organic chemicals whose effects are unknown because they have recently entered the environment and are displaying up in wastewater treatment facilities. Pharmaceutical pollutants in discharged wastewater have become a danger to animals, marine species, humans, and the environment. Although their presence in drinking water has generated significant concerns, little is known about their destiny and environmental effects. As a result, there is a rising need for selective, sensitive, quick, easy-to-handle, and low-cost early monitoring detection systems. This study aims to deliver an overview of a low-cost carbon-based composite to detect and remove pharmaceutical components from wastewater using the literature reviews and bibliometric analysis technique from 1970 to 2021 based on the web of science (WoS) database. Various pollutants in water and soil were reviewed, and different methods were introduced to detect pharmaceutical pollutants. The advantages and drawbacks of varying carbon-based materials for sensing and removing pharmaceutical wastes were also introduced. Finally, the available techniques for wastewater treatment, challenges and future perspectives on the recent progress were highlighted. The suggestions in this article will facilitate the development of novel on-site methods for removing emerging pollutants from pharmaceutical effluents and commercial enterprises.
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Affiliation(s)
- Walid Nabgan
- School of Chemical and Energy Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia; Centre of Hydrogen Energy, Institute of Future Energy, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia.
| | - Aishah Abdul Jalil
- School of Chemical and Energy Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia; Centre of Hydrogen Energy, Institute of Future Energy, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia.
| | - Bahador Nabgan
- School of Chemical and Energy Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia
| | - Muhammad Ikram
- Solar Cell Applications Research Lab, Department of Physics, Government College University Lahore, 54000, Punjab, Pakistan.
| | - Mohamad Wijayanuddin Ali
- School of Chemical and Energy Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia; Centre of Hydrogen Energy, Institute of Future Energy, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia
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22
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Ming-Liang G, Yi Z, Fang-Fang C, Hang-Hang W, Ling-Run L, Xin J, Ya-Nan Z, Tian-Shu W, Pei-Dong C, Wei-Feng Y, Bei-Hua B, Li Z. A gradient-based discriminant analysis method for process quality control of carbonized TCM via Fourier transform near infrared spectroscopy: A case study on carbonized Typhae Pollen. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 265:120363. [PMID: 34562862 DOI: 10.1016/j.saa.2021.120363] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 08/28/2021] [Accepted: 08/31/2021] [Indexed: 06/13/2023]
Abstract
Carbonized traditional Chinese medicine (TCM) is a kind of distinctive traditional drug which has been widely used in various bleeding syndromes for over two thousand years, and most of them are still in clinical use. Although they share similar processing method: stir-frying, there are no specific quality standards and few quality control researches carried out on carbonized TCM up until now. Carbonized Typhae Pollen (CTP) is a typical carbonized TCM with efficacy of eliminating blood stasis and stanching bleeding. In this study, a novel process quality control model coupled with near infrared spectroscopy was established, called Gradient-based Discriminant Analysis method (GDA). Compared with conventional modeling methods (Convolutional Neural Network, Linear Discriminant Analysis, Standard Normal Variate-LDA), GDA model applied in fiber optic probe acquisition mode exhibited highest test accuracy (0.961), satisfactory correct identification (internal validation, 100%; external validation, 97.1%) and excellent model stability. This method provided a perfect guideline for process quality control of Carbonized TCM as well as ensured their clinical efficacy.
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Affiliation(s)
- Gao Ming-Liang
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, PR China
| | - Zhang Yi
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, PR China
| | - Cheng Fang-Fang
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, PR China
| | - Wang Hang-Hang
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, PR China
| | - Liu Ling-Run
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, PR China
| | - Jin Xin
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, PR China
| | - Zhou Ya-Nan
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, PR China
| | - Wang Tian-Shu
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, PR China.
| | - Chen Pei-Dong
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, PR China
| | - Yao Wei-Feng
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, PR China
| | - Bao Bei-Hua
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, PR China.
| | - Zhang Li
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, PR China
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23
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Losso K, Bec KB, Mayr S, Grabska J, Stuppner S, Jones M, Jakschitz T, Rainer M, Bonn GK, Huck CW. Rapid discrimination of Curcuma longa and Curcuma xanthorrhiza using Direct Analysis in Real Time Mass Spectrometry and Near Infrared Spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 265:120347. [PMID: 34537630 DOI: 10.1016/j.saa.2021.120347] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 08/13/2021] [Accepted: 08/28/2021] [Indexed: 05/25/2023]
Abstract
This study describes a newly developed method for the fast and straightforward differentiation of two turmeric species using Direct Analysis in Real Time mass spectrometry and miniaturized Near Infrared spectroscopy. Multivariate analyses (PCA and LDA) were performed on the mass spectrometric data, thus creating a powerful model for the discrimination of Curcumalonga and Curcumaxanthorrhiza. Cross-validation of the model revealed correctness-scores of 100% with 20-fold as well as leave-one-out validation techniques. To further estimate the models prediction power, seven retail samples of turmeric powder were analyzed and assorted to a species. Looking for a fast, non-invasive, cost-efficient and laboratory independent method, miniaturized NIR spectrometers offer an alternative for quality control of turmeric species. However, different technologies implemented to compensate for their small size, lead to different applicability of these spectrometers. Therefore, we investigated the three handheld spectrometers microPHAZIR, MicroNIR 2200 and MicroNIR 1700ES for their application in spice analysis in hyphenation to PCA, LDA and ANN methods used for the discriminant analysis. While microPHAZIR proved to be the most valuable device for differentiating C.longa and C.xanthorrhiza, MicroNIR 1700ES offered the worst results. These findings are interpreted on the basis of a quantum chemical simulation of the NIR spectrum of curcumin as the representative constituent. It was found that the information accessible to MicroNIR 1700ES that is relevant to the analyzed constituents is located in the spectral region prone to interferences with the matrix, likely limiting the performance of this spectrometer in this analytical scenario.
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Affiliation(s)
- Klemens Losso
- Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria; Austrian Drug Screening Institute GmbH (ADSI), Innrain 66a, 6020 Innsbruck, Austria
| | - Krzysztof B Bec
- Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Sophia Mayr
- Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Justyna Grabska
- Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Stefan Stuppner
- Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria; Austrian Drug Screening Institute GmbH (ADSI), Innrain 66a, 6020 Innsbruck, Austria
| | - Michael Jones
- Waters Corporation, Stamford Avenue, Wilmslow, Cheshire, United Kingdom
| | - Thomas Jakschitz
- Austrian Drug Screening Institute GmbH (ADSI), Innrain 66a, 6020 Innsbruck, Austria
| | - Matthias Rainer
- Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria.
| | - Günther K Bonn
- Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria; Austrian Drug Screening Institute GmbH (ADSI), Innrain 66a, 6020 Innsbruck, Austria
| | - Christian W Huck
- Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
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24
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Rapid Estimation of Potato Quality Parameters by a Portable Near-Infrared Spectroscopy Device. SENSORS 2021; 21:s21248222. [PMID: 34960316 PMCID: PMC8707853 DOI: 10.3390/s21248222] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 11/30/2021] [Accepted: 12/06/2021] [Indexed: 12/02/2022]
Abstract
The aim of the present work was to determine the main quality parameters on tuber potato using a portable near-infrared spectroscopy device (MicroNIR). Potato tubers protected by the Protected Geographical Indication (PGI “Patata de Galicia”, Spain) were analyzed both using chemical methods of reference and also using the NIR methodology for the determination of important parameters for tuber commercialization, such as dry matter and reducing sugars. MicroNIR technology allows for the attainment/estimation of dry matter and reducing sugars in the warehouses by directly measuring the tubers without a chemical treatment and destruction of samples. The principal component analysis and modified partial least squares regression method were used to develop the NIR calibration model. The best determination coefficients obtained for dry matter and reducing sugars were of 0.72 and 0.55, respectively, and with acceptable standard errors of cross-validation. Near-infrared spectroscopy was established as an effective tool to obtain prediction equations of these potato quality parameters. At the same time, the efficiency of portable devices for taking instantaneous measurements of crucial quality parameters is useful for potato processors.
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Kovacs Z, Muncan J, Ohmido N, Bazar G, Tsenkova R. Water Spectral Patterns Reveals Similarities and Differences in Rice Germination and Induced Degenerated Callus Development. PLANTS (BASEL, SWITZERLAND) 2021; 10:1832. [PMID: 34579366 PMCID: PMC8471901 DOI: 10.3390/plants10091832] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 08/29/2021] [Accepted: 08/30/2021] [Indexed: 11/16/2022]
Abstract
In vivo monitoring of rice (Oryza sativa L.) seed germination and seedling growth under general conditions in closed Petri dishes containing agar base medium at room temperature (temperature = 24.5 ± 1 °C, relative humidity = 76 ± 7% (average ± standard deviation)), and induced degenerated callus formation with plant growth regulator, were performed using short-wavelength near-infrared spectroscopy and aquaphotomics over A period of 26 days. The results of spectral analysis suggest changes in water absorbances due to the production of common metabolites, as well as increases in biomass and the sizes of the samples. Quantitative models built to predict the day of the development provided better accuracy for rice seedlings growth compared to callus formation. Eight common water bands were identified as presenting prominent changes in the absorbance pattern. The water matrix of only rice seedlings showed three developmental stages: firstly expressing a predominantly weakly hydrogen-bonded state, then a more strongly hydrogen-bonded state, and then, again, a weakly hydrogen-bonded state at the end. In rice callus induction and proliferation, no similar change in water absorbance pattern was observed. The presented findings indicate the potential of aquaphotomics for the in vivo detection of degeneration in cell development.
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Affiliation(s)
- Zoltan Kovacs
- Department of Measurements and Process Control, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, Somlói út 14-16, 1118 Budapest, Hungary
| | - Jelena Muncan
- Biomeasurement Technology Laboratory, Graduate School of Agricultural Science, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe 657-8501, Hyogo, Japan;
| | - Nobuko Ohmido
- Department of Human Environmental Science, Graduate School of Human Development and Environment, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe 657-8501, Hyogo, Japan;
| | - George Bazar
- ADEXGO Ltd., Lapostelki u. 13, 8230 Balatonfüred, Hungary;
| | - Roumiana Tsenkova
- Biomeasurement Technology Laboratory, Graduate School of Agricultural Science, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe 657-8501, Hyogo, Japan;
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Si L, Ni H, Pan D, Zhang X, Xu F, Wu Y, Bao L, Wang Z, Xiao W, Wu Y. Nondestructive qualitative and quantitative analysis of Yaobitong capsule using near-infrared spectroscopy in tandem with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 252:119517. [PMID: 33578123 DOI: 10.1016/j.saa.2021.119517] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 01/16/2021] [Accepted: 01/19/2021] [Indexed: 06/12/2023]
Abstract
The purpose of the study is to present a nondestructive qualitative and quantitative approach of hard-shell capsule using near-infrared (NIR) spectroscopy combined with chemometrics. The Yaobitong capsule (YBTC) was used for demonstration of the proposed approach and the NIR spectra were collected using a handheld fiber probe (FP) without the damage of capsule shell. By comparing the differences and similarities of the NIR spectra of capsule shells, contents and intact capsules, a preliminary conclusion can be drawn that the NIR spectra contained the information of the contents. Characteristic variables were selected by competitive adaptive weighted resampling (CARS) method, and least squares support vector machine (LSSVM) method based on particle swarm optimization (PSO) algorithm was applied to the construction of quantitative models. The relative standard error of prediction (RSEP) values of five saponins including notoginsenoside R1, ginsenoside Rg1, Re, Rb1, and Rd were 3.240%, 5.468%, 5.303%, 5.043%, and 3.745%, respectively. In addition, for qualitative model, three different types of adulterated capsules were designed. The model established by data driven version of soft independent modeling of class analogy (DD-SIMCA) demonstrated a satisfactory result that all adulterated capsules were identified accurately after an appropriate number of principal components (PCs) were chosen. The results indicated that although the NIR spectra collection was affected by capsule shell, sufficient content information can be obtained for quantitative and qualitative analysis after combining with chemometrics. It further proved that acquired NIR spectra do contain the effective component information of the capsule. This study provided a reference for the rapid nondestructive quality analysis of traditional Chinese medicine (TCM) capsule without damaging capsule shell.
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Affiliation(s)
- Leting Si
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Hongfei Ni
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Dongyue Pan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xin Zhang
- Jiangsu Kanion Pharmaceutical Co., Ltd. Lianyungang, Jiangsu 222001, China; State Key Laboratory of New-tech for Chinese Medicine Pharmaceutical Process, Lianyungang, Jiangsu 222001, China; National & Local Joint Engineering Research Center on Intelligent Manufacturing of Traditional Chinese Medicine, Lianyungang, Jiangsu 222001, China
| | - Fangfang Xu
- Jiangsu Kanion Pharmaceutical Co., Ltd. Lianyungang, Jiangsu 222001, China; State Key Laboratory of New-tech for Chinese Medicine Pharmaceutical Process, Lianyungang, Jiangsu 222001, China; National & Local Joint Engineering Research Center on Intelligent Manufacturing of Traditional Chinese Medicine, Lianyungang, Jiangsu 222001, China
| | - Yun Wu
- Jiangsu Kanion Pharmaceutical Co., Ltd. Lianyungang, Jiangsu 222001, China; State Key Laboratory of New-tech for Chinese Medicine Pharmaceutical Process, Lianyungang, Jiangsu 222001, China; National & Local Joint Engineering Research Center on Intelligent Manufacturing of Traditional Chinese Medicine, Lianyungang, Jiangsu 222001, China
| | - Lewei Bao
- Jiangsu Kanion Pharmaceutical Co., Ltd. Lianyungang, Jiangsu 222001, China; State Key Laboratory of New-tech for Chinese Medicine Pharmaceutical Process, Lianyungang, Jiangsu 222001, China; National & Local Joint Engineering Research Center on Intelligent Manufacturing of Traditional Chinese Medicine, Lianyungang, Jiangsu 222001, China
| | - Zhenzhong Wang
- Jiangsu Kanion Pharmaceutical Co., Ltd. Lianyungang, Jiangsu 222001, China; State Key Laboratory of New-tech for Chinese Medicine Pharmaceutical Process, Lianyungang, Jiangsu 222001, China; National & Local Joint Engineering Research Center on Intelligent Manufacturing of Traditional Chinese Medicine, Lianyungang, Jiangsu 222001, China
| | - Wei Xiao
- Jiangsu Kanion Pharmaceutical Co., Ltd. Lianyungang, Jiangsu 222001, China; State Key Laboratory of New-tech for Chinese Medicine Pharmaceutical Process, Lianyungang, Jiangsu 222001, China; National & Local Joint Engineering Research Center on Intelligent Manufacturing of Traditional Chinese Medicine, Lianyungang, Jiangsu 222001, China.
| | - Yongjiang Wu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
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Yan H, Pu Z, Wang Y, Guo S, Wang T, Li S, Zhang Z, Zhou G, Zhan Z, Duan J. Rapid qualitative identification and quantitative analysis of Flos Mume based on Fourier transform near infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 249:119344. [PMID: 33360057 DOI: 10.1016/j.saa.2020.119344] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 12/09/2020] [Accepted: 12/10/2020] [Indexed: 06/12/2023]
Abstract
Flos Mume, an ancient Chinese plant, is widely used for food and medicine. There are numerous varieties of Flos Mume, whose main active components are chlorogenic acid, hyperoside and isoquercitrin. Currently, Flos Mume varieties are mainly distinguished by physical appearance and they have not been scientifically indexed for identification. Fourier transform near infrared spectroscopy (FT-NIR) is a technique that when combined with chemometrics, determines internal components of samples and classifies them. Here, to distinguish between different Flos Mume varieties, we used a qualitative identification model based on FT-NIR. Various model parameters indicated its stability and high predictive performance. We developed a rapid, non-destructive method of simultaneously analyzing 8 components but found that only neochlorogenic acid, chlorogenic acid, rutin, hyperoside, and isoquercitrin have application value. Other components were excluded due to low concentration and poor prediction. Chemometric analysis found that chlorogenic acid become an ingredient which is quite different in the different categories. The content of chlorogenic acid were the highest among these components. Different varieties of Flos Mume were distinguished based on chlorogenic acid content, indicating that chlorogenic acid has potential to become a key indicator for application in quality evaluation. The established FT-NIR model for chlorogenic acid detection had excellent predictive capacity. FT-NIR was the first time applied to Flos Mume and our findings offer theoretical reference for the rapid identification and quantitative analysis of Flos Mume based on FT-NIR. Flos Mume could be evaluated for quality quickly and easily by means of FT-NIR spectroscopy.
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Affiliation(s)
- Hui Yan
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China.
| | - Zongjin Pu
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China.
| | - Yingjun Wang
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China.
| | - Sheng Guo
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China.
| | - Tianshu Wang
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China.
| | - Simeng Li
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China.
| | - Zhenyu Zhang
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China.
| | - Guisheng Zhou
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China.
| | - Zhilai Zhan
- State Key Laboratory of Dao-di Herbs Breeding Base, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China.
| | - Jinao Duan
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and Jiangsu Key Laboratory for High Technology Research of TCM Formulae, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China.
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