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Bai L, Zhang ZT, Guan H, Liu W, Chen L, Yuan D, Chen P, Xue M, Yan G. Rapid and accurate quality evaluation of Angelicae Sinensis Radix based on near-infrared spectroscopy and Bayesian optimized LSTM network. Talanta 2024; 275:126098. [PMID: 38640523 DOI: 10.1016/j.talanta.2024.126098] [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: 02/02/2024] [Revised: 04/08/2024] [Accepted: 04/10/2024] [Indexed: 04/21/2024]
Abstract
The authentic traditional Chinese medicines (TCMs) including Angelicae Sinensis Radix (ASR) are the representative of high-quality herbals in China. However, ASR from authentic region being adulterated or counterfeited is frequently occurring, and there is still a lack of rapid quality evaluation methods for identifying the authentic ASR. In this study, the color features of ASR were firstly characterized. The results showed that the authentic ASR cannot be fully identified by color characteristics. Then near-infrared (NIR) spectroscopy combined with Bayesian optimized long short-term memory (BO-LSTM) was used to evaluate the quality of ASR, and the performance of BO-LSTM with common classification and regression algorithms was compared. The results revealed that following the pretreatment of NIR spectra, the optimal NIR spectra combined with BO-LSTM not only successfully distinguished authentic, non-authentic, and adulterated ASR with 100 % accuracy, but also accurately predicted the adulteration concentration of authentic ASR (R2 > 0.99). Moreover, BO-LSTM demonstrated excellent performance in classification and regression compared with common algorithms (ANN, SVM, PLSR, etc.). Overall, the proposed strategy could quickly and accurately evaluate the quality of ASR, which provided a reference for other TCMs.
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Affiliation(s)
- Lei Bai
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Zhi-Tong Zhang
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Huanhuan Guan
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Wenjian Liu
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Li Chen
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Dongping Yuan
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Pan Chen
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China
| | - Mei Xue
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Jiangsu Province Engineering Research Center of TCM Intelligence Health Service, Nanjing 210023, China.
| | - Guojun Yan
- School of Pharmacy, Nanjing University of Chinese Medicine, Jiangsu Engineering Research Center for Development and Application of External Drugs in Traditional Chinese Medicine, Jiangsu Province Engineering Research Center of Classical Prescription, Nanjing 210023, China.
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Jordan JA, Celani CP, Ketterer M, Lavine BK, Booksh KS. Supervised discretization for decluttering classification models. Analyst 2023; 148:6097-6108. [PMID: 37916659 DOI: 10.1039/d3an00770g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2023]
Abstract
Presented here is the first demonstration of supervised discretization to 'declutter' multivariate classification data in chemical sensor applications. The performance of multivariate classification models is often limited by the non-informative chemical variance within each target class; decluttering methods seek to reduce within-class variance while retaining between-class variance. Supervised discretization is shown to declutter classes in a manner that is superior to the state-of-the-art External Parameter Orthogonalization (EPO) by constructing a more parsimonious model with fewer parameters to optimize and is, consequently, less susceptible to overfitting and information loss. The comparison of supervised discretization and EPO is performed on three classification applications: X-ray fluorescence spectra of pine ash where the pine was grown in three distinct soil types, laser induced breakdown spectroscopy of colored artisanal glasses, and laser induced breakdown spectroscopy of exotic hardwood species.
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Affiliation(s)
| | - Caelin P Celani
- University of Delaware, Department of Chemistry and Biochemistry, Newark, DE, USA
| | - Michael Ketterer
- Northern Arizona University, Department of Chemistry and Biochemistry, Flagstaff, AZ, USA
| | - Barry K Lavine
- Oklahoma State University, Department of Chemistry, Stillwater, OK, USA
| | - K S Booksh
- University of Delaware, Department of Chemistry and Biochemistry, Newark, DE, USA
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Tanji T, Furukawa M, Fujimoto K, Takagai Y. Analysis of groundwater flow line of the Goshiki-numa pond community using elevation-considered principal component analysis. ANAL SCI 2023; 39:1585-1589. [PMID: 37204628 DOI: 10.1007/s44211-023-00357-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 04/28/2023] [Indexed: 05/20/2023]
Abstract
To reveal the dynamics of underground water flows, measuring many factors and chemical components are usually required. However, it is difficult for the human sense to find the right answers using the many chemo-data in different factors. Although principal component analysis is one of the useful means in a multivariate analysis (chemometrics) that can reduce the multivariable to only 2-D or 3-D and can successfully categorize quantitative multidata into several groups based on the similarity of water quality, underground water flows are difficult to elucidate owing to the lack of continuous information. This paper presents the dynamics of groundwater around the Goshiki-numa pond community (Goshiki-numa), National park of Japan, using multi-chemical component analysis and elevation-considered principal component analysis. Despite the difficulty of understanding the dynamics of groundwater flows of the pond community using limited factors thus far, an elevation-considered principal component analysis (e-PCA) presented herein has revealed the underground water flows around Goshiki-numa ponds by inputting 19 factors × 102 water (total 1,938 data) collected from 2011 to 2014 and 2016. This e-PCA proved to be an effective chemometrics technique for revealing underground water flows. It is believed to apply not only to analytical sciences but also to environmental sciences, civil engineering, and other fields dealing with multidata of water quality.
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Affiliation(s)
- Tamao Tanji
- Faculty of Symbiotic Systems Science, Cluster of Science and Technology, Fukushima University, 1 Kanayagawa, Fukushima, 960-1296, Japan
| | - Makoto Furukawa
- Faculty of Symbiotic Systems Science, Cluster of Science and Technology, Fukushima University, 1 Kanayagawa, Fukushima, 960-1296, Japan
- PerkinElmer Japan G.K., 134 Godo, Hodogaya, Yokohama, Kanagawa, 240-0005, Japan
| | - Katsushige Fujimoto
- Faculty of Symbiotic Systems Science, Cluster of Science and Technology, Fukushima University, 1 Kanayagawa, Fukushima, 960-1296, Japan
| | - Yoshitaka Takagai
- Faculty of Symbiotic Systems Science, Cluster of Science and Technology, Fukushima University, 1 Kanayagawa, Fukushima, 960-1296, Japan.
- Institute of Environmental Radioactivity, Fukushima University, 1 Kanayagawa, Fukushima, 960-1296, Japan.
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Saha D, Senthilkumar T, Sharma S, Singh CB, Manickavasagan A. Application of near-infrared hyperspectral imaging coupled with chemometrics for rapid and non-destructive prediction of protein content in single chickpea seed. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2022.104938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Foschi M, Tozzi L, Di Donato F, Biancolillo A, D’Archivio AA. A Novel FTIR-Based Chemometric Solution for the Assessment of Saffron Adulteration with Non-Fresh Stigmas. Molecules 2022; 28:molecules28010033. [PMID: 36615229 PMCID: PMC9821794 DOI: 10.3390/molecules28010033] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/18/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
The development of fast, non-destructive, and green methods with adequate sensitivity for saffron authentication has important implications in the quality control of the entire production chain of this precious spice. In this context, the highly suitable sensitivity of a spectroscopic method coupled with chemometrics was verified. A total number of 334 samples were analyzed using attenuated-total-reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy; the collected spectra were processed by partial-least-squares discriminant analysis (PLS-DA) to evaluate the feasibility of this study for the discrimination between compliant saffron (fresh samples produced in 2020) and saffron samples adulterated with non-fresh stigmas produced in 2018 and 2016. PLS-DA was able to classify the saffron samples in accordance with the aging time and to discriminate fresh samples from the samples adulterated with non-fresh (legally expired) stigmas, achieving 100% of both sensitivity and specificity in external prediction. Moreover, PLS regression was able to predict the adulteration level with sufficient accuracy (the root-mean-square error of prediction was approximately 3-5%). In summary, ATR-FTIR and chemometrics can be employed to highlight the illegal blending of fresh saffron with unsold stocks of expired saffron, which may be a common fraudulent practice not yet considered in the scientific literature.
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Yu DX, Guo S, Zhang X, Yan H, Zhang ZY, Chen X, Chen JY, Jin SJ, Yang J, Duan JA. Rapid detection of adulteration in powder of ginger (Zingiber officinale Roscoe) by FT-NIR spectroscopy combined with chemometrics. Food Chem X 2022; 15:100450. [PMID: 36211746 PMCID: PMC9532869 DOI: 10.1016/j.fochx.2022.100450] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 09/04/2022] [Accepted: 09/16/2022] [Indexed: 11/25/2022] Open
Abstract
Rapid detection of adulteration in GP was realized by NIR spectroscopy. PCA and PLS-DA models were successfully explored to identify adulterants in GP. Three algorithms achieved satisfactory results for discrimination of adulteration. Adulteration levels in GP can be predicted by PLSR model. The optimal pretreatment methods were compared and selected for modeling.
Ginger powder (GP) is a popular spice in the world. Duo to its nutritional value, GP is regarded as an attractive target for adulteration, which is not easily detected. In this study, chromaticity analysis and Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics were developed to identify and quantify of GP and its adulterants. The result showed that GPs and adulterated GPs cannot be completely distinguished by chromaticity analysis. While, the optimized NIR spectra could accurately distinguish the authentic GPs from those adulterated samples. Random forest and gradient boosting algorithms exhibited the highest accuracies (100%) in classification. Moreover, a quantitative model was successfully established to predict the adulteration level in GP. The optimal parameters of prediction to deviation were 8.92, 13.68, 14.61, and 4.30, for pure and adulterated GPs. Overall, FT-NIR spectroscopy is a promising tool, which can quickly identify potential adulteration in GP and track the types of adulterants.
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A new strategy based on PCA for inter-batches quality consistency evaluation. J Pharm Biomed Anal 2022; 217:114838. [DOI: 10.1016/j.jpba.2022.114838] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 04/15/2022] [Accepted: 05/13/2022] [Indexed: 11/21/2022]
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Wang HP, Chen P, Dai JW, Liu D, Li JY, Xu YP, Chu XL. Recent advances of chemometric calibration methods in modern spectroscopy: Algorithms, strategy, and related issues. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116648] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Castillejos-Mijangos LA, Acosta-Caudillo A, Gallardo-Velázquez T, Osorio-Revilla G, Jiménez-Martínez C. Uses of FT-MIR Spectroscopy and Multivariate Analysis in Quality Control of Coffee, Cocoa, and Commercially Important Spices. Foods 2022; 11:foods11040579. [PMID: 35206058 PMCID: PMC8871480 DOI: 10.3390/foods11040579] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/10/2022] [Accepted: 02/15/2022] [Indexed: 02/07/2023] Open
Abstract
Nowadays, coffee, cocoa, and spices have broad applications in the food and pharmaceutical industries due to their organoleptic and nutraceutical properties, which have turned them into products of great commercial demand. Consequently, these products are susceptible to fraud and adulteration, especially those sold at high prices, such as saffron, vanilla, and turmeric. This situation represents a major problem for industries and consumers’ health. Implementing analytical techniques, i.e., Fourier transform mid-infrared (FT-MIR) spectroscopy coupled with multivariate analysis, can ensure the authenticity and quality of these products since these provide unique information on food matrices. The present review addresses FT-MIR spectroscopy and multivariate analysis application on coffee, cocoa, and spices authentication and quality control, revealing their potential use and elucidating areas of opportunity for future research.
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Affiliation(s)
- Lucero Azusena Castillejos-Mijangos
- Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Av. Wilfrido Massieu Esq. Cda. Manuel Stampa s/n, Alcaldía Gustavo A. Madero, Ciudad de Mexico C.P. 07738, Mexico; (L.A.C.-M.); (A.A.-C.); (G.O.-R.)
| | - Aracely Acosta-Caudillo
- Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Av. Wilfrido Massieu Esq. Cda. Manuel Stampa s/n, Alcaldía Gustavo A. Madero, Ciudad de Mexico C.P. 07738, Mexico; (L.A.C.-M.); (A.A.-C.); (G.O.-R.)
| | - Tzayhrí Gallardo-Velázquez
- Departamento de Biofísica, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Prolongación de Carpio y Plan de Ayala s/n, Col. Santo Tomás, Ciudad de Mexico C.P. 11340, Mexico
- Correspondence: (T.G.-V.); or (C.J.-M.); Tel.: +52-(55)-5729-6000 (ext. 62305) (T.G.-V.); +52-(55)-5729-6000 (ext. 57871) (C.J.-M.)
| | - Guillermo Osorio-Revilla
- Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Av. Wilfrido Massieu Esq. Cda. Manuel Stampa s/n, Alcaldía Gustavo A. Madero, Ciudad de Mexico C.P. 07738, Mexico; (L.A.C.-M.); (A.A.-C.); (G.O.-R.)
| | - Cristian Jiménez-Martínez
- Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Av. Wilfrido Massieu Esq. Cda. Manuel Stampa s/n, Alcaldía Gustavo A. Madero, Ciudad de Mexico C.P. 07738, Mexico; (L.A.C.-M.); (A.A.-C.); (G.O.-R.)
- Correspondence: (T.G.-V.); or (C.J.-M.); Tel.: +52-(55)-5729-6000 (ext. 62305) (T.G.-V.); +52-(55)-5729-6000 (ext. 57871) (C.J.-M.)
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Correction of the moisture variation in wood NIR spectra for species identification using EPO and soft PLS2-DA. Microchem J 2021. [DOI: 10.1016/j.microc.2021.106839] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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