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Liu Y, Pan K, Liu Z, Dai Y, Duan X, Wang M, Shen Q. Simultaneous Determination of Four Catechins in Black Tea via NIR Spectroscopy and Feature Wavelength Selection: A Novel Approach. SENSORS (BASEL, SWITZERLAND) 2024; 24:3362. [PMID: 38894153 PMCID: PMC11174505 DOI: 10.3390/s24113362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 05/14/2024] [Accepted: 05/18/2024] [Indexed: 06/21/2024]
Abstract
As a non-destructive, fast, and cost-effective technique, near-infrared (NIR) spectroscopy has been widely used to determine the content of bioactive components in tea. However, due to the similar chemical structures of various catechins in black tea, the NIR spectra of black tea severely overlap in certain bands, causing nonlinear relationships and reducing analytical accuracy. In addition, the number of NIR spectral wavelengths is much larger than that of the modeled samples, and the small-sample learning problem is rather typical. These issues make the use of NIRS to simultaneously determine black tea catechins challenging. To address the above problems, this study innovatively proposed a wavelength selection algorithm based on feature interval combination sensitivity segmentation (FIC-SS). This algorithm extracts wavelengths at both coarse-grained and fine-grained levels, achieving higher accuracy and stability in feature wavelength extraction. On this basis, the study built four simultaneous prediction models for catechins based on extreme learning machines (ELMs), utilizing their powerful nonlinear learning ability and simple model structure to achieve simultaneous and accurate prediction of catechins. The experimental results showed that for the full spectrum, the ELM model has better prediction performance than the partial least squares model for epicatechin (EC), epicatechin gallate (ECG), epigallocatechin (EGC), and epigallocatechin gallate (EGCG). For the feature wavelengths, our proposed FIC-SS-ELM model enjoys higher prediction performance than ELM models based on other wavelength selection algorithms; it can simultaneously and accurately predict the content of EC (Rp2 = 0.91, RMSEP = 0.019), ECG (Rp2 = 0.96, RMSEP = 0.11), EGC (Rp2 = 0.97, RMSEP = 0.15), and EGCG (Rp2 = 0.97, RMSEP = 0.35) in black tea. The results of this study provide a new method for the quantitative determination of the bioactive components of black tea.
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Affiliation(s)
| | | | | | | | | | | | - Qiang Shen
- Tea Research Institute, Guizhou Academy of Agricultural Sciences, Guiyang 550025, China; (Y.L.); (K.P.); (Z.L.); (Y.D.); (X.D.); (M.W.)
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2
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Zhang S, Shan X, Niu L, Chen L, Wang J, Zhou Q, Yuan H, Li J, Wu T. The Integration of Metabolomics, Electronic Tongue, and Chromatic Difference Reveals the Correlations between the Critical Compounds and Flavor Characteristics of Two Grades of High-Quality Dianhong Congou Black Tea. Metabolites 2023; 13:864. [PMID: 37512571 PMCID: PMC10385030 DOI: 10.3390/metabo13070864] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 07/17/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
Tea's biochemical compounds and flavor quality vary depending on its grade ranking. Dianhong Congou black tea (DCT) is a unique tea category produced using the large-leaf tea varieties from Yunnan, China. To date, the flavor characteristics and critical components of two grades of high-quality DCT, single-bud-grade DCT (BDCT), and special-grade DCT (SDCT) manufactured mainly with single buds and buds with one leaf, respectively, are far from clear. Herein, comparisons of two grades were performed by the integration of human sensory evaluation, an electronic tongue, chromatic differences, the quantification of major components, and metabolomics. The BDCT possessed a brisk, umami taste and a brighter infusion color, while the SDCT presented a comprehensive taste and redder liquor color. Quantification analysis showed that the levels of total polyphenols, catechins, and theaflavins (TFs) were significantly higher in the BDCT. Fifty-six different key compounds were screened by metabolomics, including catechins, flavone/flavonol glycosides, amino acids, phenolic acids, etc. Correlation analysis revealed that the sensory features of the BDCT and SDCT were attributed to their higher contents of catechins, TFs, theogallin, digalloylglucose, and accumulations of thearubigins (TRs), flavone/flavonol glycosides, and soluble sugars, respectively. This report is the first to focus on the comprehensive evaluation of the biochemical compositions and sensory characteristics of two grades of high-quality DCT, advancing the understanding of DCT from a multi-dimensional perspective.
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Affiliation(s)
- Shan Zhang
- School of Landscape Architecture and Horticulture Sciences, Southwest Forestry University, Kunming 650224, China
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Xujiang Shan
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Linchi Niu
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Le Chen
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
- College of Environment, Zhejiang University of Technology, Hangzhou 310014, China
| | - Jinjin Wang
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Qinghua Zhou
- College of Environment, Zhejiang University of Technology, Hangzhou 310014, China
| | - Haibo Yuan
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Jia Li
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
| | - Tian Wu
- School of Landscape Architecture and Horticulture Sciences, Southwest Forestry University, Kunming 650224, China
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Khandasammy SR, Halámková L, Baudelet M, Lednev IK. Identification and highly selective differentiation of organic gunshot residues utilizing their elemental and molecular signatures. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 291:122316. [PMID: 36634494 DOI: 10.1016/j.saa.2023.122316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 12/22/2022] [Accepted: 01/01/2023] [Indexed: 06/17/2023]
Abstract
Firearm related evidence is of great significance to forensic science. In recent years, many researchers have focused on exploring the probative value of organic gunshot residue (OGSR) evidence, which is often bolstered by many factors including recoverability. In addition, OGSR analysis has shown the potential to achieve differentiation between OGSRs generated from various ammunition brands and/or calibers. Raman spectroscopy is a vibrational spectroscopic technique which has been used in the past for gunshot residue analysis-including OGSR specifically. Raman spectroscopy is a nondestructive, highly-selective, simple, and rapid technique which provides molecular information about samples. LIBS or Laser-Induced Breakdown Spectroscopy is a simple, robust, and rapid analytical method which requires minimal to no sample preparation and a small amount of sample for analysis. LIBS provides information on the elemental compositions of samples. In this study, Raman spectroscopy and LIBS were used together in sequence in an attempt to achieve the specific identification and characterization of OGSR particles from ammunition types which were closely related. The main goal was to determine if this method had the potential to differentiate between various ammunition types of the same caliber and produced by the same manufacturer, and generated under identical firing conditions. High-resolution optical microscopy documented the OGSR particles' morphologies and Raman spectroscopy was used to identify particles as OGSRs. Finally, LIBS analysis of the OGSR particles was carried out. Advanced chemometric techniques were shown to allow for very successful differentiation between the OGSR samples analyzed.
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Affiliation(s)
- Shelby R Khandasammy
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, NY 12222, United States
| | - Lenka Halámková
- Department of Environmental Toxicology, Texas Tech University, 2500 Broadway, Lubbock, TX 79409, United States
| | - Matthieu Baudelet
- Department of Chemistry, University of Central Florida, 4111 Libra Drive, Physical Sciences Bld. Rm. 255, Orlando, FL 32816, United States; National Center for Forensic Science, University of Central Florida, 12354 Research Parkway #225, Orlando, FL 32826, United States; CREOL - The College of Optics and Photonics, University of Central Florida, 4304 Scorpius Street, Orlando, FL 32816, United States
| | - Igor K Lednev
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, NY 12222, United States.
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Geographical Origin Identification of Chinese Tomatoes Using Long-Wave Fourier-Transform Near-Infrared Spectroscopy Combined with Deep Learning Methods. FOOD ANAL METHOD 2023. [DOI: 10.1007/s12161-023-02444-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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Li L, Lu L, Zhao X, Hu D, Tang T, Tang Y. Nondestructive detection of tomato quality based on multiregion combination model. J FOOD PROCESS ENG 2022. [DOI: 10.1111/jfpe.14100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Li Li
- School of Physics Guizhou University Guiyang China
| | - Li‐Min Lu
- School of Physics Guizhou University Guiyang China
| | | | - De‐Yuan Hu
- School of Physics Guizhou University Guiyang China
| | - Tian‐Yu Tang
- School of Physics Guizhou University Guiyang China
| | - Yan‐Lin Tang
- School of Physics Guizhou University Guiyang China
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Ding Y, Yan Y, Li J, Chen X, Jiang H. Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM. Foods 2022; 11:foods11111658. [PMID: 35681408 PMCID: PMC9180160 DOI: 10.3390/foods11111658] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 05/27/2022] [Accepted: 05/31/2022] [Indexed: 02/04/2023] Open
Abstract
In this paper, we propose a method for classifying tea quality levels based on near-infrared spectroscopy. Firstly, the absorbance spectra of Huangshan Maofeng tea samples were obtained in a wavenumber range of 10,000~4000 cm−1 using near-infrared spectroscopy. The spectral data were then converted to transmittance and smoothed using the Savitzky–Golay (SG) algorithm. The denoised transmittance spectra were dimensionally reduced using principal component analysis (PCA). The characteristic variables obtained using PCA were used as the input variables and the tea level was used as the output to establish a support vector machine (SVM) classification model. The penalty factor c and the kernel function parameter g in the SVM model were optimized using particle swarm optimization (PSO) and comprehensive-learning particle swarm optimization (CLPSO) algorithms. The final experimental results show that the CLPSO-SVM method had the best classification performance, and the classification accuracy reached 99.17%.
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Affiliation(s)
- Yuhan Ding
- Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Ministry of Education, Zhenjiang 212013, China; (Y.D.); (J.L.)
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
- Institute of High-Performance Electrical Machine System and Intelligent Control, Jiangsu University, Zhenjiang 212013, China
| | - Yuli Yan
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (Y.Y.); (X.C.)
| | - Jun Li
- Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Ministry of Education, Zhenjiang 212013, China; (Y.D.); (J.L.)
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
- School of Automation, Southeast University, Nanjing 210096, China
| | - Xu Chen
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (Y.Y.); (X.C.)
| | - Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (Y.Y.); (X.C.)
- Correspondence:
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Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves. REMOTE SENSING 2021. [DOI: 10.3390/rs13183719] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The production of high-quality tea by Camellia sinensis (L.) O. Ktze is the goal pursued by both producers and consumers. Rapid, nondestructive, and low-cost monitoring methods for monitoring tea quality could improve the tea quality and the economic benefits associated with tea. This research explored the possibility of monitoring tea leaf quality from multi-spectral images. Threshold segmentation and manual sampling methods were used to eliminate the image background, after which the spectral features were constructed. Based on this, the texture features of the multi-spectral images of the tea canopy were extracted. Three machine learning methods, partial least squares regression, support vector machine regression, and random forest regression (RFR), were used to construct and train multiple monitoring models. Further, the four key quality parameters of tea polyphenols, total sugars, free amino acids, and caffeine content were estimated using these models. Finally, the effects of automatic and manual image background removal methods, different regression methods, and texture features on the model accuracies were compared. The results showed that the spectral characteristics of the canopy of fresh tea leaves were significantly correlated with the tea quality parameters (r ≥ 0.462). Among the sampling methods, the EXG_Ostu sampling method was best for prediction, whereas, among the models, RFR was the best fitted modeling algorithm for three of four quality parameters. The R2 and root-mean-square error values of the built model were 0.85 and 0.16, respectively. In addition, the texture features extracted from the canopy image improved the prediction accuracy of most models. This research confirms the modeling application of a combination of multi-spectral images and chemometrics, as a low-cost, fast, reliable, and nondestructive quality control method, which can effectively monitor the quality of fresh tea leaves. This provides a scientific reference for the research and development of portable tea quality monitoring equipment that has general applicability in the future.
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Ren G, Liu Y, Ning J, Zhang Z. Assessing black tea quality based on visible–near infrared spectra and kernel-based methods. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.103810] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Rapid Biochemical Methane Potential Evaluation of Anaerobic Co-Digestion Feedstocks Based on Near Infrared Spectroscopy and Chemometrics. ENERGIES 2021. [DOI: 10.3390/en14051460] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Biochemical methane potential (BMP) of anaerobic co-digestion (co-AD) feedstocks is an essential basis for optimizing ratios of materials. Given the time-consuming shortage of conventional BMP tests, a rapid estimated method was proposed for BMP of co-AD—with straw and feces as feedstocks—based on near infrared spectroscopy (NIRS) combined with chemometrics. Partial least squares with several variable selection algorithms were used for establishing calibration models. Variable selection methods were constructed by the genetic simulated annealing algorithm (GSA) combined with interval partial least squares (iPLS), synergy iPLS, backward iPLS, and competitive adaptive reweighted sampling (CARS), respectively. By comparing the modeling performances of characteristic wavelengths selected by different algorithms, it was found that the model constructed using 57 characteristic wavelengths selected by CARS-GSA had the best prediction accuracy. For the validation set, the determination coefficient, root mean square error and relative root mean square error of the CARS-GSA model were 0.984, 6.293 and 2.600, respectively. The result shows that the NIRS regression model—constructed with characteristic wavelengths, selected by CARS-GSA—can meet actual detection requirements. Based on a large number of samples collected, the method proposed in this study can realize the rapid and accurate determination of the BMP for co-AD raw materials in biogas engineering.
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Ren G, Gan N, Song Y, Ning J, Zhang Z. Evaluating Congou black tea quality using a lab-made computer vision system coupled with morphological features and chemometrics. Microchem J 2021. [DOI: 10.1016/j.microc.2020.105600] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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Ren G, Ning J, Zhang Z. Intelligent assessment of tea quality employing visible-near infrared spectra combined with a hybrid variable selection strategy. Microchem J 2020. [DOI: 10.1016/j.microc.2020.105085] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Qin Y, Fang F, Wang R, Zhou J, Li L. Differentiation between wild and artificial cultivated Stephaniae tetrandrae radix using chromatographic and flow-injection mass spectrometric fingerprints with the aid of principal component analysis. Food Sci Nutr 2020; 8:4223-4231. [PMID: 32884703 PMCID: PMC7455950 DOI: 10.1002/fsn3.1717] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Revised: 05/25/2020] [Accepted: 05/26/2020] [Indexed: 12/17/2022] Open
Abstract
High-performance liquid chromatographic (HPLC) and flow-injection mass spectrometric (FIMS) fingerprinting profiles were used to differentiate between wild and artificial cultivated Stephaniae tetrandrae Radix samples. HPLC and FIMS fingerprints of 15 wild S. tetrandrae Radix samples and 12 artificial cultivated S. tetrandrae Radix samples were obtained and analyzed with the aid of principal component analysis (PCA). PCA of the fingerprints showed that the chemical differences between wild and artificial cultivated S. tetrandrae Radix samples could be differentiated by either HPLC or FIMS fingerprints. The HPLC fingerprints provided more chemical information but required longer analytical time compared with FIMS fingerprints. This study indicated that the wild samples contained higher concentrations of almost all of the major compounds than the cultivated samples. Three characteristic compounds which were responsible for the differences between the samples were tentatively identified with the aid of MS data. Furthermore, these three compounds, tetrandrine (TET), fangchinoline (FAN), and cyclanoline (CYC), were quantified. The HPLC and FIMS fingerprints combined with PCA could be used for quality assessment of wild and artificial cultivated S. tetrandrae Radix samples.
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Affiliation(s)
- Ya‐dong Qin
- College of Life SciencesAnhui Normal UniversityWuhuChina
- Pharmacy DepartmentAnhui College of Traditional Chinese MedicineWuhuChina
| | - Feng‐man Fang
- College of Life SciencesAnhui Normal UniversityWuhuChina
| | - Rong‐bin Wang
- College of Life SciencesAnhui Normal UniversityWuhuChina
- Pharmacy DepartmentAnhui College of Traditional Chinese MedicineWuhuChina
| | - Juan‐juan Zhou
- Pharmacy DivisionWuhu Hospital of Traditional Chinese MedicineWuhuChina
| | - Lin‐hua Li
- Pharmacy DepartmentAnhui College of Traditional Chinese MedicineWuhuChina
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Tarapoulouzi M, Kokkinofta R, Theocharis CR. Chemometric analysis combined with FTIR spectroscopy of milk and Halloumi cheese samples according to species' origin. Food Sci Nutr 2020; 8:3262-3273. [PMID: 32724591 PMCID: PMC7382104 DOI: 10.1002/fsn3.1603] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 04/02/2020] [Accepted: 04/03/2020] [Indexed: 12/14/2022] Open
Abstract
Food adulteration is an issue of major concern, as numerous foodstuffs and beverages do not follow their labeling. Our research interest is in the field of authenticity of dairy products and particularly cheese. Adulteration of dairy products is a well-known phenomenon, and there are numerous published studies specifically on the authenticity of cheese. In fact, substitution of a portion of fat and/or proteins, adulteration with milk of other species' origin, and mislabeling of ingredients are some of the main issues that the science of dairy products' authenticity is regularly facing. Discrimination of dairy products can be determined through several chemical or microbiological methods as presented in the literature. In addition, chemometric analysis is an important tool for interpretation of a huge load of measurements. The aim of this study is to discriminate between various milk samples, which is the primary ingredient of dairy products. Milk samples with different trademarks were analyzed. That data was combined with Halloumi cheese samples for chemometric discrimination of species' origin. The innovative point of this study is the fact that it is the first time that a research study related to dairy products includes Halloumi cheese which is a traditional Cypriot cheese, not well-studied until now. The first step of the methodology was the freeze-drying via lyophilization of the samples. Fourier transformed infrared spectroscopy (FTIR) was chosen for their chemical characterization. Moreover, interpretation of the measurements was carried out by chemometric analysis using SIMCA software. For this study, FTIR data combined with chemometrics have given a very good discrimination of the samples according to their species' origin. Chemometric methods such as PCA and OPLS-DA have been used with great success. In the future, this model will be studied regarding geographical origin of the samples.
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