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Tian M, Ma X, Liang M, Zang H. Application of Rapid Identification and Determination of Moisture Content of Coptidis Rhizoma From Different Species Based on Data Fusion. J AOAC Int 2023; 106:1389-1401. [PMID: 37171863 DOI: 10.1093/jaoacint/qsad058] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/25/2023] [Accepted: 05/08/2023] [Indexed: 05/13/2023]
Abstract
BACKGROUND For thousands of years, traditional Chinese medicine (TCM) has been clinically proven, and doctors have highly valued the differences in utility between different species. OBJECTIVE This study aims to replace the complex methods traditionally used for empirical identification by compensating for the information loss of a single sensor through data fusion. The research object of the study is Coptidis rhizoma (CR). METHOD Using spectral optimization and data fusion technology, near infrared (NIR) and mid-infrared (MIR) spectra were collected for CR. PLS-DA (n = 134) and PLSR (n = 63) models were established to identify the medicinal materials and to determine the moisture content in the medicinal materials. RESULTS For the identification of the three species of CR, the mid-level fusion model performed better than the single-spectrum model. The sensitivity and specificity of the prediction set coefficients for NIR, MIR, and data fusion qualitative models were all higher than 0.95, with an AUC value of 1. The NIR data model was superior to the MIR data model. The results of low-level fusion were similar to those of the NIR optimization model. The RPD of the test set of NIR and low-level fusion model was 3.6420 and 3.4216, respectively, indicating good prediction ability of the model. CONCLUSIONS Data fusion technology using NIR and MIR can be applied to identify CR species and to determine the moisture content of CR. It provides technical support for the rapid determination of moisture content, with a fast analysis speed and without the need for complex pretreatment methods. HIGHLIGHTS This study is the first to introduce spectral data fusion technology to identify CR species. Data fusion technology is feasible for multivariable calibration model performance and reduces the cost of manual identification. The moisture content of CR can be quickly evaluated, reducing the difficulty of traditional methods.
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Affiliation(s)
- Mengyin Tian
- Shandong University, NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Cheeloo College of Medicine, Jinan, Shandong 250012, China
- Shandong University, Key Laboratory of Chemical Biology (Ministry of Education), Jinan, Shandong 250012, China
| | - Xiaobo Ma
- Shandong University, NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Cheeloo College of Medicine, Jinan, Shandong 250012, China
- Shandong University, Key Laboratory of Chemical Biology (Ministry of Education), Jinan, Shandong 250012, China
| | - Mengying Liang
- Shandong University, NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Cheeloo College of Medicine, Jinan, Shandong 250012, China
- Shandong University, Key Laboratory of Chemical Biology (Ministry of Education), Jinan, Shandong 250012, China
| | - Hengchang Zang
- Shandong University, NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Cheeloo College of Medicine, Jinan, Shandong 250012, China
- Shandong University, Key Laboratory of Chemical Biology (Ministry of Education), Jinan, Shandong 250012, China
- Shandong University, National Glycoengineering Research Center, Jinan, Shandong 250012, China
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Quintanilla-Casas B, Torres-Cobos B, Guardiola F, Servili M, Alonso-Salces RM, Valli E, Bendini A, Toschi TG, Vichi S, Tres A. Geographical authentication of virgin olive oil by GC-MS sesquiterpene hydrocarbon fingerprint: Verifying EU and single country label-declaration. Food Chem 2022; 378:132104. [PMID: 35078099 DOI: 10.1016/j.foodchem.2022.132104] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 12/31/2021] [Accepted: 01/05/2022] [Indexed: 12/28/2022]
Abstract
According to the last report from the European Union (EU) Food Fraud Network, olive oil tops the list of the most notified products. Current EU regulation states geographical origin as mandatory for virgin olive oils, even though an official analytical method is still lacking. Verifying the compliance of label-declared EU oils should be addressed with the highest priority level. Hence, the present work tackles this issue by developing a classification model (PLS-DA) based on the sesquiterpene hydrocarbon fingerprint of 400 samples obtained by HS-SPME-GC-MS to discriminate between EU and non-EU olive oils, obtaining an 89.6% of correct classification for the external validation (three iterations), with a sensitivity of 0.81 and a specificity of 0.95. Subsequently, multi-class discrimination models for EU and non-EU countries were developed and externally validated (with three different validation sets) with successful results (average of 92.2% of correct classification for EU and 96.0% for non-EU countries).
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Affiliation(s)
- Beatriz Quintanilla-Casas
- Departament de Nutrició, Ciències de l'Alimentació i Gastronomia, Campus de l'Alimentació Torribera, Facultat de Farmàcia i Ciències de l'Alimentació, Universitat de Barcelona. Av Prat de la Riba, 171. 08921 Santa Coloma de Gramenet, Spain; Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Universitat de Barcelona. Av Prat de la Riba, 171. 08921 Santa Coloma de Gramenet, Spain
| | - Berta Torres-Cobos
- Departament de Nutrició, Ciències de l'Alimentació i Gastronomia, Campus de l'Alimentació Torribera, Facultat de Farmàcia i Ciències de l'Alimentació, Universitat de Barcelona. Av Prat de la Riba, 171. 08921 Santa Coloma de Gramenet, Spain; Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Universitat de Barcelona. Av Prat de la Riba, 171. 08921 Santa Coloma de Gramenet, Spain
| | - Francesc Guardiola
- Departament de Nutrició, Ciències de l'Alimentació i Gastronomia, Campus de l'Alimentació Torribera, Facultat de Farmàcia i Ciències de l'Alimentació, Universitat de Barcelona. Av Prat de la Riba, 171. 08921 Santa Coloma de Gramenet, Spain; Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Universitat de Barcelona. Av Prat de la Riba, 171. 08921 Santa Coloma de Gramenet, Spain
| | - Maurizio Servili
- Dipartimento di Scienze Agrarie, Alimentari ed Ambientali, Università di Perugia, Via San Costanzo S.n.c., 06126 Perugia, Italy
| | - Rosa Maria Alonso-Salces
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Departamento de Biología, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata (UNMdP), Funes 3350, 7600 Mar del Plata, Argentina
| | - Enrico Valli
- Dipartimento di Scienze e Tecnologie Agro-alimentari, Alma Mater Studiorum - Università di Bologna, Piazza Goidanich, 60, I-47521, Cesena, Italy
| | - Alessandra Bendini
- Dipartimento di Scienze e Tecnologie Agro-alimentari, Alma Mater Studiorum - Università di Bologna, Piazza Goidanich, 60, I-47521, Cesena, Italy
| | - Tullia Gallina Toschi
- Dipartimento di Scienze e Tecnologie Agro-alimentari, Alma Mater Studiorum - Università di Bologna, Piazza Goidanich, 60, I-47521, Cesena, Italy
| | - Stefania Vichi
- Departament de Nutrició, Ciències de l'Alimentació i Gastronomia, Campus de l'Alimentació Torribera, Facultat de Farmàcia i Ciències de l'Alimentació, Universitat de Barcelona. Av Prat de la Riba, 171. 08921 Santa Coloma de Gramenet, Spain; Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Universitat de Barcelona. Av Prat de la Riba, 171. 08921 Santa Coloma de Gramenet, Spain.
| | - Alba Tres
- Departament de Nutrició, Ciències de l'Alimentació i Gastronomia, Campus de l'Alimentació Torribera, Facultat de Farmàcia i Ciències de l'Alimentació, Universitat de Barcelona. Av Prat de la Riba, 171. 08921 Santa Coloma de Gramenet, Spain; Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Universitat de Barcelona. Av Prat de la Riba, 171. 08921 Santa Coloma de Gramenet, Spain
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Geographical authentication of virgin olive oil by GC-MS sesquiterpene hydrocarbon fingerprint: Scaling down to the verification of PDO compliance. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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4
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Tata A, Massaro A, Damiani T, Piro R, Dall'Asta C, Suman M. Detection of soft-refined oils in extra virgin olive oil using data fusion approaches for LC-MS, GC-IMS and FGC-Enose techniques: The winning synergy of GC-IMS and FGC-Enose. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108645] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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The use of analytical techniques coupled with chemometrics for tracing the geographical origin of oils: A systematic review (2013-2020). Food Chem 2021; 366:130633. [PMID: 34332421 DOI: 10.1016/j.foodchem.2021.130633] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 06/14/2021] [Accepted: 07/16/2021] [Indexed: 11/20/2022]
Abstract
The global market for imported, high-quality priced foods has grown dramatically in the last decade, as consumers become more conscious of food originating from around the world. Many countries require the origin label of food to protect consumers need about true characteristics and origin. Regulatory authorities are looking for an extended and updated list of the analytical techniques for verification of authentic oils and to support law implementation. This review aims to introduce the efforts made using various analytical tools in combination with the multivariate analysis for the verification of the geographical origin of oils. The popular analytical tools have been discussed, and scientometric assessment that underlines research trends in geographical authentication and preferred journals used for dissemination has been indicated. Overall, we believe this article will be a good guideline for food industries and food quality control authority to assist in the selection of appropriate methods to authenticate oils.
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Zhu H, Xu JL. Authentication and Provenance of Walnut Combining Fourier Transform Mid-Infrared Spectroscopy with Machine Learning Algorithms. Molecules 2020; 25:E4987. [PMID: 33126520 PMCID: PMC7662659 DOI: 10.3390/molecules25214987] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 10/25/2020] [Accepted: 10/27/2020] [Indexed: 01/18/2023] Open
Abstract
Different varieties and geographical origins of walnut usually lead to different nutritional values, contributing to a big difference in the final price. The conventional analytical techniques have some unavoidable limitations, e.g., chemical analysis is usually time-expensive and labor-intensive. Therefore, this work aims to apply Fourier transform mid-infrared spectroscopy coupled with machine learning algorithms for the rapid and accurate classification of walnut species that originated from ten varieties produced from four provinces. Three types of models were developed by using five machine learning classifiers to (1) differentiate four geographical origins; (2) identify varieties produced from the same origin; and (3) classify all 10 varieties from four origins. Prior to modeling, the wavelet transform algorithm was used to smooth and denoise the spectrum. The results showed that the identification of varieties under the same origin performed the best (i.e., accuracy = 100% for some origins), followed by the classification of four different origins (i.e., accuracy = 96.97%), while the discrimination of all 10 varieties is the least desirable (i.e., accuracy = 87.88%). Our results implicated that using the full spectral range of 700-4350 cm-1 is inferior to using the subsets of the optimal spectral variables for some classifiers. Additionally, it is demonstrated that back propagation neural network (BPNN) delivered the best model performance, while random forests (RF) produced the worst outcome. Hence, this work showed that the authentication and provenance of walnut can be realized effectively based on Fourier transform mid-infrared spectroscopy combined with machine learning algorithms.
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Affiliation(s)
- Hongyan Zhu
- College of Electronic Engineering, Guangxi Normal University, Guilin 541004, China;
| | - Jun-Li Xu
- UCD School of Biosystems and Food Engineering, University College of Dublin (UCD), Belfield Dublin 4, Ireland
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7
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Zhou Y, Zuo Z, Xu F, Wang Y. Origin identification of Panax notoginseng by multi-sensor information fusion strategy of infrared spectra combined with random forest. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 226:117619. [PMID: 31606667 DOI: 10.1016/j.saa.2019.117619] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 07/14/2019] [Accepted: 10/06/2019] [Indexed: 06/10/2023]
Abstract
Traditional Chinese medicine Panax notoginseng is a valuable geo-authentic herbal material. The difference of growth environment in different producing areas has significant influence on the quality of traditional Chinese medicine, and origin identification is an important part of the quality assessment of P. notoginseng. In this study, Fourier transform mid-infrared (FT-MIR) and near infrared (NIR) sensor technologies combined with single spectra analysis and multi-sensor information fusion strategy (low-, mid- and high-level) for the origin identification of 210 P. notoginseng samples from five cities in Yunnan Province, China. FT-MIR spectra were considered to play a greater role in data analysis than NIR spectra. Random forest (RF) was used to establish classification models. The result of the random forest Boruta (RF-Bo) model and the random forest variable selection (RF-Vs) model based on high-level multi-sensor information fusion strategy was satisfactory. In addition, the RF-Bo model based on high-level multi-sensor information fusion strategy was faster and simpler in data analysis and the accuracy was 95.6%.
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Affiliation(s)
- Yuhou Zhou
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, 650500, PR China
| | - Zhitian Zuo
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming, 650200, PR China
| | - Furong Xu
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, 650500, PR China.
| | - Yuanzhong Wang
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming, 650200, PR China.
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Cavallini N, Savorani F, Bro R, Cocchi M. Fused adjacency matrices to enhance information extraction: The beer benchmark. Anal Chim Acta 2019; 1061:70-83. [DOI: 10.1016/j.aca.2019.02.023] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Revised: 01/31/2019] [Accepted: 02/04/2019] [Indexed: 12/01/2022]
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9
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10
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Has your ancient stamp been regummed with synthetic glue? A FT-NIR and FT-Raman study. Talanta 2015; 149:250-256. [PMID: 26717838 DOI: 10.1016/j.talanta.2015.11.059] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2015] [Revised: 11/19/2015] [Accepted: 11/23/2015] [Indexed: 11/24/2022]
Abstract
The potential of FT-NIR and FT-Raman spectroscopies to characterise the gum applied on the backside of ancient stamps was investigated for the first time. This represents a very critical issue for the collectors' market, since gum conditions heavily influence stamp quotations, and fraudulent application of synthetic gum onto damaged stamp backsides to increase their desirability is a well-documented practice. Spectral data were processed by exploratory pattern recognition tools. In particular, application of principal component analysis (PCA) revealed that both of the spectroscopic techniques provide information useful to characterise stamp gum. Examination of PCA loadings and their chemical interpretation confirmed the robustness of the outcomes. Fusion of FT-NIR and FT-Raman spectral data was performed, following both a low-level and a mid-level procedure. The results were critically compared with those obtained separately for the two spectroscopic techniques.
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Pirro V, Oliveri P, Ferreira CR, González-Serrano AF, Machaty Z, Cooks RG. Lipid characterization of individual porcine oocytes by dual mode DESI-MS and data fusion. Anal Chim Acta 2014; 848:51-60. [PMID: 25263116 DOI: 10.1016/j.aca.2014.08.001] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2014] [Revised: 07/31/2014] [Accepted: 08/02/2014] [Indexed: 01/10/2023]
Abstract
The development of sensitive measurements to analyze individual cells is of relevance to elucidate specialized roles or metabolic functions of each cell under physiological and pathological conditions. Lipids play multiple and critical roles in cellular functions and the application of analytical methods in the lipidomics area is of increasing interest. In this work, in vitro maturation of porcine oocytes was studied. Two independent sources of chemical information (represented by mass spectra in the positive and negative ion modes) from single oocytes (immature oocytes, 24-h and 44-h in vitro matured oocytes) were acquired by using desorption electrospray ionization-mass spectrometry (DESI-MS). Low and mid-level data fusion strategies are presented with the aim of better exploring the large amount of chemical information contained in the two mass spectrometric lipid profiles. Data were explored by principal component analysis (PCA) within the two multi-block approaches to include information on free fatty acids, phospholipids, cholesterol-related molecules, di- and triacylglycerols. After data fusion, clearer differences among immature and in vitro matured porcine oocytes were observed, which provide novel information regarding lipid metabolism throughout oocyte maturation. In particular, changes in TAG composition, as well as increase in fatty acid metabolism and membrane complexity were evidenced during the in vitro maturation process. This information can assist the improvement of in vitro embryo production for porcine species.
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Affiliation(s)
- Valentina Pirro
- Department of Chemistry, University of Turin, Via Pietro Giuria 7, Turin 10125, Italy.
| | - Paolo Oliveri
- Department of Pharmacy, University of Genoa, Via Brigata Salerno 13, Genoa 16147, Italy
| | | | | | - Zoltan Machaty
- Department of Animal Sciences, Purdue University, 915 W. State St., West Lafayette, IN 47907, USA
| | - Robert Graham Cooks
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN 47907, USA
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Data-fusion for multiplatform characterization of an Italian craft beer aimed at its authentication. Anal Chim Acta 2014; 820:23-31. [PMID: 24745734 DOI: 10.1016/j.aca.2014.02.024] [Citation(s) in RCA: 139] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2013] [Revised: 02/10/2014] [Accepted: 02/17/2014] [Indexed: 11/21/2022]
Abstract
Five different instrumental techniques: thermogravimetry, mid-infrared, near-infrared, ultra-violet and visible spectroscopies, have been used to characterize a high quality beer (Reale) from an Italian craft brewery (Birra del Borgo) and to differentiate it from other competing and lower quality products. Chemometric classification models were built on the separate blocks using soft independent modeling of class analogies (SIMCA) and partial least squares-discriminant analysis (PLS-DA) obtaining good predictive ability on an external test set (75% or higher depending on the technique). The use of data fusion strategies - in particular, the mid-level one - to integrate the data from the different platforms allowed the correct classification of all the training and validation samples.
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