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Silva EMDA, Samila Lima Castro I, Pereira Aguilar A, Teixeira Caixeta E, Antônio de Oliveira Mendes T. New genetic markers for 100% arabica coffee demonstrate high discriminatory potential for InDel-HRM-based coffee authentication. Food Res Int 2023; 173:113424. [PMID: 37803761 DOI: 10.1016/j.foodres.2023.113424] [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: 06/14/2023] [Revised: 08/28/2023] [Accepted: 08/29/2023] [Indexed: 10/08/2023]
Abstract
Food authenticity is crucial in today's society, given the heightened consumer awareness and attention to the products they consume. Reliable and efficient techniques are needed to quickly detect potential food adulterations that can negatively impact product quality and economic value. Coffee, a globally traded agricultural product, holds immense economic importance, with an estimated value of USD 83 billion. It is widely consumed and recognized as a functional food that provides minerals (K, Mg, Mn, Cr), niacin, and antioxidants. However, the preferred coffee species, Coffea arabica, known for its superior drink quality, is often adulterated with Coffea canephora (Robusta and Conilon) beans, even in 100% Arabica coffee. To distinguish between these two coffee species, a comprehensive study was conducted using a robust approach to identify differences in Single-Ortholog Copy (SOC) based on InDel regions in these gene pairs. These differences were validated using a meticulous methodology that considered variations in amplicon size: electrophoretic profile, and high-resolution melting (HRM). The innovative combination of InDels and HRM resulted in highly distinctive HRM profiles, outperforming SNP-based methods previously used. The targeted InDel approach utilized in this study facilitated precise quantification of Coffea species beans with a detection sensitivity of 0.5%. The study's findings establish the reliability and accuracy in distinguishing between the two coffee species, showcasing the valuable application of InDels for quality control and ensuring the authenticity of coffee beans. This pioneering research contributes to the advancement of authenticity verification methods for both imported and exported coffee beans, as well as in future studies that require significant genetic differences between these species, such as C. arabica and C. canephora.
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Affiliation(s)
- Edson Mario de Andrade Silva
- Universidade Federal de Minas Gerais, Av. Pres. Antônio Carlos, 6627 - Pampulha, Belo Horizonte, MG, Brazil; Instituto de Biotecnologia Aplicada à Agropecuária (BIOAGRO), Universidade Federal de Viçosa (UFV), Avenida Peter Henry Rolfs, Biotecnologia do cafeeiro- Biocafé, Centro, 36570-000 Viçosa, MG, Brazil; Departamento de Bioquímica, Universidade Federal de Viçosa, Viçosa, Minas Gerais 36570-900, Brazil
| | - Isabel Samila Lima Castro
- Instituto de Biotecnologia Aplicada à Agropecuária (BIOAGRO), Universidade Federal de Viçosa (UFV), Avenida Peter Henry Rolfs, Biotecnologia do cafeeiro- Biocafé, Centro, 36570-000 Viçosa, MG, Brazil; Departamento de Bioquímica, Universidade Federal de Viçosa, Viçosa, Minas Gerais 36570-900, Brazil
| | - Ananda Pereira Aguilar
- Instituto de Biotecnologia Aplicada à Agropecuária (BIOAGRO), Universidade Federal de Viçosa (UFV), Avenida Peter Henry Rolfs, Biotecnologia do cafeeiro- Biocafé, Centro, 36570-000 Viçosa, MG, Brazil; Departamento de Bioquímica, Universidade Federal de Viçosa, Viçosa, Minas Gerais 36570-900, Brazil
| | - Eveline Teixeira Caixeta
- Instituto de Biotecnologia Aplicada à Agropecuária (BIOAGRO), Universidade Federal de Viçosa (UFV), Avenida Peter Henry Rolfs, Biotecnologia do cafeeiro- Biocafé, Centro, 36570-000 Viçosa, MG, Brazil; Embrapa Café, Parque Estação Biológica Pq EB W3 norte final Parque Estação Biológica, PQEB, Brasília, DF, Brazil
| | - Tiago Antônio de Oliveira Mendes
- Universidade Federal de Minas Gerais, Av. Pres. Antônio Carlos, 6627 - Pampulha, Belo Horizonte, MG, Brazil; Instituto de Biotecnologia Aplicada à Agropecuária (BIOAGRO), Universidade Federal de Viçosa (UFV), Avenida Peter Henry Rolfs, Biotecnologia do cafeeiro- Biocafé, Centro, 36570-000 Viçosa, MG, Brazil; Departamento de Bioquímica, Universidade Federal de Viçosa, Viçosa, Minas Gerais 36570-900, Brazil.
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Vieira Lyrio MV, Pereira da Cunha PH, Debona DG, Agnoletti BZ, Araújo BQ, Frinhani RQ, Filgueiras PR, Pereira LL, Ribeiro de Castro EV. SHS-GC-MS applied in Coffea arabica and Coffea canephora blend assessment. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023. [PMID: 37401176 DOI: 10.1039/d3ay00510k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
Abstract
Considering the great economic significance of Coffea arabica (arabica) associated with the lower production cost of C. canephora (conilon), blends of these coffees are commercially available to reduce costs and combine sensory attributes. Thus, analytical tools are required to ensure consistency between real and labeled compositions. In this sense, chromatographic methods based on volatile analysis using static headspace-gas chromatography-mass spectrometry (SHS-GC-MS) and Fourier transform infrared (FTIR) spectroscopy associated with chemometric tools were proposed for the identification and quantification of arabica and conilon blends. The peak integration from the total ion chromatogram (TIC) and extracted ion chromatogram (EIC) was compared in multivariate and univariate scenarios. The optimized partial least squares (PLS) models with uninformative variable elimination (UVE) and chromatographic data (TIC and EIC) have similar accuracy according to a randomized test, with prediction errors between 3.3% and 4.7% and Rp2 > 0.98. There was no difference between the univariate models for the TIC and EIC, but the FTIR model presented a lower performance than GC-MS. The multivariate and univariate models based on chromatographic data had similar accuracy. For the classification models, the FTIR, TIC, and EIC data presented accuracies from 96% to 100% and error rates from 0% to 5%. Multivariate and univariate analyses combined with chromatographic and spectroscopic data allow the investigation of coffee blends.
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Affiliation(s)
- Marcos Valério Vieira Lyrio
- Federal University of Espírito Santo (UFES), Department of Chemistry, Campus Goiabeiras, Avenida Fernando Ferrari, 514, CEP 29075-910 Vitoria, Espírito Santo, Brazil.
| | - Pedro Henrique Pereira da Cunha
- Federal University of Espírito Santo (UFES), Department of Chemistry, Campus Goiabeiras, Avenida Fernando Ferrari, 514, CEP 29075-910 Vitoria, Espírito Santo, Brazil.
| | - Danieli Grancieri Debona
- Federal University of Espírito Santo (UFES), Department of Chemistry, Campus Goiabeiras, Avenida Fernando Ferrari, 514, CEP 29075-910 Vitoria, Espírito Santo, Brazil.
| | - Bárbara Zani Agnoletti
- Federal University of Espírito Santo (UFES), Department of Chemistry, Campus Goiabeiras, Avenida Fernando Ferrari, 514, CEP 29075-910 Vitoria, Espírito Santo, Brazil.
| | - Bruno Quirino Araújo
- Federal University of Espírito Santo (UFES), Department of Chemistry, Campus Goiabeiras, Avenida Fernando Ferrari, 514, CEP 29075-910 Vitoria, Espírito Santo, Brazil.
| | - Roberta Quintino Frinhani
- Federal University of Espírito Santo (UFES), Department of Chemistry, Campus Goiabeiras, Avenida Fernando Ferrari, 514, CEP 29075-910 Vitoria, Espírito Santo, Brazil.
| | - Paulo Roberto Filgueiras
- Federal University of Espírito Santo (UFES), Department of Chemistry, Campus Goiabeiras, Avenida Fernando Ferrari, 514, CEP 29075-910 Vitoria, Espírito Santo, Brazil.
| | - Lucas Louzada Pereira
- Federal Institute of Espírito Santo, Department of Food Science and Technology, Avenida Elizabeth Minete Perim, S/N, Bairro São Rafael, CEP 29375-000 Venda Nova do Imigrante, Espírito Santo, Brazil
| | - Eustáquio Vinicius Ribeiro de Castro
- Federal University of Espírito Santo (UFES), Department of Chemistry, Campus Goiabeiras, Avenida Fernando Ferrari, 514, CEP 29075-910 Vitoria, Espírito Santo, Brazil.
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Luo S, Yan C, Chen D. Preliminary study on coffee type identification and coffee mixture analysis by light emitting diode induced fluorescence spectroscopy. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Aouadi B, Vitalis F, Bodor Z, Zinia Zaukuu JL, Kertesz I, Kovacs Z. NIRS and Aquaphotomics Trace Robusta-to-Arabica Ratio in Liquid Coffee Blends. Molecules 2022; 27:388. [PMID: 35056707 PMCID: PMC8780874 DOI: 10.3390/molecules27020388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/04/2022] [Accepted: 01/05/2022] [Indexed: 11/27/2022] Open
Abstract
Coffee is both a vastly consumed beverage and a chemically complex matrix. For a long time, an arduous chemical analysis was necessary to resolve coffee authentication issues. Despite their demonstrated efficacy, such techniques tend to rely on reference methods or resort to elaborate extraction steps. Near infrared spectroscopy (NIRS) and the aquaphotomics approach, on the other hand, reportedly offer a rapid, reliable, and holistic compositional overview of varying analytes but with little focus on low concentration mixtures of Robusta-to-Arabica coffee. Our study aimed for a comparative assessment of ground coffee adulteration using NIRS and liquid coffee adulteration using the aquaphotomics approach. The aim was to demonstrate the potential of monitoring ground and liquid coffee quality as they are commercially the most available coffee forms. Chemometrics spectra analysis proved capable of distinguishing between the studied samples and efficiently estimating the added Robusta concentrations. An accuracy of 100% was obtained for the varietal discrimination of pure Arabica and Robusta, both in ground and liquid form. Robusta-to-Arabica ratio was predicted with R2CV values of 0.99 and 0.9 in ground and liquid form respectively. Aquagrams results accentuated the peculiarities of the two coffee varieties and their respective blends by designating different water conformations depending on the coffee variety and assigning a particular water absorption spectral pattern (WASP) depending on the blending ratio. Marked spectral features attributed to high hydrogen bonded water characterized Arabica-rich coffee, while those with the higher Robusta content showed an abundance of free water structures. Collectively, the obtained results ascertain the adequacy of NIRS and aquaphotomics as promising alternative tools for the authentication of liquid coffee that can correlate the water-related fingerprint to the Robusta-to-Arabica ratio.
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Affiliation(s)
- Balkis Aouadi
- Department of Measurements and Process Control, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, 14-16. Somlói Street, H-1118 Budapest, Hungary; (B.A.); (F.V.); (Z.B.); (I.K.)
| | - Flora Vitalis
- Department of Measurements and Process Control, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, 14-16. Somlói Street, H-1118 Budapest, Hungary; (B.A.); (F.V.); (Z.B.); (I.K.)
| | - Zsanett Bodor
- Department of Measurements and Process Control, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, 14-16. Somlói Street, H-1118 Budapest, Hungary; (B.A.); (F.V.); (Z.B.); (I.K.)
- Department of Dietetics and Nutrition Faculty of Health Sciences, Semmelweis University, 17. Vas Street, H-1088 Budapest, Hungary
| | - John-Lewis Zinia Zaukuu
- Department of Food Science and Technology, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi 00233, Ghana;
| | - Istvan Kertesz
- Department of Measurements and Process Control, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, 14-16. Somlói Street, H-1118 Budapest, Hungary; (B.A.); (F.V.); (Z.B.); (I.K.)
| | - Zoltan Kovacs
- Department of Measurements and Process Control, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, 14-16. Somlói Street, H-1118 Budapest, Hungary; (B.A.); (F.V.); (Z.B.); (I.K.)
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Zhao J, Fang Y, Chu G, Yan H, Hu L, Huang L. Identification of Leaf-Scale Wheat Powdery Mildew ( Blumeria graminis f. sp. Tritici) Combining Hyperspectral Imaging and an SVM Classifier. PLANTS 2020; 9:plants9080936. [PMID: 32722022 PMCID: PMC7464903 DOI: 10.3390/plants9080936] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 07/13/2020] [Accepted: 07/22/2020] [Indexed: 11/20/2022]
Abstract
Powdery mildew (PM, Blumeria graminis f. sp. tritici) is a devastating disease for wheat growth and production. It is highly meaningful that the disease severities can be objectively and accurately identified by image visualization technology. In this study, an integral method was proposed based on a hyperspectral imaging dataset and machine learning algorithms. The disease severities of wheat leaves infected with PM were quantitatively identified based on hyperspectral images and image segmentation techniques. A technical procedure was proposed to perform the identification and evaluation of leaf-scale wheat PM, specifically including three primary steps of the acquisition and preprocessing of hyperspectral images, the selection of characteristic bands, and model construction. Firstly, three-dimensional reduction algorithms, namely principal component analysis (PCA), random forest (RF), and the successive projections algorithm (SPA), were comparatively used to select the bands that were most sensitive to PM. Then, three diagnosis models were constructed by a support vector machine (SVM), RF, and a probabilistic neural network (PNN). Finally, the best model was selected by comparing the overall accuracies. The results show that the SVM model constructed by PCA dimensionality reduction had the best result, and the classification accuracy reached 93.33% by a cross-validation method. There was an obvious improvement of the identification accuracy with the model, which achieved an 88.00% accuracy derived from the original hyperspectral images. This study can provide a reference for accurately estimating the disease severity of leaf-scale wheat PM and other plant diseases by non-contact measurement technology.
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Affiliation(s)
- Jinling Zhao
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
- Correspondence: (J.Z.); (L.H.)
| | - Yan Fang
- School of Electronics and Information Engineering, Anhui University, Hefei 230601, China; (Y.F.); (G.C.); (H.Y.); (L.H.)
| | - Guomin Chu
- School of Electronics and Information Engineering, Anhui University, Hefei 230601, China; (Y.F.); (G.C.); (H.Y.); (L.H.)
| | - Hao Yan
- School of Electronics and Information Engineering, Anhui University, Hefei 230601, China; (Y.F.); (G.C.); (H.Y.); (L.H.)
| | - Lei Hu
- School of Electronics and Information Engineering, Anhui University, Hefei 230601, China; (Y.F.); (G.C.); (H.Y.); (L.H.)
| | - Linsheng Huang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
- Correspondence: (J.Z.); (L.H.)
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Assis C, Gama EM, Nascentes CC, de Oliveira LS, Anzanello MJ, Sena MM. A data fusion model merging information from near infrared spectroscopy and X-ray fluorescence. Searching for atomic-molecular correlations to predict and characterize the composition of coffee blends. Food Chem 2020; 325:126953. [PMID: 32387940 DOI: 10.1016/j.foodchem.2020.126953] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 04/19/2020] [Accepted: 04/29/2020] [Indexed: 12/14/2022]
Abstract
This article aims to develop and validate a multivariate model for quantifying Robusta-Arabica coffee blends by combining near infrared spectroscopy (NIRS) and total reflection X-ray fluorescence (TXRF). For this aim, 80 coffee blends (0.0-33.0%) were formulated. NIR spectra were obtained in the wavenumber range 11100-4950 cm-1 and 14 elements were determined by TXRF. Partial least squares models were built using data fusion at low and medium levels. In addition, selection of predictive variables based on their importance indices (SVPII) improved results. The best model reduced the number of variables from 1114 to 75 and root mean square error of prediction from 4.1% to 1.7%. SVPII selected NIR regions correlated with coffee components, and the following elements were chosen: Ti, Mn, Fe, Cu, Zn, Br, Rb, Sr. The model interpretation took advantage of the data fusion between atomic and molecular spectra in order to characterize the differences between these coffee varieties.
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Affiliation(s)
- Camila Assis
- Departamento de Química, Instituto de Ciências Exatas (ICEx), Universidade Federal de Minas Gerais (UFMG), 31270-901 Belo Horizonte, MG, Brazil
| | - Ednilton Moreira Gama
- Departamento de Química, Instituto de Ciências Exatas (ICEx), Universidade Federal de Minas Gerais (UFMG), 31270-901 Belo Horizonte, MG, Brazil
| | - Clésia Cristina Nascentes
- Departamento de Química, Instituto de Ciências Exatas (ICEx), Universidade Federal de Minas Gerais (UFMG), 31270-901 Belo Horizonte, MG, Brazil
| | - Leandro Soares de Oliveira
- Departamento de Engenharia Mecânica, Escola de Engenharia, Universidade Federal de Minas Gerais (UFMG), 31270-901 Belo Horizonte, MG, Brazil
| | - Michel José Anzanello
- Departamento de Engenharia Industrial, Universidade Federal do Rio Grande do Sul, 90035-190 Porto Alegre, RS, Brazil
| | - Marcelo Martins Sena
- Departamento de Química, Instituto de Ciências Exatas (ICEx), Universidade Federal de Minas Gerais (UFMG), 31270-901 Belo Horizonte, MG, Brazil; Instituto Nacional de Ciência e Tecnologia em Bioanalítica, 13083-970 Campinas, SP, Brazil.
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Roque JV, Cardoso W, Peternelli LA, Teófilo RF. Comprehensive new approaches for variable selection using ordered predictors selection. Anal Chim Acta 2019; 1075:57-70. [DOI: 10.1016/j.aca.2019.05.039] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 05/15/2019] [Accepted: 05/16/2019] [Indexed: 01/21/2023]
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Combining mid infrared spectroscopy and paper spray mass spectrometry in a data fusion model to predict the composition of coffee blends. Food Chem 2019; 281:71-77. [DOI: 10.1016/j.foodchem.2018.12.044] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 12/16/2018] [Accepted: 12/17/2018] [Indexed: 02/07/2023]
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Xin Z, Jun S, Bing L, Xiaohong W, Chunxia D, Ning Y. Study on pesticide residues classification of lettuce leaves based on polarization spectroscopy. J FOOD PROCESS ENG 2018. [DOI: 10.1111/jfpe.12903] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Zhou Xin
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Sun Jun
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Lu Bing
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Wu Xiaohong
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Dai Chunxia
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Yang Ning
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
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A review on the application of chromatographic methods, coupled to chemometrics, for food authentication. Food Control 2018. [DOI: 10.1016/j.foodcont.2018.06.015] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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