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de Carvalho Couto C, Corrêa de Souza Coelho C, Moraes Oliveira EM, Casal S, Freitas-Silva O. Adulteration in roasted coffee: a comprehensive systematic review of analytical detection approaches. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2023. [DOI: 10.1080/10942912.2022.2158865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Cinthia de Carvalho Couto
- Food and Nutrition Graduate Program, the Federal University of State of Rio de Janeiro, Rio de Janeiro, Brazil
| | | | | | - Susana Casal
- LAQV/REQUIMTE, Laboratory of Bromatology and Hydrology, Faculty of Pharmacy, University of Porto, Porto, Portugal
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2
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Teye E, Amuah CLY, Yeh TS, Nyorkeh R. Nondestructive Detection of Moisture Content in Palm Oil by Using Portable Vibrational Spectroscopy and Optimal Prediction Algorithms. JOURNAL OF ANALYTICAL METHODS IN CHEMISTRY 2023; 2023:3364720. [PMID: 36760654 PMCID: PMC9904916 DOI: 10.1155/2023/3364720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/11/2022] [Accepted: 01/13/2023] [Indexed: 06/18/2023]
Abstract
Rapid and nondestructive measurement of moisture content in crude palm oil is essential for promoting the shelf-stability and quality. In this research, micro NIR spectrometer coupled with a multivariate calibration model was used to collect and analyse fingerprinted information from palm oil samples at different moisture contents. Several preprocessing methods such as standard normal variant (SNV), multiplicative scatter correction (MSC), Savitzky-Golay first derivative (SGD1), Savitzky-Golay second derivative (SGD2) together with partial least square (PLS) regression techniques, full PLS, interval PLS (iPLS), synergy interval PLS (SiPLS), genetic algorithm PLS (GAPLS), and successive projection algorithm PLS (SPA-PLS) were comparatively employed to construct an optimum quantitative prediction model for moisture content in crude palm oil. The models were evaluated according to the coefficient of determination and root mean square error in calibration (Rc and RMSEC) and prediction (Rp and RMSEC) set, respectively. The model SGD1 + SiPLS was the optimal novel algorithm obtained among the others with the performance of Rc = 0.968 and RMSEC = 0.468 in the calibration set and Rp = 0.956 and RMSEP = 0.361 in the prediction set. The results showed that rapid and nondestructive determination of moisture content in palm oil is feasible and this would go a long way to facilitating quality control of crude palm oil.
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Affiliation(s)
- Ernest Teye
- Department of Agricultural Engineering, School of Agriculture, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Charles L. Y. Amuah
- Department of Physics, Laser and Fibre Optics Centre, School of Physical Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Tai-Sheng Yeh
- Department of Food Science and Nutrition, Meiho University, Neipu Township, Taiwan
| | - Regina Nyorkeh
- Department of Agricultural Engineering, School of Agriculture, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
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Boadu VG, Teye E, Amuah CLY, Sam-Amoah LK. Rapid authentication of coffee bean varieties of different forms by using a pocket-sized spectrometer and multivariate data modelling. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:4756-4766. [PMID: 36398971 DOI: 10.1039/d2ay01480g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Coffee is the most consumed beverage and the second most valuable traded commodity in the world. In this current study, a pocket-sized spectrometer and multivariate analysis were used for rapid authentication of coffee varieties (Arabica and Robusta) in three states to check mislabelling (food fraud). Two main coffee varieties were collected from different locations in Africa. The samples were scanned in the 740-1070 nm wavelength and the spectral data were pre-treated with several methods: mean centering (MC), multiplicative scatter correction (MSC), first derivative (FD), second derivative (SD) and standard normal variate (SNV) independently while partial least squares discriminate analysis (PLS-DA), K-nearest neighbour (KNN) and support vector machine (SVM) were used to comparatively build the prediction models for coffee beans (raw, roasted and powdered). The performances of the models were evaluated by using accuracy and efficiency. Among the classification methods developed, the best results were obtained for the following: raw coffee bean SD-SVM had an accuracy of 0.92 and efficiency of 0.82. For roasted coffee beans, SD-KNN had an accuracy of 0.92 and efficiency of 0.87, while for roasted powdered coffee, FD-KNN showed an accuracy of 0.97 and efficiency of 0.97. These finding reveals that for a more accurate differentiation of coffee beans, the roasted powder offers the best results. The obtained results showed that a pocket-sized spectrometer coupled with chemometrics could be employed to provide accurate and rapid authentication of different categories of coffee bean varieties.
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Affiliation(s)
- Vida Gyimah Boadu
- University of Cape Coast, College of Agriculture and Natural Sciences, School of Agriculture, Department of Agricultural Engineering, Cape Coast, Ghana.
- Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development, Ghana Department of Hospitality and Tourism Education, Kumasi, Ghana
| | - Ernest Teye
- University of Cape Coast, College of Agriculture and Natural Sciences, School of Agriculture, Department of Agricultural Engineering, Cape Coast, Ghana.
| | - Charles L Y Amuah
- University of Cape Coast, College of Agriculture and Natural Sciences, School of Physical Sciences, Department of Physics, Cape Coast, Ghana
| | - L K Sam-Amoah
- University of Cape Coast, College of Agriculture and Natural Sciences, School of Agriculture, Department of Agricultural Engineering, Cape Coast, Ghana.
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Caredda M, Mara A, Ciulu M, Floris I, Pilo MI, Spano N, Sanna G. Use of genetic algorithms in the wavelength selection of FT-MIR spectra to classify unifloral honeys from Sardinia. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Portillo OR. El procesamiento del grano de café. Del tueste a la infusión. BIONATURA 2022. [DOI: 10.21931/rb/2022.07.03.18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
El café es una de las bebidas más consumidas en el mundo y su popularidad no está basada en su valor nutricional o sus potenciales beneficios a la salud, si no en su sabor placentero y las propiedades estimulantes de la cafeína. Esto es respaldado por las últimas estadísticas publicadas por la Organización Internacional del Café (ICO, por sus siglas en inglés) según la cual aproximadamente 1.4 billones de tazas de café son consumidas diariamente además del hecho de que la taza de consumo global se ha duplicado en los últimos 50 años por causa de la apertura de nuevos mercados.
La amplia aceptación del café está ligada a sus propiedades sensoriales las cuales a su vez están fuertemente influenciadas por una cadena de eventos que inician desde la cosecha y las practicas postcosecha (i.e., fermentación, lavado, secado, tamizado, eliminación de granos defectuosos y almacenamiento), seguidas por el tueste, molido y empacado del producto para su posterior comercialización. No obstante, existen otros factores que también afectan las propiedades organolépticas de la bebida tales como, pero no limitado a: el pH y temperatura del agua, las mezclas realizadas antes o después del tueste, la especie y/o variedad de café, las adulteraciones, la incorporación de aditivos, el método de preparación de la bebida, el tipo de recipiente en el que se sirve la infusión, entre otros.
El presente artículo presenta una breve descripción de los factores que afectan la calidad de la taza relacionados con el procesamiento del grano oro del café. Sin embargo, aunque los factores ya mencionados son tomados en consideración por los catadores, para fines comerciales, la calidad del café está y siempre estará en manos del consumidor. Después de todo la mejor prueba es cuando la persona lo prueba.
Palabras clave: organoléptica, perfil de tueste, endotérmica, exotérmico, ma-croscópica, microscópica, reacción Maillard, caramelización.
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Affiliation(s)
- Ostilio R. Portillo
- Facultad de Ingeniería, Universidad Nacional Autónoma de Honduras, (UNAH), Tegucigalpa, Honduras
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Xie JY, Tan J. Front-face synchronous fluorescence spectroscopy: a rapid and non-destructive authentication method for Arabica coffee adulterated with maize and soybean flours. J Verbrauch Lebensm 2022; 17:209-219. [PMID: 35996456 PMCID: PMC9385078 DOI: 10.1007/s00003-022-01396-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 07/06/2022] [Accepted: 07/26/2022] [Indexed: 10/31/2022]
Abstract
This article describes a novel front-face synchronous fluorescence spectroscopy (FFSFS) method for the fast and non-invasive authentication of ground roasted Arabica coffee adulterated with roasted maize and soybean flours. The detection was based on the different composition of fluorescent Maillard reaction products and caffeine in roasted coffee and cereal flours. For each roasted maize or soybean adulterant flour (5-40 wt%), principal component analysis coupled with linear discriminant analysis (PCA-LDA) was used for qualitative discrimination. Quantitative prediction models were constructed based on the combination of unfolded total synchronous fluorescence spectra and partial least square regression (PLSR), followed by fivefold cross-validation and external validation. The PLSR models produced suitable results, with the determination coefficient of prediction (R p 2) > 0.9, root mean square error of prediction (RMSEP) < 5%, relative error of prediction (REP) < 25% and residual predictive deviation (RPD) > 3. The limits of detection (LOD) were both 10% for roasted maize and soybean flours. Most relative errors for the prediction of simulated blind samples were between -30% and + 30%. The benefits of this strategy are simplicity, rapidity, and non-destructive detection. However, owing to the high similarity between roasted coffee and roasted cereal flours and the influence of the roasting degree on fluorescent Maillard reaction products, its application is limited to the preliminary screening of roasted coffee with the same roasting degree, adulterated with relatively large amounts of roasted cereal flours which are roasted to analogous color to the coffee. Supplementary Information The online version contains supplementary material available at 10.1007/s00003-022-01396-8.
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Affiliation(s)
- Jing-Ya Xie
- Tianjin Key Laboratory of Food Biotechnology, College of Biotechnology and Food Science, Tianjin University of Commerce, Tianjin, 300134 People’s Republic of China
| | - Jin Tan
- Tianjin Key Laboratory of Food Biotechnology, College of Biotechnology and Food Science, Tianjin University of Commerce, Tianjin, 300134 People’s Republic of China
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7
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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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Use of convolutional neural network (CNN) combined with FT-NIR spectroscopy to predict food adulteration: A case study on coffee. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.108816] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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Agricultural Potentials of Molecular Spectroscopy and Advances for Food Authentication: An Overview. Processes (Basel) 2022. [DOI: 10.3390/pr10020214] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Meat, fish, coffee, tea, mushroom, and spices are foods that have been acknowledged for their nutritional benefits but are also reportedly targets of fraud and tampering due to their economic value. Conventional methods often take precedence for monitoring these foods, but rapid advanced instruments employing molecular spectroscopic techniques are gradually claiming dominance due to their numerous advantages such as low cost, little to no sample preparation, and, above all, their ability to fingerprint and detect a deviation from quality. This review aims to provide a detailed overview of common molecular spectroscopic techniques and their use for agricultural and food quality management. Using multiple databases including ScienceDirect, Scopus, Web of Science, and Google Scholar, 171 research publications including research articles, review papers, and book chapters were thoroughly reviewed and discussed to highlight new trends, accomplishments, challenges, and benefits of using molecular spectroscopic methods for studying food matrices. It was observed that Near infrared spectroscopy (NIRS), Infrared spectroscopy (IR), Hyperspectral imaging (his), and Nuclear magnetic resonance spectroscopy (NMR) stand out in particular for the identification of geographical origin, compositional analysis, authentication, and the detection of adulteration of meat, fish, coffee, tea, mushroom, and spices; however, the potential of UV/Vis, 1H-NMR, and Raman spectroscopy (RS) for similar purposes is not negligible. The methods rely heavily on preprocessing and chemometric methods, but their reliance on conventional reference data which can sometimes be unreliable, for quantitative analysis, is perhaps one of their dominant challenges. Nonetheless, the emergence of handheld versions of these techniques is an area that is continuously being explored for digitalized remote analysis.
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Abstract
This review provides an overview of recent studies on the potential of spectroscopy techniques (mid-infrared, near infrared, Raman, and fluorescence spectroscopy) used in coffee analysis. It specifically covers their applications in coffee roasting supervision, adulterants and defective beans detection, prediction of specialty coffee quality and coffees’ sensory attributes, discrimination of coffee based on variety, species, and geographical origin, and prediction of coffees chemical composition. These are important aspects that significantly affect the overall quality of coffee and consequently its market price and finally quality of the brew. From the reviewed literature, spectroscopic methods could be used to evaluate coffee for different parameters along the production process as evidenced by reported robust prediction models. Nevertheless, some techniques have received little attention including Raman and fluorescence spectroscopy, which should be further studied considering their great potential in providing important information. There is more focus on the use of near infrared spectroscopy; however, few multivariate analysis techniques have been explored. With the growing demand for fast, robust, and accurate analytical methods for coffee quality assessment and its authentication, there are other areas to be studied and the field of coffee spectroscopy provides a vast opportunity for scientific investigation.
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Near-Infrared Spectroscopy Applied to the Detection of Multiple Adulterants in Roasted and Ground Arabica Coffee. Foods 2021; 11:foods11010061. [PMID: 35010188 PMCID: PMC8750839 DOI: 10.3390/foods11010061] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 12/13/2021] [Accepted: 12/16/2021] [Indexed: 12/24/2022] Open
Abstract
Roasted coffee has been the target of increasingly complex adulterations. Sensitive, non-destructive, rapid and multicomponent techniques for their detection are sought after. This work proposes the detection of several common adulterants (corn, barley, soybean, rice, coffee husks and robusta coffee) in roasted ground arabica coffee (from different geographic regions), combining near-infrared (NIR) spectroscopy and chemometrics (Principal Component Analysis—PCA). Adulterated samples were composed of one to six adulterants, ranging from 0.25 to 80% (w/w). The results showed that NIR spectroscopy was able to discriminate pure arabica coffee samples from adulterated ones (for all the concentrations tested), including robusta coffees or coffee husks, and independently of being single or multiple adulterations. The identification of the adulterant in the sample was only feasible for single or double adulterations and in concentrations ≥10%. NIR spectroscopy also showed potential for the geographical discrimination of arabica coffees (South and Central America).
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12
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Sen M. Food Chemistry: Role of Additives, Preservatives, and Adulteration. Food Chem 2021. [DOI: 10.1002/9781119792130.ch1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Gopalakrishnan K, Sharma A, Emanuel N, Prabhakar PK, Kumar R. Sensors for Non‐Destructive Quality Evaluation of Food. Food Chem 2021. [DOI: 10.1002/9781119792130.ch13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Thangaraju S, Modupalli N, Natarajan V. Food Adulteration and Its Impacts on Our Health/Balanced Nutrition. Food Chem 2021. [DOI: 10.1002/9781119792130.ch7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Three centuries on the science of coffee authenticity control. Food Res Int 2021; 149:110690. [PMID: 34600685 DOI: 10.1016/j.foodres.2021.110690] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 08/28/2021] [Accepted: 08/31/2021] [Indexed: 12/14/2022]
Abstract
Food authenticity relies on genuineness and reliability according to the information displayed on the package. Since the 18th century, when coffee became popularized in the West, adulteration began. Several methods have been developed to detect different kinds of frauds and they have evolved as demands increased and new technologies were introduced. The evolution of the science of coffee authenticity control in the past three centuries is reviewed, focusing on the discrimination between coffee and other foods or between coffee and its by-products. The earliest chemical and physical methods are presented followed by methods developed in the 20th and 21st centuries using microscopy, chromatography and spectroscopy associated with advanced statistical tools, and DNA-based methods. In addition to non-food material, before the 20th century, chicory was the most studied food-adulterant. From the 20th century on, corn, coffee by-products, and barley were the most studied, followed by chicory, rice and other food items. Matrix effects seem to be among the most challenging problems in these approaches, associated with variations in roast degree, particle size (particularly in spectroscopy-based methods), and lack of control over reference samples regarding species and purity. Limits of detection vary considerably within each category, with most limits being too high for commercial use. DNA-based methods appear to be promising to assess coffee authenticity, given that the limits of detection and quantification are low, and specificity is higher than in other methods. Nevertheless, as roast intensity increases, the sensitivity of the method decreases. So far, most reported methods have not been validated and only a few have been tested on commercial brands, except for those involving microscopy which has been highly used for monitoring coffee authenticity although not always efficiently enough.
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Zhu M, Long Y, Chen Y, Huang Y, Tang L, Gan B, Yu Q, Xie J. Fast determination of lipid and protein content in green coffee beans from different origins using NIR spectroscopy and chemometrics. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.104055] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Ameca‐Veneroso C, Sánchez‐Arellano L, Ramón‐Canul LG, Herrera‐Corredor JA, Cuervo‐Osorio VD, Quetz‐Aguirre EM, Rodríguez‐Miranda J, Cabal‐Prieto A, Ramírez‐Rivera EDJ. A modified version of the sensory Pivot technique as a possible tool for the analysis of food adulteration: A case of coffee. J SENS STUD 2021. [DOI: 10.1111/joss.12705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Carolina Ameca‐Veneroso
- Ingeniería en Industrias Alimentarias, Tecnológico Nacional de México/Instituto Tecnológico Superior de Huatusco Huatusco Veracruz México
| | - Lucia Sánchez‐Arellano
- Ingeniería en Industrias Alimentarias, Tecnológico Nacional de México/Instituto Tecnológico Superior de Huatusco Huatusco Veracruz México
| | - Lorena Guadalupe Ramón‐Canul
- División de estudios de Posgrado e Investigación Tecnológico Nacional de México/Instituto Tecnológico de Mérida Mérida Yucatán México
| | - José Andrés Herrera‐Corredor
- Programa de Innovación Agroalimentaria Sustentable, Colegio de Postgraduados, Campus Córdoba Amatlán de los Reyes Veracruz México
| | | | - Elvira María Quetz‐Aguirre
- Departamento de Ingenierías Tecnológico Nacional de México/Instituto Tecnológico de Chiná Campeche México
| | - Jesús Rodríguez‐Miranda
- Departamento de Ingeniería Química y Bioquímica Tecnológico Nacional de México/Instituto Tecnológico de Tuxtepec Tuxtepec Oaxaca México
| | - Adán Cabal‐Prieto
- Ingeniería en Industrias Alimentarias, Tecnológico Nacional de México/Instituto Tecnológico Superior de Huatusco Huatusco Veracruz México
| | - Emmanuel de Jesús Ramírez‐Rivera
- Ingeniería en Innovación Agrícola Sustentable, Tecnológico Nacional de México/Instituto Tecnológico Superior de Zongolica Zongolica Veracruz México
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Perez M, Domínguez-López I, López-Yerena A, Vallverdú Queralt A. Current strategies to guarantee the authenticity of coffee. Crit Rev Food Sci Nutr 2021; 63:539-554. [PMID: 34278907 DOI: 10.1080/10408398.2021.1951651] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
As they become more health conscious, consumers are paying increasing attention to food quality and safety. In coffee production, fraudulent strategies to reduce costs and maximize profits include mixing beans from two species of different economic value, the addition of other substances and/or foods, and mislabeling. Therefore, testing for coffee authenticity and detecting adulterants is required for value assessment and consumer protection. Here we provide an overview of the chromatography, spectroscopy, and single-nucleotide polymorphism-based methods used to distinguish between the major coffee species Arabica and Robusta. This review also describes the techniques applied to trace the geographical origin of coffee, based mainly on the chemical composition of the beans, an approach that can discriminate between coffee-growing regions on a continental or more local level. Finally, the analytical techniques used to detect coffee adulteration with other foods and/or coffee by-products are discussed, with a look at the practice of adding pharmacologically active compounds to coffee, and their harmful effects on health.
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Affiliation(s)
- Maria Perez
- Department of Nutrition, Food Science and Gastronomy XaRTA, Institute of Nutrition and Food Safety (INSA-UB), Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, Spain.,Laboratory of Organic Chemistry, Faculty of Pharmacy and Food Sciences, University of Barcelona, Spain
| | - Inés Domínguez-López
- Department of Nutrition, Food Science and Gastronomy XaRTA, Institute of Nutrition and Food Safety (INSA-UB), Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, Spain
| | - Anallely López-Yerena
- Department of Nutrition, Food Science and Gastronomy XaRTA, Institute of Nutrition and Food Safety (INSA-UB), Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, Spain
| | - Anna Vallverdú Queralt
- Department of Nutrition, Food Science and Gastronomy XaRTA, Institute of Nutrition and Food Safety (INSA-UB), Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, Spain.,Consorcio CIBER, M.P. Fisiopatología de la Obesidad y la Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
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Levate Macedo L, da Silva Araújo C, Costa Vimercati W, Gherardi Hein PR, Pimenta CJ, Henriques Saraiva S. Evaluation of chemical properties of intact green coffee beans using near-infrared spectroscopy. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:3500-3507. [PMID: 33274765 DOI: 10.1002/jsfa.10981] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 11/20/2020] [Accepted: 12/04/2020] [Indexed: 06/12/2023]
Abstract
BACKGROUND The chemical compounds in coffee are important indicators of quality. Its composition varies according to several factors related to the planting and processing of coffee. Thus, this study proposed to use near-infrared spectroscopy (NIR) associated with partial least squares (PLS) regression to estimate quickly some chemical properties (moisture content, soluble solids, and total and reducing sugars) in intact green coffee samples. For this, 250 samples produced in Brazil were analyzed in the laboratory by the standard method and also had their spectra recorded. RESULTS The calibration models were developed using PLS regression with cross-validation and tested in a validation set. The models were elaborated using original spectra and preprocessed by five different mathematical methods. These models were compared in relation to the coefficient of determination, root mean square error of cross-validation (RMSECV), root mean square error of test set validation (RMSEP), and ratio of performance to deviation (RPD) and demonstrated different predictive capabilities for the chemical properties of coffee. The best model was obtained to predict grain moisture and the worst performance was observed for the soluble solids model. The highest determination coefficients obtained for the samples in the validation set were equal to 0.810, 0.516, 0.694 and 0.781 for moisture, soluble solids, total sugar, and reducing sugars, respectively. CONCLUSION The statistics associated with these models indicate that NIR technology has the potential to be applied routinely to predict the chemical properties of green coffee, and in particular, for moisture analysis. However, the soluble solid and total sugar content did not show high correlations with the spectroscopic data and need to be improved. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Leandro Levate Macedo
- Department of Food Engineering, Center of Agrarian Sciences and Engineering, Federal University of Espírito Santo, Alegre, Brazil
| | - Cintia da Silva Araújo
- Department of Food Engineering, Center of Agrarian Sciences and Engineering, Federal University of Espírito Santo, Alegre, Brazil
| | - Wallaf Costa Vimercati
- Department of Food Engineering, Center of Agrarian Sciences and Engineering, Federal University of Espírito Santo, Alegre, Brazil
| | | | | | - Sérgio Henriques Saraiva
- Department of Food Engineering, Center of Agrarian Sciences and Engineering, Federal University of Espírito Santo, Alegre, Brazil
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Pradana-López S, Pérez-Calabuig AM, Cancilla JC, Lozano MÁ, Rodrigo C, Mena ML, Torrecilla JS. Deep transfer learning to verify quality and safety of ground coffee. Food Control 2021. [DOI: 10.1016/j.foodcont.2020.107801] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Mahanti NK, Chakraborty SK, Kotwaliwale N, Vishwakarma AK. Chemometric strategies for nondestructive and rapid assessment of nitrate content in harvested spinach using Vis-NIR spectroscopy. J Food Sci 2020; 85:3653-3662. [PMID: 32888324 DOI: 10.1111/1750-3841.15420] [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: 06/01/2020] [Revised: 06/24/2020] [Accepted: 07/22/2020] [Indexed: 11/29/2022]
Abstract
The overuse of nitrogenous fertilizers leads to an increase in the nitrate content of green leafy vegetables. Consumption of food with excess nitrate is not advisable because it results in human ailment. In this study, spinach leaves were harvested from plants grown under nine varying (0 to 400 kg/ha) nitrogenous fertilizer doses. A total of 261 samples were used to predict the nitrate content in spinach leaves using Vis-NIR (350 to 2,500 nm). The nitrate content was measured destructively using the ion-selective conductive method. Partial least square (PLS) regression models were developed using whole spectra and featured wavelengths. Spectral data were pre-processed using different spectral pre-processing techniques such as Savitzky-Golay (SG) derivative, standard normal variate (SNV), multiplicative scatter correction (MSC), baseline correction, and detrending. The predictive accuracy of the PLS model had improved after pre-processing of spectral data with MSC (RPDCV = 1.767; SECV = 545.745; biasCV = -3.107; slopeCV = 0.698) and SNV (RPDCV = 1.768; SECV = 545.337; biasCV = -3.201; slopeCV = 0.698) technique, but this was not significant (P < 0.05) as compared with raw spectral data (RPDCV = 1.679; SECV = 572.669; biasCV = -7.046; slopeCV = 0.687). The effective wavelengths for measurement nitrate content in spinach leaves were identified as 558, 706, 780, 1,000, and 1,420 nm. The performance of PLS model developed with effective wavelengths also had good prediction accuracy (RPDCV = 1.482; SECV = 648.672; biasCV = -3.805; slopeCV = 0.565) but significantly lower than the performance of model developed with full spectral data. The overall results of this study suggest that Vis-NIR spectroscopy can be an important tool and has great potential for the rapid and nondestructive assessment of nitrate content in harvested spinach, with a view to ascertain the suitability of the harvest for food uses. PRACTICAL APPLICATION: Better production and brighter color of leafy vegetable drive the farming community to overuse nitrogenous fertilizer. This has resulted in higher nitrate content in vegetables. It has been widely reported that consumption of these vegetables has carcinogenic effects on human beings. The prediction of nitrate content in leafy vegetables by traditional methods is time-consuming (30 min, including sample preparation time), destructive, and tedious; moreover, it cannot be used for inline applications. This study reports spectroscopy-based rapid (<5 s) assessment technique for nitrate measurement. A multivariable PLS model was developed using wavelengths representing nitrate content. This model can be adopted by food industries for inline applications.
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Affiliation(s)
- Naveen Kumar Mahanti
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Bhopal, India
| | - Subir Kumar Chakraborty
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Bhopal, India
| | - Nachiket Kotwaliwale
- Agro Produce Processing Division, ICAR-Central Institute of Agricultural Engineering, Bhopal, India
| | - Anand Kumar Vishwakarma
- Department of Soil Chemistry and Fertility, ICAR-Indian Institute of Soil Science, Bhopal, India
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Identification and Quantification of Adulterants in Coffee ( Coffea arabica L.) Using FT-MIR Spectroscopy Coupled with Chemometrics. Foods 2020; 9:foods9070851. [PMID: 32629759 PMCID: PMC7404773 DOI: 10.3390/foods9070851] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 06/26/2020] [Accepted: 06/28/2020] [Indexed: 12/01/2022] Open
Abstract
Food adulteration is an illegal practice performed to elicit economic benefits. In the context of roasted and ground coffee, legumes, cereals, nuts and other vegetables are often used to augment the production volume; however, these adulterants lack the most important coffee compound, caffeine, which has health benefits. In this study, the mid-infrared Fourier transform spectroscopy (FT-MIR) technique coupled with chemometrics was used to identify and quantify adulterants in coffee (Coffea arabica L.). Coffee samples were adulterated with corn, barley, soy, oat, rice and coffee husks, in proportions ranging from 1–30%. A discrimination model was developed using the soft independent modeling of class analogy (SIMCA) framework, and quantitative models were developed using such algorithms as the partial least squares algorithms with one variable (PLS1) and multiple variables (PLS2) and principal component regression (PCR). The SIMCA model exhibited an accuracy of 100% and could discriminate among all the classes. The quantitative model with the highest performance corresponded to the PLS1 algorithm. The model exhibited an R2c: ≥ 0.99, standard error of calibration (SEC) of 0.39–0.82, and standard error of prediction (SEP) of 0.45–0.94. The developed models could identify and quantify the coffee adulterants, and it was considered that the proposed methodology can be applied to identify and quantify the adulterants used in the coffee industry.
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23
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An innovative multivariate strategy for HSI-NIR images to automatically detect defects in green coffee. Talanta 2019; 199:270-276. [DOI: 10.1016/j.talanta.2019.02.049] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Revised: 02/07/2019] [Accepted: 02/10/2019] [Indexed: 11/21/2022]
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24
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Sezer B, Apaydin H, Bilge G, Boyaci IH. Coffee arabica adulteration: Detection of wheat, corn and chickpea. Food Chem 2018; 264:142-148. [DOI: 10.1016/j.foodchem.2018.05.037] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 05/02/2018] [Accepted: 05/04/2018] [Indexed: 12/11/2022]
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25
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Song HY, Jang HW, Debnath T, Lee KG. Analytical method to detect adulteration of ground roasted coffee. Int J Food Sci Technol 2018. [DOI: 10.1111/ijfs.13942] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ha Yan Song
- Department of Food Science and Biotechnology; Dongguk University-Seoul; 32, Dongguk-ro, Ilsandong-gu Goyang-si Gyeonggi-do 410-820 Korea
| | - Hae Won Jang
- Korea Food Research Institute; 1201-62, Anyangpangyo-ro, Bundag-gu Seongnam-si Gyeonggi-do 13539 Korea
| | - Trishna Debnath
- Department of Food Science and Biotechnology; Dongguk University-Seoul; 32, Dongguk-ro, Ilsandong-gu Goyang-si Gyeonggi-do 410-820 Korea
| | - Kwang-Geun Lee
- Department of Food Science and Biotechnology; Dongguk University-Seoul; 32, Dongguk-ro, Ilsandong-gu Goyang-si Gyeonggi-do 410-820 Korea
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26
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Uncu AT, Uncu AO. Plastid trnH-psbA intergenic spacer serves as a PCR-based marker to detect common grain adulterants of coffee ( Coffea arabica L.). Food Control 2018. [DOI: 10.1016/j.foodcont.2018.03.029] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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27
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Quantitative assessment of specific defects in roasted ground coffee via infrared-photoacoustic spectroscopy. Food Chem 2018; 255:132-138. [DOI: 10.1016/j.foodchem.2018.02.076] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Revised: 02/08/2018] [Accepted: 02/13/2018] [Indexed: 01/22/2023]
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28
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Caporaso N, Whitworth MB, Grebby S, Fisk ID. Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging. J FOOD ENG 2018; 227:18-29. [PMID: 29861528 PMCID: PMC5859211 DOI: 10.1016/j.jfoodeng.2018.01.009] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
Hyperspectral imaging (1000-2500 nm) was used for rapid prediction of moisture and total lipid content in intact green coffee beans on a single bean basis. Arabica and Robusta samples from several growing locations were scanned using a "push-broom" system. Hypercubes were segmented to select single beans, and average spectra were measured for each bean. Partial Least Squares regression was used to build quantitative prediction models on single beans (n = 320-350). The models exhibited good performance and acceptable prediction errors of ∼0.28% for moisture and ∼0.89% for lipids. This study represents the first time that HSI-based quantitative prediction models have been developed for coffee, and specifically green coffee beans. In addition, this is the first attempt to build such models using single intact coffee beans. The composition variability between beans was studied, and fat and moisture distribution were visualized within individual coffee beans. This rapid, non-destructive approach could have important applications for research laboratories, breeding programmes, and for rapid screening for industry.
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Affiliation(s)
- Nicola Caporaso
- Campden BRI, Chipping Campden, Gloucestershire, GL55 6LD, UK.,Division of Food Sciences, University of Nottingham, Sutton Bonington Campus, LE12 5RD, UK
| | | | - Stephen Grebby
- Nottingham Geospatial Institute, Faculty of Engineering, University of Nottingham, Innovation Park, NG7 2TU, UK
| | - Ian D Fisk
- Division of Food Sciences, University of Nottingham, Sutton Bonington Campus, LE12 5RD, UK
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29
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de Morais TCB, Rodrigues DR, de Carvalho Polari Souto UT, Lemos SG. A simple voltammetric electronic tongue for the analysis of coffee adulterations. Food Chem 2018; 273:31-38. [PMID: 30292371 DOI: 10.1016/j.foodchem.2018.04.136] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Revised: 04/16/2018] [Accepted: 04/28/2018] [Indexed: 10/17/2022]
Abstract
This work presents a simple and low-cost analytical approach to detect adulterations in ground roasted coffee by using voltammetry and chemometrics. The voltammogram of a coffee extract (prepared as simulating a home-made coffee cup) obtained with a single working electrode is submitted to pattern recognition analysis preceded by variable selection to detect the addition of coffee husks and sticks (adulterated/unadulterated), or evaluate the shelf-life condition (expired/unexpired). Two pattern recognition methods were tested: linear discriminant analysis (LDA) with variable selection by successive projections algorithm (SPA), or genetic algorithm (GA); and partial least squares discriminant analysis (PLS-DA). Both LDA models presented satisfactory results. The voltammograms were also evaluated for the quantitative determination of the percentage of impurities in ground roasted coffees. PLS and multivariate linear regression (MLR) preceded by variable selection with SPA or GA were evaluated. An excellent predictive power (RMSEP = 0.05%) was obtained with MLR aided by GA.
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Affiliation(s)
| | | | | | - Sherlan G Lemos
- Department of Chemistry, Federal University of Paraíba, C.P. 5093, 58051-970 João Pessoa, PB, Brazil.
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30
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Sheykhizadeh S, Naseri A. An efficient swarm intelligence approach to feature selection based on invasive weed optimization: Application to multivariate calibration and classification using spectroscopic data. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2018; 194:202-210. [PMID: 29353216 DOI: 10.1016/j.saa.2018.01.028] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2017] [Revised: 01/08/2018] [Accepted: 01/11/2018] [Indexed: 06/07/2023]
Abstract
Variable selection plays a key role in classification and multivariate calibration. Variable selection methods are aimed at choosing a set of variables, from a large pool of available predictors, relevant to the analyte concentrations estimation, or to achieve better classification results. Many variable selection techniques have now been introduced among which, those which are based on the methodologies of swarm intelligence optimization have been more respected during a few last decades since they are mainly inspired by nature. In this work, a simple and new variable selection algorithm is proposed according to the invasive weed optimization (IWO) concept. IWO is considered a bio-inspired metaheuristic mimicking the weeds ecological behavior in colonizing as well as finding an appropriate place for growth and reproduction; it has been shown to be very adaptive and powerful to environmental changes. In this paper, the first application of IWO, as a very simple and powerful method, to variable selection is reported using different experimental datasets including FTIR and NIR data, so as to undertake classification and multivariate calibration tasks. Accordingly, invasive weed optimization - linear discrimination analysis (IWO-LDA) and invasive weed optimization- partial least squares (IWO-PLS) are introduced for multivariate classification and calibration, respectively.
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Affiliation(s)
- Saheleh Sheykhizadeh
- Department of Analytical Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz, Iran
| | - Abdolhossein Naseri
- Department of Analytical Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz, Iran.
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31
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Wang Y, Jiang K, Wang L, Han D, Yin G, Wang J, Qin B, Li S, Wang T. Identification of Salvia species using high-performance liquid chromatography combined with chemical pattern recognition analysis. J Sep Sci 2018; 41:609-617. [PMID: 29105962 DOI: 10.1002/jssc.201701066] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Revised: 10/22/2017] [Accepted: 10/27/2017] [Indexed: 12/17/2023]
Abstract
Salvia miltiorrhiza, also known as Danshen, is a widely used traditional Chinese medicine for the treatment of cardiovascular diseases and hematological abnormalities. The root and rhizome of Salvia przewalskii and Salvia yunnanensis have been found as substitutes for Salvia miltiorrhiza in the market. In this study, the chemical information of 14 major compounds in Salvia miltiorrhiza and its substitutes were determined using a high-performance liquid chromatography method. Stepwise discriminant analysis was adopted to select the characteristic variables. Partial least squares discriminant and hierarchical cluster analysis were performed to classify Salvia miltiorrhiza and its substitutes. The results showed that all of the samples were correctly classified both in partial least squares discriminant analysis and hierarchical cluster analysis based on the four compounds (caffeic acid, rosmarinic acid, salvianolic acid B, and salvianolic acid A). This method can not only distinguish Salvia miltiorrhiza and its substitutes, but also classify Salvia przewalskii and Salvia yunnanensis. The method can be applied for the quality assessment of Salvia miltiorrhiza and identification of unknown samples.
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Affiliation(s)
- Yang Wang
- Shenzhen Institute for Drug Control, Shenzhen, China
- Shenzhen Key Laboratory of Drug Quality Standard Research, Shenzhen, China
| | - Kun Jiang
- Shenzhen Institute for Drug Control, Shenzhen, China
- Shenzhen Key Laboratory of Drug Quality Standard Research, Shenzhen, China
| | - Lijun Wang
- Shenzhen Institute for Drug Control, Shenzhen, China
- Shenzhen Key Laboratory of Drug Quality Standard Research, Shenzhen, China
- School of pharmacy, Shenyang Pharmaceutical University, Shenyang, China
| | - Dongqi Han
- Shenzhen Institute for Drug Control, Shenzhen, China
- Shenzhen Key Laboratory of Drug Quality Standard Research, Shenzhen, China
| | - Guo Yin
- Shenzhen Institute for Drug Control, Shenzhen, China
- Shenzhen Key Laboratory of Drug Quality Standard Research, Shenzhen, China
| | - Jue Wang
- Shenzhen Institute for Drug Control, Shenzhen, China
- Shenzhen Key Laboratory of Drug Quality Standard Research, Shenzhen, China
| | - Bin Qin
- Shenzhen Institute for Drug Control, Shenzhen, China
- Shenzhen Key Laboratory of Drug Quality Standard Research, Shenzhen, China
| | - Shaoping Li
- Institute of Chinese Medical Sciences, University of Macau, Macau, China
| | - Tiejie Wang
- Shenzhen Institute for Drug Control, Shenzhen, China
- Shenzhen Key Laboratory of Drug Quality Standard Research, Shenzhen, China
- School of pharmacy, Shenyang Pharmaceutical University, Shenyang, China
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32
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Correia RM, Tosato F, Domingos E, Rodrigues RR, Aquino LFM, Filgueiras PR, Lacerda V, Romão W. Portable near infrared spectroscopy applied to quality control of Brazilian coffee. Talanta 2018; 176:59-68. [DOI: 10.1016/j.talanta.2017.08.009] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Revised: 06/28/2017] [Accepted: 08/02/2017] [Indexed: 10/19/2022]
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33
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Efenberger-Szmechtyk M, Nowak A, Kregiel D. Implementation of chemometrics in quality evaluation of food and beverages. Crit Rev Food Sci Nutr 2017; 58:1747-1766. [PMID: 28128644 DOI: 10.1080/10408398.2016.1276883] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Conventional methods for food quality evaluation based on chemical or microbiological analysis followed by traditional univariate statistics such as ANOVA are considered insufficient for some purposes. More sophisticated instrumental methods including spectroscopy and chromatography, in combination with multivariate analysis-chemometrics, can be used to determine food authenticity, identify adulterations or mislabeling and determine food safety. The purpose of this review is to present the current state of knowledge on the use of chemometric tools for evaluating quality of food products of animal and plant origin and beverages. The article describes applications of several multivariate techniques in food and beverages research, showing their role in adulteration detection, authentication, quality control, differentiation of samples and comparing their classification and prediction ability.
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Affiliation(s)
| | - Agnieszka Nowak
- a Institute of Fermentation Technology and Microbiology, Lodz University of Technology , Lodz , Poland
| | - Dorota Kregiel
- a Institute of Fermentation Technology and Microbiology, Lodz University of Technology , Lodz , Poland
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34
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Suhandy D, Yulia M. Peaberry coffee discrimination using UV-visible spectroscopy combined with SIMCA and PLS-DA. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2017. [DOI: 10.1080/10942912.2017.1296861] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Diding Suhandy
- Department of Agricultural Engineering, Faculty of Agriculture, The University of Lampung, Bandar Lampung, Indonesia
| | - Meinilwita Yulia
- Department of Agricultural Technology, Lampung State Polytechnic, Rajabasa Bandar Lampung, Indonesia
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35
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36
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Ground Roast Coffee: Review of Analytical Strategies to Estimate Geographic Origin, Species Authenticity and Adulteration by Dilution. FOOD ANAL METHOD 2017. [DOI: 10.1007/s12161-016-0756-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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37
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Free tocopherols as chemical markers for Arabica coffee adulteration with maize and coffee by-products. Food Control 2016. [DOI: 10.1016/j.foodcont.2016.06.011] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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38
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Chang JD, Zheng H, Mantri N, Xu L, Jiang Z, Zhang J, Song Z, Lu H. Chemometrics coupled with ultraviolet spectroscopy: a tool for the analysis of variety, adulteration, quality and ageing of apple juices. Int J Food Sci Technol 2016. [DOI: 10.1111/ijfs.13229] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jia-Dong Chang
- College of Life Sciences; Zhejiang Sci-Tech University; Hangzhou 310018 China
- Zhejiang Province Key Laboratory of Plant Secondary Metabolism and Regulation; Hangzhou 310018 China
| | - Hong Zheng
- School of Pharmaceutical Sciences; Wenzhou Medical University; Wenzhou 325035 China
| | - Nitin Mantri
- School of Science; RMIT University; Melbourne 3000 Victoria Australia
| | - Ling Xu
- College of Life Sciences; Zhejiang Sci-Tech University; Hangzhou 310018 China
- Zhejiang Province Key Laboratory of Plant Secondary Metabolism and Regulation; Hangzhou 310018 China
| | - Zhengdong Jiang
- College of Life Sciences; Zhejiang Sci-Tech University; Hangzhou 310018 China
- Zhejiang Province Key Laboratory of Plant Secondary Metabolism and Regulation; Hangzhou 310018 China
| | - Jialei Zhang
- College of Life Sciences; Zhejiang Sci-Tech University; Hangzhou 310018 China
- Zhejiang Province Key Laboratory of Plant Secondary Metabolism and Regulation; Hangzhou 310018 China
| | - Zhipeng Song
- Qixin Honours School; Zhejiang Sci-Tech University; Hangzhou 310018 China
| | - Hongfei Lu
- College of Life Sciences; Zhejiang Sci-Tech University; Hangzhou 310018 China
- Zhejiang Province Key Laboratory of Plant Secondary Metabolism and Regulation; Hangzhou 310018 China
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39
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Chang H, Zhu L, Lou X, Meng X, Guo Y, Wang Z. A New Local Modelling Approach Based on Predicted Errors for Near-Infrared Spectral Analysis. JOURNAL OF ANALYTICAL METHODS IN CHEMISTRY 2016; 2016:5416506. [PMID: 27446631 PMCID: PMC4944088 DOI: 10.1155/2016/5416506] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Accepted: 06/07/2016] [Indexed: 06/06/2023]
Abstract
Over the last decade, near-infrared spectroscopy, together with the use of chemometrics models, has been widely employed as an analytical tool in several industries. However, most chemical processes or analytes are multivariate and nonlinear in nature. To solve this problem, local errors regression method is presented in order to build an accurate calibration model in this paper, where a calibration subset is selected by a new similarity criterion which takes the full information of spectra, chemical property, and predicted errors. After the selection of calibration subset, the partial least squares regression is applied to build calibration model. The performance of the proposed method is demonstrated through a near-infrared spectroscopy dataset of pharmaceutical tablets. Compared with other local strategies with different similarity criterions, it has been shown that the proposed local errors regression can result in a significant improvement in terms of both prediction ability and calculation speed.
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Affiliation(s)
- Haitao Chang
- School of Instrumentation Science & Opto-Electronics Engineering, Beihang University, Beijing 100191, China
| | - Lianqing Zhu
- Beijing Key Laboratory for Optoelectronic Measurement Technology, Beijing Information Science & Technology University, Beijing 100192, China
| | - Xiaoping Lou
- Beijing Key Laboratory for Optoelectronic Measurement Technology, Beijing Information Science & Technology University, Beijing 100192, China
| | - Xiaochen Meng
- Beijing Key Laboratory for Optoelectronic Measurement Technology, Beijing Information Science & Technology University, Beijing 100192, China
| | - Yangkuan Guo
- Beijing Key Laboratory for Optoelectronic Measurement Technology, Beijing Information Science & Technology University, Beijing 100192, China
| | - Zhongyu Wang
- School of Instrumentation Science & Opto-Electronics Engineering, Beihang University, Beijing 100191, China
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40
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Tolessa K, Rademaker M, De Baets B, Boeckx P. Prediction of specialty coffee cup quality based on near infrared spectra of green coffee beans. Talanta 2016; 150:367-74. [DOI: 10.1016/j.talanta.2015.12.039] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2015] [Revised: 12/11/2015] [Accepted: 12/14/2015] [Indexed: 10/22/2022]
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41
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Qu JH, Liu D, Cheng JH, Sun DW, Ma J, Pu H, Zeng XA. Applications of near-infrared spectroscopy in food safety evaluation and control: a review of recent research advances. Crit Rev Food Sci Nutr 2016; 55:1939-54. [PMID: 24689758 DOI: 10.1080/10408398.2013.871693] [Citation(s) in RCA: 89] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Food safety is a critical public concern, and has drawn great attention in society. Consequently, developments of rapid, robust, and accurate methods and techniques for food safety evaluation and control are required. As a nondestructive and convenient tool, near-infrared spectroscopy (NIRS) has been widely shown to be a promising technique for food safety inspection and control due to its huge advantages of speed, noninvasive measurement, ease of use, and minimal sample preparation requirement. This review presents the fundamentals of NIRS and focuses on recent advances in its applications, during the last 10 years of food safety control, in meat, fish and fishery products, edible oils, milk and dairy products, grains and grain products, fruits and vegetables, and others. Based upon these applications, it can be demonstrated that NIRS, combined with chemometric methods, is a powerful tool for food safety surveillance and for the elimination of the occurrence of food safety problems. Some disadvantages that need to be solved or investigated with regard to the further development of NIRS are also discussed.
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Affiliation(s)
- Jia-Huan Qu
- a College of Light Industry and Food Sciences, South China University of Technology , Guangzhou , PR China
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42
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Winkler-Moser JK, Singh M, Rennick KA, Bakota EL, Jham G, Liu SX, Vaughn SF. Detection of Corn Adulteration in Brazilian Coffee (Coffea arabica) by Tocopherol Profiling and Near-Infrared (NIR) Spectroscopy. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2015; 63:10662-10668. [PMID: 26600312 DOI: 10.1021/acs.jafc.5b04777] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Coffee is a high-value commodity that is a target for adulteration, leading to loss of quality and causing significant loss to consumers. Therefore, there is significant interest in developing methods for detecting coffee adulteration and improving the sensitivity and accuracy of these methods. Corn and other lower value crops are potential adulterants, along with sticks and coffee husks. Fourteen pure Brazilian roasted, ground coffee bean samples were adulterated with 1-20% of roasted, ground corn and were analyzed for their tocopherol content and profile by HPLC. They were also analyzed by near-infrared (NIR) spectroscopy. Both proposed methods of detection of corn adulteration displayed a sensitivity of around 5%, thus representing simple and fast analytical methods for detecting adulteration at likely levels of contamination. Further studies should be conducted to verify the results with a much larger sample size and additional types of adulterants.
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Affiliation(s)
- Jill K Winkler-Moser
- Functional Foods Research Unit, National Center for Agricultural Utilization Research, Agricultural Research Service, U.S. Department of Agriculture , 1815 North University Street, Peoria, Illinois 61604, United States
| | - Mukti Singh
- Functional Foods Research Unit, National Center for Agricultural Utilization Research, Agricultural Research Service, U.S. Department of Agriculture , 1815 North University Street, Peoria, Illinois 61604, United States
| | - Kathy A Rennick
- Functional Foods Research Unit, National Center for Agricultural Utilization Research, Agricultural Research Service, U.S. Department of Agriculture , 1815 North University Street, Peoria, Illinois 61604, United States
| | - Erica L Bakota
- Functional Foods Research Unit, National Center for Agricultural Utilization Research, Agricultural Research Service, U.S. Department of Agriculture , 1815 North University Street, Peoria, Illinois 61604, United States
| | - Gulab Jham
- Departamento de Fitopatologia, Universidade Federal de Viçosa , Viçosa, Minas Gerais, Brazil
| | - Sean X Liu
- Functional Foods Research Unit, National Center for Agricultural Utilization Research, Agricultural Research Service, U.S. Department of Agriculture , 1815 North University Street, Peoria, Illinois 61604, United States
| | - Steven F Vaughn
- Functional Foods Research Unit, National Center for Agricultural Utilization Research, Agricultural Research Service, U.S. Department of Agriculture , 1815 North University Street, Peoria, Illinois 61604, United States
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Identification of adulteration in ground roasted coffees using UV–Vis spectroscopy and SPA-LDA. Lebensm Wiss Technol 2015. [DOI: 10.1016/j.lwt.2015.04.003] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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44
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Fourier transform infrared spectroscopy and near infrared spectroscopy for the quantification of defects in roasted coffees. Talanta 2015; 134:379-386. [DOI: 10.1016/j.talanta.2014.11.038] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2014] [Revised: 11/17/2014] [Accepted: 11/18/2014] [Indexed: 11/18/2022]
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45
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Espresso beverages of pure origin coffee: mineral characterization, contribution for mineral intake and geographical discrimination. Food Chem 2015; 177:330-8. [PMID: 25660894 DOI: 10.1016/j.foodchem.2015.01.061] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2013] [Revised: 12/15/2014] [Accepted: 01/11/2015] [Indexed: 01/02/2023]
Abstract
Espresso coffee beverages prepared from pure origin roasted ground coffees from the major world growing regions (Brazil, Ethiopia, Colombia, India, Mexico, Honduras, Guatemala, Papua New Guinea, Kenya, Cuba, Timor, Mussulo and China) were characterized and compared in terms of their mineral content. Regular consumption of one cup of espresso contributes to a daily mineral intake varying from 0.002% (sodium; Central America) to 8.73% (potassium; Asia). The mineral profiles of the espresso beverages revealed significant inter- and intra-continental differences. South American pure origin coffees are on average richer in the analyzed elements except for calcium, while samples from Central America have generally lower mineral amounts (except for manganese). Manganese displayed significant differences (p<0.05) among the countries of each characterized continent. Intercontinental and inter-country discrimination between the major world coffee producers were achieved by applying canonical discriminant analysis. Manganese and calcium were found to be the best chemical descriptors for origin.
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Camino-Sánchez FJ, Delgado-Moreno B, Navarro-Fuentes P. Quick Development of a NIRS Method for the Analysis of Moisture, Protein, and Fat in Nutritional Clinical Products. FOOD ANAL METHOD 2014. [DOI: 10.1007/s12161-014-0058-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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47
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Craig AP, Franca AS, Oliveira LS, Irudayaraj J, Ileleji K. Application of elastic net and infrared spectroscopy in the discrimination between defective and non-defective roasted coffees. Talanta 2014; 128:393-400. [DOI: 10.1016/j.talanta.2014.05.001] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Revised: 04/30/2014] [Accepted: 05/02/2014] [Indexed: 10/25/2022]
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49
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Quantitative near-infrared spectroscopic analysis of trimethoprim by artificial neural networks combined with modified genetic algorithm. Chem Res Chin Univ 2014. [DOI: 10.1007/s40242-014-3410-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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50
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Barbin DF, Felicio ALDSM, Sun DW, Nixdorf SL, Hirooka EY. Application of infrared spectral techniques on quality and compositional attributes of coffee: An overview. Food Res Int 2014. [DOI: 10.1016/j.foodres.2014.01.005] [Citation(s) in RCA: 136] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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