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Matenda RT, Rip D, Fernández Pierna JA, Baeten V, Williams PJ. Differentiation of Listeria monocytogenes serotypes using near infrared hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 320:124579. [PMID: 38850824 DOI: 10.1016/j.saa.2024.124579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 05/27/2024] [Accepted: 05/30/2024] [Indexed: 06/10/2024]
Abstract
Among the severe foodborne illnesses, listeriosis resulting from the pathogen Listeria monocytogenes exhibits one of the highest fatality rates. This study investigated the application of near infrared hyperspectral imaging (NIR-HSI) for the classification of three L. monocytogenes serotypes namely serotype 4b, 1/2a and 1/2c. The bacteria were cultured on Brain Heart Infusion agar, and NIR hyperspectral images were captured in the spectral range 900-2500 nm. Different pre-processing methods were applied to the raw spectra and principal component analysis was used for data exploration. Classification was achieved with partial least squares discriminant analysis (PLS-DA). The PLS-DA results revealed classification accuracies exceeding 80 % for all the bacterial serotypes for both training and test set data. Based on validation data, sensitivity values for L. monocytogenes serotype 4b, 1/2a and 1/2c were 0.69, 0.80 and 0.98, respectively when using full wavelength data. The reduced wavelength model had sensitivity values of 0.65, 0.85 and 0.98 for serotype 4b, 1/2a and 1/2c, respectively. The most relevant bands for serotype discrimination were identified to be around 1490 nm and 1580-1690 nm based on both principal component loadings and variable importance in projection scores. The outcomes of this study demonstrate the feasibility of utilizing NIR-HSI for detecting and classifying L. monocytogenes serotypes on growth media.
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Affiliation(s)
- Rumbidzai T Matenda
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Diane Rip
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Juan A Fernández Pierna
- Quality and authentication of products Unit, Knowledge and valorization of agricultural products Department, Walloon Agricultural Research Centre (CRA-W), Chaussée de Namur,24, 5030 Gembloux, Belgium
| | - Vincent Baeten
- Quality and authentication of products Unit, Knowledge and valorization of agricultural products Department, Walloon Agricultural Research Centre (CRA-W), Chaussée de Namur,24, 5030 Gembloux, Belgium
| | - Paul J Williams
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa.
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2
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Baqueta MR, Diniz PHGD, Pereira LL, Almeida FLC, Valderrama P, Pallone JAL. An overview on the Brazilian Coffea canephora scenario and the current chemometrics-based spectroscopic research. Food Res Int 2024; 194:114866. [PMID: 39232507 DOI: 10.1016/j.foodres.2024.114866] [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: 03/22/2024] [Revised: 07/04/2024] [Accepted: 07/31/2024] [Indexed: 09/06/2024]
Abstract
This review explores the historical, botanical, sensory, and quality aspects of Coffea canephora, with a focus on Brazil's rise as a producer of specialty canephora coffees in the Amazon region, Espírito Santo, and Bahia. Brazil has gained global recognition through the first geographical indications for canephora: Matas de Rondônia for robusta amazônico coffee and Espírito Santo for conilon coffee. Despite this, comprehensive insights into how variety, terroir, environmental conditions, and cultivation practices influence the chemical and sensory attributes of Brazilian canephora remain underdeveloped compared to well-studied arabica coffee. Producers and researchers are working to elevate canephora coffees to higher market levels, despite technological, production, and perception challenges stemming from its historical reputation for poor quality. Ensuring the sustainability of Amazonian canephora coffee without deforestation is particularly challenging due to the need to verify practices across numerous small-scale farms. There is also a critical need for standardized production and tasting protocols for Brazilian canephora, leveraging local expertise and professional cuppers to ensure consistent quality and reliable sustainability claims. Significant opportunities exist in valuing the production chain of geographically unique canephora coffees, which could increase specialty exports, enhance economic prospects for local farmers, and support Amazon preservation. Recognizing and marketing these coffees as premium products with unique flavor profiles can boost their global appeal. Another challenge lies in establishing new specialty standards for soluble coffee from specialty canephora to meet consumer demands for convenience without compromising taste or ethical standards. In such a scenario, several analytical methods have been suggested to identify high-quality variants, combating their stigmatization. The potential of spectroscopy techniques and chemometrics-based data science is highlighted in confirming coffee quality, authenticity, traceability, and geographical origin, enhancing model interpretation and predictive accuracy through synergistic and complementary information. Non-targeted spectroscopic analyses, providing comprehensive spectral fingerprints, are contrasted with targeted analyses. Overall, this review offers valuable insights for the coffee scientific community, exporters, importers, roasters, and consumers in recognizing the potential of Brazilian canephora coffees.
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Affiliation(s)
- Michel Rocha Baqueta
- Universidade Estadual de Campinas - UNICAMP, Faculdade de Engenharia de Alimentos, Departamento de Ciência de Alimentos e Nutrição, Campinas, São Paulo, Brazil
| | | | - Lucas Louzada Pereira
- Federal Institute of Espírito Santo (IFES), Coffee Design Group, Venda Nova do Imigrante, Espírito Santo, Rua Elizabeth Minete Perim, S/N, Bairro São Rafael, Venda Nova do Imigrante, Espírito Santo 29375-000, Brazil
| | - Francisco Lucas Chaves Almeida
- Universidade Estadual de Campinas - UNICAMP, Faculdade de Engenharia de Alimentos, Departamento de Engenharia e Tecnologia de Alimentos, Campinas, São Paulo, Brazil
| | - Patrícia Valderrama
- Universidade Tecnológica Federal do Paraná - UTFPR, Campo Mourão, Paraná, Brazil
| | - Juliana Azevedo Lima Pallone
- Universidade Estadual de Campinas - UNICAMP, Faculdade de Engenharia de Alimentos, Departamento de Ciência de Alimentos e Nutrição, Campinas, São Paulo, Brazil.
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3
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Li K, Ding C, Zhang J, Du B, Song X, Wang G, Li Q, Zhang Y, Zhang Z. Accurate identification of methanol and ethanol gasoline types and rapid detection of the alcohol content using effective chemical information. Talanta 2024; 274:125961. [PMID: 38555768 DOI: 10.1016/j.talanta.2024.125961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 03/18/2024] [Accepted: 03/20/2024] [Indexed: 04/02/2024]
Abstract
Methanol and ethanol gasoline are two emerging clean energy sources with different characteristics. To achieve the qualitative identification and quantitative analysis of the alcohols present in methanol and ethanol gasoline, effective chemical information (ECI) models based on the characteristic spectral bands of the near-infrared (NIR) spectra of the methanol and ethanol molecules were developed using the partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) algorithms. The ECI model was further compared with models built from the full wavenumber (Full) spectra, variable importance in projection (VIP) spectra, and Monte Carlo uninformative variable elimination (MC-UVE) spectra to determine the predictive performance of ECI model. Among the various qualitative identification models, it was found that the ECI-PLS-DA model, which is built using the differences in molecular chemical information between methanol and ethanol, exhibited sensitivity, specificity and accuracy values of 100%. The ECI-PLS-DA model accurately identified methanol gasoline and ethanol gasoline with different contents. In the quantitative analysis model for methanol gasoline, the methanol gasoline and ethanol gasoline ECI-PLS models exhibited the smallest root mean squared error of predictions (RMSEP) of 0.18 and 0.21% (v/v), respectively, compared to the other models. Meanwhile, the F-test and T-test results revealed that the NIR method employing the ECI-PLS model showed no significant difference compared to the standard method. Compared with other spectral models examined herein, the ECI model demonstrated the highest recognition success and determination accuracy. This study therefore established a highly accurate and rapid determination model for the qualitative identification and quantitative analysis based on chemical structures. It is expected that this model could be extended to the NIR analysis of other physicochemical properties of fuel.
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Affiliation(s)
- Ke Li
- Center for Environmental Metrology, National Institute of Metrology, Beijing, 100029, China
| | - Chaomin Ding
- College of Environmental Sciences and Engineering, Dalian Maritime University, Dalian, 116026, China
| | - Jin Zhang
- College of Environmental Sciences and Engineering, Dalian Maritime University, Dalian, 116026, China
| | - Biao Du
- Beijing Yixingyuan Petrochemical Technology Co. Ltd., Beijing, 101301, China
| | - Xiaoping Song
- Center for Environmental Metrology, National Institute of Metrology, Beijing, 100029, China
| | - Guixuan Wang
- Beijing Yixingyuan Petrochemical Technology Co. Ltd., Beijing, 101301, China
| | - Qi Li
- Center for Environmental Metrology, National Institute of Metrology, Beijing, 100029, China
| | - Yinglan Zhang
- Leibniz Institut für Polymerforschung Dresden e.V., Hohe Straße 6, Dresden, 01069, Germany; Institut für Werkstoffwissenschaft, Technische Universität Dresden, Dresden, 01062, Germany
| | - Zhengdong Zhang
- Center for Environmental Metrology, National Institute of Metrology, Beijing, 100029, China.
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Giussani B, Gorla G, Riu J. Analytical Chemistry Strategies in the Use of Miniaturised NIR Instruments: An Overview. Crit Rev Anal Chem 2024; 54:11-43. [PMID: 35286178 DOI: 10.1080/10408347.2022.2047607] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Miniaturized NIR instruments have been increasingly used in the last years, and they have become useful tools for many applications on a broad variety of samples. This review focuses on miniaturized NIR instruments from an analytical point of view, to give an overview of the analytical strategies used in order to help the reader to set up their own analytical methods, from the sampling to the data analysis. It highlights the uses of these instruments, providing a critical discussion including current and future trends.
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Affiliation(s)
- Barbara Giussani
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, Como, Italy
| | - Giulia Gorla
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, Como, Italy
| | - Jordi Riu
- Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Tarragona, Spain
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5
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Baqueta MR, Marini F, Rocha RB, Valderrama P, Pallone JAL. Authentication and discrimination of new Brazilian Canephora coffees with geographical indication using a miniaturized near-infrared spectrometer. Food Res Int 2023; 172:113216. [PMID: 37689959 DOI: 10.1016/j.foodres.2023.113216] [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: 03/21/2023] [Revised: 06/27/2023] [Accepted: 06/29/2023] [Indexed: 09/11/2023]
Abstract
New Brazilian Canephora coffees (Conilon and Robusta) of high added value from specific origins have been protected by geographical indication to guarantee their origin and quality. Recently, benchtop near-infrared (NIR) spectroscopy combined with chemometrics has demonstrated its usefulness to discriminate them. It was the first study, however, and therefore the possibility exists to develop a new portable NIR method for this purpose. This work assessed a miniaturized NIR as a cheaper spectrometer to discriminate and authenticate new Brazilian Canephora coffees with certified geographical origins and to differentiate them from specialty Arabica. Discriminant chemometric and class modeling techniques have been applied and have obtained good predictive ability on external test sets. In addition, models with similar classification purpose were compared with those obtained in previous research carried out with benchtop NIR for the same samples, obtaining comparable results. In this context, the portable method was used as a laboratory technique and has the advantage of being cheaper than benchtop NIR spectrometer. Furthermore, it brings a high possibility to be implemented in small coffee cooperatives, industries or control agencies in the future that do not have high economic resources.
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Affiliation(s)
- Michel Rocha Baqueta
- University of Campinas - UNICAMP, School of Food Engineering, Department of Food Science and Nutrition, Campinas, São Paulo, Brazil; Department of Chemistry, University of Rome "La Sapienza", Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Federico Marini
- Department of Chemistry, University of Rome "La Sapienza", Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Rodrigo Barros Rocha
- Empresa Brasileira de Pesquisa Agropecuária - EMBRAPA Rondônia, Porto Velho, Rondônia, Brazil
| | - Patrícia Valderrama
- Universidade Tecnológica Federal do Paraná - UTFPR, Campo Mourão, Paraná, Brazil.
| | - Juliana Azevedo Lima Pallone
- University of Campinas - UNICAMP, School of Food Engineering, Department of Food Science and Nutrition, Campinas, São Paulo, Brazil.
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6
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Gullifa G, Barone L, Papa E, Giuffrida A, Materazzi S, Risoluti R. Portable NIR spectroscopy: the route to green analytical chemistry. Front Chem 2023; 11:1214825. [PMID: 37818482 PMCID: PMC10561305 DOI: 10.3389/fchem.2023.1214825] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 09/07/2023] [Indexed: 10/12/2023] Open
Abstract
There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019-2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the "true green analytical chemistry" which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.
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Affiliation(s)
- G. Gullifa
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - L. Barone
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - E. Papa
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - A. Giuffrida
- Department of Chemical Sciences, University of Catania, Catania, Italy
| | - S. Materazzi
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - R. Risoluti
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
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Guerrero-Peña A, Vázquez-Hernández L, Bucio-Galindo A, Morales-Ramos V. Chemical analysis and NIR spectroscopy in the determination of the origin, variety and roast time of Mexican coffee. Heliyon 2023; 9:e18675. [PMID: 37554778 PMCID: PMC10404687 DOI: 10.1016/j.heliyon.2023.e18675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/20/2023] [Accepted: 07/25/2023] [Indexed: 08/10/2023] Open
Abstract
Coffee is a product whose quality and price are associated with its geographical, genetic and processing origin; therefore, the development of analytical techniques to authenticate the above mentioned is important to avoid adulteration. The objective of this study was to compare conventional analytical methods with NIR technology for the authentication of roasted and ground coffee samples from different producing regions in Mexico (origins) and different varieties. A second objective was to determine, under the same processing conditions, if roasting times can be differentiated by using this technology. A total of 120 samples of roasted and ground commercial coffee were obtained from the states of Chiapas, Oaxaca, Tabasco and Veracruz in Mexico, 30 locally available samples per state. Samples from Veracruz included three different varieties, grown on the same farm and processed under the same conditions. One of these varieties was selected to evaluate the chemical composition of samples roasted at 185 °C using four different roasting times (15, 17, 19 and 21 min). Samples from different producing regions showed significant differences (P < 0.05) in fat content (from 7.45 ± 0.42% in Tabasco to 18.40 ± 2.95% in Chiapas), which was associated with the altitude of coffee plantations (Pearson's r = 0.96). The results indicate that NIR technology generates sufficient useful information to authenticate roasted and ground coffee from different geographical origins in Mexico and different varieties from the same coffee plantation, with similar results to those obtained by conventional analytical methods.
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Affiliation(s)
- Armando Guerrero-Peña
- Colegio de Postgraduados Campus Tabasco, Periférico Carlos A. Molina s/n, Km 3 carretera Cárdenas-Huimanguillo, Cárdenas, Tabasco, 86500, Mexico
| | - Lorena Vázquez-Hernández
- Colegio de Postgraduados Campus Tabasco, Periférico Carlos A. Molina s/n, Km 3 carretera Cárdenas-Huimanguillo, Cárdenas, Tabasco, 86500, Mexico
| | - Adolfo Bucio-Galindo
- Colegio de Postgraduados Campus Tabasco, Periférico Carlos A. Molina s/n, Km 3 carretera Cárdenas-Huimanguillo, Cárdenas, Tabasco, 86500, Mexico
| | - Victorino Morales-Ramos
- Colegio de Postgraduados Campus-Córdoba. km 348 carretera federal Córdoba-Veracruz, Col. Manuel León, Amatlán de los Reyes, Veracruz, 94946, Mexico
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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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Dharmawan A, Masithoh RE, Amanah HZ. Development of PCA-MLP Model Based on Visible and Shortwave Near Infrared Spectroscopy for Authenticating Arabica Coffee Origins. Foods 2023; 12:foods12112112. [PMID: 37297358 DOI: 10.3390/foods12112112] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 05/17/2023] [Accepted: 05/21/2023] [Indexed: 06/12/2023] Open
Abstract
Arabica coffee, one of Indonesia's economically important coffee commodities, is commonly subject to fraud due to mislabeling and adulteration. In many studies, spectroscopic techniques combined with chemometric methods have been massively employed in classification issues, such as principal component analysis (PCA) and discriminant analyses, compared to machine learning models. In this study, spectroscopy combined with PCA and a machine learning algorithm (artificial neural network, ANN) were developed to verify the authenticity of Arabica coffee collected from four geographical origins in Indonesia, including Temanggung, Toraja, Gayo, and Kintamani. Spectra from pure green coffee were collected from Vis-NIR and SWNIR spectrometers. Several preprocessing techniques were also applied to attain precise information from spectroscopic data. First, PCA compressed spectroscopic information and generated new variables called PCs scores, which would become inputs for the ANN model. The discrimination of Arabica coffee from different origins was conducted with a multilayer perceptron (MLP)-based ANN model. The accuracy attained ranged from 90% to 100% in the internal cross-validation, training, and testing sets. The error in the classification process did not exceed 10%. The generalization ability of the MLP combined with PCA was superior, suitable, and successful for verifying the origin of Arabica coffee.
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Affiliation(s)
- Agus Dharmawan
- Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Bulaksumur, Yogyakarta 55281, Indonesia
| | - Rudiati Evi Masithoh
- Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Bulaksumur, Yogyakarta 55281, Indonesia
| | - Hanim Zuhrotul Amanah
- Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Bulaksumur, Yogyakarta 55281, Indonesia
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Baqueta MR, Valderrama P, Alves EA, Pallone JAL, Marini F. Discrimination of Robusta Amazônico coffee farmed by indigenous and non-indigenous people in Amazon: comparing benchtop and portable NIR using ComDim and duplex. Analyst 2023; 148:1524-1533. [PMID: 36866727 DOI: 10.1039/d3an00104k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/04/2023]
Abstract
Robusta Amazônico is the name given to the Amazonian coffee that has been becoming popular and has recently been registered as a geographical indication in Brazil. It is produced by indigenous and non-indigenous coffee producers in regions that are geographically very close to one another. There is a need to authenticate whether coffee is truly produced by indigenous people and near-infrared (NIR) spectroscopy is an excellent technique for this. To meet the substantial trend towards NIR spectroscopy miniaturization, this work compared benchtop and portable NIR instruments to discriminate Robusta Amazônico samples using partial least squares discriminant analysis (PLS-DA). To ensure the results to be fairly comparable and, at the same time, to guarantee representative selection of both training and test set for the discriminant analysis, a sample selection strategy based on coupling ComDim multi-block analysis and the duplex algorithm was applied. Different pre-processing techniques were tested to create multiple matrices to be used in ComDim, as well as to build the discriminant models. The best PLS-DA model for benchtop NIR provided an accuracy of 96% for the test samples, while for the portable NIR the correct classification rate was 92%. It was demonstrated that portable NIR provides similar results to benchtop NIR for coffee origin classification by performing an unbiased sample selection strategy.
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Affiliation(s)
- Michel Rocha Baqueta
- Department of Food Science and Nutrition, School of Food Engineering, State University of Campinas - UNICAMP, Campinas, São Paulo, Brazil. .,Department of Chemistry, University of Rome "La Sapienza", Piazzale Aldo Moro 5, 00185 Rome, Italy.
| | - Patrícia Valderrama
- Universidade Tecnológica Federal do Paraná - UTFPR, Campo Mourão, Paraná, Brazil
| | - Enrique Anastácio Alves
- Empresa Brasileira de Pesquisa Agropecuária - EMBRAPA Rondônia, Porto Velho, Rondônia, Brazil
| | - Juliana Azevedo Lima Pallone
- Department of Food Science and Nutrition, School of Food Engineering, State University of Campinas - UNICAMP, Campinas, São Paulo, Brazil.
| | - Federico Marini
- Department of Chemistry, University of Rome "La Sapienza", Piazzale Aldo Moro 5, 00185 Rome, Italy.
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11
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Classification of instant coffees based on caffeine content and roasting degree using NIR spectrometry and multivariate analysis. Microchem J 2023. [DOI: 10.1016/j.microc.2023.108624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/15/2023]
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12
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Shen G, Chen Y, Zhang J, Wu Y, Yi Y, Li S, Yin S. Quantitative analysis of index factors in agricultural compost by infrared spectroscopy. Heliyon 2023; 9:e14010. [PMID: 36938392 PMCID: PMC10015198 DOI: 10.1016/j.heliyon.2023.e14010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 02/15/2023] [Accepted: 02/17/2023] [Indexed: 02/26/2023] Open
Abstract
Hyperspectral technology, with its high spectrum resolution and nanometer continuous spectral information acquisition ability, provide a possibility for rapidly and nondestructive evaluating compost maturity. In this study, the near-infrared spectroscopy (NIRS) analysis techniques was used to analyze quantitatively organic matter (OM) content, total nitrogen (TN) content and carbon-nitrogen (C/N) ratio in compost based on two different composting procedures. In the basis of spectra preprocessing and strategies of variable selection, the nonlinear modeling LBC-siPLS-PLSR for OM, MSC-SPA-PLSR for TN and R-SPA-PLSR for C/N ratio was respectively constructed using partial least squares regression (PLSR). LBC-siPLS-PLSR, MSC-SPA-PLSR and R-SPA-PLSR provided a better prediction capability with root mean square error of prediction, the coefficient of determination for prediction and residual predictive deviation values of 4.061, 0.746 and 2.02 for OM, values of 0.205, 0.65 and 1.71 for TN and values of 1.11, 0.706 and 2.07 for C/N ratio, respectively. These results showed that the NIRS technique could be fitted to each element, using specific spectrum pretreatment, in order to achieve an acceptable accuracy in the prediction.
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Affiliation(s)
- Guangrong Shen
- School of Agriculture and Biology and Research Centre for Low-Carbon Agriculture, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai, 200240, China
- Key Laboratory of Urban Agriculture, Ministry of Agriculture, Shanghai, 200240, China
- Shanghai Yangtze River Delta Eco-Environmental Change and Management Observation and Research Station,Ministry of Science and Technology, Ministry of Education, Shanghai Urban Forest Ecosystem Research Station, National Forestry and Grassland Administration, 800 Dongchuan Rd., Shanghai, 200240, China
- Corresponding author. School of Agriculture and Biology and Research Centre for Low-Carbon Agriculture, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai, 200240, China.
| | - Yanchi Chen
- School of Agriculture and Biology and Research Centre for Low-Carbon Agriculture, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai, 200240, China
- Key Laboratory of Urban Agriculture, Ministry of Agriculture, Shanghai, 200240, China
| | - Jingying Zhang
- School of Agriculture and Biology and Research Centre for Low-Carbon Agriculture, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai, 200240, China
- Key Laboratory of Urban Agriculture, Ministry of Agriculture, Shanghai, 200240, China
| | - Yu Wu
- School of Agriculture and Biology and Research Centre for Low-Carbon Agriculture, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai, 200240, China
- Key Laboratory of Urban Agriculture, Ministry of Agriculture, Shanghai, 200240, China
| | - Yang Yi
- Shanghai Yangtze River Delta Eco-Environmental Change and Management Observation and Research Station,Ministry of Science and Technology, Ministry of Education, Shanghai Urban Forest Ecosystem Research Station, National Forestry and Grassland Administration, 800 Dongchuan Rd., Shanghai, 200240, China
- Key Laboratory of National Forestry and Grassland Administration on Ecological Landscaping of Challenging Urban Sites, National Innovation Alliance of National Forestry and Grassland Administration on Afforestation and Landscaping of Challenging Urban Sites, Shanghai Academy of Landscape Architecture Science and Planning, Shanghai, 200232, China
| | - Shengyong Li
- Department of Civil Engineering, Hetao College, Yunzhong Street, Bayannur City, Inner Mongolia, 015000, China
| | - Shan Yin
- School of Agriculture and Biology and Research Centre for Low-Carbon Agriculture, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai, 200240, China
- Key Laboratory of Urban Agriculture, Ministry of Agriculture, Shanghai, 200240, China
- Shanghai Yangtze River Delta Eco-Environmental Change and Management Observation and Research Station,Ministry of Science and Technology, Ministry of Education, Shanghai Urban Forest Ecosystem Research Station, National Forestry and Grassland Administration, 800 Dongchuan Rd., Shanghai, 200240, China
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13
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Infrared-Photoacoustic Spectroscopy and Multiproduct Multivariate Calibration to Estimate the Proportion of Coffee Defects in Roasted Samples. BEVERAGES 2023. [DOI: 10.3390/beverages9010021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
Infrared-photoacoustic spectroscopy (IR-PAS) and partial least squares (PLS) were tested as a rapid alternative to conventional methods to evaluate the proportion of coffee defects in roasted and ground coffees. Multiproduct multivariate calibration models were obtained from spectra of healthy beans of Coffea canephora and C. arabica (Arabica) and blends composed of defective and healthy beans of Arabica in different proportions. The blends, named selections, contained sour, black, broken, whole beans, skin, and coffee woods. Six models were built using roasted and ground coffee samples. The model was optimized through outlier evaluation, and the parameters of merit such as accuracy, sensitivity, limits of detection and quantification, the inverse of analytical sensitivity, linearity, and adjustment were computed. The models presented predictive capacity and high sensitivity in determining defects, all being predicted with suitable correlation coefficients (ranging from 0.7176 to 0.8080) and presenting adequate performance. The parameters of merit displayed promising results, and the prediction models developed for %defects can be safely used as an alternative to the reference method. Furthermore, the new method is fast, efficient, and suitable for in-line application in quality control industrial coffee processing.
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14
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Cheng Y, Bi X, Xu Y, Liu Y, Li J, Du G, Lv X, Liu L. Artificial intelligence technologies in bioprocess: Opportunities and challenges. BIORESOURCE TECHNOLOGY 2023; 369:128451. [PMID: 36503088 DOI: 10.1016/j.biortech.2022.128451] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 12/01/2022] [Accepted: 12/03/2022] [Indexed: 06/17/2023]
Abstract
Bioprocess control and optimization are crucial for tapping the metabolic potential of microorganisms, and which have made great progress in the past decades. Combination of the current control and optimization technologies with the latest computer-based strategies will be a worth expecting way to improve bioprocess further. Recently, artificial intelligence (AI) emerged as a data-driven technique independent of the complex interactions used in mathematical models and has been gradually applied in bioprocess. In this review, firstly, AI-guided modeling approaches of bioprocess are discussed, which are widely applied to optimize critical process parameters (CPPs). Then, AI-assisted rapid detection and monitoring technologies employed in bioprocess are summarized. Next, control strategies according to the above two technologies in bioprocess are analyzed. Lastly, current research gaps and future perspectives on AI-guided optimization and control technologies are discussed. This review provides theoretical guidance for developing AI-guided bioprocess optimization and control technologies.
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Affiliation(s)
- Yang Cheng
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Xinyu Bi
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Yameng Xu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Yanfeng Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Jianghua Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Guocheng Du
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China.
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15
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Riu J, Vega A, Boqué R, Giussani B. Exploring the Analytical Complexities in Insect Powder Analysis Using Miniaturized NIR Spectroscopy. Foods 2022; 11:foods11213524. [PMID: 36360137 PMCID: PMC9659064 DOI: 10.3390/foods11213524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/20/2022] [Accepted: 11/01/2022] [Indexed: 11/09/2022] Open
Abstract
Insects have been a food source for humans for millennia, and they are actively consumed in various parts of the world. This paper aims to ascertain the feasibility of portable near-infrared (NIR) spectroscopy as a reliable and fast candidate for the classification of insect powder samples and the prediction of their major components. Commercially-available insect powder samples were analyzed using two miniaturized NIR instruments. The samples were analyzed as they are and after grinding, to study the effect of the granulometry on the spectroscopic analyses. A homemade sample holder was designed and optimized for making reliable spectroscopic measurements. Classification was then performed using three classification strategies, and partial least squares (PLS) regression was used to predict the macronutrients. The results obtained confirmed that both spectroscopic sensors were able to classify insect powder samples and predict macronutrients with an adequate detection limit.
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Affiliation(s)
- Jordi Riu
- Universitat Rovira i Virgili, Department of Analytical Chemistry and Organic Chemistry, Carrer Marcel·lí Domingo 1, 43007 Tarragona, Spain
| | - Alba Vega
- Universitat Rovira i Virgili, Department of Analytical Chemistry and Organic Chemistry, Carrer Marcel·lí Domingo 1, 43007 Tarragona, Spain
| | - Ricard Boqué
- Universitat Rovira i Virgili, Department of Analytical Chemistry and Organic Chemistry, Carrer Marcel·lí Domingo 1, 43007 Tarragona, Spain
| | - Barbara Giussani
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell’Insubria, Via Valleggio, 9, 22100 Como, Italy
- Correspondence: ; Tel.: +39-031-238-6434
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Baqueta MR, Alves EA, Valderrama P, Pallone JAL. Brazilian Canephora coffee evaluation using NIR spectroscopy and discriminant chemometric techniques. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.105065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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17
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A Systematic Mapping Study of Coffee Quality throughout the Production-to-Consumer Chain. J FOOD QUALITY 2022. [DOI: 10.1155/2022/8019251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Coffee is one of the most consumed beverages in the world and is crucial in the economy of many developing countries. The search to improve coffee quality comes from many fronts, as do the many ways to measure quality and the factors that affect it. Several techniques are used to measure the different metrics to assess coffee quality, across different types of coffee samples and species, and throughout the entire process from farm to cup. In this work, we conducted a systematic mapping study of 1,470 articles to identify the aspects of quality that are the most important in the scientific literature to evaluate coffee throughout the processing chain. The study revealed that cup quality and biochemical composition are the most researched quality attributes. The main objective of the reviewed studies is the correlation between different quality measurements. The most used techniques are the analytical chemistry methods. The most studied species is Coffea arabica. The most used sample presentation is green coffee. The postharvest stage is the most researched, in which quality control receives more attention. In the preharvest stage, management practices stand out. Finally, the most used type of research was the evaluation research.
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18
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Rapid Determination of Cellulose and Hemicellulose Contents in Corn Stover Using Near-Infrared Spectroscopy Combined with Wavelength Selection. Molecules 2022; 27:molecules27113373. [PMID: 35684314 PMCID: PMC9182057 DOI: 10.3390/molecules27113373] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 05/20/2022] [Accepted: 05/22/2022] [Indexed: 11/17/2022] Open
Abstract
The contents of cellulose and hemicellulose (C and H) in corn stover (CS) have an important influence on its biochemical transformation and utilization. To rapidly detect the C and H contents in CS by near-infrared spectroscopy (NIRS), the characteristic wavelength selection algorithms of backward partial least squares (BIPLS), competitive adaptive reweighted sampling (CARS), BIPLS combined with CARS, BIPLS combined with a genetic simulated annealing algorithm (GSA), and CARS combined with a GSA were used to select the wavelength variables (WVs) for C and H, and the corresponding regression correction models were established. The results showed that five wavelength selection algorithms could effectively eliminate irrelevant redundant WVs, and their modeling performance was significantly superior to that of the full spectrum. Through comparison and analysis, it was found that CARS combined with GSA had the best comprehensive performance; the predictive root mean squared errors of the C and H regression model were 0.786% and 0.893%, and the residual predictive deviations were 3.815 and 12.435, respectively. The wavelength selection algorithm could effectively improve the accuracy of the quantitative analysis of C and H contents in CS by NIRS, providing theoretical support for the research and development of related online detection equipment.
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Chang YT, Hsueh MC, Hung SP, Lu JM, Peng JH, Chen SF. Prediction of specialty coffee flavors based on near-infrared spectra using machine- and deep-learning methods. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:4705-4714. [PMID: 33491774 DOI: 10.1002/jsfa.11116] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 01/12/2021] [Accepted: 01/25/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Specialty coffee fascinates people with its bountiful flavors. Currently, flavor descriptions of specialty coffee beans are only offered by certified coffee cuppers. However, such professionals are rare, and the market demand is tremendous. The hypothesis of this study was to investigate the feasibility to train machine learning (ML) and deep learning (DL) models for predicting the flavors of specialty coffee using near-infrared spectra of ground coffee as the input. Successful model development would provide a new and objective framework to predict complex flavors in food and beverage products. RESULTS In predicting seven categories of coffee flavors, the models developed using the ML method (i.e. support vector machine) and the deep convolutional neural network (DCNN) achieved similar performance, with the recall and accuracy being 70-73% and 75-77% respectively. Through the proposed visualization method - a focusing plot - the potential correlation among the highly weighted spectral region of the DCNN model, the predicted flavor categories, and the corresponding chemical composition are presented. CONCLUSION This study has proven the feasibility of applying ML and DL methods on the near-infrared spectra of ground coffee to predict specialty coffee flavors. The effective models provided moderate prediction for seven flavor categories based on 266 samples. The results of classification and visualization indicate that the DCNN model developed is a promising and explainable method for coffee flavor prediction. © 2021 Society of Chemical Industry.
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Affiliation(s)
- Yu-Tang Chang
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan (ROC)
| | - Meng-Chien Hsueh
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan (ROC)
| | - Shu-Pin Hung
- Information and Communications Research Lab, Industrial Technology Research Institute, Hsinchu, Taiwan (ROC)
- Institute of Management of Technology, College of Management, National Chiao Tung University, Hsinchu, Taiwan (ROC)
| | - Juin-Ming Lu
- Information and Communications Research Lab, Industrial Technology Research Institute, Hsinchu, Taiwan (ROC)
| | - Jia-Hung Peng
- Information and Communications Research Lab, Industrial Technology Research Institute, Hsinchu, Taiwan (ROC)
| | - Shih-Fang Chen
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan (ROC)
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20
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Liu J, Jin S, Bao C, Sun Y, Li W. Rapid determination of lignocellulose in corn stover based on near-infrared reflectance spectroscopy and chemometrics methods. BIORESOURCE TECHNOLOGY 2021; 321:124449. [PMID: 33285506 DOI: 10.1016/j.biortech.2020.124449] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 11/22/2020] [Accepted: 11/23/2020] [Indexed: 06/12/2023]
Abstract
In this study, a rapid detection method based on near-infrared reflectance spectroscopy was proposed for measuring the contents of cellulose, hemicellulose and lignin in corn stover. In the basis of strategies of variable selection, feature extraction and nonlinear modeling, BiPLS-PCA-SVM was constructed using backward interval partial least squares combined with principal component analysis and support vector machine, which was used to improve the performance of spectral regression calibration model. For BiPLS-PCA-SVM model, the determination coefficients, root mean squared error and residual predictive deviation for the validation set were 0.906, 0.900% and 3.213 for cellulose; 0.987, 0.797% and 9.071 for hemicellulose; and 0.936, 0.264% and 4.024 for lignin, correspondingly. The results indicate that near-infrared reflectance spectroscopy combined with BiPLS-PCA-SVM can provide a reliable alternative strategy to detect contents of lignocellulosic components for pretreated corn stover in the anaerobic digestion process.
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Affiliation(s)
- Jinming Liu
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, PR China
| | - Shuo Jin
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, PR China
| | - Changhao Bao
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, PR China
| | - Yong Sun
- College of Engineering, Northeast Agricultural University, Harbin 150030, PR China.
| | - Wenzhe Li
- College of Engineering, Northeast Agricultural University, Harbin 150030, PR China
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21
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dos Santos VJ, Baqueta MR, Março PH, Valderrama P, Visentainer JV. Human Milk Lactation Phases Evaluation Through Handheld Near-Infrared Spectroscopy and Multivariate Classification. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-020-01924-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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