Geographical and genotypic classification of arabica coffee using Fourier transform infrared spectroscopy and radial-basis function networks

Link, Jade Varaschim; Lemes, André Luis Guimarães; Marquetti, Izabele; Scholz, Maria Brígida dos Santos; Bona, Evandro

doi:10.1016/j.chemolab.2014.04.008

Cited by 37 publications

(19 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Caffeic acid when esterified with other trans ‐cinnamic acids may form chlorogenic acids (Clifford, ). According to Link, Lemes, Marquetti, Scholz, and Bona (), the identification of secondary metabolites, such as caffeine and phenolic acids, using infrared spectroscopy proved to be an effective technique for the recognition and classification of coffee beans. In relation to the physicochemical properties, pH values differed significantly ( P = 0.014) among the geographic origins, varying from 4.99 to 5.31.…”

Section: Resultsmentioning

confidence: 99%

Chemometric Authentication of Brazilian Coffees Based on Chemical Profiling

Monteiro

Santos

Rodionova

et al. 2019

Journal of Food Science

View full text Add to dashboard Cite

In this work, different chemometric tools were compared to classify n = 26 conventional (CONV) and n = 19 organic (ORG) coffees from the main Brazilian producing regions based on the chemical composition, physicochemical properties, and antioxidant activity. Principal component analysis separated ORG and CONV coffees but the distinction among the producing regions of Brazilian coffee was not possible. Partial least squares discriminant analysis classified all ORG and CONV coffees in the external validation. Similarly, linear discriminant analysis was able to discriminate 100% and 81% of ORG and CONV coffees in the external validation, respectively, in which total phenolic content (TPC), ferric reducing antioxidant activity, and caffeic acid were the main discriminant variables. Overall 100% of samples from Paraná, Minas Gerais, and blended samples were correctly classified, where TPC, flavonoids, inhibition of lipid peroxidation, caffeic acid, pH, and soluble solids were the main discriminant variables. Support vector machines classified 95% ORG and 88% CONV, 100% Coffea arabica, and 88% and 78% coffees produced in São Paulo and Minas Gerais. k‐Nearest neighbors was effective in distinguishing 100% CONV, 89% ORG, 100% coffees from São Paulo, and 100% C. arabica coffees. Overall, HPLC data and simple physicochemical parameters allied to chemometrics were effective in authenticating the cultivation system and the botanical origin of Brazilian coffees. Practical Application Coffee adulteration is a serious problem in the food chain as some fraudsters replace coffee powder by other cheaper products. In the case of organic coffee, this scenario is even worse as still there is not a universal method to differentiate conventionally grown coffee from its organic counterpart. In addition, Brazilian coffee is produced in different regions and the commercial value varies. Therefore, we analyzed some physicochemical, chemical, and antioxidant properties of Brazilian coffees from distinct origins and classified the samples using chemometrics. Our approach seems to be interesting for quality control purposes.

show abstract

Section: Resultsmentioning

confidence: 99%

Chemometric Authentication of Brazilian Coffees Based on Chemical Profiling

Monteiro

Santos

Rodionova

et al. 2019

Journal of Food Science

View full text Add to dashboard Cite

show abstract

“…FTIR spectroscopy associated with radial-basis function network (RBF), an artificial neural network (ANN) that is widely used for pattern classification, was successfully employed by Link et al for the geographic and genotypic classification of Arabica coffees [ 47 ]. This chemometric procedure was able to classify the samples, both geographically (100% correct classification) and genotypically (94.44%), exhibiting a superior performance when compared with other methods such as Soft Independent Modeling of Class Analogies (SIMCA) that have also been applied to the same test set [ 48 ].…”

Section: Application Of Ft-mir To Food Analysismentioning

confidence: 99%

Mid-Infrared Spectroscopy as a Valuable Tool to Tackle Food Analysis: A Literature Review on Coffee, Dairies, Honey, Olive Oil and Wine

Mendes

Duarte

2021

Foods

View full text Add to dashboard Cite

Nowadays, food adulteration and authentication are topics of utmost importance for consumers, food producers, business operators and regulatory agencies. Therefore, there is an increasing search for rapid, robust and accurate analytical techniques to determine the authenticity and to detect adulteration and misrepresentation. Mid-infrared spectroscopy (MIR), often associated with chemometric techniques, offers a fast and accurate method to detect and predict food adulteration based on the fingerprint characteristics of the food matrix. In the first part of this review the basic concepts of infrared spectroscopy, sampling techniques, as well as an overview of chemometric tools are summarized. In the second part, recent applications of MIR spectroscopy to the analysis of foods such as coffee, dairy products, honey, olive oil and wine are discussed, covering a timespan from 2010 to mid-2020. The literature gathered in this article clearly reveals that the MIR spectroscopy associated with attenuated total reflection acquisition mode and different chemometric tools have been broadly applied to address quality, authenticity and adulteration issues. This technique has the advantages of being simple, fast and easy to use, non-destructive, environmentally friendly and, in the future, it can be applied in routine analyses and official food control.

show abstract

“…MLP-ANN achieved a prediction ability of 93% and specificity of 98% while the corresponding metrics for LDA were 81% and 94% [136]. In a separate study designed for classification of arabica coffee by genotypic and geographical origin, Link et al [137] used RBF-ANN to obtain 100% correct geographic classification and 94.4% genotypic classification. Bona et al [138] used SVM to produced 100% accuracy for geographical classification of different genotypes of arabica coffee.…”

Section: Vegetablesmentioning

confidence: 99%

Comparison of Chemometric Problems in Food Analysis using Non-Linear Methods

2020

View full text Add to dashboard Cite

Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

show abstract

Geographical and genotypic classification of arabica coffee using Fourier transform infrared spectroscopy and radial-basis function networks

Cited by 37 publications

References 21 publications

Chemometric Authentication of Brazilian Coffees Based on Chemical Profiling

Chemometric Authentication of Brazilian Coffees Based on Chemical Profiling

Mid-Infrared Spectroscopy as a Valuable Tool to Tackle Food Analysis: A Literature Review on Coffee, Dairies, Honey, Olive Oil and Wine

Comparison of Chemometric Problems in Food Analysis using Non-Linear Methods

Contact Info

Product

Resources

About