Chemometric Studies for Quality Control of Processed Brazilian Coffees Using Drifts (original) (raw)
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ETP International Journal of Food Engineering, 2016
Given the successful application of spectroscopic methods in the field of coffee analysis as fast and reliable routine techniques, the objective of this work was to evaluate the feasibility of employing Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) for discrimination between roasted coffees that presented distinct sensory characteristics and were submitted to a range of roasting conditions. Samples consisted of coffees obtained from Nespresso type capsules of intensity levels ranging from 2 to 12. Principal Component Analysis (PCA) of the processed spectra provided separation of the samples into three groups: low (positive PC1), medium (scattered) and high (negative PC1) intensity. Group separation was related to both roasting intensity and sensory parameters, with a clear separation between samples described as low roasted with fruity and floral flavors in comparison to samples described as being intense and very roasted. PLS-DA models were constructed and provided satisfactory discrimination according to sensory characteristics. Samples were classified according to flavor as sugar browning, enzymatic, or dry distillation. Such results confirm the potential of DRIFTS in the discrimination and classification of roasted and ground coffees.
Research, Society and Development
Currently, Brazil is the largest exporter and producer of coffee in the world, and it is the second most consumed beverage in the world, only behind water. In the years 2019 and 2020 it is estimated that the world consumption of coffee was 168.84 million bags of 60 kg, Brazil consumed 20 million bags of coffee, the second-largest consumer in the world, only behind the United States with 25 million bags. The techniques such as infrared spectroscopy has been applied in the food industry, as it is a fast, easy technique, without the need for reagents, free from polluting processes, and capable of analyzing the simultaneous composition of the constituents. The present study aims to analyze the changes in the chemical constituents of Brazilian commercial coffees as a function of shelf life through Fourier transformed infrared spectroscopy (FT-IR) associated with chemometric methods. The experiments were carried out within the expiration date, 6 months, and a year after the expiration dat...
Food Research International, 2014
During the last two decades, near and mid-infrared spectral analyses have emerged as a reliable and promising analytical tool for objective assessment of coffee quality attributes. The literature presented in this review clearly reveals that near and mid-infrared approaches have a huge potential for gaining rapid information about the chemical composition and related properties of coffee. In addition to its ability for effectively quantifying and characterizing quality attributes of some important features of coffee such as moisture, lipids and caffeine content, classification into quality grades and determination of sensory attributes, it is able to measure multiple chemical constituents simultaneously avoiding extensive sample preparation. Developing a quality evaluation system based on infrared spectral information to assess the coffee quality parameters and to ensure its authentication would bring economical benefits to the coffee industry by increasing consumer confidence in the quality of products. This paper provides an overview of the recently developed approaches and latest research carried out in near and mid-infrared spectral technology for evaluating the quality and composition of coffee and the possibility of its widespread deployment.
Journal of Food Science, 2011
The objective of this work was to evaluate the potential of Fourier transform infrared spectroscopy (FTIR) in the characterization and discrimination between immature and mature or ripe coffee beans. Arabica coffee beans were submitted to FTIR analysis by reflectance readings employing attenuated total reflectance (ATR) and diffuse reflectance (DR) accessories. The obtained spectra were similar, but in general higher absorbance values were observed for nondefective beans in comparison to immature ones. Multivariate statistical analysis (principal component analysis, PCA, and agglomerative hierarchical clustering, AHC) was performed in order to verify the possibility of discrimination between immature and mature coffee samples. A clear separation between immature and mature coffees was observed based on AHC and PCA analyses of the normalized spectra obtained by employing both ATR and DR accessories. Linear discriminant analysis was employed for developing classification models, with recognition and prediction abilities of 100%. Such results showed that FTIR analysis presents potential for the development of a simple routine methodology for separation of immature and mature coffee beans.
Foods
The Specialty Coffee Association (SCA) sensory analysis protocol is the methodology that is used to classify specialty coffees. However, because the sensory analysis is sensitive to the taster’s training, cognitive psychology, and physiology, among other parameters, the feasibility of instrumental approaches has been recently studied for complementing such analyses. Spectroscopic methods, mainly near infrared (NIR) and mid infrared (FTIR—Fourier Transform Infrared), have been extensively employed for food quality authentication. In view of the aforementioned, we compared NIR and FTIR to distinguish different qualities and sensory characteristics of specialty coffee samples in the present study. Twenty-eight green coffee beans samples were roasted (in duplicate), with roasting conditions following the SCA protocol for sensory analysis. FTIR and NIR were used to analyze the ground and roasted coffee samples, and the data then submitted to statistical analysis to build up PLS models in...
Journal of Agricultural and Food Chemistry, 2007
Near-infrared spectroscopy (NIRS), combined with diverse feature selection techniques and multivariate calibration methods, has been used to develop robust and reliable reduced-spectrum regression models based on a few NIR filter sensors for determining two key parameters for the characterization of roasted coffees, which are extremely relevant from a quality assurance standpoint: roasting color and caffeine content. The application of the stepwise orthogonalization of predictors (an "old" technique recently revisited, known by the acronym SELECT) provided notably improved regression models for the two response variables modeled, with root-mean-square errors of the residuals in external prediction (RMSEP) equal to 3.68 and 1.46% for roasting color and caffeine content of roasted coffee samples, respectively. The improvement achieved by the application of the SELECT-OLS method was particularly remarkable when the very low complexities associated with the final models obtained for predicting both roasting color (only 9 selected wavelengths) and caffeine content (17 significant wavelengths) were taken into account. The simple and reliable calibration models proposed in the present study encourage the possibility of implementing them in online and routine applications to predict quality parameters of unknown coffee samples via their NIR spectra, thanks to the use of a NIR instrument equipped with a proper filter system, which would imply a considerable simplification with regard to the recording and interpretation of the spectra, as well as an important economic saving.
Influences of pH and temperature on infrared spectroscopic features of brewed coffee
Procedia food science, 2011
We developed an infrared spectroscopic evaluation method of brewed coffee, whose quality and taste highly depend on the chemical contents, the interactions between the components, the pH value and the temperature, using a Fourier transform infrared (FT-IR) spectrometer equipped with an attenuated total reflection (ATR) accessory. The objective of this study is to understand the influences of the pH values and temperature on the spectral features of brewed coffee and the main components, since the component balances of the organic acids originating from coffee beans and being produced during processes such as roasting and extraction could closely relate to the brewed coffee characteristics. The absorption peak sifts of the ATR spectra of brewed coffee were observed as the influences of the pH and temperatures. Therefore, by analyzing the spectra of the coffee components under the various pH and temperature conditions based on the ionic dissociation equilibrium theory, the spectral behavior of the brewed coffee model due to the pH and temperature changes could mainly result from those of the organic acids as the main components. Consequently, the infrared spectral information analysis would be acceptable as a new method to evaluate a profile of brewed coffee for the quality evaluation relating to the taste and the non-intensive on-line monitoring of the coffee process.
Classification of Brazilian Coffee Using Near-Infrared Spectroscopy and Multivariate Calibration
Analytical Letters, 2012
This work describes the use of near infrared spectroscopy (NIRS) and chemometric techniques calibration for the classification of coffee samples from different lots and producers acquired in supermarkets and roasting industries in some Brazilian cities. Seventy-three samples of finely ground roasted coffee were acquired in the market and 91 samples of roasted ground Arabica beans were analyzed in the full NIR spectral range (800–2500 nm) using a diffuse reflectance accessory coupled to an MB160 Bomem spectrophotometer. Two classification models were constructed: Soft Independent Modeling Class Analogy (SIMCA) and PLS Discriminant Analysis (PLS-DA). All findings reveal that NIR spectroscopy, coupled with either SIMCA or PLS-DA multivariate models, can be a useful tool to differentiate roasted coffee grains and to replace sensory tests.
Talanta, 2011
Mathematical models based on chemometric analyses of the coffee beverage sensory data and NIR spectra of 51 Arabica roasted coffee samples were generated aiming to predict the scores of acidity, bitterness, flavour, cleanliness, body and overall quality of coffee beverage. Partial least squares (PLS) were used to construct the models. The ordered predictor selection (OPS) algorithm was applied to select the wavelengths for the regression model of each sensory attribute in order to take only significant regions into account. The regions of the spectrum defined as important for sensory quality were closely related to the NIR spectra of pure caffeine, trigonelline, 5-caffeoylquinic acid, cellulose, coffee lipids, sucrose and casein. The NIR analyses sustained that the relationship between the sensory characteristics of the beverage and the chemical composition of the roasted grain were as listed below: 1-the lipids and proteins were closely related to the attribute body; 2-the caffeine and chlorogenic acids were related to bitterness; 3-the chlorogenic acids were related to acidity and flavour; 4-the cleanliness and overall quality were related to caffeine, trigonelline, chlorogenic acid, polysaccharides, sucrose and protein.