Combining mid infrared spectroscopy and paper spray mass spectrometry in a data fusion model to predict the composition of coffee blends

Assis, Camila; Pereira, Hebert Vinicius; Amador, Victoria Silva; Augusti, Rodinei; Oliveira, Leandro S.; Sena, Marcelo M.

doi:10.1016/j.foodchem.2018.12.044

Cited by 48 publications

(23 citation statements)

References 39 publications

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“…In the literature, some analytical approaches have been proposed to evaluate coffee blends, either to determine and predict their chemical composition (Assis, Pereira, Amador, Augusti, de Oliveira, & Sena, 2019;Jumhawan, Putri, Yusianto, & T., & Fukusaki, E., 2016;Souard, Delporte, Stoffelen, Thévenot, Noret, Dauvergne, & Stévigny, 2018) or to identify and quantify adulterations (Bertone, Venturello, Giraudo, Pellegrino, & Geobaldo, 2016;Combes, Joët, & Lashermes, 2018;Wermelinger, D'Ambrosio, Klopprogge, & Yeretzian, 2011). However, to date, no study has been carried out to characterize the quality and composition of coffee blends with different cup and roasting profiles, a subject that deserves attention.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, the demand of the modern industry has propagated several claims and arguments to develop fast, simple, robust, and ecofriendly analytical methods with industrial application in the coffee market (Assis et al, 2019;Baqueta et al, 2020Baqueta et al, , 2019Barbin, Felicio, Sun, Nixdorf, & Hirooka, 2014;Correia et al, 2018;Craig et al, 2018;dos Santos, Alvarenga, & Boffo, 2020;Milani, Rossini, Catelani, Pezza, Toci, & Pezza, 2020). These solutions include instrumental techniques such as proton Nuclear Magnetic Resonance ( 1 H NMR) spectroscopy and Near-Infrared (NIR) spectroscopy (including portable and bench-top NIR equipment), which have achieved popularity for solving problems related to the coffee analytical evaluation (Barbin et al, 2014;Consonni, Polla, & Cagliani, 2018;Correia et al, 2018;Monteiro et al, 2018;Toci et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Integrated 1H NMR fingerprint with NIR spectroscopy, sensory properties, and quality parameters in a multi-block data analysis using ComDim to evaluate coffee blends

et al. 2021

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Coffee quality is determined by several factors and, in the chemometric domain, the multi-block data analysis methods are valuable to study multiple information describing the same samples. In this industrial study, the Common Dimension (ComDim) multi-block method was applied to evaluate metabolite fingerprints, nearinfrared spectra, sensory properties, and quality parameters of coffee blends of different cup and roasting profiles and to search relationships between these multiple data blocks. Data fusion-based Principal Component Analysis was not effective in exploiting multiple data blocks like ComDim. However, when a multi-block was applied to explore the data sets, it was possible to demonstrate relationships between the methods and techniques investigated and the importance of each block or criterion involved in the industrial quality control of coffee. Coffee blends were distinguished based on their qualities and metabolite composition. Blends with high cup quality and lower roasting degrees were generally differentiated from those with opposite characteristics.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrated 1H NMR fingerprint with NIR spectroscopy, sensory properties, and quality parameters in a multi-block data analysis using ComDim to evaluate coffee blends

et al. 2021

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“…Consequently, coffee beverage quality depends on the chemical constituents present in coffee beans, this composition is determinant for beverages' sensory characteristics, promoting the difference in aroma and flavor (Assis et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Application of mid-infrared vibrational spectroscopy with Fourier transform (FTIR) in quality evaluation in commercial coffees

Fontes

Pereira

Lyra

et al. 2022

RSD

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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 date. Spectra were obtained in the range from 4000 to 500 cm-1. The studies of Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were made as discrimination methods. The areas in the region from 2970 to 2830 cm-1 and 1765 to 1720 cm-1 were calculated to analyze the alteration as a function over time. The results suggest that these bands in coffee are sensitive over time and to the storage conditions, promoting changes in aroma and flavor.

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“…The spectroscopic-based method, using different electromagnetic regions along with chemometrics, has been successfully applied for cereal adulteration quantification in ground roasted coffee, both in single and multiple adulterants using near-infrared (NIR), ultraviolet–visible (UV–Vis), mid-infrared, Raman, nuclear magnetic resonance (NMR), and laser-induced breakdown spectroscopy (LIBS) [ 11 , 12 , 13 , 14 , 15 , 16 ]. Most of these methods are less expensive in the device and faster in sample preparation (little or no need for sample preparation).…”

Section: Introductionmentioning

confidence: 99%

Quantification of Corn Adulteration in Wet and Dry-Processed Peaberry Ground Roasted Coffees by UV–Vis Spectroscopy and Chemometrics

Yulia

Suhandy

2021

Molecules

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In this present research, a spectroscopic method based on UV–Vis spectroscopy is utilized to quantify the level of corn adulteration in peaberry ground roasted coffee by chemometrics. Peaberry coffee with two types of bean processing of wet and dry-processed methods was used and intentionally adulterated by corn with a 10–50% level of adulteration. UV–Vis spectral data are obtained for aqueous samples in the range between 250 and 400 nm with a 1 nm interval. Three multivariate regression methods, including partial least squares regression (PLSR), multiple linear regression (MLR), and principal component regression (PCR), are used to predict the level of corn adulteration. The result shows that all individual regression models using individual wet and dry samples are better than that of global regression models using combined wet and dry samples. The best calibration model for individual wet and dry and combined samples is obtained for the PLSR model with a coefficient of determination in the range of 0.83–0.93 and RMSE below 6% (w/w) for calibration and validation. However, the error prediction in terms of RMSEP and bias were highly increased when the individual regression model was used to predict the level of corn adulteration with differences in the bean processing method. The obtained results demonstrate that the use of the global PLSR model is better in predicting the level of corn adulteration. The error prediction for this global model is acceptable with low RMSEP and bias for both individual and combined prediction samples. The obtained RPDp and RERp in prediction for the global PLSR model are more than two and five for individual and combined samples, respectively. The proposed method using UV–Vis spectroscopy with a global PLSR model can be applied to quantify the level of corn adulteration in peaberry ground roasted coffee with different bean processing methods.

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Combining mid infrared spectroscopy and paper spray mass spectrometry in a data fusion model to predict the composition of coffee blends

Cited by 48 publications

References 39 publications

Integrated 1H NMR fingerprint with NIR spectroscopy, sensory properties, and quality parameters in a multi-block data analysis using ComDim to evaluate coffee blends

Integrated 1H NMR fingerprint with NIR spectroscopy, sensory properties, and quality parameters in a multi-block data analysis using ComDim to evaluate coffee blends

Application of mid-infrared vibrational spectroscopy with Fourier transform (FTIR) in quality evaluation in commercial coffees

Quantification of Corn Adulteration in Wet and Dry-Processed Peaberry Ground Roasted Coffees by UV–Vis Spectroscopy and Chemometrics

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