Exploiting near infrared spectroscopy as an analytical tool for on-line monitoring of acidity during coffee roasting

Santos, João Rodrigo; Lopo, Miguel; Rangel, António O.S.S.; Lopes, João A.

doi:10.1016/j.foodcont.2015.08.007

Cited by 47 publications

(38 citation statements)

References 27 publications

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“…The spectral behavior observed is similar to that reported by Santos et al, 12 with the absorption peaks observed in regions 1388-1470 and 1923-2000 nm being attributed to the absorption of water. The first overtone of O H stretching appears at 1450 nm, and a combination of O H stretching and deformation bands occurs at 1940 nm.…”

Section: Discussionsupporting

confidence: 88%

“…determined the region 1208 to 1222 nm as being ideal for determination of acidity in coffee beverage, and those related to the second overtone of CH and CH 2 groups. The chosen region also presents proximity with what was reported by Santos et al ., who found the region between 1063 and 1338 nm as being extremely important for acidity monitoring in the process of roasting of coffee. For Esteban‐Díez et al ., the region between 1100 and 1250 nm might be related to the absorption of chlorogenic acids.…”

Section: Discussionsupporting

confidence: 79%

“…The first overtone of O H stretching appears at 1450 nm, and a combination of O H stretching and deformation bands occurs at 1940 nm. 26 Santos et al 12 believe that the region between 1666 and 2500 nm contains the largest amount of chemical information for coffee, particularly the spectral region between 2000 and 2500 nm, which provides a large amount of information due to the combination bands between C H and C C. However, because of a large number of organic molecules that make up coffee, it is difficult to say precisely which structures are responsible for the information in a particular region of the spectrum.…”

Section: Discussionmentioning

confidence: 99%

“…According to Balzer, acetic, citric, chlorogenic, malic and quinic acids are the most abundant in coffee. The acid content in the composition of coffee depends on several factors, among them the altitude of the place of cultivation, ripeness of the beans, roasting and occurrence of fermentation during the drying process . The pH value can be indicative of changes in coffee beans, such as undesirable fermentations that can occur before or after harvesting .…”

Section: Introductionmentioning

confidence: 99%

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Determination of pH and acidity in green coffee using near‐infrared spectroscopy and multivariate regression

et al. 2020

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BACKGROUND Coffee is a raw material of global interest. Due to its relevance, this work evaluated the performance of calibration models constructed from spectral data obtained using near‐infrared spectroscopy (FT‐NIR) to determine the pH values and acidity in coffee beans in a practical and non‐destructive way. Partial least squares regression was used during the calibration and the cross‐validation to optimize the number of latent variables. The predictive capacity of the spectral pre‐processing methods was also accessed. RESULTS The results obtained showed that the best methods of pre‐processing were the first derivative for the pH variable and the standard normal variate for the acidity, which produced models with correlations of 0.78 and 0.92, ratios of prediction to deviation of 2.061 and 2.966 and biases of −0.00011 and −0.152 to test set validation, respectively. The average errors between predicted and experimental values were lower than 7%. CONCLUSIONS FT‐NIR was successfully applied to predict properties related to the quality of coffee. The method was demonstrated to be a fast and non‐destructive tool which allows the rapid inline evaluation of samples facilitating industrial and commercial processing. © 2020 Society of Chemical Industry

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Section: Discussionsupporting

confidence: 88%

Section: Discussionsupporting

confidence: 79%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Determination of pH and acidity in green coffee using near‐infrared spectroscopy and multivariate regression

et al. 2020

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“…Color measurements have been successfully applied to predict the optimal temperature and period of roasting (Mendes, De Menezes, Aparecida, & Da Silva, ), acrylamide level (Gökmen & Şenyuva, ), and to discriminate between raw and roasted Arabica and Robusta beans (Mendonca, Franca, & Oliveira, ). Fine color correlations with sucrose content (Santos, Viegas, & Pascoa, ), acidity (Santos, Lopo, Rangel, & Lopes, ), moisture, density (Alessandrini, Romani, Pinnavaia, & Rosa, ), and caffeine content (Pizarro, Esteban‐Diez, Gonzalez‐Sáiz, & Forina, ) have also been established. Online roasting control systems via online image monitoring also work on the principle of evaluating variation in surface color/brightness and surface area of coffee beans (Hernandez, Heyd, & Trystram, ).…”

Section: Primary Processingmentioning

confidence: 90%

Farm to Consumer: Factors Affecting the Organoleptic Characteristics of Coffee. II: Postharvest Processing Factors

Hameed

Hussain

Ijaz

et al. 2018

Comp Rev Food Sci Food Safe

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The production and consumption of coffee are increasing despite the roadblocks to its agriculture and global trade. The unique, refreshing, and stimulating final cupping quality of coffee is the only reason for this rising production and consumption. Coffee quality is a multifaceted trait and is inevitably influenced by the way it is successively processed after harvesting. Reportedly, 60% of the quality attributes of coffee are governed by postharvest processing. The current review elaborates and establishes for the first time the relationship between different methods of postharvest processing of coffee and its varying organoleptic and sensory quality attributes. In view of the proven significance of each processing step, this review has been subdivided into three sections, secondary processing, primary processing, and postprocessing variables. Secondary processing addresses the immediate processing steps on the farm after harvest and storage before roasting. The primary processing section adheres specifically to roasting, grinding and brewing/extraction, topics which have been technically addressed more than any others in the literature and by industry. The postprocessing attribute section deals generally with interaction of the consumer with products of different visual appearance. Finally, there are still some bottlenecks which need to be addressed, not only to completely understand the relationship of varying postharvest processing methods with varying in-cup quality attributes, but also to devise the next generation of coffee processing technologies.

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Evaluation of chemical properties of intact green coffee beans using near‐infrared spectroscopy

et al. 2020

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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.

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Exploiting near infrared spectroscopy as an analytical tool for on-line monitoring of acidity during coffee roasting

Cited by 47 publications

References 27 publications

Determination of pH and acidity in green coffee using near‐infrared spectroscopy and multivariate regression

Determination of pH and acidity in green coffee using near‐infrared spectroscopy and multivariate regression

Farm to Consumer: Factors Affecting the Organoleptic Characteristics of Coffee. II: Postharvest Processing Factors

Evaluation of chemical properties of intact green coffee beans using near‐infrared spectroscopy

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