Prediction of specialty coffee cup quality based on near infrared spectra of green coffee beans

Tolessa, Kassaye; Rademaker, Michaël; Baets, Bernard De; Boeckx, Pascal

doi:10.1016/j.talanta.2015.12.039

Cited by 81 publications

(63 citation statements)

References 34 publications

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“…Fick's second law of diffusion in Equation () (Crank, ) describes a phenomenon of liquid diffusion in the drying of food materials (Aidani et al, ; Nilnont et al, ; Muhidong et al, ; Ghosh, & Venkatachalapathy, 2014; Tolessa, Rademaker, Rademaker, & Boeckx, ; Siqueire et al, ; Deeto et al, ). The cylindrical analytical solution of Equation () was presented in Equation (), which assumes that the diffusivity of the materials is constant with a negligible reduction (Crank, ; McMinn & Magee, ; Ramachandran, Paliwal, & Cenkowski., ).

\frac{\partial M}{\partial t} = \nabla (D_{eff} (\nabla M))

MR = \frac{M_{t} - M_{e}}{M_{o} - M_{e}} = \frac{8 r^{2}}{l^{2}} {false\sum}_{i = 1}^{\infty} {false\sum}_{j = 1}^{\infty} \frac{1}{λ_{i}^{2} β_{j}^{2}} exp (- (λ_{i}^{2} + β_{j}^{2}) \frac{D_{eff} t}{r^{2}})

where D eff : the effective moisture diffusivity (m 2 /s), r : the parchment coffee radius,

l

: the length of parchment coffee (m), t : the drying time ( s ), and λ i : ith root of the Bessel function (2.405, 5.520, 8.654,...) of zero order, i = 1, 2, 3, and.

β_{j} = \frac{false(2 j - 1 false) π r}{2 l}, j = 1, 2, 3

…”

Section: Methodsmentioning

confidence: 99%

“…Fick's second law of diffusion in Equation (5) (Crank, 1975) describes a phenomenon of liquid diffusion in the drying of food materials (Aidani et al, 2016;Nilnont et al, 2012;Muhidong et al, 2013;Ghosh, & Venkatachalapathy, 2014;Tolessa, Rademaker, Rademaker, & Boeckx, 2016;Siqueire et al, 2017;Deeto et al, 2018). The cylindrical analytical solution of Equation (5) was presented in Equation (6), which assumes that the diffusivity of the materials is constant with a negligible reduction (Crank, 1975;McMinn & Magee, 1999;Ramachandran, Paliwal, & Cenkowski., 2018).…”

Section: Diffusivities Of Parchment Coffeementioning

confidence: 99%

See 1 more Smart Citation

Thin‐layer drying of parchment Arabica coffee by controlling temperature and relative humidity

Phitakwinai

Thepa

Nilnont

2019

Food Science & Nutrition

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This paper presents thin‐layer drying of parchment coffee (Coffea arabica). Thin‐layer drying of parchment coffee was conducted under controlled temperatures (50°C, 60°C, and 70°C) and relative humidities (10%–30%). The temperature of the drying air was important for drying at a high temperature, which results in the rapid removal of moisture and reduced time for drying. Nine thin‐layer drying models (Newton, Page, Henderson and Pabis, logarithmic, two‐term, modified Henderson and Pabis, two‐term exponential, approximation diffusion, and modified‐Midilli) were fitted to the experimental data for parchment coffee. The drying parameters of parchment coffee were related to temperature and relative humidity. The best model was the modified‐Midilli model, which can be used to design the optimal dryer. The effective moisture diffusivity of parchment coffee drying was determined by minimizing the sum of squares of the deviations between the experimental data for the moisture content and the predicted values of thin‐layer drying. The effective moisture diffusivity as a function of the temperature at each relative humidity was expressed by the Arrhenius‐type equation.

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\frac{\partial M}{\partial t} = \nabla (D_{eff} (\nabla M))

MR = \frac{M_{t} - M_{e}}{M_{o} - M_{e}} = \frac{8 r^{2}}{l^{2}} {false\sum}_{i = 1}^{\infty} {false\sum}_{j = 1}^{\infty} \frac{1}{λ_{i}^{2} β_{j}^{2}} exp (- (λ_{i}^{2} + β_{j}^{2}) \frac{D_{eff} t}{r^{2}})

where D eff : the effective moisture diffusivity (m 2 /s), r : the parchment coffee radius,

l

: the length of parchment coffee (m), t : the drying time ( s ), and λ i : ith root of the Bessel function (2.405, 5.520, 8.654,...) of zero order, i = 1, 2, 3, and.

β_{j} = \frac{false(2 j - 1 false) π r}{2 l}, j = 1, 2, 3

…”

Section: Methodsmentioning

confidence: 99%

Section: Diffusivities Of Parchment Coffeementioning

confidence: 99%

Thin‐layer drying of parchment Arabica coffee by controlling temperature and relative humidity

Phitakwinai

Thepa

Nilnont

2019

Food Science & Nutrition

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“…[22] As an alternative analytical approach, the dielectric spectroscopy approach losses the resonant nature of other approaches that have proven to be effective for this type of coffee studies. [30][31][32][33][34] One of the most notable advantages of this approach is that the samples do not require of any sort of previous handling and that the wide frequency range offers the possibility of exploring several behaviors. Consequently, this technology offers an intermediate alternative between data availability and easiness of analysis and use.…”

Section: Resultsmentioning

confidence: 99%

Determination of the complex permittivity of cherry, pulped, green, and roasted coffee using a planar dielectric platform and a coaxial probe between 0.3 and 6 GHz

Velasquez

Peña

Bohórquez

et al. 2018

International Journal of Food Properties

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“…NIR spectroscopy is characterized by speed, reliability, simplicity, security, low operation cost, minimum sample processing, low instrumental maintenance, allowing various analyses simultaneously and not requiring the use of chemicals. 8,17,18 This technique has already proven useful for the prediction of several properties in coffee samples and has been applied in the determination of antioxidants, 19,20 caffeine, [21][22][23] cafestol and kahweol, 24 roasting degree prediction, 25 chemical composition of defective beans, 11 sensory characteristics 18,26,27 and geographical origin. 28 In this context, the objective of the work presented here was to evaluate the potential of NIR spectroscopy and spectral pre-processing methods associated with partial least squares regression (PLS-R) in determining the pH and acidity of green coffee samples.…”

Section: Introductionmentioning

confidence: 99%

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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Prediction of specialty coffee cup quality based on near infrared spectra of green coffee beans

Cited by 81 publications

References 34 publications

Thin‐layer drying of parchment Arabica coffee by controlling temperature and relative humidity

Thin‐layer drying of parchment Arabica coffee by controlling temperature and relative humidity

Determination of the complex permittivity of cherry, pulped, green, and roasted coffee using a planar dielectric platform and a coaxial probe between 0.3 and 6 GHz

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

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