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Diding Suhandy - One of the best experts on this subject based on the ideXlab platform.
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Partial least squares with discriminant analysis and UV– visible spectroscopy for qualitative evaluation of Arabica and Robusta Coffee in Lampung
2018Co-Authors: Meinilwita Yulia, Aniessa Rinny Asnaning, Sri Waluyo, Diding SuhandyAbstract:Arabica Coffee is considered to be of better quality than Robusta Coffee. It has superior taste and aroma, better than Robusta Coffee. To develop an authentication system for Arabica Coffee, it is highly necessary to discriminate between pure Arabica Coffee and Arabica adulterated with Robusta Coffee. Ground roasted Coffee samples are most difficult to discriminate from each other: visual inspection by the naked eye or even machine vision methods becomes very problematic. For this reason, we here propose a relatively new analytical method based on UV–visible spectroscopy for discrimination the pure and adulterated Arabica ground roasted Coffee. In this study, 100 samples were used as samples with different degrees of adulteration (0%–60% of Robusta concentration in an Arabica–Robusta Coffee blend). Spectral data of samples were acquired using a UV–visible spectrometer in the range of 190–1100 nm (Genesys 10s, Thermo Scientific, USA). Partial least square discriminant analysis (PLS-DA) was applied to discriminate between the pure and adulterated Arabica Coffee based on UV–visible spectra data. Several pre-processing spectra were also tested to determine which one provides an appropriate discrimination model. The PLS-DA model has coefficient of correlation 0.89 (R2 = 0.79) with low Root mean square error of calibration (RMSEC) 0.226. The full-cross validation resulted in Q2 = 0.74 and low Root mean squared error of cross-validation (RMSECV) 0.254. Using this PLS-DA model, a total rate of correct classification of 97.5% was obtained in the prediction set. In conclusion, UV–visible spectroscopy in tandem with PLS-DA is a promising analytical method for differentiating between pure and adulterated Arabica ground roasted Coffee.
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partial least squares with discriminant analysis and uv visible spectroscopy for qualitative evaluation of arabica and Robusta Coffee in lampung
2018Co-Authors: Meinilwita Yulia, Aniessa Rinny Asnaning, Sri Waluyo, Diding SuhandyAbstract:Arabica Coffee is considered to be of better quality than Robusta Coffee. It has superior taste and aroma, better than Robusta Coffee. To develop an authentication system for Arabica Coffee, it is highly necessary to discriminate between pure Arabica Coffee and Arabica adulterated with Robusta Coffee. Ground roasted Coffee samples are most difficult to discriminate from each other: visual inspection by the naked eye or even machine vision methods becomes very problematic. For this reason, we here propose a relatively new analytical method based on UV–visible spectroscopy for discrimination the pure and adulterated Arabica ground roasted Coffee. In this study, 100 samples were used as samples with different degrees of adulteration (0%–60% of Robusta concentration in an Arabica–Robusta Coffee blend). Spectral data of samples were acquired using a UV–visible spectrometer in the range of 190–1100 nm (Genesys 10s, Thermo Scientific, USA). Partial least square discriminant analysis (PLS-DA) was applied to discriminate between the pure and adulterated Arabica Coffee based on UV–visible spectra data. Several pre-processing spectra were also tested to determine which one provides an appropriate discrimination model. The PLS-DA model has coefficient of correlation 0.89 (R2 = 0.79) with low Root mean square error of calibration (RMSEC) 0.226. The full-cross validation resulted in Q2 = 0.74 and low Root mean squared error of cross-validation (RMSECV) 0.254. Using this PLS-DA model, a total rate of correct classification of 97.5% was obtained in the prediction set. In conclusion, UV–visible spectroscopy in tandem with PLS-DA is a promising analytical method for differentiating between pure and adulterated Arabica ground roasted Coffee.
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identification of fresh and expired ground roasted Robusta Coffee using uv visible spectroscopy and chemometrics
MATEC Web of Conferences, 2018Co-Authors: Meinilwita Yulia, Diding SuhandyAbstract:The freshness of ground roasted Coffee escapes extremely fast. For this reason, the evaluation of conservation state of ground roasted Coffee must be taken into account for acceptability of Coffee. Unfortunately, it is difficult to discriminate the fresh and expired ground roasted Coffee physically by our naked eyes. Thus, it is desired to develop an analytical method to evaluate the fresh and expired ground roasted Coffee using reliable methods. The objective of this research was to evaluate the potential of UV-visible spectroscopy and chemometrics method for classification of fresh and expired ground roasted Robusta Coffee. A number of 200 samples of Robusta fresh Coffee and 200 samples of Robusta expired Coffee was used. The spectral data were pre-treated using standard normal variate (SNV), moving average smoothing (window: 9) and Savitzky-Golay 2nd derivative (order: 2; window: 11). The analysis data was done statistically using multivariate chemometric techniques, including principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) in the spectral range of 230-400 nm. PCA with PC1 = 94% and PC2 = 4% showed clear clustering of samples (p ≤ 0.05). UV-visible spectroscopy with SIMCA analysis allowed to classify between fresh and expired ground roasted Robusta Coffee with a correct classification rate of 100%.
Nikolai Kuhnert - One of the best experts on this subject based on the ideXlab platform.
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hierarchical scheme for liquid chromatography multi stage spectrometric identification of 3 4 5 triacyl chlorogenic acids in green Robusta Coffee beans
Rapid Communications in Mass Spectrometry, 2010Co-Authors: Rakesh Jaiswal, Nikolai KuhnertAbstract:Liquid chromatography/multi-stage spectrometry (LC/MS(n)) (n = 2-4) has been used to detect and characterize in green Robusta Coffee beans eight quantitatively minor triacyl chlorogenic acids with seven of them not previously reported in nature. These comprise 3,4,5-tricaffeoylquinic acid (Mr 678); 3,5-dicaffeoyl-4-feruloylquinic acid, 3-feruloyl-4,5-dicaffeoylquinic acid and 3,4-dicaffeoyl-5-feruloylquinic acid (Mr 692); 3-caffeoyl-4,5-diferuloylquinic acid and 3,4-diferuloyl-5-caffeoylquinic acid (Mr 706); and 3,4-dicaffeoyl-5-sinapoylquinic acid and 3-sinapoyl-4,5-dicaffeoylquinic acid (Mr 722). Structures have been assigned on the basis of LC/MS(n) patterns of fragmentation. A new hierarchical key for the identification of triacyl quinic acids is presented, based on previously established rules of fragmentation. Fifty-two chlorogenic acids have now been characterized in green Robusta Coffee beans. In this study five samples of green Robusta Coffee beans and fifteen samples of Arabica Coffee beans were analyzed with triacyl chlorogenic acids only found in Robusta Coffee bean extracts. These triacyl chlorogenic acids could be considered as useful phytochemical markers for the identification of Robusta Coffee beans.
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Hierarchical scheme for liquid chromatography/multi-stage spectrometric identification of 3,4,5-triacyl chlorogenic acids in green Robusta Coffee beans
Rapid communications in mass spectrometry : RCM, 2010Co-Authors: Rakesh Jaiswal, Nikolai KuhnertAbstract:Liquid chromatography/multi-stage spectrometry (LC/MS(n)) (n = 2-4) has been used to detect and characterize in green Robusta Coffee beans eight quantitatively minor triacyl chlorogenic acids with seven of them not previously reported in nature. These comprise 3,4,5-tricaffeoylquinic acid (Mr 678); 3,5-dicaffeoyl-4-feruloylquinic acid, 3-feruloyl-4,5-dicaffeoylquinic acid and 3,4-dicaffeoyl-5-feruloylquinic acid (Mr 692); 3-caffeoyl-4,5-diferuloylquinic acid and 3,4-diferuloyl-5-caffeoylquinic acid (Mr 706); and 3,4-dicaffeoyl-5-sinapoylquinic acid and 3-sinapoyl-4,5-dicaffeoylquinic acid (Mr 722). Structures have been assigned on the basis of LC/MS(n) patterns of fragmentation. A new hierarchical key for the identification of triacyl quinic acids is presented, based on previously established rules of fragmentation. Fifty-two chlorogenic acids have now been characterized in green Robusta Coffee beans. In this study five samples of green Robusta Coffee beans and fifteen samples of Arabica Coffee beans were analyzed with triacyl chlorogenic acids only found in Robusta Coffee bean extracts. These triacyl chlorogenic acids could be considered as useful phytochemical markers for the identification of Robusta Coffee beans.
Shahbaz Mushtaq - One of the best experts on this subject based on the ideXlab platform.
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Performance of a process-based model for predicting Robusta Coffee yield at the regional scale in Vietnam
Ecological Modelling, 2021Co-Authors: Louis Kouadio, Philippe Tixier, Vivekananda Byrareddy, Torben Marcussen, Shahbaz Mushtaq, Roger StoneAbstract:Reliable and timely prediction of Robusta Coffee (Coffea canephora Pierre ex A. Froehner) yield is pivotal to the profitability of the Coffee industry worldwide. In this study we assess the performance of a simple process-based model for simulating and predicting Robusta Coffee yield at the regional scale in Vietnam. The model includes the key processes of Coffee growth and development and simulates its response to variation in climate and potential water requirements throughout the growing season. The model was built and evaluated for the major Vietnamese Robusta Coffee-producing provinces Dak Lak, Dak Nong, Gia Lai, Kon Tum, and Lam Dong, using official provincial Coffee yield data and climate station data for the 2001-2014 period, and field data collected during a 10-year (2008-2017) survey. Overall, good agreements were found between the observed and predicted Coffee yields. Root mean square error (RMSE) and mean absolute percentage error (MAPE) values ranged from 0.24 to 0.33 t ha(-1), and 9% to 14%, respectively. Willmott's index of agreement (WI) was greater than or equal to 0.710 in model evaluation steps for three out of five provinces. The relatively low values of WI were found for provinces with relatively low inter-annual yield variability (i.e. Dak Lak and Dak Nong). Moreover, the model was successfully tested using remote sensing satellite and model-based gridded climate data: MAPE values were
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Temperature and rainfall impacts on Robusta Coffee bean characteristics
Climate Risk Management, 2021Co-Authors: Jarrod Kath, Vivekananda Byrareddy, Shahbaz Mushtaq, A.c.w. Craparo, Mario PorcelAbstract:Abstract Robusta Coffee is the primary source of income for millions of smallholder farmers throughout the world’s tropics. The price smallholder farmers can get for their Coffee is strongly influenced by bean characteristics (i.e. beans are of a sufficient size and have minimal defects). Climate is a key determinant of successful Coffee production, but scant research has been undertaken to test and quantify climate impacts on Robusta Coffee bean physical characteristics. Here we investigate how climate relates to the risk of poor Coffee bean characteristics in one of South East Asia’s key Coffee producing areas, the central highlands of Vietnam. We use 5 years (2012–2016) of Coffee bean characteristic data from 60 farms. Hierarchical modelling was used to investigate how rainfall and temperature related to two indicators of Coffee bean characteristics (1) the probability of below average Coffee bean size and (2) the probability of above average Coffee bean defects. Low rainfall ( 80% probability) of below average Coffee bean size. Conversely, high rainfall (>750 mm) and high mean minimum temperature (>22 °C) during harvest (October-December) increased the risk (>75% probability) of above average Coffee bean defects. Various Coffee bean characteristic subcomponents (e.g. insect damage and mouldy beans) and different bean sizes were also examined and were affected by a range of rainfall and temperature predictors across the flowering, growing and harvest seasons. With this information targeted risk-management strategies (e.g. targeted irrigation during hot and dry growing seasons, adjusting harvest timing and employing drying techniques during wet and cold harvest periods) could be developed to minimise the effect of climate conditions that increase the risk of Coffee bean defects. Successfully managing the impacts identified here, could decrease Coffee bean defects and in turn increase the incomes of smallholder Coffee farmers.
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not so robust Robusta Coffee production is highly sensitive to temperature
Global Change Biology, 2020Co-Authors: Jarrod Kath, Vivekananda Byrareddy, Shahbaz Mushtaq, A.c.w. Craparo, Thong Nguyenhuy, Loc Cao, Laurent BossolascoAbstract:Coffea canephora (Robusta Coffee) is the most heat‐tolerant and ‘robust’ Coffee species and therefore considered more resistant to climate change than other types of Coffee production. However, the optimum production range of Robusta has never been quantified, with current estimates of its optimal mean annual temperature range (22–30°C) based solely on the climatic conditions of its native range in the Congo basin, Central Africa. Using 10 years of yield observations from 798 farms across South East Asia coupled with high‐resolution precipitation and temperature data, we used hierarchical Bayesian modeling to quantify Robusta's optimal temperature range for production. Our climate‐based models explained yield variation well across the study area with a cross‐validated mean R2 = .51. We demonstrate that Robusta has an optimal temperature below 20.5°C (or a mean minimum/maximum of ≤16.2/24.1°C), which is markedly lower, by 1.5–9°C than current estimates. In the middle of Robusta's currently assumed optimal range (mean annual temperatures over 25.1°C), Coffee yields are 50% lower compared to the optimal mean of ≤20.5°C found here. During the growing season, every 1°C increase in mean minimum/maximum temperatures above 16.2/24.1°C corresponded to yield declines of ~14% or 350–460 kg/ha (95% credible interval). Our results suggest that Robusta Coffee is far more sensitive to temperature than previously thought. Current assessments, based on Robusta having an optimal temperature range over 22°C, are likely overestimating its suitable production range and its ability to contribute to Coffee production as temperatures increase under climate change. Robusta supplies 40% of the world's Coffee, but its production potential could decline considerably as temperatures increase under climate change, jeopardizing a multi‐billion dollar Coffee industry and the livelihoods of millions of farmers.
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Sustainable Production of Robusta Coffee under a Changing Climate: A 10-Year Monitoring of Fertilizer Management in Coffee Farms in Vietnam and Indonesia
Agronomy, 2019Co-Authors: Vivekananda Byrareddy, Louis Kouadio, Shahbaz Mushtaq, Roger StoneAbstract:Assessing and prescribing fertilizer use is critical to profitable and sustainable Coffee production, and this is becoming a priority concern for the Robusta Coffee industry. In this study, annual survey data of 798 farms across selected Robusta Coffee-producing provinces in Vietnam and Indonesia between 2008 and 2017 were used to comparatively assess the fertilizer management strategies in these countries. Specifically, we aimed to characterize fertilizer use patterns in the key Coffee-growing provinces and discuss the potential for improving nutrient management practices. Four types of chemical (NPK, super phosphate, potassium chloride and urea) and two of natural (compost and lime) fertilizers were routinely used in Vietnam. In Indonesia, NPK and urea were supplemented only with compost. Farmers in Vietnam applied unbalanced quantities of chemical fertilizers (i.e., higher rates than recommended) and at a constant rate between years whereas Indonesian farmers applied well below the recommended rates because of poor accessibility and financial support. The overuse of chemical fertilizers in Vietnam threatens the sustainability of Robusta Coffee farming. Nevertheless, there is a potential for improvement in both countries in terms of nutrient management and sustainability of Robusta Coffee production by adopting the best local fertilizer management practices.
Vivekananda Byrareddy - One of the best experts on this subject based on the ideXlab platform.
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Performance of a process-based model for predicting Robusta Coffee yield at the regional scale in Vietnam
Ecological Modelling, 2021Co-Authors: Louis Kouadio, Philippe Tixier, Vivekananda Byrareddy, Torben Marcussen, Shahbaz Mushtaq, Roger StoneAbstract:Reliable and timely prediction of Robusta Coffee (Coffea canephora Pierre ex A. Froehner) yield is pivotal to the profitability of the Coffee industry worldwide. In this study we assess the performance of a simple process-based model for simulating and predicting Robusta Coffee yield at the regional scale in Vietnam. The model includes the key processes of Coffee growth and development and simulates its response to variation in climate and potential water requirements throughout the growing season. The model was built and evaluated for the major Vietnamese Robusta Coffee-producing provinces Dak Lak, Dak Nong, Gia Lai, Kon Tum, and Lam Dong, using official provincial Coffee yield data and climate station data for the 2001-2014 period, and field data collected during a 10-year (2008-2017) survey. Overall, good agreements were found between the observed and predicted Coffee yields. Root mean square error (RMSE) and mean absolute percentage error (MAPE) values ranged from 0.24 to 0.33 t ha(-1), and 9% to 14%, respectively. Willmott's index of agreement (WI) was greater than or equal to 0.710 in model evaluation steps for three out of five provinces. The relatively low values of WI were found for provinces with relatively low inter-annual yield variability (i.e. Dak Lak and Dak Nong). Moreover, the model was successfully tested using remote sensing satellite and model-based gridded climate data: MAPE values were
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Temperature and rainfall impacts on Robusta Coffee bean characteristics
Climate Risk Management, 2021Co-Authors: Jarrod Kath, Vivekananda Byrareddy, Shahbaz Mushtaq, A.c.w. Craparo, Mario PorcelAbstract:Abstract Robusta Coffee is the primary source of income for millions of smallholder farmers throughout the world’s tropics. The price smallholder farmers can get for their Coffee is strongly influenced by bean characteristics (i.e. beans are of a sufficient size and have minimal defects). Climate is a key determinant of successful Coffee production, but scant research has been undertaken to test and quantify climate impacts on Robusta Coffee bean physical characteristics. Here we investigate how climate relates to the risk of poor Coffee bean characteristics in one of South East Asia’s key Coffee producing areas, the central highlands of Vietnam. We use 5 years (2012–2016) of Coffee bean characteristic data from 60 farms. Hierarchical modelling was used to investigate how rainfall and temperature related to two indicators of Coffee bean characteristics (1) the probability of below average Coffee bean size and (2) the probability of above average Coffee bean defects. Low rainfall ( 80% probability) of below average Coffee bean size. Conversely, high rainfall (>750 mm) and high mean minimum temperature (>22 °C) during harvest (October-December) increased the risk (>75% probability) of above average Coffee bean defects. Various Coffee bean characteristic subcomponents (e.g. insect damage and mouldy beans) and different bean sizes were also examined and were affected by a range of rainfall and temperature predictors across the flowering, growing and harvest seasons. With this information targeted risk-management strategies (e.g. targeted irrigation during hot and dry growing seasons, adjusting harvest timing and employing drying techniques during wet and cold harvest periods) could be developed to minimise the effect of climate conditions that increase the risk of Coffee bean defects. Successfully managing the impacts identified here, could decrease Coffee bean defects and in turn increase the incomes of smallholder Coffee farmers.
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not so robust Robusta Coffee production is highly sensitive to temperature
Global Change Biology, 2020Co-Authors: Jarrod Kath, Vivekananda Byrareddy, Shahbaz Mushtaq, A.c.w. Craparo, Thong Nguyenhuy, Loc Cao, Laurent BossolascoAbstract:Coffea canephora (Robusta Coffee) is the most heat‐tolerant and ‘robust’ Coffee species and therefore considered more resistant to climate change than other types of Coffee production. However, the optimum production range of Robusta has never been quantified, with current estimates of its optimal mean annual temperature range (22–30°C) based solely on the climatic conditions of its native range in the Congo basin, Central Africa. Using 10 years of yield observations from 798 farms across South East Asia coupled with high‐resolution precipitation and temperature data, we used hierarchical Bayesian modeling to quantify Robusta's optimal temperature range for production. Our climate‐based models explained yield variation well across the study area with a cross‐validated mean R2 = .51. We demonstrate that Robusta has an optimal temperature below 20.5°C (or a mean minimum/maximum of ≤16.2/24.1°C), which is markedly lower, by 1.5–9°C than current estimates. In the middle of Robusta's currently assumed optimal range (mean annual temperatures over 25.1°C), Coffee yields are 50% lower compared to the optimal mean of ≤20.5°C found here. During the growing season, every 1°C increase in mean minimum/maximum temperatures above 16.2/24.1°C corresponded to yield declines of ~14% or 350–460 kg/ha (95% credible interval). Our results suggest that Robusta Coffee is far more sensitive to temperature than previously thought. Current assessments, based on Robusta having an optimal temperature range over 22°C, are likely overestimating its suitable production range and its ability to contribute to Coffee production as temperatures increase under climate change. Robusta supplies 40% of the world's Coffee, but its production potential could decline considerably as temperatures increase under climate change, jeopardizing a multi‐billion dollar Coffee industry and the livelihoods of millions of farmers.
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Sustainable Production of Robusta Coffee under a Changing Climate: A 10-Year Monitoring of Fertilizer Management in Coffee Farms in Vietnam and Indonesia
Agronomy, 2019Co-Authors: Vivekananda Byrareddy, Louis Kouadio, Shahbaz Mushtaq, Roger StoneAbstract:Assessing and prescribing fertilizer use is critical to profitable and sustainable Coffee production, and this is becoming a priority concern for the Robusta Coffee industry. In this study, annual survey data of 798 farms across selected Robusta Coffee-producing provinces in Vietnam and Indonesia between 2008 and 2017 were used to comparatively assess the fertilizer management strategies in these countries. Specifically, we aimed to characterize fertilizer use patterns in the key Coffee-growing provinces and discuss the potential for improving nutrient management practices. Four types of chemical (NPK, super phosphate, potassium chloride and urea) and two of natural (compost and lime) fertilizers were routinely used in Vietnam. In Indonesia, NPK and urea were supplemented only with compost. Farmers in Vietnam applied unbalanced quantities of chemical fertilizers (i.e., higher rates than recommended) and at a constant rate between years whereas Indonesian farmers applied well below the recommended rates because of poor accessibility and financial support. The overuse of chemical fertilizers in Vietnam threatens the sustainability of Robusta Coffee farming. Nevertheless, there is a potential for improvement in both countries in terms of nutrient management and sustainability of Robusta Coffee production by adopting the best local fertilizer management practices.
Meinilwita Yulia - One of the best experts on this subject based on the ideXlab platform.
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Partial least squares with discriminant analysis and UV– visible spectroscopy for qualitative evaluation of Arabica and Robusta Coffee in Lampung
2018Co-Authors: Meinilwita Yulia, Aniessa Rinny Asnaning, Sri Waluyo, Diding SuhandyAbstract:Arabica Coffee is considered to be of better quality than Robusta Coffee. It has superior taste and aroma, better than Robusta Coffee. To develop an authentication system for Arabica Coffee, it is highly necessary to discriminate between pure Arabica Coffee and Arabica adulterated with Robusta Coffee. Ground roasted Coffee samples are most difficult to discriminate from each other: visual inspection by the naked eye or even machine vision methods becomes very problematic. For this reason, we here propose a relatively new analytical method based on UV–visible spectroscopy for discrimination the pure and adulterated Arabica ground roasted Coffee. In this study, 100 samples were used as samples with different degrees of adulteration (0%–60% of Robusta concentration in an Arabica–Robusta Coffee blend). Spectral data of samples were acquired using a UV–visible spectrometer in the range of 190–1100 nm (Genesys 10s, Thermo Scientific, USA). Partial least square discriminant analysis (PLS-DA) was applied to discriminate between the pure and adulterated Arabica Coffee based on UV–visible spectra data. Several pre-processing spectra were also tested to determine which one provides an appropriate discrimination model. The PLS-DA model has coefficient of correlation 0.89 (R2 = 0.79) with low Root mean square error of calibration (RMSEC) 0.226. The full-cross validation resulted in Q2 = 0.74 and low Root mean squared error of cross-validation (RMSECV) 0.254. Using this PLS-DA model, a total rate of correct classification of 97.5% was obtained in the prediction set. In conclusion, UV–visible spectroscopy in tandem with PLS-DA is a promising analytical method for differentiating between pure and adulterated Arabica ground roasted Coffee.
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partial least squares with discriminant analysis and uv visible spectroscopy for qualitative evaluation of arabica and Robusta Coffee in lampung
2018Co-Authors: Meinilwita Yulia, Aniessa Rinny Asnaning, Sri Waluyo, Diding SuhandyAbstract:Arabica Coffee is considered to be of better quality than Robusta Coffee. It has superior taste and aroma, better than Robusta Coffee. To develop an authentication system for Arabica Coffee, it is highly necessary to discriminate between pure Arabica Coffee and Arabica adulterated with Robusta Coffee. Ground roasted Coffee samples are most difficult to discriminate from each other: visual inspection by the naked eye or even machine vision methods becomes very problematic. For this reason, we here propose a relatively new analytical method based on UV–visible spectroscopy for discrimination the pure and adulterated Arabica ground roasted Coffee. In this study, 100 samples were used as samples with different degrees of adulteration (0%–60% of Robusta concentration in an Arabica–Robusta Coffee blend). Spectral data of samples were acquired using a UV–visible spectrometer in the range of 190–1100 nm (Genesys 10s, Thermo Scientific, USA). Partial least square discriminant analysis (PLS-DA) was applied to discriminate between the pure and adulterated Arabica Coffee based on UV–visible spectra data. Several pre-processing spectra were also tested to determine which one provides an appropriate discrimination model. The PLS-DA model has coefficient of correlation 0.89 (R2 = 0.79) with low Root mean square error of calibration (RMSEC) 0.226. The full-cross validation resulted in Q2 = 0.74 and low Root mean squared error of cross-validation (RMSECV) 0.254. Using this PLS-DA model, a total rate of correct classification of 97.5% was obtained in the prediction set. In conclusion, UV–visible spectroscopy in tandem with PLS-DA is a promising analytical method for differentiating between pure and adulterated Arabica ground roasted Coffee.
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identification of fresh and expired ground roasted Robusta Coffee using uv visible spectroscopy and chemometrics
MATEC Web of Conferences, 2018Co-Authors: Meinilwita Yulia, Diding SuhandyAbstract:The freshness of ground roasted Coffee escapes extremely fast. For this reason, the evaluation of conservation state of ground roasted Coffee must be taken into account for acceptability of Coffee. Unfortunately, it is difficult to discriminate the fresh and expired ground roasted Coffee physically by our naked eyes. Thus, it is desired to develop an analytical method to evaluate the fresh and expired ground roasted Coffee using reliable methods. The objective of this research was to evaluate the potential of UV-visible spectroscopy and chemometrics method for classification of fresh and expired ground roasted Robusta Coffee. A number of 200 samples of Robusta fresh Coffee and 200 samples of Robusta expired Coffee was used. The spectral data were pre-treated using standard normal variate (SNV), moving average smoothing (window: 9) and Savitzky-Golay 2nd derivative (order: 2; window: 11). The analysis data was done statistically using multivariate chemometric techniques, including principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) in the spectral range of 230-400 nm. PCA with PC1 = 94% and PC2 = 4% showed clear clustering of samples (p ≤ 0.05). UV-visible spectroscopy with SIMCA analysis allowed to classify between fresh and expired ground roasted Robusta Coffee with a correct classification rate of 100%.
