Monitoring Chemical Changes of Coffee Beans During Roasting Using Real-time NIR Spectroscopy and Chemometrics

Grassi, Silvia; Giraudo, Alessandro; Novara, Chiara; Cavallini, Nicola; Geobaldo, Francesco; Casiraghi, E.; Savorani, Francesco

doi:10.1007/s12161-023-02473-w

Cited by 15 publications

(7 citation statements)

References 34 publications

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“…There are different types of controlled fermentation used by producers in which yeasts are used to degrade the coffee mucilage without affecting the quality of the bean. However, the pH of coffee is also influenced by factors such as the coffee variety and the geographical location of production [42]. The measurement of • Brix is an indication of the concentration of soluble solids; most of these soluble solids are sucrose.…”

Section: Discussionmentioning

confidence: 99%

Olfactory Evaluation of Geisha Coffee from Panama Using Electronic Nose

Santamaría,

Meléndez,

Arroyo

et al. 2023

Chemosensors

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Geisha arabica coffee from Panama is featured in the world specialty coffee market. Its quality is assessed by sensory analysis with a panel of experts over several days. However, there is a risk of commercial fraud where cheaper coffees are mixed with pure specialty coffees. For these reasons, having an electronic nose (EN) device proves advantageous in supporting the cupping panel. It allows a greater number of fragrance and aroma analyses to be conducted per day, providing more objective results. In this study, an experimental EN equipped with a metal oxide semiconductor (MOS) gas sensor array was used. Olfactory evaluation of ground and infused Geisha coffee of different roast levels, brewing process, and purity was studied by EN, sensory analysis and chromatography. The sensory analysis perceived significant differences in fragrance and aromas in the light and dark roast levels of the samples. A total of 57 volatile organic compounds (VOC) were studied by gas chromatography. The EN data were analyzed chemometrically with principal component analysis (PCA) and predictive partial least squares (PLS). The data variances for two components were explained with values greater than 82%. The EN demonstrated its ability to differentiate the three levels of roasting, two production processes, and adulteration in the analyzed samples.

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Section: Discussionmentioning

confidence: 99%

Olfactory Evaluation of Geisha Coffee from Panama Using Electronic Nose

Santamaría,

Meléndez,

Arroyo

et al. 2023

Chemosensors

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show abstract

“…In this paper, assuming that the data obtained from the laboratory is represented as model Q, the spectral information obtained from whole leaf scanning is denoted as X, the spectral information obtained from leaf fragment scanning is denoted as Z, and the tobacco chemical composition values measured using continuous flow method are denoted as Y. Based on partial least squares (PLS) modeling, we obtain the model equation ( 2), and then we can calculate the parameters of the model equation (1) using equation (3). Equation ( 1) represents the predictive model we need.…”

Section: Migration Methodsmentioning

confidence: 99%

“…This method only requires obtaining the near-infrared spectral data of the sample, offering fast detection, high timeliness, and non-destructive testing of the sample. Currently, NIR analysis technology has been applied in numerous fields, including agriculture, petrochemicals, pharmaceuticals, food, and clinical settings [2][3][4][5] .…”

Section: Introductionmentioning

confidence: 99%

Near-infrared spectroscopy based on transfer learning, detection of tobacco chemical composition

Wang,

Tian,

Gao

et al. 2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

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The use of near-infrared spectroscopy enables rapid and efficient detection of tobacco leaf chemical components. However, changes in environmental and climatic factors can affect the accuracy of the originally established prediction model. To reduce the workload involved in re-sampling and modeling, a more stable prediction model was established using transfer learning, transferring valuable information from fragmented leaf spectra to enhance the stability of the whole-leaf spectral measurement model. New prediction models were developed for total sugars, reducing sugars, total alkaloids, potassium, and chlorine contents. Experimental results show that the mathematical model established through transfer learning provides highly consistent predictions with results obtained through continuous flow methods. It is more stable than existing models and can be directly applied for model transfer without the need for re-sampling. It can be used for the analysis of tobacco leaf chemical components in the tobacco production process, meeting the requirements for rapid detection of routine chemical components in tobacco leaves. The model exhibits high accuracy in predicting new samples and can meet the demands of tobacco production companies.

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“…Recent studies have demonstrated growing interest in the application of NIR coupled with chemometrics for analyzing coffee samples, encompassing areas such as geographical origin discrimination [ 9 , 17 , 18 ], quality classification [ 19 ], monitoring chemical changes during roasting [ 20 ], predicting stable isotopes and trace elements [ 21 ], and predicting sensory cupping scores [ 15 , 22 ]. While NIR spectra have been employed to predict roast degrees both online and inline, no studies have incorporated initial data from GC-MS to gain additional insights into the volatile composition of different roast degrees, with the ultimate goal of roast degree discrimination.…”

Section: Introductionmentioning

confidence: 99%

The Potential of NIR Spectroscopy and Chemometrics to Discriminate Roast Degrees and Predict Volatiles in Coffee

Green,

Fanning,

Sim

et al. 2024

Molecules

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This study aimed to establish a rapid and practical method for monitoring and predicting volatile compounds during coffee roasting using near-infrared (NIR) spectroscopy coupled with chemometrics. Washed Arabica coffee beans from Ethiopia and Congo were roasted to industry-validated light, medium, and dark degrees. Concurrent analysis of the samples was performed using gas chromatography-mass spectrometry (GC-MS) and NIR spectroscopy, generating datasets for partial least squares (PLS) regression analysis. The results showed that NIR spectroscopy successfully differentiated the differently roasted samples, similar to the discrimination achieved by GC-MS. This finding highlights the potential of NIR spectroscopy as a rapid tool for monitoring and standardizing the degree of coffee roasting in the industry. A PLS regression model was developed using Ethiopian samples to explore the feasibility of NIR spectroscopy to indirectly measure the volatiles that are important in classifying the roast degree. For PLSR, the data underwent autoscaling as a preprocessing step, and the optimal number of latent variables (LVs) was determined through cross-validation, utilizing the root mean squared error (RMSE). The model was further validated using Congo samples and successfully predicted (with R2 values > 0.75 and low error) over 20 volatile compounds, including furans, ketones, phenols, and pyridines. Overall, this study demonstrates the potential of NIR spectroscopy as a practical and rapid method to complement current techniques for monitoring and predicting volatile compounds during the coffee roasting process.

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Monitoring Chemical Changes of Coffee Beans During Roasting Using Real-time NIR Spectroscopy and Chemometrics

Cited by 15 publications

References 34 publications

Olfactory Evaluation of Geisha Coffee from Panama Using Electronic Nose

Olfactory Evaluation of Geisha Coffee from Panama Using Electronic Nose

Near-infrared spectroscopy based on transfer learning, detection of tobacco chemical composition

The Potential of NIR Spectroscopy and Chemometrics to Discriminate Roast Degrees and Predict Volatiles in Coffee

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