The Potential of NIR Spectroscopy and Chemometrics to Discriminate Roast Degrees and Predict Volatiles in Coffee
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, a...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/29/2/318 |
| _version_ | 1797339571558547456 |
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| author | Stella Green Emily Fanning Joy Sim Graham T. Eyres Russell Frew Biniam Kebede |
| author_facet | Stella Green Emily Fanning Joy Sim Graham T. Eyres Russell Frew Biniam Kebede |
| author_sort | Stella Green |
| collection | DOAJ |
| description | 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 R<sup>2</sup> 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. |
| first_indexed | 2024-03-08T09:50:07Z |
| format | Article |
| id | doaj.art-bcf1911cf7e0463a8e32d73e03c9bc1a |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-08T09:50:07Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-bcf1911cf7e0463a8e32d73e03c9bc1a2024-01-29T14:07:30ZengMDPI AGMolecules1420-30492024-01-0129231810.3390/molecules29020318The Potential of NIR Spectroscopy and Chemometrics to Discriminate Roast Degrees and Predict Volatiles in CoffeeStella Green0Emily Fanning1Joy Sim2Graham T. Eyres3Russell Frew4Biniam Kebede5Department of Food Science, University of Otago, Dunedin 9054, New ZealandDepartment of Food Science, University of Otago, Dunedin 9054, New ZealandDepartment of Food Science, University of Otago, Dunedin 9054, New ZealandDepartment of Food Science, University of Otago, Dunedin 9054, New ZealandOritain Global Limited, 167 High Street, Dunedin 9016, New ZealandDepartment of Food Science, University of Otago, Dunedin 9054, New ZealandThis 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 R<sup>2</sup> 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.https://www.mdpi.com/1420-3049/29/2/318coffeeroastingNIRrapid analysisvolatileprediction |
| spellingShingle | Stella Green Emily Fanning Joy Sim Graham T. Eyres Russell Frew Biniam Kebede The Potential of NIR Spectroscopy and Chemometrics to Discriminate Roast Degrees and Predict Volatiles in Coffee Molecules coffee roasting NIR rapid analysis volatile prediction |
| title | The Potential of NIR Spectroscopy and Chemometrics to Discriminate Roast Degrees and Predict Volatiles in Coffee |
| title_full | The Potential of NIR Spectroscopy and Chemometrics to Discriminate Roast Degrees and Predict Volatiles in Coffee |
| title_fullStr | The Potential of NIR Spectroscopy and Chemometrics to Discriminate Roast Degrees and Predict Volatiles in Coffee |
| title_full_unstemmed | The Potential of NIR Spectroscopy and Chemometrics to Discriminate Roast Degrees and Predict Volatiles in Coffee |
| title_short | The Potential of NIR Spectroscopy and Chemometrics to Discriminate Roast Degrees and Predict Volatiles in Coffee |
| title_sort | potential of nir spectroscopy and chemometrics to discriminate roast degrees and predict volatiles in coffee |
| topic | coffee roasting NIR rapid analysis volatile prediction |
| url | https://www.mdpi.com/1420-3049/29/2/318 |
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