Feasibility of NIR spectroscopy coupled with chemometrics for classification of Brazilian specialty coffee

Mutz, Yhan S.; Rosário, Denes Kaic Alves do; Galvan, Diego; Schwan, Rosane Freitas; Bernardes, Patrícia Campos; Conté-Júnior, Carlos Adam

doi:10.1016/j.foodcont.2023.109696

Cited by 17 publications

(8 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Near and mid-infrared spectral studies have evolved as a valid and promising analytical method for objectively assessing coffee quality features over the last two decades [19][20][21][22][23]. These studies demonstrate that near and mid-infrared techniques have enormous potential for rapidly obtaining information about the chemical composition and related aspects of coffee.…”

Section: Infrared Spectroscopymentioning

confidence: 97%

“…Soft independent modeling of class analogies (SIMCA) model was built by Mutz et al [22] employing a portable near-infrared (NIR) spectrometer and a dataset of 182 coffee samples. The goal was to distinguish between distinct coffee qualities.…”

Section: Infrared Spectroscopymentioning

confidence: 99%

“…There are several studies in the literature designed to classify Arabic coffee and coffee, in general, using various sources of information and technologies, including coffee bean images [15][16][17][18], infrared spectroscopy [19][20][21][22][23], and electronic nose [24][25][26]. To the best of our knowledge, none of them, however, address the chemical classification of Arabic coffee.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants

et al. 2023

View full text Add to dashboard Cite

This study investigates the classification of Arabic coffee into three major variations (light, medium, and dark) using simulated data gathered from the actual measurements of color information, antioxidant laboratory testing, and chemical composition tests. The goal is to overcome the restrictions of limited real-world data availability and the high costs involved with laboratory testing. The Monte Carlo approach is used to generate new samples for each type of Arabic coffee using the mean values and standard deviations of publicly available data. Using these simulated data, multiple machine-learning algorithms are used to classify Arabic coffee, while also investigating the importance of features in identifying the key chemical components. The findings emphasize the importance of color information in accurately recognizing Arabic coffee types. However, depending purely on antioxidant information results in poor classification accuracy due to increased data complexity and classifier variability. The chemical composition information, on the other hand, has exceptional discriminatory power, allowing faultless classification on its own. Notably, particular characteristics like crude protein and crude fiber show high relationships and play an important role in coffee type classification. Based on these findings, it is suggested that a mobile application be developed that uses image recognition to examine coffee color while also providing chemical composition information. End users, especially consumers, would be able to make informed judgments regarding their coffee preferences.

show abstract

Section: Infrared Spectroscopymentioning

confidence: 97%

Section: Infrared Spectroscopymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Several approaches have been utilized to perform the classification of several specialty coffees based on different geographical origins using high-performance liquid chromatography (HPLC), near-infrared (NIR), Raman, Terahertz, fluorescence, and UV-visible spectroscopy methods [6][7][8][9][10][11][12]. Most of these methods utilize benchtop-type instrumentation.…”

Section: Introductionmentioning

confidence: 99%

Portable fluorescence spectroscopy for single-origin coffee authentication with 365 nm wavelength as excitation source

Yulia,

Analianasari,

Widodo

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Specialty coffee with geographical indications (GI) labels has been regarded as one of the most expensive commercial coffee in Indonesia. In this research, we utilize a new approach to classify specialty coffee from Indonesia with different geographical origins using portable fluorescence spectroscopy. For samples, 80 coffee samples with 1 gram of each sample were provided: 40 samples of Gayo arabica coffee (origin: Gayo, Aceh), and 40 samples of Java Mocha robusta coffee (origin: Java). All coffee samples were subjected to the following sample preparation: roasting (at 200°C for 15 min), grinding, and sieving using mesh size 40 to obtain samples with a homogenous size of 420 micrometers. The extraction of coffee samples was done by using hot distilled water. After extraction and dilution, coffee samples were injected into the 10 mm standard cell. The fluorescence spectral acquisition was performed by using a GoyaLab fluorescence spectrometer. This spectrometer has four LED lamps with 365 nm peaks used as excitation sources. The principal component analysis (PCA) of preprocessed spectral data results showed that most coffee samples could be differentiated into two different clusters. The proposed method is easy, and affordable to be adopted by many coffee industries in Indonesia.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Feasibility of NIR spectroscopy coupled with chemometrics for classification of Brazilian specialty coffee

Cited by 17 publications

References 42 publications

Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants

Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants

Portable fluorescence spectroscopy for single-origin coffee authentication with 365 nm wavelength as excitation source

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

Contact Info

Product

Resources

About