Recognition of coffee roasting degree using a computer vision system

Leme, Dimas Samid; Silva, Sabrina Alves da; Barbosa, Bruno Henrique Groenner; Borém, Flávio Meira; Pereira, Rosemary Gualberto Fonseca Alvarenga

doi:10.1016/j.compag.2018.11.029

Cited by 42 publications

(27 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In particular, the colour change of the grain through the process is due to the formation of melanoidins, which are dark molecules resulting from the Maillard and caramelization reactions. Both chemical reactions are closely related to the final sensory properties, so the correct definition of the degree of roasting of coffee is of paramount importance, since it directly influences the quality of the beverage [5]. On the other hand, there are many ways to define the degree of roasting (colour development, moisture loss, content of chlorogenic acids, etc.…”

Section: Introductionmentioning

confidence: 99%

“…In the coffee industry, due to the variations in the raw material and environmental conditions, the roasting process depends mostly on toaster masters that, based on their experience, use subjective and empirical indicators (mostly sensorial factors: smell, sight, and hearing) to decide temperature-time profile along the roasting process to achieve the desired toasting degree. In the best of the cases, they may use colorimeters or spectrophotometers that are economically unfeasible for small coffee maker industries [5,12]. Recently, some efforts have been carried out to technify the tracking of the roasting degree along the roasting process, such as classification methods by hyperspectral imaging analysis [12][13][14][15], coffee roasting acoustics [16], and prediction of brightness and surface area variations [17].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cascade Control of Coffee Roasting Degree in a Spouted Bed Batch Process Based on a Real-Time Imaging Analysis

Gómez-Gómez

Guatemala-Morales

García-Sandoval

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

On this work, the experimental implementation of a cascade control algorithm to regulate coffee roasting degree in a batch spouted bed process is presented. The control algorithm is composed of an inner control loop that regulates the temperature of the hot air inflow used to roast coffee grains inside a spouted bed, while an outer control loop, based on imaging processing techniques, tracks on real time the coffee roasting degree and decides if the inflow air temperature must be modified. To achieve this goal, a colour-matching algorithm is used to compare a colour spectrum obtained from images acquired on real time, from a peephole on the spouted bed, with the colour spectrums of several reference images with different degrees of roasting. Match scores are computed based on the similarity between the colour spectrums. With the match scores, a roasting index is finally calculated to assess the degree of roasting, allowing to automatically track the roasting progress to decide if the batch roasting process has achieved the desired roasting degree. The experimental results show that the control scheme is able to robustly achieve the desired roasting degree with excellent effectiveness.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cascade Control of Coffee Roasting Degree in a Spouted Bed Batch Process Based on a Real-Time Imaging Analysis

Gómez-Gómez

Guatemala-Morales

García-Sandoval

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…There are currently a large number of studies about digital image processing techniques and methodologies for classification of nutritional and physical defects of various parts of the plant, such as root, stem, flower, fruit and leaf (BARESEL et al, 2017;LEME et al, 2019;PATEL et al, 2012;VASSALLO-BARCO et al, 2017). However, most of the research is focused on maize, rice and vegetable crops, with a lack of information in coffee production (MAJUMDAR et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Open Source Iterative Bayesian Classifier Algorithm for Quality Assessment of Processed Coffee Beans

Santos

Telles

Rosas

et al. 2020

NAT

View full text Add to dashboard Cite

The selection of coffee beans plays a key role in the product's final quality. After processing, coffee beans are classified according to their quantity of defects. Traditionally this classification is performed manually, which makes the process laborious and time-consuming. This problem can be solved with digital image processing techniques since defective grains have unique visual characteristics. Considering the difficulty of manual classification of the defects, this study aimed to elaborate a Bayesian classifier algorithm to identify these defects in benefited coffee beans, based on its shape and color. To do so, 630 grains of arabica coffee were used, composing eight images in total. The algorithm aimed to classify four classes, which were: regular beans, normal broken beans, black beans, and black broken beans. In order to evaluate the accuracy of the classifier algorithm, it was calculated the global accuracy and the Kappa coefficient, which allows inferring if the classifier is better than a random classification. It was concluded that the developed algorithm presented a global accuracy of 76% and kappa equals to 0.6. Also, the proposed methodology showed great potential for application in the quality evaluation of other products, whose shape and spectral parameters are relevant in evaluating its quality.

show abstract

“…Electronic Eyes have proven advantageous in various foodstuff areas, such as process monitoring, quality control, freshness assessment, shelf life investigation, and authenticity assessment. Over the last few years, it has been possible to find applications related to the evaluation of the quality in alcoholic beverages [12,13], fruit ripening analysis [14][15][16], vegetables [17,18], cereals [19,20], meat products [21,22], fish and seafood [23][24][25], coffee [26,27], tea [28,29], olive oil [30,31], and others [32,33].…”

Section: Introductionmentioning

confidence: 99%

Electronic Eye for Identification of Tequila Samples

Gómez

Bueno

Gutiérrez

2020

The 1st International Electronic Conference on Biosensors

View full text Add to dashboard Cite

The present work reports the potential of a bio-inspired system based on spectrometry, also known as Electronic Eye (EE), capable of detecting different Tequila samples. The reported system analyzes small volumes of Tequila Reposado and Blanco by calculating samples’ absorbances, using a low cost and portable instrumentation employing a CCD camera. The absorbance imaging method consisted of exciting samples with light passes through an 8MP camera connected to a Raspberry Pi Card. The camera’s image data are analyzed using MATLAB 2018b to be represented in Red, Green and Blue (RGB) components for each pixel, in order to get an approximation of the absorbance and the Surface Color Index (Isc) associated with sample concentration. Using the developed EE, it was possible to identify seven different kinds and brands of Tequila. From the obtained results, it was observed that the average absorbance of the Tequila Reposado was greater than the absorbance of the Tequila Blanco. Otherwise, with the Isc, the Tequila Blanco color index is lower concerning the Tequila Reposado’s. Finally, the EE allowed the identification of Tequila samples with reproducibility and repeatability.

show abstract

Recognition of coffee roasting degree using a computer vision system

Cited by 42 publications

References 23 publications

Cascade Control of Coffee Roasting Degree in a Spouted Bed Batch Process Based on a Real-Time Imaging Analysis

Cascade Control of Coffee Roasting Degree in a Spouted Bed Batch Process Based on a Real-Time Imaging Analysis

Open Source Iterative Bayesian Classifier Algorithm for Quality Assessment of Processed Coffee Beans

Electronic Eye for Identification of Tequila Samples

Contact Info

Product

Resources

About