Estimation of percentage of impurities in coffee using a computer vision system

ABSTRACT The quality and price of coffee drinks can be affected by contamination with impurities during roasting and grinding. Methods that enable quality control of marketed products are important to meet the standards required by consumers and the industry. The purpose of this study was to estimate the percentage of impurities contained in coffee using textural and colorimetric descriptors obtained from digital images. Arabica coffee beans (Coffea arabica L.) at 100% purity were subjected to roasting and grinding processes, and the initially pure ground coffee was gradually contaminated with impurities. Digital images were collected from coffee samples with 0, 10, 30, 50, and 70% impurities. From the images, textural descriptors of the histograms (mean, standard deviation, entropy, uniformity, and third moment) and colorimetric descriptors (RGB color space and HSI color space) were obtained. The principal component regression (PCR) method was applied to the data group of textural and colorimetric descriptors for the development of linear models to estimate coffee impurities. The selected models for the textural descriptors data group and the colorimetric descriptors data group were composed of two and three principal components, respectively. The model from the colorimetric descriptors showed a greater capacity to estimate the percentage of impurities in coffee when compared to the model from the textural descriptors.

Genetic Variability and Correlation of Biochemical and Sensory Characteristics of Coffee

Organoleptic and biochemical attributes in the coffee bean determine the final cup quality of coffee which is a critical factor in the price determination of coffee in the market. The study aimed at determining the genetic variability of the green coffee bean. The trial sites were located at Siaya and Busia counties in Kenya. Nineteen different genotypes were established and included Arabusta coffee hybrids, backcrosses of Arabica to tetraploid Robusta, Arabica coffee, Robusta coffee, and Arabusta coffee. Randomized Complete Block Design with three replications in each site was used in conducting the experiment. The coffee beans were harvested in the year 2018 and extraction and calculation of sucrose, trigonelline, caffeine, and chlorogenic acids was carried using the recommended methods. The cupping procedure involved the use of five judges in assessing the flavor, aroma, balance, overall standard, acidity, body, and aftertaste of the roasted coffee beans. The sensory evaluation used the Specialty Coffee Association (SCA) method. There were significant variations recorded for the traits that were measured. All the traits were highly heritable registering values of > 50% for heritability whereby, caffeine and oil were highly heritable traits with 90.8% and 88.9% respectively. Oil had a high phenotypic coefficient of variation, genotypic variation, and response values when compared to the other traits. All the organoleptic traits were positively correlated with sucrose, trigonelline, and oil but the correlation with caffeine and chlorogenic acids was negative. The genotypic effects contributed largely to the high heritability recorded with a low influence from the environmental factors.

Antioxidant and Sensory Assessment of Innovative Coffee Blends of Reduced Caffeine Content

Considering the current trend in the global coffee market, which involves an increased demand for decaffeinated coffee, the aim of the present study was to formulate coffee blends with reduced caffeine content, but with pronounced antioxidant and attractive sensory properties. For this purpose, green and roasted Arabica and Robusta coffee beans of different origins were subjected to the screening analysis of their chemical and bioactive composition using standard AOAC, spectrophotometric and chromatographic methods. From roasted coffee beans, espresso, Turkish and filter coffees were prepared, and their sensory evaluation was performed using a 10-point hedonic scale. The results showed that Arabica coffee beans were richer in sucrose and oil, while Robusta beans were characterized by higher content of all determined bioactive parameters. Among all studied samples, the highest content of 3-O-caffeoylquinic acid (14.09 mg g−1 dmb), 4-O-caffeoylquinic acid (8.23 mg g−1 dmb) and 5-O-caffeoylquinic acid (4.65 mg g−1 dmb), as well as caffeine (22.38 mg g−1 dmb), was detected in roasted Robusta beans from the Minas Gerais region of Brazil, which were therefore used to formulate coffee blends with reduced caffeine content. Robusta brews were found to be more astringent and recognized as more sensorily attractive, while Arabica decaffeinated brews were evaluated as more bitter. The obtained results point out that coffee brews may represent a significant source of phenolic compounds, mainly caffeoylquinic acids, with potent antioxidant properties, even if they have reduced caffeine content.

Effect of the Post-Harvest Processing on Protein Modification in Green Coffee Beans by Phenolic Compounds

The protein fraction, important for coffee cup quality, is modified during post-harvest treatment prior to roasting. Proteins may interact with phenolic compounds, which constitute the major metabolites of coffee, where the processing affects these interactions. This allows the hypothesis that the proteins are denatured and modified via enzymatic and/or redox activation steps. The present study was initiated to encompass changes in the protein fraction. The investigations were limited to major storage protein of green coffee beans. Fourteen Coffea arabica samples from various processing methods and countries were used. Different extraction protocols were compared to maintain the status quo of the protein modification. The extracts contained about 4–8 µg of chlorogenic acid derivatives per mg of extracted protein. High-resolution chromatography with multiple reaction monitoring was used to detect lysine modifications in the coffee protein. Marker peptides were allocated for the storage protein of the coffee beans. Among these, the modified peptides K.FFLANGPQQGGK.E and R.LGGK.T of the α-chain and R.ITTVNSQK.I and K.VFDDEVK.Q of β-chain were detected. Results showed a significant increase (p < 0.05) of modified peptides from wet processed green beans as compared to the dry ones. The present study contributes to a better understanding of the influence of the different processing methods on protein quality and its role in the scope of coffee cup quality and aroma.

Improvement of Robusta Coffee Aroma with L-leucine Powder

L-leucine powder (LP) were added to improve the aroma of Robusta coffee beans. Treatment was a short soaking (M1) or spraying procedure (M2), then LP was added at varying levels up to 3% (w/w). All samples were roasted (240 °C/15 min) and extracted using an espresso machine. Volatile compounds were analysed by solid-phase microextraction−gas chromatography−mass selective detection. Thirty volatile compounds (6 pyrroles, 8 pyrazines, 3 phenols, 9 furans, 2 ketones, 2 aldehydes) were analysed. In 15 coffee samples, the levels of total volatile compounds (based on peak area ratios) ranged from 8.9 (M1-1) to 15.5 (non-treated Robusta: NTR). Robusta coffee has lower levels of bitter aroma compounds when pre-treated with LP. The sum of bitter volatiles (phenols, pyrroles, pyrazines) was lowest in M1-5 (3% LP), M2-1 (1% LP; both dried at 50 °C/15 min) and M2-7 (3% LP, dried at 70 °C/15 min) compared with NTR (p < 0.05).

Application of colour, shape, and texture parameters for classifying the defect of Gayo Arabica green coffee bean using computer vision

Coffee is the most important commodity in the trading industry. Determination of the quality of coffee is still done manually so that it cannot separate good quality coffee beans with bad quality coffee beans. This research conducted the development of a visual-based intelligent system using computer vision to be able to classify the quality of rice coffee based on the Indonesian National Standard (SNI). The models used in the study are the K-Nearest Neighbour (K-NN) method and the Support Vector Machine (SVM) method with 13 parameters used such as; area, contrast, energy, correlation, homogeneity, circularity, perimeter, and colour index R(red), G (green), B (blue), L*, a* and b*. A total of 1200 Arabica green coffee bean captured using Kinect V2 camera with training data of 1000 samples and testing data of 200 samples.