In-line monitoring of the coffee roasting process with near infrared spectroscopy: Measurement of sucrose and colour

Rapid discrimination of different primary processing Arabica coffee beans using FT-IR and machine learning

In this study, fourier transform infrared spectroscopy (FT-IR) analysis was combined with machine learning, while various analytical techniques such as colorimetry, low-field nuclear magnetic resonance spectroscopy, scanning electron microscope, two-dimensional correlation spectroscopy (2D-COS), and multivariate statistical analysis were employed to rapidly distinguish and compare three different primary processed Arabica coffee beans. The results showed that the sun-exposed processed beans (SPB) exhibited the highest total color difference value and the largest pore size. Meanwhile, the wet-processed beans (WPB) retained the most bound and immobilized water in green and roast coffee beans. The FT-IR data analysis results indicated that the functional group composition was similar across the three different primary processed coffee beans, while significant differences in structural characteristics were observed in 2D-COS. The multivariate statistical analysis demonstrated that the orthogonal partial least squares-discriminant analysis model could effectively distinguish the different types of coffee beans. The machine learning results indicated that the six models could rapidly identify different samples of primary processed coffee beans. Notably, the SNV-Voting model demonstrated superior predictive performance, with an average precision, recall, and F1-score of 88.67%, 88.67%, and 0.88 for three primary processing coffee beans, respectively.

An eco-friendly approach for analysing sugars, minerals, and colour in brown sugar using digital image processing and machine learning

Brown sugar is a natural sweetener obtained by thermal processing, with interesting nutritional characteristics. However, it has significant sensory variability, which directly affects product quality and consumer choice. Therefore, developing rapid methods for its quality control is desirable. This work proposes a fast, environmentally friendly, and accurate method for the simultaneous analysis of sucrose, reducing sugars, minerals and ICUMSA colour in brown sugar, using an innovative strategy that combines digital image processing acquired by smartphone cell with machine learning. Data extracted from the digital images, as well as experimentally determined contents of the physicochemical characteristics and elemental profile were the variables adopted for building predictive regression models by applying the kNN algorithm. The models achieved the highest predictive capacity for the Ca, ICUMSA colour, Fe and Zn, with coefficients of determination (R2) ≥ 92.33 %. Lower R2 values were observed for sucrose (81.16 %), reducing sugars (85.67 %), Mn (83.36 %) and Mg (86.97 %). Low data dispersion was found for all the predictive models generated (RMSE < 0.235). The AGREE Metric assessed the green profile and determined that the proposed approach is superior in relation to conventional methods because it avoids the use of solvents and toxic reagents, consumes minimal energy, produces no toxic waste, and is safer for analysts. The combination of digital image processing (DIP) and the kNN algorithm provides a fast, non-invasive and sustainable analytical approach. It streamlines and improves quality control of brown sugar, enabling the production of sweeteners that meet consumer demands and industry standards.

Investigating the Impact of Dietary Fibers on Mycotoxin Bioaccessibility during In Vitro Biscuit Digestion and Metabolites Identification

Mycotoxins contamination is a real concern worldwide due to their high prevalence in foods and high toxicity; therefore, strategies that reduce their gastrointestinal bioaccessibility and absorption are of major relevance. The use of dietary fibers as binders of four mycotoxins (zearalenone (ZEA), deoxynivalenol (DON), HT-2, and T-2 toxins) to reduce their bioaccessibility was investigated by in vitro digestion of biscuits enriched with fibers. K-carrageenan is a promising fiber to reduce the bioaccessibility of ZEA, obtaining values lower than 20%, while with pectin a higher reduction of DON, HT-2, and T-2 (50-88%) was achieved. Three metabolites of mycotoxins were detected, of which the most important was T-2-triol, which was detected at higher levels compared to T-2. This work has demonstrated the advantages of incorporating dietary fibers into a biscuit recipe to reduce the bioaccessibility of mycotoxins and to obtain healthier biscuits than when a conventional recipe is performed due to its high content of fiber.

Quality Evaluation of Hainan Robusta Coffee Bean Oil Produced by Ultrasound Coupled with Coconut Oil Extraction

This study investigates the treatment of coconut oil using thermosonic treatment in combination with green coffee beans. Under a defined ratio of coconut oil to green coffee beans, the effect of different thermosonic time on the quality parameters, active substance content, antioxidant capacity, and thermal oxidative stability of coconut oil were investigated as a strategy to potentially improve the quality of oil. Results showed that the β-sitosterol content of CCO (coconut coffee oil) treated with the thermal method combined with green coffee bean treatment reached up to 393.80 ± 11.13 mg/kg without affecting the lipid structure. In addition, DPPH clearance equivalents increased from 5.31 ± 1.30 mg EGCG/g to 71.34 ± 0.98 mg EGCG/g, and the ABTS clearance equivalent was 45.38 ± 0.87 mg EGCG/g versus 0 for the untreated sample. The improvement in thermal oxidation stability of treated coconut oil is also significant. The TG (Thermogravimetry) onset temperature was elevated from 277.97 °C to 335.08 °C and the induction time was elevated up to 24.73 ± 0.41 h from 5.17 ± 0.21 h. Thermosonic treatment in combination with green coffee beans is an ideal option to improve the quality of coconut oil. The results of this article provide new ideas for the development of plant-blended oil products and the new utilization of coconut oil and coffee beans.

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

Variations occurring in coffee beans during roasting are ascribable to several chemical-physical phenomena: to quickly track the whole process and to ensure its reproducibility, a process analytical technology (PAT) approach is needed.

In this study, a method combining in-line Fourier transform near-infrared (FT-NIR) spectroscopy and chemometric modelling was investigated to get real-time and practical knowledge about the roasting effects on coffee’s chemical-physical composition. In-line spectra were acquired by inserting a NIR probe into a laboratory coffee roaster, running twenty-four roasting experiments, planned spanning different coffee species (Arabica and Robusta), four roasting temperature settings (TS1–TS4) and times (650–1580 s).

Multivariate curve resolution-alternate least squares (MCR-ALS) was used to model the chemical-physical changes occurring during the roasting process, and information about maximum rate, acceleration and deceleration of the process was obtained, also highlighting potential effects due to the different roasting temperatures and coffee varieties.

The proposed approach provides the groundwork for direct real-time implementation of rapid, non-invasive automated monitoring of the roasting process at industrial scale.

Analysis of the chemical composition of natural carbohydrates - An overview of methods

Natural carbohydrates are gaining importance over a wide spectrum of human activity due to their versatile functionalities. The properties of carbohydrates are currently used in many branches of industry and new possibilities of their utilization, like in medicine or materials science, are demonstrated systematically. The attractive properties of carbohydrates result from their chemical structure and ability to form macromolecules and derivatives. Each application of carbohydrate requires a knowledge of their chemical composition, which due to the number and differentiation of monosaccharides and their spatial forms is often challenging. This review presents an overview on sample preparation and the methods used for the determination of the fine chemical structure of natural carbohydrates. Most popular and reliable colorimetric, chromatographic and spectroscopic methods are presented with an emphasis on their pros and cons.

Prediction of coffee aroma from single roasted coffee beans by hyperspectral imaging

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This paper applied hyperspectral imaging (HSI) to predict roasted coffee aroma profile.

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Individual roast coffee beans were analysed by HSI and aroma by GC–MS.

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PLS models successfully predicted volatile aroma compounds in single coffee beans.

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Beans were successfully segregated into two batches with different aroma profiles.

Coffee aroma is critical for consumer liking and enables price differentiation of coffee. This study applied hyperspectral imaging (1000–2500 nm) to predict volatile compounds in single roasted coffee beans, as measured by Solid Phase Micro Extraction-Gas Chromatography-Mass Spectrometry and Gas Chromatography-Olfactometry. Partial least square (PLS) regression models were built for individual volatile compounds and chemical classes. Selected key aroma compounds were predicted well enough to allow rapid screening (R² greater than 0.7, Ratio to Performance Deviation (RPD) greater than 1.5), and improved predictions were achieved for classes of compounds - e.g. aldehydes and pyrazines (R² ∼ 0.8, RPD ∼ 1.9). To demonstrate the approach, beans were successfully segregated by HSI into prototype batches with different levels of pyrazines (smoky) or aldehydes (sweet). This is industrially relevant as it will provide new rapid tools for quality evaluation, opportunities to understand and minimise heterogeneity during production and roasting and ultimately provide the tools to define and achieve new coffee flavour profiles.

Nutritional Characterization of Strychnos madagascariensis Fruit Flour Produced by Mozambican Communities and Evaluation of Its Contribution to Nutrient Adequacy

The indigenous fruit Strychnos madagascariensis is usually processed to flour, called nfuma, being highly consumed during staple food shortage. This study aimed to evaluate the nutritional composition of nfuma and its nutrient adequacy. Flours from four districts of Mozambique were analyzed using AOAC methods for proximate composition, HPLC for sugar, amino acids (AA), vitamin E and carotenoids and ICP-MS and FAAS for minerals. The results showed that nfuma stands out for its high content of fat (26.3–27.8%), mainly oleic acid, fiber (>6%), vitamin E (6.7 to 8.0 mg/100 g) and carotenes (2.2 to 2.6 mg/100 g). The main amino acids of nfuma protein were Arg, Asp and Glu, and Lys was the limiting one. The mineral composition reveals K (~1200 to 1700 mg/100 g) as the main macromineral followed by Mg > Ca > Na. The main trace element was Mn (~4 mg/100 g) followed by Fe > Zn > Cu > Cr > Co. Aluminum (~3 mg/100 g) was the main non-essential element and Rb, Ni, Sr, Ba, V, Cd were also quantified. Assuming the daily consumption of 50 g, nfuma provides 82% of Vitamin A dietary reference value for toddlers, while the consumption of 100 g contributes to 132% and 60% of Mn and vitamin A DRV for adults, respectively. Despite the nutritional advantages of nfuma, this flour can be a source of Ni, highlighting the importance of the study of good practices in its preparation to decrease the exposure to non-essential elements.

Benefits of coffee consumption for human health: an overview

Background:

Coffee is one of the most consumed beverages worldwide and is popular for its characteristic flavor and rich organoleptic properties.

Aim:

Based on published articles, the aims of this review are i) study the association between coffee consumption and benefits to human health; ii) the effects of coffee consumption on some pathologies; and iii) provide a description of coffee’s bioactive compounds.

Discussion:

Coffee presents bioactive compounds, which include phenolic compounds, especially chlorogenic acid (caffeoylquinic acid), trigonelline, and diterpenes, such as cafestol and kahweol. These compounds are related to the beneficial effects for human health, including high antioxidant activity, antimutagenic activity, hepatoprotective action, reduced incidence of type 2 diabetes mellitus, reduced risk of cardiovascular diseases, decreased incidence of inflammatory diseases, reduced menopausal symptoms, and others. Coffee’s bioactive compounds are caffeine, chlorogenic acid, trigonelline, cafestol and kahweol, which are closely related to coffee’s beneficial effects.

Conclusion:

The present review clarified that the benefits of moderate coffee consumption outweigh the associated risks.

The Potential of Spectroscopic Techniques in Coffee Analysis—A Review

This review provides an overview of recent studies on the potential of spectroscopy techniques (mid-infrared, near infrared, Raman, and fluorescence spectroscopy) used in coffee analysis. It specifically covers their applications in coffee roasting supervision, adulterants and defective beans detection, prediction of specialty coffee quality and coffees’ sensory attributes, discrimination of coffee based on variety, species, and geographical origin, and prediction of coffees chemical composition. These are important aspects that significantly affect the overall quality of coffee and consequently its market price and finally quality of the brew. From the reviewed literature, spectroscopic methods could be used to evaluate coffee for different parameters along the production process as evidenced by reported robust prediction models. Nevertheless, some techniques have received little attention including Raman and fluorescence spectroscopy, which should be further studied considering their great potential in providing important information. There is more focus on the use of near infrared spectroscopy; however, few multivariate analysis techniques have been explored. With the growing demand for fast, robust, and accurate analytical methods for coffee quality assessment and its authentication, there are other areas to be studied and the field of coffee spectroscopy provides a vast opportunity for scientific investigation.

Near-infrared spectroscopy and multivariate calibration as an alternative to the Agtron to predict roasting degrees in coffee beans and ground coffees

Agtron method is widely used in the industry to determine roasting degrees in whole and ground coffee but it suffers from some inconveniences associated with unavailability of equipment, high cost, and lack of reproductive results. This study investigates the feasibility to determine roasting degrees in coffee beans and ground specialty coffees using near-infrared (NIR) spectroscopy combined with multivariate calibration based on partial least squares (PLS) regression. Representative data sets were considered to cover all Agtron roasting profiles for whole and ground coffees. Proper development of models with outlier evaluation and complete validation using parameters of merit such as accuracy, adjust, residual prediction deviation, linearity, analytical sensitivity, and limits of detection and quantification are presented to prove their performance. The results indicated that predictive chemometric models, for intact coffee beans and ground coffee, could be used in the coffee industry as an alternative to Agtron, thus digitalizing the roasting quality control.

Evaluation of chemical properties of intact green coffee beans using near-infrared spectroscopy

BACKGROUND

The chemical compounds in coffee are important indicators of quality. Its composition varies according to several factors related to the planting and processing of coffee. Thus, this study proposed to use near-infrared spectroscopy (NIR) associated with partial least squares (PLS) regression to estimate quickly some chemical properties (moisture content, soluble solids, and total and reducing sugars) in intact green coffee samples. For this, 250 samples produced in Brazil were analyzed in the laboratory by the standard method and also had their spectra recorded.

RESULTS

The calibration models were developed using PLS regression with cross-validation and tested in a validation set. The models were elaborated using original spectra and preprocessed by five different mathematical methods. These models were compared in relation to the coefficient of determination, root mean square error of cross-validation (RMSECV), root mean square error of test set validation (RMSEP), and ratio of performance to deviation (RPD) and demonstrated different predictive capabilities for the chemical properties of coffee. The best model was obtained to predict grain moisture and the worst performance was observed for the soluble solids model. The highest determination coefficients obtained for the samples in the validation set were equal to 0.810, 0.516, 0.694 and 0.781 for moisture, soluble solids, total sugar, and reducing sugars, respectively.

CONCLUSION

The statistics associated with these models indicate that NIR technology has the potential to be applied routinely to predict the chemical properties of green coffee, and in particular, for moisture analysis. However, the soluble solid and total sugar content did not show high correlations with the spectroscopic data and need to be improved. © 2020 Society of Chemical Industry.

Smart Online Coffee Roasting Process Control: Modelling Coffee Roast Degree and Brew Antioxidant Capacity for Real-Time Prediction by Resonance-Enhanced Multi-Photon Ionization Mass Spectrometric (REMPI-TOFMS) Monitoring of Roast Gases

Process control with high time resolution is essential to maintain high product quality in coffee roasting. However, analytical techniques for quality assurance or measurements of desired coffee properties are often labor-intensive and can only be conducted after dropping the coffee beans. Resonance-enhanced multi-photon ionization time-of-flight mass spectrometry (REMPI-TOFMS) at 248 nm and 266 nm was applied to analyze the composition of the roast gas from small-scale Arabica coffee roasting. Coffee beans were dropped after different roasting times, ground and analyzed by Colorette to obtain the roast degree. Additionally, the antioxidant capacity of the coffee brew was determined by Folin–Ciocalteu (FC) assay. Models for the prediction of Colorette and FC values from REMPI mass spectra were constructed by partial least squares (PLS) regression. REMPI-TOFMS enables the prediction of Colorette values with a root-mean-square error in prediction (RMSE_P) below 5 for both wavelengths. FC values could be predicted using REMPI at 248 nm with an RMSE_P of 80.3 gallic acid equivalents (GA-eq) mg L⁻¹, while REMPI at 266 nm resulted in RMSE_P of 151 GA-eq mg L⁻¹. Finally, the prediction of Colorette and FC value at 5 s time resolution were demonstrated with online measurements.

A data fusion model merging information from near infrared spectroscopy and X-ray fluorescence. Searching for atomic-molecular correlations to predict and characterize the composition of coffee blends

This article aims to develop and validate a multivariate model for quantifying Robusta-Arabica coffee blends by combining near infrared spectroscopy (NIRS) and total reflection X-ray fluorescence (TXRF). For this aim, 80 coffee blends (0.0-33.0%) were formulated. NIR spectra were obtained in the wavenumber range 11100-4950 cm-1 and 14 elements were determined by TXRF. Partial least squares models were built using data fusion at low and medium levels. In addition, selection of predictive variables based on their importance indices (SVPII) improved results. The best model reduced the number of variables from 1114 to 75 and root mean square error of prediction from 4.1% to 1.7%. SVPII selected NIR regions correlated with coffee components, and the following elements were chosen: Ti, Mn, Fe, Cu, Zn, Br, Rb, Sr. The model interpretation took advantage of the data fusion between atomic and molecular spectra in order to characterize the differences between these coffee varieties.

Farm to Consumer: Factors Affecting the Organoleptic Characteristics of Coffee. II: Postharvest Processing Factors

The production and consumption of coffee are increasing despite the roadblocks to its agriculture and global trade. The unique, refreshing, and stimulating final cupping quality of coffee is the only reason for this rising production and consumption. Coffee quality is a multifaceted trait and is inevitably influenced by the way it is successively processed after harvesting. Reportedly, 60% of the quality attributes of coffee are governed by postharvest processing. The current review elaborates and establishes for the first time the relationship between different methods of postharvest processing of coffee and its varying organoleptic and sensory quality attributes. In view of the proven significance of each processing step, this review has been subdivided into three sections, secondary processing, primary processing, and postprocessing variables. Secondary processing addresses the immediate processing steps on the farm after harvest and storage before roasting. The primary processing section adheres specifically to roasting, grinding and brewing/extraction, topics which have been technically addressed more than any others in the literature and by industry. The postprocessing attribute section deals generally with interaction of the consumer with products of different visual appearance. Finally, there are still some bottlenecks which need to be addressed, not only to completely understand the relationship of varying postharvest processing methods with varying in-cup quality attributes, but also to devise the next generation of coffee processing technologies.

Fault Detection Based on Near-Infrared Spectra for the Oil Desalting Process

The fault detection problem of the oil desalting process is investigated in this paper. Different from the traditional fault detection approaches based on measurable process variables, near-infrared (NIR) spectroscopy is applied to acquire the process fault information from the molecular vibrational signal. With the molecular spectra data, principal component analysis was explored to calculate the Hotelling T² and squared prediction error, which act as fault indicators. Compared with the traditional fault detection approach based on measurable process variables, NIR spectra-based fault detection illustrates more sensitivity to early failure because of the fact that the changes in the molecular level can be identified earlier than the physical appearances on the process. The application results show that the detection time of the proposed method is earlier than the traditional method by about 200 min.

Development of Noninvasive Classification Methods for Different Roasting Degrees of Coffee Beans Using Hyperspectral Imaging

This study aimed to develop an approach for quickly and noninvasively differentiating the roasting degrees of coffee beans using hyperspectral imaging (HSI). The qualitative properties of seven roasting degrees of coffee beans (unroasted, light, moderately light, light medium, medium, moderately dark, and dark) were assayed, including moisture, crude fat, trigonelline, chlorogenic acid, and caffeine contents. These properties were influenced greatly by the respective roasting degree. Their hyperspectral images (874–1734 nm) were collected using a hyperspectral reflectance imaging system. The spectra of the regions of interest were manually extracted from the HSI images. Then, principal components analysis was employed to compress the spectral data and select the optimal wavelengths based on loading weight analysis. Meanwhile, the random frog (RF) methodology and the successive projections algorithm were also adopted to pick effective wavelengths from the spectral data. Finally, least squares support vector machine (LS-SVM) was utilized to establish discriminative models using spectral reflectance and corresponding labeled classes for each degree of roast sample. The results showed that the LS-SVM model, established by the RF selecting method, with eight wavelengths performed very well, achieving an overall classification accuracy of 90.30%. In conclusion, HSI was illustrated as a potential technique for noninvasively classifying the roasting degrees of coffee beans and might have an important application for the development of nondestructive, real-time, and portable sensors to monitor the roasting process of coffee beans.

Attenuated Total Reflectance Fourier Transform Spectroscopy (ATR-FTIR) and chemometrics for discrimination of espresso coffees with different sensory characteristics

Coffee cup quality, determined by the sensory attributes evaluated by professional tasters, is a decisive factor for evaluating coffee, with the "Specialty Coffee Association of America" (SCAA) classification being nowadays considered the most suitable. Panels of trained coffee tasters are used by the industry to describe and evaluate beverage quality, but those evaluations can be subjective and time demanding. Recent studies have demonstrated the potential of spectroscopy-based methods for establishing parameters of quality in the analysis of food products, including coffee. Thus, the aim of this study was to evaluate the potential of ATR-FTIR and chemometrics to discriminate espresso coffees with different sensory characteristics reported by a panel of coffee tasters. The results showed good consistency among coffee tasters. PLS-DA models based on spectroscopic data were able to classify samples according to sensory attributes, confirming the potential of FTIR and chemometrics in coffee quality evaluation.

Prediction of key aroma development in coffees roasted to different degrees by colorimetric sensor array

We developed a colorimetric sensor array (CSA) that is sensitive to highly contributory volatile compounds of coffee aroma for discrimination of coffee samples roasted to different roast degrees. Strecker aldehydes and α-diketones were significantly higher for the medium roast than the other roast degrees. The development of several sulfur compounds was pronounced in the medium-dark and dark roasts, except for dimethyl sulfide, which was only detected in the light roast. The CSA method coupled with principal component analysis or hierarchical cluster analysis successfully distinguished the roasted coffee samples according to roast degree. Partial least squares regression results showed that the CSA responses were well-correlated with the concentrations of volatile compounds in the coefficient of determination (r_p²) range of 0.686-0.955. These results demonstrate that the CSA rapidly responded to coffee aroma compounds and was capable of predicting coffee aroma development.