Rapid assessment of bioactive phenolics and methylxanthines in spent coffee grounds by FT-NIR spectroscopy

High-sensitivity and stability electrochemical sensors for chlorogenic acid detection based on optimally engineered nanomaterials

Developing cost-effective and efficient analytical methods is essential for detecting chlorogenic acid (CGA), as excessive consumption of CGA, despite its significant antioxidant, anticancer, and anti-inflammatory properties, can cause serious health problems. The remarkable progress and adjustable features of nanomaterials have significantly improved the analytical capabilities of electrochemical sensors for CGA. This review examines the use of optimally engineered nanomaterials in CGA electrochemical sensors, emphasizing the design and modification strategies of various nanomaterials. It starts with an introduction to the basic principles of electrochemical sensors, detailing their components and the analytical methods employed. Subsequently, the review explores how structural and compositional adjustments in electrocatalysts from different nanomaterial categories enhance CGA detection performance. In conclusion, it discusses the challenges and opportunities linked to designing nanomaterials for modified electrodes in CGA sensors. This review seeks to enhance the understanding of the connection between nanomaterial structures and the performance of CGA electrochemical sensors, offering new perspectives for the future design of highly efficient CGA electrochemical sensors.

Rapid discrimination of Alismatis Rhizoma and quantitative analysis of triterpenoids based on near-infrared spectroscopy

This study developed a rapid, accurate, objective and economic method to identify and evaluate the quality of Alismatis Rhizoma (AR) commodities. Traditionally, the identification of plant species and geographical origins of AR commodities mainly relied on experienced staff. However, the subjectivity and inaccuracy of human identification negatively impacted the trade of AR. Besides, liquid chromatographic methods such as ultra-high-performance liquid chromatography (UPLC) and high-performance liquid chromatography (HPLC), the major approach for the determination of triterpenoid contents in AR was time-consuming, expensive, and highly demanded in manoeuvre specialists. In this study, the combination of near-infrared (NIR) spectroscopy and chemometrics as the method was developed and utilised to address the two common issues of identifying the quality of AR commodities. Through the discriminant analysis (DA), the raw NIR spectroscopy data on 119 batches samples from two species and four origins in China were processed to the best pre-processed data. Subsequently, orthogonal partial least squares-discriminant analysis (OPLS-DA) and random forest (RF) as the major chemometrics were used to analyse the best pre-processed data. The accuracy rates by OPLS-DA and RF were respectively 100% and 97.2% for the two species of AR, and respectively100% and 94.4% for the four origins of AR. Meanwhile, a quantitative correction model was established to rapidly and economically predict the seven triterpenoid contents of AR through combining the partial least squares (PLS) method and NIR spectroscopy, and taking the triterpenoid contents measured by UPLC as the reference value, and carry out spectral pre-processing methods and band selection. The final quantitative model correlation coefficients of the seven triterpenoid contents of AR ranged from 0.9000 to 0.9999, indicating that prediction ability of this model had good stability and applicability.

Near-infrared spectroscopy as a green technology to monitor coffee roasting

Wet chemistry methods are traditionally used to evaluate the quality of a coffee beverage and its chemical characteristics. These old methods need to be replaced with more rapid, objective, and simple analytical methods for routine analysis. Near-infrared spectroscopy is an increasingly popular technique for nondestructive quality evaluation called a green technology. Our study aimed to apply near-infrared spectroscopy to evaluate the quality of coffee samples of different origin (Brazil, Guatemala, Peru, and Kongo). Particularly, we analyzed the roasting time and its effect on the quality of coffee. The colorimetric method determined a relation between the coffee color and the time of roasting. Partial least squares regression analysis assessed a possibility of predicting the roasting conditions from the near-infrared spectra. The regression results confirmed the possibility of applying near-infrared spectra to estimate the roasting conditions. The correlation between the spectra and the roasting time had R2 values of 0.96 and 0.95 for calibration and validation, respectively. The root mean square errors of prediction were low – 0.92 and 1.05 for calibration and validation, respectively. We also found a linear relation between the spectra and the roasting power. The quality of the models differed depending on the coffee origin and sub-region. All the coffee samples showed a good correlation between the spectra and the brightness (L* parameter), with R2 values of 0.96 and 0.95 for the calibration and validation curves, respectively. According to the results, near-infrared spectroscopy can be used together with the chemometric analysis as a green technology to assess the quality of coffee.

Rapid determination of the total content of oleanolic acid and ursolic acid in Chaenomelis Fructus using near-infrared spectroscopy

Chaenomelis Fructus is a widely used traditional Chinese medicine with a long history in China. The total content of oleanolic acid (OA) and ursolic acid (UA) is taken as an important quality marker of Chaenomelis Fructus. In this study, quantitative models for the prediction total content of OA and UA in Chaenomelis Fructus were explored based on near-infrared spectroscopy (NIRS). The content of OA and UA in each sample was determined using high-performance liquid chromatography (HPLC), and the data was used as a reference. In the partial least squares (PLS) model, both leave one out cross validation (LOOCV) of the calibration set and external validation of the validation set were used to screen spectrum preprocessing methods, and finally the multiplicative scatter correction (MSC) was chosen as the optimal pretreatment method. The modeling spectrum bands and ranks were optimized using PLS regression, and the characteristic spectrum range was determined as 7,500-4,250 cm^-1, with 14 optimal ranks. In the back propagation artificial neural network (BP-ANN) model, the scoring data of 14 ranks obtained from PLS regression analysis were taken as input variables, and the total content of OA and UA reference values were taken as output values. The number of hidden layer nodes of BP-ANN was screened by full-cross validation (Full-CV) of the calibration set and external validation of the validation set. The result shows that both PLS model and PLS-BP-ANN model have strong prediction ability. In order to evaluate and compare the performance and prediction ability of models, the total content of OA and UA in each sample of the test set were detected under the same HPLC conditions, the NIRS data of the test set were input, respectively, to the optimized PLS model and PLS-BP-ANN model. By comparing the root-mean-square error (RMSEP) and determination coefficient (R ²) of the test set and ratio of performance to deviation (RPD), the PLS-BP-ANN model was found to have better performance with RMSEP of 0.59 mg·g^-1, R ² of 95.10%, RPD of 4.53 and bias of 0.0387 mg·g^-1. The results indicated that NIRS can be used for the rapid quality control of Chaenomelis Fructus.

Spent coffee grounds: A sustainable approach toward novel perspectives of valorization

Coffee is one of the most popular and preferred drinks in the world, being consumed for its refreshing and energizing properties. As a result, the consumption of coffee generates millions of tons of waste, in particular, spent coffee grounds (SCG). On the contrary, food waste recovery is an incredibly sustainable and convenient solution to the growing need for materials, fuels, and chemicals. SCG has been developed as a precious resource of several high value-added products (oil, proteins, minerals, fatty acids, sterols….). Thus, a transformative pathway to a circular economy that involves the valorization of coffee wastes and by-products is currently attracting the attention of researchers worldwide. The potential growth of scientific papers and publications promotes a comprehensive review to determine the research hotspots, knowledge structure, and to consider future avenues and challenges. Therefore, in this paper, we conducted a systematic review based on 275 indexed papers on the composition and valorization of SCG as a prospective environmental source. PRACTICAL APPLICATIONS: SCG can be applied in agro-food industries.

Application of NIR spectroscopy for rapid quantification of acid and peroxide in crude peanut oil coupled multivariate analysis

Two key parameters (acidity and peroxide content) for evaluation of the oxidation level in crude peanut oil have been studied. The titrimetric analysis was carried out for reference data collection. Then, near-infrared spectroscopy in combination with chemometric algorithms such as partial least square (PLS); bootstrapping soft shrinkage-PLS (BOSS-PLS); uninformative variable elimination-PLS (UVE-PLS), and competitive-adaptive reweighted sampling-PLS (CARS-PLS) were attempted and assessed. The correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) were used to individually evaluate the performance of the models. Optimum results were noticed with CARS-PLS, 0.9517 ≤ Rc ≤ 0.9670, 0.9503 ≤ Rp ≤ 0.9637, 0.0874 ≤ RMSEP ≤ 0.5650, and 3.14 ≤ RPD ≤ 3.64. Therefore, this affirmed that the near-infrared spectroscopy coupled with CARS-PLS could be used as a simple, fast, and non-invasive technique for quantifying acid value and peroxide value in crude peanut oil.

A Review on Electrochemical Sensors and Biosensors Used in Chlorogenic Acid Electroanalysis

Chlorogenic acid (5-O-caffeoylquinic acid) is a phenolic compound from the hydroxycinnamic acid family. Epidemiological, biological, and biochemical studies concur to support the beneficial role of chlorogenic acid in human health, along with other dietary phenolic compounds. Thus, chlorogenic acid has been reported to exert inhibitory effects on carcinogenesis in the large intestine, liver, and tongue, and a protective action on oxidative stress in vivo, together with anti-inflammatory, antidiabetic and antihypertensive activities. It is also claimed to have antifungal, antibacterial and antiviral effects with relatively low toxicity and side effects, alongside properties that do not lead to antimicrobial resistance. Due to its importance, numerous methods for determining chlorogenic acid (CGA), as well as for its derivatives from coffee beans and other plants, were elaborated. The most frequently used methods are infrared spectroscopy, high performance liquid chromatography (HPLC), capillary electrophoresis, liquid chromatography-mass spectrometry and chemiluminescence. Although these methods proved to be efficient for quantifying CGA and its derived products, a number of deficiencies were identified: they are time consuming, laborious, and require expensive instruments. Therefore, electrochemical methods have been developed and used in the determination of CGA in different nutraceuticals or food products. The present review aims to present the main progresses and performance characteristics of electrochemical sensors and biosensors used to detect CGA, as it is reported in a high number of relevant scientific papers published mainly in the last decade.

Rapid quantification of dissolved solids and bioactives in dried root vegetable extracts using near infrared spectroscopy

Artificial neural networks (ANN) were developed for prediction of total dissolved solids, polyphenol content and antioxidant capacity of root vegetables (celery, fennel, carrot, yellow carrot, purple carrot and parsley) extracts prepared from the (i) fresh vegetables, (ii) vegetables dried conventionally at 50 °C and 70 °C, and (iii) the lyophilised vegetables. Two types of solvents were used: organic solvents (acetone mixtures and methanol mixtures) and water. Near-infrared (NIR) spectra were recorded for all samples. Principal Component Analysis (PCA) of the pre-treated spectra using Savitzky-Golay smoothing showed specific grouping of samples in two clusters (1st: extracts prepared using methanol mixtures and water as the solvents; 2nd: extracts prepared using acetone mixtures as the solvents) for all four types of extracts. Furthermore, obtained results showed that the developed ANN models can reliably be used for prediction of total dissolved solids, polyphenol content and antioxidant capacity of dried root vegetable extracts in relation to the recorded NIR spectra.

Development of a fast and simple method to identify pure Arabica coffee and blended coffee by Infrared Spectroscopy

ABSTRACT

Coffee is the second most consumed beverage in the world and Brazil is the biggest coffee producer. The main coffee species are Arabica (Coffea arabica) and Robusta (Coffea canephora). Arabica presents superior sensorial characteristics, as flavor and aroma, and Robusta is less expensive to produce. Pure Arabica coffee presents a market share of 70% and Arabic and Robusta are mixed to produce blended coffee. In this work, a fast and simple method to identify Arabica and blended coffee was proposed. The samples were analyzed by Infrared Spectroscopy in the mid and near-infrared regions and the spectra were used to develop a discriminant method. Using the method, the purity varied from 99.44 to 99.94% for pure Arabica coffees. To evaluate the method, the samples were characterized by gas chromatography coupled to mass spectrometry. It was possible to identify Arabica and blended coffee with high accuracy, in one minute, without complex analyses or sample preparations. The method is useful when Arabica is blended with more than 20% of Robusta and the practical application of the method can be extended to all coffee producers and distributors to ensure quality and to identify frauds or blended coffees and pure Arabica coffees.

GRAPHICAL ABSTRACT

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13197-021-05176-4.

In Vitro Bioaccessibility and Antioxidant Activity of Polyphenolic Compounds from Spent Coffee Grounds-Enriched Cookies

Spent coffee ground (SCG) is a significant by-product generated by the coffee industry. It is considered a great source of bioactive molecules well-recognized for exerting biological properties. This study aimed to implement SCG in a baked foods, such as cookies (SCGc), to increase their bioactive potential. A comprehensive study of the polyphenolic fraction of the SCG and SCGc using a high-resolution mass spectrometry analysis was performed. Moreover, the polyphenol bioaccessibility and change in antioxidant activity during simulated gastrointestinal digestion (GiD) were assessed. Data showed that SCGc provided 780 mg of melanoidins, 16.2 mg of chlorogenic acid (CGA), 6.5 mg of caffeine, and 0.08 mg of phenolic acids per 100 g of sample. Moreover, the 5-caffeoylquinic acid was the most relevant CGA found in SCG (116.4 mg/100 g) and SCGc (8.2 mg/100 g) samples. The antioxidant activity evaluated through three spectrophotometric tests, and the total phenolic compounds of SCGc samples exhibited significantly higher values than the control samples. Furthermore, during simulated GiD, the highest bioaccessibility of SCGc polyphenols was observed after the colonic stage, suggesting their potential advantages for human health. Therefore, SCG with high content in bioactive molecules could represent an innovative ingredient intended to fortify baked food formulations.

Study of Influential Parameters of the Caffeine Extraction from Spent Coffee Grounds: From Brewing Coffee Method to the Waste Treatment Conditions

This article aims to study the interest of spent coffee grounds (SCG) valorization through caffeine recovery. In an original way, this study takes into account all the parameters such as (i) the brewing coffee methods (household, coffee shops, etc.); (ii) the storage conditions, in particular the drying step; (iii) the solid/liquid extraction parameters such as the nature of solvent, the temperature, the extraction time and the solid/liquid ratio; and (iv) the liquid/liquid purification parameters such as the nature, the volume and the pH of extraction medium. Results have shown that spent coffee grounds from coffee-shops obtained by percolation contain a higher amount of caffeine than spent coffee grounds from households obtained from spent pods or filters. A drying treatment is not required when extraction is performed under one week after the spent coffee grounds collection with 96.4% of not degraded caffeine. Solid/liquid extraction performed with 25 mL.g−1 SCG of hydroalcoholic solvent (water/EtOH, v/v 60/40) at 60 °C during 15 min have given a caffeine yield up to 4.67 mg.g−1 SCG. When using ethyl acetate, 93.4% of the caffeine has been selectively recovered by liquid/liquid extraction. Finally, the extraction of caffeine for the valorization of spent coffee grounds is a promising and easy way, which fits with an already important and well established market.

Improving the Recovery of Phenolic Compounds from Spent Coffee Grounds (SCG) by Environmentally Friendly Extraction Techniques

The aim of this study was to investigate and compare the effects of different extraction techniques (high hydrostatic pressure-assisted extraction (HHPE), ultrasound-assisted extraction (UAE), and classical solvent extraction (CSE)) on phenolic compounds from spent coffee grounds (SCG). Different HHPE parameters (300, 400 and 500 MPa at 25 °C for 5, 10 and 15 min) and UAE parameters (40%, 50%, and 60% amplitude at 25 °C for 5, 10 and 15 min) were used. These techniques were compared with CSE (at 50 °C for 30 min) according to total phenolic content (TPC), antioxidant activity (AA), high-performance liquid chromatography (HPLC), scanning electron microscopy (SEM), and infrared (IR) spectroscopy. The results showed that eco-friendly techniques increased the TPC and AA compared to CSE and morphological changes were verified by SEM results. Furthermore, chlorogenic and caffeic acid were also quantified by using HPLC. Chlorogenic acid was found as the main phenolic compound in spent coffee grounds (SCG). The highest chlorogenic acid was detected as 85.0 ± 0.6 mg/kg FW with UAE at 60% amplitude for 15 min. In brief, for the extraction of phenolic compounds from waste SCG eco-friendly techniques such as HHPE and/or UAE were more convenient than CSE.

Rapid determination of total phenolic content of whole wheat flour using near-infrared spectroscopy and chemometrics

Phenolics in whole wheat products provide many health benefits. Wheat breeders, producers, and end-users are becoming increasingly interested in wheats with higher total phenolic content (TPC). Whole wheat flour with higher phenolics may have greater marketing value in the future. However, conventional methods determining TPC are costly and labor-intensive, which are not practical for wheat breeders to analyze several thousands of lines within a limited timeframe. We presented a novel application of near-infrared spectroscopy for TPC prediction in whole wheat flour. The optimal regression model demonstrated R² values of 0.92 and 0.90 for the calibration and validation sets, and a residual prediction deviation value of 3.4. The NIR method avoids the tedious extraction and TPC assay procedures, making it more convenient and cost-effective. Our result also demonstrated that NIR can accurately quantify phenolics even at low concentration (less than 0.2%) in the food matrix such as whole wheat flour.

Application of Detrended Fluctuation Analysis and Yield Stability Index to Evaluate Near Infrared Spectra of Green and Roasted Coffee Samples

Coffee quality, and therefore its price, is determined by coffee species and varieties, geographic location, the method used to process green coffee beans, and particularly the care taken during coffee production. Determination of coffee quality is often done by the nondestructive and fast near infrared spectroscopy (NIRS), which provides a huge amount of data about the samples. NIRS data require sophisticated, multivariate data analysis methods, such as principal component analysis, or linear discriminant analysis. Since the obtained data are a set of spectra, they can also be analyzed by signal processing methods. In the present study, the applications of two novel methods, detrended fluctuation analysis (DFA) and yield stability index (YSI), is introduced on NIR spectra of different roasting levels of coffee samples. Fourteen green coffee samples from all over the world have been roasted on three different levels and their NIR spectra were analyzed. DFA successfully differentiated the green samples from the roasted ones, however, the joint analysis of all samples was not able to differentiate the roasting levels. On the other hand, DFA successfully differentiated the roasting levels on samples level, which was strengthened by a 100% accurate agglomerative hierarchical clustering. YSI was first used in NIR signal processing and was able to detect that a light roast is the most stable among all roasting levels. Future research should focus on the application of DFA in terms of the analysis of the effects of other transformation methods of the spectra.

Investigating the effects of geographical origin, roasting degree, particle size and brewing method on the physicochemical and spectral properties of Arabica coffee by PCA analysis

Effects of geographical origin of Coffee Arabica beans (Brazil, Colombia and Peru), roasting degree, particle size and different brewing methods (French press, chemex and cold brew) on physicochemical and spectral properties of coffee samples were investigated in this study. Analyses of pH, total phenolic content (TPC) and total antioxidant activity (TA), UV and fluorescence spectroscopy measurements were performed. Principal component analysis (PCA) was used to obtain the most effective parameters on chemical changes. Results indicated that the increase of roasting degree caused decrements in the intensities of UV-spectra because of the degradation of trigonelline and chlorogenic acid molecules while counterpart trend was observed in the fluorescence spectra due to formation of fluorescence Maillard reaction products (MRP) during roasting. French press and cold brew methods caused similar TPC (1873.33-3818.33 and 2648.88-3824.44 μg/mL gallic acid equivalent, respectively) and TA (0.18-0.32 and 0.16-0.27 μmol/mL Trolox equivalent, respectively) values whereas chemex method showed different physicochemical properties (TPC: 1008.88-3543.88 μg/mL gallic acid equivalent and TA: 0.08-0.26 μmol/mL Trolox equivalent). Roasting degree and brewing method-compared to other parameters-were the most discriminating factors on the basis of UV spectra and fluorescence spectra of coffee brew samples, respectively. All roasting degrees could be distinguished with the rate of 71.42% on PC1 and 23.45% on PC2 of total variance according to UV-spectra while chemex and French press-cold brew methods could be differentiated with the rate of 97.24% on PC1 and 1.79% on PC2 of total variance based on fluorescence spectra on PCA score graphs.

Activity of catechins and their applications

Background

Catechins, which are polyphenol compounds found in many plants and are an important component of tea leaves, are strong anti-oxidants.

Research

Many studies seek to enhance the effects of catechins on the human body and boost their protective power against UV radiation. There are many examples of the positive anti-microbial, anti-viral, anti-inflammatory, anti-allergenic, and anti-cancer effects of catechins. Catechins increase the penetration and absorption of healthy functional foods and bio cosmetics into the body and the skin, thus improving their utility. High value-added anti-oxidant substances have been extracted from food and plant sludge, and experiments have shown that catechins are safe when applied to the human body. The stability of catechins is very important for their absorption into the human body and the effectiveness of their anti-oxidant properties.

Conclusion

Continued research on the strong anti-oxidant effects of catechins is expected to result in many advances in the food, cosmetics, and pharmaceutical industries.

Antioxidant Activity of Blueberry (Vaccinium spp.) Cultivar Leaves: Differences Across the Vegetative Stage and the Application of Near Infrared Spectroscopy

Blueberries production has increased in the last few years boosted somehow by the World Health Organization (WHO) recommendations for a healthier nutrition and their recognized potential to treat several diseases. The production increase lead to high amounts of discarded leaves that could be very valuable. In this context, the antioxidant activity of Vaccinium spp. leaves, by means of the total phenolic (TPC) and flavonoid (TFC) content and total antioxidant capacity (TAC) was determined. Adult leaves of twenty-seven Vaccinium cultivars collected in three geographic regions and three seasons of the year were included. The antioxidant activity was additionally estimated with near infrared (NIR) spectroscopy and data transferability across the regions and seasons was evaluated. The TPC, TFC and TAC ranged from 39.6–272.8 mg gallic acid, 41.2–269.1 mg catechin and 22.6–124.8 mM Trolox per g of dry leaf, respectively. Globally through the seasons, the higher values of the three parameters were obtained in December. Regarding the geographic region, region A provided the cultivars with the higher antioxidant content. Titan was the cultivar with higher TPC and TAC and Misty the one with the higher TFC. NIR spectroscopy combined with the partial least squares analysis was able to successfully predict the antioxidant activity with coefficients of determination and range error ratios ranging from 0.84–0.99 and 11.2–26.8. Despite some identified limitations on data transferability, NIR spectroscopy proved to be a reliable, low cost and quick method to predict the antioxidant activity of the considered cultivar leaves.

Geographic identification of Boletus mushrooms by data fusion of FT-IR and UV spectroscopies combined with multivariate statistical analysis

Boletus griseus and Boletus edulis are two well-known wild-grown edible mushrooms which have high nutrition, delicious flavor and high economic value distributing in Yunnan Province. In this study, a rapid method using Fourier transform infrared (FT-IR) and ultraviolet (UV) spectroscopies coupled with data fusion was established for the discrimination of Boletus mushrooms from seven different geographical origins with pattern recognition method. Initially, the spectra of 332 mushroom samples obtained from the two spectroscopic techniques were analyzed individually and then the classification performance based on data fusion strategy was investigated. Meanwhile, the latent variables (LVs) of FT-IR and UV spectra were extracted by partial least square discriminant analysis (PLS-DA) and two datasets were concatenated into a new matrix for data fusion. Then, the fusion matrix was further analyzed by support vector machine (SVM). Compared with single spectroscopic technique, data fusion strategy can improve the classification performance effectively. In particular, the accuracy of correct classification of SVM model in training and test sets were 99.10% and 100.00%, respectively. The results demonstrated that data fusion of FT-IR and UV spectra can provide higher synergic effect for the discrimination of different geographical origins of Boletus mushrooms, which may be benefit for further authentication and quality assessment of edible mushrooms.

New fluorescence spectroscopic method for the simultaneous determination of alkaloids in aqueous extract of green coffee beans

BACKGROUND

There is no fluorescence spectroscopic method for the determination of trigonelline and theobromine in green coffee beans. Therefore, the objective of this study was to develop a new fluorescence spectroscopic method to determine the alkaloids simultaneously in the aqueous extract of green coffee beans.

RESULTS

The calibration curves were linear in the range 2-6, 1-6, 1-5 mg/L for caffeine, theobromine and trigonelline, respectively, with R² ≥ 0.9987. The limit of detection and limit of quantification were 2, 6 and 7 µg/L and 40, 20 and 20 µg/L for caffeine, theobromine and trigonelline, respectively. Caffeine and trigonelline exhibited well separated fluorescence excitation spectra and therefore the two alkaloids were selectively quantified in the aqueous extract of green coffee. While theobromine showed overlapping fluorescence excitation spectra with caffeine and hence theobromine could not be determined in the aqueous extract of green coffee beans. The amount of caffeine and trigonelline in the three samples of green coffee beans were found to be 0.95-1.10 and 1.00-1.10% (w/w), respectively. The relative standard deviations (RSD ≤ 4%) of the method for the three compounds of interest were of very good. The accuracy of the developed analytical method was evaluated by spiking standard caffeine and trigonelline to green coffee beans and the average recoveries were 99 ± 2% for both the alkaloids.

CONCLUSIONS

A fast, sensitive and reliable fluorescence method for the simultaneous determination of caffeine and trigonelline in the aqueous extract of green coffee beans was developed and validated. The developed method reflected an effective performance to the direct determination of the two alkaloids in the aqueous extract of green coffee beans.

Crude ethanolic extract from spent coffee grounds: Volatile and functional properties

Espresso capsule consumption and spent coffee ground (SCG) generation have increased, and the present study was undertaken to evaluate the volatile profile (VP), the antioxidant activity (AA) and the sun protection factor (SPF) of the Crude ethanolic extract obtained from the SCG in capsules. The extract yield was superior to the ether yield because a higher unsaponifiable matter (U.M.) amount was recovered by ethanol. The obtained VP (70 compounds) was typical of roasted coffee oil. Furthermore, chemometric analysis using principal components (PCA) discriminated the extracts and grouped the replicates for each sample, which showed the repeatability of the extraction process. The AA ranged from 18.4 to 23.6 (mg extract mg DPPH^-1) and the SPF from 2.27 to 2.76. The combination of the coffee VP, AA and SPF gave the espresso SCG's crude ethanolicextract, desirable properties that can be used in cosmetic and food industries.

The Use of Partial Least Square Regression and Spectral Data in UV-Visible Region for Quantification of Adulteration in Indonesian Palm Civet Coffee

Asian palm civet coffee or kopi luwak (Indonesian words for coffee and palm civet) is well known as the world's priciest and rarest coffee. To protect the authenticity of luwak coffee and protect consumer from luwak coffee adulteration, it is very important to develop a robust and simple method for determining the adulteration of luwak coffee. In this research, the use of UV-Visible spectra combined with PLSR was evaluated to establish rapid and simple methods for quantification of adulteration in luwak-arabica coffee blend. Several preprocessing methods were tested and the results show that most of the preprocessing spectra were effective in improving the quality of calibration models with the best PLS calibration model selected for Savitzky-Golay smoothing spectra which had the lowest RMSECV (0.039) and highest RPD_cal value (4.64). Using this PLS model, a prediction for quantification of luwak content was calculated and resulted in satisfactory prediction performance with high both RPD _p and RER values.

Development and validation of ultrasound-assisted solid-liquid extraction of phenolic compounds from waste spent coffee grounds

In this current work, Box-Behnken statistical experimental design (BBD) was adopted to evaluate and optimize USLE (ultrasound-assisted solid-liquid extraction) of phytochemicals from spent coffee grounds. Factors employed in this study are ultrasonic power, temperature, time and solid-liquid (SL) ratio. Individual and interactive effect of independent variables over the extraction yield was depicted through mathematical models, which are generated from the experimental data. Determined optimum process conditions are 244W of ultrasonic power, 40°C of temperature, 34min of time and 1:17g/ml of SL ratio. The predicted values were in correlation with experimental values with 95% confidence level, under the determined optimal conditions. This indicates the significance of selected method for USLE of phytochemicals from SCG.