Direct infusion electrospray ionization mass spectrometry applied to the detection of forgeries: Roasted coffees adulterated with their husks

rIDIMS: A novel tool for processing direct-infusion mass spectrometry data

Metabolomics using mass spectrometry-only (MS) analysis either by continuous or intermittent direct infusion (DIMS) and ambient ionization techniques (AMS) has grown in popularity due to their rapid, high-throughput nature and the advantage of performing fast analysis with minimal or no sample pretreatments. But currently, end-users without programming knowledge do not find applications with Graphical User Interface (GUI) specialized in processing DIMS or AMS data. Specifically, there is a lack of standardized workflow for processing data from limited sample sizes and scans from different total ion chronograms (TIC).To address this gap, we present rIDIMS, a browser-based application that offers a straightforward and fast workflow focusing on high-quality scan selection, grouping of isotopologues and adducts, data alignment, binning, and filtering. We also introduce a novel function for selecting TIC scans that is reproducible and statistically reliable, which is a feature particularly useful for studies with limited sample sizes. After processing in rIDIMS, the result is exported in an HTML report document that presents publication-quality figures, statistical data and tables, ready to be customized and exported. We demonstrate rIDIMS functionality in three cases: (i) Classification of coffee bean species through the chemical profile obtained with Mass Spec Pen; (ii) Public repository DIMS data from lipid profiling in monogenic insulin resistance syndromes, and (iii) Lipids for lung cancer classification. We show that our implementation facilitates the processing of AMS and DIMS data through an easy and intuitive interface, contributing to reproducible and reliable metabolomic investigations. Indeed, rIDIMS function asa user-friendly GUI based Shiny web application for intuitive use by end-users (available at https://github.com/BioinovarLab/rIDIMS).

Determination of adulteration, geographical origins, and species of food by mass spectrometry

Food adulteration, mislabeling, and fraud, are rising global issues. Therefore, a number of precise and reliable analytical instruments and approaches have been proposed to ensure the authenticity and accurate labeling of food and food products by confirming that the constituents of foodstuffs are of the kind and quality claimed by the seller and manufacturer. Traditional techniques (e.g., genomics-based methods) are still in use; however, emerging approaches like mass spectrometry (MS)-based technologies are being actively developed to supplement or supersede current methods for authentication of a variety of food commodities and products. This review provides a critical assessment of recent advances in food authentication, including MS-based metabolomics, proteomics and other approaches.

Comparative Profiling of Hot and Cold Brew Coffee Flavor Using Chromatographic and Sensory Approaches

Coffee brewing is a complex process from roasted coffee bean to beverage, playing an important role in coffee flavor quality. In this study, the effects of hot and cold brewing on the flavor profile of coffee were comprehensively investigated on the basis of chromatographic and sensory approaches. By applying gas chromatography–mass spectrometry and odor activity value calculation, most pyrazines showed higher contribution to the aroma profile of cold brew coffee over hot brew coffee. Using liquid chromatography, 18 differential non-volatiles were identified, most of which possessed lower levels in cold brew coffee than hot brew coffee. The sensory evaluation found higher fruitiness and lower bitterness and astringent notes in cold brew coffee than hot brew coffee, which was attributed by linalool, furfural acetate, and quercetin-3-O-(6″-O-p-coumaroyl) galactoside. This work suggested coffee brewing significantly affected its flavor profile and sensory properties.

Near-Infrared Spectroscopy Applied to the Detection of Multiple Adulterants in Roasted and Ground Arabica Coffee

Roasted coffee has been the target of increasingly complex adulterations. Sensitive, non-destructive, rapid and multicomponent techniques for their detection are sought after. This work proposes the detection of several common adulterants (corn, barley, soybean, rice, coffee husks and robusta coffee) in roasted ground arabica coffee (from different geographic regions), combining near-infrared (NIR) spectroscopy and chemometrics (Principal Component Analysis—PCA). Adulterated samples were composed of one to six adulterants, ranging from 0.25 to 80% (w/w). The results showed that NIR spectroscopy was able to discriminate pure arabica coffee samples from adulterated ones (for all the concentrations tested), including robusta coffees or coffee husks, and independently of being single or multiple adulterations. The identification of the adulterant in the sample was only feasible for single or double adulterations and in concentrations ≥10%. NIR spectroscopy also showed potential for the geographical discrimination of arabica coffees (South and Central America).

Quantification of Corn Adulteration in Wet and Dry-Processed Peaberry Ground Roasted Coffees by UV-Vis Spectroscopy and Chemometrics

In this present research, a spectroscopic method based on UV–Vis spectroscopy is utilized to quantify the level of corn adulteration in peaberry ground roasted coffee by chemometrics. Peaberry coffee with two types of bean processing of wet and dry-processed methods was used and intentionally adulterated by corn with a 10–50% level of adulteration. UV–Vis spectral data are obtained for aqueous samples in the range between 250 and 400 nm with a 1 nm interval. Three multivariate regression methods, including partial least squares regression (PLSR), multiple linear regression (MLR), and principal component regression (PCR), are used to predict the level of corn adulteration. The result shows that all individual regression models using individual wet and dry samples are better than that of global regression models using combined wet and dry samples. The best calibration model for individual wet and dry and combined samples is obtained for the PLSR model with a coefficient of determination in the range of 0.83–0.93 and RMSE below 6% (w/w) for calibration and validation. However, the error prediction in terms of RMSEP and bias were highly increased when the individual regression model was used to predict the level of corn adulteration with differences in the bean processing method. The obtained results demonstrate that the use of the global PLSR model is better in predicting the level of corn adulteration. The error prediction for this global model is acceptable with low RMSEP and bias for both individual and combined prediction samples. The obtained RPD_p and RER_p in prediction for the global PLSR model are more than two and five for individual and combined samples, respectively. The proposed method using UV–Vis spectroscopy with a global PLSR model can be applied to quantify the level of corn adulteration in peaberry ground roasted coffee with different bean processing methods.

Three centuries on the science of coffee authenticity control

Food authenticity relies on genuineness and reliability according to the information displayed on the package. Since the 18th century, when coffee became popularized in the West, adulteration began. Several methods have been developed to detect different kinds of frauds and they have evolved as demands increased and new technologies were introduced. The evolution of the science of coffee authenticity control in the past three centuries is reviewed, focusing on the discrimination between coffee and other foods or between coffee and its by-products. The earliest chemical and physical methods are presented followed by methods developed in the 20th and 21st centuries using microscopy, chromatography and spectroscopy associated with advanced statistical tools, and DNA-based methods. In addition to non-food material, before the 20th century, chicory was the most studied food-adulterant. From the 20th century on, corn, coffee by-products, and barley were the most studied, followed by chicory, rice and other food items. Matrix effects seem to be among the most challenging problems in these approaches, associated with variations in roast degree, particle size (particularly in spectroscopy-based methods), and lack of control over reference samples regarding species and purity. Limits of detection vary considerably within each category, with most limits being too high for commercial use. DNA-based methods appear to be promising to assess coffee authenticity, given that the limits of detection and quantification are low, and specificity is higher than in other methods. Nevertheless, as roast intensity increases, the sensitivity of the method decreases. So far, most reported methods have not been validated and only a few have been tested on commercial brands, except for those involving microscopy which has been highly used for monitoring coffee authenticity although not always efficiently enough.

Detection of coffee adulteration with soybean and corn by capillary electrophoresis-tandem mass spectrometry

The detection of coffee adulteration with soybean and corn by capillary electrophoresis-tandem mass spectrometry was accomplished by evaluating the monosaccharides profile obtained after acid hydrolysis of the samples. The acid hydrolysis, using H2SO4 as a catalyst, increases the ionic strength of the sample impairing the electrophoretic separation. Therefore, Ba(OH)2 was used to both neutralize the medium and reduce the content of sulfate by precipitation of BaSO4. The best separation of nine determined monosaccharides (fucose, galactose, arabinose, glucose, rhamnose, xylose, mannose, fructose and ribose) plus inositol as internal standard was obtained in 500 mmol·L-1 triethylamine, pH 12.3. The monosaccharides are separated as anionic species at this pH. The proposed method is simple, fast (<12.0 min), present linear calibration curves (r2 = 0.995), and relative standard deviation for replicate injections lower than 5%. The LOQ for all monosaccharides was lower than 0.01 mmol·L-1, which is in accordance with the tolerable limits for coffee. Principal component analysis (PCA) was used to evaluate interrelationships between the monosaccharide profile and the coffee adulteration with different proportions of soybean and corn. Fucose, galactose, arabinose, glucose, sucrose, rhamnose, xylose, mannose, fructose, and ribose were quantified in packed roast-and-ground commercial coffee samples, and differences between adulterated and unadulterated coffees could be detected.

Detection of Corn Adulteration in Brazilian Coffee (Coffea arabica) by Tocopherol Profiling and Near-Infrared (NIR) Spectroscopy

Coffee is a high-value commodity that is a target for adulteration, leading to loss of quality and causing significant loss to consumers. Therefore, there is significant interest in developing methods for detecting coffee adulteration and improving the sensitivity and accuracy of these methods. Corn and other lower value crops are potential adulterants, along with sticks and coffee husks. Fourteen pure Brazilian roasted, ground coffee bean samples were adulterated with 1-20% of roasted, ground corn and were analyzed for their tocopherol content and profile by HPLC. They were also analyzed by near-infrared (NIR) spectroscopy. Both proposed methods of detection of corn adulteration displayed a sensitivity of around 5%, thus representing simple and fast analytical methods for detecting adulteration at likely levels of contamination. Further studies should be conducted to verify the results with a much larger sample size and additional types of adulterants.