Multielement analysis of green coffee and its possible use for the determination of origin

Potentially toxic elements (PTEs) in coffee: a comprehensive review of toxicity, prevalence, and analytical techniques

Coffee is one of the most popular non-alcoholic beverages, consumed as a raw material in different food sectors. The popularity of coffee is induced by its pleasant flavor, taste, and highly nutritious nature. However, the absorption of potentially toxic elements (PTEs) through preharvest, harvesting, and post-harvest stages makes it a potentially rich source. Essential elements are potentially toxic at a higher concentration than required for the human body to work. PTEs intake through food systems may lead to health risks, including mutagenicity, teratogenicity, carcinogenicity, and embryotoxic effects. Different analytical techniques such as spectroscopy, electromigration, and electrochemical. are used for PTEs content determination of coffee. Considering the importance of PTEs in human health and the worldwide popularity of coffee, their monitoring of coffee is crucial. Therefore, this study is aimed to investigate the classification, prevalence, and determination techniques of PTEs in different coffee types.

Support vector regression for prediction of stable isotopes and trace elements using hyperspectral imaging on coffee for origin verification

The potential of using rapid and non-destructive near-infrared - hyperspectral imaging (HSI-NIR) for the prediction of an integrated stable isotope and multi-element dataset was explored for the first time with the help of support vector regression. Speciality green coffee beans sourced from three continents, eight countries, and 22 regions were analysed using a push-broom HSI-NIR (700-1700 nm), together with five isotope ratios (δ13C, δ15N, δ18O, δ2H, and δ34S) and 41 trace elements. Support vector regression with the radial basis function kernel was conducted using X as the HSI-NIR data and Y as the geochemistry markers. Model performance was evaluated using root mean squared error, coefficient of determination, and mean absolute error. Three isotope ratios (δ18O, δ2H, and δ34S) and eight elements (Zn, Mn, Ni, Mo, Cs, Co, Cd, and La) had an R2predicted 0.70 - 0.99 across all origin scales (continent, country, region). All five isotope ratios were well predicted at the country and regional levels. The wavelength regions contributing the most towards each prediction model were highlighted, including a discussion of the correlations across all geochemical parameters. This study demonstrates the feasibility of using HSI-NIR as a rapid and non-destructive method to estimate traditional geochemistry parameters, some of which are origin-discriminating variables related to altitude, temperature, and rainfall differences across origins.

Stable isotope and trace element analyses with non-linear machine-learning data analysis improved coffee origin classification and marker selection

BACKGROUND

This study investigated the geographical origin classification of green coffee beans from continental to country and regional levels. An innovative approach combined stable isotope and trace element analyses with non-linear machine learning data analysis to improve coffee origin classification and marker selection. Specialty green coffee beans sourced from three continents, eight countries, and 22 regions were analyzed by measuring five isotope ratios (δ¹³ C, δ¹⁵ N, δ¹⁸ O, δ² H, and δ³⁴ S) and 41 trace elements. Partial least squares discriminant analysis (PLS-DA) was applied to the integrated dataset for origin classification.

RESULTS

Origins were predicted well at the country level and showed promise at the regional level, with discriminating marker selection at all levels. However, PLS-DA predicted origin poorly at the continental and Central American regional levels. Non-linear machine learning techniques improved predictions and enabled the identification of a higher number of origin markers, and those that were identified were more relevant. The best predictive accuracy was found using ensemble decision trees, random forest and extreme gradient boost, with accuracies of up to 0.94 and 0.89 for continental and Central American regional models, respectively.

CONCLUSION

The potential for advanced machine learning models to improve origin classification and the identification of relevant origin markers was demonstrated. The decision-tree-based models were superior with their embedded variable identification features and visual interpretation. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Near-infrared reflectance spectroscopy accurately predicted isotope and elemental compositions for origin traceability of coffee

Stable isotope ratios and trace elements are well-established tools that act as signatures of the product's environmental conditions and agricultural processes; but they involve time, money, and environmentally destructive chemicals. In this study, we tested for the first time the potential of near-infrared reflectance spectroscopy (NIR) to estimate/predict isotope and elemental compositions for the origin verification of coffee. Green coffee samples from two continents, 4 countries, and 10 regions were analysed for five isotope ratios (δ¹³C, δ¹⁵N, δ¹⁸O, δ²H, and δ³⁴S) and 41 trace elements. NIR (1100-2400 nm) calibrations were developed using pre-processing with extended multiplicative scatter correction (EMSC) and mean centering and partial-least squares regression (PLS-R). Five elements (Mn, Mo, Rb, B, La) and three isotope ratios (δ¹³C, δ¹⁸O, δ²H) were moderately to well predicted by NIR (R²: 0.69 to 0.93). NIR indirectly measured these parameters by association with organic compounds in coffee. These parameters were related to altitude, temperature and rainfall differences across countries and regions and were previously found to be origin discriminators for coffee.

Aluminum in Coffee

This study investigated the aluminum content in one of the most consumed daily beverages: coffee. The total Al concentration in 10 different samples of coffee beans and their water-extractable fraction were determined. We then tested the influence of different brewing methods on the concentration of the extracted Al in the final beverage. Metal analyses were performed using graphite furnace atomic absorption spectroscopy (GF-AAS) after microwave-assisted acid digestion. The results showed highly variable Al contents in coffee beans (1.5-15.5 mg kg^-1), of which ∼2-10% were water-extractable. The brewing technique had a major influence on the Al content in the beverage: significantly higher Al concentrations (72.57 ± 23.96 μg L^-1) occurred in coffee brewed in an aluminum moka pot. Interestingly, using ground coffee with this method even reduced the Al content in the final beverage compared to the brewing water used. Coffee brewed from Al capsules did not contain significantly higher Al concentrations compared to other methods.

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.

A predictive spatial model for roasted coffee using oxygen isotopes of α-cellulose

RATIONALE

Fraudulent region-of-origin labeling is a concern for high-value, globally traded commodities such as coffee. The oxygen isotope ratio of cellulose is a useful geographic tracer, as it integrates climate and source water signals. A predictive spatial model ("isoscape") of the δ¹⁸ O values of coffee bean cellulose is generated to evaluate coffee region-of-origin claims.

METHODS

The oxygen isotope ratio of α-cellulose extracted from roasted coffee beans was measured via high-temperature conversion elemental analyzer/isotope ratio mass spectrometry (TC-EA/IRMS) and used to calculate the δ¹⁸ O value of coffee bean water. The ¹⁸ O enrichment of coffee bean water relative to the δ¹⁸ O value of local precipitation was modeled as a function of local temperature and humidity. This function was incorporated into a mechanistic model of cellulose δ¹⁸ O values to predict the δ¹⁸ O values of coffee bean cellulose across coffee-producing regions globally.

RESULTS

The δ¹⁸ O values of analyzed coffee bean cellulose ranged from approximately +22‰ to +42‰ (V-SMOW). As expected, coffees grown in the same region tended to have similar isotope ratios, and the δ¹⁸ O value of coffee bean cellulose was generally higher than the δ¹⁸ O value of modeled stem cellulose for the region. Modeled δ¹⁸ O values of coffee cellulose were within ±2.3‰ of the measured δ¹⁸ O value of coffee cellulose.

CONCLUSIONS

The oxygen isotope ratio of coffee bean cellulose is a useful indicator of region-of-origin and varies predictably in response to climatic factors and precipitation isotope ratios. The isoscape of coffee bean cellulose δ¹⁸ O values from this study provides a quantitative tool that can be applied to region-of-origin verification of roasted coffee at the point-of-sale.

Distinguishing the region-of-origin of roasted coffee beans with trace element ratios

Determining coffee region-of-origin is most appropriately addressed through analyses of the product available to the consumer. We analyzed the concentrations of 44 trace elements in 53 samples of roasted Arabica coffee beans (Coffea arabica) from 21 different countries. Variations in absolute elemental concentrations of coffee beans arise through varying degrees of roasting (from green through dark roasts). Since trace elements are not volatilized at roasting temperatures, we conducted analyses of element ratios to evaluate concentration-related differences among beans of different origins. We used kernel density estimates to compare the distributions of 1892 element ratios for each of these countries with the combined distribution of coffee samples from the other countries. Using this quantitative approach, we demonstrated that many of the world's coffee-producing regions can be distinguished from other regions of the world on the basis of element ratios.

Differentiating the geographical origin of Ethiopian coffee using XRF- and ICP-based multi-element and stable isotope profiling

To test the potential of different analytical tools to determine the geographical origin of Ethiopian coffee, 103 green arabica coffee samples from four coffee regions in Ethiopia were subjected to multi-elements and δ¹³C, δ¹⁵N and δ¹⁸O determinations. Multi-elements were determined by using inductively coupled plasma (ICP)- and X-ray fluorescence spectrometry (XRF)-based techniques, and δ¹³C, δ¹⁵N and δ¹⁸O were determined by using elemental analyzer-isotope ratio mass spectrometry. Using linear discriminant analysis, XRF-based multi-elements with and without δ¹³C appeared to be most effective in discriminating the geographical origin of coffee, giving higher classification accuracy (89 and 86%, respectively) than ICP-based multi-elements with and without stable isotopes (80%, each). These results demonstrate the potential of XRF-based multi-element profiling as a relatively fast and low-cost tool to trace the geographical origin of Ethiopian coffee. All together this study offers the proof of concept for a promising method that, upon standardization, could be used for coffee provenance authentication and fraud detection.

Elemental analysis of Brazilian coffee with ion beam techniques: From ground coffee to the final beverage

Brazilian coffee is well known worldwide due to its quality and richness in taste. The aim of the present study is to provide the elemental characterization of Brazilian coffee along different stages of the drip brewing process. To that end, samples from roasted ground coffee, spent coffee, paper filters and the final beverage were analyzed with one single ion beam technique, namely particle-induced X-ray emission (PIXE). In total, over 140 samples from 8 different Brazilian brands of ground coffee were analyzed. Large differences in some elemental concentrations were observed among different brands and among different batches of a single brand, which leads to high variances in the data. Concerning the beverage preparation, the analysis of the spent coffee shows that the transfer ratio from the ground coffee to the beverage differs for each element. Our results indicate that potassium and chlorine have the highest transfer ratio. Moreover, the concentration of rubidium is relatively high in drinking coffee. Finally, there is no influence of the elemental composition of paper filter in the preparation of drinking coffee.

Elemental composition of green coffee and its contribution to dietary intake

The concentration of twenty-seven elements (Li, Be, B, Mg, Al, P, K, Ca, Cr, Mn, Co, Ni, Cu, Zn, As, Se, Sr, Mo, Cd, Sn, Sb, Ba, Hg, Pb, Bi, Th, and U) in green coffee samples and their infusions were determined by using inductively coupled plasma-mass spectrometry (ICP-MS). Prior to analysis, green coffee samples were prepared by microwave digestion, while infusions were analyzed without any pre-treatment. The accuracy and precision of the proposed methods were verified by recovery experiments. Considering samples; K, Cu, and Al had the highest mean concentrations with 6714.5μgg(-1), 12.1μgg(-1), and 25.9μgg(-1) among major, trace and toxic elements, respectively. The impact of brewing type on leachability of elements was also studied and the results outlined that mean leachability of elements to Turkish coffee were greater than to mud coffee. Furthermore, dietary element intakes through green coffee consumption were also estimated. This is the first study presenting wide range of elements in green coffee brews and calculating dietary intakes.

Elemental profiling and geographical differentiation of Ethiopian coffee samples through inductively coupled plasma-optical emission spectroscopy (ICP-OES), ICP-mass spectrometry (ICP-MS) and direct mercury analyzer (DMA)

This study was aimed to establish the elemental profiling and provenance of coffee samples collected from eleven major coffee producing regions of Ethiopia. A total of 129 samples were analyzed for forty-five elements using inductively coupled plasma (ICP)-optical emission spectroscopy (OES), ICP-mass spectrometry (MS) and direct mercury analyzer (DMA). Among the macro elements, K showed the highest levels whereas Fe was found to have the lowest concentration values. In all the samples, Ca, K, Mg, P and S contents were statistically significant (p<0.05). Micro elements showed the concentrations order of: Mn>Cu>Sr>Zn>Rb>Ni>B. Contents of the trace elements were lower than the permissible standard values. Inter-regions differentiation by cluster analysis (CA), linear discriminant analysis (LDA) and principal component analysis (PCA) showed that micro and trace elements are the best chemical descriptors of the analyzed coffee samples.

A simplified determination of total concentrations of Ca, Fe, Mg and Mn in addition to their bioaccessible fraction in popular instant coffee brews

A direct analysis of instant coffee brews with HR-CS-FAAS spectrometry to determine the total Ca, Fe, Mg and Mn content has been developed and validated. The proposed method is simple and fast; it delivers good analytical performance; its accuracy being within -3% to 3%, its precision--2-3% and detection limits--0.03, 0.04, 0.004 and 0.01 mg l(-1) for Ca, Fe, Mg and Mn, respectively. In addition, Ca, Fe, Mg and Mn bioaccessibility in instant coffee brews was measured by means of the in vitro gastrointestinal digestion with the use of simulated gastric and intestinal juice solutions. Absorption of metals in intestinal villi was simulated by means of ultrafiltration over semi-permeable membrane with a molecular weight cut-off of 5 kDa. Ca, Fe, Mg and Mn concentrations in permeates of instant coffee gastrointestinal incubates were measured with HR-CS-FAA spectrometry.

The content of Ca, Cu, Fe, Mg and Mn and antioxidant activity of green coffee brews

A simple and fast method of the analysis of green coffee infusions was developed to measure total concentrations of Ca, Cu, Fe, Mg and Mn by high resolution-continuum source flame atomic absorption spectrometry. The precision of the method was within 1-8%, while the accuracy was within -1% to 2%. The method was used to the analysis of infusions of twelve green coffees of different geographical origin. It was found that Ca and Mg were leached the easiest, i.e., on average 75% and 70%, respectively. As compared to the mug coffee preparation, the rate of the extraction of elements was increased when infusions were prepared using dripper or Turkish coffee preparation methods. Additionally, it was established that the antioxidant activity of green coffee infusions prepared using the mug coffee preparation was high, 75% on average, and positively correlated with the total content of phenolic compounds and the concentration of Ca in the brew.

Espresso beverages of pure origin coffee: mineral characterization, contribution for mineral intake and geographical discrimination

Espresso coffee beverages prepared from pure origin roasted ground coffees from the major world growing regions (Brazil, Ethiopia, Colombia, India, Mexico, Honduras, Guatemala, Papua New Guinea, Kenya, Cuba, Timor, Mussulo and China) were characterized and compared in terms of their mineral content. Regular consumption of one cup of espresso contributes to a daily mineral intake varying from 0.002% (sodium; Central America) to 8.73% (potassium; Asia). The mineral profiles of the espresso beverages revealed significant inter- and intra-continental differences. South American pure origin coffees are on average richer in the analyzed elements except for calcium, while samples from Central America have generally lower mineral amounts (except for manganese). Manganese displayed significant differences (p<0.05) among the countries of each characterized continent. Intercontinental and inter-country discrimination between the major world coffee producers were achieved by applying canonical discriminant analysis. Manganese and calcium were found to be the best chemical descriptors for origin.

Geographic determination of coffee beans using multi-element analysis and isotope ratios of boron and strontium

This study aims to evaluate the feasibility of using chemical and isotopic compositions of coffee beans to identify their geographic origins. Twenty-one Coffea arabica beans collected from 14 countries in 3 major coffee-producing regions, Africa, America and Asia, were analysed for multi-element of B, Rb, Sr, Ba, Fe, Mn and Zn, as well as isotopic compositions of B and Sr. Our results demonstrate that the geographic origin of coffee beans could be classified based on concentrations of Rb, Sr and Ba. However, the isotope ratios of B and Sr provide more sensitive information for the growth localities. Combined with literature data, this study indicates that B and Sr isotopes are excellent indicators of the origin of coffee beans.

The suitability of the simplified method of the analysis of coffee infusions on the content of Ca, Cu, Fe, Mg, Mn and Zn and the study of the effect of preparation conditions on the leachability of elements into the coffee brew

A fast and straightforward method of the analysis of coffee infusions was developed for measurements of total concentrations of Ca, Cu, Fe, Mg, Mn and Zn by flame atomic absorption spectrometry. Its validity was proved by the analysis of spiked samples; recoveries of added metals were found to be within 98-104% while the precision was better than 4%. The method devised was used for the analysis of re-distilled water infusions of six popular ground coffees available in the Polish market. Using the mud coffee preparation it was established that percentages of metals leached in these conditions varied a lot among analysed coffees, especially for Ca (14-42%), Mg (6-25%) and Zn (1-24%). For remaining metals, the highest extractabilities were assessed for Mn (30-52%) while the lowest for Fe (4-16%) and Cu (2-12%). In addition, it was found that the water type and the coffee brewing preparation method influence the concentration of studied metals in coffee infusions the most.

Discrimination between arabica and robusta green coffee varieties according to their chemical composition

Arabica and robusta green coffee varieties have been differentiated by using pattern recognition procedures. Chlorogenic acid, caffeine, trigonelline, aqueous extract, amino acids and polyphenols have been analysed in 41 samples of green coffee and used as chemical descriptors. Principal component and cluster analysis in addition with the K-nearest neighbours method have been applied.

Using elemental profiles and stable isotopes to trace the origin of green coffee beans on the global market

A broad elemental profile incorporating 54 elements (Li, Be, B, Na, Mg, Al, P, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Rb, Sr, Y, Mo, Pd, Ag, Cd, Sn, Sb, Te, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Dy, Er, Tm, Yb, Re, Ir, Pt, Au, Hg, Tl, Pb, Bi and U) in combination with δ(2) H, δ(13) C, δ(15) N and δ(18) O was used to characterise the composition of 62 green arabica (Coffea arabica) and robusta (Coffea canephora) coffee beans grown in South and Central America, Africa and Asia, the four most internationally renowned areas of production. The δ(2) H, Mg, Fe, Co and Ni content made it possible to correctly assign 95% of green coffee beans to the appropriate variety. Canonical discriminant analysis, performed using δ(13) C, δ(15) N, δ(18) O, Li, Mg, P, K, Mn, Co, Cu, Se, Y, Mo, Cd, La and Ce correctly traced the origin of 98% of coffee beans.

Isotopes as tracers of the Hawaiian coffee-producing regions

Green coffee bean isotopes have been used to trace the effects of different climatic and geological characteristics associated with the Hawaii islands. Isotope ratio mass spectrometry (IRMS) and inductively coupled plasma mass spectrometry ((MC)-ICP-SFMS and ICP-QMS) were applied to determine the isotopic composition of carbon (δ13C), nitrogen (δ15N), sulfur (δ34S), and oxygen (δ18O), the isotope abundance of strontium (87Sr/86Sr), and the concentrations of 30 different elements in 47 green coffees. The coffees were produced in five Hawaii regions: Hawaii, Kauai, Maui, Molokai, and Oahu. Results indicate that coffee plant seed isotopes reflect interactions between the coffee plant and the local environment. Accordingly, the obtained analytical fingerprinting could be used to discriminate between the different Hawaii regions studied.

Feasibility study on chemometric discrimination of roasted Arabica coffees by solvent extraction and Fourier transform infrared spectroscopy

In this feasibility study, Fourier transform infrared (FTIR) spectroscopy and chemometric analysis were adopted to discriminate coffees from different geographical origins and of different roasting degrees. Roasted coffee grounds were extracted using two methods: (1) solvent alone (dichloromethane, ethyl acetate, hexane, acetone, ethanol, or acetic acid) and (2) coextraction using a mixture of equal volume of the solvent and water. Experiment results showed that the coextraction method resulted in cleaner extract and provided a greater amount of spectral information, which was important for sample discrimination. Principal component analysis of infrared spectra of ethyl acetate extracts for dark and medium roast coffees showed separated clusters according to their geographical origins and roast degrees. Classification models based on soft independent modeling of class analogy analysis were used to classify different coffee samples. Coffees from four different countries, which were roasted to dark, were 100% correctly classified when ethyl acetate was used as a solvent. The FTIR-chemometric technique developed here may serve as a rapid tool for discriminating geographical origin of roasted coffees. Future studies involving green coffee beans and the use of larger sample size are needed to further validate the robustness of this technique.

Stable isotope and trace element profiling combined with classification models to differentiate geographic growing origin for three fruits: effects of subregion and variety

Classifications of geographic growing origin of three fresh fruits combining elemental profiles with various modeling approaches were determined. Elemental analysis (Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, P, V, and Zn) of strawberry, blueberry, and pear samples was performed using inductively coupled plasma argon atomic emission spectrometer. Bulk stable carbon and nitrogen isotope analyses in pear were performed using mass spectrometry as an alternative fingerprinting technique. Each fruit, strawberry (Fragaria x ananassa), blueberry (Vaccinium caesariense/corymbosum), and pear (Pyrus communis), was analyzed from two growing regions: Oregon vs Mexico, Chile, and Argentina, respectively. Principal component analysis and canonical discriminant analysis were used for data visualization. The data were modeled using linear discriminant function, quadratic discriminant function, neural network, genetic neural network, and hierarchical tree models with successful classification ranging from 70 to 100% depending on commodity and model. Effects of Oregon subregional and variety classification were investigated with similar success rates.

Chemical discrimination of arabica and robusta coffees by Fourier transform Raman spectroscopy

This article deals with the potential of Fourier transform (FT) Raman spectroscopy in discrimination of botanical species of green and roasted coffees. There are two species of commercial importance: Coffea arabica (arabica) and Coffea canephora (robusta). It is recognized that they differ in their lipid fraction, especially in the content of the diterpene kahweol, which is present at 0.1-0.3% dry matter basis in arabica beans and only in traces (<0.01%) in robusta. The visual examination of the Raman spectra of the lipid fraction extracted from arabica, robusta and liberica samples shows differences in the mid-wavenumbers region: arabica spectra have two characteristic scattering bands at 1567 and 1478 cm(-1). The spectrum of the pure kahweol shows the same bands. Principal component analysis is applied to the spectra and reveals clustering according to the coffee species. The first principal component (PC1) explains 93% of the spectral variation and corresponds to the kahweol concentration. Using the PC1 score plot, two groups of arabica can be distinguished as follows: one group with high kahweol content and another group with low kahweol content. The first group includes samples coming from Kenya and Jamaica; the second group includes samples from Australia. The main difference between these coffees is that those from Kenya and Jamaica are well-known for growing at a high altitude whereas those ones from Australia are grown at a low altitude. To our knowledge, the application of Raman spectroscopy has never been used in coffee analysis.

Chemical profiling to differentiate geographic growing origins of coffee

The objective of this research was to demonstrate the feasibility of this method to differentiate the geographical growing regions of coffee beans. Elemental analysis (K, Mg, Ca, Na, Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Mo, S, Cd, Pb, and P) of coffee bean samples was performed using ICPAES. There were 160 coffee samples analyzed from the three major coffee-growing regions: Indonesia, East Africa, and Central/South America. A computational evaluation of the data sets was carried out using statistical pattern recognition methods including principal component analysis, discriminant function analysis, and neural network modeling. This paper reports the development of a method combining elemental analysis and classification techniques that may be widely applied to the determination of the geographical origin of foods.

Daily intake of trace metals through coffee consumption in India

The trace element contents of five varieties of instant coffee powder available in the Indian market have been analysed. Ca, Cr, Fe, K, Mg, Mn, Ni, Sr, Zn and Pb, Cd, Cu have been determined using atomic absorption spectrophotometry and differential pulse anodic stripping voltammetry, respectively. The metal levels in the coffee powders observed in this study are comparable with those reported for green coffe beans (Arabica and Robusta variety) reported worldwide with the exception of Sr and Zn, which were on the lower side of the reported values. Concentrations of these metals have been converted into intake figures based on coffee consumption. The daily intakes of the above metals through ingestion of coffee are 1.4 mg, 1.58 microg, 124 microg, 41.5 mg, 4.9 mg, 17.9 microg, 2.9 microg, 3.8 microg, 12.5 microg, 0.2 microg, 0.03 microg and 15.5 microg, respectively. The values, which were compared with the total dietary, intake of metals through ingestion by the Mumbai population, indicate that the contribution from coffee is less than or around 1% for most of the elements except for Cr and Ni which are around 3%.

Correction of mass spectral drift using artificial neural networks

For pyrolysis mass spectrometry (PyMS) to be used for the routine identification of microorganisms, for quantifying determinands in biological and biotechnological systems, and in the production of useful mass spectral libraries, it is paramount that newly acquired spectra be compared to those previously collected. Neural network and other multivariate calibration models have been used to relate mass spectra to the biological features of interest. As commonly observed, however, mass spectral fingerprints showed a lack of long-term reproducibility, due to instrumental drift in the mass spectrometer; when identical materials were analyzed by PyMS at dates from 4 to 20 months apart, neural network models produced at earlier times could not be used to give accurate estimates of determinand concentrations or bacterial identities. Neural networks, however, can be used to correct for pyrolysis mass spectrometer instrumental drift itself, so that neural network or other multivariate calibration models created using previously collected data can be used to give accurate estimates of determinand concentration or the nature of bacteria (or, indeed, other materials) from newly acquired pyrolysis mass spectra. This approach is not limited solely to pyrolysis mass spectrometry but is generally applicable to any analytical tool which is prone to instrumental drift, such as IR, ESR, NMR and other spectroscopies, and gas and liquid chromatography, as well as other types of mass spectrometry.