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n this paper (open access) the authors used of solid-phase microextraction (SPME)-gas chromatography-time-of-flight mass spectrometry (GC/Q-ToF-MS) combined with chemometrics to detect key differences between adulterated and non-adulterated ground roast coffee. They drilled into these differences and found two potential chemical markers for common adulterants.

They compared the aroma profiles of ground roasted coffee with some commonly used adulterants (ground roasted barley, corn and soybean). The SPME fibre collected and concentrated the headspace volatiles. Non-adulterated and adulterated samples were distinguished after applying some chemometric tools (principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA)) on the obtained chromatographic data. Two volatile compounds (1H-imidazole-4-methanol and benzene-2-(1,3-butadienyl)-1,3,5-trimethyl) were identified as potential markers for the determination of adulterants (ground roasted barley, corn or soybean) in ground roasted coffee (p-value cut-off<0.001 and fold change (FC) cut-off>10). Also, 2-furanmethanol and 2-formyl-1-methylprrrole were found as marker candidates for roasted coffee powder.

The authors tested this approach and were able to detect selected herbal adulterants (5% w/w) found in ground coffee.

This paper (open access) looked at classifying quinoa, amaranth and wheat flours.

Reference mixtures were prepared in-house:

i) Pure flours, including wheat, quinoa, and amaranth, with two varieties analysed for both quinoa and amaranth;

ii) Double mixtures, which comprised binary combinations of quinoa:wheat flours at 50:50 and 25:75 ratios, and amaranth:wheat flours at 20:80 and 10:90 ratios; and

iii) Triple mixtures, involving combinations of quinoa, amaranth, and wheat flours at 25:10:65 and 12.5:5:82.5 ratios

Volatile profiles of all reference mixtures were measured by both SPME-GC-MS and using a previously-published “electronic nose” sensor (a multiplex of 8 electrochemical sensors).

Twenty-four volatile compounds were identified, including limonene, 1R-α-pinene, and L-β-pinene, which were exclusive to pseudocereal flours, and hexanal, abundant in wheat flour as an oxidation indicator. The authors report that the E-nose achieved 89.7 % accuracy in discriminating between quinoa, amaranth, and wheat flours and effectively separated double and triple mixtures. A PLS model revealed a strong correlation between E-nose data and concentrations of limonene, α-pinene, and β-pinene (R²CV = 0.94–0.95). The integration of GC-MS and E-nose proved highly efficient for flour authentication, with canonical discriminant analysis successfully identifying pseudocereal flours in mixtures with wheat flour,

Photo by Vlad Kutepov on Unsplash

This study (open access) aimed to identify the volatile compound components in chicken, beef, pork, and mixed (2-to-1 proportions) pork-containing satay, as well as determined the biomarker compounds for each type of satay meat.  The satay products were as commonly eaten in Indonesia, with cubes of meat barbequed on a skewer before adding the sauce.  Cooking in this manner gives the meat a distinct flavour, and the aim was to differentiate this by analysis of volatiles.

The volatile components in satay were extracted using the solid-phase microextraction (SPME) and analysed by gas chromatography-mass spectrometry (GC-MS). The data were processed using multivariate data analysis. 15 key volatile chemicals were measured.

Each type of satay meat exhibited good separation with the multivariate model. Beef and chicken satay were distinctly separated, whereas samples of pork and mixed pork-containing satay were positioned closely together.

The volatile compounds with the highest intensity in beef satay samples were nonanal, carbon disulfide, hexadecanal, and benzaldehyde. Chicken satay samples showed the highest levels of benzaldehyde, nonanal, hexadecanal, and hexadecane among the volatile compounds. In pork satay, the highest volatile compounds were cyclohexanol, 5-methyl-2-(1-methylethyl)-(1.alpha.,2.beta.,5.alpha.), hexanal, nonanal, benzaldehyde, and hexadecanal. Each type of satay meat was effectively separated, and mixed meat satay was positioned close to the pork satay group. The compounds identified as markers in beef satay were hexadecanal, nonanoic acid, ethylbenzene, pentadecanal, and heptadecanal. Chicken satay marker components included benzaldehyde; 2,3,5-trimethyl-6-ethylpyrazine; 2-nonenal, (E)-; linalool; 2-methylbutanal; and 3-methylbutanal. The marker components for pork satay and its mixtures were hexanal; thiophene, 2-methyl-; cyclodecene, (E)-; 2-methyl-2-butenal; and cyclodecene, (Z)-. These marker compounds present in each meat were highly correlated in the separation of satay samples.

The authors conclude that SPME-GC-MS successfully differentiated the satay meats and determined the compounds contributing most strongly to the separation.

Photo by Keriliwi on Unsplash

In this study (open access) a non-targeted method of headspace-solid phase microextraction with gas chromatography coupled to mass spectrometry (HS-SPME-GC–MS) was developed to achieve the characterization, classification, and authentication of different coffee samples according to geographical production region, and variety (arabica/robusta). Moreover, decaffeinated and non-decaffeinated instant coffee samples were analyzed. Some samples of chicory, a potential coffee adulterant, were also been included. The GC–MS fingerprints were used to classify and characterise the analyzed coffees using principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and partial least squares (PLS) regression.

185 reference samples were used.  42 were chicory (a typical coffee substitute or adulterant), 96 were coffee from three geographical production regions (Vietnam, Cambodia, and Costa Rica) and species (Arabica, Robusta, and Arabica-Robusta mixture), and 47 samples were soluble coffee (decaffeinated and non-decaffeinated). The chicory samples were purchased from Barcelona supermarkets and the coffee samples were from Vietnam, Cambodia and Costa Rica local supermarkets. Each paired PLS-DA model was built using 70 % of samples randomly selected from each group as the calibration set while the remaining 30 % of the samples were employed as the prediction set.  The authors compared models generated using two different GC columns and operating conditions.

The authors tested their model on mixtures prepared in-house from the same reference set:: Vietnamese Arabica Coffee adulterated with Vietnamese Robusta Coffee and Vietnamese Robusta Coffee adulterated with chicory.  They reported that the model could classify adulteration levels down to 15%.

(image from the paper)

This peer-reviewed pre-print (open access) reports a classification model for different Greek olive oil cultivars using combined data from two analytical techniques: volatile component analysis (6 marker compounds) by solid phase microextraction – gas chromatography (SPME-GC-MS) and spectral analysis by attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR)

The model was built to differentiate Greek oils from 3 cultivars: Koroneiki, Megaritiki and Amfissis.  The reference database was constructed from samples collected over 3 harvest seasons.  The authors report that application of the supervised methods of linear and quadratic discriminant cross-validation analysis, based on volatile component data, provided a correct classification score of 97.4 and 100.0%, respectively. The corresponding statistical analyses were used in the mid-infrared spectra where the 96.1% of samples were discriminated correctly.

The authors conclude that ATR-FTIR and SPME-GC-MS, in conjunction with the appropriate feature selection algorithm and classification methods, are powerful tools for the authentication of Greek olive oil. They consider that the proposed methodology could be used in industrial settings for the determination of Greek olive oil botanical origin.