Fiber fractions, multielemental and isotopic composition of a tropical C4 grass grown under elevated atmospheric carbon dioxide

Investigation of fraud in the production of butter: a forensic case study of criminal association

Butter is among the most popular and commercially valuable dairy products. Its high commercial value makes it a major target for adulteration, which aims to reduce production costs by using lower-quality fats and oils from other sources. The annual global market is around USD 30 billion (2023), expected to reach USD 36 billion in 2028, which also justifies the enormous interest in adulteration. In this work, a confirmed case of butter adulteration was studied by Nuclear Magnetic Resonance (NMR) and Stable Carbon Isotopic Ratio Analysis (SCIRA) techniques, employed to detect the inclusion in butter production of vegetable oils, such as soybean and palm oils. A total of 21 samples seized by the Brazilian Federal Police were analysed by NMR and SCIR, and compared to original butter obtained from commercial sources. The composition of all the seized samples was a mixture of butter (dairy fat of animal origin) with fat of vegetable origin (soybean and palm oil) and did not contain milk as a major component. While NMR was an unequivocal choice to discriminate the chemical composition of food samples, identifying the short-chain saturated fatty acids present in milk fat, including the butyryl alkyl chain, SCIRA was able to discriminate the origin of fat present in the butter samples as C3 sources, such as palm vegetable oils.

The main effects of elevated CO2 and soil-water deficiency on 1H NMR-based metabolic fingerprints of Coffea arabica beans by factorial and mixture design

The metabolic response of Coffea arabica trees in the face of the rising atmospheric concentration of carbon dioxide (CO₂) combined with the reduction in soil-water availability is complex due to the various (bio)chemical feedbacks. Modern analytical tools and the experimental advance of agronomic science tend to advance in the understanding of the metabolic complexity of plants. In this work, Coffea arabica trees were grown in a Free-Air Carbon Dioxide Enrichment dispositive under factorial design (2²) conditions considering two CO₂ levels and two soil-water availabilities. The ¹H NMR mixture design-fingerprinting effects of CO₂ and soil-water levels on beans were strategically investigated using the principal component analysis (PCA), analysis of variance (ANOVA) - simultaneous component analysis (ASCA) and partial least squares-discriminant analysis (PLS-DA). From the ASCA, the CO₂ factor had a significant effect on changing the ¹H NMR profile of fingerprints. The soil-water factor and interaction (CO₂ × soil-water) were not significant. ¹H NMR fingerprints with PCA, ASCA and PLS-DA analysis determined spectral profiles for fatty acids, caffeine, trigonelline and glucose increases in beans from current CO₂, while quinic acid/chlorogenic acids, malic acid and kahweol/cafestol increased in coffee beans from elevated CO₂. PLS-DA results revealed a good classification performance between the significant effect of the atmospheric CO₂ levels on the fingerprints, regardless of the soil-water availabilities. Finally, the PLS-DA model showed good prediction ability, successfully classifying validation data-set of coffee beans collected over the vertical profile of the plants and included several fingerprints of different extracting solvents. The results of this investigation suggest that the association of experimental design, mixture design, PCA, ASCA and PLS-DA can provide accurate information on a series of metabolic changes provoked by climate changes in products of commercial importance, in addition to minimizing the extra work necessary in classic analytical approaches, encouraging the development of similar strategies.