Natural Contaminants in Wines: Determination of Biogenic Amines by Chromatographic Techniques.

Biogenic amines (BAs) are natural contaminants of wine that originate from decarboxylase microorganisms involved in fermentation processes. The primary relevance of biogenic amines in food could have both toxic effects on consumers' health (i.e., allergic reactions, nausea, tremors, etc.), if present at high concentrations, and concurrently it can be considered as a remarkable indicator of quality and/or freshness. Therefore, the presence of nine biogenic amines [Tryptamine (TRP), ß-phenylethylamine (ß-PEA), putrescine (PUT), cadaverine (CAD), histamine (HIS), serotonin (SER), tyramine (TYR), spermidine (SPD), and spermine (SPM)] was investigated in red and white wine samples, which differed in the winemaking processes. The qualitative-quantitative determination of BAs was carried out by chromatographic methods (HPLC-UV/Vis and LC-ESI-MS). The analysis showed that both winemaking processes had all the nine BAs considered in the study at different amounts. Data showed that red wines had a higher concentration of PUT (10.52 mg L-1), TYR (7.57 mg L-1), and HIS (6.5 mg L-1), the BAs most involved in food poisoning, compared to white wines, probably related to the different type of fermentation (alcoholic and malolactic).

Bioactive compounds in cherry tomatoes (Solanum Lycopersicum var. Cerasiforme): Cultivation techniques classification by multivariate analysis.

This study aimed to assess bioactive compounds contents in cherry tomatoes and classify samples according to cultivation techniques. Simple and cost-effective analyses were conducted on 128 samples cultivated using hydroponic, organic, and conventional practices. An HPLC-FD method was validated in-house for eight biogenic amines while antioxidants were evaluated using total phenolic content and anti-radical activity assays (DPPH• and ABTS•+ assays). Chemometric evaluation was applied to extrapolate significant information from data-sets. Variable selection using correlation matrix and the Fisher test was performed. Principal component analysis and linear discriminant analysis were used to construct a mathematical model to classify samples. Correct classifications were achieved in training (95.2%), validation (98.5%), and testing (100%). The results showed that cherry tomatoes cultivated using different techniques could be discriminated based on bioactive profiles using chemometric approaches.