The Quality of Ecologically and Conventionally Grown White and Brown Agaricus bisporus Mushrooms

Preventing mislabeling of organic white button mushrooms (Agaricus bisporus) combining NMR-based foodomics, statistical, and machine learning approach

Organic foods are among the most susceptible to fraud and mislabeling since the differentiation between organic and conventionally grown food relies on a paper-trail-based system. This study aimed to develop a differentiation model that combines nuclear magnetic resonance (NMR), statistical approach (principal component analysis - PCA and partial least square discriminant analysis - PLS-DA), and classification artificial neural network (cANN). The model was tested for hydrophilic and lipophilic extracts of Agaricus bisporus. As linear techniques, the PCA and PLS-DA analyses and cANN as a non-linear classification tool successfully discriminated organic from conventional samples regarding their NMR data. PLS-DA revealed higher similarity among the hydrophilic samples within the organic class and among the lipophilic samples within the conventional class. Both applied approaches demonstrated high statistical quality, but a higher level of classification confidence in the case of lipophilic extracts. The metabolites responsible for discrimination and observed (dis)similarities between classes were considered according to cultivation specificities.

Changes in Lacto-Fermented Agaricus bisporus (White and Brown Varieties) Mushroom Characteristics, including Biogenic Amine and Volatile Compound Formation

This study aimed to evaluate the changes in Agaricus bisporus (white and brown) characteristics (colour and acidity parameters, lactic acid bacteria (LAB) and mould/yeast counts, biogenic amine content, fatty acid (FA) and volatile compound (VC) profiles, overall acceptability, and emotions induced for consumers) during a 48 h lactic acid fermentation with Lacticaseibacillus casei No. 210, Lactiplantibacillus plantarum No. 135, Lacticaseibacillus paracasei No. 244, and Pediococcus acidilactici No. 29 strains. Fermented white and brown A. bisporus showed higher LAB count and lower pH, lightness, redness, and yellowness than non-fermented ones. Yeast and fungi counts were similar between non-fermented and fermented samples. All samples contained spermidine (on average, 191.5 mg/kg) and some of the fermented samples had tyramine (on average, 80.7 mg/kg). Saturated FA was the highest in non-fermented brown A. bisporus. The highest monounsaturated and polyunsaturated FA contents were found in Lp. plantarum No. 135 fermented white and brown A. bisporus, respectively. For the first time, the VC profile of fermented A. bisporus was analysed. 1-Octen-3-ol content significantly decreased while benzyl alcohol, acetoin, and 2,3-butanediol increased in most fermented samples. Fermented A. bisporus received good acceptability scores. The emotional evaluation showed that the LAB strain and the interaction of the LAB strain and A. bisporus variety were significant on the intensity of emotions "happy" and "sad", while all analysed factors and their interactions were significant on the intensity of "angry" and "disgusted" (p ≤ 0.05). The findings of this study show the potential of the selected LAB strains and contribute to the increasing body of research on fermented mushrooms.

Chromatographic fingerprint application possibilities in food authentication

The aim of the study was to compare the effectiveness of the use of low-peak chromatographic fingerprints for the differentiation of various food products. Three groups of unprocessed products (mushrooms, hazelnuts and tomatoes), food preparations (bread, dried herbs and tomato juice) and alcoholic beverages (vodka and two types of blended whiskey) were examined. A commercial electronic nose based on ultrafast gas chromatography (acquisition time 90 s) with a flame ionization detector was used for the research. Static headspace was used as a green procedure to extract volatile compounds without modifying the food matrix. Individual extraction conditions were used for each product group. Similarities and differences between profiles were analyzed by simple Principal Components Analysis. The similarity rating was determined using the Euclidean distances. Global model was built for recognition chromatographic fingerprints of food samples. The best recognition results were 100% and 89% for tomato juices, spices, separate champignon elements and hazelnuts. On the other hand, the worst recognition results were 56% and 77% for breads and strong alcoholic beverages.