Body structural and cellular aging of women with low socioeconomic status in Hungary: A pilot study.

OBJECTIVES: The health status of an individual is determined not only by their genetic background but also by their physical environment, social environment and access and use of the health care system. The Roma are one of the largest ethnic minority groups in Hungary. The majority of the Roma population live in poor conditions in segregated settlements in Hungary, with most experiencing higher exposure to environmental health hazards. The main aim of this study was to examine the biological health and aging status of Roma women living in low socioeconomic conditions in Hungary. METHODS: Low SES Roma (n: 20) and high SES non-Roma women (n: 30) aged between 35 and 65 years were enrolled to the present analysis. Body mass components were estimated by body impedance analysis, bone structure was estimated by quantitative ultrasound technique. Cellular aging was assessed by X chromosome loss estimation. Data on health status, lifestyle and socioeconomic factors were collected by questionnaires. RESULTS: The results revealed that low SES women are prone to be more obese, have a higher amount of abdominal body fat, and have worse bone structure than the national reference values. A positive relationship was found between aging and the rate of X chromosome loss was detected only in women with low SES. Waist to hip ratio, existence of cardiovascular diseases and the number of gravidities were predictors of the rate of X chromosome loss in women. CONCLUSIONS: The results suggested that age-adjusted rate of X chromosome loss could be related to the socioeconomic status

Nutritional Quality and Safety Assessment of Pork Meat Cuts from Romania: Fatty Acids and Elemental Profile

In this study, the fatty acids and elemental profiles of 53 pork cut samples were determined. To offer insights into their potential health implications, we computed 18 key nutritional indices. These indices included parameters such as saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), polyunsaturated fatty acids (PUFAs), unsaturated fatty acids (UFAs), the MUFAs/SFAs ratio, PUFAs/SFAs ratio, atherogenic index (AI), thrombogenic index (TI), the hypocholesterolemic to hypercholesterolemic ratio (h/H), health-promoting index (HPI), hypocholesterolemic index (HI), unsaturation index (UI), saturation index (SI), peroxidizability index (PI), nutritional value index (NVI), hypocholesterolemic index of fatty acids (DFAs), hypercholesterolemic index of fatty acids (OFAs), and the DFAs/OFAs ratio. These indices were calculated based on their fatty acid composition to provide comprehensive nutritional information. A health risk assessment revealed the safety and minimum health risk for the population from consuming the investigated pork cuts using the Target Hazard Quotient (THQ), Hazard Index (HI), and target cancer risk (TR). The ANOVA test showed significant differences in the levels of K, Fe, Mn, Zn, MUFAs, and AI among the pork cut samples. It was noted that by employing the correlation between the fatty acids profile, nutritional indices, and elemental concentrations and an unsupervised statistical method, such as PCA, a perfect separation from the different pork cuts could not be obtained

Edible Oils Differentiation Based on the Determination of Fatty Acids Profile and Raman Spectroscopy—A Case Study

This study proposes a comparison between two analytical techniques for edible oil classification, namely gas-chromatography equipped with a flame ionization detector (GC-FID), which is an acknowledged technique for fatty acid analysis, and Raman spectroscopy, as a real time noninvasive technique. Due to the complexity of the investigated matrix, we used both methods in connection with chemometrics processing for a quick and valuable evaluation of oils. In addition to this, the possible adulteration of investigated oil varieties (sesame, hemp, walnut, linseed, sea buckthorn) with sunflower oil was also tested. In order to extract the meaningful information from the experimental data set, a supervised chemometric technique, namely linear discriminant analysis (LDA), was applied. Moreover, for possible adulteration detection, an artificial neural network (ANN) was also employed. Based on the results provided by ANN, it was possible to detect the mixture between sea buckthorn and sunflower oil

Evaluation of Mushrooms Based on FT-IR Fingerprint and Chemometrics

Edible mushrooms have been recognized as a highly nutritional food for a long time, thanks to their specific flavor and texture, as well as their therapeutic effects. This study proposes a new, simple approach based on FT-IR analysis, followed by statistical methods, in order to differentiate three wild mushroom species from Romanian spontaneous flora, namely, Armillaria mellea, Boletus edulis, and Cantharellus cibarius. The preliminary data treatment consisted of data set reduction with principal component analysis (PCA), which provided scores for the next methods. Linear discriminant analysis (LDA) managed to classify 100% of the three species, and the cross-validation step of the method returned 97.4% of correctly classified samples. Only one A. mellea sample overlapped on the B. edulis group. When kNN was used in the same manner as LDA, the overall percent of correctly classified samples from the training step was 86.21%, while for the holdout set, the percent rose to 94.74%. The lower values obtained for the training set were due to one C. cibarius sample, two B. edulis, and five A. mellea, which were placed to other species. In any case, for the holdout sample set, only one sample from B. edulis was misclassified. The fuzzy c-means clustering (FCM) analysis successfully classified the investigated mushroom samples according to their species, meaning that, in every partition, the predominant species had the biggest DOMs, while samples belonging to other species had lower DOMs