Evidence of seasonal variation of ethyl glucuronide in hair: Modeling a seven-year data series.

The assessment of chronic excessive alcohol consumption by ethyl glucuronide (EtG) determination in hair is generally based on a cut-off value of 30 pg/mg recognized by regulatory authorities and scientific societies that guide the decision process. The ongoing debate about the risks connected with the straightforward application of this cut-off refers to the factors that may influence the detected EtG concentration. The present contribution to this debate evaluates the seasonal variation of the averaged EtG values along a seven-year period. Over 65 000 data points have been statistically analyzed to provide a mathematical model that interprets the data, gives insight into several influencing factors, and forecasts progressive data-points of the time series. This model shows that there is an annual pattern in the data exhibiting lower EtG concentrations during warm seasons and higher values in cold seasons. The estimated EtG cycles are characterized by the seasonal variation of ±2.78 pg/mg above and below the overall mean (with 5.56 pg/mg absolute difference overall). This seasonal factor associated with EtG quantification might result in a potential source of bias, at least in the regional/climatic conditions observed in the samples' collection area. Moreover, the EtG time series reveals that the change in the sample pre-treatment procedure has an effect on the modeled pattern as an abrupt increment (+38%) in the mean value of the EtG concentration. This change corresponds to the time when the former protocol of cutting hair into small segments before extraction was substituted by pulverization with a ball mill.

Development and evaluation of semi-destructive, ultrasound assisted extraction method followed by gas chromatography coupled to mass spectrometry enabling discrimination of red lipstick samples.

In this study, the effective and robust semi-destructive ultrasound assisted extraction (UAE) method followed by gas chromatography coupled to mass spectrometry (GC-MS) method used for analysis of red lipsticks samples was developed, optimized and evaluated. 43 red lipsticks of a very similar hue representing of 21 different manufacturers were investigated. The red lipsticks (approximately 0.05 mg) were applied on a disc (of ø 2 mm) placed on specially designed stand printed with a 3D printer. In order to optimize the main factors affecting extraction process, Doehlert experimental design with response surface methodology was applied. The optimal for all analysed lipsticks UAE extraction conditions were: 21 min - time, 35 °C - temperature of the ultrasonic bath, and the 100 µL of extraction mixture of acetonitrile, methanol and acetone (50:30:20,  v/v/v). The developed, qualitative UAE/GC-MS method was evaluated and then successfully used for the differentiation of 43 red lipsticks. In this case, the two approaches were utilized: the visual inspection of chromatograms and the likelihood ratio model. The results confirmed that the proposed method has a great potential in lipsticks differentiation and after adaptation to real samples it seems to be a good alternative to the methods routinely used in forensic science investigations.

A likelihood ratio model for the determination of the geographical origin of olive oil.

Food fraud or food adulteration may be of forensic interest for instance in the case of suspected deliberate mislabeling. On account of its potential health benefits and nutritional qualities, geographical origin determination of olive oil might be of special interest. The use of a likelihood ratio (LR) model has certain advantages in contrast to typical chemometric methods because the LR model takes into account the information about the sample rarity in a relevant population. Such properties are of particular interest to forensic scientists and therefore it has been the aim of this study to examine the issue of olive oil classification with the use of different LR models and their pertinence under selected data pre-processing methods (logarithm based data transformations) and feature selection technique. This was carried out on data describing 572 Italian olive oil samples characterised by the content of 8 fatty acids in the lipid fraction. Three classification problems related to three regions of Italy (South, North and Sardinia) have been considered with the use of LR models. The correct classification rate and empirical cross entropy were taken into account as a measure of performance of each model. The application of LR models in determining the geographical origin of olive oil has proven to be satisfactorily useful for the considered issues analysed in terms of many variants of data pre-processing since the rates of correct classifications were close to 100% and considerable reduction of information loss was observed. The work also presents a comparative study of the performance of the linear discriminant analysis in considered classification problems. An approach to the choice of the value of the smoothing parameter is highlighted for the kernel density estimation based LR models as well.