Effect of wax separation on macro- and micro-elements, phenolic compounds, pesticide residues, and toxic elements in propolis.

Propolis, a natural product with many biological activities, is a resinous material produced by honeybees. It contains not only valuable components but also some possible contaminants in varying amounts. Hence, this study aimed to examine how the process step of wax separation affects certain elements, pesticide residues, and phenolic compounds in propolis. Total phenolics, elements, and some pesticide residues were analyzed in the crude propolis (CP samples), wax portion (W samples), and remaining propolis fraction (PF samples) after wax separation. Total phenolics of the CP samples were determined in the range of 31.90-45.00 mg GAE g-1 sample, while those of the PF samples were in the range of 54.97-162.09 mg GAE g-1 sample. Loss/reduction values by means of wax separation for phenolics were calculated as 10.88% and 17.89%, respectively. Pb contents of all PF samples were low (0.232-1.520 mg kg-1), but it was also noteworthy that nearly 40% or even more of Cr, As, Cd, and Pb were removed by wax separation. Removal of significant amounts of carbendazim (38.09%-67.35%), metalaxyl (81.57%-72.67%), tebuconazole (65.99%-78.36%), and propargite (88.46%-83.05%) was also achieved. Wax separation enables the removal of toxic substances from crude propolis without causing huge losses in phenolic compounds.

Protein based evaluation of meat species by using laser induced breakdown spectroscopy.

Meat adulteration through partial substitution with cheaper species or mislabeling causes significant problems in terms of health, religious beliefs, economy, and product quality. Therefore, identification of meat species is crucial for monitoring and prevention of meat fraud. In the present study, protein based laser induced breakdown spectroscopy method was developed for the first time to identify three meat species (beef, chicken and pork) by using bulk proteins and protein fractions, namely actin and myosin. LIBS spectra were evaluated with principal component analysis for clustering pattern of meat species, and partial least square analysis was performed to determine adulteration ratio. In PLS analysis, limit of detection (LOD) values for beef adulteration with chicken and pork meat were calculated as 2.84% and 3.89% by using bulk proteins, respectively.

Identification of offal adulteration in beef by laser induced breakdown spectroscopy (LIBS).

Minced meat is the major ingredient in sausages, beef burgers, and similar products; and thus it is the main product subjected to adulteration with meat offal. Determination of this kind of meat adulteration is crucial due to religious, economic and ethical concerns. The aim of the present study is to discriminate the beef meat and offal samples by using laser induced breakdown spectroscopy (LIBS). To this end, LIBS and multivariate data analysis were used to discriminate pure beef and offal samples qualitatively and to determine the offal mixture adulteration quantitatively. In this analysis, meat samples were frozen and LIBS analysis were performed. The results indicate that by using principal component analysis (PCA), discrimination of pure offal and offal mixture adulterated beef samples can be achieved successfully. Besides, adulteration ratio can be determined using partial least square analysis method (PLS) with 0.947 coefficient of determination (R2) and 3.8% of limit of detection (LOD) values for offal mixture adulterated beef samples.

Detection and quantification of a toxic salt substitute (LiCl) by using laser induced breakdown spectroscopy (LIBS).

The use of Li salts in foods has been prohibited due to their negative effects on central nervous system; however, they might still be used especially in meat products as Na substitutes. Lithium can be toxic and even lethal at higher concentrations and it is not approved in foods. The present study focuses on Li analysis in meatballs by using laser induced breakdown spectroscopy (LIBS). Meatball samples were analyzed using LIBS and flame atomic absorption spectroscopy. Calibration curves were obtained by utilizing Li emission lines at 610nm and 670nm for univariate calibration. The results showed that Li calibration curve at 670nm provided successful determination of Li with 0.965 of R2 and 4.64ppm of limit of detection (LOD) value. While Li Calibration curve obtained using emission line at 610nm generated R2 of 0.991 and LOD of 22.6ppm, calibration curve obtained at 670nm below 1300ppm generated R2 of 0.965 and LOD of 4.64ppm.

Determination of terpenoid contents of aromatic plants using NIRS.

The method was developed in order to provide a fast, simple and non-destructive analysis of terpenoid compounds of aromatic plants. For this purpose, spectroscopic data were collected on the surface of dried plant samples by using near infrared spectroscopic (NIRS) analysis. Volatile substances were extracted from aromatic plants using hydro-distillation method to determine terpenoid composition by using gas chromatography-mass spectrometry (GC-MS). Multivariate calibration methods namely, partial least squares (PLS) regression, were used for data analysis. The correlation between NIRS spectral data and the concentrations of terpenoid contents were established with coefficient of determination (R2) values in the range of 0.953-0.997. The model validation results showed that the contents of 24 terpenoids were predicted accurately with a satisfactory limit of detection (LOD) values. In this study, 24 terpenoids, the major constituents of volatile compounds, from nine aromatic plants were investigated.

Use of Raman spectroscopy for determining erucic acid content in canola oil.

This study presents a novel method to determine erucic acid in canola oil samples by using Raman spectroscopy and chemometric analysis. The oil mixtures were prepared at various concentrations of erucic acid ranging from 0% to 33.56% (w/w) through binary combinations of different oils. In order to predict erucic acid content, Raman spectroscopy and GC results were correlated by means of partial least squares analysis. High coefficient of determination values was obtained for both calibration and validation data sets, which are 0.990 and 0.982, respectively. The results of the present study reveal the potential of Raman spectroscopy for rapid determination (45s) of erucic acid in canola oil. Further research would be useful to improve the method to put it forward as an alternative to GC in the erucic acid analysis.

Identification of meat species by using laser-induced breakdown spectroscopy.

The aim of the present study is to identify meat species by using laser-induced breakdown spectroscopy (LIBS). Elemental composition differences between meat species were used for meat identification. For this purpose, certain amounts of pork, beef and chicken were collected from different sources and prepared as pellet form for LIBS measurements. The obtained LIBS spectra were evaluated with some chemometric methods, and meat species were qualitatively discriminated with principal component analysis (PCA) method with 83.37% ratio. Pork-beef and chicken-beef meat mixtures were also analyzed with partial least square (PLS) method quantitatively. Determination coefficient (R(2)) and limit of detection (LOD) values were found as 0.994 and 4.4% for pork adulterated beef, and 0.999 and 2.0% for chicken adulterated beef, respectively. In the light of the findings, it was seen that LIBS can be a valuable tool for quality control measurements of meat as a routine method.

Differentiation of fresh and frozen-thawed fish samples using Raman spectroscopy coupled with chemometric analysis.

The potential of Raman spectroscopy was investigated in terms of its capability to discriminate the species of the fish samples and determine their freshness according to the number of freezing/thawing cycles they exposed. Species discrimination analysis was carried out on sixty-four fish samples from six different species, namely horse mackerel (Trachurus trachurus), European anchovy (Engraulis encrasicolus), red mullet (Mullus surmuletus), Bluefish (Pomatamus saltatrix), Atlantic salmon (Salmo salar) and flying gurnard (Trigla lucerna). Afterwards, fish samples were exposed to different numbers of freezing/thawing cycles and separated into three batches, namely (i) fresh, (ii) once frozen-thawed (OF) and (iii) twice frozen-thawed (TF) samples, in order to perform the freshness analysis. Raman data collected were used as inputs for chemometric analysis, which enabled us to develop two main PCA models to successfully terminate the studies for both species discrimination and freshness determination analysis.

A novel method for discrimination of beef and horsemeat using Raman spectroscopy.

A new approach, based on the usage of Raman spectroscopy in combination with chemometrics, was developed for the rapid determination of beef adulteration with horsemeat. The data mining process of collected Raman spectra was performed with principal component analysis (PCA). Pure fat samples, extracted from forty-nine meat beef and horsemeat samples, were analysed using the Raman spectroscopy. All meat samples were classified successfully according to their origins. The presence of different concentrations (25%, 50%, 75%, w/w) of horsemeat in beef was also differentiated using the developed model system. This study offers a rapid assay for determination of meat adulteration by discriminating beef and horsemeat with high accuracy, a short analysis time (30s) and no requirement for time-consuming sample preparation procedures.

Investigating the effects of ingredient levels on physical quality properties of cooked hamburger patties using response surface methodology and image processing technology.

A three-factor central composite design was adopted to determine the interactive effects of fat (15-30%), water (10-20%) and textured soy protein (3-9%) content on the shrinkage, fat loss and moisture loss of hamburger patties after cooking. Image processing was used to estimate the shrinkage of hamburger patties. Textured soy protein (TSP) content was found to be the most important factor for minimizing fat and moisture loss. Both fat and water content were found to be significantly effective (P<0.05) in the model for shrinkage and moisture loss in linear form. The changes in shrinkage due to fat, water and TSP content were also in linear form. The model for fat loss was in linear and quadratic form, whereas the model for moisture loss was in full quadratic form. The models for shrinkage, fat loss and moisture loss had the R-square values of 0.954, 0.969 and 0.964, respectively.