No abstract

Honey adulteration with cheap sweeteners such as corn syrup or invert syrup results in honey of lesser quality that can harm the objectives of both manufacturers and consumers. Therefore, there is a growing interest for the development of a fast and simple method for adulteration detection. In this work, near-infrared spectroscopy (NIR) was used for the detection of honey adulteration and changes in the physical and chemical properties of the prepared adulterations. Fifteen (15) acacia honey samples were adulterated with glucose syrup in a range from 10% to 90%. Raw and pre-processed NIR spectra of pure honey samples and prepared adulterations were subjected to Principal Component Analysis (PCA), Partial Least Squares (PLS) regression, and Artificial Neural Network (ANN) modeling. The results showed that PCA ensures distinct grouping of samples in pure honey samples, honey adulterations, and pure adulteration using NIR spectra after the Multiplicative Scatter Correction (MSC) method. Furthermore, PLS models developed for the prediction of the added adulterant amount, moisture content, and conductivity can be considered sufficient for screening based on RPD and RER values (1.7401 < RPD < 2.7601; 7.7128 < RER < 8.7157) (RPD of 2.7601; RER of 8.7157) and can be moderately used in practice. The R2validation of the developed ANN models was greater than 0.86 for all outputs examined. Based on the obtained results, it can be concluded that NIR coupled with ANN modeling can be considered an efficient tool for honey adulteration quantification.

Honey is a naturally sweet and viscous product for which the addition of any substance is prohibited by international regulation. Detection of adulteration in honey is a technical problem: adulteration of honey with invert sugar and syrup may not be reliably detected by direct sugar analysis because its constituents are identical to the major natural components of the honey. Therefore, it is important to develop a rapid and reliable analytical method to detect such additions. We used near-infrared spectroscopy (NIR) combined with principle component analysis (PCA) and artificial neural networks (ANN) modelling to discriminate between honey and corn syrup in adulterated honey. Fifteen honey samples from north-west Croatia (Krapina-Zagorje County) were intentionally supplemented with differing proportions of corn syrup ranging from 10-90%. We collected a total of 460 NIR spectra using the Control Development NIR128L-1.7 spectrophotometer (Control Development, South Bend, Indiana, USA) with their software Spec32 software anda HL-2000 halogen light source. For each of the prepared samples, we measured water content by refractometer (Brouwland, Belgium), conductivity byconductometer (SevenCompact, MettlerToledo, Switzerland), and colour using a PCE-CSM3 colorimeter (PCE Instruments, Germany). Prior to ANN modelling, PCA was used to identify patterns and highlight similarities and differences in data of the individual set of the experiment. The goal of PCA is to extract important information from the data table and to express this information as a set of new orthogonal variables called principal components or factors (PCs or Fs). We conducted PCA of raw spectra using the Unscrambler® X 10.4 software (CAMO software, Norway). Data were divided into ANN model training, test, and validation datasets at a 70:15:15 ratio using the first five PCs. ANNs were calibrated using model training data, and evaluated using model test and model validation datasets for their ability to predict: i) the amount of added adultering substance in honey, ii) water content, iii) conductivity and iv) colour of the adulterated honey. Multiple layer perception (MLP) networks were developed in Statistica v.10.0 software (StatSoft, Tulsa, USA). Back error propagation algorithm available in Statistica v.10.0 was applied for the model training. Model performance was evaluated using R2 and root mean squared error (RMSE) values for model training, test, and validation datasets. Results show that network MLP 5-8-6 with five neurons in the input layer, 8 neurons in the hidden layer and 6 neurons in the output layer predicts the analysed output variables with high precision (R2validation,concentration = 0.995, R2validation,water content = 0.993, R2validation,conductivity = 0.992, R2validation,L = 0.939, R2validation,a = 0.895, R2validation,b = 0.924).

Virgin olive oil has a high resistance to oxidative deterioration due to both a triacylglycerol composition low in polyunsaturated fatty acids and a group of phenolic antioxidants composed mainly of polyphenols and tocopherols. This essay discusses the effect of microwave heating on the oxidative stability of extra virgin olive oil with or without the addition of antioxidants and synergists. Of natural antioxidants are used rosemary extract and green tea extract and citric acid synergist. Oil samples with or without the addition of antioxidants and synergists were heated in a microwave oven at a constant power of 300 W over a different time period (5, 10, 15 and 20 minutes). Samples were also heated at different power levels (180, 300, 450W) in a constant time period of 5 minutes. The result of the accelerated oxidation test of olive oil is expressed by the peroxide number. Microwave heating of the samples during a longer heating time increases the temperature and the value of the peroxide number, which results in an increase in oxidative oil degradation. Addition of antioxidants and synergists increased the stability of olive oil. The highest stability of extra virgin olive oil was achieved by a combination of green tea extract and synergistic citric acid.