No abstract

There has been growing public awareness about the health benefits of olive oil throughout the world in recent years, resulting in a significant increase in its consumption as part of the daily diet. This demand has attracted fraudulent attempts to market olive oil which has been adulterated with cheaper oils. This study focuses on the near infrared (NIR) spectroscopic determination of adulteration of olive oil by vegetable oils using multivariate calibration. The binary, ternary and quaternary mixtures of olive, soybean, cotton, corn, canola and sunflower oils were prepared using a random design. The absorbance spectra of these synthetic samples were measured by a near infrared (NIR) spectrometer. A genetic algorithm-based variable selection algorithm, coupled with an inverse least squares multivariate calibration method (GILS) was used to build calibration models for possible adulterants and olive oil in the adulterated mixtures. The correlation coefficients of actual versus predicted concentrations resulting from multivariate calibration models for the different oils were between 0.90 and 0.99. The results demonstrated that NIR spectroscopy in conjunction with the GILS method makes it possible to determine the adulteration of olive oils regardless of adulterant vegetable oils over a wide range of concentrations.

A new procedure for calibrating multiple instruments is presented in which spectra from each are used simultaneously during the construction of multivariate calibration models. The application of partial least-squares (PLS) and genetic regression (GR) to the problem of generating these hybrid calibrations is presented. Spectra of ternary mixtures of methylene chloride, ethyl acetate, and methanol were collected on a dispersive and a Fourier transform spectrometer. Calibration models were generated by using differing numbers of spectra from each instrument simultaneously in the calibration and prediction sets, and then validated by using a set of spectra from each instrument separately. Calibration models were found that perform well on both instruments, even when only a single spectrum from the second instrument was used during the calibration process. As a benchmark, comparison with PLS showed that GR is more effective than PLS in building these hybrid calibration models.

The fact that the models were generated with several honey samples coming from various different botanical and geographical origins, quite successful results were obtained for the detection of adulterated honey samples with a simple Fourier transform infrared spectroscopy technique. Having a genetic algorithm for variable selection helped to build somewhat better models with GILS compared with PLS. © 2018 Society of Chemical Industry.

Virgin olive oils (VOOs) obtained from olives grown in different regions of Turkey under changing climatic conditions sometimes show different sensory and chemical properties. This study was planned to determine whether these deviations are due to climatic changes or not. For this purpose, five different olive varieties (Ayvalık, Memecik, Gemlik, Nizip Yağlık, Kilis Yağlık) of commercial importance were harvested from the provinces/districts (four different region) where cultivation is intense during the 2017/2018–2020/2021 harvest years. Every year, olive samples were collected from 3 orchards from 13 provinces/districts. One hundred and fifty‐six samples were subjected to the purity, quality and sensory analysis. Basic climatic values (average, minimum and maximum temperature, humidity and precipitation) were examined for four consecutive years. All of the examined olive oil samples were determined within the legal limits in terms of fatty acid composition and fatty acid ethyl ester values. However, delta‐7‐stigmastenol value from the sterol composition was found to be above 0.5% in some samples in all the years studied (total 21 samples). Delta‐7‐stigmastenol values of olive oil samples varied between 0.16% and 1.14%. Multiple linear regression analysis was applied using a genetic algorithm‐based inverse least squares method to determine whether there is a relationship between climate data and delta‐7‐stigmastenol values. According to this result, it has been determined that the delta‐7‐stigmastenol value is high when the annual average relative humidity is low and the annual average temperature is high. There is an urgent need to make forward‐looking plans due to climate change.

Simultaneous determination of binary mixtures pyridoxine hydrochloride and thiamine hydrochloride in a vitamin combination using UV-visible spectrophotometry and classical least squares (CLS) and three newly developed genetic algorithm (GA) based multivariate calibration methods was demonstrated. The three genetic multivariate calibration methods are Genetic Classical Least Squares (GCLS), Genetic Inverse Least Squares (GILS) and Genetic Regression (GR). The sample data set contains the UV-visible spectra of 30 synthetic mixtures (8 to 40 m mg/ml) of these vitamins and 10 tablets containing 250 mg from each vitamin. The spectra cover the range from 200 to 330 nm in 0.1 nm intervals. Several calibration models were built with the four methods for the two components. Overall, the standard error of calibration (SEC) and the standard error of prediction (SEP) for the synthetic data were in the range of Ͻ0.01 and 0.43 m mg/ml for all the four methods. The SEP values for the tablets were in the range of 2.91 and 11.51 mg/tablets. A comparison of genetic algorithm selected wavelengths for each component using GR method was also included.

The applicability of genetic regression (GR) to multi-instrument calibration was demonstrated by using several UV-visible spectrophotometers. GR is a calibration technique that optimizes linear regression using a genetic algorithm (GA). Sample spectra of ternary and quaternary mixtures of the pharmaceuticals furaltadone (Fd), doxycycline (Dx), sulfadiazine (Sd), and trimethoprim (Tm) were collected on four different UV-visible spectrophotometers, including one single-beam diode array and three double-beam dispersive instruments. Hybrid calibration models (HCMs) were generated by combining the data collected on multiple instruments into one calibration model as if they had all been collected on a single instrument. For comparison, single-instrument calibration models were also generated for each instrument. Both HCMs and single-instrument models were tested by using a validation set measured on all four instruments. Results obtained from single-instrument models were comparable with a previous study in which partial least-squares (PLS) regression was used for multivariate calibration of these compounds. HCMs for double-instrument cases performed equally well as single-instrument models and slightly worse for the four-instruments models.

Determination of quality parameters such as lignin and extractive content of wood samples by wet chemistry analyses takes a long time. Near-infrared (NIR) spectroscopy coupled with multivariate calibration offers a fast and nondestructive alternative to obtain reliable results. However, due to the complexity of the NIR spectra, some wavelength selection is generally required to improve the predictive ability of multivariate calibration methods. Pinus brutia Ten. is the most growing pine species in Turkey. Its rotation period is around 80 years; the forest products industry has widely accepted the use of Pinus brutia Ten. because of its ability to grow on a wide range of sites and its suitability to produce desirable products. Pinus brutia Ten. is widely used in construction, window door panel, floor covering, etc. Determination of lignin and extractive content of wood provides information to tree breeders on when to cut and how much chemicals are needed for the pulping and bleaching process. In this study, 58 samples of Pinus brutia Ten. trees were collected in Isparta region of Turkey, and their lignin and extractive content were determined with standard reference (TAPPI) methods. Then, the same samples were scanned with near-infrared spectrometer between 1,000 and 2,500 nm in diffuse reflectance mode, and multivariate calibration models were built with genetic inverse least squares method for both lignin and extractive content using the concentration information obtained from wet standard reference method. Overall, standard error of calibration (SEC) and standard error of prediction (SEP) ranged between 0.35% (w/w) and 2.40% (w/w).