Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2022Authentication of hazelnut oil by portable ft-nir, ft-mir and raman spectrometers combined with multivariate data analysis
Aykas, Didem Peren; Menevseoglu, Ahmed
This research studied the authentication of hazelnut oil by portable FT-NIR, FT-MIR, and Raman spectrometers. Hazelnut oils were adulterated with vegetable oils at various concentrations (0-25%) (w/w). Collected spectra were analyzed using Principal Component Analysis (PCA) and Soft Independent Modelling of Class Analogy (SIMCA) to generate classification models to authenticate pure hazelnut oil and Partial Least Squares Regression (PLSR) to predict the fatty acids and adulterant levels. For confirmation, oil’s fatty acid profile was determined by gas chromatography. In all three instruments, SIMCA provided distinct clusters for pure and adulterated samples with interclass distance (ICD)>3. All instruments showed excellent performance in predicting fatty acids and adulteration levels with rᵥₐₗ>0.93 and standard error prediction (SEP)<1.75%. Specifically, the FT-MIR unit provided the best performances. Still, all the units can be used as an alternative to traditional methods. These units showed great potential for in-situ surveillance to detect hazelnut oil adulterations.
- MakaleYalnız künye2021A rapid method to detect green pea and peanut adulteration in pistachio by using portable FT-MIR and FT-NIR spectroscopy combined with chemometrics
Aykas, Didem Peren; Menevseoglu, Ahmed
In this research, the determination of green pea and peanut adulteration in pistachio by portable Fourier transform mid-infrared (FT-MIR) and Fourier transform near-infrared (FT-NIR) spectroscopy was studied. A rapid method to detect adulteration in pistachio is a necessity in the food industry because of its high commodity value and being one of the most consumed tree nuts. Pistachio nut is a target for economically motivated adulteration, and green pea, spinach, and peanut are the most seen adulterants in pistachio. Portable FT-MIR and FT-NIR spectrometers are simple, non-invasive, rapid, easy to transport, and cheaper alternatives to traditional methods, including chromatography (GC, HPLC), mass-spectroscopy (GC-MS, LC-MS) for the detection of pistachio adulteration. Powdered pistachio samples were adulterated with powdered green pea and peanut samples at different concentrations (5–40%). Spectra were collected with a portable FT-MIR and FT-NIR spectrometers and analyzed by Soft Independent Modeling of Class Analogy (SIMCA) to generate a classification model to authenticate pure pistachio, and Partial Least Square Regression (PLSR) to predict the levels of adulterants in pistachio. SIMCA provided, in both units, very distinct clusters for pure and adulterated samples with inter-class distance (ICD) over 3. Both units also showed superior performance in predicting adulterant levels with rVal>0.99, standard error of prediction (SEP) < 2.5%. Based on SIMCA and PLSR models, portable FT-NIR spectrometer provided more advantages over portable FT-MIR unit because the prediction with the FT-NIR spectrometer was more precise than the FT-MIR unit. Both portable FT-MIR and FT-NIR units can be used as alternative methods to traditional methods and showed great potential for real-time surveillance to detect green pea and peanut adulteration in pistachio.
- MakaleYalnız künye2021Non-targeted approach to detect green pea and peanut adulteration in pistachio by using portable FT-IR, and UV-Vis spectroscopy
Menevseoglu, Ahmed; Aykas, Didem Peren; Adal, Eda
Pistachio is one of the most expensive nuts with having high economic importance in Turkey. It has become more prone to adulteration because of its high commodity value. Peanut with color additives and green pea are generally used to adulterate ground pistachio. Vibrational spectroscopy is a potential technique to detect adulterations in pistachio. The objective of this study was to generate a non-targeted method for portable FT-IR and UV-Vis spectrometers to authenticate pistachio and detect green pea and peanut adulterations. Pistachio granules were adulterated with green pea and peanut at different concentrations (5 to 40% w/w). Spectra were collected by a portable FT-IR spectrometer and by a conventional UV-Vis spectrometer and analyzed by Soft Independent Modeling of Class Analogy (SIMCA) to generate classification algorithms to authenticate pistachio, and Partial Least Square Regression (PLSR) to predict the concentrations of adulterants. SIMCA showed very distinct clusters for pure samples. Moreover, adulterated pistachio samples were discriminated by SIMCA even in low levels of adulteration (5%). Portable FTIR showed excellent performance (r(val) > 0.93) of predicting the adulterant levels with a standard error of prediction (SEP) 0.66% and 0.80% for green pea and peanut, respectively. Similarly, UV-VIS predicted (r(val) > 0.93) the adulterant levels with SEP 0.58% and 0.14% for green pea and peanut, respectively. The results supported that portable FT-IR, and UV-Vis units present great potential for real-time surveillance of green pea and peanut adulteration in pistachio.
- MakaleAçık Erişim2020In situ monitoring of sugar content in breakfast cereals using a novel ft-nir spectrometer
Aykas, Didem Peren; Ball, Christopher; Menevseoglu, Ahmed; Rodriguez-Saona, Luis E.
This research demonstrates simultaneous predictions of individual and total sugars in breakfast cereals using a novel, handheld near-infrared (NIR) spectroscopic sensor. This miniaturized, battery-operated unit based on Fourier Transform (FT)-NIR was used to collect spectra from both ground and intact breakfast cereal samples, followed by real-time wireless data transfer to a commercial tablet for chemometric processing. A total of 164 breakfast cereal samples (60 store-bought and 104 provided by a snack food company) were tested. Reference analysis for the individual (sucrose, glucose, and fructose) and total sugar contents used high-performance liquid chromatography (HPLC). Chemometric prediction models were generated using partial least square regression (PLSR) by combining the HPLC reference analysis data and FT-NIR spectra, and associated calibration models were externally validated through an independent data set. These multivariate models showed excellent correlation (Rpre ≥ 0.93) and low standard error of prediction (SEP ≤ 2.4 g/100 g) between the predicted and the measured sugar values. Analysis results from the FT-NIR data, confirmed by the reference techniques, showed that eight store-bought cereal samples out of 60 (13%) were not compliant with the total sugar content declaration. The results suggest that the FT-NIR prototype can provide reliable analysis for the snack food manufacturers for on-site analysis.