Trends in authentication of edible oils using vibrational spectroscopic techniques.

The authentication of edible oils has become increasingly important for ensuring product quality, safety, and compliance with regulatory standards. Some prevalent authenticity issues found in edible oils include blending expensive oils with cheaper substitutes or lower-grade oils, incorrect labeling regarding the oil's source or type, and falsely stating the oil's origin. Vibrational spectroscopy techniques, such as infrared (IR) and Raman spectroscopy, have emerged as effective tools for rapidly and non-destructively analyzing edible oils. This review paper offers a comprehensive overview of recent advancements in using vibrational spectroscopy for authenticating edible oils. The fundamental principles underlying vibrational spectroscopy are introduced and chemometric approaches that enhance the accuracy and reliability of edible oil authentication are summarized. Recent research trends highlighted in the review include authenticating newly introduced oils, identifying oils based on their specific origins, adopting handheld/portable spectrometers and hyperspectral imaging, and integrating modern data handling techniques into the use of vibrational spectroscopic techniques for edible oil authentication. Overall, this review provides insights into the current state-of-the-art techniques and prospects for utilizing vibrational spectroscopy in the authentication of edible oils, thereby facilitating quality control and consumer protection in the food industry.

Formulation of Gluten-Free Cookies Utilizing Chickpea, Carob, and Hazelnut Flours through Mixture Design.

Legume flours, which offer high nutritional quality, present viable options for gluten-free bakery products. However, they may have an objectionable flavor and taste for some consumers. In this study, it was aimed to improve the gluten-free cookie formulation by incorporating carob and hazelnut flours to pre-cooked chickpea flour and to investigate the techno-functional properties of the formulated cookies. The flours used in the formulations were assessed for their chemical and physical properties. This study employed a mixture design (simplex-centroid) to obtain the proportions of the flours to be used in the cookie formulations. The rheological characteristics of the doughs and the technological attributes of the baked cookies were determined. The addition of the hazelnut and carob flours had the overall effect of reducing the rheological characteristics of the cookie doughs. Furthermore, the textural attribute of the hardness of the baked cookies decreased as the ratio of hazelnut flour in the formulations was raised. The analysed results and sensory evaluation pointed to a formulation consisting of 30% pre-cooked chickpea/30% carob/30% hazelnut flours, which exhibited improved taste and overall acceptability scores. A total of 16.82 g/100 g of rapidly digestible starch, 5.36 g/100 g of slowly digestible starch, and 8.30 g/100 g of resistant starch exist in this particular cookie. As a result, combinations of chickpea, hazelnut, and carob flours hold promise as good alternatives for gluten-free cookie ingredients and warrant further exploration in the development of similar products.

Authentication of Turkish olive oils by using detailed pigment profile and spectroscopic techniques.

BACKGROUND: Minor compounds of olive oil could have discriminatory characteristics in the authentication of this product. It was aimed to determine the detailed pigment profiles of Turkish olive oils and use them in differentiation of the samples in comparison to fast, reliable, and environmentally friendly Fourier-transform infrared (FTIR) and ultraviolet (UV)-visible spectroscopic techniques. Pigment contents of 91 olive oils obtained from different locations for two consecutive harvesting years were determined with chromatographic analysis and FTIR and UV-visible spectra of these samples were also obtained. All data were analyzed with orthogonal partial least-squares discriminant analysis to investigate the differentiation ability of these methods with regard to their detailed pigment and spectroscopic profiles. RESULTS: Pheophytin a (2.78-8.98 mg kg-1 ) and lutein (1.19-4.07 mg kg-1 ) were the major pigments in all samples. Pigment profiles provided successful classification of olive oils with respect to their designated origins and harvesting year with average correct classification rates of 97%. UV-visible spectroscopy has quite similar results with pigment profiles in terms of its discriminatory power. In addition, FTIR and fused data were slightly better in discrimination of the samples, and the fused dataset has the highest correct classification rate of 100%. CONCLUSION: Use of detailed pigment profiles is quite promising in authentication of olive oils. However, UV-visible and FTIR spectroscopic techniques could be reliable alternatives for the same purposes. All of the techniques studied have great potential in 'protected designation of origin' certification studies. © 2020 Society of Chemical Industry.

Use of FTIR and UV-visible spectroscopy in determination of chemical characteristics of olive oils.

It was aimed to predict fatty acid ethyl ester (FAEE), wax, diacylglycerol (DAG) and color pigment contents of olive oils by using rapid and non-destructive spectroscopic techniques (FTIR and UV-vis) individually and in combination. Prediction models were constructed by using partial least squares (PLS) regression with cross and external validation. FAEEs were estimated best with FTIR + UV-Vis spectroscopy (R2cv. = 0.84, R2pred. = 0.90, and RPD = 3.0). PLS model with R2cv = 0.79, R2pred = 0.71, and RPD = 1.9 was obtained for the estimation of 1,2 DAG using FTIR spectral data. Major pigments, lutein, pheophytin a and their derivatives and total xanthophylls were quantified successfully by FTIR + UV-Vis with a range of R2cv of 0.71-0.85, R2pred of 0.70-0.84, and RPD = 1.5-2.5 values but the prediction of the rest of the pigments were poor (R2cv = 0.60-0.76, R2pred:0.42-0.62, and RPD = 1.2-1.5). Combination of two spectral data resulted in average prediction of wax content of oils (R2cal. = 0.95, R2pred. = 0.75, and RPD = 1.9). FTIR and UV-vis spectroscopic techniques in combination with PLS regression provided promising results for the prediction of several chemical parameters of olive oils; therefore, they could be alternatives to traditional analysis methods.

Combination of visible and mid-infrared spectra for the prediction of chemical parameters of wines.

Rapid and environmentally friendly methods for the prediction of chemical compositions have been an interest in the wine industry. The objective of the study was to show the potentials of combined use of visible and mid-infrared (MIR) spectroscopies to improve the prediction of various chemical compounds of wine as opposed to using mid-infrared range only. Wine samples of twelve grape varieties from two harvest years were analyzed. The chemical composition of wine samples was related to MIR and visible spectra using orthogonal partial least square (OPLS) regression technique. The prediction abilities were tested with crossvalidation and independent validation sets. The coefficient of determination of validation (R2val) for anthocyanin compounds of red wines were between 0.76 and 0.90, and that for total phenol content was 0.90. Range of R2val for glycerol, glycerol/ethanol ratio, malic acid, o-coumaric acid and °Brix were between 0.77 and 0.96. The spectral ranges that played significant roles in the predictions were also determined. The validations with independent data sets showed that the combination of visible and MIR ranges with multivariate methods improved the prediction of anthocyanin compounds and total phenols; produced comparable results for the rest of the parameters as MIR. This is the first study in the literature that shows the practical use of visible spectra along MIR. The combined use of these spectral ranges with multivariate models can be applied for the rapid, on-line determination of quality parameters and chemical profiles of wines.

Effects of malaxation temperature and harvest time on the chemical characteristics of olive oils.

The aim of the study was to determine the effects of harvest time and malaxation temperature on chemical composition of olive oils produced from economically important olive varieties with a full factorial experimental design. The oils of Ayvalik and Memecik olives were extracted in an industrial two-phase continuous system. The quality parameters, phenolic and fatty acid profiles were determined. Harvest time, olive variety and their interaction were the most significant factors. Malaxation temperature was significant for hydroxytyrosol, tyrosol, p-coumaric acid, pinoresinol and peroxide value. Early and mid-harvest oils had high hydroxytyrosol and tyrosol (maximum 20.7mg/kg) and pigment concentrations (maximum chlorophyll and carotenoids as 4.6mg/kg and 2.86mg/kg, respectively). Late harvest oils were characterized with high peroxide values (9.2-25meqO2/kg), stearic (2.4-3.1%) and linoleic acids (9.3-10.4%). Multivariate regression analysis showed that oxidative stability was affected positively by hydroxytyrosol, tyrosol and oleic acid and negatively by polyunsaturated fatty acids.

Comparison of some chemical parameters of a naturally debittered olive (Olea europaea L.) type with regular olive varieties.

Some olives grown in Karaburun peninsula in the west part of Turkey and mostly coming from Erkence variety lose their bitterness while still on the tree and are called Hurma among locals. This olive type does not require further processing to remove the bitter compounds. In this study, sugar, organic acid and fatty acid profiles of Hurma, Erkence (not naturally debittered) and Gemlik (commonly consumed as table olive) olives were determined throughout 8weeks of maturation period for two consecutive harvest seasons, and the results were analysed by principal component analysis (PCA). PCA of sugar and organic acid data revealed a differentiation in terms of harvest year but not on variety. Hurma olive is separated from others due to its fatty acid profile, and it has higher linoleic acid content compared to others. This might be an indication of increased desaturase enzyme activity for Hurma olives during natural debittering phase.

Phenolics profile of a naturally debittering olive in comparison to regular olive varieties.

BACKGROUND: Hurma, an olive variety that grows in a specific area in Turkey, loses its bitterness before harvesting, and therefore does not need further processing steps for the production of table olives. The total phenol content and phenolic profiles of (1) this naturally debittered olive type, Hurma; (2) the same olive variety, but not a naturally debittered type, Erkence; and (3) another variety, Gemlik, which is commonly consumed as table olive, were determined during their maturation period for two harvest years. RESULTS: The total phenol content of Hurma is the lowest compared to the other types regardless of harvest year, which has a significant effect on the phenolic content and composition of individual components for all olive types. All three olive types can be differentiated from each other especially during the late phase of maturation using the phenolics profile in combination with principal component analysis. CONCLUSION: The natural debittering phenomenon of Hurma olive on the tree involves a decrease in phenol content and a change in phenol composition. The differentiation in phenol composition especially becomes very significant in the late of period of maturation.

Antimicrobial and antioxidant activities of Turkish extra virgin olive oils.

Turkish extra virgin olive oils (EVOO) from different varieties/geographical origins and their phenolic compounds were investigated in terms of their antimicrobial and antioxidant properties in comparison to refined olive, hazelnut, and canola oils. Antimicrobial activity was tested against three foodborne pathogenic bacteria, Escherichia coli O157:H7, Listeria monocytogenes , and Salmonella Enteritidis. Although all EVOOs showed a bactericidal effect, the individual phenolic compounds demonstrated only slight antimicrobial activity. Moreover, refined oil samples did not show any antimicrobial activity. Among the phenolic compounds, cinnamic acid (2 mg/kg of oil) had the highest percent inhibition value with 0.25 log reduction against L. monocytogenes. The synergistic interactions of tyrosol, vanillin, vanillic, and cinnamic acids were also observed against Salmonella Enteritidis. The antioxidant activities of oils were tested by beta-carotene-linoleate model system and ABTS method. In both methods, EVOOs showed higher antioxidant activities, whereas refined oils had lower activity. The ABTS method provided a higher correlation (0.89) with total phenol content.

Modeling of polygalacturonase enzyme activity and biomass production by Aspergillus sojae ATCC 20235.

Aspergillus sojae, which is used in the making of koji, a characteristic Japanese food, is a potential candidate for the production of polygalacturonase (PG) enzyme, which of a major industrial significance. In this study, fermentation data of an A. sojae system were modeled by multiple linear regression (MLR) and artificial neural network (ANN) approaches to estimate PG activity and biomass. Nutrient concentrations, agitation speed, inoculum ratio and final pH of the fermentation medium were used as the inputs of the system. In addition to nutrient conditions, the final pH of the fermentation medium was also shown to be an effective parameter in the estimation of biomass concentration. The ANN parameters, such as number of hidden neurons, epochs and learning rate, were determined using a statistical approach. In the determination of network architecture, a cross-validation technique was used to test the ANN models. Goodness-of-fit of the regression and ANN models was measured by the R (2) of cross-validated data and squared error of prediction. The PG activity and biomass were modeled with a 5-2-1 and 5-9-1 network topology, respectively. The models predicted enzyme activity with an R (2) of 0.84 and biomass with an R (2) value of 0.83, whereas the regression models predicted enzyme activity with an R (2) of 0.84 and biomass with an R (2) of 0.69.