Validation of Fluorescence Spectroscopy to Detect Adulteration of Edible Oil in Extra Virgin Olive Oil (EVOO) by Applying Chemometrics

Rapid quantification of grapeseed oil multiple adulterations using near-infrared spectroscopy coupled with a novel double ensemble modeling method

As a high-quality edible oil, grapeseed oil is often adulterated with low-price/quality vegetable oils. A novel ensemble modeling method is proposed for quantitative analysis of grapeseed oil adulterations combined with near-infrared (NIR) spectroscopy. The method combines Monte Carlo (MC) sampling and whale optimization algorithm (WOA) to build numerous partial least squares (PLS) sub-models, named MC-WOA-PLS. A total of 80 adulterated grapeseed oil samples were prepared by mixing grapeseed oil with soybean oil, palm oil, cottonseed oil, and corn oil with the designed mass percentages. NIR spectra of the 80 samples were measured in a transmittance mode in the range of 12,000-4000 cm-1. Parameters in MC-WOA-PLS including the number of latent variables (LVs) in PLS, iteration number of WOA, whale number, number of PLS sub-models, and percentage of training subsets were optimized. To validate the prediction performance of the model, root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean squared error of prediction (RMSEP), correlation coefficient (R), residual predictive deviation (RPD), and standard deviation (S.D.) were used. Compared with PLS, standard normal variate-PLS (SNV-PLS), uninformative variable elimination-PLS (UVE-PLS), Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS), randomization test-PLS (RT-PLS), variable importance in projection-PLS (VIP-PLS), and WOA-PLS, MC-WOA-PLS achieves the best prediction accuracy and stability for quantification of the five pure oils in adulterated grapeseed oil samples.

Fluorescence Spectroscopy Based Characterization of Flaxseed Oil

Fluorescence spectroscopy has been employed for the compositional analysis of flaxseed oil, detection of its adulteration and investigation of the thermal effects on its molecular composition. Excitation wavelengths from 320 to 420 nm have been used to explore the valued ingredients in flaxseed oil. The emission bands of flaxseed oil centred at 390, 414, 441, 475, 515 and 673/720 nm represent vitamin K, isomers of vitamin E, carotenoids and chlorophylls, which can be used as a marker for quality analysis. Due to its high quality, it is highly prone to adulteration and in this study, detection of its adulteration with canola oil is demonstrated by applying principal component analysis. Moreover, the effects of temperature on the molecular composition of cold pressed flaxseed oil has been explored by heating them at cooking temperatures of 100, 110, 120, 130, 140, 150, 160, 170 and 180 °C, each for 30 min. On heating, the deterioration of vitamin E, carotenoids and chlorophylls occurred with an increase in the oxidation products. However, it was found that up to 140 °C, flaxseed oil retains much of its natural composition whereas up to 180 oC, it loses much of its valuable ingredients along with increase of oxidized products.

Adulteration Detection and Quantification in Olive Oil Using Excitation-Emission Matrix Fluorescence Spectroscopy and Chemometrics

This research investigates the use of excitation-emission matrix fluorescence (EEMF) in conjunction with chemometric models to rapidly identify and quantify adulteration in olive oil, a critical concern where sample availability is limited. Adulteration is simulated by blending soybean, peanut, and linseed oils into olive oil, creating diverse adulterated samples. Principal component analysis (PCA) was applied to the EEMF spectral data as an initial exploratory measure to cluster and differentiate adulterated samples. Spatial clustering enabled vivid visualization of the variations and trends in the spectra. The novel application of parallel factor analysis (PARAFAC) for data decomposition in this paper focuses on unraveling correlations between the decomposed components and the actual adulterated components, which offers a novel perspective for accurately quantifying adulteration levels. Additionally, a comparative analysis was conducted between the PCA and PARAFAC methodologies. Our study not only unveils a new avenue for the quantitative analysis of adulterants in olive oil through spectral detection but also highlights the potential for applying these insights in practical, real-world scenarios, thereby enhancing detection capabilities for various edible oil samples. This promises to improve the detection of adulteration across a range of edible oil samples, offering significant contributions to food safety and quality assurance.

Evaluation of different regression models for detection of adulteration of mustard and canola oil with argemone oil using fluorescence spectroscopy coupled with chemometrics

Mustard and canola oils are commonly used cooking oils in Asian countries such as India, Nepal, and Bangladesh, making them prone to adulteration. Argemone is a well-known adulterant of mustard oil, and its alkaloid sanguinarine has been linked with health conditions such as glaucoma and dropsy. Utilising a non-destructive spectroscopic method coupled with a chemometric approach can serve better for the detection of adulterants. This work aimed to evaluate the performance of various regression algorithms for the detection of argemone in mustard and canola oils. The spectral dataset was acquired from fluorescence spectrometer analysis of pure as well as adulterated mustard and canola oils with some local and commercial samples also. The prediction performance of the eight regression algorithms for the detection of adulterants was evaluated. Extreme gradient boosting regressor (XGBR), Category gradient boosting regressor (CBR), and Random Forest (RF) demonstrate potential for predicting adulteration levels in both oils with high R2 values.

Quality Analysis of Canola and Mustard Oil Using Fluorescence Spectroscopy

The potential of Fluorescence spectroscopy has been utilized for the quality analysis of canola and mustard oil along with the effect of heating on their molecular composition has been investigated. Laser diode at 405 nm has been employed directly to oil surface to excite both oil type samples and their emission spectra has been recorded by an in-house developed Fluorosensor. The emission spectra of both oil types unveiled that they contain carotenoids, isomers of vitamin E and chlorophylls that exhibit their fluorescence at 525 and 675/720 nm, and these can be used as markers for their quality assurance. Fluorescence spectroscopy is a fast, reliable and non-destructive analytical technique for the quality assessment of both oil types. Moreover, the effect of temperature on their molecular composition has been investigated by heating them at 110, 120, 130, 140, 150, 170, 180 and 200 °C, each sample for 30 min which was done because both oils are used for cooking and frying. On heating, the deterioration of carotenoids and isomers of vitamin E in both oil types occurred with an increase in the oxidised products. However, it was found that up to 150 °C, both oil types can be used safely for cooking/frying purpose where they do not lose much of their valuable ingredients and up to 180 °C for deep frying, both oils can be used with less deterioration and after that both deteriorated much due to rapid increase of the oxidized products. The portable Fluorosensor, therefore, proved as an excellent device for quality screening of edible oils based on carotenoids and vitamin E.

Thermal Effects on the Quality Parameters of Extra Virgin Olive Oil Using Fluorescence Spectroscopy

Extra virgin olive oil is one of the superlative due to its health benefits. In this work, the Fluorescence spectra of extra virgin olive oil (EVOO) from different olive growing regions of Pakistan and Al-Jouf region from the Kingdom of Saudi Arabia (KSA) were obtained. The emission bands depicted relative intensity variations in all non-heated and heated EVOO samples. Prominent emission bands at 385, 400, 435 and 470 nm represent oxidized products of fatty acids, bands at 520 and 673 nm has been assigned to beta carotene and chlorophyll isomers respectively. All EVOO samples collected from Al-Jouf region, KSA and from Pakistan (Loralai Baluchistan, Barani Agricultural Research Institute, Chakwal and Morgha Biodiversity Park, Rawalpindi) regions showed thermal stability. Other EVOO samples from Chaman Baluchistan and one sample from wild specie (Baluchistan) bought directly from farmers showed denatured spectra even without heating. Chemical characteristics of all EVOO samples changed significantly at 200 °C. Relatively, EVOO samples from Al-Jouf showed more thermal stability which might be due to geographical distribution, environmental effects, genetic background and processing or storage conditions. These results demonstrated fluorescence spectroscopy as a quick, cost-effective and reliable approach to assess the quality and thermal stability of EVOO. These characteristics of fluorescence spectroscopy may lead to the development of portable device for the onsite monitoring of EVOO.

Evaluation of Steady-State and Time-Resolved Fluorescence Spectroscopy as a Method for Assessing the Impact of Photo-Oxidation on Refined Soybean Oils

The type of material used in packaging, lighting, and storage time can impact food quality during storage. This study aimed to investigate the progress of photosensitized oxidation in refined soybean oil using steady-state and time-resolved fluorescence spectroscopy. The experiment was conducted through accelerated photo-oxidation with Light-Emitting Diode (LED) in samples stored for ten days at room temperature (26.0 ± 2.0 °C) in clear polyethylene terephthalate (PET) packaging of different colors and different transmission spectra in the UV and visible range. Emission spectra were obtained with excitation at 373, 405, and 500 nm, resulting in two main emission peaks: the first with maximum emission between 430 and 555 nm and the second at around 660 nm. Fluorescence decay curves were obtained with excitation at 340 and 405 nm. The results indicated that transparent PET bottles are not effective in protecting soybean oil from photosensitized oxidation under the studied conditions. Strong correlations were observed between fluorescence parameters and peroxide and conjugated diene values, indicators of lipid oxidation progress. Fluorescence spectroscopy has several advantages over traditional methods as it is a simple, fast, low-cost, and low-waste technique.

Detection and Quantification of Adulteration in Krill Oil with Raman and Infrared Spectroscopic Methods

Raman and infrared spectroscopy, used as individual and low-level fused datasets, were evaluated to identify and quantify the presence of adulterants (palm oil, PO; ω-3 concentrates in ethyl ester, O3C and fish oil, FO) in krill oil. These datasets were qualitatively analysed with principal component analysis (PCA) and classified as adulterated or unadulterated using support vector machines (SVM). Using partial least squares regression (PLSR), it was possible to identify and quantify the adulterant present in the KO mixture. Raman spectroscopy performed better (r² = 0.98; RMSEP = 2.3%) than IR spectroscopy (r² = 0.91; RMSEP = 4.2%) for quantification of O3C in KO. A data fusion approach further improved the analysis with model performance for quantification of PO (r² = 0.98; RMSEP = 2.7%) and FO (r² = 0.76; RMSEP = 9.1%). This study demonstrates the potential use of Raman and IR spectroscopy to quantify adulterants present in KO.

An Affordable NIR Spectroscopic System for Fraud Detection in Olive Oil

Adulterations of olive oil are performed by adding seed oils to this high-quality product, which are cheaper than olive oils. Food safety controls have been established by the European Union to avoid these episodes. Most of these methodologies require expensive equipment, time-consuming procedures, and expert personnel to execute. Near-infrared spectroscopy (NIRS) technology has many applications in the food processing industry. It analyzes food safety and quality parameters along the food chain. Using principal component analysis (PCA), the differences and similarities between olive oil and seed oils (sesame, sunflower, and flax oil) have been evaluated. To quantify the percentage of adulterated seed oil in olive oils, partial least squares (PLS) have been employed. A total of 96 samples of olive oil adulterated with seed oils were prepared. These samples were used to build a spectra library covering various mixtures containing seed oils and olive oil contents. Eighteen chemometric models were developed by combining the first and second derivatives with Standard Normal Variable (SNV) for scatter correction to classify and quantify seed oil adulteration and percentage. The results obtained for all seed oils show excellent coefficients of determination for calibration higher than 0.80. Because the instrumental aspects are not generally sufficiently addressed in the articles, we include a specific section on some key aspects of developing a high-performance and cost-effective NIR spectroscopy solution for fraud detection in olive oil. First, spectroscopy architectures are introduced, especially the Texas Instruments Digital Light Processing (DLP) technology for spectroscopy that has been used in this work. These results demonstrate that the portable prototype can be used as an effective tool to detect food fraud in liquid samples.

Adulteration detection technologies used for halal/kosher food products: an overview

In the Islamic and Jewish religions, there are various restrictions that should be followed in order for food products to be acceptable. Some food items like pork or dog meat are banned to be consumed by the followers of the mentioned religions. However, illegally, some food producers in various countries use either the meat or the fat of the banned animals during food production without being mentioned in the label on the final products, and this considers as food adulteration. Nowadays, halal or kosher labeled food products have a high economic value, therefore deceiving the consumers by producing adulterated food is an illegal business that could make large gains. On the other hand, there is an insistent need from the consumers for getting reliable products that comply with their conditions. One of the main challenges is that the detection of food adulteration and the presence of any of the banned ingredients is usually unnoticeable and cannot be determined by the naked eye. As a result, scientists strove to develop very sensitive and precise analytical techniques. The most widely utilized techniques for the detection and determination of halal/kosher food adulterations can be listed as High-Pressure Liquid Chromatography (HPLC), Capillary Electrophoresis (CE), Gas Chromatography (GC), Electronic Nose (EN), Polymerase Chain Reaction (PCR), Enzyme-linked Immuno Sorbent Assay (ELISA), Differential Scanning Calorimetry (DSC), Nuclear Magnetic Resonance (NMR), Near-infrared (NIR) Spectroscopy, Laser-induced Breakdown Spectroscopy (LIBS), Fluorescent Light Spectroscopy, Fourier Transform Infrared (FTIR) Spectroscopy and Raman Spectroscopy (RS). All of the above-mentioned techniques were evaluated in terms of their detection capabilities, equipment and analysis costs, accuracy, mobility, and needed sample volume. As a result, the main purposes of the present review are to identify the most often used detection approaches and to get a better knowledge of the existing halal/kosher detection methods from a literature perspective.

Olive oil classification with Laser-induced fluorescence (LIF) spectra using 1-dimensional convolutional neural network and dual convolution structure model

Laser-induced fluorescence (LIF) spectroscopy is widely used for the analysis and classification of olive oil. This paper proposes the classification of LIF data using a specific 1-dimensional convolutional neural network (1D-CNN) model, which does not require pre-processing steps such as normalisation or denoising and can be flexibly applied to massive data. However, by adding a dual convolution structure (Dual-conv) to the model, the features of the 1-dimensional spectra are more scattered within one convolution-pooling process; thus, the classification effects are improved. The models were validated through an olive oil classification experiment which contained a total of 72,000 sets of LIF spectra data, and the classification accuracy rate reached ∼99.69%. Additionally, a common classification approach, the support vector machine (SVM), was utilised for the comparison of the results. The results show that the neural networks perform better than the SVM. The Dual-conv model structure has a faster convergence speed and higher evaluation parameters than those of the 1D-CNN in the same period of iterations, without increasing the data dimension.

Characterization of Corn Oil Using Fluorescence Spectroscopy

In these studies, Fluorescence spectroscopy has been utilized for the characterization of pure and commercially available corn oil. The best excitation wavelength of 380 nm has been investigated, where maximum spectral information can be assessed. The emission spectra from pure and commercial corn oil samples disclosed that pure corn oil contained oleic acid, beta-carotenes, chlorophylls, isomers of vitamin E and traces of oxidized products which exhibit fluorescence at 406, 525, 675, 440 and 435/475 nm respectively. Whereas, commercial corn oils lack these valuable ingredients and only contain fats along with their primary and secondary oxidized products that emit a broad emission band centred at 440 nm. The study has also depicted that Fluorescence spectroscopy can even be used to select best quality corn oil among pure corn oil samples with different varieties and seed origins. In addition, the effect of temperature on the composition of pure and commercial corn oil samples have also been investigated by heating them at 100, 120,140, 160, 180 and 200 °C each sample for 30 min. This was done because corn oil is being used for cooking where it is generally heated up to 120 °C and for deep frying up to 180 °C. On heating, in pure corn oil, deterioration of Vitamin-E and beta-carotenes occurred with an increase in the oxidation products, whereas, in commercial oil samples, only the concentration of oxidation products increased. However, it was found that up to 140 °C, pure corn oil can be used safely for cooking purpose where it does not lose much of its valuable ingredients while in commercial corn oils, fat composition does not alter much up to 180 °C and after that oxidized products start to increase rapidly.

Comparative study of three fingerprint analytical approaches based on spectroscopic sensors and chemometrics for the detection and quantification of argan oil adulteration

BACKGROUND

Argan oil is one of the purest and rarest oils in the world, so that the addition of any further product is strictly prohibited by international regulations. Consequently, it is necessary to establish reliable analytical methods to ensure its authenticity. In this study, three multivariate approaches have been developed and validated using fluorescence, UV-visible, and attenuated total reflectance Fourier transform mid-infrared (FT-MIR) spectroscopies.

RESULTS

The application of a partial least squares discriminant analysis model showed an accuracy of 100%. The quantification of adulteration have been evaluated using partial least squares (PLS) regression. The PLS model developed from fluorescence spectroscopy provided the best results for the calibration and cross-validation sets, as it showed the highest R² (0.99) and the lowest root mean square error of calibration and cross-validation (0.55, 0.79). The external validation of the three multivariate approaches by the accuracy profile shows that these approaches guarantee reliable and valid results of 0.5-32%, 7-32%, and 10-32% using fluorescence, FT-MIR and UV-visible spectroscopies respectively.

CONCLUSION

This study confirmed the feasibility of using spectroscopic sensors (routine technique) for rapid determination of argan oil falsification. © 2021 Society of Chemical Industry.

Estimating bulk optical properties of AFB1 contaminated edible oils in 300-900 nm by combining double integrating spheres technique with laser induced fluorescence spectroscopy

An optical detection platform based on laser induced spectroscopy and double integrating spheres techniques was developed to obtain absorption (μ_a), reduced scattering coefficients (μ'_s) and fluorescence intensity of oil. The validation experiment carried on liquid phantoms showed that the developed system could achieve high linearity, and the results of spectra analysis indicated that the fluorescence intensity has a significant negative correlation with both μ_a and μ'_s. A total of 1620 oils with six categories were detected. The reason for the difference of fluorescence and μ_a spectra was analyzed by comparing the measured chlorophyll, polyphenol and α-tocopherol contents. Linear discriminant analysis combined with principal component analysis based on fluorescence and μ_a spectra was employed, to calibrate the AFB₁ classification models. The discrimination results manifested that by integrating μ_a with fluorescence signal, the correct classification rate could be improved by more than 10%, and the false negative rate was greatly reduced.

Sensitivity of Proton NMR Relaxation and Proton NMR Diffusion Measurements to Olive Oil Adulterations with Vegetable Oils

Olive oils and, in particular, extra-virgin olive oils (EVOOs) are one of the most frauded food. Among the different adulterations of EVOOs, the mixture of high-quality olive oils with vegetable oils is one of the most common in the market. The need for fast and cheap techniques able to detect extra-virgin olive oil adulterations was the main motivation for the present research work based on ¹H NMR relaxation and diffusion measurements. In particular, the ¹H NMR relaxation times, T₁ and T₂, measured at 2 and 100 MHz on about 60 EVOO samples produced in Italy are compared with those measured on four different vegetable oils, produced from macadamia nuts, linseeds, sunflower seeds, and soybeans. Self-diffusion coefficients on this set of olive oils and vegetable oil samples were measured by means of the ¹H NMR diffusion ordered spectroscopy (DOSY) technique, showing that, except for the macadamia oil, other vegetable oils are characterized by an average diffusion coefficient sensibly different from extra-virgin olive oils. Preliminary tests based on both NMR relaxation and diffusometry methods indicate that eventual adulterations of EVOO with linseed oil and macadamia oil are the easiest and the most difficult frauds to be detected, respectively.

Edible Oils Adulteration: A Review on Regulatory Compliance and Its Detection Technologies

Various events of edible oils adulteration with inferior ingredients were reported regularly in recent years. This review is aimed to provide an overview of edible oils adulteration practices, regulatory compliance and detection technologies. Many detection technologies for edible oils adulteration were developed in the past such as methods that are based on chromatography or spectroscopy. Electrochemical sensors like electric nose and tongue are also gaining popularity in the detection of adulterated virgin olive oil and virgin coconut oil. It can be concluded that these detection technologies are essential in the combat with food adulterers and can be improved.

An improved Method for Determining Transient Fuel Dilution of Oil in an Internal-Combustion Engine Using Laser-Induced Florescence and Multivariate Least Square Calibration

An optical diagnostic, based on laser-induced fluorescence (LIF), has been developed for on-engine measurements of real-time fuel dilution of engine oil or fuel in oil (FIO). Fuel dilution of oil is broadly relevant to advancing engine technology including durability, calibration, and catalyst-system management, and believed to promote destructive stochastic pre-ignition (SPI) during high-load engine operations. While standard (e.g., ASTM D3524-90) methods are not capable of real-time transient measurements, the LIF technique resolves transient dilution on the minutes time scale. We have expanded on our original FIO instrument development by introducing an improved analysis based on multivariate least square chemometrics analysis. The measurement uses a fuel dye (180-1300 parts per million, by mass) and monitors for its presence in the oil using 532 nm excitation and LIF. While the original FIO instrument utilized a two-color ratio method for analysis, the improved chemometric analysis uses the fully resolved LIF dye spectra to provide better predictive FIO accuracy (>92%) over a wide FIO range (1.5-14%) typical of engine application. We also investigate the effect of oil temperature on the LIF signal. Limited engine applications for demonstrating and validating the improved FIO instrument are shown, and the related data used to quantify practical detection limit and sensitivity. The improved analysis is insensitive to laser power fluctuation and change in detector integration time, providing an excellent FIO sensitivity (1-2%) and detection limit (0.01 %FIO) over a wide range of loads and injection timings, illustrating this updated approach to be a promising tool for advancing engine technology.

Rapid Detection of Fatty Acids in Edible Oils Using Vis-NIR Reflectance Spectroscopy with Multivariate Methods

The composition and content of fatty acids are critical indicators to identify the quality of edible oils. This study was undertaken to establish a rapid determination method for quality detection of edible oils based on quantitative analysis of palmitic acid, stearic acid, arachidic acid, and behenic acid. Seven kinds of oils were measured to obtain Vis-NIR spectra. Multivariate methods combined with pretreatment methods were adopted to establish quantitative analysis models for the four fatty acids. The model of support vector machine (SVM) with standard normal variate (SNV) pretreatment showed the best predictive performance for the four fatty acids. For the palmitic acid, the determination coefficient of prediction (RP2) was 0.9504 and the root mean square error of prediction (RMSEP) was 0.8181. For the stearic acid, RP2 and RMSEP were 0.9636 and 0.2965. In the prediction of arachidic acid, RP2 and RMSEP were 0.9576 and 0.0577. In the prediction of behenic acid, the RP2 and RMSEP were 0.9521 and 0.1486. Furthermore, the effective wavelengths selected by successive projections algorithm (SPA) were useful for establishing simplified prediction models. The results demonstrate that Vis-NIR spectroscopy combined with multivariate methods can provide a rapid and accurate approach for fatty acids detection of edible oils.

Exploration of Spanish Olive Oil Quality with a Miniaturized Low-Cost Fluorescence Sensor and Machine Learning Techniques

Extra virgin olive oil (EVOO) is the highest quality of olive oil and is characterized by highly beneficial nutritional properties. The large increase in both consumption and fraud, for example through adulteration, creates new challenges and an increasing demand for developing new quality assessment methodologies that are easier and cheaper to perform. As of today, the determination of olive oil quality is performed by producers through chemical analysis and organoleptic evaluation. The chemical analysis requires advanced equipment and chemical knowledge of certified laboratories, and has therefore limited accessibility. In this work a minimalist, portable, and low-cost sensor is presented, which can perform olive oil quality assessment using fluorescence spectroscopy. The potential of the proposed technology is explored by analyzing several olive oils of different quality levels, EVOO, virgin olive oil (VOO), and lampante olive oil (LOO). The spectral data were analyzed using a large number of machine learning methods, including artificial neural networks. The analysis performed in this work demonstrates the possibility of performing the classification of olive oil in the three mentioned classes with an accuracy of 100%. These results confirm that this minimalist low-cost sensor has the potential to substitute expensive and complex chemical analysis.

Molecular characterization of Leucoanthocyanidin reductase and Flavonol synthase gene in Arachis hypogaea

Arachis hypogaea (peanut) is a potential source of bioactive compounds including flavonols and proanthocyanidins, which have gained particular interest of metabolic engineering owing to their significance in the growth, development and defense responses in plants. To gain insight of proanthocyanidins and flavonols production in A. hypogaea, Leucoanthocyanidin reductase (AhLAR) and Flavonol synthase (AhFLS) enzymes responsible for their production, have been structurally, transcriptionally and functionally characterized. Structural and functional analysis of putative protein sequence of AhFLS indicated two functional motifs 2OG-FeII_Oxy and DIOX_N, while six functional motifs belonging to the families of NAD-dependent dehydratase, 3, β hydroxysteroid dehydrogenase and NmrA-like family were observed in case of AhLAR. Promoter sequence analysis unraveled several promoter elements related to the development regulation, environmental stress responses and hormonal signaling. Furthermore, the expression analysis of AhFLS and AhLAR and accumulation pattern analysis of proanthocyanidins and flavonols in three selected cultivars of A. hypogaea under saline environment confirmed their role against salinity in genotype-dependent and stress level-dependent manner. Correlation studies revealed that AhFLS and AhLAR expression is not directly dependent on the antioxidant enzymes activity, biochemical and growth parameters but higher Pearson r value depicted some level of dependency. This detailed study of AhLAR and AhFLS can assist in the metabolic engineering of flavonoid biosynthetic pathway to produce stress tolerant varieties and production of proanthocyanidins and flavonols at an industrial scale.

A comparative study of ATR-FTIR, UV-visible and fluorescence spectroscopy combined with chemometrics for quantification of squalene in extra virgin olive oils

Attenuated total reflectance-Fourier transform infrared (ATR-FTIR), ultraviolet-visible (UV-Vis), and fluorescence (FL) spectroscopic techniques, combined with partial least-square (PLS) regression with various spectral derivatization methods were tested for the quantitative determination of squalene content of extra virgin olive oils (EVOOs). A set of 90 calibration standards covering a wide range of squalene (3.25-12.54 mg/kg) was used to build up the calibration models. The root mean square error of calibration (RMSEC), the root mean square error of cross-validation (RMSECV), and the root mean square error of prediction (RMSEP) were calculated for evaluation of 18 different calibration models. 50 different brands of EVOOs, which are also analyzed in terms of quality indexes, fatty acid composition, and squalene concentration (3.25-12.54 g/kg) were used for checking the predictive capacities of the calibration models. The best predictions were achieved using normal spectra in FL spectroscopy with the lowest RMSEC of 0.1065, RMSEV of 0.1310, and RMSEP of 0.1500 in the spectral region 250-730 nm. Thus, FL spectroscopy combined with PLS regression is proposed as a fast, accurate, and environmentally friendly approach that can be efficiently used in determining squalene in EVOOs.

Fluorescence Spectroscopy Based Detection of Adulteration in Desi Ghee

Desi ghee, obtained by buffalo and cow milk, is highly expensive because it contains valuable vitamins and conjugated linoleic acid (CLA). Its high demand and cost result in to its adulteration with inferior banaspati ghee. In this study, Fluorescence spectroscopy along with multivariate analysis has been utilised for the detection and quantification of adulteration. Spectroscopic analysis showed that buffalo ghee contains more vitamins and CLA than cow, whereas cow ghee is enriched with beta-carotene. For multivariate analysis, principle component analysis (PCA) and partial least square regression (PLSR) have been applied on the spectral data for the determination of adulteration. PLSR model was authenticated by predicting 23 unknown samples including 3 commercial brands of desi ghee. The root mean square error in prediction (RMSEP) of unknown samples was found to be 1.7 which is a reasonable value for quantitative prediction. Due to non-destructive and requiring no sample pre-treatment, this method can effectively be employed as on line characterization tool for the food safety assurance.

Excitation wavelength analysis of a laser-induced fluorescence technique for quantification of extra virgin olive oil adulteration

The performance of the laser-induced fluorescence (LIF) technique is greatly affected by the excitation wavelength (EW). This study aims to find an appropriate EW that can be used for analyzing extra virgin olive oil (EVOO) adulteration quantification by comparing the effect of different EWs. The EWs of 405 nm, 450 nm, and 532 nm were selected to perform the comparative experiments. By using the three EWs in the experiments, the LIF spectra of EVOO samples adulterated with peanut oil (PO) or soybean oil (SO) in different proportions, as well as the prediction models established through different multivariate analysis algorithms were analyzed. The linear discriminant analysis (LDA) was applied for qualitative analysis, while the partial least squares regression (PLSR), backpropagation neural network, and k-nearest neighbor were employed for quantitative analysis. The results show that the performance of 450 nm EW is always superior to that of 405 and 532 nm EWs in any model, with a smaller root mean square error (RMSE). Using the LDA-PLSR model, the RMSE is 1.35% for SO adulterants and 1.36% for PO adulterants, respectively.

Improved continuous locality preserving projection for quantification of extra virgin olive oil adulteration by using laser-induced fluorescence

An optimized dimensionality reduction technique is proposed as the improved continuous locality preserving projection (ICLPP), which was developed by modifying and optimizing the weighting functions and weighting factors of the continuous locality preserving projection (CLPP) algorithm. With only one adjustable parameter, this optimized technique not only enhances CLPP's capability of maintaining the continuity of the massive data, but also results in better simplicity and adaptability of the algorithm. In this paper, the performance of ICLPP is validated through quantification analysis of the adulteration of extra virgin olive oil (EVOO) with low-cost oils based on laser-induced fluorescence spectroscopy. Through cross validation and comparative studies, ICLPP, combined with the regression algorithm, is employed to predict and screen adulteration in EVOO, and is found to generally outperform other state-of-the-art dimensionality reduction algorithms, especially for prediction of adulterants at low level (<10%). It is evidenced that the ICLPP-based framework is superior in detecting adulteration by using spectral data.

Characterization of canola oil extracted by different methods using fluorescence spectroscopy

The potential of fluorescence spectroscopy has been utilized for the characterization of three types of canola oil samples: the first type was obtained by dissolving its seeds in hexane solvent, the second by cold press method, and the third from eight commercial brands. Fluorescence spectra from all samples have been acquired by using excitation wavelengths from 280 to 420 nm with step of 10 nm to investigate their valuable ingredients. The emission bands at 375, 525 and 673 nm that represent vitamin E/beta-carotene and chlorophyll, are present only in canola oil samples extracted by chemical and cold press methods and absolutely absent from all commercial brands. The emission band at 440 nm appearing only in the commercial oil brands, is assigned to oxidized products of isomers of vitamin E and fatty acids. In addition, the effect of temperature on the canola oil extracted by cold press method has been investigated which showed that up to 180 ^oC it does not lose much of its natural molecular composition. However, it showed a trend of thermal oxidation with rise of temperature.