A detection method of vegetable oils in edible blended oil based on three-dimensional fluorescence spectroscopy technique

Quick and reagent-free monitoring of edible oil saponification values using a handheld Raman device

Saponification value, the average molecular weight of fatty acids, is a crucial parameter for detecting adulteration of edible oils. Conventionally, it is determined in a laboratory setup through a time-consuming, laborious titration process using chemical reagents. Herein, the application of Raman spectroscopy for quick SV estimation of oils is demonstrated. It was hypothesized that the SV can be predicted from Raman spectra since the spectral patterns reflect the composition of fatty acid triglycerides. Two model oil systems were studied: coconut-gingelly oil and coconut-sunflower oil. Univariate models built from Raman spectra were successful only for the specific oil system; hence, PLS-Regression was executed across the two systems. The PLSR model on the validation set returned the average error, percentage error, and root mean square error of prediction as 2.1, 0.99 %, and 2.4, respectively. This method offers several advantages of portability, little reagent use, minimal sample preparation, and reduced analysis time.

Machine learning-enabled high-throughput industry screening of edible oils

Long-term consumption of mixed fraudulent edible oils increases the risk of developing of chronic diseases which has been a threat to the public health globally. The complicated global supply-chain is making the industry malpractices had often gone undetected. In order to restore the confidence of consumers, traceability (and accountability) of every level in the supply chain is vital. In this work, we shown that machine learning (ML) assisted windowed spectroscopy (e.g., visible-band, infra-red band) produces high-throughput, non-destructive, and label-free authentication of edible oils (e.g., olive oils, sunflower oils), offers the feasibility for rapid analysis of large-scale industrial screening. We report achieving high-level of discriminant (AUC > 0.96) in the large-scale (n ≈ 11,500) of adulteration in olive oils. Notably, high clustering fidelity of 'spectral fingerprints' achieved created opportunity for (hypothesis-free) self-sustaining large database compilation which was never possible without machine learning. (137 words).

Quantitative Analysis of Blended Oils Based on Intensity Ratios of Marker Ions in MALDI-MS Spectra

Determination of quantitative compositions of blended oils is an essential but challenging step for the quality control and safety assurance of blended oils. We herein report a method for the quantitative analysis of blended oils based on the intensity ratio of triacylglycerol marker ions, which could be obtained from the highly reproducible spectra acquired by using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) to directly analyze blended oils in their oily states. We demonstrated that this method could provide good quantitative results to binary, ternary, and quaternary blended oils, with simultaneous quantitation of multiple compositions, and was applicable for quantitative analysis of commercial blended oil products. Moreover, the intensity ratio-based method could be used to rapidly measure the proportions of oil compositions in blended oils, only based on the spectra of the blended oils and related pure oils, making the method as a high-throughput approach to meet the sharply growing analytical demands of blended oils.

Toward commercial applications of LED and laser-induced fluorescence techniques for food identity, quality, and safety monitoring: A review

The assessment of food safety and quality is a matter of paramount importance, especially considering the challenges posed by climate change. Convenient, eco-friendly, and non-destructive techniques have attracted extensive attention in the food industry because they can retain food safety and quality. Fluorescence radiation, the process by which fluorophore emits light upon the absorption of ultraviolet or visible light, offers the advantages of high sensitivity and selectivity. The use of excitation-emission matrix (EEM) has been extensively explored in the food industry, but on-site detection of EEMs remain a challenge. To address this limitation, laser-induced fluorescence (LIF) and light emitting diode-induced fluorescence (LED-IF) have been implemented in many cases to facilitate the transition of fluorescence measurements from the laboratory to commercial applications. This review provides an overview of the application of commercially available LIF/LED-IF devices for non-destructive food measurement and recent studies that focus on the development of LIF/LED-IF devices for commercial applications. These studies were categorized into two stages: the preliminary exploration stage, which emphasizes the selection of an appropriate excitation wavelength based on the combination of EEM and chemometrics, and the pre-application stage, where experiments were conducted on scouting with specific excitation wavelength. Although commercially available devices have emerged in many research fields, only a limited number have been reported for use in the food industry. Future studies should focus on enhancing the diversity of test samples and parameters that can be measured by a single device, exploring the application of LIF techniques for detecting low-concentration substances in food, investigating more quantitative approaches, and developing embedded computing devices.

Quantitative analysis of phenanthrene in soil by fluorescence spectroscopy coupled with the CARS-PLS model

Polycyclic aromatic hydrocarbons (PAHs) are typical organic pollutants in soil and are teratogenic and carcinogenic. Therefore, rapid and accurate analysis of PAHs in soil can provide a theoretical basis and data support for soil contamination risk assessment. In this work, a fluorescence spectroscopy technique combined with partial least squares (PLS) was proposed for rapid quantitative analysis of phenanthrene (PHE) in soil. At first, the fluorescence spectra of 29 soil samples with different concentrations (0.3-10 mg g-1) of PHE were collected by RF-5301 PC fluorescence spectrophotometer. Secondly, the effects of different spectral preprocessing methods were investigated on the prediction performance of the PLS calibration model. And then, the influence of competitive adaptive reweighted sampling (CARS) wavelength points on the prediction performance of PLS calibration model was discussed. Finally, according to the selected wavelength points, a quantitative analytical model for PHE content in soil was constructed using the PLS calibration method. To further explore the predictive performance of the CARS-PLS calibration model, the predictive results were compared with those of the RAW spectrum-partial least squares calibration model (RAW-PLS) and the wavelet transform-standard normal variation (WT-SNV) calibration model. The CARS-PLS calibration model showed the optimal predictive performance and its coefficient of determination of cross-validation (R cv 2) and root mean square error of 10-fold cross-validation (RMSEcv) were 0.9957 and 18.98%, respectively. The coefficient of determination of prediction set (R p 2) and root mean square error of prediction set (RMSEp) were 0.9963 and 16.13%, respectively. Hence, the CARS algorithm based on fluorescence spectrum coupled with PLS can give a rapid and accurate quantitative analysis of the PHE content in soil.

Texture staling of pound cakes assessed by front face fluorescence spectroscopy in tandem with chemometric analysis

Two vegetal oils have been selected for the formulation of pound cakes, rapeseed oil (RO) and palm oil (PO). The freshness of these products has been monitored during 58 days of storage. The RO pound cakes of 58 days as well as PO pound cakes presented higher values (p < .05) of hardness (N), Young's modulus (N.cm^-2 ) and compressive stress (N.cm^-2 ) than those aged of 5 days. For both vitamin A and tryptophan spectra, the maximum of fluorescence intensity varied according to the aging period. For both RO and PO recipes, lipid oxidation indicators (peroxide value, para-anisidine value, and total oxidation value [TOTOX]) were interestingly correlated to Young's modulus (R²  = 0.99) as well as compressive stress (R²  = 0.99), using polynomial model. The application of partial least squares regression to the vitamin A spectra provided excellent predictive results for both compressive stress (R²  = 0.99) and TOTOX (R²  = 0.99). Good results were observed for tryptophan spectra for the prediction of TOTOX (R²  = 0.77). Thus, the use of front face fluorescence spectroscopy may provide rapid and accurate results for the monitoring of pound cakes freshness throughout storage.

A Review of Advanced Methods for the Quantitative Analysis of Single Component Oil in Edible Oil Blends

Edible oil blends are composed of two or more edible oils in varying proportions, which can ensure nutritional balance compared to oils comprising a single component oil. In view of their economical and nutritional benefits, quantitative analysis of the component oils in edible oil blends is necessary to ensure the rights and interests of consumers and maintain fairness in the edible oil market. Chemometrics combined with modern analytical instruments has become a main analytical technology for the quantitative analysis of edible oil blends. This review summarizes the different oil blend design methods, instrumental techniques and chemometric methods for conducting single component oil quantification in edible oil blends. The aim is to classify and compare the existing analytical techniques to highlight suitable and promising determination methods in this field.

Study of vegetable oils and their blends using infrared reflectance spectroscopy and refractometry

The present study aims to perform a comparative analysis of vegetable oils and their two-component blends using infrared spectroscopy and refractometry. The study was conducted in Almaty (Kazakhstan) in 2020. Three samples of 44 vegetable oils and their blends made from two components were examined. Fractometry and infrared spectroscopy were used to investigate the properties of blended vegetable oils. To this end, the fatty acid fraction (in percentage), iodine number, and index of refraction (IOR) were calculated. Afterward, the spectrograms obtained for the blends were analyzed. It was found that the difference between the intensities of weak bands and the band expansion of 722 cm^-1 indicates greater expressiveness. When low-intensity bands (1653 cm^-1) become more distinct due to vibrations of double carbon bonds (C-bonds), the level of unsaturated fatty acids in the blend increases as well.

Near infrared spectroscopy combined with chemometrics for quantitative analysis of corn oil in edible blend oil

In this study, near infrared (NIR) spectroscopy combined with chemometrics was used for the quantitative analysis of corn oil in binary to hexanary edible blend oil. Sesame oil, soybean oil, rice oil, sunflower oil and peanut oil were mixed with corn oil subsequently to form binary, ternary, quaternary, quinary and hexanary blend oil datasets. NIR spectra for the five order blend oil datasets were measured in a transmittance mode in the range of 12000-4000 cm^-1. Partial least square (PLS) was used to build models for the five datasets. Six spectral preprocessing methods and their combinations were investigated to improve the prediction performance. Furthermore, the optimal preprocessing-PLS models were further optimized by uninformative variable elimination (UVE), Monte Carlo uninformative variable elimination (MCUVE) and randomization test (RT) variable selection methods. The optimal models acquire root mean square error of prediction (RMSEP) of 1.7299, 2.2089, 2.3742, 2.5608 and 2.6858 for binary, ternary, quaternary, quinary and hexanary blend oil datasets, respectively. The determination coefficients of prediction set (R²_P) and residual predictive deviations (RPDs) for the five datasets are all above 0.93 and 3. Results show that the prediction accuracy is gradually decreased with the increasing of mixture order of blend oil. However, with proper spectral preprocessing and variable selection, the optimal models present good prediction accuracy even for the higher order blend oil. It demonstrates that NIR technology is feasible for determining the pure oil contents in binary to hexanary blend oil.

A simple and quick method to detect adulterated sesame oil using 3D fluorescence spectra

Adulterated sesame oil seriously damages the interests of consumers and the health of market. In this paper, a simple, fast and real-time model for identifying adulterated sesame oil (ASO) was proposed by combining 3D fluorescence spectra with wavelet moments (WMs). First, noise and data volume of the experimental data were reduced by wavelet multiresolution decomposition (WMRSD), which improved the stability and real-time of the model. Next, WMs were used to extract the features of the 3D fluorescence spectra and proved to be effective by hierarchical clustering results. Then, the qualitative quality of WMs of the same orders, different orders and the combinations were evaluated by Dunn's validity index (DVI), and the rules were given, respectively. Finally, the target WMs for identifying ASO were determined. This model is simple and fast, and expandable to online measurement, providing a reference for identification and adulteration of vegetable oils.

Rapid adulteration detection of cold pressed oils with their refined versions by UV-Vis spectroscopy

The aim of this study is the rapid detection of food pressed oils adulteration with their refined versions, using UV–Vis spectroscopy. The study investigates some common oil physico-chemical parameters such are: density, viscosity, refractive index, acid index, peroxide value, saponification index, to detect differences between cold pressed oils versus refined ones, for some food-grade oils found on Romanian market, as well as FT-IR spectroscopy and GC–MS analytical method, obtaining similar results to those presented in the literature data. The difference between some of the obtained results is not relevant for telling the cold-pressed oils from their refined version for adulteration investigation purpose. Colour analysis instead is a very good method to differentiate a cold pressed oil from a refined one. Taking this into account, the cold pressed oils and their refined versions were mixed in different proportions, and their colour properties were analyzed, obtaining linear dependences for a* and b* CIE L*a*b* parameters with cold pressed oil content in the mixture. Dependence equations were proposed.

Study on the long-term effects of DOM on the adsorption of BPS by biochar

Bisphenol S (BPS), regarded as a valid alternative to Bisphenol A (BPA), has been found to induce acute toxicity, genotoxicity. In this paper, BPS pollution was repaired by corn straw biochar, and the effect of dissolved organic matter (DOM) on the remediation mechanism was investigated. Different DOMs were obtained by decomposing corn straw in red soil, yellow soil and brown soil. The DOMs were characterized by Elemental analysis, Fourier infrared spectroscopy (FT-IR), ultraviolet-visible spectroscopy (Uv-vis) spectroscopy, Three-dimensional excitation-emission matrix (3D-EEM). Different kinds of DOMs were added into the biochar adsorption system to determine the optimal pH, optimal dosage, equilibrium adsorption capacity, adsorption kinetics, adsorption isotherms, thermodynamic characteristics, and explore the influence mechanism of DOM on the adsorption of BPS by biochar. The results of the adsorption experiments showed that DOM would suppress the BPS adsorption capacity on biochar. In addition, the DOM, produced by decomposition of corn straw with brown soil, had the strongest inhibitory effect on adsorption, and red soil was the soil with the lowest inhibitory effect on organic pollution removal by biochar.

Simultaneous determination of carbendazim and chlorothalonil pesticide residues in peanut oil using excitation-emission matrix fluorescence coupled with three-way calibration method

A novel fluorescence application for simultaneous determination of two common fungicide pesticides (carbendazim and chlorothalonil) in peanut oil is presented. Using the strategy of combining excitation-emission matrix (EEM) fluorescence with three-way calibration methods, the proposed approach successfully achieved quantitative analysis of carbendazim and chlorothalonil pesticide residues in peanut oil, even with highly overlapped spectra. It needs little preparation, using "mathematical separation" instead of "analytical separation" to achieve concentration prediction of target analytes in complex systems. Each analyte was performed using fluorescence spectroscopy after instrument spectral correction and scatter removal. Then the data were modeled with two three-way calibration algorithms, including alternating trilinear decomposition (ATLD) and alternating penalty trilinear decomposition (APTLD). The results indicated that APTLD performed slightly better than ATLD for this system. The carbendazim and chlorothalonil can be recognized simultaneously with the correlation coefficients all above 0.96 between resolved spectra and actual spectra. Satisfactory results have been achieved with the average recoveries (mean ± standard deviation) of carbendazim and chlorothalonil being 100.2 ± 6.7% and 99.7 ± 6.7%, respectively. Moreover, as for carbendazim and chlorothalonil, the sensitivity (SENs) are 1.50 × 10² and 3.80 × 10² mL ng^-1, the limits of detection (LODs) are 11 ng mL^-1 and 4.3 ng mL^-1, the limit of quantitation (LOQ) are 33.33 ng mL^-1 and 13.03 ng mL^-1, respectively. The above results demonstrated that the proposed method is sensitive, fast and accurate for direct quantitative analysis of multiple pesticide residues in complex matrix such as that of peanut oil.

Enhancing the fluorescence spectrum of frying oil using a nanoscale probe

This study attempted to develop a probe-based fluorescence technology as a rapid method to discriminate the oxidation degree of oil. The fluorescence probe was made by dissolving the selected probe in a chloroform solvent. A fluorescence landscape profile for each oil sample was obtained to discriminate fluorescence changes during the oxidation process. Parallel factor analysis (PARAFAC) was performed to extract characteristic fluorescence peaks. Then, these characteristic fluorescence peaks and chemical indicators were used to build regression models based on support vector regression (SVR). The correlation coefficients (R) and mean squared error (MSE) in the prediction sets were applied as the assessment parameters for the SVR models as follows: R = 0.9724, MSE = 0.0088 for an acid value. This research suggests that probe-based fluorescence spectroscopy is useful for oxidation evaluation of oil and perhaps other food containing high amounts of oil.

Thermal aging of edible oils: spectrophotometric study

The aim of the present study was to determine the spectrophotometric and thermal aging properties of various edible oils (olive, peanut, rapeseed, soybean and sunflower oils) which are commonly available in the Czech market. The samples were measured by UV/VIS absorption spectrometry and fluorescence spectroscopy. Detected substances of UV/VIS spectra were compared to expected oil composition; the highest absorbance values were detected in a wavelength range 300-550 nm which can be related to the presence of unsaturated fatty acids. The mixtures of oils were characterized by fluorescence spectroscopy; the individual oils were successfully distinguished according to their excitation-emission profiles. This method was also used to detect the samples of adulterated oils, i.e., the adulteration of high-quality oils with soybean oil. From a physicochemical point of view, the influence of temperature on the compounds of extra virgin olive oil was examined by thermal stress simulation. This thermal aging analysis demonstrated that the amount of oxidation products in olive oil increased during the heating whereas the chlorophyll content decreased. The results showed the ability of the techniques used, UV/VIS absorption spectrometry and fluorescence spectroscopy, to characterize the quality and composition of oils, and to distinguish individual oils in blends. UV/VIS spectrometry was also successfully employed for the evaluation of olive oil qualitative parameters according to the standard quality parameters by the "International Olive Council" (EEC 702/2007).

Fluorescence Spectroscopy for the Monitoring of Food Processes

Different analytical techniques have been used to examine the complexity of food samples. Among them, fluorescence spectroscopy cannot be ignored in developing rapid and non-invasive analytical methodologies. It is one of the most sensitive spectroscopic approaches employed in identification, classification, authentication, quantification, and optimization of different parameters during food handling, processing, and storage and uses different chemometric tools. Chemometrics helps to retrieve useful information from spectral data utilized in the characterization of food samples. This contribution discusses in detail the potential of fluorescence spectroscopy of different foods, such as dairy, meat, fish, eggs, edible oil, cereals, fruit, vegetables, etc., for qualitative and quantitative analysis with different chemometric approaches.