Discrimination of Adulterated Sesame Oil Using Mid-infrared Spectroscopy and Chemometrics

Study of the Antioxidant Capacity and Oxidation Products of Resveratrol in Soybean Oil

Resveratrol (3,5,4'-trihydroxystilbene), a naturally occurring polyphenol that is widely utilized in functional food due to its antioxidant, anti-inflammatory, anti-cancer and anti-aging properties. In the present study, the antioxidant capacity and oxidation products of resveratrol in soybean oil were investigated. The antioxidant activity of resveratrol was evaluated by employing various in vitro antioxidant assays such as DPPH scavenging activities, ferric reducing abilities (FRAP) and oxygen radical absorbance capacity (ORAC). Furthermore, monitoring the peroxide value and the acid value of soybean oil with the addition of 200-1000 μg/g of resveratrol at 60 and 180 °C. It was found that when the concentration of resveratrol in soybean oil was 600 µg/g, the antioxidant capacity was most effective. Resveratrol and its thermal degradation products were identified using liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS). There were seven nonvolatile oxidation products with mass-to-charge ratios of 138.03, 171.04, 185.10, 157.03, 436.13, 244.07 and 306.09 kg/C and two volatile oxidation products with mass-to-charge ratios of 100.05 and 158.13 kg/C were identified. The research findings may provide essential information for the development of resveratrol as functional oils in future.

Identification of the interference spectra of edible oil samples based on neighborhood rough set attribute reduction

Due to numerous edible oil safety problems in China, an automatic oil quality detection technique is urgently needed. In this study, rough set theory and Fourier transform spectrum are combined for proposing a digital identification method for edible oil. First, the Fourier transform spectra of three different types of edible oil samples, including colza oil, waste oil, and peanut oil, are measured. After the input spectra are differentially and smoothly processed, the characteristic wavelength bands are selected with neighborhood rough set attribution reduction (NRSAR). Moreover, the classification models are established based on random forest (RF) and extreme learning machine (ELM) algorithms. Finally, confusion matrix, classification accuracy, sensitivity, specificity, and the distribution of judgment are calculated for evaluating the classification performances of different models and determining the optimal oil identification model. The results show that by using the third-order difference pre-processing method, 193 wavelength bands in the visible range can be reduced to 10 characteristic wavelengths, with a compression ratio of over 88.61%. Using the established NRS-RF and NRS-ELM models, the total identification accuracies are 91.67% and 93.33%, respectively. In particular, the identification accuracy of peanut oil using the NRS-ELM model reaches up to 100%, whereas the identification accuracies obtained using the principal component analysis (PCA)-based models that are commonly used in information processing (PCA-RF and PCA-ELM) are 81.67% and 90.00%, respectively. As compared with feature extraction methods, the proposed NRSAR shows directive advantages in terms of precision, sensitivity, specificity, and the distribution of judgment. In addition, the execution time is also reduced by approximately 1/3. Conclusively, the NRSAR method and NRS-ELM the model in the spectral identification of edible oil show favorable performance. They are expected to bring forth insightful oil identification techniques.

Applications of ultrasound techniques in tandem with non-destructive approaches for the quality evaluation of edible oils

Edible oils include triglycerides that are extracted from oil seeds or fruits such as sunflowers, palms, olives, soys, rapeseeds, cocoa and many others. They are the elementary origins of unsaturated fats and vitamins especially vitamin 'E' in people's diets. Edible oils are at risk of intentional (such as inadequate storage conditions) and unintentional adulteration, so it is necessary to pay attention to their safety, health and fraud. Generally, this evaluation can be destructive or non-destructive. There are numerous methods to evaluate quality of edible oils which include sensory analysis, chemical analysis, chromatography, ultrasound, etc. The Ultrasonic approach is a non-destructive way and also fast, accurate, inexpensive, repeatable, portable and simple. Combination of ultrasound with other techniques such as electronic nose, electronic tongue, visible spectroscopic fingerprints, chemical descriptors, Raman spectroscopy, mid-infrared and machine vision, will improve quality evaluation and detection accuracy. This review summarizes the ultrasound idea and the latest knowledge of its application with other techniques on evaluation of edible oils.

Chia Oil Adulteration Detection Based on Spectroscopic Measurements

Chia oil is a valuable source of omega-3-fatty acids and other nutritional components. However, it is expensive to produce and can therefore be easily adulterated with cheaper oils to improve the profit margins. Spectroscopic methods are becoming more and more common in food fraud detection. The aim of this study was to answer following questions: Is it possible to detect chia oil adulteration by spectroscopic analysis of the oils? Is it possible to identify the adulteration oil? Is it possible to determine the amount of adulteration? Two chia oils from local markets were adulterated with three common food oils, including sunflower, rapeseed and corn oil. Subsequently, six chia oils obtained from different sites in Kenya were adulterated with sunflower oil to check the results. Raman, NIR and fluorescence spectroscopy were applied for the analysis. It was possible to detect the amount of adulterated oils by spectroscopic analysis, with a minimum R² of 0.95 for the used partial least square regression with a maximum RMSEP_range of 10%. The adulterations of chia oils by rapeseed, sunflower and corn oil were identified by classification with a median true positive rate of 90%. The training accuracies, sensitivity and specificity of the classifications were over 90%. Chia oil B was easier to detect. The adulterated samples were identified with a precision of 97%. All of the classification methods show good results, however SVM were the best. The identification of the adulteration oil was possible; less than 5% of the adulteration oils were difficult to detect. In summary, spectroscopic analysis of chia oils might be a useful tool to identify adulterations.

Prediction of chemical parameters and authentication of various cold pressed oils with fluorescence and mid-infrared spectroscopic methods

It was aimed to compare the performances of two spectroscopic methods, fluorescence and mid-infrared spectroscopy, in terms of their adulteration detection and estimation of several chemical properties for various cold pressed seed oils. Spectroscopic profiles, fatty acid, free fatty acid and total phenol contents of pumpkin seed, grape seed, black cumin oil, and sesame seed oils were determined and these oils were mixed with sunflower oil at 1-50% (v/v). Both spectroscopic techniques provided comparable results for determination of adulteration of each oil type and the most successful prediction was obtained for pumpkin seed oil at levels >%1. Combined data set of oils resulted in successful quantification of their free fatty acid value, total phenol and major fatty acids contents with both spectroscopic methods regardless of oil type. Both techniques could be used as reliable, fast and environmentally friendly alternatives in the analyses of different types of seed oils.

Detection of adulteration in food based on nondestructive analysis techniques: a review

In recent years, people pay more and more attention to food quality and safety, which are significantly relating to human health. Food adulteration is a world-wide concerned issue relating to food quality and safety, and it is difficult to be detected. Modern detection techniques (high performance liquid chromatography, gas chromatography-mass spectrometer, etc.) can accurately identify the types and concentrations of adulterants in different food types. However, the characteristics as expensive, low efficient and complex sample preparation and operation limit the use of these techniques. The rapid, nondestructive and accurate detection techniques of food adulteration is of great and urgent demand. This paper introduced the principles, advantages and disadvantages of the nondestructive analysis techniques and reviewed the applications of these techniques in food adulteration screen in recent years. Differences among these techniques, differences on data interpretation and future prospects were also discussed.

Rapid Classification and Quantification of Camellia (Camellia oleifera Abel.) Oil Blended with Rapeseed Oil Using FTIR-ATR Spectroscopy

Currently, the authentication of camellia oil (CAO) has become very important due to the possible adulteration of CAO with cheaper vegetable oils such as rapeseed oil (RSO). Therefore, we report a Fourier transform infrared (FTIR) spectroscopic method for detecting the authenticity of CAO and quantifying the blended levels of RSO. In this study, two characteristic spectral bands (1119 cm^-1 and 1096 cm^-1) were selected and used for monitoring the purity of CAO. In combination with principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares regression (PLSR) analysis, qualitative and quantitative methods for the detection of camellia oil adulteration were proposed. The results showed that the calculated I₁₁₁₉/I₁₀₉₆ intensity ratio facilitated an initial check for pure CAO and six other edible oils. PCA was used on the optimized spectral region of 1800-650 cm^-1. We observed the classification of CAO and RSO as well as discrimination of CAO with RSO adulterants. LDA was utilized to classify CAO from RSO. We could differentiate and classify RSO adulterants up to 1% v/v. In the quantitative PLSR models, the plots of actual values versus predicted values exhibited high linearity. Root mean square error of calibration (RMSEC) and root mean square error of cross validation (RMSECV) values of the PLSR models were 1.4518%-3.3164% v/v and 1.7196%-3.8136% v/v, respectively. This method was successfully applied in the classification and quantification of CAO adulteration with RSO.

Multispecies Adulteration Detection of Camellia Oil by Chemical Markers

Adulteration of edible oils has attracted attention from more researchers and consumers in recent years. Complex multispecies adulteration is a commonly used strategy to mask the traditional adulteration detection methods. Most of the researchers were only concerned about single targeted adulterants, however, it was difficult to identify complex multispecies adulteration or untargeted adulterants. To detect adulteration of edible oil, identification of characteristic markers of adulterants was proposed to be an effective method, which could provide a solution for multispecies adulteration detection. In this study, a simple method of multispecies adulteration detection for camellia oil (adulterated with soybean oil, peanut oil, rapeseed oil) was developed by quantifying chemical markers including four isoflavones, trans-resveratrol and sinapic acid, which used liquid chromatography tandem mass spectrometry (LC-MS/MS) combined with solid phase extraction (SPE). In commercial camellia oil, only two of them were detected of daidzin with the average content of 0.06 ng/g while other markers were absent. The developed method was highly sensitive as the limits of detection (LODs) ranged from 0.02 ng/mL to 0.16 ng/mL and the mean recoveries ranged from 79.7% to 113.5%, indicating that this method was reliable to detect potential characteristic markers in edible oils. Six target compounds for pure camellia oils, soybean oils, peanut oils and rapeseed oils had been analyzed to get the results. The validation results indicated that this simple and rapid method was successfully employed to determine multispecies adulteration of camellia oil adulterated with soybean, peanut and rapeseed oils.