Method For Detection Of Adulteration Research Articles

Adulteration detection of essence in sesame oil based on headspace gas chromatography-ion mobility spectrometry

Sesame oil is a traditional and delicious edible oil in China and Southeast Asia with a high price. However, sesame oil essence was often illegally added to cheaper edible oils to counterfeit sesame oil. In this study, a rapid and accurate headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) method was proposed to detect the counterfeit sesame oil where the other cheap oils were adulterated with essence. Combined with chemometric methods including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and random forest (RF), authentic and counterfeit sesame oils adulterated with sesame essence (0.5%, w/w) were easily separated into two groups. More importantly, 2-methylbutanoic acid, 2-furfurylthiol, methylpyrazine, methional, and 2,5-dimethylpyrazine were found to be markers of sesame essence, which were used to directly identify the sesame essence. The determination of volatile compounds based on HS-GC-IMS was proven to be an effective method for adulteration detection of essence in sesame oil.

Portable through Bottle SORS for the Authentication of Extra Virgin Olive Oil

The authenticity of olive oil has been a significant long-term challenge. Extra virgin olive oil (EVOO) is the most desirable of these products and commands a high price, thus unscrupulous individuals often alter its quality by adulteration with a lower grade oil. Most analytical methods employed for the detection of food adulteration require sample collection and transportation to a central laboratory for analysis. We explore the use of portable conventional Raman and spatially-offset Raman spectroscopy (SORS) technologies as non-destructive approaches to assess the adulteration status of EVOO quantitatively and for SORS directly through the original container, which means that after analysis the bottle is intact and the oil would still be fit for use. Three sample sets were generated, each with a different adulterant and varying levels of chemical similarity to EVOO. These included EVOO mixed with sunflower oil, pomace olive oil, or refined olive oil. Authentic EVOO samples were stretched/diluted from 0% to 100% with these adulterants and measured using two handheld Raman spectrometers (excitation at 785 or 1064 nm) and handheld SORS (830 nm). The PCA scores plots displayed clear trends which could be related to the level of adulteration for all three mixtures. Conventional Raman (at 785 or 1064 nm) and SORS (at 830 nm with a single spatial offset) conducted in sample vial mode resulted in prediction errors for the test set data ranging from 1.9–4.2% for sunflower oil, 6.5–10.7% for pomace olive oil and 8.0–12.8% for refined olive oil; with the limit of detection (LOD) typically being 3–12% of the adulterant. Container analysis using SORS produced very similar results: 1.4% for sunflower, 4.9% for pomace, and 10.1% for refined olive oil, with similar LODs ranging from 2–14%. It can be concluded that Raman spectroscopy, including through-container analysis using SORS, has significant potential as a rapid and accurate analytical method for the non-destructive detection of adulteration of extra virgin olive oil.

A rapid reversed-phase thin layer chromatographic detection protocol for adulteration in some edible fats and oils food formulation

The problems of adulteration in the vegetable oil and fat have been the major draw backs in the food products formulation, in spite of the various adulteration detection methods in different applications that have been reported. However, the detection tools that can be fast and reliable for the routine analysis necessitated the current work. The two groups of three different samples: vegetable fat containing sample (Blue Band, Golden Penny, La Prairie Classic) and animal fat containing samples (Kell Salad Cream, Crosse & Blackwell and Nola) was used for the purity check using the reversed phased - thin layer chromatographic (RPTLC) method of analysis were developed. The average Rf ratio of 0.95 and 0.92, found for the vegetable and animal fat groups were reported, respectively. The Rf = 0.03 difference between the two groups indicated the presence of sistosterol (plant sterol) and cholesterol (animal sterol), an improvement over color detection methods to screen oils and fats to ascertain purity.
 Keywords: Sistosterol, Cholesterol, Adulteration, Animal fat, Vegetable oil

A Qualitative Analysis of the Machine Learning Methods in Food Adultery: A Focus on Milk Adulteration Detection

A qualitative analysis of the machine learning focus on milk adulteration detection methods in food adultery: a - Food adultery

Ribose-induced Maillard Reaction as an Analytical Method for Detection of Adulteration and Differentiation of Chilled and Frozen-thawed Minced Veal.

Quality control of meat products is one of the main concerns of consumers, governmental control authorities, and retailers. The purpose of this study was to employ ribose-induced Maillard reaction in detection of meat adulteration and differentiation of fresh-chilled from frozen-thawed minced veal. The browning intensity was assessed through measuring the absorbance at 420 nm with a spectrophotometer as well as the direct analysis of the color and pH. The results showed that CIE b*, CIE a*, and A420* values in the extract of fresh-chilled veal were significantly (p<0.05) higher than frozen-thawed samples. The extract of frozen meat samples stored at −18°C became significantly darker and more yellowish compared to −4°C. The results showed that the A420* value in the frozen-thawed veal stored at −4°C and −18°C was reduced by approximately 17.22±3.53% and 11.68±2.49%, respectively, compared with fresh-chilled veal. The findings also showed that the storage temperature of minced veal and the heating time in this reaction had a significant effect on all tested variables (p<0.0001). The proposed method can be considered as an easy, quick, and inexpensive test for differentiating between the fresh-chilled and frozen-thawed minced veal.

Detection of flaxseed oil multiple adulteration by near-infrared spectroscopy and nonlinear one class partial least squares discriminant analysis

Near-infrared (NIR) spectroscopy is widely used to detect fraudulent food products. However, NIR spectroscopy has difficulty in detecting multiple adulterations of edible oils, which is a commonly used countermeasure against adulteration detection methods. In this study, a targeted detection approach was proposed by using NIR for multiple adulteration of flaxseed oil. A variable selection method was designed to significantly reduce the number of variables and improve the accuracy of adulterant detection. After the important variables were selected by orthogonal partial least squares discriminant analysis (OPLS-DA), a one-class partial least squares (OCPLS) was used to build a detection model from a set of Gaussian radial basis functions (GRBF) that could identify flaxseed oil adulterated at a 5% level with blends of cheaper oils. This model was validated by two independent test sets. The results indicated that this model could effectively detect single, dual, or multiple adulterants with a high accuracy of 95.8% (68 out of 71). Compared with previous studies, the model built by OPLS-OCPLS provided a rapid and effective targeted detection approach for multiple adulteration of flaxseed oil.

An efficient DNA barcoding based method for the authentication and adulteration detection of the powdered natural spices

Natural spices are widely used as food additives, coloring and flavoring agents, preservatives, and traditional medicines. However, the authentication and adulteration detection of spices, particularly the powdered ones, is a significant challenge and public health concern in the growing spice industry. To address this issue, a DNA barcoding-based identification method was developed in this study. 16 types of representative spices and their common adulterants were successfully identified using ITS2 and psbA-trnH sequences, proving that this is an efficient and powerful method for adulteration detection. Using this method, 91 commercial spices commodities were analyzed for adulteration. Of the 91, the ITS2 and psbA-trnH sequences of respectively 78 (85.7%) and 77 (84.6%) samples were successfully obtained. Identification results using BLAST search of the ITS2 and psbA-trnH sequences indicated that only two types of natural spices (fennel and licorice) were correctly labeled, the other 14 spices had a different amounts of adulteration. Spices were commonly adulterated with congeneric species; cheaper substitutes with similar color or appearance; or crop-based products such as rice, corn, or wheat flour. These results indicate that the DNA barcoding method developed in this study is an efficient, simple, and accurate identification method, which may be a powerful tool for the quality control of powdered spices.

Mid-infrared spectroscopic detection of sunflower oil adulteration with safflower oil

The oil industry is in need of rapid analysis techniques to differentiate mixtures of safflower-sunflower oils from pure oils. The current adulteration detection methods are generally cumbersome and detection limits are questionable. The aim of this study was to test the capability of a mid-infrared spectroscopic method to detect the adulteration of sunflower oil with safflower oil compared to fatty acid analysis. Mid-infrared spectra of pure oils and their mixtures at the 10–60% range were obtained at 4000–650 cm-1 wavenumber and fatty acid profiles were determined. Data were analyzed by multivariate statistical analysis techniques. The lowest level of detection was obtained with mid-infrared spectroscopy at 30% while the fatty acid profile could determine adulteration at around 60%. Adulteration levels were predicted successfully using PLS regression analysis of infrared data with R2 (calibration) = 0.96 and R2 (validation) = 0.93. As a rapid and minimum waste generating technique, mid-infrared spectroscopy could be a useful tool for the screening of raw material to detect safflower-sunflower oil mixtures.

Facile and Accelerated Method for Detection of Adulteration in Commercially Available Lime Juice Products in Iranian Marke

Background: The present study was aimed to introduce a rapid, simple and cost-effective method to detect adulterated lime juice products commercially available in the Iranian market. Methods: Twelve different brands of marketed lime juice products were randomly purchased from the local stores in Tabriz, Iran. Fresh lime juice, as a control, was prepared by squeezing lime fruits using a plain hand juicer. Ehrlich’s reagent was used for a colorimetric analysis; solid phase extraction-thin layer chromatography (SPE-TLC) technique was utilized for fingerprinting of methanol/water fractions of samples on the silica gel plates at two wavelengths of 366 and 254 nm, before and after spraying aluminum chloride reagent; and attenuated total reflectance-infrared spectroscopy (ATR-IR) was used for further studies. Results: The obtained results from colorimetric analysis indicated appearance of orange-brown color indicating presence of limonoids in fresh lime juice and only five commercially available products. Regarding SPE-TLC fingerprinting, except for five products with identical or comparable TLC patterns to the fresh lime juice, other samples represented completely different TLC patterns, suggesting the presence of adulterants in these products. Therefore, the developed colorimetric and SPE-TLC techniques revealed some extents of adulteration in half of the studied products (58.3%). However, in the case of ATR-IR analysis, the obtained spectra were not illustrating enough to be applied for the detection of adulterated samples. Conclusion: It can be concluded that the described colorimetric method in combination with the developed SPE-TLC technique could be utilized for the preliminary screening of adulterated lime juice products.

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.

Targeted multivariate adulteration detection based on fatty acid profiles and Monte Carlo one-class partial least squares

To develop effective adulteration detection methods is essential as food quality and safety draw particular concern all over the world. In this study, Monte Carlo one-class partial least squares (MCOCPLS) was proposed and employed as a novel one class classification model for authentication identification by using virgin olive oil (VOO) as an example. Monte Carlo sampling was proposed for selecting variable subspace to improve the performance of one-class partial least squares (OCPLS) classifier. MCOCPLS was used to establish a one-class model, the performance of which was validated by an independent test set consisting of 5000 adulterated oils simulated by the Monte Carlo method. The prediction for the best model of MCOCPLS reaches a correct rate of 99.10%. Moreover, authentic VOOs were analyzed and assessed for the adulteration risk. In conclusion, the proposed MCOCPLS method could be used to effectively detect olive oils adulterated with other vegetable oils at a concentration of as low as 3%. Therefore, MCOCPLS provides an effective tool and new insights in adulteration detection for edible oils and other foods.

Detection of olive oil adulteration with waste cooking oil via Raman spectroscopy combined with iPLS and SiPLS

Olive oil adulteration with waste cooking oil was detected and quantified by combining optical Raman scattering spectroscopy and chemometrics. Spectra of 96 olive oil samples with waste cooking oil (2.5%, 5%, 10%, 20%, 30% and 50%) were collected by the portable Raman spectroscopy system. iPLS and SiPLS quantitative analysis models were established. The results revealed that spectral data after SNV processing are the best for synergy interval partial least square (SiPLS) modeling and forecast. The root mean squared error of calibration (RMSEC) is 0.0503 and the root mean squared error of validation (RMSEV) is 0.0485. The lower limit of application (LLA) of the proposed method is c[WCO]=0.5%. According to linear regression calculation, the theoretical limit of detection (LOD) of the proposed method is about c[WCO]=0.475%. The established model could make effective quantitative analysis on adulteration of waste cooking oil. It provides a quick accurate method for adulteration detection of waste cooking oil in olive oil.

Interlaboratory Validation of Modified Classical Qualitative Methods for Detection of Adulterants in Milk: Starch, Chloride, and Sucrose

Interlaboratory validation procedures were proposed and performed to confirm the effectiveness of modified classical qualitative methods for the detection of adulterants in milk, including starch, chloride, and sucrose, which were previously validated by a single laboratory approach. Raw milk samples that were adulterated with 150 g L−1 of water and 0.0, 0.3, 0.8, and 1.2 g L−1 of starch, 0, 1.5, 2.0, and 2.5 g L−1 of chlorides, and 0.0, 2.4, 3.0, and 3.6 g L−1 of sucrose were sent to 10 collaborators in Brazil that represent the government, food control, food industry, and university affiliations. Reliability rates of 93 to 100, 98 to 100, and 99 to 100 % were obtained for the starch, chlorides, and sucrose methods, respectively. The prediction intervals for the probability of detection proved the sensitivity and selectivity of the methods. Concordance values were greater than 0.85 to starch, 0.98 to chlorides, and 0.99 to sucrose, indicating precision and that the procedures were properly standardized between the collaborators. The estimated detection limits and unreliability regions confirmed the fitness of the modified methods for their respective purposes.

FTMIR-PLS as a promising method for rapid detection of adulteration by waste whey in raw milk

In this paper, we propose a feasible, sample preparation free and fast validated methodology for the detection and quantitative analysis of Minas Frescal cheese waste whey as an adulterant in raw milk using mid-infrared spectroscopy with Fourier transform (FTMIR) along with the chemometric technique of partial least squares (PLS). The PLS model was built in accordance with Brazilian and international guidelines and was analytically validated through the estimate of figures of merit parameters, in accordance with ASTM E1655-05 standard. This model showing an effective and feasible method for quality control of raw milk can be adopted for the quality control by regulatory agencies, as shown by the satisfactory results obtained for all estimated figures of merit with no systematic errors and low errors, with R = 0.99.

Performance improvement and single laboratory validation of classical qualitative methods for the detection of adulterants in milk: starch, chlorides and sucrose

Simple modifications in classical methods for detection of milk adulteration were validated with significant performance improvement.

The identification of the relationship between chemical and electrical parameters of honeys using artificial neural networks

A number of significant scientific studies have confirmed the health benefits of honey. Due to the high price of natural honey, it is a common target for adulteration which reduces its medicinal value. Adulteration detection methods require specific laboratory equipment and are very expensive. The development of measurement techniques enables the measurement of electrical characteristics of strained honey. Honey electrical parameters can possibly be used for its quality assessment. The identification of the relationship between chemical and electrical parameters of honeys and analysis to determine if there are frequency-dependent changes, can help in developing of that group of methods. The aim of this research was to determine how the chemical parameters of certain honeys influence the dielectric loss factor and the permittivity of strained honey measured in various frequencies. Another aim was to determine whether the percentage influence of certain chemical parameters of honeys on electrical characteristics significantly depends on frequency value. The research was based on neural network models and sensitivity analysis. The percentage influence of certain chemical parameters on electrical characteristics significantly depends on frequency value.

Design and Fabrication of Silicon Oxynitride Based Evanescent Optical Waveguide Sensor for Rapid Detection of Adulteration in Petrol

In this paper we report on the design and fabrication of an Evanescent Optical Waveguide Sensor (EOWS) using silicon oxynitride (SiON) as the core layer on silica-silicon wafer and its implementation for rapid detection of adulterant traces in pure petrol incorporating composite planar waveguide geometry. The embedded waveguide of length ∼ 10,000μm and core width ∼ 50μm was fabricated using standard Plasma Enhanced Chemical Vapour Deposition (PECVD) technologies and Reactive Ion Etching (RIE) technique. The main aim of this work is to encompass an abrupt choice to the time-consuming existing adulteration detection methods which generally requires some time to give the consequence. Using Simple Effective Index Method (SEIM), the theoretical predictions and experimental results at wavelength 632.8nm are analyzed and presented, which demonstrated that the sensitivity of the proposed EOWS is ∼40 times more than that of the existing planar waveguide sensors and also 20 times more than that of asymmetric waveguide structure thereby allowing rapid detection of adulterant traces in pure petrol without involving the use of chemicals. It is found that the sensor structure is polarization independent and advantages include high sensitivity, easy fabrication and more importantly, requirement of very minimal amount of sample volume for detecting adulteration.

MONITORING THE ADULTERATION OF VIRGIN COCONUT OIL BY SELECTED VEGETABLE OILS USING DIFFERENTIAL SCANNING CALORIMETRY

ABSTRACT The crystallization and melting enthalpy of virgin coconut oil adulterated with palm kernel oil (PKO) and soybean oil (SBO) were studied by using differential scanning calorimetry. Virgin coconut oil was spiked separately with PKO and SBO from 2% to 40% (w/w) of adulterant oils. Fatty acids of all oils were determined to complement the differential scanning calorimetry data. The heating curve of SBO‐adulterated samples showed the adulteration peak appearing at the lower temperature region at 10% adulteration level. Regression analyses using stepwise multiple linear regression were used to predict the percentage adulterant with R2 of 0.9490. PKO‐adulterated oils did not show any adulteration peak but demonstrated a gradual decrease in the peak height of the major exothermic peak.PRACTICAL APPLICATIONSAn alternative method for detection of adulteration based on differential scanning calorimetry in virgin coconut oil is presented. Application of differential scanning calorimetry is rapid, does not require sample preparation and does not involve use of solvents or toxic chemicals.

The application of NMR and MS methods for detection of adulteration of wine, fruit juices, and olive oil. A review.

This review covers two important techniques, high resolution nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), used to characterize food products and detect possible adulteration of wine, fruit juices, and olive oil, all important products of the Mediterranean Basin. Emphasis is placed on the complementary use of SNIF-NMR (site-specific natural isotopic fractionation nuclear magnetic resonance) and IRMS (isotope-ratio mass spectrometry) in association with chemometric methods for detecting the adulteration.

Citrus fruits. Part II. Chemistry, technology, and quality evaluation. C. quality evaluation

In Part II of this review on citrus fruits, the literature on chemistry, technology, and quality evaluation are critically considered. Sweet oranges, mandarin, grapefruit, lemon and lime are generally used for processing. The literature on chemical components of citrus fruits reviewed and discussed in Section A includes the following: sugars, polysaccharides, organic acids, nitrogenous constituents, and lipids; carotenoids which contribute to color; vitamins and minerals, and flavonoids; limonoids, some of which impart bitterness to the juice, and the volatile components which contribute to aroma. Chilled and pasteurized juices, juice concentrates, and beverages are the important products manufactured commercially, and to a limited extent powdered citrus juices, canned segments, and marmalades. The literature on the manufacture of these products as well as new types of juice and oil extractors, TASTE, and other types of evaporators, tank farms to store juice and concentrate in bulk, aseptic filling in bulk containers and retail packs, alternate flexible and rigid containers other than glass and tin and recovery of volatile flavoring constituents during juice processing are some of the important technological developments in the recent past which were discussed in Section B. Bitterness in citrus juices and its control, composition of cloud and its stability, and changes during storage have been reviewed. Essential oils, pectin, frozen and dried juice sacs, dried pulp and molasses, flavonoids, seed oil, and meal are the important by‐products, the manufacture of which is given in essential details. Generally, consumers judge the product on the basis of its sensory attributes. The quality of the finished products is dependent upon the raw materials used and control of processes. In this section, the USDA standards for different products, physico‐chemical and microbiological parameters prescribed as indices of quality of fruit, juice, concentrate, and other products, composition of essential oils, and aroma concentrates are discussed in relation to sensory quality. Analytical methods for compounds affecting quality and methods for detection of adulteration in different citrus products are briefly reviewed. The importance of sensorily evaluating quality of citrus products to select and develop quality control indices is emphasized. Areas where further research is required are indicated. A comprehensive bibliography is provided to aid further study and research.