Adulterant Concentration Research Articles

Quantifying colorimetric assays in µPAD for milk adulterants detection using colorimetric android application

A microfluidic paper-based analytical device (μ-PAD) and handheld colorimeter android application for quantifying the precise concentration of adulterants in milk are developed. A simple method to pattern polydimethylsiloxane for creating a hydrophobic barrier on filter paper using custom-designed rubber stamp containing the design of μ-PAD was used. The experiments were performed for the detection of urea, starch, salt and detergent ranging from 1 to 100 mg in 10 ml of milk. The user just needs to pipette drops of adulterated milk on a test spot and the results are obtained instantly. The authors believe that the developed platform can be used as a basis for low-cost, portable system for various fluid adulterants’ tests. The experimental database was used for the development of a colorimetric android application to calibrate the colour range for adulterants’ concentration detection. The limit of detection of colorimetric android application for adulterants urea is 5 mg, starch is 17 mg, salt is 29 mg and for detergent is 20 mg in 10 ml of milk. This kind of system is attractive for use in developing countries, in the field, or as an inexpensive alternative as compared with other methods for water and food safety.

Chaotic parameters from fluorescence spectra to resolve fraudulent mixtures of fresh and expired protected designation of origin extra virgin olive oils

One of the most profitable products from the Mediterranean basin is extra virgin olive oil (EVOO), and, therefore, some of them have protected designation of origin (PDO) labels. In order to prevent fraudulent practices, a method to quantify adulterants has been developed. 459 binary blends composed of PDO EVOO in date (Saqura, Oleoestepa, and Duque de Baena) mixed with expired PDO EVOO (Quinta do Vallouto, Señorío de Segura, and Planeta) to serve as adulterants (<17%) have been analyzed. Using a laser diode as a source light, the fluorescence emission has been measured and 20 chaotic parameters from the resulting spectra have been calculated. Using these as independent variables of multi-parameter regression models, the concentration of adulterant has been estimated. Every model was evaluated through the mean square error, adjusted correlation coefficient, Mallows’ Cp, Akaike information criterion, Hannan-Quinn criterion, and Bayesian information criterion. This approach was validated by the leave-one-out cross-validation method and the results were promising (lower than 10% quantification error). Additionally, the structure of the sensor has been designed and developed by a 3D printer and has the potential of being applied in situ for real-time and cost-effective analysis at oil mills or for quality control.

Chemometric classification and quantification of cold pressed grape seed oil in blends with refined soybean oils using attenuated total reflectance–mid infrared (ATR–MIR) spectroscopy

Of the cold pressed edible oils, grape seed oil (GSO) is one of the highest quality and, consequently the most expensive oil. Because of that, adulteration by mixing GSOs up with lower price edible oils is frequently seen. The classification and determination of adulterant concentration for GSO is hence a focus of great interest. The aim of this study was to investigate the feasibility of ATR–MIR spectroscopy coupled with the multivariate methodology for qualitative and quantitative analyses of cold pressed GSO adulteration with refined soybean oil (SBO), by using characteristic wavenumber regions. Thirty three pure oils and ninety six blends were analyzed using ATR–MIR spectroscopy in combination with PCA, LDA, SIMCA and PLSR analysis. SIMCA models provided an excellent classification for pure GSO and other samples. The classification limits by ATR–MIR spectroscopy was also below 5%. Quantitative analyses were performed by minimization of RMSE of cross-validation values, which resulted in excellent predictions (R2 > 0.99). The prediction parameters for the sample sets were: the RMSEC were in the range 0.59–2.09% and, RMSECV were in the range 0.92–5.60%, hence on the basis of the PLSR models, quantification of adulterant could be determined at levels <0.59%.

Suitability evaluation of new endogenous biomarkers for the identification of nitrite-based urine adulteration in mass spectrometry methods.

Urine adulteration to circumvent positive drug testing is a fundamental challenge for toxicological laboratories all over the world. Untargeted mass spectrometry (MS) methods used in metabolomics had previously revealed uric acid (UA), histidine, methylhistidine, and their oxidation products, for example 5-hydroxyisourate (HIU) as potential biomarkers for urine adulteration using potassium nitrite (KNO2 ). These markers should be further evaluated for their reliability, stability, and routine applicability. Influence of KNO2 concentration, urinary pH, reaction time, and stability at room temperature, 4°C, and-20°C was determined in urine under varying conditions. Analysis was performed after protein precipitation with acetonitrile by liquid chromatography-high resolution mass spectrometry (LC-HRMS). Receiver operating characteristics (ROC) analysis was applied for cut-off evaluation after biomarker quantification (n=100 per group). Blinded measurements (n=50) were performed to check the general applicability to identify adulterated samples under routine conditions. The higher the adulterant concentration, the lower the concentrations of histidine, methylhistidine, and UA. In return, amounts of their oxidation products increased. Highest changes were observed under weak acid conditions (pH4-5). Storage at -20°C ensured sufficient stability for all oxidative markers over onemonth. ROC evaluated biomarker performance and application to unknown samples revealed satisfying results, with HIU as the most suitable biomarker (positive predictive value (PPV) 100%), followed by UA (PPV 93%). HIU and UA proved suitable markers to identify urine adulteration using KNO2 and are ready for implementation into routine MS procedures.

Stimulated Brillouin scattering in combination with visible absorption spectroscopy for authentication of vegetable oils and detection of olive oil adulteration.

Vegetable oils provide high nutritional value in the human diet. Specifically, extra virgin olive oil (EVOO) possesses a higher price than that of other vegetable oils. Adulteration of pure EVOO with other types of vegetable oils has attracted increasing attentions. In this work, a stimulated Brillouin scattering (SBS) combined with visible absorption spectroscopy method is proposed for authentication of vegetable oils and detection of olive oil adulteration. The results provided here have demonstrated that the different vegetable oils and adulteration oils exhibit significant differences in normalized absorbance values of two relevant wavelengths (455 and 670 nm) and frequency shifts of SBS. The normalized absorbance values of all spectra at the two relevant wavelengths of 670 nm and 455 nm linearly decrease with the increase of the adulteration concentration. The Brillouin frequency shifts exponentially increase with the increase of the adulteration concentration. Due to non-destructive and requiring no sample pretreatment procedure, this method can be effectively employed for authentication and detection of oils adulteration.

Cocaine and adulterants analysis in seized drug samples by infrared spectroscopy and MCR-ALS

Cocaine use has been reported for more than 2500 years, but along this period of time its damage to society in general became evident. Nowadays, cocaine is classified as an illicit drug and studies which give support to drug enforcement institutions, particularly in the area of police intelligence, are very relevant. Often, trafficked cocaine is not traded in its pure form, but mixed with adulterants and diluents. In the analysis of seized samples, the reference method is based on gas chromatography, however the interest in the use of vibrational spectroscopy has increased. This work aims at developing a method for determination of the concentrations of cocaine, adulterants and diluents in cocaine samples employing Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) associated with Multivariate Curve Resolution with Alternating Least-Squares (MCR-ALS). A total of 6 adulterants, 3 diluents, 2 forms of cocaine (base and hydrochloride) were determined and the method developed based on 55 synthetic mixtures prepared according to a mixture design and 20 seized samples. For validation purposes 708 seized samples and 9 synthetic mixtures were analyzed. The results proved to be satisfactory, showing that the proposed method has a great potential in the classification of the chemical form of cocaine, identification of adulterants, diluents and cocaine in seized samples, as well as providing an estimate for their concentration.

FTIR spectroscopy coupled with multivariate classification methods to identify different powdered infant formulas adulterated with melamine and cyanuric acid

Fourier transform infrared spectroscopy (FTIR) is a nondestructive, simple, rapid, and cheap measurement technique for analysis of many multicomponent chemical systems, e.g., detection of adulterants in food samples. In this respect, this study proposes combining FTIR spectroscopy with multivariate classification methods for classification and discrimination of different samples of infant formulas adulterated by melamine or/and cyanuric acid. Different parametric and non-parametric multivariate classification methods including the linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbors (KNN), and classification and regression tree (CART) approaches were used to classify the recorded FTIR data. Assessing the performance of the multivariate methods according to their sensitivity, specificity and percent of correct prediction results demonstrated that coupling FTIR spectroscopy with multivariate classification can be applied as a rapid and powerful technique to the simultaneous detection of melamine and cyanuric acid in powdered infant formulas. This combinatorial method is efficient for adulterant concentrations as low as 0.0001 w/w%.

DETERMINACIÓN DE ADULTERANTE EN LECHE MEDIANTE EL USO DE UNALENGUA ELECTRÓNICA VOLTAMÉTRICA PORTABLE

The aim of this work was to evaluate the application of a portable voltammetric electronic tongue device based on PSoC technology and an electrochemical sensor array of polypyrrole in the discrimination of samples of fresh milk adulterated with starch. The results showed that the device is able to perfectly discriminate the adulterated samples, separating them into well differentiated groups according to the amount of adulterant. In addition, the analysis of the correlation circle showed that all the sensors in the array provide useful information for the classification of the samples. Additionally, the ability of the device to determine the concentration of adulterant was explored through a PLS regression analysis. The obtained results allow to establish that the electronic tongue device can be used for quantitative determinations. However, the concentrations can present an average variation of 4.54%. This small deviation is compensated with some advantages of the electronic language such as the short analysis time, the elimination of pretreatment of the samples, the ease of handling of the device and its portability.

Calibration and testing of a Raman hyperspectral imaging system to reveal powdered food adulteration.

The potential adulteration of foodstuffs has led to increasing concern regarding food safety and security, in particular for powdered food products where cheap ground materials or hazardous chemicals can be added to increase the quantity of powder or to obtain the desired aesthetic quality. Due to the resulting potential health threat to consumers, the development of a fast, label-free, and non-invasive technique for the detection of adulteration over a wide range of food products is necessary. We therefore report the development of a rapid Raman hyperspectral imaging technique for the detection of food adulteration and for authenticity analysis. The Raman hyperspectral imaging system comprises of a custom designed laser illumination system, sensing module, and a software interface. Laser illumination system generates a 785 nm laser line of high power, and the Gaussian like intensity distribution of laser beam is shaped by incorporating an engineered diffuser. The sensing module utilize Rayleigh filters, imaging spectrometer, and detector for collection of the Raman scattering signals along the laser line. A custom-built software to acquire Raman hyperspectral images which also facilitate the real time visualization of Raman chemical images of scanned samples. The developed system was employed for the simultaneous detection of Sudan dye and Congo red dye adulteration in paprika powder, and benzoyl peroxide and alloxan monohydrate adulteration in wheat flour at six different concentrations (w/w) from 0.05 to 1%. The collected Raman imaging data of the adulterated samples were analyzed to visualize and detect the adulterant concentrations by generating a binary image for each individual adulterant material. The results obtained based on the Raman chemical images of adulterants showed a strong correlation (R>0.98) between added and pixel based calculated concentration of adulterant materials. This developed Raman imaging system thus, can be considered as a powerful analytical technique for the quality and authenticity analysis of food products.

Wavenumber selection method to determine the concentration of cocaine and adulterants in cocaine samples

Street cocaine is typically altered with several compounds that increase its harmful health-related side effects, most notably depression, convulsions, and severe damages to the cardiovascular system, lungs, and brain. Thus, determining the concentration of cocaine and adulterants in seized drug samples is important from both health and forensic perspectives. Although FTIR has been widely used to identify the fingerprint and concentration of chemical compounds, spectroscopy datasets are usually comprised of thousands of highly correlated wavenumbers which, when used as predictors in regression models, tend to undermine the predictive performance of multivariate techniques. In this paper, we propose an FTIR wavenumber selection method aimed at identifying FTIR spectra intervals that best predict the concentration of cocaine and adulterants (e.g. caffeine, phenacetin, levamisole, and lidocaine) in cocaine samples. For that matter, the Mutual Information measure is integrated into a Quadratic Programming problem with the objective of minimizing the probability of retaining redundant wavenumbers, while maximizing the relationship between retained wavenumbers and compounds’ concentrations. Optimization outputs guide the order of inclusion of wavenumbers in a predictive model, using a forward-based wavenumber selection method. After the inclusion of each wavenumber, parameters of three alternative regression models are estimated, and each model’s prediction error is assessed through the Mean Average Error (MAE) measure; the recommended subset of retained wavenumbers is the one that minimizes the prediction error with maximum parsimony. Using our propositions in a dataset of 115 cocaine samples we obtained a best prediction model with average MAE of 0.0502 while retaining only 2.29% of the original wavenumbers, increasing the predictive precision by 0.0359 when compared to a model using the complete set of wavenumbers as predictors.

Second-derivative laser-induced fluorescence spectroscopy combined with chemometrics for authentication of the adulteration of camellia oil

ABSTRACTIn this study, second-derivative laser-induced fluorescence spectroscopy (SD-LIF) and its derivative intrinsic-ratiometric laser-induced fluorescence spectroscopy (IR-LIF) were developed for the qualitative and quantitative analysis of camellia oil adulterated with sunflower oil or rapeseed oil, respectively. In the qualitative analysis, three pure oils and two blended oils were successfully discriminated based on SD-LIF and linear discriminant analysis. The influence of fluorescence concentration quenching on the adulterant concentration analysis was discussed. IR-LIF was established by using 646 nm chlorophyll b fluorescence as the insensitive reference and proved to be an effective approach to reduce the possible quenching influence on the full spectrum. Then, the combination of IR-LIF and partial least squares regression was employed to determine the adulterant concentration, and the results were satisfactory. The plots of the observed versus predicted adulteration concentration values showed high linearity. Root-mean-square errors of prediction lower than 3% were achieved.

Feasibility Assessment of Synchronous Fluorescence Spectral Fusion by Application to Argan Oil for Adulteration Analysis.

Synchronous fluorescence spectroscopy (SFS) is used for quantitative analysis as well as for qualitative analysis, such as with classification methods. With SFS, determination of a useful wavelength interval between the excitation and emission wavelengths (Δλ) is required. There are a multitude of Δλ intervals that can be evaluated and optimization of the best one is complex. Presented here is a fusion approach for combining Δλ intervals, thereby negating the need to perform the selection by a skilled operator. To demonstrate the feasibility of omitting selection of the best Δλ interval, adulterated argan oil samples are studied. Argan oil is made from the argan tree, endemic to southwestern Morocco, and is well-known for its cosmetic, pharmaceutical, and nutritional applications. It is considered a luxury product and exported from Morocco around the world. Consequently, detection of argan oil adulteration followed by quantitative analysis of the adulterant concentration is important. This study uses fusion of SFS spectra obtained at ten Δλ intervals to first detect adulteration of argan oil by corn oil and then determination of the corn oil content. For detection of adulteration, 15 one-class classification methods were used simultaneously over the ten Δλ sets of SFS spectra. For tuning parameter dependent classifiers such as Mahalanobis distance, non-optimized classifiers are used. Raw classification values are used, removing the need to set classifier-dependent threshold values, albeit, ultimately, a fusion decision rule is needed for classification. For quantitative analysis, two calibration approaches are evaluated with fusion of these ten Δλ SFS spectral data sets. One is multivariate calibration by partial least squares (PLS). The second approach is a univariate calibration process where the SFS spectra are summed over respective SFS spectral ranges, also known as the area under the curve (AUC). For adulteration detection and quantitation of the corn oil, prediction errors decrease with fusion compared to individually using the ten Δλ interval SFS specific data sets. For this argan oil data set, the AUC method generally provides equivalent prediction errors to PLS.

MCR-ALS and PLS coupled to NIR/MIR spectroscopies for quantification and identification of adulterant in biodiesel-diesel blends

Fourier transforming near infrared (FT-NIR) and mid infrared (FT-MIR) spectroscopies combined with multivariate curve resolution alternating least squares (MCR-ALS) and partial least squares (PLS) regression were utilized to identify and quantify residual vegetable oil adulteration in commercial diesel. In addition, physiochemical methods following ASTM standards were performed for comparison in identifying diesel adulteration. In total, 16 commercial diesel samples containing 8% biodiesel and 10mg sulfur/kg (named S10B8) were prepared with the addition of residual vegetable oil (1–60%) and were analyzed by FT-NIR and FT-MIR spectroscopies. The physical–chemical properties analyzed were atmospheric distillation, flash point, kinematic viscosity and specific mass tests. Only the distillation curve was able to identify the adulteration. A paired t-test and an F-test were performed for the MCR-NIR, MCR-MIR, PLS-NIR and PLS-MIR models. Through the results, it can be stated that all the models used in this work are valid for a 95% confidence level. Furthermore, no statistical difference was observed between the estimated concentrations of adulterant and the reference values. In this work, MCR-ALS was applied as a spectral decomposition tool as a new approach to identify the adulterant profile on biodiesel/diesel blends, followed by its quantification. MCR-ALS was able to recover the related pure spectral profile of the fuels and adulterant, and to accurately predict the concentration of frying oil in biodiesel/diesel samples.

Adding Adulterants to Coffee Reduces Bioactive Compound Levels and Antioxidant Activity

The present study aimed to evaluate the effect of coffee adulteration on antioxidant activity in vitro. Coffee beverages were adulterated with different concentrations of coffee hulls, coffee straw, and corn (0%, 10%, 20%, 30%, 40%, 50%, and 100%) and tested separately. Each coffee beverage was prepared according to the same methods in all of the treatments. Phenolic compound, caffeine, trigonelline, and chlorogenic acid levels were determined in the beverages. Antioxidant activity in vitro was evaluated using the DPPH radical scavenging activity, reducing power, iron chelating activity, and lipoperoxidation inhibition methods. Phenolic compound, caffeine, and chlorogenic acid levels of the samples decreased with increasing adulterant concentration. Adding adulterant reduced the antioxidant capacity tested using all of the methods. The results show that adding coffee hulls, coffee straw, and corn affect the antioxidant capacity of the coffee beverages, reducing protection against oxidative stress.

Rapid detection of adulteration of cold pressed sesame oil adultered with hazelnut, canola, and sunflower oils using ATR-FTIR spectroscopy combined with chemometric

Sesame oil is one of the most valuable oil due to its bioactive properties, unique taste, odor, and flavor. Therefore, consumers tend to consume and willing to pay a higher price for sesame oil due to increasing awareness of consuming healthier and better food. However, sesame oil is subjected to adulteration risks with lower price vegetable oils which impairs consumer rights and human health. In this study, we purposed an ATR-FTIR based method for the rapid detection of food adulteration in sesame oil. For this purpose, sesame oil was adulterated with hazelnut, canola, and sun flower oils in different concentrations ranged from 1 to 50%. Cluster analysis of FTIR spectra was performed for differentiation and classification of pure sesame oil from adultured vegetable oil samples. In addition, a calibration curve was obtained to determine relationship between actual adulterant concentration and FTIR predicted concentrations using partial least square (PLS) method for each oil samples. According to these results, it could be concluded that ATR-FTIR technique has a potential for adulteration of sesame oil as a non-destructive, rapid, and effective alternative method.

A novel FT-IR spectroscopic method based on lipid characteristics for qualitative and quantitative analysis of animal-derived feedstuff adulterated with ruminant ingredients

The objective of this study was to explore the ability of Fourier transform infrared (FT-IR) spectroscopy to authenticate adulterated animal-derived feedstuff. A total of 18 raw meat and bone meals (MBMs), including 9 non-ruminant MBMs and 9 ruminant MBMs, were mixed to obtain 81 binary mixtures with specific proportions (1–35%). Lipid spectral characteristics were analyzed by FT-IR spectroscopy combined with chemometrics. Changes in FT-IR spectra were observed as adulterant concentration was varied. The results illustrate ruminant adulteration can be successfully distinguished based on lipid characteristics. PLS model was established to quantify ruminant adulteration, which was shown to be valid (R2P>0.90). Furthermore, the ratios of CC/CO and CC/CH(CH2), as well as the number of CH(CH2) in the fatty acids of adulterated lipids, were calculated, which showed that differences in the trans fatty acid content and the degree of unsaturation were the main contributors to determination of adulteration based on FT-IR spectroscopy.

Multimode Fiber Optic Sensor for Adulterant traces in Edible Oil using Nanotechnology Technique

Fiber optic sensors for detection and determination of adulterant traces in edible oil is designed in the laboratory. The fundamental principle of detection is the sensitive dependence of the wavelength shift due to the refractive index changes of the surrounding medium of multimode fiber cladding. Building up of this low cost multimode fiber optic sensor is helpful in detection and determination of adulteration in oils. Fiber optic sensor coupled with gold-nanoparticles shows an enhancement in the sensitivity of designed sensor in the determination of concentration of adulteration in coconut oil.

Classification of adulterated honeys by multivariate analysis.

In this research, honey samples were adulterated with date syrup (DS) and invert sugar syrup (IS) at three concentrations (7%, 15% and 30%). 102 adulterated samples were prepared in six batches with 17 replications for each batch. For each sample, 32 parameters including color indices, rheological, physical, and chemical parameters were determined. To classify the samples, based on type and concentrations of adulterant, a multivariate analysis was applied using principal component analysis (PCA) followed by a linear discriminant analysis (LDA). Then, 21 principal components (PCs) were selected in five sets. Approximately two-thirds were identified correctly using color indices (62.75%) or rheological properties (67.65%). A power discrimination was obtained using physical properties (97.06%), and the best separations were achieved using two sets of chemical properties (set 1: lactone, diastase activity, sucrose - 100%) (set 2: free acidity, HMF, ash - 95%).

Detection for Non-Milk Fat in Dairy Product by Gas Chromatography.

The aim of this study was to evaluate the potential use of fatty acids, triacylglycerols, and cholesterol in the detection of adulterated milk fat. The fatty acid, triacylglycerol, and cholesterol profiles of the mixtures of milk and non-milk fat (adulteration ratios of 10%, 30%, 50%, 70%, and 90%) were analyzed by gas chromatography. The results showed that concentrations of the fatty acids with oleic acid (C18:1n9c) and linoleic acid (C18:2n6c), triglycerides with C52 and C54, and cholesterol detected are proportional to the adulteration ratios remarkably. Oleic acid (C18:1n9c), linoleic acid (C18:2n6c), C52, and C54 were lower in pure milk fat than in adulterated mixtures. In contrast, pure milk has a higher cholesterol concentration than all adulterated mixtures (adulteration concentration in the range 10-90%). Thus, we suggest that oleic acid (C18:1n9c), linoleic acid (C18:2n6c), C52, C54, and cholesterol are suitable indicators and can be used as biomarkers to rapidly detect adulterated milk fat by gas chromatography. This study is expected to provide basic data for adulteration and material usage. Moreover, this new approach can detect the presence of foreign oils and fats in the milk fat of cheese and can find application in related studies.

Sugar Detection in Adulterated Honey via Fiber Optic Displacement Sensor for Food Industrial Applications

A fiber optic displacement sensor (FODS) using a transmission technique was developed to detect sugar contents in honey. This was achieved by measuring the output signal of non- and adulterated honey with different addition percentage volumes of sugar (fructose and glucose). Five types of honey samples were adulterated with different percentages of fructose and glucose ranging from 0% to 10%. Blue diode laser at 435 nm was used as a light source in this FODS. The performance of sensor showed that the voltage signal linearly increased with the concentration of adulteration of sugar. In contrast, the refractive index of the adulterated honey is found decreasing with the concentration of sugar. This means the higher sugar contents in adulterated honey the lower is the refractive index leading to high transmission in FODS. Thus, FODS based on a transmission technique has a potential to detect the sugar content in adulterated honey. The simplicity, faster, and cost effective made FODS as a promising technique for food industry.