Adulterant Content Research Articles

Mid-infrared spectroscopy and chemometrics in the detection of adulteration in chia oil (Salvia hispanica L) and α-linolenic acid content prediction

Chia oil has high commercial value due to polyunsaturated fatty acids (PUFAs), especially α-linolenic acid (ALA), and suffers from tampering. Traditional adulterant detection in oils applies gas chromatography, but this approach has disadvantages such as time consumption. The development of fast analytical methods like infrared spectroscopy is important to detect oil fraud. The study aims to employ mid-infrared (FTIR) and chemometrics to detect adulteration in chia oil. Chia oil was extracted by cold pressing and adulterated with sunflower, corn, and soybean oils. FTIR-ATR spectra were obtained using a Fourier transform infrared spectrophotometer and horizontal attenuated reflectance accessory (HATR). Partial least square (PLS) models were adjusted to predict the adulteration content in chia oil and to predict the fatty acid content, including ALA. Gas chromatography was the reference method for the fatty acid content, and the adulteration content was known. The model obtained for adulteration content in chia oil had a high predictive capacity with r2 = 0.9868 for the prediction set and a low limit of detection (1.47%) and limit of quantification (4.40%). The models for fatty acid content also had good prediction capabilities (0.90 < r2, RMSE <21 mg g−1, RSD <6.5%, LOD <12 mg g−1, and LOQ <36 mg g−1). The results indicate that it is possible to quantify fraud in chia oil even using different adulterants when analyzing FTIR-ATR spectra in tandem with PLS. The proposed method is an important, fast, low-cost alternative for monitoring adulterations in vegetable oils.

Identification of Adulterants in Chili Powder Based on the Histogram of Oriented Gradients Algorithm by Using an Electronic Nose

Driven by economic interests, adding adulterations in chili powder is a problem which threatens people’s health. Thanks to its nondestructive, rapid, and portable advantages, electronic nose has more potential to be used for adulteration detection than the traditional methods. An approach for identifying the adulterants in chili powder was proposed in this paper. Firstly, an electronic nose system with 10 gas sensors was designed, and then the response images were drawn based on the response signals of the electronic nose. Afterwards, gas features were extracted from those response images by using a histogram of oriented gradients (HOG) algorithm. Finally, an SVM-based identification model was constructed to achieve the identification of plant adulterants in chili powder. The experimental results showed that the identification accuracy of the adulterant categories (almond shell, red beetroot, and tomato peel) based on the HOG features could reach up to 98.3%, and the identification results for adulterant content were 94.2%, 93.3%, and 95%, respectively. Furthermore, in order to compare the efficiency of the proposed identification approach, the widely used model AlexNet was also investigated and discussed.

Rapid and Low-Cost Quantification of Adulteration Content in Camellia Oil Utilizing UV-Vis-NIR Spectroscopy Combined with Feature Selection Methods.

This study aims to explore the potential use of low-cost ultraviolet-visible-near infrared (UV-Vis-NIR) spectroscopy to quantify adulteration content of soybean, rapeseed, corn and peanut oils in Camellia oil. To attain this aim, test oil samples were firstly prepared with different adulterant ratios ranging from 1% to 90% at varying intervals, and their spectra were collected by an in-house built experimental platform. Next, the spectra were preprocessed using Savitzky-Golay (SG)-Continuous Wavelet Transform (CWT) and the feature wavelengths were extracted using four different algorithms. Finally, Support Vector Regression (SVR) and Random Forest (RF) models were developed to rapidly predict adulteration content. The results indicated that SG-CWT with decomposition scale of 25 and the Iterative Variable Subset Optimization (IVSO) algorithm can effectively improve the accuracy of the models. Furthermore, the SVR model performed best for predicting adulteration of camellia oil with soybean oil, while the RF models were optimal for camellia oil adulterated with rapeseed, corn, or peanut oil. Additionally, we verified the models' robustness by examining the correlation between the absorbance and adulteration content at certain feature wavelengths screened by IVSO. This study demonstrates the feasibility of using low-cost UV-Vis-NIR spectroscopy for the authentication of Camellia oil.

Rapid Prediction of Adulteration Content in Atractylodis rhizoma Based on Data and Image Features Fusions from Near-Infrared Spectroscopy and Hyperspectral Imaging Techniques.

Atractylodis rhizoma (AR) is an herb and food source with great economic, medicinal, and ecological value. Atractylodes chinensis (DC.) Koidz. (AC) and Atractylodes lancea (Thunb.) DC. (AL) are its two botanical sources. The commercial fraud of AR adulterated with Atractylodes japonica Koidz. ex Kitam (AJ) frequently occurs in pursuit of higher profit. To quickly determine the content of adulteration in AC and AL powder, two spectroscopic techniques, near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI), were introduced. The partial least squares regression (PLSR) algorithm was selected for predictive modeling of AR adulteration levels. Preprocessing and feature variable extraction were used to optimize the prediction model. Then data and image feature fusions were developed to obtain the best predictive model. The results showed that if only single-spectral techniques were considered, NIRS was more suitable for both tasks than HSI techniques. In addition, by comparing the models built after the data fusion of NIRS and HSI with those built by the single spectrum, we found that the mid-level fusion strategy obtained the best models in both tasks. On this basis, combined with the color-texture features, the prediction ability of the model was further optimized. Among them, for the adulteration level prediction task of AC, the best strategy was combining MLF data (at CARS level) and color-texture features (C-TF), at which time the R2T, RMSET, R2P, and RMSEP were 99.85%, 1.25%, 98.61%, and 5.06%, respectively. For AL, the best approach was combining MLF data (at SPA level) and C-TF, with the highest R2T (99.92%) and R2P (99.00%), as well as the lowest RMSET (1.16%) and RMSEP (2.16%). Therefore, combining data and image features from NIRS and HSI is a potential strategy to predict the adulteration content quickly, non-destructively, and accurately.

Effect of different spectral processing methods on the quantitative model of Astragalus polysaccharides detected by NIR spectroscopy

Quality control of Chinese herbal medicines requires reliable and rapid detection techniques, Many scholars have used NIR spectroscopy for research in Chinese herbal medicine industry. The aim of this study was to evaluate the feasibility of combining NIR spectroscopy and machine learning to predict the adulteration content of Astragalus polysaccharides. The astragalus polysaccharide and rice flour were mixed to form astragalus polysaccharide adulterant with different concentration. the Quantitative prediction models are built using the support vector machine (SVM). Different pre-processing methods of SG, SNV, WT, SG+SNV, SG+WT were used to process the spectra, and then the continuous projection algorithm (SPA) and uninformative variable elimination (UVE) were used to select the characteristic wavelengths. The SVM prediction model based on SG+SNV+SPA is optimal with an RPD of 2.653, indicating that the model has good predictive ability. The results showed that NIR technique could be used for quantitative analysis of Astragalus polysaccharides.

N-glycan profiles as a tool in qualitative and quantitative analysis of goat milk adulteration

Goat milk is closer to human milk in some respects than cow milk, and therefore preferred by many consumers. Because of the short lactation period and consequently less milk production of goats, the price of goat milk is often higher than that of cow milk, so that adulteration of goat milk is common. N-glycans have stability and thus have a good potential for acting as a new biomarker for identifying dairy adulteration. In this study, the N-glycan structures of goat milk and cow milk were analyzed by Ultra-high performance liquid chromatography (UPLC) and MALDI-TOF-MS. Based on the high species specificity of N-glycans, a method for identifying goat milk mixed with cow milk was established. The adulteration content of 5% cow milk in goat milk could be qualitatively and quantitatively detected. A prediction model of adulteration in goat milk was established by using partial least squares (PLS).

Fast quantification of matcha adulterants with laser-induced breakdown spectroscopy spectrum and image

Matcha adulteration is a critical issue in food production, which may reduce the effective components in matcha and have a detrimental effect on human health. Hence, laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was used to fast quantify the adulterant content. Two types of matcha adulteration, including mixing two grades of matcha (Fuding and Jiukeng) with barley seedling powder, were quantified with fusion of LIBS spectrum and ablation image. In addition, important features of spectrum and ablation image were extracted by extreme gradient boosting (XGBoost) and gray-level co-occurrence matrix (GLCM), respectively. Support vector machine regression models were established based on full spectrum, spectral features, and feature fusion. The models based on fusion of spectral and textural features achieved the best performance with R of 0.9046, RMSE of 0.12 for Fuding, and R of 0.9200, RMSE of 0.11 for Jiukeng. This study provides a fast approach for quantifying matcha adulteration.

Rapid, simultaneous and non-destructive determination of multiple adulterants in Panax notoginseng powder by front-face total synchronous fluorescence spectroscopy

The authentication of traditional herbal medicines in powder form is of great significance, as they are always of high values but vulnerable to adulteration. Based on the distinct fluorescence of protein tryptophan, phenolic acids and flavonoids, front-face synchronous fluorescence spectroscopy (FFSFS) was applied for the fast and non-invasive authentication of Panax notoginseng powder (PP) adulterated with the powder of rhizoma curcumae (CP), maize flour (MF) and whole wheat flour (WF). For either single or multiple adulterants in the range of 5–40% w/w, prediction models were built based on the combination of unfolded total synchronous fluorescence spectra and partial least square (PLS) regression, and were validated by both five-fold cross-validation and external validation. The constructed PLS2 models simultaneously predicted the contents of multiple adulterants in PP and gave suitable results, with most of the determination coefficients of prediction (Rp2) >0.9, the root mean square error of prediction (RMSEP) no >4% and residual predictive deviation (RPD) >2. The limits of detections (LODs) were 12.0, 9.1 and 7.6% for CP, MF and WF, respectively. All the relative prediction errors for simulated blind samples were between −22% and + 23%. FFSFS offers a novel alternative to the authentication of powdered herbal plants.

Identification of common buckwheat (Fagopyrum esculentum Moench) adulterated in Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) flour based on near-infrared spectroscopy and chemometrics

Near-infrared spectroscopy (NIRS) presents great potential in the identification of food adulteration due to its advantages of nondestructive, simple, and easy to operate. In this paper, a method based on NIRS and chemometrics was proposed to predict the content of common buckwheat (Fagopyrum esculentum Moench) flour in Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) flour. Partial least squares regression (PLSR) and support vector regression (SVR) models were used to analyze the spectrum data of adulterated samples and predict the adulteration level. Various preprocessing methods, parameter-optimization methods, and competitive adaptive reweighted sampling (CARS) wavelength-selection methods were used to optimize the model prediction accuracy. The results of PLSR and SVR modeling for predicting of Tartary buckwheat adulteration content were satisfactory, and the correlation coefficients of the optimum identification models were above 0.99. In conclusion, the combinations of NIRS and chemometrics indicated excellent predictive performance and applicability to analyze the adulteration of common buckwheat flour in Tartary buckwheat flour. This work provides a promising method to identify the adulteration of Tartary buckwheat flour and results obtained can give theoretical and data support for adulteration identification of agro-products.

Rapid detection of Ganoderma lucidum spore powder adulterated with dyed starch by NIR spectroscopy and chemometrics

As a valuable traditional Chinese medicine, Ganoderma lucidum spore powder is often mixed with dyed starch to reduce production costs. In this report, we developed a near-infrared (NIR) spectroscopy method to better identify the authenticity of spore powder samples and predict their adulteration content. The model based on NIR spectral data of samples combined with partial least squares discriminant analysis rapidly and accurately identified the adulterated G. lucidum spore powder. Partial least square (PLS) and artificial neural network models were used for regression analysis to predict the level of adulteration. The results showed that the PLS model was better in determining the adulteration content of G. lucidum spore powder, with correlation coefficients in calibration and validation greater than 0.99, and the root mean square errors of cross-validation and prediction were 0.9218% and 3.1127%, respectively. Thus, combining NIR spectroscopy and chemometric methods is an effective and rapid approach to identifying and quantifying the adulteration level of G. lucidum spore powder.

Comparison of Various Signal Processing Techniques and Spectral Regions for the Direct Determination of Syrup Adulterants in Honey Using Fourier Transform Infrared Spectroscopy and Chemometrics

Honey consumption has become increasingly popular worldwide. However, the increase in demand for honey has also caused an increase in its adulteration, a deliberate fraud which involves adding of other substances to pure honey for economic purposes. This process not only lowers the quality of honey, but also has potential health risks, including high blood sugar, increased risk of diabetes, and weight gain. Herein, we develop an easy-to-use and direct method of quantifying corn, cane, beet, and rice syrup adulterants in honey using Fourier transform infrared spectroscopy and chemometrics. Various signal processing techniques, including derivatives, moving average, binning, Savitzky–Golay, and standard normal variate using the entire spectral region (3996–650 cm−1) and specific spectral region (1501–799 cm−1), were compared. Optimum results were obtained using first derivative signal processing for both the entire and specific spectral regions. The first derivative signal processing technique garnered the most optimum results using the specific spectral range (1501–799 cm−1) (RMSECVaverage = 0.021, RMSEPaverage = 0.014, R2average = 0.859) across all syrup adulterants. An exploratory analysis to assess the utility of this specific spectral region in pattern recognition of samples based on their adulterant content show that this region is effective in discriminating samples according to the presence or absence of honey syrup adulterants.

Predicting Vodka Adulteration: A Combination of Electronic Tongue and Artificial Neural Networks

An artificial neural network was used to build models caple of predicting and quantifying vodka adulteration with methanol and/or tap water. A voltammetric electronic tongue based on gold and copper microelectrodes was used, and 310 analyses were performed. Vodkas were adulterated with tap water (5 to 50% (v/v)), methanol (1 to 13% (v/v)), and with a fixed addition of 5% methanol and tap water varying from 5 to 50% (v/v). The classification model showed 99.5% precision, and it correctly predicted the type of adulterant in all samples. Regarding the regression model, the root mean squared error was 3.464% and 0.535% for the water and methanol addition, respectively, and the prediction of the adulterant content presented an R2 0.9511 for methanol and 0.9831 for water adulteration.

Rapid detection and quantification of adulteration in Chinese hawthorn fruits powder by near-infrared spectroscopy combined with chemometrics

The aim of this study is to explore the feasibility of detection and quantification of two cheap adulterants (maltodextrin and starch) in Chinese functional food, hawthorn fruits powder (HFP), by using near infrared (NIR) spectroscopy coupled with chemometrics methods. The partial least squares discriminant analysis (PLS-DA) models were developed to discriminate the adulterated HFP from the authentic HFP, while the partial least squares regression (PLSR) models were employed to determine the contents of adulterants. In order to yield the best results, various spectra pretreatment methods and wavelength selection methods were carefully investigated. The models’ qualities were assessed by the self-consistency test, the independent test and the rigorous leave-one-out cross-validation test. The metrics for the PLS-DA discriminative model included error rate, true positive rate, true negative rate and F1 score, while the metrics for the PLSR quantitative model were determination coefficient, root mean square error and residual prediction deviation. Finally, very satisfying results were obtained, which indicate that our method is quite robust and applicable, and thus has great potential for rapid detection of adulteration in powder of many other herbal plants or functional foods.

ДНК-идентификация животных для выявления фальсификаций продуктов питания

The article describes state-of-the-art approaches to the species and breed identification of even-toed ungulates, including the determination of subspecies of the Bovinae subfamily and the belonging of breeds of bovine (Bos taurus) subspecies using breed-specific SNP markers that differentiate the gene pool of meat or dairy cattle; the accredited activity results of the Institute of Genetics and Cytology of the National Academy of Sciences of Belarus on the species identification of the meat ingredients of animals and poultry in food products and raw material are provided to detect adulterations and prove the quality conformance. The authors demonstrate their own results related to the studies on the COI gene polymorphism of mitochondrial DNA of the European bison (Bison bonasus), the American bison (Bison bison), cattle (Bos taurus taurus). A need for developing of species and breed identification technologies allowing to determine the belonging of an individual to a specific subspecies or breed to obtain information that may be used in forensic science is substantiated. The importance of developing of quantitative PCR techniques for the rigorous calculation of an adulteration content in meat products and differentiation between the deliberately produced fake and the technically inevitable contamination of food raw material arising from technological meat processing is justified.

Fast Quantification of Honey Adulteration with Laser-Induced Breakdown Spectroscopy and Chemometric Methods

Honey adulteration is a major issue in food production, which may reduce the effective components in honey and have a detrimental effect on human health. Herein, laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was used to fast quantify the adulterant content. Two common types of adulteration, including mixing acacia honey with high fructose corn syrup (HFCS) and rape honey, were quantified with univariate analysis and partial least squares regression (PLSR). In addition, the variable importance was tested with univariable analysis and feature selection methods (genetic algorithm (GA), variable importance in projection (VIP), selectivity ratio (SR)). The results indicated that emissions from Mg II 279.58, 280.30 nm, Mg I 285.25 nm, Ca II 393.37, 396.89 nm, Ca I 422.70 nm, Na I 589.03, 589.64 nm, and K I 766.57, 769.97 nm had compact relationship with adulterant content. Best models for detecting the adulteration ratio of HFCS 55, HFCS 90, and rape honey were achieved by SR-PLSR, VIP-PLSR, and VIP-PLSR, with root-mean-square error (RMSE) of 8.9%, 8.2%, and 4.8%, respectively. This study provided a fast and simple approach for detecting honey adulteration.

Development of a high-performance liquid chromatography with tandem mass spectrometry method for identifying common adulterant content in saffron (Crocus sativus L.).

This study aimed to develop an efficient and reliable method for estimating common adulterants in saffron by detecting their characteristic components to warrant its efficacy and regular use as a highly valuable medicinal herb. A selective and sensitive high-performance liquid chromatography with tandem mass spectrometry (HPLC-MS/MS) method was developed to estimate the common adulterants in saffron from corn stigma, chrysanthemum and safflower through the simultaneous determination of specific constituents including allantoin, chlorogenic acid (ChA) and hydroxysafflor yellow A (HSYA). Peak identification of each target compound was confirmed from product ions obtained using multiple reaction monitoring triggered enhanced product ions mass chromatogram. Method validation in terms of linearity, sensitivity, reproducibility, accuracy and stability was systematically performed according to official guidelines. Satisfactory separation of the three components was achieved on a C18 column (4.6×250mm, 5μm) with methanol-acetonitrile-ammonium acetate (3.0mm) as the mobile phase at gradient elution. The identification of these specific constituents was accomplished using the multiple reaction monitoring mode in combination with enhanced product ion supplementary confirmation. The established method was validated in terms of linearity, sensitivity, reproducibility, accuracy and recovery, which were found satisfactory for sensitive detection of the three target compounds. By detecting the specific constituents allantoin, ChA and HSYA in one run, the adulterants of corn stigma, chrysanthemum and safflower can be effectively identified and estimated in saffron. This is the first report on developing a simple, sensitive and operational method for the identification and estimation of common adulterants of saffron, that was forwarded for broaden application.

Untargeted headspace gas chromatography – Ion mobility spectrometry analysis for detection of adulterated honey

The recognized properties of honey together with its price have, almost inevitably, led to economically motivated adulteration. In this work, headspace gas chromatography coupled to ion mobility spectrometry (HS-GC-IMS) is proposed for the differentiation of honey according to its purity and the level of adulteration by sugar cane or corn syrups. An easy and rapid sample treatment, consisting of incubating 1 g of honey at 100 °C for 15 min and then injecting 750 μL of the sample headspace into the GC-IMS system, is proposed. A 3-dimensional data map is obtained in 32 min. The proposed method was used for the analysis of 198 honey samples (56 pure honeys of different botanical origins, 71 honeys adulterated with sugar cane syrup and 71 adulterated with corn syrup). The influence of the adulterant on variations in the honey sample spectrum was studied. In order to obtain chemometric models for the detection of adulterated honey samples, the data obtained by HS-GC-IMS were processed selecting the significant markers of the spectrum fingerprint. OPLS-DA models were constructed using 80% of the samples, and the remaining 20% were used for method validation. The differentiation between pure and adulterated honeys had a validation success of 97.4%, and the assessment of adulterant content was obtained with a 93.8% validation success rate for both adulterant agents assayed. Nine commercial honey samples were analysed using the proposed methodology, and seven of them were classified as adulterated.

Determination of Adulteration Content in Extra Virgin Olive Oil Using FT-NIR Spectroscopy Combined with the BOSS-PLS Algorithm.

This work applied the FT-NIR spectroscopy technique with the aid of chemometrics algorithms to determine the adulteration content of extra virgin olive oil (EVOO). Informative spectral wavenumbers were obtained by the use of a novel variable selection algorithm of bootstrapping soft shrinkage (BOSS) during partial least-squares (PLS) modeling. Then, a PLS model was finally constructed using the best variable subset obtained by the BOSS algorithm to quantitative determine doping concentrations in EVOO. The results showed that the optimal variable subset including 15 wavenumbers was selected by the BOSS algorithm in the full-spectrum region according to the first local lowest value of the root-mean-square error of cross validation (RMSECV), which was 1.4487 % v/v. Compared with the optimal models of full-spectrum PLS, competitive adaptive reweighted sampling PLS (CARS–PLS), Monte Carlo uninformative variable elimination PLS (MCUVE–PLS), and iteratively retaining informative variables PLS (IRIV–PLS), the BOSS–PLS model achieved better results, with the coefficient of determination (R2) of prediction being 0.9922, and the root-mean-square error of prediction (RMSEP) being 1.4889 % v/v in the prediction process. The results obtained indicated that the FT-NIR spectroscopy technique has the potential to perform a rapid quantitative analysis of the adulteration content of EVOO, and the BOSS algorithm showed its superiority in informative wavenumbers selection.

Attenuated Total Reflectance–Fourier Transform Infrared (ATR–FTIR) Spectroscopy Combined with Chemometrics for Rapid Determination of Cold-Pressed Wheat Germ Oil Adulteration

This paper describes the feasibility of attenuated total reflectance–Fourier transform infrared (ATR–FTIR) spectroscopy combined with multivariate data analyses for rapid determination of cold-pressed wheat germ oil (WGO) adulteration. Thirty-six pure edible oils, as well as 216 binary blends of WGO adulterated with cheaper refined oils, sunflower (SFO), and soybean oil (SBO) (1–50%) were analyzed by using ATR–FTIR spectroscopy in combination with PCA, LDA, SIMCA, and PLSR analyses. SIMCA models provide excellent classification for pure cold-pressed WGO and refined edible oil samples, with 95% significance level. The classification limits for detection of SFO and SBO adulterations in WGO were below 1%. Furthermore, a total of 100% of studied samples were correctly classified on the basis of their origin in calibration and in cross-validation by LDA models. Under the optimum conditions, the PLS–R plots of actual versus predicted values exhibited high linearity (R2 > 0.9990). The content of SFO and SBO adulterants has been successively quantified using PLSR at levels < 0.56% and < 0.99% in an unknown mixture. RMSEC and RMSECV values for the binary mixtures of WGO–SFO were between 0.56–1.98% and 0.68–4.46%, for the binary mixtures of WGO–SBO were between 0.99–1.77% and 1.09–5.12%, respectively.

Raman Imaging for the Detection of Adulterants in Paprika Powder: A Comparison of Data Analysis Methods

Raman imaging requires the effective extraction of chemical information from the corresponding datasets, which can be achieved by a range of analytical methods. However, since each of these methods exhibits both strengths and weaknesses, we herein directly compare univariate, bivariate, and multivariate analyses of Raman imaging data by evaluating their performance in the quantitation of two adulterants in paprika powder. Univariate and bivariate models were developed based on the spectral features of the target adulterants, whereas spectral angle mapper (SAM), adopted as a multivariate analysis method, utilized the complete dataset. The obtained results demonstrate that despite being simple and easily implementable, the univariate method affords false positive pixels in the presence of background noise. Luckily, the above problem can be easily resolved using the bivariate method, which utilizes the multiplication of two band images wherein the same adulterant shows high-intensity peaks exhibiting the least overlap with those of other sample constituents. Finally, images produced by SAM contain abundant false negative pixels of adulterants, particularly for low-concentration samples. Notably, the bivariate method affords results closely matching the theoretical adulterant content, exhibiting the advantages of using non-complex data (only two bands are utilized) and being well suited to online applications of Raman imaging in the agro-food sector.