Journal articles: 'Adulterated samples'

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Florea, Anca, Jonas Schram, Mats de Jong, Joy Eliaerts, Filip Van Durme, Balwinder Kaur, Nele Samyn, and Karolien De Wael. "Electrochemical Strategies for Adulterated Heroin Samples." Analytical Chemistry 91, no. 12 (May 22, 2019): 7920–28. http://dx.doi.org/10.1021/acs.analchem.9b01796.

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Zhao, Haixiang, Yongli Wang, Xiuli Xu, Heling Ren, Li Li, Li Xiang, and Weike Zhong. "Detection of Adulterated Vegetable Oils Containing Waste Cooking Oils Based on the Contents and Ratios of Cholesterol, β-Sitosterol, and Campesterol by Gas Chromatography/Mass Spectrometry." Journal of AOAC INTERNATIONAL 98, no. 6 (November 1, 2015): 1645–54. http://dx.doi.org/10.5740/jaoacint.15-053.

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Abstract A simple and accurate authentication method for the detection of adulterated vegetable oils that contain waste cooking oil (WCO) was developed. This method is based on the determination of cholesterol, β-sitosterol, and campesterol in vegetable oils and WCO by GC/MS without any derivatization. A total of 148 samples involving 12 types of vegetable oil and WCO were analyzed. According to the results, the contents and ratios of cholesterol, β-sitosterol, and campesterol were found to be criteria for detecting vegetable oils adulterated with WCO. This method could accurately detect adulterated vegetable oils containing 5% refined WCO. The developed method has been successfully applied to multilaboratory analysis of 81 oil samples. Seventy-five samples were analyzed correctly, and only six adulterated samples could not be detected. This method could not yet be used for detection of vegetable oils adulterated with WCO that are used for frying non-animal foods. It provides a quick method for detecting adulterated edible vegetable oils containing WCO.

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RUKUNDO, ISAAC R., and MARY-GRACE C. DANAO. "Identifying Turmeric Powder by Source and Metanil Yellow Adulteration Levels Using Near-Infrared Spectra and PCA-SIMCA Modeling." Journal of Food Protection 83, no. 6 (February 7, 2020): 968–74. http://dx.doi.org/10.4315/jfp-19-515.

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ABSTRACT Turmeric sourced from six retailers was processed into a powder and adulterated with metanil yellow (MY) at concentrations of 0.0 to 30% (w/w). A handheld near-infrared spectrometer was used to obtain spectral scans of the samples, which were preprocessed using Savitzky-Golay first-derivative (SG1) approximation using 61 smoothing points and second-order polynomial. The preprocessed spectra were analyzed using principal component analysis (PCA) followed by classification by soft independent modeling class analogy (SIMCA) and were used to group the adulterated turmeric powder samples according to the source (i.e., processor) of adulteration. Results showed the first principal component (PC1) of PCA models was sensitive to adulteration level, but when coupled with SIMCA, unadulterated and adulterated samples could be classified according to their source despite having high levels of MY. At 5% level of significance, all of the samples were correctly classed for origin during validation. Some samples were classified under two groups, indicating possible inherent similarities. When the PCA model was built using only unadulterated samples, the PCA-SIMCA model could not classify the adulterated samples but could classify those with very low levels (≤2%, w/w) of MY, allowing for segregation of adulterated samples but not identification of sources. The combination of near-infrared and PCA-SIMCA modeling is a great tool not only to detect adulterated turmeric powder but also, potentially, to deter it in the future because the source of adulterated food can be traced back to the source of adulteration. HIGHLIGHTS

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Le Nguyen Doan, Duy, Quoc Cuong Nguyen, Federico Marini, and Alessandra Biancolillo. "Authentication of Rice (Oryza sativa L.) Using Near Infrared Spectroscopy Combined with Different Chemometric Classification Strategies." Applied Sciences 11, no. 1 (January 1, 2021): 362. http://dx.doi.org/10.3390/app11010362.

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Rice is a staple food in Vietnam, and the concern about rice is much greater than that for other foods. Preventing fraud against this product has become increasingly important in order to protect producers and consumers from possible economic losses. The possible adulteration of this product is done by mixing, or even replacing, high-quality rice with cheaper rice. This highlights the need for analytical methodologies suitable for its authentication. Given this scenario, the present work aims at testing a rapid and non-destructive approach to detect adulterated rice samples. To fulfill this purpose, 200 rice samples (72 authentic and 128 adulterated samples) were analyzed by near infrared (NIR) spectroscopy coupled, with partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogies (SIMCA). The two approaches provided different results; while PLS-DA analysis was a suitable approach for the purpose of the work, SIMCA was unable to solve the investigated problem. The PLS-DA approach provided satisfactory results in discriminating authentic and adulterated samples (both 5% and 10% counterfeits). Focusing on authentic and 10%-adulterated samples, the accuracy of the approach was even better (with a total classification rate of 82.6% and 82.4%, for authentic and adulterated samples, respectively).

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Hong, Victor, and Ronald E. Wrolstad. "Detection of Adulteration in Commercial Cranberry Juice Drinks and Concentrates." Journal of AOAC INTERNATIONAL 69, no. 2 (March 1, 1986): 208–13. http://dx.doi.org/10.1093/jaoac/69.2.208.

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Abstract Thirty-one samples of commercial cranberry juice drink and one sample of commercial cranberry juice concentrate were analyzed for nonvolatile acids and anthocyanidin profiles by liquid chromatography (LC). Ultraviolet-visible spectral measurements were used to measure pigment concentration, polymeric color, and percent polymeric color. Nineteen of the 31 samples analyzed were found to be adulterated. The adulterated samples exhibited nonvolatile organic acid profiles indicative of added malic and/or citric acid. Anthocyanidin profiles of the adulterated samples showed the presence of substantial quantities of delphinidin and malvidin, neither of which are present in cranberries in significant amounts. Grape skin extract is believed to be the added colorant.

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K., Abidfaheem T., Baby S. Nayak, and Maxie Andrade. "FOOD ADULTERATION AND FAMILY'S KNOWLEDGE ON FOOD ADULTERATION IN SELECTED VILLAGE OF UDUPI TALUK, KARNATAKA." Journal of Health and Allied Sciences NU 03, no. 02 (June 2013): 033–37. http://dx.doi.org/10.1055/s-0040-1703650.

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AbstractA cross sectional survey was under taken among 75 families using structured knowledge questionnaire and observation technique. The collected data was analysed using SPSS version 16. The study findings showed that majority (60%)of the subjects had moderate knowledge on food adulteration. Out of 75 samples tested, 2.7% chili powder samples were adulterated with artificial color and 10.7% of the common salt samples were adulterated with insoluble impurities. In tea powder 12 % were adulterated with iron fillings and 2.7% 2 were adulterated with artificial color. There was significant association of knowledge score on food adulteration with age (X2 (2) =8.627 p= (2), 2 0.013) and educational status (X2 (4) =9.876, p= 0.043) of the respondents. The study concludes that the food adulteration even though (4) low, still it is existing. However awareness of the public in relation to food adulteration should be ongoing especially to the general public with lower level of education.

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Khamsopha, Duangkamolrat, and Sontisuk Teerachaichayut. "Detection of Adulteration of Tapioca Starch with Dolomite by near Infrared Hyperspectral Imaging." Key Engineering Materials 862 (September 2020): 46–50. http://dx.doi.org/10.4028/www.scientific.net/kem.862.46.

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Tapioca starch adulterated with dolomite is sold in markets, but this adulteration cannot be identified by normal visual inspection. Near infrared (NIR) hyperspectral imaging has been successfully used as a non-destructive method of identifying various characteristics of food, therefore it was tested to identify dolomite adulteration. Adulterated tapioca starch samples were prepared by adding dolomite in the range of 0.5-100% (wt/wt). Samples (N=400) of pure tapioca starch (0) and adulterated tapioca starch (1) were divided into calibration set (N=300) and a prediction set (N=100). All samples were scanned using NIR hyperspectral imaging (935-1720 nm) and spectra were pre-processed using Savitzky-Golay first derivative differentiation pretreatment in order to obtain the optimal conditions for establishing a classification model. Partial least squares-discriminant analysis was carried out to evaluate the accuracy of classification tapioca starch adulterated with dolomite. The results showed the total accuracy of prediction for classification was 100%. Therefore, NIR hyperspectral imaging was demonstrated to have a potential for use in detecting adulteration of tapioca starch with dolomite.

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J.M.N. Marikkar. "Principal Component Analysis of Fatty Acid Data to Detect Virgin Coconut Oil Adulteration by Palm Olein." CORD 34, no. 1 (October 1, 2018): 9. http://dx.doi.org/10.37833/cord.v34i1.24.

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Authentication of virgin coconut oil (VCO) is important to safeguard customers from adulteration practices. A study was carried out to distinguish VCO from VCO adulterated with palm olein (PO) using principal component analysis (PCA) of fatty acid (FA) compositional data. Six samples of VCO, and six samples of palm olein were obtained from oil producers’ companies in Malaysia. Six samples of adulterated VCO were prepared by mixing with palm olein in 5% increment of adulteration. Fatty acid compositions of all oil samples were determined individually and the data were analyzed statistically. PCA analysis showed that lauric, palmitic and oleic acids were the most influencing parameters to discriminate VCO from adulterated VCO. Out of the thirteen FA variables investigated, ten were found to display high correlation with increasing adulteration. Predictive models showing higher coefficient of determination (R2) and good confidence limits were useful for quantification purposes.

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Aliaño-González, María José, Marta Ferreiro-González, Estrella Espada-Bellido, Miguel Palma, and Gerardo F. Barbero. "A Screening Method Based on Headspace-Ion Mobility Spectrometry to Identify Adulterated Honey." Sensors 19, no. 7 (April 4, 2019): 1621. http://dx.doi.org/10.3390/s19071621.

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Nowadays, adulteration of honey is a frequent fraud that is sometimes motivated by the high price of this product in comparison with other sweeteners. Food adulteration is considered a deception to consumers that may have an important impact on people’s health. For this reason, it is important to develop fast, cheap, reliable and easy to use analytical methods for food control. In the present research, a novel method based on headspace-ion mobility spectrometry (HS-IMS) for the detection of adulterated honey by adding high fructose corn syrup (HFCS) has been developed. A Box–Behnken design combined with a response surface method have been used to optimize a procedure to detect adulterated honey. Intermediate precision and repeatability studies have been carried out and coefficients of variance of 4.90% and 4.27%, respectively, have been obtained. The developed method was then tested to detect adulterated honey. For that purpose, pure honey samples were adulterated with HFCS at different percentages (10–50%). Hierarchical cluster analysis (HCA) and principal component analysis (PCA) showed a tendency of the honey samples to be classified according to the level of adulteration. Nevertheless, a perfect classification was not achieved. On the contrary, a full classification (100%) of all the honey samples was performed by linear discriminant analysis (LDA). This is the first time the technique of HS-IMS has been applied for the determination of adulterated honey with HFCS in an automatic way.

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Smithson, Shayla, Boluwatife Fakayode, Siera Henderson, John Nguyen, and Sayo Fakayode. "Detection, Purity Analysis, and Quality Assurance of Adulterated Peanut (Arachis hypogaea) Oils." Foods 7, no. 8 (July 31, 2018): 122. http://dx.doi.org/10.3390/foods7080122.

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The intake of adulterated and unhealthy oils and trans-fats in the human diet has had negative health repercussions, including cardiovascular disease, causing millions of deaths annually. Sadly, a significant percentage of all consumable products including edible oils are neither screened nor monitored for quality control for various reasons. The prospective intake of adulterated oils and the associated health impacts on consumers is a significant public health safety concern, necessitating the need for quality assurance checks of edible oils. This study reports a simple, fast, sensitive, accurate, and low-cost chemometric approach to the purity analysis of highly refined peanut oils (HRPO) that were adulterated either with vegetable oil (VO), canola oil (CO), or almond oil (AO) for food quality assurance purposes. The Fourier transform infrared spectra of the pure oils and adulterated HRPO samples were measured and subjected to a partial-least-square (PLS) regression analysis. The obtained PLS regression figures-of-merit were incredible, with remarkable linearity (R2 = 0.994191 or better). The results of the score plots of the PLS regressions illustrate pattern recognition of the adulterated HRPO samples. Importantly, the PLS regressions accurately determined percent compositions of adulterated HRPOs, with an overall root-mean-square-relative-percent-error of 5.53% and a limit-of-detection as low as 0.02% (wt/wt). The developed PLS regressions continued to predict the compositions of newly prepared adulterated HRPOs over a period of two months, with incredible accuracy without the need for re-calibration. The accuracy, sensitivity, and robustness of the protocol make it desirable and potentially adoptable by health departments and local enforcement agencies for fast screening and quality assurance of consumable products.

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Windarsih, Anjar, Theresia Wijayanti, Irnawati Irnawati, and Abdul Rohman. "The Use of ¹H-NMR Spectroscopy Coupled with Chemometrics for Authentication of Curcuma xanthorrhiza Adulterated with Curcuma aeruginosa." Key Engineering Materials 884 (May 2021): 320–26. http://dx.doi.org/10.4028/www.scientific.net/kem.884.320.

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Curcuma xanthorrhiza rhizome is known to have several pharmacological activities and it is potential to be adulterated with other species having lower price such as Curcuma aeruginosa to gain more economic benefit. The objective of this study was to develop 1H-NMR spectroscopy combined with chemometrics of principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA), and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) for authentication of C. xanthorrhiza. PCA could be used for differentiation between pure and adulterated C. xanthorrhiza. Chemometrics of PLS-DA showed clear and better separation between authentic and adulterated samples. The obtained R2X was 0.975, R2Y was 0.993, and Q2(cum) was 0.986. OPLS-DA using two principal components and one orthogonal variabel provided complete separation between authentic and adulterated samples better than using PCA and PLS-DA. The model has a good fit indicated by high value of R2X (0.939) and R2Y (0.932) and a good predictivity indicated by its Q2 value (0.925). It can be concluded that combination of 1H-NMR spectroscopy and chemometrics of PCA, PLS-DA, and OPLS-DA could be used for authentication of C. xanthorrhiza adulterated with C. aeruginosa with OPLS-DA showed the best classification.

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Doner,, Landis W., Allan R. Brause, and Donald R. Petrus. "δ18 Measurements in Water for Detection of Sugar Beet-Derived Syrups in Frozen Concentrated Orange Juice: Collaborative Study." Journal of AOAC INTERNATIONAL 75, no. 6 (November 1, 1992): 1107–11. http://dx.doi.org/10.1093/jaoac/75.6.1107.

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Abstract Stable isotope ratio mass spectrometry has shown that pure frozen concentrated orange juices (FCOJ) of 63-67° brix possess a mean δ18 value of +14.28%o, with a standard deviation of 1.80. Beet invert syrups, which are produced using ground water, possess negative values. As a result, δ18 values decrease on addition of such syrups to FCOJ. Samples with values less than +8.9%o (3 standard deviations from the mean for pure FCOJs) can confidently be considered as adulterated. A collaborative study was conducted in which a pure FCOJ and 4 samples adulterated to various levels with medium beet invert syrup were sent to each of 6 collaborators. In all but 2 instances, juices containing more than 10% beet syrup would have been classified as adulterated by the collaborators; none would have classified pure juice as adulterated. The plot of mean δ18 values for all collaborators at each adulteration level has a correlation coefficient >0.999. The method has been adopted first action by AOAC International.

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Windarsih, Anjar, Abdul Rohman, and Respati Tri Swasono. "AUTHENTICATION OF TURMERIC USING PROTON-NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY AND MULTIVARIATE ANALYSIS." International Journal of Applied Pharmaceutics 10, no. 6 (November 22, 2018): 174. http://dx.doi.org/10.22159/ijap.2018v10i6.29014.

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Objective: The objective of this study was to apply 1H-NMR spectroscopy-based metabolite fingerprinting in combination with multivariate analysis for authentication of turmeric (Curcuma longa) from C. heyneana and C. manga.Methods: Partial least square-discriminant analysis (PLS-DA) and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) were used for differentiation of authentic and adulterated C. longa with C. manga and C. heyneana. The variables used were peaks with certain chemical shifts at optimized 1H-NMR spectra of authentic and adulterated C. longa.Results: All of the authentic C. longa samples were clearly separated from the adulterated ones. The multivariate calibration of partial least square (PLS) was successfully applied to predict of adulterants in C. longa. The lower RMSEC (root mean square error of calibration) values, 0.94% for adulterated C. longa with C. heyneana and 1.37% for adulterated C. longa with C. manga, and the lower RMSEP (root mean square error of prediction) values, 0.83% for adulterated C. longa with C. heyneana and 1.34% for adulterated C. longa with C. manga indicated the good of accuracy and precision of the calibration models.Conclusion: The combination of 1H-NMR spectroscopy and chemometrics of multivariate analysis PLS-DA, OPLS-DA, and PLS proves an adequate technique for authentication of turmeric.

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Kinkennon, Amy E., David L. Black, Timothy A. Robert, and Peter R. Stout. "Analysis of Nitrite in Adulterated Urine Samples by Capillary Electrophoresis." Journal of Forensic Sciences 49, no. 5 (2004): 1–7. http://dx.doi.org/10.1520/jfs2003361.

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Cody, J. T., S. Valtier, and J. Kuhlman. "Analysis of Morphine and Codeine in Samples Adulterated with Stealth." Journal of Analytical Toxicology 25, no. 7 (October 1, 2001): 572–75. http://dx.doi.org/10.1093/jat/25.7.572.

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Dramićanin, Tatjana, Lea Lenhardt Acković, Ivana Zeković, and Miroslav D. Dramićanin. "Detection of Adulterated Honey by Fluorescence Excitation-Emission Matrices." Journal of Spectroscopy 2018 (July 2, 2018): 1–6. http://dx.doi.org/10.1155/2018/8395212.

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Honey is a frequent target of adulteration through inappropriate production practices and origin mislabelling. Current methods for the detection of adulterated honey are time and labor consuming, require highly skilled personnel, and lengthy sample preparation. Fluorescence spectroscopy overcomes such drawbacks, as it is fast and noncontact and requires minimal sample preparation. In this paper, the application of fluorescence spectroscopy coupled with statistical tools for the detection of adulterated honey is demonstrated. For this purpose, fluorescence excitation-emission matrices were measured for 99 samples of different types of natural honey and 15 adulterated honey samples (in 3 technical replicas for each sample). Statistical t-test showed that significant differences between fluorescence of natural and adulterated honey samples exist in 5 spectral regions: (1) excitation: 240–265 nm, emission: 370–495 nm; (2) excitation: 280–320 nm, emission: 390–470 nm; (3) excitation: 260–285 nm, emission: 320–370 nm; (4) excitation: 310–360 nm, emission: 370–470 nm; and (5) excitation: 375–435 nm, emission: 440–520 nm, in which majority of fluorescence comes from the aromatic amino acids, phenolic compounds, and fluorescent Maillard reaction products. Principal component analysis confirmed these findings and showed that 90% of variance in fluorescence is accumulated in the first two principal components, which can be used for the discrimination of fake honey samples. The classification of honey from fluorescence data is demonstrated with a linear discriminant analysis (LDA). When subjected to LDA, total fluorescence intensities of selected spectral regions provided classification of honey (natural or adulterated) with 100% accuracy. In addition, it is demonstrated that intensities of honey emissions in each of these spectral regions may serve as criteria for the discrimination between natural and fake honey.

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Cardoso, Thiago M. G., Robert B. Channon, Jaclyn A. Adkins, Márcio Talhavini, Wendell K. T. Coltro, and Charles S. Henry. "A paper-based colorimetric spot test for the identification of adulterated whiskeys." Chemical Communications 53, no. 56 (2017): 7957–60. http://dx.doi.org/10.1039/c7cc02271a.

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Faqeerzada, Mohammad Akbar, Santosh Lohumi, Geonwoo Kim, Rahul Joshi, Hoonsoo Lee, Moon Sung Kim, and Byoung-Kwan Cho. "Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method." Sensors 20, no. 20 (October 16, 2020): 5855. http://dx.doi.org/10.3390/s20205855.

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The widely used techniques for analyzing the quality of powdered food products focus on targeted detection with a low-throughput screening of samples. Owing to potentially significant health threats and large-scale adulterations, food regulatory agencies and industries require rapid and non-destructive analytical techniques for the detection of unexpected compounds present in products. Accordingly, shortwave-infrared hyperspectral imaging (SWIR-HSI) for high throughput authenticity analysis of almond powder was investigated in this study. Two different varieties of almond powder, adulterated with apricot and peanut powder at different concentrations, were imaged using the SWIR-HSI system. A one-class classifier technique, known as data-driven soft independent modeling of class analogy (DD-SIMCA), was used on collected data sets of pure and adulterated samples. A partial least square regression (PLSR) model was further developed to predict adulterant concentrations in almond powder. Classification results from DD-SIMCA yielded 100% sensitivity and 89–100% specificity for different validation sets of adulterated samples. The results obtained from the PLSR analysis yielded a high determination coefficient (R2) and low error values (<1%) for each variety of almond powder adulterated with apricot; however, a relatively higher error rates of 2.5% and 4.4% for the two varieties of almond powder adulterated with peanut powder, which indicates the performance of quantitative analysis model could vary with sample condition, such as variety, originality, etc. PLSR-based concentration mapped images visually characterized the adulterant (apricot) concentration in the almond powder. These results demonstrate that the SWIR-HSI technique combined with the one-class classifier DD-SIMCA can be used effectively for a high-throughput quality screening of almond powder regarding potential adulteration.

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Wu, Nao, Stéphane Balayssac, Saïda Danoun, Myriam Malet-Martino, and Véronique Gilard. "Chemometric Analysis of Low-field 1H NMR Spectra for Unveiling Adulteration of Slimming Dietary Supplements by Pharmaceutical Compounds." Molecules 25, no. 5 (March 6, 2020): 1193. http://dx.doi.org/10.3390/molecules25051193.

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The recent introduction of compact or low-field (LF) NMR spectrometers that use permanent magnets, giving rise to proton (1H) NMR frequencies between 40 and 80 MHz, have opened up new areas of application. The two main limitations of the technique are its insensitivity and poor spectral resolution. However, this study demonstrates that the chemometric treatment of LF 1H NMR spectral data is suitable for unveiling medicines as adulterants of slimming dietary supplements (DS). To this aim, 66 DS were analyzed with LF 1H NMR after quick and easy sample preparation. A first PLS-DA model built with the LF 1H NMR spectra from forty DS belonging to two classes of weight-loss DS (non-adulterated, and sibutramine or phenolphthalein-adulterated) led to the classification of 13 newly purchased test samples as natural, adulterated or borderline. This classification was further refined when the model was made from the same 40 DS now considered as representing three classes of DS (non-adulterated, sibutramine-adulterated, and phenolphthalein-adulterated). The adulterant (sibutramine or phenolphthalein) was correctly predicted as confirmed by the examination of the 1H NMR spectra. A limitation of the chemometric approach is discussed with the example of two atypical weight-loss DS containing fluoxetine or raspberry ketone.

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Harnly, James M., Devanand Luthria, and Pei Chen. "Detection of Adulterated Ginkgo biloba Supplements Using Chromatographic and Spectral Fingerprints." Journal of AOAC INTERNATIONAL 95, no. 6 (November 1, 2012): 1579–87. http://dx.doi.org/10.5740/jaoacint.12-096.

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Abstract The fingerprints of 18 commercially available Ginkgo biloba supplements, 12 samples of raw G. biloba leaves, and three G. biloba standard reference materials from the National Institute of Standards and Technology were acquired directly (no chromatography) by UV spectrometry and after separation using HPLC with a diode array detector. The fingerprints consisted of the UV spectral images, the chromatographic images, and the areas of the 21 most prominent chromatographic peaks. Data were analyzed by principal component analysis and one-class soft independent modeling of class analogy (SIMCA). It was determined that three of the commercial products were adulterated with rutin, four with quercetin, and one with an unidentified flavonol glycoside. One-class SIMCA of the authentic products allowed the adulterated products to be easily distinguished using Q-residuals. Authentic supplements and raw leaf materials were easily distinguished. The finely powdered samples were also analyzed by near-IR (NIR) spectrometry. The authentic and adulterated products could not be distinguished by NIR spectrometry because of the excipients.

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Belkin, Yury. "Identification and Traceability of Spiritous Drinks based on UV-spectrometry and Conductometry." International Journal of Engineering and Advanced Technology 10, no. 3 (February 28, 2021): 9–12. http://dx.doi.org/10.35940/ijeat.c2122.0210321.

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This article is devoted to the identification and detection of adulterated alcoholic beverages on the example of brandy and vodka. According to different opinions, up to 40% of spiritous beverages turn out to be adulterated or counterfeit. Most commonly used to identify such adulteration analytical methods are either too expensive or time-demanding. The other important thing that there is no standard method developed for objective tracing of spirits to the level of a production lot. The paper proposes an express method for detecting adulterated alcoholic beverages based on the use of two analytical methods of different nature: conductometry and UV-spectrometry. The possibility of these methods’ joint use for identification of alcoholic beverages with the standard or by common features (non-signature identification) is proved by the usage of statistical methods on data got from analytical measurements and sensory evaluation of 171 drinks’ samples. The samples represent different countries of product origin, brands, ageing periods. Firstly we found out informative wavelength bands, spectral curve forms and conductivity limits for different types of drinks, both genuine and adulterated – adulteration was previously revealed by sensory test. Then, using k-means cluster analysis we developed typical spectra for 2 ‘styles’ of genuine brandies and adulterated brandies, for vodkas the same work was done using simpler statistical techniques (means and variation analysis). Finally, an algorithm for spiritous beverages identification and tracing is given. Additionally, changes in the values of the electrical conductivity and UV spectra during shelf life that do not affect the suitability of the proposed method were studied.

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Vlahov, Giovanna. "13C Nuclear Magnetic Resonance Spectroscopic Detection of the Adulteration of Extra Virgin Olive Oils Extracted from Different Cultivars with Cold-Pressed Hazelnut Oil." Journal of AOAC INTERNATIONAL 92, no. 6 (November 1, 2009): 1747–54. http://dx.doi.org/10.1093/jaoac/92.6.1747.

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Abstract 13C NMR spectroscopy was applied to detect the adulteration of olive oils with hazelnut oil. Considering that the linolenate chain and the squalene hydrocarbon were absent in hazelnut oil, unlike olive oil, a 13C NMR spectroscopy method was developed to measure in addition to the triglyceride normal chains (i.e., saturated, oleate, and linoleate chains), the resonances of the linolenate chain and of squalene hydrocarbon. Acyl chain and squalene resonances highly discriminated olive oil samples by cultivars. Nevertheless, the hazelnut oil percentage factor prevailed over the cultivar factor, thus correctly classifying 86 of the authentic and adulterated olive oil samples according to the hazelnut oil percentages. In particular, 85.7, 73.7, and 100.0 of the authentic olive oil samples, and the samples adulterated with 5 and 20 of hazelnut oil, were correctly classified through cross-validation.

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Bari, L., MR Hoque, MSA Reza, MA Hossain, and A. Islam. "Adulteration of Raw Milk in Selected Regions of Tangail District of Bangladesh." Journal of Environmental Science and Natural Resources 8, no. 1 (August 24, 2015): 41–44. http://dx.doi.org/10.3329/jesnr.v8i1.24662.

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At the present time adulteration of food especially raw milk is becoming a burning issue. Normally milk is adulterated with water, starch, skim milk powder, sugar, detergent, and fat, recently formalin also added with milk to increase the shelf life of milk. Thats why it is very essential to detect the prevalence of adulteration of milk. The present experiment was conducted to know the adulteration of milk. Total thirty milk samples were collected from five different bazaars (Tangail town, Santosh Bazaar, Porabari Bazaar, Boilla Bazaar, Bajitpur Bazaar) of Tangail district where six samples were taken from each entry point. The mean Specific gravity was 1.021, 1.020, 1.019, 1.019 and 1.020 respectively which were lower than standard 1.032 (BSTI, 2000). The results clearly suggest that water was the most common adulterant in almost all sample of raw milk collected. 56.67% and 50% samples were adulterated with starch and skim milk powder respectively. Two types of preservatives namely borax and sodium bicarbonate were detected in the fluid milk from the study areas. Out of all samples, 23.33% was adulterated with sodium bicarbonate and 16.66% with borax. However, none of the samples contained either formalin or hydrogen peroxide. It could therefore be inferred that, the consumers need to be more cautious regarding the quality of raw milk.J. Environ. Sci. & Natural Resources, 8(1): 41-44 2015

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Voldřich, M., A. Rajchl, H. Čížková, and P. Cuhra. "Detection of Foreign Enzyme Addition into the Adulterated Honey." Czech Journal of Food Sciences 27, Special Issue 1 (June 24, 2009): S280—S282. http://dx.doi.org/10.17221/1066-cjfs.

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Natural honey contains several enzymes, which are produced by bees (salivary secretion) and some are found in the nectar or pollen. The most important enzymes are amylases, invertases, glusidases, catalases, fosfatases and other. The activity of diastase (α-, β-, γ-amylase) is the important quality parameter of honey, according to the Directive 2001/110/CE the diastase activity (diastase number) must not be less than or equal to 8, for some kinds of honey also higher or equal to 3 (in these cases the HMF must not be higher than 15 mg/kg). Diastase is used as a marker to evaluate the freshness or the heat damage of honey. When honey is adulterated by addition of inverted sucrose or hydrolysed starch namely high fructose corn syrup (HFCS), then such dilution of honey leads to the reduction of diastase number. Such adulteration can be masked by addition of foreign amylases, e.g. bakery mould amylases. Recently several suspect samples of honey with inconsistent diastase number were found in the market. The possibilities of detection of foreign amylase addition based on the comparison of diastase determination using the Schade and Phadebas procedures are evaluated. The both tests are based on the determination of hydrolytic activity (the Schade number is expressed as g of starch hydrolysed 1 h at 40°C per 100 g honey), but the results depend on the substrate used for the trial (according to the standard procedure an insoluble blue dyed cross-linked type of starch should be used). The results of Schade test are therefore often affected by the choice of substrate. The model samples of honeys with addition of foreign amylase (Aspergillus oryzae) were analysed, the methods of adulteration detection based on the substrate specificity of enzymes is proposed. Keywords: falsification; honey; diastase number; Schade; amylase addition

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Mazivila, Sarmento Júnior, Letícia Maria de Souza, Ibrahim Muhammad Abubakar, Elisa Langa Mavulula, and Waldomiro Borges Neto. "Determination of residual automotive lubricant oil and residual solvent used in a dry wash as adulterants in Brazilian S-10 diesel (B7) using mid-infrared spectroscopy data and chemometric methods." Analytical Methods 8, no. 27 (2016): 5427–34. http://dx.doi.org/10.1039/c6ay01330a.

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Contal, Laure, Vanessa León, and Gerard Downey. "Detection and Quantification of Apple Adulteration in Strawberry and Raspberry Purées Using Visible and near Infrared Spectroscopy." Journal of Near Infrared Spectroscopy 10, no. 4 (October 2002): 289–99. http://dx.doi.org/10.1255/jnirs.345.

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Adulteration of strawberry and raspberry purées with apple is a potential authenticity issue. In purée form, detection of such adulteration is not easy. A series of pure strawberry ( n = 32) and raspberry ( n = 32) purées was prepared. Strawberry and raspberry samples were adulterated at levels of 10, 20, 30, 50 and 75% (w/w) apple using different ( n = 28) apple samples. Reflectance spectra were recorded using two different sample cell types. Classification of pure and adulterated fruit samples was investigated using soft independent modelling of class analogy. Best models permitted detection of apple adulteration at levels of > 20% w/w (strawberry) and > 10 < 20% w/w (raspberry). Quantification of adulterant content was by partial least squares regression. The most accurate models produced prediction errors of 3.4% apple (in raspberry) and 5.5% apple (in strawberry).

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Krueger, Dana A. "Determination of Adulterated Natural Bitter Almond Oil by Carbon Isotopes." Journal of AOAC INTERNATIONAL 70, no. 1 (January 1, 1987): 175–76. http://dx.doi.org/10.1093/jaoac/70.1.175.

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Abstract Bitter almond oil (benzaldehyde), a flavoring compound used in many foods, was isolated from apricot kernels; 2 synthetic benzaldehyde samples were obtained from commercial sources. All samples were analyzed for radiocarbon (l4C) content. The natural sample yielded a value consistent with its natural origin (approximately 116% of Modern Standard Activity), while the synthetic samples were devoid of 14C activity as expected for a petrochemical material. Implications for quality control of bitter almond oil are discussed

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McVey, Claire, Una Gordon, Simon A. Haughey, and Christopher T. Elliott. "Assessment of the Analytical Performance of Three Near-Infrared Spectroscopy Instruments (Benchtop, Handheld and Portable) through the Investigation of Coriander Seed Authenticity." Foods 10, no. 5 (April 27, 2021): 956. http://dx.doi.org/10.3390/foods10050956.

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The performance of three near-infrared spectroscopy (NIRS) instruments was compared through the investigation of coriander seed authenticity. The Thermo Fisher iS50 NIRS benchtop instrument, the portable Ocean Insights Flame-NIR and the Consumer Physics handheld SCiO device were assessed in conjunction with chemometric modelling in order to determine their predictive capabilities and use as quantitative tools through regression analysis. Two hundred authentic coriander seed samples and ninety adulterated samples were analysed on each device. Prediction models were developed and validated using SIMCA 15 chemometric software. All instruments correctly predicted 100% of the adulterated samples. The best models resulted in correct predictions of 100%, 98.5% and 95.6% for authentic coriander samples using spectra from the iS50, Flame-NIR and SCiO, respectively. The development of regression models highlighted the limitations of the Flame-NIR and SCiO for quantitative analysis, compared to the iS50. However, the results indicate their use as screening tools for on-site analysis of food, at various stages of the food supply chain.

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Lapcharoensuk, Ravipat, Kitticheat Danupattanin, Chaowarin Kanjanapornprapa, and Tawin Inkawee. "Combination of NIR spectroscopy and machine learning for monitoring chili sauce adulterated with ripened papaya." E3S Web of Conferences 187 (2020): 04001. http://dx.doi.org/10.1051/e3sconf/202018704001.

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This research aimed to study the combination of NIR spectroscopy and machine learning for monitoring chilli sauce adulterated with papaya smoothie. The chilli sauce was produced by the famous community enterprise of chilli sauce processing in Thailand. The ingredients of the chilli sauce consisted of 45% chilli, 25% sugar, 20% garlic, 5% vinegar, and 5% salt. The chilli sauce sample was mixed with ripened papaya (Khaek Dam variety) smoothie with 9 levels from 10 to 90 %w/w. The NIR spectra of pure chilli sauce, papaya smoothie and 9 adulterated chilli sauce samples were recorded using FT-NIR spectrometer in the wavenumber range of 12500 and 4000 cm-1. Three machine learning algorithms were applied to develop a model for monitoring adulterated chilli sauce, including partial least squares regression (PLS), support vector machine (SVM), and backpropagation neural network (BPNN). All model presented performance of prediction in the validation set with R2al = 0.99 while RMSEP of PLS, SVM and BPNN were 1.71, 2.18 and 3.27% w/w respectively. This finding indicated that NIR spectroscopy coupled with machine learning approaches were shown to be an alternative technique to monitor papaya smoothie adulterated in chilli sauce in the global food industry.

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Gusti, Nina, Dinda Oktarina, Rina Elvia, Euis Nursa’adah, Rendy W. Wardhana, Agus Sudaryono, and M. Lutfi Firdaus. "Facile Detection of Oil Adulteration using UV-Visible Spectroscopy Coupled with Chemometrics Analysis." Science and Technology Indonesia 6, no. 1 (January 13, 2021): 14. http://dx.doi.org/10.26554/sti.2021.6.1.14-18.

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Engine and machine oils, better known as lubricant, is a fast-moving part within the motorcycle and automobile industries. Due to its high demand, these oils are often counterfeited by irresponsible people to get more profit. The thing most often done to adulterate oil is by mixing it with other low-quality or used oil. Here, we propose a simple analytical method to identify oil adulteration by using UV-Visible spectroscopy. A number of 425 genuine and adulterated oils were used as samples. After appropriate dilution using n-hexane, the samples were analyzed by UV-Visible spectrophotometer followed by Principle Component Analysis (PCA) and Principle Component Regression (PCR) as part of the chemometrics analysis. The results show that prediction samples were accurately classified into their corresponding groups with PCA scores of 49% and 27% for principal component 1 and 2, respectively. PLS model achieved a good prediction to detect lubricant oil adulteration, with R-Square of predicted and reference samples were 0.9257 and 0.9204, respectively. The proposed method shows a promising alternative to the conventional chemical method using a more sophisticated instruments such as GC-MS and HPLC for oil or other organic compound identification.

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Hirashima, Karen, Simone Alves da Silva, Miriam Solange Fernandes Caruso, and Sabria Aued-Pimentel. "Encapsulated specialty oils commercialized in São Paulo state, Brazil: evaluation of identity (fatty acid profile) and compliance of fatty acids and Vitamin E contents with nutrition labeling." Food Science and Technology 33, no. 1 (March 19, 2013): 107–15. http://dx.doi.org/10.1590/s0101-20612013005000022.

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Encapsulated specialty oils commercialized in São Paulo state, Brazil, were evaluated for their identity (fatty acids profile) and compliance with nutrition labeling (fatty acids and Vitamin E (alpha tocopherol) contents). Twenty one samples [flaxseed oil (6), evening primrose (5), safflower (8), borage (1), and black currant (1)] purchased from local markets or collected by the health surveillance agency were analyzed. The fatty acids and vitamin E contents were analyzed by gas chromatography with flame ionization detector and liquid chromatography with UV detector, respectively. Nine samples were adulterated (5 samples of safflower oil, 3 of flaxseed oil, and one of evening primrose). Among them, 3 flaxseed and 2 safflower oil samples were probably adulterated by the addition of soybean oil. Conjugated linoleic acid (CLA) was found in two safflower oils samples although the sale of oils with conjugated linoleic acid (CLA) is not permitted by the National Health Surveillance Agency in Brazil (ANVISA). Only two samples presented all values in compliance with nutrition labeling (one safflower oil sample and one borage oil sample). The results show that a continuous monitoring of encapsulated specialty oils commercialized in Brazil is necessary including a greater number of samples and sanitary surveillance.

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Baimatova, Nassiba, Olga Demyanenko, and Ahmad Zia. "Sensitive determination of impurities in samples of vodka by gas chromatography with flame-ionization detection." Chemical Bulletin of Kazakh National University, no. 2 (June 30, 2015): 3. http://dx.doi.org/10.15328/cb613.

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Vodka is one of the most popular alcoholic drinks around the world. Adulteration of vodkas lead to many negative consequences. A number of analytical methods covers a wide range of analytes and allows detection of adulterated alcoholic beverages, however, most of these methods are very labor and time consuming or require expensive analytical instrumentation. The simplest and thus most popular method is based on direct analysis by gas chromatography (GC) with flame-ionization detection (FID). The main drawback of this method for discovery of adulterated vodka samples is insufficient sensitivity. The aim of this study was to increase sensitivity of the method for determination of key vodka impurities. Optimized parameters included split ratio 10:1, inlet temperature >120 °C, initial oven temperature 60 °C, flow rates of make-up gas, air and hydrogen 50, 400 and 40 mL/min, respectively. Obtained calibration plots are linear in the concentration range between 1 and 1000 mg/L with approximation coefficients R2>0.99. Compared to a standard method, slope factors are about 4 times higher when optimized method is used proving its higher sensitivity.

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Jakubíková, Michaela, Jana Sádecká, and Pavel Májek. "Determination of adulterants in adulterant-fruit spirit blends using excitation-emission matrix fluorescence spectroscopy." Acta Chimica Slovaca 8, no. 1 (April 1, 2015): 52–58. http://dx.doi.org/10.1515/acs-2015-0010.

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Abstract This study introduces a reliable method to detect adulteration of spirit drinks. Excitation-emission matrix (EEM) fluorescence in combination with parallel factor analysis (PARAFAC) and partial least squares (PLS) regression was used to determine the content of water and ethanol in adulterated fruit spirit samples. EEM fluorescence spectra recorded in the emission wavelength range of 315–450 nm and in the excitation wavelength range of 240–305 nm were used for PARAFAC. The model created using PARAFAC-PLS was able to predict the water and ethanol level in adulterated apple spirit with the root mean square error of prediction (RMSEP) values of 1.9 % and 1.8 %, respectively. Regarding adulterated plum spirit, the RMSEP values of 0.7 % and 3.5 % were obtained for water and ethanol, respectively. The aim of this work was to determine whether EEM-PARAFAC can be used to distinguish between plum and apple spirit. Better results were obtained for apple spirit and the method is useful also for water-apple spirit blends.

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Azmi, M. F. J., D. Jamaludin, S. Abd. Aziz, Y. A. Yusof, and A. M. Mustafah. "Adulterated stingless bee honey identification using VIS-NIR spectroscopy technique." Supplementary 1 5, S1 (January 17, 2021): 85–93. http://dx.doi.org/10.26656/fr.2017.5(s1).035.

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The objective of this study was to study the ability of the VIS-NIR spectroscopy to classify the pure and adulterated stingless bee honey across the wavelength range of 450– 969 nm using an optical spectrometer. The physicochemical properties such as soluble solid content (SSC) and moisture content (refractive index, RI) of pure and adulterated honey has also been investigated using a refractometer. The result showed that pure stingless bee honey has the highest transmittance rate, SSC and RI value compared to adulterated honey. There are significant differences (P < 0.0001) in the transmittance rate, SSC and RI of stingless bee honey over five different types of treatments. The results also showed that VIS-NIR data were good in classifying the samples into different treatments with 99.33% accuracy rate. About thirty-four wavelengths were found to be the most significant to discriminate the different treatments by principal component analysis (PCA) and linear discriminant analysis (LDA) techniques.

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Bohačenko, I., and Z. Veselý. "Proof of authenticity of pure instant coffee." Czech Journal of Food Sciences 18, No. 5 (January 1, 2000): 165–69. http://dx.doi.org/10.17221/8337-cjfs.

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An HPLC method with refractometric detection was worked out for the determination of the limiting contents of marker saccharides (free mannitol and total glucose and xylose) used for the proof of authenticity of pure instant coffee. This method, even though more laborious, yields results comparable with those obtained by the HPAE-PAD method and is intended mainly for those laboratories where the current HPLC technique with refractometric detection is presently used for saccharide analysis. The survey of market supply showed that instant coffee imported in bulk and subsequently packaged in the Czech Republic is most frequently adulterated – only one out of 7 samples examined contained authentic coffee. On the other hand, only one out of 10 samples of instant coffee imported in original packaging did not meet the authenticity criteria. The samples of instant coffee by domestic producers indicated that one producer placed on the market an adulterated product, whereas the other brand is authentic coffee.

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Low, Nicholas H., Allan Brause, and Eric Wilhelmsen. "Normative Data for Commercial Pineapple Juice from Concentrate." Journal of AOAC INTERNATIONAL 77, no. 4 (July 1, 1994): 965–75. http://dx.doi.org/10.1093/jaoac/77.4.965.

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Abstract Normative data for pineapple juice from concentrate were determined for 19 samples, including 5 that had been aseptically processed and representing 4 of the major pineapple growing regions of the world. Values are reported for sugars, organic acids, including isocitric acid, metals (specifically potassium, sodium calcium, and magnesium), δ13C, and oligosaccharides. Although geographical variation existed, the observed ranges and variances were small enough to be useful in describing authentic pineapple juice. Two concentrates (one aseptically and one nonaseptically processed) were intentionally adulterated (individually) with 3 commercially available inexpensive sweeteners (high fructose corn syrup, cane invert syrup, and beet medium invert syrup). Oligosaccharide analysis of these samples either by liquid chromatography or by capillary gas chromatography yielded oligosaccharide patterns that were useful for the detection of these sweeteners at 10% levels. Principal-component analysis (PCA) was used to represent graphically both the pure and adulterated samples based on their measured chemical parameters.

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Suhandy, Diding, and Meinilwita Yulia. "Classification of Lampung robusta Specialty Coffee According to Differences in Cherry Processing Methods Using UV Spectroscopy and Chemometrics." Agriculture 11, no. 2 (February 1, 2021): 109. http://dx.doi.org/10.3390/agriculture11020109.

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The postharvest processing factors including cherry processing methods highly influence the final quality of coffee beverages, especially in the composition of several coffee metabolites such as glucose, fructose, the amino acid (glutamic acid), and chlorogenic acids (CGA) as well as trigonelline contents. In this research, UV spectroscopy combined with chemometrics was used to classify a ground roasted Lampung robusta specialty coffee according to differences in the cherry processing methods. A total of 360 samples of Lampung robusta specialty coffee with 1 g of weight for each sample from three different cherry processing methods were prepared as samples: 100 samples of pure dry coffee (DRY), 100 samples of pure semi-dry coffee (SMD), 100 samples of pure wet coffee (WET) and 60 samples of adulterated coffee (ADT) (SMD coffee was adulterated with DRY and WET coffee). All samples were extracted using a standard protocol as explained by previous works. A low-cost benchtop UV-visible spectrometer (Genesys™ 10S UV-Vis, Thermo Scientific, Waltham, MA, USA) was utilized to obtain UV spectral data in the interval of 190–400 nm using the fast scanning mode. Using the first three principal components (PCs) with a total of 93% of explained variance, there was a clear separation between samples. The samples were clustered into four possible groups according to differences in cherry processing methods: dry, semi-dry, wet, and adulterated. Four supervised classification methods, partial least squares–discriminant analysis (PLS-DA), principal component analysis–linear discriminant analysis (PCA-LDA), linear discriminant analysis (LDA) and support vector machine classification (SVMC) were selected to classify the Lampung robusta specialty coffee according to differences in the cherry processing methods. PCA-LDA is the best classification method with 91.7% classification accuracy in prediction. PLS-DA, LDA and SVMC give an accuracy of 56.7%, 80.0% and 85.0%, respectively. The present research suggested that UV spectroscopy combining with chemometrics will be highly useful in Lampung robusta specialty coffee authentication.

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Prodolliet, Jacques, Melene Bruelhart, Maurice B. Blanc, Valerie Leloup, Genevieve Cherk, Catherine M. Donnelly, and Rinantonio Viani. "Adulteration of Soluble Coffee with Coffee Husks and Parchments." Journal of AOAC INTERNATIONAL 78, no. 3 (May 1, 1995): 761–67. http://dx.doi.org/10.1093/jaoac/78.3.761.

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Abstract Commercial soluble coffee can be adulterated with coffee husks or parchments. Xylose is a good tracer for this type of mispractice. The analysis of total xylose in a wide selection of green beans and the assessment of its fate during processing allowed the derivation of a maximum total xylose limit of 0.40%, above which a soluble coffee should be considered as adulterated. Out of the 700 commercial soluble coffees analyzed, 81 exhibited a total xylose level above this limit. Of the samples with total xylose level lower than the limit, 99% displayed concentrations in free mannitol and total glucose below 0.30 and 2.10%, respectively.

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León, Lorenzo, J. Daniel Kelly, and Gerard Downey. "Detection of Apple Juice Adulteration Using Near-Infrared Transflectance Spectroscopy." Applied Spectroscopy 59, no. 5 (May 2005): 593–99. http://dx.doi.org/10.1366/0003702053945921.

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Near-infrared transflectance spectroscopy was used to detect adulteration of apple juice samples. A total of 150 apple samples from 19 different varieties were collected in two consecutive years from orchards throughout the main cultivation areas in Ireland. Adulterant samples at 10, 20, 30, and 40% w/w were prepared using two types of adulterants: a high fructose corn syrup (HFCS) with 45% fructose and 55% glucose, and a sugars solution (SUGARS) made with 60% fructose, 25% glucose, and 15% sucrose (the average content of these sugars in apple juice). The results show that NIR analysis can be used to predict adulteration of apple juices by added sugars with a detection limit of 9.5% for samples adulterated with HFCS, 18.5% for samples adulterated with SUGARS, and 17% for the combined (HFCS + SUGARS) adulterants. Discriminant partial least squares (PLS) regression can detect authentic apple juice with an accuracy of 86–100% and adulterant apple juice with an accuracy of 91–100% depending on the adulterant type and level of adulteration considered. This method could provide a rapid screening technique for the detection of this type of apple juice adulteration, although further work is required to demonstrate model robustness.

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Chanda, T., GK Debnath, ME Hossain, MA Islam, and MK Begum. "Adulteration of raw milk in the rural areas of Barisal district of Bangladesh." Bangladesh Journal of Animal Science 41, no. 2 (March 10, 2013): 112–15. http://dx.doi.org/10.3329/bjas.v41i2.14126.

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The study was undertakento detect the type of adulterants and preservatives added to the incoming fluid milk from rural areas to the Barisal city of Bangladesh. Total fifty milk samples were collected from five different entry points of Karapure, Dabdopia, Shaestabad, Goriarpar and Khanpura of Barisal district where ten samples were taken from each entry point. The results indicated that, 100% of the milk samples were adulterated with water irrespective of collection points and months. Cane sugar, powdered milk and starch were detected as 26.0, 14.0 and 12.0% in the milk samples, respectively. Two types of preservatives namely formalin and sodium bicarbonate were detected in the fluid milk from the study areas. Out of all samples, 10.0% was adulterated with formalin and 20.0% with sodium bicarbonate. However, none of the samples contained either hydrogen peroxide or borax. There was a positive relationship between day temperature and intensity of adding preservatives to the fluid milk. As the day temperature increased, the number of preservative positive samples also increased in a linear fashion and vice versa. It could therefore be inferred that, the consumers need to be more cautious regarding the quality of raw milk specially in summer season.DOI: http://dx.doi.org/10.3329/bjas.v41i2.14126Bang. J. Anim. Sci. 2012. 41 (2): 112-115

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Raval, Parth, B. R. Patel, V. J. Shukla, Bhavesh Patil, Nehaparmar ., Preeti Pandya, and Shivangi Bhardwaj. "A comparative study on different market samples and standard samples of Shalaparni through physicochemical methods and near infrared spectroscopy." Indian Journal of Pharmaceutical and Biological Research 2, no. 04 (December 31, 2014): 20–25. http://dx.doi.org/10.30750/ijpbr.2.4.4.

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Dashmoolais one of the most important groups explained in Mishrakagana. One of which, Shalaparni is a potent drug used single as well as in various formulations mentioned in classics. Adulteration in Dashmoolaplants is a very big issue now days and this is because of the lack of availability of the original drugs. In this study market samples of Shalaparni (Desmodium gangeticum DC.) collected from the different part of India; compared with the standard Shalaparni authenticated sample which was collected from the natural source; by using physicochemical parameter and near infrared spectroscopy. Results were statistically processed by PCA. The results show that there is no similarity found outbetween the standard drug and market samples of Shalaparni which were collected from different regions of India. The market samples were observed for different adulterated material having poor quality.

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Bhat, Mohammad Mansoor, Heena Jalal, Parveez Ahmad Para, and Subha Ganguly. "Effect of Meat Processing and Cooking on DNA Extraction and Detection of Meat Adulteration in Mutton Rista (Kashmiri Meat Product)." Indian Journal of Nutrition and Dietetics 53, no. 4 (December 1, 2016): 459. http://dx.doi.org/10.21048/ijnd.2016.53.4.8403.

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The processing and cooking of meat, during meat product preparation, affects the DNA quality and its concentration during DNA isolation. In this study, the effect of processing and cooking, during Rista preparation, on meat speciation of beef and buffalo meat in mutton Rista was studied. The study material involved three types of meat i.e. unprocessed meat, Rista emulsion and the final cooked Rista product. In each type of meat, pure meat samples of mutton, beef and buffalo meat were studied along with the adulterated mutton sample having 10% beef and 10% buffalo meat adulteration level. The meat samples were subjected to mtDNA isolation and multiplex PCR analysis. The results of this study showed that processing and cooking decreases the concentration of extracted DNAs but does not affect the detection of beef and buffalo meat in adulterated mutton Rista (unprocessed, processed and cooked) at 10% level of adulteration.

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Saleemi, Sarah, Steven J. Pennybaker, Missi Wooldridge, and Matthew W. Johnson. "Who is ‘Molly’? MDMA adulterants by product name and the impact of harm-reduction services at raves." Journal of Psychopharmacology 31, no. 8 (July 10, 2017): 1056–60. http://dx.doi.org/10.1177/0269881117715596.

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Methylenedioxymethamphetamine (MDMA), often sold as ‘Ecstasy’ or ‘Molly’, is commonly used at music festivals and reported to be responsible for an increase in deaths over the last decade. Ecstasy is often adulterated and contains compounds that increase morbidity and mortality. While users and clinicians commonly assume that products sold as Molly are less-adulterated MDMA products, this has not been tested. Additionally, while pill-testing services are sometimes available at raves, the assumption that these services decrease risky drug use has not been studied. This study analyzed data collected by the pill-testing organization, DanceSafe, from events across the United States from 2010 to 2015. Colorimetric reagent assays identified MDMA in only 60% of the 529 samples collected. No significant difference in the percentage of samples testing positive for MDMA was determined between Ecstasy and Molly. Individuals were significantly less likely to report intent to use a product if testing did not identify MDMA (relative risk (RR) = 0.56, p = 0.01). Results suggest that Molly is not a less-adulterated substance, and that pill-testing services are a legitimate harm-reduction service that decreases intent to consume potentially dangerous substances and may warrant consideration by legislators for legal protection. Future research should further examine the direct effects of pill-testing services and include more extensive pill-testing methods.

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Angelis, Elisabetta De, Rosa Pilolli, Alice Bejjani, Rocco Guagnano, Cristiano Garino, Marco Arlorio, and Linda Monaci. "Optimization of an Untargeted DART-HRMS Method Envisaging Identification of Potential Markers for Saffron Authenticity Assessment." Foods 10, no. 6 (May 29, 2021): 1238. http://dx.doi.org/10.3390/foods10061238.

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Saffron is one of the most expensive agricultural products in the world and as such, the most commonly adulterated spice, with undeclared plant-based surrogates or synthetic components simulating color and morphology. Currently, saffron quality is certificated in the international trade market according to specific ISO guidelines, which test aroma, flavor, and color strength. However, it has been demonstrated that specific adulterants such as safflower, marigold, or turmeric up to 20% (w/w) cannot be detected under the prescribed approach; therefore, there is still a need for advanced and sensitive screening methods to cope with this open issue. The current investigation aims to develop a rapid and sensitive untargeted method based on an ambient mass spectrometry ionization source (DART) and an Orbitrap™high-resolution mass analyzer to discriminate pure and adulterated saffron samples with either safflower or turmeric. The metabolic profiles of pure and adulterated model samples prepared at different inclusion levels were acquired. Unsupervised multivariate analysis was carried out based on hierarchical cluster analysis and principal component analysis as first confirmation of the discriminating potential of the metabolic profile acquired under optimized DART-HRMS conditions. In addition, a preliminary selection of potential markers for saffron authenticity was accomplished, identifying compounds able to discriminate the type of adulteration down to a concentration level of 5%.

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Lagurin, Lolita G., Mark Joseph M. Garrovillas, and Fabian M. Dayrit. "The Application of 13C NMR and Untargeted Multivariate Analysis for Classifying Virgin Coconut Oil." Proceedings 70, no. 1 (November 10, 2020): 54. http://dx.doi.org/10.3390/foods_2020-07718.

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Virgin coconut oil (VCO) is produced from fresh mature coconut meat without the use of chemicals or high heat. VCO can be made using three processes: fermentation, centrifuge, and expeller. To determine quality, it is important to be able to differentiate control VCO (fresh) from old VCO, refined bleached and deodorized coconut oil (RBDCO), and VCO which has been adulterated with RBDCO. Differentiating these types of samples has remained a challenge because of their chemical similarity. This study investigated the ability of 13C NMR and multivariate analysis to differentiate these different coconut oil samples. The methodology used the standard 13C NMR pulse sequence with broadband 1H decoupling with dioxane as the internal standard (IS). After pre-processing of the spectra (alignment, bucketing/binning, normalization with respect to dioxane IS peak), untargeted multivariate analyses, both unsupervised and supervised, were done on the bins of the 13C peaks. Principal components analysis (PCA), a linear unsupervised method, was able to differentiate control VCO (n = 57) from RBDCO (n = 21), adulterated VCO (n = 9), and old VCO (n = 11). Partial least squares–discriminant analysis (PLS–DA) was used as the supervised linear binary classifier. Using overall accuracy and AUC-ROC curves (by 100 cross validation and single validation using manual holdout), the supervised dataset with an optimized model gave performances that were 99%, 95%, and 80% improved in differentiating control VCO vs. RBDCO, old VCO, and adulterated VCO (one vs. one), respectively. Predictive ability (Q2 < 0.20) and overall accuracy (<0.80) were poor compared to the previous models for binary classifier models (one vs. rest) to differentiate among the three VCO processes. This may be due to the variations in production conditions and methods that different VCO producers use. We conclude that 13C NMR combined with linear techniques can be used to accurately differentiate fresh VCO from RBDCO, old VCO, and adulterated VCO.

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Mburu, Monica, Clement Komu, Olivier Paquet-Durand, Bernd Hitzmann, and Viktoria Zettel. "Chia Oil Adulteration Detection Based on Spectroscopic Measurements." Foods 10, no. 8 (August 4, 2021): 1798. http://dx.doi.org/10.3390/foods10081798.

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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 RMSEPrange 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.

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Florea, Anca, Todd Cowen, Sergey Piletsky, and Karolien De Wael. "Polymer platforms for selective detection of cocaine in street samples adulterated with levamisole." Talanta 186 (August 2018): 362–67. http://dx.doi.org/10.1016/j.talanta.2018.04.061.

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Singh, Jasbir, James A. Elberling, D. Gary Hemphill, and Jeffrey Holmstrom. "The Measurement of Nitrite in Adulterated Urine Samples by High-Performance Ion Chromatography." Journal of Analytical Toxicology 23, no. 3 (May 1, 1999): 137–40. http://dx.doi.org/10.1093/jat/23.3.137.

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Shen, F., Q. Wu, A. Su, P. Tang, X. Shao, and B. Liu. "Detection of adulteration in freshly squeezed orange juice by electronic nose and infrared spectroscopy." Czech Journal of Food Sciences 34, No. 3 (June 17, 2016): 224–32. http://dx.doi.org/10.17221/303/2015-cjfs.

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The use of electronic nose and attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) as rapid tools for detection of orange juice adulteration has been preliminarily investigated and compared. Freshly squeezed orange juices were tentatively adulterated with 100% concentrated orange juices at levels ranging from 0% to 30% (v/v). Then the E-nose response signals and FTIR spectra collected from samples were subjected to multivariate analysis by principal component analysis (PCA) and linear discriminant analysis (LDA). PCA indicated that authentic juices and adulterated ones could be approximately separated. For the classification of samples with different adulteration levels, the overall accuracy obtained by LDA in prediction was 91.7 and 87.5% for E-nose and ATR-FTIR, respectively. Gas chromatography-mass spectrometry (GC-MS) results verified that there existed an obvious holistic difference in flavour characteristics between fresh squeezed and concentrated juices. These results demonstrated that both E-nose and FTIR might be used as rapid screening techniques for the detection of this type of juice adulteration.

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Islam, Md Khairul, Kevin Vinsen, Tomislav Sostaric, Lee Yong Lim, and Cornelia Locher. "Detection of syrup adulterants in manuka and jarrah honey using HPTLC-multivariate data analysis." PeerJ 9 (September 22, 2021): e12186. http://dx.doi.org/10.7717/peerj.12186.

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High-Performance Thin-Layer Chromatography (HPTLC) was used in a chemometric investigation of the derived sugar and organic extract profiles of two different honeys (Manuka and Jarrah) with adulterants. Each honey was adulterated with one of six different sugar syrups (rice, corn, golden, treacle, glucose and maple syrups) in five different concentrations (10%, 20%, 30%, 40%, and 50% w/w). The chemometric analysis was based on the combined sugar and organic extract profiles’ datasets. To obtain the respective sugar profiles, the amount of fructose, glucose, maltose, and sucrose present in the honey was quantified and for the organic extract profile, the honey’s dichloromethane extract was investigated at 254 and 366 nm, as well as at T (Transmittance) white light and at 366 nm after derivatisation. The presence of sugar syrups, even at a concentration of only 10%, significantly influenced the honeys’ sugar and organic extract profiles and multivariate data analysis of these profiles, in particular cluster analysis (CA), principal component analysis (PCA), principal component regression (PCR), partial least-squares regression (PLSR) and Machine Learning using an artificial neural network (ANN), were able to detect post-harvest syrup adulterations and to discriminate between neat and adulterated honey samples. Cluster analysis and principal component analysis, for instance, could easily differentiate between neat and adulterated honeys through the use of CA or PCA plots. In particular the presence of excess amounts of maltose and sucrose allowed for the detection of sugar adulterants and adulterated honeys by HPTLC-multivariate data analysis. Partial least-squares regression and artificial neural networking were employed, with augmented datasets, to develop optimal calibration for the adulterated honeys and to predict those accurately, which suggests a good predictive capacity of the developed model.

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Journal articles on the topic 'Adulterated samples'

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