Rapid and accurate detection of cinnamon oil adulteration in perilla leaf oil using atmospheric solids analysis probe-mass spectrometry.

Perilla leaf oil (PLO) is a global premium vegetable oil with abundant nutrients and substantial economic value, rendering it susceptible to potential adulteration by unscrupulous entrepreneurs. The addition of cinnamon oil (CO) is one of the main adulteration avenues for illegal PLOs. In this study, new and real-time ambient mass spectrometric methods were developed to detect CO adulteration in PLO. First, atmospheric solids analysis probe tandem mass spectrometry combined with principal component analysis and principal component analysis-linear discriminant analysis was employed to differentiate between authentic and adulterated PLO. Then, a spectral library was established for the instantaneous matching of cinnamaldehyde in the samples. Finally, the results were verified using the SRM mode of ASAP-MS/MS. Within 3 min, the three methods successfully identified CO adulteration in PLO at concentrations as low as 5% v/v with 100% accuracy. The proposed strategy was successfully applied to the fraud detection of CO in PLO.

Quantitative predictions of protein and total flavonoids content in Tartary and common buckwheat using near-infrared spectroscopy and chemometrics.

A rapid method was developed for determining the total flavonoid and protein content in Tartary buckwheat by employing near-infrared spectroscopy (NIRS) and various machine learning algorithms, including partial least squares regression (PLSR), support vector regression (SVR), and backpropagation neural network (BPNN). The RAW-SPA-CV-SVR model exhibited superior predictive accuracy for both Tartary and common buckwheat, with a high coefficient of determination (R2p = 0.9811) and a root mean squared error of prediction (RMSEP = 0.1071) for flavonoids, outperforming both PLSR and BPNN models. Additionally, the MMN-SPA-PSO-SVR model demonstrated exceptional performance in predicting protein content (R2p = 0.9247, RMSEP = 0.3906), enhancing the effectiveness of the MMN preprocessing technique for preserving the original data distribution. These findings indicate that the proposed methodology could efficiently assess buckwheat adulteration analysis. It can also provide new insights for the development of a promising method for quantifying food adulteration and controlling food quality.

Identification and quantification of goat milk adulteration using mid-infrared spectroscopy and chemometrics.

The fraudulent adulteration of goat milk with cheaper and more available milk of other species such as cow milk is occurrence. The aims of the present study were to investigate the effect of goat milk adulteration with cow milk on the mid-infrared (MIR) spectrum and further evaluate the potential of MIR spectroscopy to identify and quantify the goat milk adulterated. Goat milk was adulterated with cow milk at 5 different levels including 10%, 20%, 30%, 40%, and 50%. Statistical analysis showed that the adulteration had significant effect on the majority of the spectral wavenumbers. Then, the spectrum was preprocessed with standard normal variate (SNV), multiplicative scattering correction (MSC), Savitzky-Golay smoothing (SG), SG plus SNV, and SG plus MSC, and partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSR) were used to establish classification and regression models, respectively. PLS-DA models obtained good results with all the sensitivity and specificity over 0.96 in the cross-validation set. Regression models using raw spectrum obtained the best result, with coefficient of determination (R2), root mean square error (RMSE), and the ratio of performance to deviation (RPD) of cross-validation set were 0.98, 2.01, and 8.49, respectively. The results preliminarily indicate that the MIR spectroscopy is an effective technique to detect the goat milk adulteration with cow milk. In future, milk samples from different origins and different breeds of goats and cows should be collected, and more sophisticated adulteration at low levels should be further studied to explore the potential and effectiveness of milk mid-infrared spectroscopy and chemometrics.

A review of the emerging technologies and systems to mitigate food fraud in supply chains.

Food fraud has serious consequences including reputational damage to businesses, health and safety risks and lack of consumer confidence. New technologies targeted at ensuring food authenticity has emerged and however, the penetration and diffusion of sophisticated analytical technologies are faced with challenges in the industry. This review is focused on investigating the emerging technologies and strategies for mitigating food fraud and exploring the key barriers to their application. The review discusses three key areas of focus for food fraud mitigation that include systematic approaches, analytical techniques and package-level anti-counterfeiting technologies. A notable gap exists in converting laboratory based sophisticated technologies and tools in high-paced, live industrial applications. New frontiers such as handheld laser-induced breakdown spectroscopy (LIBS) and smart-phone spectroscopy have emerged for rapid food authentication. Multifunctional devices with hyphenating sensing mechanisms together with deep learning strategies to compare food fingerprints can be a great leap forward in the industry. Combination of different technologies such as spectroscopy and separation techniques will also be superior where quantification of adulterants are preferred. With the advancement of automation these technologies will be able to be deployed as in-line scanning devices in industrial settings to detect food fraud across multiple points in food supply chains.

Near-infrared spectroscopy combined with support vector machine for the identification of Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) adulteration using wavelength selection algorithms.

The frequent occurrence of adulterating Tartary buckwheat powder with crop flours in the market necessitates an urgent need for a simple analysis method to ensure the quality of Tartary buckwheat. This study employed near-infrared spectroscopy (NIRS) for the collection of spectral data from Tartary buckwheat samples adulterated with whole wheat, oat, soybean, barley, and sorghum flours. The competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were deployed to identify informative wavelengths. By integrating support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA), we constructed qualitative models to discern Tartary buckwheat adulteration. The PLS-DA model exhibited prediction accuracies between 89.78 % and 94.22 %, while the mean-centering (MC)-PLS-DA model showcased impressive predictive accuracy of 93.33 %. Notably, the feature-based Autoscales-CARS-CV-SVM model achieved more excellent identification accuracy. These findings exhibit the excellent potential of chemometrics as a powerful tool for detecting food product adulteration.

Molecular quantification of fritillariae cirrhosae bulbus and its adulterants.

BACKGROUND: Fritillariae Cirrhosae Bulbus (FCB) is frequently adulterated with its closely related species due to personal or non-man made factors, leading to alterations in the composition of its constituents and compromising the efficacy of its products. METHODS: The specific single nucleotide polymorphisms (SNPs) were screened by comparing candidate barcodes of Fritillaria and verified by amplification and sequencing. Herb molecular quantification (Herb-Q) was established by detecting specific SNPs, and the methodological validation was performed. Quantitative standard curves were established for FCB mixed with each adulterated species, and the quantitative validity of this method was verified based on external standard substance. In addition, eight commercial Shedan Chuanbei capsules (SDCBs) randomly selected were detected. RESULTS: FCB and its five adulterants can be distinguished based on the ITS 341 site. The methodological investigation of Herb-Q shows optimal accuracy, and repeatability, which exhibited good linearity with an R2 of 0.9997 (> 0.99). An average bias in quantitative validity was 5.973% between the measured and actual values. Four of eight commercial SDCBs were adulterated with F. ussuriensis or F. thunbergia with adulteration levels ranging from 9 to 15% of the total weight. CONCLUSION: This study confirmed that Herb-Q can quantitatively detect both the mixed herbs and Chinese patent medicines (CPMs) containing FCB with high reproducibility and accuracy. This method provides technical support for market regulation and helps safeguard patient rights.

Comparison of strength and adulteration between illicit drugs obtained from cryptomarkets versus off-line.

BACKGROUND AND AIMS: Drugs sold on cryptomarkets are thought to have lower levels of adulteration and higher strength compared with those sourced off-line. The present study aimed to determine whether cryptomarket and off-line-sourced 3,4-methylenedioxy-N-methamphetamine (MDMA), cocaine, amphetamine, methamphetamine and lysergic acid diethylamide (LSD) differed in adulteration and strength. DESIGN AND SETTING: A between-groups design was used to compare cryptomarket versus off-line-sourced drugs. Regression analyses controlling for year and service were conducted. Drug-checking services were conducted in Spain (Energy Control) and the Netherlands (Drugs Information and Monitoring System). CASES: The cases comprised drug samples that underwent drug checking between 2016 and 2021 and were expected to contain MDMA (tablets; n = 36 065; powder: n = 6179), cocaine (n = 11 419), amphetamine (n = 6823), methamphetamine (n = 293) and LSD (n = 1817). MEASUREMENTS: Drugs were measured for (1) matching the advertised substance (i.e. containing any amount of the expected substance); (2) strength; (3) presence of adulteration; and (4) number of adulterants. FINDINGS: The expected drug was more likely to be identified when sourced from cryptomarkets versus off-line for MDMA tablets [adjusted odds ratio (AOR) = 2.10, 95% confidence interval (CI) = 1.28-3.43], MDMA powder (AOR = 2.64, CI = 1.55-4.51), cocaine (AOR = 3.65, CI = 1.98-6.71) and LSD (AOR = 1.75, CI = 1.13-2.72). Cryptomarket-sourced MDMA powder (ß = 0.03, P = 0.012), cocaine (ß = 0.08, P < 0.001) and methamphetamine (ß = 0.15, P = 0.028) were statistically significantly higher in strength than substances from off-line sources. Conversely, MDMA tablets (ß = -0.01, P = 0.043) and amphetamine (ß = -0.07, P < 0.001) from cryptomarkets were statistically significantly lower in strength than from off-line sources. MDMA powder (AOR = 0.53, CI = 0.33-0.86) and cocaine (AOR = 0.66, CI = 0.55-0.79) were statistically significantly less likely to be adulterated if sourced from cryptomarkets. However, amphetamine (AOR = 1.54, CI = 1.25-1.90) and LSD (AOR = 1.31, CI = 1.00-1.71) were found to be more likely to be adulterated when purchased from cryptomarkets. Cocaine from cryptomarkets exhibited fewer adulterants (incidence rate ratio = 0.71, CI = 0.60-0.85). CONCLUSION: The relationship between on-line drug market-places and substance quality varies depending on both the specific substance and the dynamics of the cryptomarket.

Photonic Dipstick Immunosensor to Detect Adulteration of Ewe, Goat, and Donkey Milk with Cow Milk through Bovine κ-Casein Detection.

The quality and authenticity of milk are of paramount importance. Cow milk is more allergenic and less nutritious than ewe, goat, or donkey milk, which are often adulterated with cow milk due to their seasonal availability and higher prices. In this work, a silicon photonic dipstick sensor accommodating two U-shaped Mach-Zehnder Interferometers (MZIs) was employed for the label-free detection of the adulteration of ewe, goat, and donkey milk with cow milk. One of the two MZIs of the chip was modified with bovine κ-casein, while the other was modified with bovine serum albumin to serve as a blank. All assay steps were performed by immersion of the chip side where the MZIs are positioned into the reagent solutions, leading to a photonic dipstick immunosensor. Thus, the chip was first immersed in a mixture of milk with anti-bovine κ-casein antibody and then in a secondary antibody solution for signal enhancement. A limit of detection of 0.05% v/v cow milk in ewe, goat, or donkey milk was achieved in 12 min using a 50-times diluted sample. This fast, sensitive, and simple assay, without the need for sample pre-processing, microfluidics, or pumps, makes the developed sensor ideal for the detection of milk adulteration at the point of need.

Quality Assessment of Tomato Paste Products on the Ghanaian Market: An Insight Into Their Possible Adulteration.

Tomato paste is the most consumed tomato product on the Ghanaian market, the majority of which are imported into the country. This food product is easily adulterated, and thus, routine quality checks are necessary. Therefore, the current study is aimed at assessing the quality of eight tomato paste products on the Ghanaian market and checking for the presence of starch and artificial colourant erythrosine as possible adulterants. Routine quality metrics such as the pH, titratable acidity, total solids, and total soluble solids were assessed using standard methods. An HPLC method was employed to detect the presence of the colourant erythrosine, whereas starch content was determined by an enzymatic method using α-amylase and then amyloglucosidase. Fifty percent of the products did not qualify to be called tomato paste based on total solid estimation. All the sampled products contained some amount of starch, with three having more than 10 g/100 g of this thickener. Additionally, the banned colourant erythrosine was detected in two of the products. All other parameters were consistent with regulatory standards. The present study has shown that some tomato paste products on the Ghanaian market contain additives that are not permitted under any circumstance and fall short of regulatory standards.

Statistical machine learning techniques applied to NIR spectral data for rapid detection of sudan dye-I in turmeric powders with optimized pre-processing and wavelength selection.

Machine learning techniques were applied systematically to the spectral data of near-infrared (NIR) spectroscopy to find out the sudan dye I adulterants in turmeric powders. Turmeric powder is one of the most commonly used spice and a simple target for adulteration. Pure turmeric powder was prepared at the laboratory and spiked with sudan dye I adulterants. The spectral data of these adulterated mixtures were obtained by NIR spectrometer and investigated accordingly. The concentrations of the adulterants were 1%, 5%, 10%, 15%, 20%, 25%, 30% (w/w) respectively. Exploratory data analysis was done for the visualization of the adulterant classes by principal component analysis (PCA). Optimization of the pre-processing and wavelength selection was done by cross-validation techniques using a partial least squares regression (PLSR) model. For quantitative analysis four different regression techniques were applied namely ensemble tree regression (ENTR), support vector regression (SVR), principal component regression (PCR), and PLSR, and a comparative analysis was done. The best method was found to be PLSR. The accuracy of the PLSR analysis was determined with the coefficients of determination (R2) of greater than 0.97 and with root mean square error (RMSE) of less than 0.93 respectively. For the verification of the robustness of the model, the Figure of merit (FOM) of the model was derived with the help of the Net analyte signal (NAS) theory. The current study established that the NIR spectroscopy can be applied to detect and quantify the amount of sudan dye I adulterants added to the turmeric powders with satisfactory accuracy. Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-024-05971-9.

Authentication of edible herbal materials and food products using mass spectrometry based metabolites and inorganic constituents.

Medicinal food homologous (MFH) substances not only provide nutrition but also serve as a traditional means to overcome many health issues. Authentication of these products verifies their efficacity and assures consumers of a genuine product. In this review paper, we focus the determination of MFH authenticity including geographical identification and adulteration detection using mass spectrometry (liquid and gas chromatography) based metabolites and inorganic constituents (muti-elements and stable isotopes). The application of these techniques to determine product identification characteristics combined with chemometrics are discussed, along with the limitations of these techniques. Multi-elements, stable isotopes, and metabolite analysis are shown to provide an effective combination of techniques to resolve the origin of various MFH products. Most organic compounds from MFH products are identified using chromatographic separation techniques (HPLC, GC) combined with different detection methods. Chemometric analysis of organic and inorganic fingerprints offers a robust method to detect and classify mislabeled and suspected fraudulent samples of different MFH products.

Application of handheld near infrared spectrometer in quality control of traditional Chinese medicine: Rapid screening and quantitative analysis of Lonicerae Japonicae Flos adulteration.

Traditional Chinese medicine (TCM) prescription, with its intricate formulations and nuanced compositions, is a cornerstone of holistic health practices. However, the expansion of the TCM market has led to a surge in herb adulteration, which significantly undermines the quality and safety of these medicinal products. A case in point is Lonicerae Japonicae Flos (LJF), a widely utilized herb for treating colds, which has been adulterated by the cheaper Lonicerae Flos (LF), thereby affecting its therapeutic effectiveness. Therefore, a method utilizing handheld NIR spectroscopy combined with chemometrics has been developed to provide a portable, real-time solution for the rapid and accurate detection and quantification of adulterants in TCM. By collecting NIR spectra from LJF, LF and adulterated samples (AS), we've established a spectral database enabling deep insights into the correlation between spectral features and sample compositions. Resultantly, a classification model with a 99.58 % cross-validation accuracy, reaching 100 % for test set, effectively identified adulterants. And further spectral similarity analysis and classification identification of samples with different adulteration ratios were carried out. The cross-validation accuracy under the optimal model reached 98.38 %, and the test set accuracy was 99.20 %. In addition, the study extends to quantifying different levels of adulteration, employing 20 standard adulterated samples across a 0-100 % adulteration gradient. Via data preprocessing, feature extraction, and regression techniques, the full concentration prediction models were developed, later refined by segmenting samples based on high and low adulteration ratios. Under the SGFD_CARS_PLS (Savitzky-Golay smoothing with the first derivative_competitive adaptive reweighted sampling_partial least squares) model, exceptional performance was achieved, with a R2p of 0.983, RMSEp of 3.402, and RPDp of 7.757 for the homemade adulterated prediction set. In conclusion, the application of this technology not only improves the efficiency and accuracy of screening, but also has the advantages of low cost, easy operation and rapid results compared with traditional chemical analysis methods. It effectively protects the safety of drugs for consumers, maintains the integrity of the TCM market, and provides a strong technical support for the on-site rapid detection of TCM.

Applications of Capillary Electrophoresis for the Detection of Adulterants in Dietary Supplements.

In recent years, the consumption of dietary supplements, particularly those incorporating plant-based ingredients, has increased greatly, driven by the perception of their natural origins and purported minimal health risks. However, one significant safety concern revolves around the adulteration of dietary supplements, wherein unscrupulous manufacturers may illegally incorporate pharmaceutical substances or their analogs into these products to achieve increased efficiency and bolster sales. This review assesses the role of capillary electrophoresis (CE) in ensuring the quality control of dietary supplement products over the past two decades. This study provides an overview of various applications of CE in analyzing dietary supplements, outlining the typical attributes of natural product analysis using CE. These analyses demonstrate the broad versatility of CE, exemplified by its diverse applications and detection modes. Moreover, the review highlights the growing prominence of CE as a separation technique in quality control, by comparison with more conventional methods like high-performance liquid chromatography (HPLC). Through this exploration, the review elucidates the pivotal role of CE in upholding the integrity and safety of dietary supplements, in connection with a landscape of evolving regulatory challenges and consumer demands.

How does adulteration of wax foundation affect phenoloxidase and lysozyme activities as selected parameters of immunity in Apis mellifera?

Introduction: The adulteration of wax foundation is, for many reasons, a growing problem of modern beekeeping not only in Europe but also around the world. Wax foundation contaminated with stearin addition leads to a brood die-off, while paraffin addition negatively affects the strength of combs. It is tenable that such adulterated wax foundation reduces bees' immunity. The aim of the study was to determine the activities of two bee immune enzymes, lysozyme and phenoloxidase, in the haemolymph of worker bees which had emerged from combs with wax foundations contaminated with stearin or paraffin. Material and Methods: Combs built with stearin- or paraffin-adulterated wax (both adulterants at concentrations of 10%, 30% or 50%) or pure wax (0% adulterated) foundations were placed in the colonies, one for each adulterant and percentage. The workers were marked upon emergence from these combs and those bees were introduced into one strong colony per adulterant and percentage. Phenoloxidase and lysozyme activities were determined in the haemolymph of 1-, 7- and 14-day-old workers. Results: The higher the concentrations of stearin and paraffin in the wax foundation, the lower the phenoloxidase activities were. These activities increased with the bee age. In contrast, the trends in lysozymes were opposite. Paraffin seems to be less toxic than stearin. Conclusion: Adulteration of wax foundation with even a small amount of stearin or paraffin has negative effects on the functioning of the bee.

Authentication of Argentinean extra-virgin olive oils using three-way fluorescence and two-way near-infrared data fused with multi-block DD-SIMCA.

A trending problem of Extra Virgin Olive Oil (EVOO) adulteration is investigated using two analytical platforms, involving: (1) Near Infrared (NIR) spectroscopy, resulting in a two-way data set, and (2) Fluorescence Excitation-Emission Matrix (EEFM) spectroscopy, producing three-way data. The related instruments were employed to study genuine and adulterated samples. Each data set was first separately analyzed using the Data Driven-Soft Independent Modeling of Class Analogies (DD-SIMCA) method, based on Principal Component Analysis (for the two-way NIR data) and PARallel FACtor analysis (for the three-way EEFM data). The data sets were then processed together using the multi-block fusion method, based on the concept of Cumulative Analytical Signal (CAS). A comparison of the data processing methods in terms of sensitivity, specificity and selectivity showed the following order of excellence: NIR < EEFM < NIR + EEFM. This finding confirms the effectiveness of multi-block data fusion, which cumulatively improves the model performance.

FT-NIR combined with machine learning was used to rapidly detect the adulteration of pericarpium citri reticulatae (chenpi) and predict the adulteration concentration.

Pericarpium citri reticulatae (PCR) has been used as a food and spice for many years and is known for its rich nutritional content and unique aroma. However, price increases are often accompanied by adulteration. In this study, two kinds of adulterants (Orange peel-OP and Mandarin Rind-MR) were identified by chromaticity analysis, FT-NIR and machine learning algorithm, and the doping concentration was predicted quantitatively. The results show that colorimetric analysis cannot completely differentiate between PCR and adulterants. Using spectral preprocessing combined with machine learning algorithms, PCR and two adulterants were successfully distinguished, with classification accuracy reaching 99.30 % and 98.64 % respectively. After selecting characteristic wavelengths, the R2 P of the adulterated quantitative model is greater than 0.99. Generally, this study proposes to use FT-NIR to study the adulteration of PCR for the first time, which fills the technical gap in the adulteration research of PCR, and provides an important method to solve the increasingly serious adulteration problem of PCR.

Lead content in cinnamon and its health risk assessment for Ecuadorian consumers.

Lead (Pb) is a poisonous metal that affects organs and the nervous system. Its presence in spices such as cinnamon has been identified as a potential human exposure pathway. In late October 2023, a safety alert was issued in the United States regarding four children with elevated Pb levels in their blood after consuming apple-cinnamon fruit puree manufactured and exported by an Ecuadorian company. Thus, this study aimed to determine the Pb content in 61 ground and stick cinnamon samples, from different commercial brands and lots sold in Ecuador. Results showed that ground cinnamon samples had almost twice the level of Pb (0.80 ± 0.75 mg/kg) than stick samples (0.36 ± 0.28 mg/kg). Three ground samples had Pb content above the maximum level established by Ecuadorian and European Union regulations (2.0 mg/kg). A Kruskal-Wallis test showed significant differences in Pb content between ground and stick cinnamon (p < 0.05). The HQ values showed negligible non-carcinogenic effects for children and adults, even at the highest Pb content. However, the carcinogenic risk of ground cinnamon at maximum and mean Pb concentrations was found for the population. Our study highlights the deficiencies in current surveillance systems and the lack of effective national regulations for exposure to foodborne metals.

Safeguarding Public Health: Advanced Detection of Food Adulteration Using Nanoparticle-Based Sensors.

Food adulteration, whether intentional or accidental, poses a significant public health risk. Traditional detection methods often lack the precision required to detect subtle adulterants that can be harmful. Although chromatographic and spectrometric techniques are effective, their high cost and complexity have limited their widespread use. To explore and validate the application of nanoparticle-based sensors for enhancing the detection of food adulteration, focusing on their specificity, sensitivity, and practical utility in the development of resilient food safety systems. This study integrates forensic principles with advanced nanomaterials to create a robust detection framework. Techniques include the development of nanoparticle-based assays designed to improve the detection specificity and sensitivity. In addition, sensor-based technologies, including electronic noses and tongues, have been assessed for their capacity to mimic and enhance human sensory detection, offering objective and reliable results. The use of nanomaterials, including functionalized nanoparticles, has significantly improved the detection of trace amounts of adulterants. Nanoparticle-based sensors demonstrate superior performance in terms of speed, sensitivity, and selectivity compared with traditional methods. Moreover, the integration of these sensors into food safety protocols shows promise for real-time and onsite detection of adulteration. Nanoparticle-based sensors represent a cutting-edge approach for detecting food adulteration, and offer enhanced sensitivity, specificity, and scalability. By integrating forensic principles and nanotechnology, this framework advances the development of more resilient food-safety systems. Future research should focus on optimizing these technologies for widespread application and adapting them to address emerging adulteration threats.

Dataset of Raman spectroscopy responses for over-the-counter drugs in Paraguay, including acetylsalicylic acid, paracetamol, and ibuprofen.

This dataset includes spectra obtained through Raman spectroscopy of acetylsalicylic acid, paracetamol, and ibuprofen commercialized in San Lorenzo, Central Department of Paraguay. The pharmaceuticals were randomly purchased from pharmacies, official sales points, and street vendors, simulating purchases for self-consumption. These drugs were selected due to their high demand and consumption by the population, aiming to document and facilitate the identification of adulterations or alterations in their original structures caused by poor storage conditions. Additionally, this database will support multivariate studies for clustering using various techniques, both supervised and unsupervised, and will allow for signal processing and spectroscopic data handling.