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.

Ability of visible imaging and machine learning in detection of chickpea flour adulterant in original cinnamon and pepper powders.

Adulteration detection in plant-based medicinal powders is necessary to provide high quality products due to the economic and health importance of them. According to advantages of imaging technology as non-destructive tool with low cost and time, the present research aims to evaluate the ability of the visible imaging combined with machine learning for distinguish original products and the adulterated samples with different levels of chickpea flour. The original products were black pepper, red pepper, and cinnamon, the adulterant was chick pea, and the adulteration levels were 0, 5, 15, 30, and 50 %. The results showed that the accuracies of the classifier based on the artificial neural networks method for classification of black pepper, red pepper, and cinnamon were 97.8, 98.9, and 95.6 %, respectively. The results for support vector machine with one-to-one strategy were 93.33, 97.78 and 92.22 %, respectively. Visible imaging combined with machine learning are reliable technologies to detect adulteration in plant-based medicinal powders so that can be applied to develop industrial systems and improving performance and reducing operation costs.

Comprehensive lipidomics study of basa catfish and sole fish using ultra-performance liquid chromatography Q-extractive orbitrap mass spectrometry for fish authenticity.

The authenticity of fish products has become a widespread issue in markets due to substitution and false labeling. Lipidomics combined with chemometrics enables the fraudulence identification of food through the analysis of a large amount of data. This study utilized ultra-high-performance liquid chromatography (UHPLC)-QE Orbitrap MS technology to comprehensively analyze the lipidomics of commercially available basa catfish and sole fish. In positive and negative ion modes, a total of 779 lipid molecules from 21 lipid subclasses were detected, with phospholipid molecules being the most abundant, followed by glycerides molecules. Significant differences in the lipidome fingerprinting between the two fish species were observed. A total of 165 lipid molecules were screened out as discriminative features to distinguish between basa catfish and sole fish, such as TAG(16:0/16:0/18:1), PC(14:0/22:3), and TAG(16:1/18:1/18:1), etc. This study could provide valuable insights into authenticating aquatic products through comprehensive lipidomics analysis, contributing to quality control and consumer protection in the food industry.

Screening the quality of legal and illegal dietary supplements by LC-MS/MS.

Dietary supplements are widely consumed. However, the lack of mandatory testing results in limited data on their quality, particularly in Eastern Europe. In this study, 21 legally registered and 9 illegal supplements, seized from an underground facility by the Polish Police, were examined. Contaminants were screened by utilising high-performance liquid chromatography coupled with untargeted mass spectrometry. The analysis identified 32 contaminants in the 30 dietary supplements examined. Untargeted analysis revealed a concerning issue: the intentional adulteration of both legal and illegal supplements with pharmacologically active substances that are prohibited in this category of products. This study indicated that many dietary supplements are of low quality due to deliberate adulteration or inadequate manufacturing conditions. The presence of unregistered or unapproved substances in these supplements poses serious health risks. Strong legal regulations are essential to address this issue effectively.

Digital identification and adulteration analysis of Pulsatilla Radix and Pulsatilla Cernua based on "digital identity" and UHPLC-QTOF-MSE.

Under the background of digitalization of traditional Chinese medicine (TCM), to realize the quick identification and adulteration analysis of Pulsatilla Radix (PR), adhering to digital conviction, this study conducted UHPLC-QTOF-MSE analysis on PR and its adulterant-Pulsatilla Cernua (PC) from different batches and based on digital conversion, the shared ions were extracted from different batches of PR and PC as their "ions representation", respectively. Further, the data set of unique ions of PR relative to PC and PC relative to PR were screened out as the "digital identities" of PR and PC respectively. Further, above the "digital identities" of PR and PC were used as the benchmarks for matching and identifying to feedback give a matching credibility (MC). The results showed that based on the "digital identities" of PR and PC, the digital identification of two herbal samples can be realized efficiently and accurately at the individual level with the MC≥70.00 %, even if 5 % of PC in the mixed samples can still be identified efficiently and accurately. The study is of great practical significance for improving the identification efficiency of PR and PC, cracking down on adulterated and counterfeit drugs, and strengthening the quality control of PR. In addition, it has important reference significance for developing non-targeted digital identification of herbal medicines at the individual level based on UHPLC-QTOF-MSE and the "digital identity", which was beneficial to the construction of digital Chinese medicine and digital quality control.

Convenient analysis of sartan adulteration in herbal oral liquids using cotton fiber-supported liquid extraction and with high-performance liquid chromatography-fluorescence detection.

This research introduces a novel approach for detecting sartan antihypertensive drug adulteration in herbal oral liquids using cotton fiber-supported liquid extraction (CF-SLE) combined with high-performance liquid chromatography-fluorescence detection (HPLC-FLD). Optimal extraction parameters were determined through systematic method development, establishing a sample solution with a pH of 3.0, using 200 mg of cotton fiber, ethyl acetate as the extraction solvent, and a solvent volume of 4 mL. These conditions demonstrated robust extraction efficiency and were further validated for precision and accuracy, with intra- and inter-day relative standard deviations consistently below 7.5 % and relative recoveries ranging from 88.5 % to 106.1 %. The method exhibited excellent linearity for sartans, with R² values greater than 0.993 across a concentration range of 10-2000 ng/mL. Detection limits were effectively established in the range of 2.6-3.1 ng/mL, indicating that the method's sensitivity is adequate for the intended screening purposes. This validated method was then applied to real sample analysis, confirming its potential for routine use in detecting illegal additives within complex herbal matrices, thereby ensuring consumer safety and supporting regulatory compliance.

Monitoring illicit pentobarbital availability in the United States: A National Drug Early Warning System briefing.

BACKGROUND: Pentobarbital is a Schedule II/III short-acting barbiturate with limited medical use in humans. Veterinary professionals use pentobarbital to euthanize dogs, cats, and other companion animals. Pentobarbital is also utilized in capital punishment and small amounts are illegally shipped or diverted to assist in suicides. However, five kilograms of pentobarbital smuggled in from Mexico was recently seized by an organized crime drug enforcement task force (along with fentanyl, heroin, and cocaine), which may suggest a shift in illicit supply. We investigated potential indicators of illicit pentobarbital use or availability in the US to help determine whether this drug is becoming an emerging public health concern. METHODS: The National Drug Early Warning System requested information on pentobarbital from its sentinel surveillance sites and collaborators and conducted a search of current literature. RESULTS: In early 2024, multiple batches of counterfeit pills (e.g., pressed as "M30s" to represent oxycodone) confiscated near the Southwest border tested positive for pentobarbital plus combinations of fentanyl, fentanyl analogs, and xylazine. Other indicators suggest pentobarbital is being smuggled in powder form and possibly sold as another drug such as heroin. One national drug analysis program detected pentobarbital in 217 drug submissions from 2020 to 2023, and there were at least 12 fatal exposures linked to use from 2020 to 2022. CONCLUSION: Continued monitoring of illicit use and availability is needed as pentobarbital may continue to appear on the illicit market. Unknown exposure can occur if the drug is mixed into counterfeit pills or sold in powder form represented to be another drug.

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.

HPTLC sequentially coupled to UV and SERS: A cost-effective tool for confirmative identification and quantitation of sildenafil in falsified herbal products.

The detection of falsified drugs usually requires multi-disciplinary analysis for confirmative identification. Among hyphenated techniques with high specificity detection, thin-layer chromatography coupled with surface-enhanced Raman spectroscopy (TLC-SERS) is an efficient choice, especially for herbal products with diversified matrix. In this study, HPTLC was coupled to two detection techniques: UV absorption and Raman scattering with silver colloid enhancement for the analysis of sildenafil adulterated in herbal products. With this approach, orthogonal UV and SERS spectral data was collected, so that confirmative results could be obtained within a single TLC analysis. How this approach helped to reduce chances of false positive or false negative results was also discussed. The HPTLC sequentially coupled to UV and SERS (HPTLC-UV-SERS) method was developed and validated parallelly on the UV and SERS signals. To improve the repeatability of the SERS signal, several analytical conditions were optimized, so that direct quantitation with TLC-SERS was feasible without chemometric data extrapolation. The determination was done with UV scanning at 304 nm for HPTLC and with SERS signal at 1580 cm-1 (excitation 633 nm). The TLC-SERS method had a detection limit of 1.65 ng/spot, 95 times lower than HPTLC method (157 ng/spot). The HPTLC-UV-SERS method was applied on 24 real herbal samples collected from the market, among which 3 real samples were positive to sildenafil, and quantitation results by UV and SERS were in consistency. Not only this method was proved feasible for practical applications, but the recommendations for TLC-SERS procedures could also be useful in TLC-SERS method development for other compounds.

Analytical approaches for the determination of adulterated animal fats and vegetable oils in food and non-food samples.

Edible oils and fats are crucial components of everyday cooking and the production of food products, but their purity has been a major issue for a long time. High-quality edible oils are contaminated with low- and cheap-quality edible oils to increase profits. The adulteration of edible oils and fats also produces many health risks. Detection of main and minor components can identify adulterations using various techniques, such as GC, HPLC, TLC, FTIR, NIR, NMR, direct mass spectrometry, PCR, E-Nose, and DSC. Each detection technique has its advantages and disadvantages. For example, chromatography offers high precision but requires extensive sample preparation, while spectroscopy is rapid and non-destructive but may lack resolution. Direct mass spectrometry is faster and simpler than chromatography-based MS, eliminating complex preparation steps. DNA-based oil authentication is effective but hindered by laborious extraction processes. E-Nose only distinguishes odours, and DSC directly studies lipid thermal properties without derivatization or solvents. Mass spectrometry-based techniques, particularly GC-MS is found to be highly effective for detecting adulteration of oils and fats in food and non-food samples. This review summarizes the benefits and drawbacks of these analytical approaches and their use in conjunction with chemometric tools to detect the adulteration of animal fats and vegetable oils. This combination provides a powerful technique with enormous chemotaxonomic potential that includes the detection of adulterations, quality assurance, assessment of geographical origin, assessment of the process, and classification of the product in complex matrices from food and non-food samples.

Detection Limits of Insect Fragments in Spiked Whole Wheat Flour Using Multiplex Polymerase Chain Reaction (PCR).

The need for a sensitive molecular method to detect specific species of insect contaminants in food products remains a significant challenge in the food industry. This study evaluated the detection limit of a multiplex end-point PCR assay for detecting insects in food. The assay amplifies two fragments of the cytochrome oxidase subunit I gene (COI-Fa and COI-Fb) and one fragment of the protein-coding wingless (wg) gene found in insects. Five insect species, comprising three vectors of foodborne pathogens (the housefly, Musca domestica, the American cockroach, Periplaneta americana, and the pharaoh ant, Monomorium pharaonis) and two storage insect pests (the red flour beetle, Tribolium castaneum and the Indian meal moth, Plodia interpunctella), were spiked separately and in combination at levels of 1, 0.1, 0.01, and 0.001% in whole wheat flour. At spike levels greater than 0.01%, amplicon bands of expected sizes were seen in 100% of samples containing fragments from distinct insect species. At least 25% of spiked samples at the lowest spike level had amplicon bands, except for samples spiked with M. domestica. Results showed an 18.9% probability (with 11.3% and 30% lower and upper confidence limits, respectively) of detecting insect fragments at the lowest spike level (0.001%, corresponding to 3-22 fragments), which is far below the FDA's regulatory level of less than 75 fragments per 50 g of wheat flour. The intensity of amplicon bands in the gel images was higher at higher spike levels. However, this method is not quantitative enough to extrapolate the intensity of the amplicon bands to the number of insect fragments present in a sample. This multiplex assay was also evaluated in a variety of market food samples derived from plants and animals, showing its potential use in various food types. Overall, the sensitivity and specificity of this molecular approach suggest that it could be used in the future as a screening tool for detecting insect contaminants in food.

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.

A method validation of determination of melamine in marketed infant formula and milk powder of Iran.

Melamine is a recognized food contaminant that can arise incidentally or intentionally in specific categories of food. This study aimed to validate the melamine detection and quantification in infant formula and milk powders and also analyzed 40 samples consist of infant formula and milk powders from commercially available food products, from different geographic regions of Iran. The approximate content of melamine in samples was measured by High-performance liquid chromatography-ultra violet (HPLC-UV) system. A calibration curve (R2 = 0.9925) was established for detection of melamine in the range of 0.1-1.2 µg mL-1. Limit of quantification and limit of detection were 1 µg mL-1 and 3 µg mL-1, respectively. The presence of melamine in infant formula and milk powdered was investigated and it was observed that the amount of melamine in samples of infant formulas and milk powders was 0.001-0.095 mg kg-1 and 0.001-0.004 mg kg-1, respectively. These values were found to be within the prescribed limits by the European Union and Codex Alimentarius Commission legislation. It is important to note that the consumption of these dairy products, which contain low content of melamine, does not pose any significant threat to consumer health. Furthermore, the results of the risk assessment confirmed this issue.

Unveiling the mix-up: investigating species and unauthorized tissues in beef-based meat products.

Customers are very concerned about high-quality products whose provenance is healthy. The identification of meat authenticity is a subject of growing concern for a variety of reasons, including religious, economic, legal, and public health. Between March and April of 2023, 150 distinct marketable beef product samples from various retailers in El-Fayoum, Egypt, were gathered. There were 30 samples of each of the following: luncheon, kofta, sausage, burger, and minced meat. Every sample underwent a histological investigation as well as subjected to a standard polymerase chain reaction (PCR) analysis to identify meat types that had not been stated by Egyptian regulations. According to the obtained data, the meat products under scrutiny contained a variety of unauthorized tissues which do not match Egyptian regulations. Furthermore, the PCR results indicated that the chicken, camels, donkeys, and pigs derivatives were detected in 60%, 30%, 16%, and 8% of examined samples, respectively. In conclusion, besides displaying a variety of illegal tissues, the majority of the meat items under examination were tainted with flesh from many species. As a result, it is crucial to regularly inspect these products before they are put on the market to ensure that they comply with the law and don't mislead customers Furthermore, it is advisable for authorities to implement rigorous oversight of food manufacturing facilities to ensure the production of safe and wholesome meat.

Investigation of fraud in the production of butter: a forensic case study of criminal association.

Butter is among the most popular and commercially valuable dairy products. Its high commercial value makes it a major target for adulteration, which aims to reduce production costs by using lower-quality fats and oils from other sources. The annual global market is around USD 30 billion (2023), expected to reach USD 36 billion in 2028, which also justifies the enormous interest in adulteration. In this work, a confirmed case of butter adulteration was studied by Nuclear Magnetic Resonance (NMR) and Stable Carbon Isotopic Ratio Analysis (SCIRA) techniques, employed to detect the inclusion in butter production of vegetable oils, such as soybean and palm oils. A total of 21 samples seized by the Brazilian Federal Police were analysed by NMR and SCIR, and compared to original butter obtained from commercial sources. The composition of all the seized samples was a mixture of butter (dairy fat of animal origin) with fat of vegetable origin (soybean and palm oil) and did not contain milk as a major component. While NMR was an unequivocal choice to discriminate the chemical composition of food samples, identifying the short-chain saturated fatty acids present in milk fat, including the butyryl alkyl chain, SCIRA was able to discriminate the origin of fat present in the butter samples as C3 sources, such as palm vegetable oils.

Toothpick DNA extraction combined with handheld LAMP microfluidic platform for simple and rapid meat authentication.

Adulteration of meat is a global issue, necessitating rapid, inexpensive, and simple on-site testing methods. Therefore, the present study aimed to develop a one-minute toothpick-based DNA extraction method, a handheld microfluidic chip, and a smartphone-controlled portable analyzer for detecting multiple meat adulterations. A toothpick was inserted into the meat to promote DNA release and adsorption. Furthermore, a handheld microfluidic chip was designed for DNA elution on toothpicks and fluid distribution. Finally, a smartphone-actuated portable analyzer was developed to function as a heater, signal detector, and result reader. The portable device comprises a microcontroller, a fluorescence detection module, a step scanning unit, and a heating module. The proposed device is portable, and the app is user-friendly. This simple design, easy operation, and fast-response system could rapidly detect as little as 1% of simulated adulterated samples (following UK standards) within 40 min at a cost of less than USD 1 per test.

Application of machine learning approach on halal meat authentication principle, challenges, and prospects: A review.

Meat is a source of essential amino acids that are necessary for human growth and development, meat can come from dead, alive, Halal, or non-Halal animal species which are intentionally or economically (adulteration) sold to consumers. Sharia has prohibited the consumption of pork by Muslims. Because of the activities of adulterators in recent times, consumers are aware of what they eat. In the past, several methods were employed for the authentication of Halal meat, but numerous drawbacks are attached to this method such as lack of flexibility, limited application, time,consumption and low level of accuracy and sensitivity. Machine Learning (ML) is the concept of learning through the development and application of algorithms from given data and making predictions or decisions without being explicitly programmed. The techniques compared with traditional methods in Halal meat authentication are fast, flexible, scaled, automated, less expensive, high accuracy and sensitivity. Some of the ML approaches used in Halal meat authentication have proven a high percentage of accuracy in meat authenticity while other approaches show no evidence of Halal meat authentication for now. The paper critically highlighted some of the principles, challenges, successes, and prospects of ML approaches in the authentication of Halal meat.

Rapid detection and quantification of melamine, urea, sucrose, water, and milk powder adulteration in pasteurized milk using Fourier transform infrared (FTIR) spectroscopy coupled with modern statistical machine learning algorithms.

There is an evident requirement for a rapid, efficient, and simple method to screen the authenticity of milk products in the market. Fourier transform infrared (FTIR) spectroscopy stands out as a promising solution. This work employed FTIR spectroscopy and modern statistical machine learning algorithms for the identification and quantification of pasteurized milk adulteration. Comparative results demonstrate modern statistical machine learning algorithms will improve the ability of FTIR spectroscopy to predict milk adulteration compared to partial least square (PLS). To discern the types of substances utilized in milk adulteration, a top-performing multiclassification model was established using multi-layer perceptron (MLP) algorithm, delivering an impressive prediction accuracy of 97.4 %. For quantification purposes, bayesian regularized neural networks (BRNN) provided the best results for the determination of both melamine, urea and milk powder adulteration, while extreme gradient boosting (XGB) and projection pursuit regression (PPR) gave better results in predicting sucrose and water adulteration levels, respectively. The regression models provided suitable predictive accuracy with the ratio of performance to deviation (RPD) values higher than 3. The proposed methodology proved to be a cost-effective and fast tool for screening the authenticity of pasteurized milk in the market.

Time-of-Flight Secondary Ion Mass Spectrometry Coupled with Unsupervised Methods for Advanced Saffron Authenticity Screening.

Saffron, renowned for its aroma and flavor, is susceptible to adulteration due to its high value and demand. Current detection methods, including ISO standards, often fail to identify specific adulterants such as safflower or turmeric up to 20% (w/w). Therefore, the quest continues for robust screening methods using advanced techniques to tackle this persistent challenge of safeguarding saffron quality and authenticity. Advanced techniques such as time-of-flight secondary ion mass spectrometry (TOF-SIMS), with its molecular specificity and high sensitivity, offer promising solutions. Samples of pure saffron and saffron adulterated with safflower and turmeric at three inclusion levels (5%, 10%, and 20%) were analyzed without prior treatment. Spectral analysis revealed distinct signatures for pure saffron, safflower, and turmeric. Through principal component analysis (PCA), TOF-SIMS effectively discriminated between pure saffron and saffron adulterated with turmeric and safflower at different inclusion levels. The variation between the groups is attributed to the characteristic peaks of safflower and the amino group peaks and mineral peaks of saffron. Additionally, a study was conducted to demonstrate that semi-quantification of the level of safflower inclusion can be achieved from the normalized values of its characteristic peaks in the saffron matrix.

Organic-Acid-Sensitive Visual Sensor Array Based on Fenton Reagent-Phenol/Aniline for the Rapid Species and Adulteration Assessment of Baijiu.

Baijiu is an ancient, distilled spirit with a complicated brewing process, unique taste, and rich trace components. These trace components play a decisive role in the aroma, taste, and especially the quality of baijiu. In this paper, the redox reaction between the Fenton reagent and four reducing agents, including o-phenylenediamine (OPD), p-phenylenediamine (PPD), 4-aminophenol (PAP), and 2-aminophenol (OAP), was adopted to construct a four-channel visual sensor array for the rapid detection of nine kinds of common organic acids in baijiu and the identification of baijiu and its adulteration. By exploiting the color-changing fingerprint response brought by organic acids, each organic acid could be analyzed accurately when combined with an optimized variable-weighted least-squares support vector machine based on a particle swarm optimization (PSO-VWLS-SVM) model. What is more, this novel sensor also could achieve accurate semi-quantitative analysis of the mixed organic acid samples via partial least squares discriminant analysis (PLSDA). Most importantly, the sensor array could be further used for the identification of baijiu with different species through the PLSDA model and the adulteration assessment with the one-class partial least squares (OCPLS) model simultaneously.