Search Results (609)

Essential oils are natural products frequently subject to economically motivated adulteration with cheaper substances like vegetable oils, mineral oils, or organic solvents. This study developed and validated a rapid high-temperature gas chromatography with flame ionization detection (HTGC-FID) method for the simultaneous determination of high-boiling adulterants: triacylglycerides (vegetable oils) and medicinal white oil (mineral oil) in essential oils. The method utilizes on-column injection onto a DB-5 capillary column (30 m × 0.53 mm, 0.88 μm) with a temperature program from 60 to 380 °C and hydrogen carrier gas. Validation parameters demonstrated excellent linearity (R² = 0.9957–0.9978), high repeatability (content RSD < 3%), and sufficient sensitivity (LOQ of 0.03% for triacylglycerides, and 0.63% for medicinal white oil). The method was successfully applied to 20 commercial essential oils. While medicinal white oil was undetected, several samples contained triacylglycerides (up to 3.79%) and other adulterants (up to 52%). Significantly reduced response factors confirmed extensive adulteration in some products. The proposed HTGC-FID method represents a simple, cost-effective, and efficient tool for routine quality control, enabling direct quantification of high-boiling adulterants without tedious sample preparation. Full article

The demand for reliable, rapid, and low-cost tools for quality control analysis has driven the development and application of different instrumental approaches in the wine industry. Thus, this review aims to gather and discuss the most relevant analytical methodologies reported in the literature, with emphasis on spectroscopic, chromatographic, electroanalytical, and sensor-based techniques, including electronic noses and tongues, as well as the integration of these techniques with chemometric tools. The studied methods demonstrate, in varying levels of precision, the potential for determining chemical composition, detecting contaminants and adulterations, evaluating attributes related to sensory quality, and monitoring fermentation and aging processes. Advances in non-destructive methods with high analytical throughput are highlighted, as these approaches have gained relevance due to their applicability in routine analyses which is desired for process control. Despite the progress observed, challenges related to sensitivity, selectivity, matrix effects, and method standardization still persist, limiting their industrial implementation. Finally, this review identifies research gaps, therefore pointing to perspectives for the development of standardization routines for the different methodologies, and the integration of analytical methods in the decision-making framework of the winemaking industry. Full article

Olive oil adulteration with low-cost vegetable oils poses a serious food safety concern. This study proposes a Dynamic Task Priority Multi-Gate Mixture-of-Experts (DTP-MMoE) model based on Raman spectroscopy to simultaneously perform the qualitative discrimination of adulteration types and quantitative prediction of adulteration ratios. The model learns shared spectral representations through expert networks and task-specific gating mechanisms, while a dynamic task priority loss function adaptively balances optimization between the classification and regression tasks. Experimental results demonstrated that the DTP-MMoE model achieved a classification accuracy of 99.15% and a coefficient of determination (R²) of 0.99 for prediction, significantly outperforming conventional single-task and multi-task baselines. Ablation studies confirmed the critical contributions of the gating mechanism, expert network configuration, and dynamic weighting strategy. Furthermore, external validation on commercial blended oil samples not involved in training yielded a mean absolute error (MAE) of 0.317%, an RMSE of 0.459%, and a MAPE of 6.34%, demonstrating good generalization capability. The proposed method provides an efficient, non-destructive, and reliable analytical tool for rapid screening of olive oil authenticity, showing considerable promise for application in food quality control and regulatory practice. Full article

Hyperspectral imaging has emerged as a powerful tool for food quality assessment, yet most existing methods rely on supervised classification and require prior knowledge of adulterant categories. This study applies a non-targeted screening approach based on a deep spectral autoencoder to detect adulterants in Fritillaria. While autoencoder-based anomaly detection has been established in other hyperspectral domains, its application to congeneric species discrimination and exogenous adulterant screening in Fritillaria has not been systematically explored. A deep spectral autoencoder was constructed and trained exclusively on pure samples to learn the intrinsic spectral distribution of authentic materials. During inference, reconstruction error was used as an anomaly score, and samples deviating from the learned spectral manifold were identified as suspicious. Spectral data augmentation and band trimming were applied to enhance model robustness, while the anomaly threshold was determined solely from the distribution of pure samples. The proposed method achieved strong discrimination performance, with an area under the receiver operating characteristic curve (AUC) of 0.9903 and high detection rates across multiple adulterant types. Typical exogenous adulterants such as starch and talc powder were completely detected, while congeneric species also showed high detection sensitivity despite their spectral similarity to authentic samples. Latent space visualization and residual spectral analysis further revealed clear separation patterns and interpretable spectral deviations. These results demonstrate the proof-of-concept viability of the proposed non-targeted framework for open-set screening of adulteration risks. However, the authentic samples used for training originated from a single source, and only a limited set of anomaly types was tested. Therefore, the current model should be regarded as an early proof-of-concept only, not as a ready-to-deploy screening tool. Further validation with diverse authentic samples and a wider range of adulterants under realistic variability is necessary before the method can be considered a practical strategy for quality control. Full article

This study presents a novel analytical approach combining headspace solid-phase microextraction (HS-SPME) with gas chromatography–mass spectrometry (GC–MS) and advanced chemometric techniques to detect adulteration in coriander honey. A total of 34 volatile compounds were identified and quantified, revealing a progressive decrease in both profile complexity and compound abundance with increasing levels of invert sugar and high-fructose corn syrup (HFCS) adulteration. Chromatographic and chemometric analyses effectively distinguished authentic from adulterated samples, with the Extreme Gradient Boosting (XGBoost) model achieving a high classification performance of 95.83% accuracy. The study highlights the critical impact of adulteration on honey’s chemical composition and confirms the efficacy of integrating modern analytical and machine learning tools for rapid, sensitive, and reliable honey authenticity assessment. This methodology offers a valuable framework for food quality control and fraud prevention, addressing current challenges in the honey market and protecting consumer interests. Full article

Food adulteration with melamine represents a serious threat to food safety due to its toxic effects and its ability to falsely elevate protein values measured by nitrogen-based methods. Visual inspection and visible reflectance spectroscopy are unsuitable for identifying low-level adulteration. This study evaluates Fourier Transform Infrared (FTIR) spectroscopy combined with chemometric tools for the identification of melamine in brown rice flour adulterated at 0–2.00% (w/w). Under the tested conditions, no clear FTIR-detectable interactions between melamine and starch or proteins were observed, suggesting that melamine primarily acts as a physical admixture. Characteristic melamine absorption bands were identified at 3466, 3415, 1431, and 810 cm⁻¹. Spectral normalization and second-order derivative processing improved sensitivity and enabled quantitative calibration models. The method achieved a limit of detection of 1408 mg/kg. Although this value is above the regulatory threshold of 2.5 mg/kg, the approach provides a rapid, non-destructive screening tool for identifying highly adulterated samples and prioritizing them for confirmatory chromatographic or mass spectrometric analysis. Overall, FTIR spectroscopy combined with chemometric analysis offers an efficient first-line approach for identification of melamine adulteration in brown rice flour. Full article

Apple juice is one of the world’s most widely consumed fruit juices and is therefore a common target for economically motivated adulteration (EMA). Such adulteration may involve dilution with water, substitution with other juices, or the addition of exogenous sugars, each requiring robust analytical methods for detection. In this study, we present an improved analytical method for identifying exogenous C4-type sugars in apple juice which utilizes the naturally occurring sorbitol as an endogenous isotopic reference marker. The method uses liquid chromatography coupled to isotope ratio mass spectrometry (LC-IRMS) to determine the δ¹³C values of the major endogenous sugars in apple juice. The study shows that the δ¹³C value of sorbitol can be measured in the same analytical run as the other major sugar components and remains unaffected by the addition of exogenous C4-type sugars to the apple juice. This method offers significant advantages over existing approaches, notably by eliminating the need for extensive sample preparation and multiple analytical methods thereby improving both analytical throughput and ease of use. Full article

This research aimed to develop a rapid, non-destructive, and accurate method for detecting adulteration in turmeric using Visible–Near-Infrared (UV/Vis and NIR) spectroscopy combined with machine learning algorithms. Spectral data from the samples were collected and analyzed in two ranges: 170–870 nm (UV/Vis) and 900–2170 nm (NIR). Four supervised learning algorithms, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), the Multilayer Perceptron (MLP) neural network, and Decision Tree, were evaluated for modeling. To quantitatively assess model performance, we employed not only the accuracy metric but also complementary performance indicators including precision, recall, and the F1-score to provide a more comprehensive evaluation of classification effectiveness. The models developed in the 900–2170 nm spectral range demonstrated highly significant performance, with most models achieving 100% accuracy on the independent test set. To reduce data dimensionality and enhance computational efficiency, a hybrid feature selection method combining SVM with five algorithms—League Championship Algorithm (LCA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Imperialist Competitive Algorithm (ICA)—was employed. Upon evaluation of each method, the SVM-LCA was selected as the optimal feature selection technique. This algorithm successfully extracted the most effective wavelengths with the highest correlation and lowest error, which maintained or improved the accuracy of the classification models. This study confirms the high potential of UV/Vis and NIR spectroscopy as rapid, non-destructive, and precise tools for detecting adulteration in turmeric. The findings can pave the way for the development of intelligent quality control systems in the food and pharmaceutical industries, playing a crucial role in ensuring consumer health and safety. Full article

This paper presents a comprehensive review on near-infrared (NIR) spectroscopy applied in food analysis, systematically elaborating its core principles, widespread industrial applications, advanced chemometric strategies, and cutting-edge technological progress. NIR spectroscopy (760–2500 nm), characterized by rapid, non-destructive detection and minimal sample preparation, has been widely implemented in quality evaluation and safety monitoring of grains, meat, fruits and vegetables, dairy, fermented products, tea, coffee, and other processed foods, realizing quantitative analysis of nutrients, freshness assessment, texture prediction, adulteration identification, origin tracing, and rapid preliminary screening of toxin/pesticide residues. A series of chemometric methods, including spectral preprocessing (SNV, MSC, S-G smoothing), feature extraction, and variable selection (CARS, PSO-CMW, ICPA), as well as linear/nonlinear modeling algorithms (PLS, SVM, BP-ANN, fuzzy clustering) significantly boost the accuracy and robustness of spectral analysis. Meanwhile, portable NIR devices and online monitoring systems promote on-site and real-time detection in food supply chains. Despite existing challenges such as calibration transfer, matrix interference, and model generalization, innovations like multimodal data fusion, deep learning integration, and intelligent algorithm optimization offer effective solutions. This review not only summarizes the latest research advances of NIR technology in the food field but also emphasizes its significant advantages as a rapid, non-destructive complementary tool to traditional destructive detection methods, providing theoretical support and technical reference for accelerating the industrial translation and standardized application of NIR spectroscopy, and ultimately safeguarding global food quality and safety. Full article

Milk adulteration is a common form of food fraud, particularly in high-value dairy products from small ruminants. A frequent practice involves dilution with water, often combined with the addition of sugars to mask physicochemical changes and avoid detection during routine quality control. This study aimed to develop an analytical approach for detecting combined adulteration in goat and sheep milk involving both water dilution and sucrose addition. Controlled experiments were conducted by diluting milk samples with water (1–15%) followed by the addition of sucrose solutions. Changes in physicochemical parameters, including fat, protein, total solids, lactose, density, freezing point depression, mineral content, and pH, were evaluated using an automated milk analyzer. In parallel, a suspected adulterant powder was characterized using conventional chemical analysis, ICP-AES, and HPLC-RI, revealing a composition predominantly of sucrose (91.4% w/w) with elevated sodium levels. Sucrose in milk samples was subsequently quantified using an enzymatic spectrophotometric method. Water dilution reduced protein, total solids, and density, while sucrose addition partially restored these parameters, masking adulteration effects. However, sucrose was reliably detected at concentrations above 0.1%. The proposed workflow may provide a practical and cost-effective complementary tool for routine dairy authenticity surveillance and fraud prevention. Full article

Determining reducing sugars in honey is key to ensuring its quality and authenticity. Honey mainly contains fructose and glucose, which represent around 90–95% of the total sugar content, and is one of the most adulterated foodstuffs worldwide. Fraudulent practices such as the addition of water, chemicals, or cheap sweeteners affect honey quality properties and pose health risks. This work presents a spot-test paper-based analytical device (PAD) for the colorimetric quantification of reducing sugars in honey, expressed as the sum of fructose and glucose, which was determined using Benedict’s reaction. Images of the spot-test PAD are captured with a smartphone camera under controlled light conditions and processed with the free web application Trigit. The increase in the color intensity in the blue channel gives a linear correlation with the concentration of reducing sugars in the range of 50–400 mg/L. The obtained limit of detection and quantification values were 11 mg/L and 37 mg/L, respectively. The proposed spot-test PAD offers a low-cost, biodegradable, and easily portable material for the rapid and simple quantification of reducing sugars in honey samples. Likewise, using the smartphone as a digitization system enables rapid data acquisition and analysis, making the method useful as a practical screening tool in food safety and quality control in small or non-centralized laboratories. Full article

Saffron (Crocus sativus L.), one of the world’s most valuable spices, is highly vulnerable to adulteration due to its premium market price and the limitations of conventional analytical methods for rapid, non-destructive authentication. Although recent deep learning-based approaches have reported promising accuracy, many rely on single models or naïve ensembles and lack rigorous validation and statistical reliability assessment. This study proposes a validation-driven and explainable deep ensemble framework for image-based saffron adulteration detection. Multiple pretrained convolutional neural networks (DenseNet169, ResNet50, and VGG16) are integrated using a validation-driven weighted ensemble strategy in which fusion weights are computed exclusively from validation performance within the training folds and fixed prior to evaluation on the held-out fold, thereby preventing information leakage between model selection and performance assessment. The proposed framework achieved 98.61% classification accuracy, 98.17% F1-score, and 98.61% AUC, outperforming the best individual base model by up to 1.4% in F1-score. Stratified five-fold cross-validation demonstrated stable performance, with a mean accuracy of 97.81% ± 0.53, confirming robustness across data splits. Statistical validation using McNemar’s test (p < 0.05) and 5 × 2 cross-validated significance testing verified that performance improvements over constituent models are statistically reliable. Grad-CAM-based explainability and background-invariance analysis further demonstrated that predictions are driven primarily by intrinsic filament-level characteristics, with only a marginal (~0.9%) performance reduction under ROI-cropped evaluation. The proposed framework provides a robust, interpretable, and statistically validated solution for saffron authentication and offers methodological insights for reliable image-based food adulteration detection under limited data conditions. Full article

To combat poultry meat adulteration, we developed a poultry universal primer-based fluorescent PCR (PUP-fPCR). Through comprehensive genomic alignment analysis, a poultry-specific nuclear DNA sequence containing phylogenetically conserved regions and hypervariable segments with interspecies nucleotide polymorphisms was employed to develop universal primers targeting conserved flanking sequences and TaqMan probes for hypervariable segments. Then, a multiplex quantitative PCR method incorporating universal primers with four TaqMan probes was developed with high specificity and sensitivity (limit of detection: 0.005 ng). Analytical performance evaluation using prepared DNA mixtures revealed robust accuracy (relative deviation: 0.80–5.05%) and precision (relative standard deviation: 0.94–13.84%). This single-tube multiplex system leverages the spectral discrimination of TaqMan probes to simultaneously detect four poultry species, overcoming primer competition issues inherent in conventional multiplex PCR designs. This integrated approach reduces system complexity while maintaining detection efficiency, providing regulatory agencies with a robust tool for combating meat adulteration and ensuring food quality supervision. Full article

Nyaope is a highly addictive street drug that is widely used in South Africa, particularly in urban and peri-urban settings. Although it is traditionally consumed by smoking, increasing injection use has raised serious public health concerns due to an elevated risk of bloodborne viral infections and other drug-related health complications. The composition of nyaope is highly variable, frequently adulterated, and continually evolving, thus highlighting the need for detailed chemical characterization to support forensic investigations and public health interventions. An exploratory study design was conducted using eight nyaope samples seized from six sites within the City of Tshwane Metropolitan Municipality that were provided by the South African Police Service Forensic Science Chemistry Laboratory (SAPS-FSCL). Samples were analyzed using Ultra-High-Performance Liquid Chromatography coupled to Quadrupole-Time-of-Flight Mass Spectrometry (UHPLC-qTOF-MS) operated in data-dependent acquisition mode under positive ionization. Raw data from the methanolic extracts of nyaope was converted to mzML format and processed using SIRIUS software for compound annotation based on isotope pattern ranking and fragmentation analysis. Chemical profiling revealed multiple opiate-related compounds, including noscapine, heroin, papaverine, and codeine. Molecular networking revealed chemically diverse yet structurally related metabolites consistent with a poppy-derived botanical origin. In addition, multiple synthetic pharmaceutical adulterants were detected. Notably, one sample contained formaline, a toxic rodenticide structurally related to protopine, highlighting the risk of misidentification using less advanced analytical approaches. This study demonstrates the value of advanced computational metabolomics, including molecular networking and machine-learning-assisted mass spectrometry interpretation, for comprehensive characterization of complex illicit drug mixtures. These approaches enhance forensic accuracy and support informed public health and law-enforcement responses. Full article

Hyperspectral imaging (HSI) has become a reliable nondestructive method for evaluating food quality and safety, surpassing traditional methods that are typically destructive and labor-intensive. HSI integrates spectral signatures with spatial distribution, enabling real-time, high-sensitivity analysis of both internal and external food attributes. Recently, there has been a growing number of studies focusing on food quality and safety detection using the HSI technique. This review offers a comprehensive summary of advancements in detecting food quality and safety in key areas, such as assessing the quality of fruits, vegetables, meat, grains, and tea; measuring moisture content; identifying variety and geographic origin; detecting adulterants and additives; and evaluating heavy metals and pesticide residues. Additionally, challenges and perspectives, including data dimensionality, the trade-off between signal-to-noise ratio and resolution, hardware costs, and the gap between laboratory research and applications under variable environmental conditions, are proposed. This review highlights the great potential of the HSI technique for rapidly and nondestructively detecting and monitoring food quality and safety in food and agricultural applications. Full article