Food Analytical Methods最新文献

Pine nuts are highly valued for their nutritional content but are prone to mold contamination during transportation and storage, potentially leading to the production of carcinogenic aflatoxins.To address this issue, we propose a rapid, non-destructive mold detection method using hyperspectral imaging combined with a lightweight three-dimensional convolutional neural network (Light3DCNN). A novel differentiable band-selection layer—feature selection (FS)—is integrated directly into the network’s training pipeline. Leveraging the Gumbel-Softmax relaxation technique and a straight-through estimator, FS enables gradient back-propagation through discrete spectral-band selections.This end-to-end learning mechanism dynamically selects the ten most informative bands by aligning the selection strategy with the final classification objective. In parallel, a lightweight feature-extraction unit combines deep convolution with point-wise convolution to independently capture spectral signatures and fuse multi-channel information with minimal computational overhead. This research compares the performance of the feature-selection-based FS-Light3DCNN model with other models. While FS-Light3DCNN achieves 99.34% accuracy—slightly lower than the full-band Light3DCNN (99.69%)—it significantly reduces training parameters and processing time by nearly 70%. FS-Light3DCNN outperforms models using SVM with UVE and CARS feature-selection algorithms, GLCM texture features, and traditional 1DCNN, 2DCNN, HybridSN, and 3DCNN models. Additionally, compared to PCA-Light3DCNN and RF-Light3DCNN, which use ten key bands selected by PCA and RF, FS-Light3DCNN improves accuracy by 6% and 5%, respectively. Experimental results demonstrate that FS-Light3DCNN excels in both accuracy and efficiency, effectively distinguishing between healthy and varying levels of mold contamination. This model provides a fast, reliable, and non-destructive method for assessing mold contamination in pine nuts and offers potential for broader applications in food quality testing.

The aging process has a significant impact on the quality and market value of white tea (WT), as described in a traditional proverb: “One year’s tea, three years’ medicinal value, and seven years becomes a treasure.” However, the lack of a rapid and reliable method for age identification poses challenges for market regulation. This study proposes a novel approach that combines surface-enhanced Raman spectroscopy (SERS) with machine learning (ML) to accurately identify the age of white tea. SERS is used to obtain molecular fingerprint spectra from Fujian white tea samples covering seven different years (2011−2023). Seven machine learning models, including logistic regression (LR), support vector machine (SVM), and random forest (RF), were systematically evaluated. The LR model, after being preprocessed through principal component analysis—logistic discriminant analysis, demonstrated relatively excellent performance. This SERS-ML method can perform rapid analysis and requires very little samples preparation. Our work establishes a robust, efficient, and field-deployable strategy for identifying the age of white tea, which is of great significance for combating fraud and protecting consumers.

Graphical Abstract

This study employs digital image processing and machine learning techniques to predict all colorimetric characteristics (color intensity, density, tonality, and color index percentages) of colored wines in a novel, cost-effective, and rapid manner. To determine the values of colorimetric characteristics, ultraviolet-visible (UV-Vis) absorbance was measured at three key wavelengths (A420, A520, and A620) using UV-Vis spectrophotometry, corresponding to the yellow, red, and blue color percentages, respectively. Simultaneously, the pictures of 86 wine samples were acquired, and the corresponding RGB and HSV color values were extracted from the images to serve as input features for multiple regression models. The models developed included principal component regression, k-nearest neighbors, linear regression, decision tree, random forest, and partial least squares (PLS). Among the models, random forest outperformed PLS in predicting A620 absorbance value due to its ability to capture non-linear patterns, whereas PLS demonstrated greater accuracy (R² > 0.95) in predicting the A420 and A520 absorbance values. According to feature selection, hue and saturation had the biggest impact on prediction accuracy. By determining absorbance values using the developed models, the complete colorimetric characteristics of the wine samples can be calculated, enabling the evaluation of their physicochemical parameters during the fermentation process or post-fermentation. As a result, all the models, improved, offer a promising alternative for quick, easy, and scalable prediction methods by reducing measurement time, eliminating the need for laboratory instruments, and introducing a new methodology to complement conventional spectroscopic techniques, with potential applications in consumer-level analysis and the process of wine quality control.

Colorimetric sensors for volatile amine (VA) detection makes meat freshness monitoring visible and convenient. However, the sensing performance still needs to be improved due to low VA concentrations in the early stage of meat spoilage. In this study, we proposed the strategy of dye solution pH regulation to improve the sensing performance of the VA colorimetric sensor. Taking methyl red (MR) as the example, the pH of the MR solution was adjusted, and MR was then adsorbed on the aerogel to fabricate the aerogel-type VA colorimetric sensor. By adjusting the pH of the MR solution to 1 prior to dye adsorption, the sensor exhibited optimal sensing performance, with a sensitivity of 3.58 ppm⁻¹ and a maximum ΔE value of 108.90 ± 2.43. Compared with the sensors without pH regulation, the sensitivity and maximum ΔE of the newly developed sensor increased by 70% and 51%, respectively. This strategy showed good generality in various pH-sensitive dye systems. The sensor also successfully applied to the real-time monitoring of shrimp. A ΔE value higher than 62.16 indicated the spoilage of the shrimp. The enhanced performance was also validated in the freshness monitoring of other types of meat. The findings provided a facile and effective approach for optimizing colorimetric sensing performance in meat and aquatic products freshness monitoring.

Foodborne pathogens, such as Escherichia coli O157:H7, pose a significant global health threat, necessitating the development of rapid and decentralized detection methods. Conventional laboratory assays often require extensive sample processing and sophisticated instrumentation, which limits their use for on-site, point-of-care testing. This work addresses the need for a simplified and accelerated platform. A rapid bi-functional linker-based immunoassay for the detection of Escherichia coli O157:H7 was developed by accelerating gold nanoparticle (AuNP) aggregation and sedimentation. The bi-functional linker facilitated antigen–antibody binding and controlled AuNP aggregation, while a 1-min centrifugation step expedited sedimentation, significantly shortening the overall assay time. Under these conditions, the assay produced an instrument-free, eye-readable visual response based on the range exhibiting a visible color change (REVC). A portable hand-powered centrifuge was fabricated and applied to detect E. coli O157:H7 in spiked tomato samples, achieving a decision level of 10³ CFU/25 g within a total assay time of just 70 min. Compared with passive settling (≈180 min), the proposed system significantly reduced detection time while maintaining consistent signal patterns, demonstrating its potential as a rapid, field-deployable biosensor for on-site foodborne pathogen detection.

The specialty coffee industry relies heavily on manual grading to maintain the ultimate cupping quality of the specialty coffee. This is a subjective and costly process, as this grading is performed by skilled labor. Therefore, this study aims to evaluate the potential of computer vision and machine learning approaches to classify green coffee beans into specialty and defective categories. In this regard, two traditional machine learning models, including random forest (RF) and support vector classifier (SVC), and three deep learning models, including a custom lightweight Convolutional Neural Network (CNN), MobileNetV2, and MobileNetV3, were evaluated on this task. Model performances were assessed using accuracy, precision, recall, F1-score, learning curves, Grad-CAM visualization, and precision-recall analysis. According to the results, the traditional machine-learning models achieved classification accuracies of 98% with RF and 95% with SVC. Similarly, the deep-learning models achieved accuracy values of 99.6% with the lightweight custom CNN, 99.6% with MobileNetV2, and 98.7% with MobileNetV3. Moreover, inference time was tested on a Raspberry Pi 5 to assess the feasibility of real-time deployment capabilities of the models on low-cost edge devices. The results demonstrated ultra-fast inference time of 0.155 ms with SVC compared to RF (1.226 ms). Similarly, average inference time for deep learning models demonstrated 94.811 ms for CNN with custom architecture, 125.144 ms with MobileNetV2, and 115.86 ms with MobileNetV3. Furthermore, this inference time was reduced significantly after the conversion of the models to a TFLite model. Based on overall evaluations of the models, the lightweight CNN with a custom architecture outperformed, maintaining consistent inference time and strong feature interpretability with generalized performance.

Surfactant-induced aqueous two-phase systems (ATPS) offer an environmentally friendly alternative for the separation and preconcentration of analytes, minimizing toxic waste generation and operational costs. In this work, we report for the first time the application of a surfactant-driven ATPS to the simultaneous extraction and preconcentration of cobalt and nickel from food matrices. The system was composed of Triton X-100 + Na₂SO₄ + H₂O in the presence of 4-(2-Pyridylazo)resorcinol (PAR) as the complexing agent, followed by detection via flame atomic absorption spectrometry. Key parameters, including pH, PAR concentration, centrifugation time, and incubation time, were optimized through multivariate analysis based on a desirability function approach. Optimal conditions were pH 9.2, centrifugation time 10 min, thermostatic bath time 11 h, and PAR concentration 0.0750% w/w. Under these conditions, the limits of detection and quantification were 0.330 and 1.10 µg·kg⁻¹ for Co, and 0.0370 and 0.890 µg·kg⁻¹ for Ni, respectively, with enrichment factors of 20.2 and 16.7. The method showed good precision, with RSDs of 6.3% for Co and 7.4% for Ni, and accuracy verified using the certified reference material NIST 1515 (apple leaves), yielding recoveries of 97.6 ± 2.7% for Co and 97.9 ± 2.9% for Ni. In real food samples, recoveries ranged from 96 to 106%, further confirming the reliability of the approach. This novel methodology, by combining micellar extraction with the principles of green chemistry, provides a reliable, cost-effective, and sustainable strategy for trace metal monitoring in food safety applications.

This study presents a voltammetric electronic tongue for the classification and electrochemical characterization of honey from Brazilian native stingless bees, an underexplored and chemically complex food matrix. Ten samples from different species and regions were analyzed using unmodified commercial screen-printed electrodes: carbon (C110), gold cured at high temperature (220AT), gold cured at low temperature (220BT), and platinum (550), under four pH conditions (pure, 2.0, 7.0, and 12.0). Au-AT and Pt were selected for their superior voltammetric definition and classification performance, assessed via principal component analysis (PCA), hierarchical cluster analysis (HCA), and artificial neural networks (ANNs). The most distinctive redox profiles emerged under neutral and alkaline conditions, with peak attribution restricted to the class level (sugars, flavonoids, and phenolic acids). NN models using combined Au-AT and Pt data at pH 7.0 and pH 12.0 achieved 91.7% accuracy in training and 100% in validation, successfully discriminating samples by both geographical origin and bee species. Overall, the minimalist bare-electrode e-tongue combined with AI enabled robust and interpretable classification, offering a powerful tool for the authentication and valorization of native stingless bee honeys.

Graphical Abstract

In this study, the Cu₇Mn₂ bimetallic cluster was synthesized via a solvothermal method using copper (II) chloride dihydrate (CuCl₂·2H₂O) and manganese (II) chloride tetrahydrate (MnCl₂·4H₂O) as the metal centers, and 1,2-cyclohexanediamine-N,N′-bis-(3-carboxylsalicylide) as the ligand. Subsequently, the Cu₇Mn₂ bimetallic cluster fiber coating was prepared using a physical coating method and applied for the solid phase microextraction (SPME) of 7 polycyclic aromatic hydrocarbons (PAHs). The coating was characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, energy dispersive X-ray spectroscopy, thermogravimetric analysis, and contact angle measurement. Based on adsorption isotherms and density functional theory (DFT) calculation, the adsorption mechanism was speculated to be hydrophobic interaction, π-π stacking, and N − H···π interaction. The coating exhibited higher extraction efficiency for 7 PAHs than the Cu cluster, Mn cluster, commercial PDMS, and PDMS/CAR fiber coatings. Under the optimized extraction conditions, an SPME-HPLC method was developed for the determination of 7 PAHs in corn oil, sesame oil, blended oil, and rapeseed oil. The linear ranges of the method were 1.0 − 1000 µg·kg^–1 for naphthalene, 0.1 − 1000 µg·kg^–1 for anthracene, phenanthrene, and pyrene, 3.0 − 1000 µg·kg^–1 for fluorene, 0.3 − 1000 µg·kg^–1 for fluoranthene, and 1.5 − 1000 µg·kg^–1 for 1-methylnaphthalene (R² > 0.99), respectively. The limits of detection (LODs, S/N = 3) and the limits of quantitation (LOQs, S/N = 10) were in the ranges of 0.030 − 1.0 µg·kg^–1 and 0.10 − 3.0 µg·kg^–1, respectively. The precisions (RSDs) were less than 10.0% and the recoveries were in the range of 82.9 − 115.2%. These results demonstrate that the Cu₇Mn₂ bimetallic cluster coating is an effective SPME adsorbent for separation and sensitive determination of PAHs in edible oils.

Graphical Abstract

Sudan dyes, a class of azo dyes, are genotoxic and carcinogenic to humans. However, they are still used illegally to color food products and spices due to their color fastness. In this study, multi-functionalized magnetic silica nanoparticles (C₁₈/DVB/NVP(4:1)-Fe₃O₄@SiO₂ MNPs) were synthesized, and solid–liquid extraction (SLE) coupled with magnetic solid phase extraction (MSPE) was employed for the extraction and preconcentration of five Sudan dyes (Sudan I − IV, Sudan Red 7B), followed by high-performance liquid chromatography/ultraviolet spectroscopy (HPLC–UV) for their detection in powdered chili pepper samples. The SLE conditions for the analytes in powdered chili pepper samples were optimized. Under optimal conditions, the extraction recovery ranged between 78.0% and 106.9%. We investigated and optimized the potential factors that may affect the efficiency of MSPE. Under these optimal conditions, calibration curves showed good linearity (R² > 0.9989) across the tested concentration range of 0.98 to 125 µg/g. The limits of detection for the five Sudan dyes were in the range of 0.06–0.26 µg/g. The intra-day and inter-day accuracy ranged from 93.5% to 106.3% with a relative standard deviation (RSD) of not more than 9.1%. The developed method was successfully applied to analyze ten different commercial brands of powdered chili pepper samples purchased from markets in Wuhan, confirming its feasibility for routine analysis of illegal Sudan dyes in these samples.