Food Analytical Methods最新文献

To accurately predict the degree of milling (DOM), contrast images were initially acquired by randomly selecting and arranging head milled rice grains alongside head brown rice grains on two distinct color cardboards. Using computer vision techniques, milled rice grains and brown rice grains within a contrast image were segmented, identified, and followed by the extraction of contour dimensions. Subsequently, the mean area ratio, length ratio, and width ratio between milled rice and brown rice were calculated, along with the mean aspect ratio of brown rice. Additionally, the color difference value between stacked milled rice and brown rice was determined using the CIEDE2000 formula. The dataset was thereby created by combining contour dimension ratios and color difference values with augmentation data generated via a multivariate linear interpolation method. Thereafter, based on evaluation results of eleven machine learning models, the support vector regression (SVR) was employed to elucidate the feature contributions to the prediction model through Shapley additive explanations (SHAP). Both macro and micro analyses unveil that the area ratio exerts the most significant impact on DOM prediction, followed by aspect ratio and color difference, while width ratio and length ratio have a comparatively minimal influence. Furthermore, the area ratio exhibits a negative effect, whereas color difference and aspect ratio demonstrate a positive effect. Ultimately, prediction tests on three additional distinct rice varieties utilizing the SVR model, achieving accuracies of 95.63% (short-grain), 93.45% (long-grain), and 95.36% (medium-grain), respectively. Moreover, tests confirm that DOM is positively correlated with the aspect ratio as unveiled by SHAP analysis.

Carthami Flos (CF) has substantial medicinal and nutritional value. However, inadequate management of production sources and insufficient quality control criteria have hindered its comprehensive development and practical application. This study prioritized geographical origin discrimination as the primary objective, employing bioactivity-associated analysis as an auxiliary screening approach to identify efficacy-related chemical markers for CF, thereby refining its quality assessment. Using ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS), 45 chemical constituents of CF were preliminarily characterized. Subsequently, high-performance liquid chromatography (HPLC) fingerprints of 55 CF batches were established and analyzed using chemometric methods, including hierarchical cluster analysis (HCA), principal component analysis (PCA), and partial least squares-discriminant analysis (PLS-DA), to screen for potential geographical discrimination markers. Concurrently, thrombin inhibitory activity was evaluated and partial least squares regression (PLSR) was performed to identify potential bioactive markers. On this basis, bioactive markers were applied as auxiliary screening criteria together with deep learning and molecular docking for verification. Ultimately, four potential bioactive-associated geographical markers capable of indicating geographical origin and exhibiting antithrombin activity were identified, including hydroxysafflor yellow A (HSYA), anhydrosafflor yellow B (AHSYB), isorhamnetin-3-O-rutinoside (IRR), and 6-hydroxykaempferol-3,6-diglucoside. This integrative strategy provides a valuable reference for the comprehensive quality evaluation of CF.

Carob (Ceratonia siliqua L.) syrup is a functional and nutritionally rich product widely used in food applications. This study compared two preparation methods, a rapid 30-min decoction (syrup 1) and a conventional 24-h maceration (syrup 2), and evaluated their conversion into molasses, using smartphone-based color assessment and artificial neural network (ANN) modeling for real-time monitoring. Physicochemical analysis showed that both syrups had slightly acidic pH and similar total soluble solids, but syrup 1 exhibited higher density, conductivity, and soluble sugar content, whereas syrup 2 retained higher mineral levels due to its prolonged extraction. Moreover, syrup 2 contained higher polyphenols (2311 vs. 581 mg GAE/100 g), flavonoids (199 vs. 128 mg QE/100 g), and tannins (93.8 vs. 595.3 mg TAE/100 g). These differences translated into superior functional properties, including stronger antioxidant activity (89.5% DPPH inhibition vs. 71.4%) and greater emulsifying capacity (61.1% vs. 35.5%). No antibacterial effect was observed against S. aureus or E. coli. Color analysis revealed progressive darkening during concentration, with a steady decline in RGB and L a b parameters. The ANN regression model accurately predicted syrup quality from smartphone-captured images, showing strong correlations between color indices and chemical/functional attributes. Overall, prolonged maceration improved the nutritional and functional quality of carob syrups, while smartphone-based ANN monitoring proved effective for rapid, non-destructive quality control in food processing.

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Roxithromycin (ROX), a broad-spectrum macrolide antibiotic, is derived from erythromycin (Markham et al.). ROX is mainly used to treat infections in humans and animals and is easily found and accumulated in the natural environment (Jiang et al. 2021). The sensor for quantitative analysis of ROX was developed via electro-polymerization (EP) on a glassy carbon electrode (GCE) using alginate (ALG), chitosan (CHIT), and titanium IV oxide nanoparticles (TiO₂NPs), and it offers a green process. The ROX/TiO₂NPs/ALG/CHIT@MIP/GCE sensor has exhibited excellent efficiency and stability for ROX. Differential pulse voltammetry (DPV) was used as the electrochemical analysis method in the developed sensor. Additionally, both surface morphology (scanning electron microscopy (SEM) and X-ray Photoelectron Spectrometry (XPS)) and electrochemical (electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV)) characterizations of the ROX/TiO₂NPs/ALG/CHIT@MIP/GCE sensor were performed. Under the optimal conditions, the sensor calibration range was found to be 1 × 10⁻¹³ M to 1 × 10⁻¹² M, with a very low limit of detection (LOD). With the designed sensor, ROX analysis of food samples and satisfactory recovery results were obtained, demonstrating the sensor's accuracy. Moreover, the effects of different interfering agents in milk and fruit juice samples on the ROX peak current were investigated to evaluate the selectivity of the designed sensor. The designed sensor demonstrated highly sensitive, reliable, repeatable, stable, and, most importantly, selective properties for ROX. Finally, four different green metric calculation methods were used to determine the study's green profile score.

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In the present research, hemp seed oil extraction was optimized using pulsed electric field (PEF) technology under varied conditions, i.e., PEF strength (0.25, 3.25, and 6.25 kV/cm) and pulse numbers (10, 30, and 50). Extraction of hemp seed oil was performed through solvent extraction following PEF processing, using different hexane-to-isopropanol ratios (0:100, 50:50, and 100:0 v/v %). The research focused on maximizing extraction efficiency, phenolic content, and antioxidant potential, while reducing peroxide value and pigmentation, via response surface methodology (RSM) in conjunction with Box-Behnken experimental design (BBD). The variance analysis indicated that PEF treatment significantly affected all variables (p < 0.05), except for phenolic content (p > 0.05), with an R2 value of more than 0.96, confirming the model's appropriateness. The optimal conditions were identified as a solvent ratio of approximately 3:2 (hexane: isopropanol), a PEF intensity of 2.02 kV/cm, and 10 pulses. Under these conditions, the oil yield reached 23.5%, total phenolic content was 3.02 mg GAE/g, antioxidant activity (DPPH inhibition) was 81.3%, peroxide value was maintained at 5.7 meq O₂/kg, and the color index was 61.4. These results demonstrate that precise optimization of PEF pretreatment can enhance both the yield and quality of hemp seed oil.

Instant noodles are a processed food product that may be quickly prepared by rehydrating them with hot water after a brief period of time. They have the advantages of easy storage, diverse flavors, and low price. The eating quality of instant noodles directly depends on the texture quality after rehydration. Current instrumental techniques for measuring food texture show poor capacity to provide objective and thorough assessments of instant noodle quality standards. Therefore, in this study, a three-ring cutting shear (A/TRCS) method was developed. At the same time, the eating qualities (hardness, elasticity, and chewiness) of instant noodles from three different companies were analyzed with the test method of A/TRCS. The results revealed that instant noodles of MFR-A had the highest hardness and good chewiness. Furthermore, the eating qualities of MFR-C instant noodles were in the middle, while MFR-B instant noodles exhibited good elasticity but poor hardness and chewiness. Notably, the A/TRCS method demonstrated strong alignment with established quality assessment techniques (r > 0.99), confirming its reliability. Compared with traditional methods, A/TRCS is an efficient and accurate texture detection method that future research could investigate for its applicability to other food matrices.

The accurate and rapid determination of wheat quality is of great importance for the wheat supply chain. Near-infrared (NIR) spectroscopy has become an established method for this purpose. So far, however, predictions for most wheat quality characteristics are not accurate enough to replace reference measurements, with the exception of protein content. This study investigates the potential to improve the prediction of 41 wheat quality parameters (protein- and starch-related parameters, solvent retention capacity, farinograph, extensograph, alveograph) based on a flour fractionation approach (sieve fractionation, dough preparation, gluten washing) and data fusion using the established techniques of NIR spectroscopy and chemometrics. Results show that preprocessing of flour significantly altered the composition of the samples, which reflected in spectral differences of their NIR spectra. This also led to a change in the prediction accuracy for many wheat quality parameters. Compared to the prediction using flour spectra, flour fractionation with or without data fusion improved the RMSECV between 5.6 and 28.6% for 35 out of the 41 quality parameters tested, leading to R²_CV between 0.80 and 0.96 for many of them. Gluten, dough, and the 50–75 µm and the 75–100 µm fractions were particularly important for the improved predictions. The best predictions were often based on data fusion of spectra from different sample types, demonstrating the importance of using complementary information from different data sources to improve predictions. The results underline the potential of this novel approach to be established in the industry as an extension of conventional NIR spectroscopy to improve wheat quality prediction.

Molecularly imprinted polymer (MIP) has been intensively investigated as a solid phase extraction material for water treatment in environmental engineering, as well as a pre-concentration media for chromatographic analysis of trace analytes in complex samples. The selective binding capability of MIPs toward target template molecules has also been exploited in the development of chemical sensors for the selective detection of analytes in intricate sample environments. In this paper, we present the development of a novel optical fiber chemical sensor for the detection of chlorogenic acid (CGA), based on the integration of a MIP with evanescent wave fiber optic UV/Vis absorption spectrometry. A chitosan-based MIP, synthesized using CGA as the template molecule, was applied as a membrane coating on the surface of a bent optical fiber core. The MIP selectively binds and concentrates CGA into the membrane from a sample solution, which was detected by monitoring variations in the light intensity transmitted through the optical fiber probe. The sensor exhibited high selectivity toward CGA and was capable of detecting the target analyte in complex sample matrices. Owing to the concentration effect imparted by the MIP membrane, the sensor demonstrated high sensitivity, achieving a detection limit of 6.7 ng/mL. The sensor’s performance was validated through the analysis of CGA extracted from powdered green coffee beans and a commercial CGA dietary supplement. The results obtained were in close agreement with those from conventional optical absorption spectrometric analysis, confirming the reliability and accuracy of the proposed sensing approach. The relative standard deviation for analyzing CGA in test sample solutions in concentration range from 3.2 to 5.7 µg/mL was from 2.5% to 3.5%.

Cold-pressed grape seed oil provides a complex mixture of volatile organic compounds (VOCs) that contribute to its aroma and overall quality. However, the analysis of these compounds is challenging due to their wide range of individual boiling points and the limited sample volumes available because of the labor-intensive oil extraction process. This study aimed at evaluating and comparing three extraction techniques—multiple static headspace (MHS), dynamic headspace (DHS), and direct thermal desorption using slitted microvials (ATEX)—based on standard validation criteria, including repeatability and reproducibility, linearity, coefficient of determination, accuracy, and lower limit of quantification. Additionally, chromatographic performance was assessed by measuring signal intensity and the number of detectable peaks. Separation and detection were performed using gas chromatography-electron ionization-single quadrupole mass spectrometry (GC-EI-MS). Overall, DHS showed the best performance across the validation parameters for cold-pressed grape seed oil, while ATEX proved to be the most suitable option when only limited sample material is available, whereas MHS offered simplicity in handling. These findings indicate that DHS is a robust and versatile method for analyzing grape seed oil’s VOC, which is particularly suitable for untargeted screening and compound identification, and may also be applicable to other edible oils such as olive oil.

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Coenzyme Q10 (CoQ10) is a natural antioxidant that plays a key role in cellular energy production and offers various health benefits, research on its stability during food processing is lacking. This study validated a vortex-assisted mechanochemical extraction (VAME) coupled with high-performance liquid chromatography (HPLC) analysis for quantification of CoQ10 in minced beef meat with and without 0.1% w/w black pepper oil fortification. Sample pretreatment included vortex extraction with 0.1% FeCl3 for 30 s, followed by homogenization with 5 ml acetonitrile using a bead mill homogenizer (0.8 m/s, 1.4 mm bead, 30 s). Separation was achieved on an RP18 column with CH3CN: THF: Water (55:40:5, v/v) at 275 nm. The method showed good linearity (R² > 0.998), recovery (95–105%), precision (RSD < 10.4%), LOD (0.11 mg/kg), and LOQ (0.33 mg/kg). It was applied to assess the impact of conventional cooking techniques, UV irradiation (1 and 3 min), and HeNe laser irradiation (10 and 30 s) with and without fortification. Proximate analysis revealed significant effects of cooking on fat, moisture, and protein. Results indicated significant degradation of CoQ10 under cooking (p < 0.05). Oven-grilled (39.00 ± 0.41 mg/kg) and fried (48.00 ± 0.28 mg/kg) samples showed the lowest concentrations. In contrast, HeNe laser irradiation of pepper fortified minced meat increased CoQ10 levels compared with controls, alongside high antioxidant activity (DPPH assay, IC50 2.26 ± 0.03, n = 3), suggesting synergistic protective interactions between pepper antioxidants and laser treatment. Coupling cooking and irradiation enhanced CoQ10 retention.