Journal of Food Engineering最新文献

In this study, yeast β-glucan (YG) and edible dock protein (EDP) were used to develop the nanomicelles for delivering apigenin (Api) via self-assembly. Results showed that a stable and uniform Api-EDP-YG composite nanomicelles could be formed when the additive amount of YG was 0.5 wt%, giving the particle size of 351.2 nm and the zeta-potential of −22.59 mV. The composite nanomicelles exhibited a core-shell structure, wherein Api-EDP was a core and YG was a shell. Moreover, hydrogen bonding and van der Waals forces drove the formation of Api-EDP-YG nanomicelles. Meanwhile, the composite nanomicelles can delay the degradation of apigenin in SSF and make it slowly release in SIF, which is benefit for improving its stability and bioavailability. Importantly, the apigenin within the composite nanomicelles displayed a good storage stability and cellular compatibility. These results indicated that the Api-EDP-YG nanomicelles might have a potential application in precision nutritional and healthy foods.

Biogenic amines (BAs) generally result from the decarboxylation reaction of free amino acids as a result of the activity of different microorganisms. A build-up of these compounds can result in food being spoilt. Therefore, the rapid and precise detection of BAs like histamine is an important task for food safety. This research aimed to explore the potential of Fourier-Transform Mid-Infrared (FT-MIR) spectroscopy combined with chemometric methods to assess histamine in fresh tuna quantitatively. Based on the FT-MIR data, partial least squares regression models for the prediction of histamine were successfully constructed with R² > 0.90. Machine learning algorithms (partial least squares-discrimination analysis, k-nearest neighbors, and support vector machine) were applied, and excellent discrimination results were achieved based on the limits specified in two different legislations (EU and FDA). The results support the use of a rapid, economic and reliable approach for the discrimination of samples that could pose a health risk to consumers.

Volatile flavor is a key indicator of food quality which can directly affect consumer preference and purchase intention. Electronic nose is considered as a promising intelligent sensory analysis tool for food flavor assessment, however, extracting effective features from the gas sensor array is still a major challenge, which largely determines the performance of subsequent classifiers. Here, a parallel long short-term memory (LSTM) network is proposed as a feature extractor for automatically extracting features from the whole time series of sensor responses in flavor discrimination of five Chinese vinegars. The network was trained by the temporal data from the sensor array and yielded different feature patterns corresponding to different vinegars, which were then fed to other conventional classifiers for pattern recognition. We also evaluated the influence of the extracted feature dimension that is related to the dimension of the hidden state of the LSTM layer on the classification performance. The results indicate that a larger dimension of extracted feature is unnecessary for promoting classification accuracy, instead, the optimum dimension 4 of the hidden state gives the highest accuracy of 95.8% in this application under the softmax evaluator. Moreover, much higher accuracies were obtained when combined with other sophisticated classifiers such as support vector machine. The results demonstrate that the proposed network is competent to extract features directly and automatically from the temporal data of the electronic nose.

To address concerns regarding walnut shell damage and inadequate sorting precision during the mechanized sorting of walnuts, a walnut automatic sorting machine was designed based on deep learning and experimental research. Initially, the rationality of the design was verified through experiment. Then, three deep learning semantic segmentation algorithms, namely PSPnet, U-net, and Deeplabv3+, were selected to train walnut detection models. Results indicated that the U-net algorithm proved to be the most effective, achieving a Mean Intersection over Union of 96.71% and a Mean Pixel Accuracy value of 98.52%. Finally, performance tests were conducted on the prototype machine, yielding results with an average sorting efficiency of 51.70 kg/h, an average loss rate of 6.50%, and an average accuracy of sorting walnuts of 92.98%. The findings can provide insights for future structural improvements and operational parameter optimization of walnut automatic sorting machines.

Food breakage is an important issue in industry and is coupled to acoustic signals. The aim of this work was to analyse the breakage phenomenon with time in commercial crispy and brittle foods, using acoustic techniques and mechanical-textural features using Digital Image Analysis (DIA) and fractal analysis. Breakage patterns were coupled to the acoustic signals of six brittle foods by capturing the audio during breaking and then, applying DIA to audio signals and breakage patterns, which depicted multifractal spectra. Results showed that samples with less irregular acoustic patterns presented higher spectrum amplitudes and low generalised fractal dimension (GFD) (1.245 ± 0.045) and lacunarity (Ʌ) (0.269 ± 0.209) while materials presenting the largest irregularity had shorter spectrum amplitudes and depicted high GFD (1.656 ± 0.177) and Ʌ(0.745 ± 0.007). For global analysis of the transverse and longitudinal rupture pattern, four breakage zones were defined. In all cases, multifractal breakage patterns were associated to multifractal acoustic signals.

Mislabelling frozen-thawed fish fillets as fresh is one of the most important fraudulent practices during commercialisation. This study aimed to determine the ability of two portable miniaturised low-cost near-infrared spectroscopy (NIR) devices intended for consumers to discriminate between fresh and thawed mackerel (submitted to one or two frozen-thawed cycles) and between different freezing systems. The effect of different fish seasonal characteristics on the performance of the model was also evaluated. The low-cost NIR devices were able to discriminate between fresh and thawed samples with a classification rate of 90.3% and 94.1% and the freezing system to which they were submitted with a classification rate of 91.2% and 89.7%. These findings suggest that low-cost portable NIR spectroscopy can be a valuable tool for detecting mislabeled frozen-thawed products sold as fresh, providing consumers with a rapid and affordable method for fraud detection.

In this research, curcumin (Cur) was encapsulated in succinylated soy protein isolate (SSPI) emulsion and combined with chitosan (CS) to prepare a smart film. The good adsorption ability of SSPI at the oil-water interface provides good physical stability for preparing Cur-loaded emulsion. These CS smart films containing SSPI emulsions with different curcumin content were evaluated on the structure, mechanical, barrier, antioxidant, antibacterial properties, and controlled release behavior. SEM and FT-IR results showed that the Cur-loaded emulsion was compatible with the CS matrix. By adding the emulsion, the film blocked 97.11% of ultraviolet radiation, reduced the water vapor transmission rate by 42%, and improved the swelling degree (32.26%), water solubility (12%), thermal stability and elongation at break (70.79 %) (p < 0.05). In addition, the film has a high antibacterial effect on Staphylococcus aureus and Escherichia coli (Bacterial inhibition zone diameter: 19.59 mm and 18.66 mm) and the release rate of curcumin in the film reaches 82.60%, mainly following the Fickian diffusion. The film gradually turns red under alkaline conditions, a property that makes the films successful in monitoring the deterioration of pork during storage. Adding SSPI emulsions and curcumin to films has great potential for food freshness monitoring and packaging.

Among various size reduction techniques, high-energy ball milling is one of the most attractive means for plant-based foods. The objectives of the work were to investigate the influence of ball diameters (3, 6, and 13 mm) and milling time (2, 4, and 6 h) on particle size and microstructural properties of quinoa flours. Particle size analysis demonstrated that ball-milled particles were mostly in the range of nanoscales (122–295 nm). A longer milling time with larger balls significantly increased the particles to microscale (3.58 μm). The scanning electron microscopy displayed the conversion of quinoa starch granules into flakes after ball milling, however, the X-ray diffraction crystallinity peak observed at a 2θ value of 19–20° did not change. The AFM roughness parameters, arithmetic and squared mean heights of flours increased with increasing ball diameters. These results provided new insights for the application of ball milling, in particular in functional foods and pickering emulsion.

Wheat germ is an agricultural but low-economic by-product for animal feed or waste due to its susceptibility of hydrolytic/oxidative rancidities. Here, we use controllable extrusion to treat wheat germ, and with assistance of exogenous starch as lipid protective factor at different ratios (0:10 2:8, 3:7, 4:6). Oxidation of optimized germ extrudate was slowed down during storage, with total lipid retention rate reach up to ∼88.3%. Extrusion dynamic analysis showed that relatively high screw speed (100–150 rpm) significantly shortened mean residence time, increased axial diffusion velocity and reduced the loss of free and bound lipid. Type Ⅱ starch-lipid complex was changed to type Ⅰ during extrusion, with thermal transition peak declined. Wheat germ lipid was most evenly distributed under 100 rpm extrusion. The hydrogen bonding interaction between exogenous starch and lipids in wheat germ was strengthened, with significant modification in water absorption, water solubility, expansion and textual indexes.