Analyzing the effects of nonthermal pretreatments on the quality of microwave vacuum dehydrated beef using terahertz time-domain spectroscopy and near-infrared hyperspectral imaging

Stacked long and short-term memory (SLSTM) - assisted terahertz spectroscopy combined with permutation importance for rapid red wine varietal identification

Mislabeling of low-value red wines as high-value ones is common, which seriously undermines consumer rights and interests. However, traditional sensory and chemical analysis methods have limitations, which highlights the need for novel detection techniques. To address above issues, terahertz time-domain spectroscopy (THz-TDS) combined with deep learning (DL) was employed to distinguish different red wine varieties quickly and non-destructively, contributing to correctly identifying red wine labels. Compared with the other models, the stacked long and short-term memory (SLSTM) model based on the first derivative (1-st der) spectra performed the best (Precision: 85.72 %, Recall: 85.61 %, F1-score: 85.59 %, Accuracy: 85.61 %). In addition, feature selection (FS) was used to explore the feasibility of improving model accuracy and reducing prediction time by eliminating redundant frequencies. Compared to full frequency, the 1-st der-SLSTM model based on permutation importance (PI) performed slightly lower (Precision: 84.42 %, Recall: 84.10 %, F1-score: 84.14 %, Accuracy: 84.18 %), but the prediction time was reduced by 2 s. Therefore, different models can be selected based on different detection needs by weighing accuracy and prediction time. In conclusion, the current research demonstrates that the SLSTM-assisted THz-TDS technology provides a novel approach for fast, accurate and non-destructive for fast, accurate and non-destructive discrimination of red wine labels, facilitating the maintenance of market discipline.

Effects of chain lengths and unsaturation degrees of fatty acids on microwave-processed wheat starch-fatty acid complexes: Structure, digestion, and storage stability

Wheat starch and fatty acid can combine as wheat starch-fatty acid (WS-FA) complexes playing crucial roles in health but are sensitive to ambient humidity. Therefore, microwave-processed WS-FA complexes incorporating fatty acids in different carbon chain lengths (C12 to C18) and unsaturation degrees (C18:0 to C18:3) were prepared, of which structural information, digestive characteristics and humidity-induced storage stability were investigated. Results showed that both chain lengths and unsaturation degrees of fatty acids affected the structural and digestive properties of WS-FA complexes. According to the moisture absorption behaviours, WS-FA complexes exhibited lower critical absorption relative humidities (CARH: 44 %-68 % RH) and equilibrium moisture values (Me: 17.7 %-20.5 %) than pure wheat starch (CARH: 72 % RH; Me: 27.7 %), indicating ambient humidity >44 % RH significantly impacts their storage stability. Beyond structural and digestive analysis, this study first investigates humidity-induced instability of WS-FA complexes for the first time, highlighting the need for customized standards for starch-lipid complex preservation.

Exploring the moisture absorption behaviours of microwave-processed wheat starch-stearic acid complexes from a molecular view

The ambient humidity significantly affects the quality characteristics of starch-lipid complexes, and the moisture absorption behaviours remain further explored. In this study, microwave-processed wheat starch-stearic acid (WS-SA) complexes were prepared, of which the moisture absorption mechanism was investigated using experimental methods and molecular dynamics (MD) simulation. Results showed that the denser structure could endow the WS-SA complexes with lower equilibrium moisture (Me = 19.0 %) as compared with the pure WS (Me = 27.7 %), indicating their higher wetting resistance. MD simulation showed that the amylose-SA could effectively prevent water infiltration by the denser structures and the hydrophobicity of the non-complexed SA. This study reveals the moisture absorption behaviours of WS-SA complexes from a molecular view, embodying the theoretical and scientific significance of the current work. For future industrial reference, the current work can guide the development of humidity control strategies to mitigate the quality deterioration risks of starch-lipid complexes.

Highly cost-effective wheat starch-stearic acid complexes enabled by microwave processing: Structural properties, anti-digestion, and molecular dynamics simulation

Microwave (MW) heating shows higher efficiency in preparing wheat starch-stearic acid (WS-SA) complexes than the traditional water bath (WB) heating method, while the detailed "time-energy-quality" evaluations and the potential anti-digestion mechanism of the MW-processed WS-SA remain further exploration. In this study, 95 % time cost and 73 % energy consumption were saved when using MW processing WS-SA, and the MW-processed complexes were verified to show significantly higher relative crystallinity, short-range ordered structure degree, thermal stability, complex index, and resistant starch content. Molecular dynamics (MD) simulation demonstrated that MW treatment notably facilitated the binding rate of amylose and SA molecules, generating a tight and stable helical structure through hydrogen bonds and van der Waals forces. Analyses of solvent-accessible surface area and water status cross-verified that the denser structure could endow the MW-processed complexes with higher resistance to water solvation effects and correspondingly reduce the water mobility for enzymatic hydrolysis reactions, ultimately making the MW-processed complexes more undigestible. This study provides a further understanding of the anti-digestion mechanisms of the MW-processed WS-SA from the molecular level, and it is expected that the current work could attract more concerns to the highly cost-effective MW heating method for processing starchy food.

Nondestructive intelligent detection of total viable count in pork based on miniaturized spectral sensor

Changes in the freshness of pork due to microbial action during complex transportation and storage indicate an urgent need for in-situ, real-time monitoring techniques for chemical spoilage of meat. This study reported the use of a portable detection device based on a miniaturized visible/near-infrared spectrometer, combined with data noise reduction and machine learning methods, to predict the total viable count (TVC) in pork samples. A rapid detection device for pork TVC was designed based on the miniaturized spectrometer; a spectral preprocessing method based on the resolution of the spectrometer was proposed; the effects of different preprocessing methods on the full-wavelength modeling effect were compared; and four different feature wavelength extraction algorithms were utilized for the feature wavelengths of pork TVC. The modeling effects of different simplified models were compared. The results showed that resolution interval correction combined with standard normal variation was the most effective in full-wavelength modeling, with correlation coefficients of prediction set (RP), root mean square errors in prediction set (RMSEP), and relative percent deviation (RPD) of 0.918, 0.464 (lg CFU/g), and 2.508, respectively; interval random frog - partial least squares regression (iRF-PLSR) had the best predictive ability among all simplified models, the number of wavelengths used in the simplified model was reduced by 85.45% compared with the full-wavelength model. In contrast, the model performance was improved with RP, RMSEP, and RPD of 0.948, 0.392 (lg CFU/g) and 2.970, respectively. The combination of a rational spectral acquisition setup and a data processing methodology, the miniaturized spectrometer showed competitive results with the complex detection system in predicting meat TVC.

Prediction of freezing point and moisture distribution of beef with dual freeze-thaw cycles using hyperspectral imaging

The freezing point (FP) is an important quality indicator of the superchilled meat. Currently, the potential of hyperspectral imaging (HSI) for predicting beef FP as affected by multiple freeze-thaw (F-T) cycles was explored. Correlation analysis revealed that the FP had a negative correlation with the proportion of bound water (P21) and a positive correlation with the proportion of immobilized water (P22). Moreover, the optimal wavelengths were selected by principal component analysis (PCA). Principal component regression (PCR) and partial least squares regression (PLSR) models were successfully developed based on the optimal wavelengths for predicting FP with determination coefficient in prediction (RP2) of 0.76, 0.76 and root mean square errors in prediction (RMSEP) of 0.12, 0.12, respectively. Additionally, PLSR based on full wavelengths was established for predicting P21 with RP2 of 0.80 and RMSEP of 0.67, and PLSR based on the optimal wavelengths was established for predicting P22 with RP2 of 0.87 and RMSEP of 0.66. The results show the potential of hyperspectral technology to predict the FP and moisture distribution of meat as a nondestructive method.

Enhanced osmotic dehydration of watermelon rind using honey-sucrose solutions: A study on pre-treatment efficacy and mass transfer kinetics

This study investigates the osmotic dehydration process of watermelon rind using a solution composed of honey and sucrose. The impact of the ratio of rind-to-solution and temperature on the process is illustrated. Pre-treatments such as blanching, microwaves, and ultrasonication were utilized. Ultrasonication reduces the time needed for osmosis in a sample, resulting in increased fluid loss and solute uptake; therefore, it was selected as the method to investigate the kinetics and modelling of mass transfer. The effective diffusivities for water loss (ranging from 3.02 × 10-5 to 4.21 × 10-4 m2 s-1) and solid gain (ranging from 1.94 × 10-6 to 3.21 × 10-6 m2 s-1) were shown to increase with process variables such as temperature and the rind-to-solution ratio. The activation energy decreased as the process temperature increased, ranging from 3.723 to 0.928 kJ mol-1 for water loss and from 1.733 to 0.903 kJ mol-1 for solid gain, respectively. The sample treated with microwaves exhibited the maximum dehydration coefficient, rendering it appropriate for producing dehydrated products. Five empirical models were utilized, with the power law model (R 2 = 0.983) and the Magee model (R 2 = 0.950) being the most suitable for water loss data and solid gain, respectively.

Unveiling microwave and Roasting-Steam heating mechanisms in regulating fat changes in pork using cell membrane simulation

Fat reduction due to heating or cooking is an important issue in a healthy diet. In the current study, pork subcutaneous back fat was treated via microwave heating (MH) within 10-90 s and roasting - steam heating (RSH) within 2-30 min and their dynamic changes of individual adipocytes were explored by using vesicles as a bio-membrane model. The result showed that MH and RSH significantly increased fat loss (P < 0.05), with the maximum losses being 74.1 % and 65.6 %, respectively. The mechanical strength of connective tissue decreased and then increased slightly. The microstructure demonstrated that MH and RSH treatments facilitated a large outflow of fat, showing that the particle size of the vesicle and individual adipocytes increased and then decreased. It is thus feasible to study the dynamic changes of individual adipocytes in regulating fat reduction using cell membrane simulation.

Terahertz spectra reconstructed using convolutional denoising autoencoder for identification of rice grains infested with Sitophilus oryzae at different growth stages

Rice grains are often infected by Sitophilus oryzae due to improper storage, resulting in quality and quantity losses. The efficacy of terahertz time-domain spectroscopy (THz-TDS) technology in detecting Sitophilus oryzae at different stages of infestation in stored rice was employed in the current research. Terahertz (THz) spectra for rice grains infested by Sitophilus oryzae at different growth stages were acquired. Then, the convolutional denoising autoencoder (CDAE) was used to reconstruct THz spectra to reduce the noise-to-signal ratio. Finally, a random forest classification (RFC) model was developed to identify the infestation levels. Results showed that the RFC model based on the reconstructed second-order derivative spectrum with an accuracy of 84.78%, a specificity of 86.75%, a sensitivity of 86.36% and an F1-score of 85.87% performed better than the original first-order derivative THz spectrum with an accuracy of 89.13%, a specificity of 91.38%, a sensitivity of 88.18% and an F1-score of 89.16%. In addition, the convolutional layers inside the CDAE were visualized using feature maps to explain the improvement in results, illustrating that the CDAE can eliminate noise in the spectral data. Overall, THz spectra reconstructed with the CDAE provided a novel method for effective THz detection of infected grains.