Relationship between masticatory variables and bolus characteristics of meat with different textures

Rapid identification of horse oil adulteration based on deep learning infrared spectroscopy detection method

As a natural oil, horse oil has unique biological activity ingredients and therapeutic characteristics, which has important application value and market potential in healthcare, food, skin care and other fields. However, fraud is rampant in the horse oil market, and traditional methods such as chemical analysis and physical property detection are time-consuming, costly, and have low accuracy in detecting adulteration. Excessive adulteration may cause health risks, skin problems, and economic losses. Therefore, it is urgent to establish a rapid method for identifying adulteration in horse oil. Infrared spectroscopy exhibits substantial potential within detection applications, attributable to its fast analysis speed, non-destructive, and easy operation. This study collected four types of samples: horse oil, butter, sheep oil, and lard, and mixed them in different proportions (5%, 10%, 20%, 30%, 40%, 50%). The infrared spectral data were enhanced by Gaussian white noise and preprocessed by Standard normal variable transformation and detrending (SNV-DT), and 591 × 3601 infrared spectral data were obtained for each adulteration ratio. In terms of model selection, by comparing CNN, RNN, Transformer, and ResNet, which are commonly used in foods, cosmetics and other fields, it is found that the fine-tuning ResNet can achieve the best results in identifying adulterated horse oil applications. For the first time, this study proposed a method for rapid detection of horse oil adulteration by combining infrared spectroscopy and deep learning, which reflected the significance of combining deep learning and infrared spectroscopy in the field of adulteration, and laid a foundation for qualitative detection in this field.

Association of physical function with masticatory ability and masticatory habits: a cohort study

BACKGROUND

Few studies have evaluated masticatory ability and habits in relation to physical function. This study aimed to investigate the association of physical function with both masticatory ability and masticatory habits.

METHODS

In this cohort study, we followed up with 146 community-dwelling older adults aged 65-84 years for 1 year. Physical function domain scores on the Kihon Checklist were used to assess physical function. Masticatory ability was examined using objective measurements and self-administered questionnaires. Data on masticatory habits were obtained using self-administered questionnaires. The Mann-Whitney U test was used to analyze the association between masticatory ability and masticatory habits as exposures; logistic regression analysis was used to analyze the effect of exposure on the outcome.

RESULTS

A relationship was found between objective and subjective masticatory ability; however, no relationship was found between objective masticatory ability and masticatory habits. Furthermore, subjective masticatory ability and masticatory habits appeared to influence physical function 1 year later (odds ratio [OR]: 6.00, 95% confidence interval [CI]: 1.44-25.05; OR: 6.49, 95% CI: 2.45-17.22).

CONCLUSION

Masticatory ability and habits may be associated with a decline in physical function after 1 year in community-dwelling older adults. To maintain the physical function of these individuals, early intervention that addresses not only masticatory ability but also masticatory habits is necessary.

Hardness Analysis of Foods in a Diet Based on the Mediterranean Diet and Adapted to Chilean Gastronomy

The human diet is a factor for disease prevention and the extension of life expectancy. Loss of teeth can adversely affect chewing capacity, which can lead patients to modify their diet and subsequently result in a poor dietary intake. This work is conducted within the framework of an ongoing research project in the Dentistry School of Universidad de la Frontera aimed at designing a diet for patients with complete removable dental prostheses (CRDP). This study aimed to evaluate the hardness of foods in a diet designed for patients using CRDP, using texture profile analysis (TPA). TPA was used to measure the hardness of 43 foods, categorized into seven groups, dairy, animal protein, fruits, vegetables, cereals and grains, high-lipid foods, and vegetable protein, to understand their impact on masticatory performance in CRDP wearers. TPA consists of two compression cycles where the food sample is compressed until it reaches a pre-established deformation. The first force peak achieved in the first cycle is used as a measure of sample hardness. Significant differences in hardness were identified within each food group, indicating a wide spectrum of textural properties that could influence chewing behavior. These findings suggest that assessing food hardness can help tailor dietary recommendations to improve masticatory efficiency in patients with dental prostheses.

Resolution enhancement of tongue tactile image based on deconvolution neural network

To reproduce the tactile perception of multiple contacts on the human tongue surface, it is necessary to use a pressure measurement device with high spatial resolution. However, reducing the size of the array sensing unit and optimizing the lead arrangement still pose challenges. This article describes a deconvolution neural network (DNN) for improving the resolution of tongue surface tactile imaging, which alleviates this tradeoff between tactile sensing performance and hardware simplicity. The model can work without high-resolution tactile imaging data of tongue surface: First, in the compression test using artificial tongues, the tactile image matrix (7 × 7) with low resolution can be acquired by sensor array with a sparse electrode arrangement. Then, through finite element analysis modeling, combined with the distribution rule of additional stress on the two-dimensional plane, the pressure data around the existing detection points are calculated, further expanding the tactile image matrix data amount. Finally, the DNN, based on its efficient nonlinear reconstruction attributes, uses the low-resolution and high-resolution tactile imaging matrix generated by compression test and finite element simulation, respectively, to train, and outputs high-resolution tactile imaging information (13 × 13) closer to the tactile perception of the tongue surface. The results show that the overall accuracy of the tactile image matrix calculated by this model is above 88%. Then, we deduced the spatial difference graph of the resilience index of the three kinds of ham sausages through the high-resolution tactile imaging matrix.

Non-Destructive Detection of the Freshness of Air-Modified Mutton Based on Near-Infrared Spectroscopy

Monitoring and identifying the freshness levels of meat holds significant importance in the field of food safety as it directly relates to human dietary safety. Traditional packaging methods for lamb meat quality assessment present issues such as cumbersome operations and irreversible damage. This research proposes a quality assessment method for modified atmosphere packaging lamb meat using near-infrared spectroscopy and multi-parameter fusion. Fresh lamb meat quality is taken as the research subject, comparing various physicochemical indicators and near-infrared spectroscopic information under different temperatures (4 °C and 10 °C) and different modified atmosphere packaging combinations. Through precision parameter comparison, rebound and TVB-N values are selected as the modeling parameters. Six spectral preprocessing methods (multi-scatter calibration, MSC; standard normal variate transformation, SNV; normalization; Savitzky-Golay smoothing, SG; Savitzky-Golay 1 derivative, SG-1st; and Savitzky-Golay 2 derivative, SG-2nd), and three feature wavelength selection methods (competitive adaptive reweighted sampling, CARS; successive projections algorithm, SPA; and uninformative variable elimination, UVE) are compared. Partial least squares (PLS) and support vector machine (SVM) are used to construct prediction models for chilled fresh lamb meat quality. The results show that when rebound is used as a parameter, the SG-2nd-SPA-PLSR model has the highest accuracy, with a determination coefficient R²p of 0.94 for the prediction set. When TVB-N is used as a parameter, the MSC-UVE-SVM model has the highest accuracy, with an R²p of 0.95 for the prediction set. In conclusion, the use of near-infrared spectroscopic analysis enables rapid and non-destructive prediction and evaluation of lamb meat freshness, including its textural characteristics and TVB-N content under different modified atmosphere packaging. This study provides a theoretical basis and technical support for further encapsulating the models into portable devices and developing portable near-infrared spectrometers to rapidly determine lamb meat freshness.

A dynamic in vitro oral mastication system to study the oral processing behavior of soft foods

A bolus-oriented artificial oral mastication system was developed to simulate the dynamics of food mastication in the human mouth. The system consists of a chewing unit, a bolus forming unit, and provisions for the dynamic incorporation of saliva during mastication. The system performance was validated with in vivo trials (n = 25) considering time-dependent changes in particle size, textural attributes and rheological behavior of the bolus. Idli, a fermented and steamed black gram-rice-based Indian food was considered the model soft food for all trials measured in triplicates. The mastication dynamics were evaluated by analyzing bolus properties during every 3 s of mastication. Large strain shear rheology tests revealed that the viscosity of the sample decreased over time. Results of in vivo trials follow close trends in particle size and rheological behavior and have no significant change in correlation with in vitro mastication results. Similar observations were made in the half softening time of idli during mastication as determined using the relative change in hardness (hardness ratio (H_t/H₀)) values fitted to the Weibull model. Also, a model to simulate the time-dependent changes in bolus adhesiveness was developed.

Meat texture modification for dysphagia management and application of hydrocolloids: A review

Dysphagia is a medical condition that describes the difficulty of swallowing food, and texture modified food (TMF) is the best intervention for dysphagia. The relevant guidelines to identify dysphagia food are provided by the International Dysphagia Diet Standardization Initiative (IDDSI). Developing texture modified meat is a challenging task due to its fibrous microstructure and harder texture. Various meat tenderization attempts are therefore evaluated in the literature. Meat texture modification for dysphagia is not just limited to tenderization but should be focused on safe swallowing attributes as well. The application of hydrocolloids for designing TMF has a major research focus as it is a cost-effective method and offers an opportunity for careful control. The present review focuses on the meat texture modification attempts that have been used in the past and present, with special attention to the use of hydrocolloids. Several studies have shown improvements in texture upon the addition of various hydrocolloids; however, few studies have attempted to develop texture modified meat for people with dysphagia. This area has to be further developed along with the sensory evaluations conducted with the dysphagia population, to validate the industrial application of hydrocolloids to TMF.