Exploring the relationships between oral sensory physiology and oral processing with mid infrared spectra of saliva

Masticatory simulators based on oral physiology in food research: A systematic review

A masticatory simulator is a mechanical device that mimics the physiological structures of the human oral cavity, chewing movement system, and functions. The advantage of this device lies in real-time tracking and analysis of food boluses within a sealed oral space, offering a direct validation platform for food experiments without constraints related to time, space, and individual variations. The degree to which the masticatory simulator simulates physiological structures reflects its efficacy in replicating oral physiological processes. This review mainly discusses the physiological structures of the oral cavity, the simulation of biomimetic components, and the development, feasibility assessment, applications, and prospects of masticatory simulators in food. The highlight of this review is the analogy of biomimetic component designs in masticatory simulators over the past 15 years. It summarizes the limitations of masticatory simulators and their biomimetic components, proposing potential directions for future development. The purpose of this review is to assist readers in understanding the research progress and latest literature findings on masticatory simulators while also offering insights into the design and innovation of masticatory simulators.

Creating similar food boluses as that in vivo using a novel in vitro bio-inspired oral mastication simulator (iBOMS-Ⅲ): The cases with cooked rice and roasted peanuts

Food bolus is the major outcome of oral processing of foods. Its structure and properties are crucial for safe swallowing and subsequent gastric digestion. However, collecting the ready-to-swallow bolus for further analysis in either normal or deficient human subjects is difficult, regulatorily or practically. Here, a novel in vitro bio-inspired oral mastication simulator (iBOMS-Ⅲ) was developed to be capable of replicating food boluses comparable to those in vivo. Cooked rice and roasted peanuts were used as the model foods (soft and hard) respectively. Particle size distribution, moisture content and rheology of the food boluses produced in the iBOMS-Ⅲ were assessed. A conventional food blender was also employed as a non-consequential comparation. Eighteen healthy young volunteers of the ages from 20-30 years (10 male and 8 female) were invited to provide the in vivo data. For cooked rice boluses produced by the iBOMS-Ⅲ with 10, 12, 14, and 20 chewing number of cycles, the moisture content exhibited minimal variation (68.3-68.8 wt%), aligning closely with values obtained from the average value of the human subjects (67.5 wt%). Similarly, the boluses from roasted peanut displayed similar moisture contents across masticatory number of cycles (36, 40, and 44 number of cycles), averaging at 35.3 %, mirroring the average in vivo results (33.8 wt%). Furthermore, the shear viscosity of both cooked rice and roasted peanut boluses exhibited minimal variations with iBOMS-Ⅲ chewing number of cycles. The particle size distributions of the boluses produced with 14 and 44 chewing number of cycles matched well with the in vivo data for cooked rice and roasted peanuts, with median particle size (d50) being 1.07 and 0.78 mm, respectively. The physical properties of the food boluses collected from the food blender, with varying grinding times, differed significantly. This study demonstrates the value of the iBOMS-Ⅲ in achieving realistic boluses with two very different food textures.

Unlocking the Diagnostic Potential of Saliva: A Comprehensive Review of Infrared Spectroscopy and Its Applications in Salivary Analysis

Infrared (IR) spectroscopy is a noninvasive and rapid analytical technique that provides information on the chemical composition, structure, and conformation of biomolecules in saliva. This technique has been widely used to analyze salivary biomolecules, owing to its label-free advantages. Saliva contains a complex mixture of biomolecules including water, electrolytes, lipids, carbohydrates, proteins, and nucleic acids which are potential biomarkers for several diseases. IR spectroscopy has shown great promise for the diagnosis and monitoring of diseases such as dental caries, periodontitis, infectious diseases, cancer, diabetes mellitus, and chronic kidney disease, as well as for drug monitoring. Recent advancements in IR spectroscopy, such as Fourier-transform infrared (FTIR) spectroscopy and attenuated total reflectance (ATR) spectroscopy, have further enhanced its utility in salivary analysis. FTIR spectroscopy enables the collection of a complete IR spectrum of the sample, whereas ATR spectroscopy enables the analysis of samples in their native form, without the need for sample preparation. With the development of standardized protocols for sample collection and analysis and further advancements in IR spectroscopy, the potential for salivary diagnostics using IR spectroscopy is vast.

Interpretable machine learning assisted spectroscopy for fast characterization of biomass and waste

The combination of machine learning and infrared spectroscopy was reported as effective for fast characterization of biomass and waste (BW). However, this characterization process is lack of interpretability towards its chemical insights, leading to less satisfactory recognition for its reliability. Accordingly, this paper aimed to explore the chemical insights of the machine learning models in the fast characterization process. A novel dimensional reduction method with significant physicochemical meanings was thus proposed, where the high loading spectral peaks of BW were selected as input features. Combined with functional groups attribution of these spectral peaks, the machine learning models established based on the dimensionally reduced spectral data could be explained with clear chemical insights. The performance of classification and regression models between the proposed dimensional reduction method and principal component analysis method was compared. The influence mechanism of each functional group on the characterization results were discussed. CH deformation, CC stretch & CO stretch and ketone/aldehyde CO stretch played essential roles in C, H/ LHV and O prediction, respectively. The results of this work demonstrated the theoretical fundamentals of the machine learning and spectroscopy based BW fast characterization method.

Capturing the impact of oral processing behavior and bolus formation on the dynamic sensory perception and composition of steamed sturgeon meat

The effect of oral processing on flavor release and change in composition of steamed sturgeon meat was investigated. Oral processing caused changes in the concentrations of taste compounds including amino acids, 5'-nucleotides, organic acids, and Na⁺. Sensory omics demonstrated that the concentrations of 12 volatile compounds increased significantly (p < 0.05) during the initial stage of oral processing. There is no significant difference in microstructure, texture, and particle size of meat bolus. The top fifteen differential lipids which including eight phospholipids in all processed samples significantly (p < 0.05) correlated with the flavor release. A total of 589 differential proteins were detected in three samples with different chewing times (0, 12, and 30 s). Analysis of the correlations between odorants and 19 differential proteins was performed. Enriched pathways including fatty acid degradation, valine, leucine and isoleucine degradation, glycine, serine and threonine metabolism, and arachidonic acid metabolism were associated with flavor release during oral processing. This study aimed to investigate potential links between flavor release and biological processes during oral processing from a proteomics perspective.

Holistic approach to effects of foods, human physiology, and psychology on food intake and appetite (satiation & satiety)

Appetite (satiation and satiety) is an essential element for the control of eating behavior, and as a consequence human nutrition, body weight, and chronic disease risk. A better understanding of appetite mechanisms is necessary to modulate eating behavior and food intake, and also provide a practical approach for weight management. Although many researchers have investigated the relationships between satiation/satiety and specific factors including human physiology, psychology, and food characteristics, limited information on the interactions between factors or comparisons between the relative importance of factors in contributing to satiation/satiety have been reported. This article reviews progress and gaps in understanding individual attributes contributing to perceived satiation/satiety, the advantages of considering multiple factors together in appetite experiments, as well as the applications of nondestructive sensing in evaluating human factors contributing to relative appetite perception. The approaches proposed position characterization of appetite (satiation and satiety) for personalized and precision nutrition in relation to human status and healthy diets. In particular, it is recommended that future studies of appetite perception recognize the inter-dependence of food type and intake, appetite (satiation and satiety), and individual status.

Integrating Effects of Human Physiology, Psychology, and Individual Variations on Satiety–An Exploratory Study

Satiety can influence food intake, and as a consequence has the potential to affect weight and obesity. Human factors such as physiology and psychology are likely to be important in determining satiety. However, it is not well-understood how these factors (individual variations) alone or combined contribute to satiety feelings. In addition, there have been limited or no attempts to use a holistic approach to evaluate satiety. In this study, three plant-based foods were used as mid-morning snack for 52 participants to evaluate satiety response (during three consecutive days, one-day-one-food type). The foods were served ad libitum until participants felt comfortably full prior to satiety monitoring. The study explored diverse human factors (n = 30) that might contribute to satiety including those related to oral physiology, metabolic factors, body composition and psychology. It identified important variables for satiety as well as the interactions among them and the influences of age, gender, and low satiety phenotype (consistently lower reported fullness scores) on satiety. Overall, combinations of factors rather than individual ones contributed to self-reported satiety. Food factors (e.g., type, composition) had limited effects, but there were only three types used in the study. The combination of metabolic factors [respiratory quotient, age, and body energy usage type (e.g., carbohydrate or fat)], oral sensitivity & processing, personality traits (agreeableness, conscientiousness, and neuroticism), and eating behavior (e.g., emotional and external eating) were the most important for explaining individual satiety responses. Older participants had significantly higher reported satiety than younger participants, associated with significant differences in oral physiology, increased body fat, and mature psychological characters. Moreover, different satiety phenotypes had significant differences in relationships with body fat, oral physiology, personalities, food neophobia, and eating behaviors. The results of this study indicate that much greater insights into the factors determining satiety responses can be obtained by combining multiple food and human physiological and psychological characteristics. This study used more diverse measures of individual variation than previous studies of satiety and points the way toward a more holistic approach to understanding the (control of) perceptions of fullness at both individual and group levels.

Predicting Satiety from the Analysis of Human Saliva Using Mid-Infrared Spectroscopy Combined with Chemometrics

The aim of this study was to evaluate the ability of mid-infrared (MIR) spectroscopy combined with chemometrics to analyze unstimulated saliva as a method to predict satiety in healthy participants. This study also evaluated features in saliva that were related to individual perceptions of human–food interactions. The coefficient of determination (R²) and standard error in cross validation (SECV) for the prediction of satiety in all saliva samples were 0.62 and 225.7 satiety area under the curve (AUC), respectively. A correlation between saliva and satiety was found, however, the quantitative prediction of satiety using unstimulated saliva was not robust. Differences in the MIR spectra of saliva between low and high satiety groups, were observed in the following frequency ratios: 1542/2060 cm⁻¹ (total protein), 1637/3097 cm⁻¹ (α-amino acids), and 1637/616 (chlorides) cm⁻¹. In addition, good to excellent models were obtained for the prediction of satiety groups defined as low or high satiety participants (R² 0.92 and SECV 0.10), demonstrating that this method could be used to identify low or high satiety perception types and to select participants for appetite studies. Although quantitative PLS calibration models were not achieved, a qualitative model for the prediction of low and high satiety perception types was obtained using PLS-DA. Furthermore, this study showed that it might be possible to evaluate human/food interactions using MIR spectroscopy as a rapid and cost-effective tool.