Using OPLS-DA to Fingerprint Key Free Amino and Fatty Acids in Understanding the Influence of High Pressure Processing in New Zealand Clams.

This study investigated the effect of high pressure processing (HPP) on the fatty acids and amino acids content in New Zealand Diamond Shell (Spisula aequilatera), Storm Shell (Mactra murchisoni), and Tua Tua (Paphies donacina) clams. The clam samples were subjected to HPP with varying levels of pressure (100, 200, 300, 400, 500, and 600 MPa) and holding times (5 and 600 s) at 20 °C. Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) were deployed to fingerprint the discriminating amino and fatty acids post-HPP processing while considering their inherent biological variation. Aspartic acid (ASP), isoleucine (ILE), leucine (LEU), lysine (LYS), methionine (MET), serine (SER), threonine (THR), and valine (VAL) were identified as discriminating amino acids, while C18:0, C22:1n9, C24:0, and C25:5n3 were identified as discriminating fatty acids. These amino and fatty acids were then subjected to mixed model ANOVA. Mixed model ANOVA was employed to investigate the influence of HPP pressure and holding times on amino acids and fatty acids in New Zealand clams. A significant effect of pressure levels was reported for all three clam species for both amino and fatty acids composition. Additionally, holding time was a significant factor that mainly influenced amino acid content. butnot fatty acids, suggesting that hydrostatic pressure hardly causes hydrolysis of triglycerides. This study demonstrates the applicability of OPLS-DA in identifying the key discriminating chemical components prior to traditional ANOVA analysis. Results from this research indicate that lower pressure and shorter holding time (100 MPa and 5 s) resulted in the least changes in amino and fatty acids content of clams.

Design of experiments meets immersive environment: Optimising eating atmosphere using artificial neural network.

Design of experiments (DOE) is a family of statistical tools commonly used in food science to optimise recipes and facilitate new food development. In a novel cross-disciplinary twist, we propose to adapt DOE approach to the optimisation of restaurant atmosphere. In this study, an artificial neural network (ANN) with particle swarm optimisation algorithm (PSO; hereafter ANN-PSO) was selected and compared with classical Response Surface Method (RSM) as ANN-PSO has been reported to yield better reliability and predictability compared to RSM. Recent research has increasingly demonstrated that perceived food quality, enjoyment, and willingness to pay are influenced by contextual factors such as lighting, decoration, and background noise/music. Moreover, virtual reality (VR) technology, which has become increasingly accessible, sophisticated, and widespread over the past years, presents a new way to study scenarios which may be otherwise too expensive/implausible to test in real life this includes delivering immersive environment. We hereby demonstrate a novel proof-of-concept study by varying the degree of illumination and of background sound level in an immersive restaurant setup. Participants (N = 283) watched immersive 360° videos while rating situational appropriateness and food wanting for two different dishes in various ambient conditions as determined by DOE's Central Composite Design (CCD). Participants did not actually consume the foods but rather only viewed them. Optimal restaurant lighting and sound levels were then estimated using ANN-PSO model which was found to be at 289 lux and -21.38 Loudness Unit Full Scale (LUFS) for burger and 186.9 lux and -30 LUFS for pizza. While the results of our study are of obvious interest to those in the hospitality industry, this work further highlights the transferability of methods across different disciplines and the applicability of time-tested methods to new emerging areas.

Development of an Artificial Neural Network Utilizing Particle Swarm Optimization for Modeling the Spray Drying of Coconut Milk.

Spray drying techniques are one of the methods to preserve and extend the shelf-life of coconut milk. The objective of this research was to create a particle swarm optimization-enhanced artificial neural network (PSO-ANN) that could predict the coconut milk spray drying process. The parameters for PSO tuning were selected as the number of particles and acceleration constant, respectively, for both global and personal best using a 2k factorial design. The optimal PSO settings were recorded as global best, C1 = 4.0; personal best, C2 = 0; and number of particles = 100. When comparing different types of spray drying models, PSO-ANN had an MSE value of 0.077, GA-ANN had an MSE of 0.033, while ANN had an MSE of 0.082. Sensitivity analysis was conducted on all three models to evaluate the significance level of each parameter on the model, and it was discovered that inlet temperature had the most significant influence on the model performance. In conclusion, the PSO-ANN was found to be more effective than ANN but less effective than GA-ANN in predicting the quality of coconut milk powder.