Journal Articles
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915
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Item Heat-induced modifications of pea protein: Implications for solubility and digestion behaviour(Elsevier B.V., 2025-08-20) Li D; Ma Y; Acevedo-Fani A; Lu W; Singh H; Ye APlant proteins have become increasingly desirable due to their sustainability and proposed health benefits. This study initially examined the effects of heat treatment on the solubility of pea protein (PP) in a 3 % (w/w) protein solution, applying heat from 65 °C to 95 °C for varying durations across pH conditions ranging from 5.5 to 7.8. Subsequently, an advanced dynamic gastric digestion model—the Human Gastric Simulator—was employed to examine the in vitro gastric digestion behaviours of heat-treated and untreated PP. Results suggest that heat treatment reduces the protein aggregate size and enhances PP solubility, potentially due to a decrease in α-helix and β-turn structures or an increase in β-sheet content, as determined via Fourier transform infrared spectroscopy. Additionally, heat treatment elevated the surface hydrophobicity and free sulfhydryl group concentration of PP. During in vitro dynamic gastric digestion with pepsin, PP underwent notable structural and physical stability modifications. Unheated and heated PP exhibited small particles in the digesta and remained unaggregated throughout digestion. However, the heat-treated PP showed a smaller particle size during gastric digestion and a greater hydrolysis rate than the unheated protein. This study systematically evaluates the solubility and digestion behaviour of PP subjected to food processing conditions, highlighting its stability and structural changes that may influence the delivery of macronutrients from the stomach to the next phase of digestion.Item Effect of Caffeinated Chewing Gum on Maximal Strength, Muscular Power, and Muscle Recruitment During Bench Press and Back Squat Exercises(MDPI (Basel, Switzerland), 2025-07-28) Ding L; Liu J; Ma Y; Lei T-H; Barnes M; Guo L; Chen B; Cao Y; Girard OBackground/Objectives: This study aims to investigate the effects of caffeinated chewing gum on maximal strength, muscular power, and neural drive to the prime movers during bench press and back squat in resistance-trained men. Methods: Sixteen resistance-trained males participated in a double-blind, randomized trial, chewing either caffeinated gum (4 mg/kg) or placebo gum on two separate occasions, seven days apart. After chewing for 5 min, participants performed a maximal strength test followed by muscular power assessments at 25%, 50%, 75%, and 90% of their one-repetition maximum (1RM), completing with 3, 2, 1, and 1 repetition (s), respectively, for bench press and back squat. Surface electromyography data were recorded for each repetition. Results: Caffeinated gum did not significantly improve one-repetition maximum (1RM) for bench press (p > 0.05), but increased mean frequency (MF) and median frequency (MDF) in anterior deltoid, pectoralis major, and biceps brachii (all p < 0.05) compared to placebo. For back squat, 1RM increased with caffeinated gum, along with higher MF and MDF in vastus medialis (all p < 0.05). Caffeinated gum also improved mean and peak velocities, and mean and peak power outputs at 25–75% 1RM during the bench press (all p < 0.05), along with elevated MDF in pectoralis major and biceps brachii (all p < 0.05). Similar improvements were seen in mean and peak velocities during the back squat at 25–90% 1RM (all p < 0.05), along with higher MF and MDF in vastus medialis and increased normalized root mean square activity in gluteus maximus (all p < 0.05). Conclusions: Caffeinated chewing gum (4 mg/kg) enhanced muscular power (25–75% 1RM) in the bench press and improved maximal strength and muscular power (25–90% 1RM) in the back squat by increasing muscle recruitment in resistance-trained men.Item Rapid soil attribute evaluation for soil security assessments in data-poor environments in the Pacific region(Elsevier B.V., 2024-08-09) Moloney JP; Ma Y; Stockmann U; Manu VT; Minoneti V; Hui ST; Halavatau SM; Patolo S; Tukia T; Foliaki S; Carter T; Macdonald BCT; Barringer J; Roudier PMany global environments face increasing pressures on soil resources, and effective, scalable methods for assessment of soil condition and capital are essential to respond to tangible soil threats. This situation is common across Pacific Island Countries and Territories (PICTs), where high throughput soil analysis laboratories are limited, and issues such as soil organic carbon decline, acidification and fertility declines are present. Soil spectral inference presents an opportunity in such regions to provide rapid insights into soil capital and condition, though the need for robust calibration libraries remains a limiting factor. This work investigates the utility of a regionally appropriate spectral library, the New Zealand Soil Spectral Library (NZSSL) to support the development of soil spectral inference in data-poor environments, such as PICTs, through a case study on the island of Tongatapu in The Kingdom of Tonga. We contrast the performance of existing partial least squares regression (PLSR) models developed for New Zealand soils on soils from Tongatapu and explore the opportunities for enhancement of predictions formed through memory-based learning (MBL) supplemented with local data. Our work shows the potential for cost-effective and timely soil monitoring through soil spectral inference in PICTs. The work further underscores the importance of regional cooperation and data-sharing for addressing soil security.Item k-NN attention-based video vision transformer for action recognition(Elsevier B.V,, 2024-03-14) Sun W; Ma Y; Wang RAction Recognition aims to understand human behavior and predict a label for each action. Recently, Vision Transformer (ViT) has achieved remarkable performance on action recognition, which models the long sequences token over spatial and temporal index in a video. The fully-connected self-attention layer is the fundamental key in the vanilla Transformer. However, the redundant architecture of the vision Transformer model ignores the locality of video frame patches, which involves non-informative tokens and potentially leads to increased computational complexity. To solve this problem, we propose a k-NN attention-based Video Vision Transformer (k-ViViT) network for action recognition. We adopt k-NN attention to Video Vision Transformer (ViViT) instead of original self-attention, which can optimize the training process and neglect the irrelevant or noisy tokens in the input sequence. We conduct experiments on the UCF101 and HMDB51 datasets to verify the effectiveness of our model. The experimental results illustrate that the proposed k-ViViT achieves superior accuracy compared to several state-of-the-art models on these action recognition datasets.
