School of Agriculture, Food and Ecosystem Sciences - Research Publications
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ItemNo Preview AvailableAssessment of changes in sensory perception, biometrics and emotional response for space exploration by simulating microgravity positions(ELSEVIER, 2024-01)Long-term space exploration endeavors, encompassing journeys from the Earth to the Moon by 2030 and subsequent voyages from the Moon to Mars by 2040, necessitate the utilization of plant-based materials not solely for sustenance and refreshments but also the production of pharmaceuticals and repair compounds, such as plastics, among others. Nevertheless, the vital aspects of research in this domain pertain to the nutritional value and sensory perception associated with plant-based food. Prior investigations have shown altered sensory perception in space, manifested as diminished olfactory sensations and heightened taste perception (saltiness and sweetness). Nonetheless, studies concerning changes in aroma, basic tastes, and mouthfeel have been limited due to the logistical challenges associated with conducting experiments in the unique environment of space. To address this limitation, the present study employed sensory trials and biometrics from video using simulated microgravity chairs to simulate alterations in sensory perception akin to those encountered in space conditions. The findings of this study align with previous reports of changes in aroma and taste perception and contribute to the understanding of changes in the mouthfeel, heart rate, blood pressure, and emotional response that could be experienced in space environments. These experimental endeavors are critical to facilitate the advancement and development of novel plants and food materials tailored to the requirements of long-term space exploration.
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ItemNovel insights into the mechanism(s) of silicon-induced drought stress tolerance in lentil plants revealed by RNA sequencing analysis(BMC, 2023-10-17)BACKGROUND: Lentil is an essential cool-season food legume that offers several benefits in human nutrition and cropping systems. Drought stress is the major environmental constraint affecting lentil plants' growth and productivity by altering various morphological, physiological, and biochemical traits. Our previous research provided physiological and biochemical evidence showing the role of silicon (Si) in alleviating drought stress in lentil plants, while the molecular mechanisms are still unidentified. Understanding the molecular mechanisms of Si-mediated drought stress tolerance can provide fundamental information to enhance our knowledge of essential gene functions and pathways modulated by Si during drought stress in plants. Thus, the present study compared the transcriptomic characteristics of two lentil genotypes (drought tolerant-ILL6002; drought sensitive-ILL7537) under drought stress and investigated the gene expression in response to Si supplementation using high-throughput RNA sequencing. RESULTS: This study identified 7164 and 5576 differentially expressed genes (DEGs) from drought-stressed lentil genotypes (ILL 6002 and ILL 7537, respectively), with Si treatment. RNA sequencing results showed that Si supplementation could alter the expression of genes related to photosynthesis, osmoprotection, antioxidant systems and signal transduction in both genotypes under drought stress. Furthermore, these DEGs from both genotypes were found to be associated with the metabolism of carbohydrates, lipids and proteins. The identified DEGs were also linked to cell wall biosynthesis and vasculature development. Results suggested that Si modulated the dynamics of biosynthesis of alkaloids and flavonoids and their metabolism in drought-stressed lentil genotypes. Drought-recovery-related DEGs identified from both genotypes validated the role of Si as a drought stress alleviator. This study identified different possible defense-related responses mediated by Si in response to drought stress in lentil plants including cellular redox homeostasis by reactive oxygen species (ROS), cell wall reinforcement by the deposition of cellulose, lignin, xyloglucan, chitin and xylan, secondary metabolites production, osmotic adjustment and stomatal closure. CONCLUSION: Overall, the results suggested that a coordinated interplay between various metabolic pathways is required for Si to induce drought tolerance. This study identified potential genes and different defence mechanisms involved in Si-induced drought stress tolerance in lentil plants. Si supplementation altered various metabolic functions like photosynthesis, antioxidant defence system, osmotic balance, hormonal biosynthesis, signalling, amino acid biosynthesis and metabolism of carbohydrates and lipids under drought stress. These novel findings validated the role of Si in drought stress mitigation and have also provided an opportunity to enhance our understanding at the genomic level of Si's role in alleviating drought stress in plants.
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ItemNew developments and opportunities for AI in viticulture, pomology, and soft-fruit research: a mini-review and invitation to contribute articles(Frontiers Media SA, 2023)Climate change constraints on horticultural production and emerging consumer requirements for fresh and processed horticultural products with an increased number of quality traits have pressured the industry to increase the efficiency, sustainability, productivity, and quality of horticultural products. The implementation of Agriculture 4.0 using new and emerging digital technologies has increased the amount of data available from the soil–plant–atmosphere continuum to support decision-making in these agrosystems. However, to date, there has not been a unified effort to work with these novel digital technologies and gather data for precision farming. In general, artificial intelligence (AI), including machine/deep learning for data modeling, is considered the best approach for analyzing big data within the horticulture and agrifood sectors. Hence, the terms Agriculture/AgriFood 5.0 are starting to be used to identify the integration of digital technologies from precision agriculture and data handling and analysis using AI for automation. This mini-review focuses on the latest published work with a soil–plant–atmosphere approach, especially those published works implementing AI technologies and modeling strategies.
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ItemEditorial: Integrating advanced high-throughput technologies to improve plant resilience to environmental challenges(FRONTIERS MEDIA SA, 2023-05-31)
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ItemNovel Contactless Sensors for Food, Beverage and Packaging Evaluation.(MDPI AG, 2023-09-26)The use of traditional methods to evaluate food, beverages, and packaging tends to be time-consuming, labour-intensive, and usually involves high costs due to the need for expensive equipment, regular refill of consumables, skilled personnel and, in the case of sensory evaluation, incentives or payments involved for participants recruitment and/or panelists training and participation. Therefore, researchers have developed novel low-cost, rapid, and time-effective methods using digital and artificial intelligence technologies. This special issue (SI) focused on the novel methods developed using contactless sensors for food, beverages and packaging. This SI is composed of nine papers related to the use of spectral analysis using different methods, such as near-infrared spectroscopy to classify eggs into cage or free-range production practices [1], a semi-automatic low field nuclear magnetic resonance (LF-NMR) coupled with machine learning modelling to predict oxidation in edible oil [2], the use of vibrational spectroscopy to measure ethanol and methanol levels in pisco [3], and the use of an infrared laser sensor to monitor the gas-phase CO2 in Champagne headspace when swirling [4]. Other papers focus on the use of other specific sensors, such as a low-cost and portable electronic nose (e-nose) to predict aromas and roasting intensity in coffee [5] and the use of flexible sensors for monitoring oyster survival rates [6]. Furthermore, a computer vision method was presented using a conventional RGB camera coupled with machine learning modelling to predict the type of rice based on morphocolorimetric parameters obtained through the images [7]. On the other hand, two papers focused on the use of non-invasive biometrics (i) to assess self-reported responses as well as eye tracking and emotional responses towards specific regions of interest (ROI) in packaging and labels using contactless biometrics through the BioSensory© application (The University of Melbourne, Parkville, VIC, Australia) [8], and (ii) to evaluate the influence of label design and country of origin in wine labels on consumers conscious and subconscious responses using contactless biometrics also with the BioSensory© application [9].
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ItemNo Preview AvailableThe Impact of Wet Fermentation on Coffee Quality Traits and Volatile Compounds Using Digital Technologies(MDPI, 2023-01)Fermentation is critical for developing coffee’s physicochemical properties. This study aimed to assess the differences in quality traits between fermented and unfermented coffee with four grinding sizes of coffee powder using multiple digital technologies. A total of N = 2 coffee treatments—(i) dry processing and (ii) wet fermentation—with grinding levels (250, 350, 550, and 750 µm) were analysed using near-infrared spectrometry (NIR), electronic nose (e-nose), and headspace/gas chromatography–mass spectrometry (HS-SPME-GC-MS) coupled with machine learning (ML) modelling. Most overtones detected by NIR were within the ranges of 1700–2000 nm and 2200–2396 nm, while the enhanced peak responses of fermented coffee were lower. The overall voltage of nine e-nose sensors obtained from fermented coffee (250 µm) was significantly higher. There were two ML classification models to classify processing and brewing methods using NIR (Model 1) and e-nose (Model 2) values as inputs that were highly accurate (93.9% and 91.2%, respectively). Highly precise ML regression Model 3 and Model 4 based on the same inputs for NIR (R = 0.96) and e-nose (R = 0.99) were developed, respectively, to assess 14 volatile aromatic compounds obtained by GC-MS. Fermented coffee showed higher 2-methylpyrazine (2.20 ng/mL) and furfuryl acetate (2.36 ng/mL) content, which induces a stronger fruity aroma. This proposed rapid, reliable, and low-cost method was shown to be effective in distinguishing coffee postharvest processing methods and evaluating their volatile compounds, which has the potential to be applied for coffee differentiation and quality assurance and control.
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ItemNo Preview AvailableNovel packaging development, assessment and authentication using smart technologies, non-invasive biometric sensory tools and artificial intelligence(Springer Nature Singapore, 2022-01-19)Packaging creates the first impression from consumers when selecting commercial food or beverages. Different packaging components are important as they contain all areas of interest related to branding, shape, design and nutritional information, which could determine willingness to purchase and success of products in the market. However, traditional packaging acceptability assessments based on focus groups, acceptance and preference tests may be biased and subjective. Therefore, novel assessment methods have been developed based on more objective parameters, including non-invasive biometrics such as eye tracking, emotional responses from consumers and changes in physiological parameters, such as heart rate and body temperature. Emerging technologies have also been studied for packaging assessment, such as virtual/augmented reality and artificial intelligence tools, including computer vision and machine learning modelling. Furthermore, counterfeiting has been a major issue among commercial products, with food and beverages accounting for 10% counterfeited, including packaging and branding. This chapter focuses on the latest research on intelligent and digital technologies for packaging development, assessing consumer acceptability towards packaging and authentication using new and emerging digital technologies.
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ItemMethodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture(MDPI, 2022-10)When adopting remote sensing techniques in precision agriculture, there are two main areas to consider: data acquisition and data analysis methodologies [...].
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ItemEarly Detection of Fusarium oxysporum Infection in Processing Tomatoes (Solanum lycopersicum) and Pathogen-Soil Interactions Using a Low-Cost Portable Electronic Nose and Machine Learning Modeling(MDPI, 2022-11)The early detection of pathogen infections in plants has become an important aspect of integrated disease management. Although previous research demonstrated the idea of applying digital technologies to monitor and predict plant health status, there is no effective system for detecting pathogen infection before symptomatology appears. This paper presents the use of a low-cost and portable electronic nose coupled with machine learning (ML) models for early disease detection. Several artificial neural network models were developed to predict plant physiological data and classify processing tomato plants and soil samples according to different levels of pathogen inoculum by using e-nose outputs as inputs, plant physiological data, and the level of infection as targets. Results showed that the pattern recognition models based on different infection levels had an overall accuracy of 94.4-96.8% for tomato plants and between 94.81% and 96.22% for soil samples. For the prediction of plant physiological parameters (photosynthesis, stomatal conductance, and transpiration) using regression models or tomato plants, the overall correlation coefficient was 0.97-0.99, with very significant slope values in the range 0.97-1. The performance of all models shows no signs of under or overfitting. It is hence proven accurate and valid to use the electronic nose coupled with ML modeling for effective early disease detection of processing tomatoes and could also be further implemented to monitor other abiotic and biotic stressors.