School of Agriculture, Food and Ecosystem Sciences - Research Publications
Permanent URI for this collection
Search Results
1 - 10 of 57
-
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.
-
ItemNo Preview Available
-
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].
-
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.
-
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.
-
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.
-
ItemLivestock Identification Using Deep Learning for Traceability(MDPI, 2022-11)Farm livestock identification and welfare assessment using non-invasive digital technology have gained interest in agriculture in the last decade, especially for accurate traceability. This study aimed to develop a face recognition system for dairy farm cows using advanced deep-learning models and computer vision techniques. This approach is non-invasive and potentially applicable to other farm animals of importance for identification and welfare assessment. The video analysis pipeline follows standard human face recognition systems made of four significant steps: (i) face detection, (ii) face cropping, (iii) face encoding, and (iv) face lookup. Three deep learning (DL) models were used within the analysis pipeline: (i) face detector, (ii) landmark predictor, and (iii) face encoder. All DL models were finetuned through transfer learning on a dairy cow dataset collected from a robotic dairy farm located in the Dookie campus at The University of Melbourne, Australia. Results showed that the accuracy across videos from 89 different dairy cows achieved an overall accuracy of 84%. The computer program developed may be deployed on edge devices, and it was tested on NVIDIA Jetson Nano board with a camera stream. Furthermore, it could be integrated into welfare assessment previously developed by our research group.
-
ItemRapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning(MDPI, 2022-11)Rice fraud is one of the common threats to the rice industry. Conventional methods to detect rice adulteration are costly, time-consuming, and tedious. This study proposes the quantitative prediction of rice adulteration levels measured through the packaging using a handheld near-infrared (NIR) spectrometer and electronic nose (e-nose) sensors measuring directly on samples and paired with machine learning (ML) algorithms. For these purposes, the samples were prepared by mixing rice at different ratios from 0% to 100% with a 10% increment based on the rice's weight, consisting of (i) rice from different origins, (ii) premium with regular rice, (iii) aromatic with non-aromatic, and (iv) organic with non-organic rice. Multivariate data analysis was used to explore the sample distribution and its relationship with the e-nose sensors for parameter engineering before ML modeling. Artificial neural network (ANN) algorithms were used to predict the adulteration levels of the rice samples using the e-nose sensors and NIR absorbances readings as inputs. Results showed that both sensing devices could detect rice adulteration at different mixing ratios with high correlation coefficients through direct (e-nose; R = 0.94-0.98) and non-invasive measurement through the packaging (NIR; R = 0.95-0.98). The proposed method uses low-cost, rapid, and portable sensing devices coupled with ML that have shown to be reliable and accurate to increase the efficiency of rice fraud detection through the rice production chain.
-
ItemDigital Detection of Olive Oil Rancidity Levels and Aroma Profiles Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Machine Learning Modelling(MDPI, 2022-05)The success of the olive oil industry depends on provenance and quality-trait consistency affecting the consumers' acceptability/preference and purchase intention. Companies rely on laboratories to analyze samples to assess consistency within the production chain, which may be time-consuming, cost-restrictive, and untimely obtaining results, making the process more reactive than predictive. This study proposed implementing digital technologies using near-infrared spectroscopy (NIR) and a novel low-cost e-nose to assess the level of rancidity and aromas in commercial extra-virgin olive oil. Four different olive oils were spiked with three rancidity levels (N = 17). These samples were evaluated using gas-chromatography-mass-spectroscopy, NIR, and an e-nose. Four machine learning models were developed to classify olive oil types and rancidity (Model 1: NIR inputs; Model 2: e-nose inputs) and predict the peak area of 16 aromas (Model 3: NIR; Model 4: e-nose inputs). The results showed high accuracies (Models 1–2: 97% and 87%; Models 3–4: R = 0.96 and 0.93). These digital technologies may change companies from a reactive to a more predictive production of food/beverages to secure product quality and acceptability.
-
ItemEditorial: Special Issue "Implementation of Digital Technologies on Beverage Fermentation"(MDPI, 2022-03)In the food and beverage industries, implementing novel methods using digital technologies such as artificial intelligence (AI), sensors, robotics, computer vision, machine learning (ML), and sensory analysis using augmented reality (AR) has become critical to maintaining and increasing the products’ quality traits and international competitiveness, especially within the past five years [...]