Exploiting Data Mining for Authenticity Assessment and Protection of High-Quality Italian Wines from Piedmont

This paper discusses the data mining approach followed in a project called TRAQUASwine, aimed at the definition of methods for data analytical assessment of the authenticity and protection, against fake versions, of some of the highest value Nebbiolo-based wines from Piedmont region in Italy. This is a big issue in the wine market, where commercial frauds related to such a kind of products are estimated to be worth millions of Euros. The objective is twofold: to show that the problem can be addressed without expensive and hyper-specialized wine analyses, and to demonstrate the actual usefulness of classification algorithms for data mining on the resulting chemical profiles. Following Wagstaff\u2019s proposal for practical exploitation of machine learning (and data mining) approaches, we describe how data have been collected and prepared for the production of different datasets, how suitable classification models have been identified and how the interpretation of the results suggests the emergence of an active role of classification techniques, based on standard chemical profiling, for the assesment of the authenticity of the wines target of the stud

Sustainable management of the main two Maltese indigenous grape varieties for winemaking

Ġellewża and Girgentina, the main two Maltese indigenous grape varieties that are used for winemaking, are grown in small parcels of land with an average size just over 0.1 ha. Policies and regulations that influence the sector have been set up and this increased the need for studies to understand the current management practices and how can they be more sustainable. Through interviews with 30 growers of the indigenous grape varieties it was found that most vines are trained as bush vines and cane pruned with 2 or 3 canes with about 5 to 10 buds in each. Nutrition is mainly given in the form of artificial fertilizer or organic pellets, but the timing is varied. Growers also have varied strategies for plant protection, mainly based on the use of sulfur and copper sulfate, and systemic fungicides. 14 growers also used insecticides, but the use of herbicides was not found to be common. Only 12 growers irrigated their vines and the water quality was found to be different from one location to another, with very high water conductivity, chlorides, and nitrates observed in some locations, mainly those close to Għajn Rihana. Data obtained from sugar levels and yield of 2010, 2011, and 2012 harvests were analysed. Temperature, precipitation and wind speed data for those three seasons were also reviewed to understand the trends along the viticulture season. For both varieties, there was no significant correlation observed between yield and sugar levels in most seasons. The brix was not significantly different from one season to another in most scenarios considered but the yield for 2012 was found to be significantly higher from that of the two other seasons, most probably due to weather conditions. The average yield for Ġellewża was higher than the average yield for Girgentina in every season. PCA analysis showed that the brix and yield data cannot be distinguished by location of vineyard. A list of recommendations was presented to ensure that the management practices are improved and therefore prove that sustainability is the way forward for Maltese vineyards and winemaking

Nonlinear feature extraction through manifold learning in an electronic tongue classification task

A nonlinear feature extraction-based approach using manifold learning algorithms is developed in order to improve the classification accuracy in an electronic tongue sensor array. The developed signal processing methodology is composed of four stages: data unfolding, scaling, feature extraction, and classification. This study aims to compare seven manifold learning algorithms: Isomap, Laplacian Eigenmaps, Locally Linear Embedding (LLE), modified LLE, Hessian LLE, Local Tangent Space Alignment (LTSA), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to find the best classification accuracy in a multifrequency large-amplitude pulse voltammetry electronic tongue. A sensitivity study of the parameters of each manifold learning algorithm is also included. A data set of seven different aqueous matrices is used to validate the proposed data processing methodology. A leave-one-out cross validation was employed in 63 samples. The best accuracy (96.83%) was obtained when the methodology uses Mean-Centered Group Scaling (MCGS) for data normalization, the t-SNE algorithm for feature extraction, and k-nearest neighbors (kNN) as classifier.Peer ReviewedPostprint (published version

Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems

The implementation of artificial intelligence (AI), together with robotics, sensors, sensor networks, Internet of Things (IoT), and machine/deep learning modeling, has reached the forefront of research activities, moving towards the goal of increasing the efficiency in a multitude of applications and purposes related to environmental sciences. The development and deployment of AI tools requires specific considerations, approaches, and methodologies for their effective and accurate applications. This Special Issue focused on the applications of AI to environmental systems related to hazard assessment in urban, agriculture, and forestry areas

Food Recognition and Ingredient Detection Using Electrical Impedance Spectroscopy With Deep Learning Techniques to Facilitate Human-food Interactions

Food is a vital component of our everyday lives closely related to our health, well-being, and human behavior. The recent advancements of Spatial Computing technologies, particularly in Human-Food interactive (HFI) technologies have enabled novel eating and drinking experiences, including digital dietary assessments, augmented flavors, and virtual and augmented dining experiences. When designing novel HFI technologies, it is essential to recognize different food and beverages and their internal attributes (i.e., food sensing), such as volume and ingredients. As a result, contemporary research employs image analysis techniques to identify food items, notably in digital dietary assessments. These techniques, often combined with AI algorithms, analyze digital food images to extract various information about food items and quantities. However, these visual food analyzing methods are ineffective when: 1) identifying food’s internal attributes, 2) discriminating visually similar food and beverages, and 3) seamlessly integrating with people’s natural interactions while consuming food (e.g., automatically detecting the food when using a spoon to eat). This thesis presents a novel approach to digitally recognize beverages and their attributes, an essential step towards facilitating novel human-food interactions. The proposed technology has an electrical impedance measurement unit and a recognition method based on deep learning techniques. The electrical impedance measurement unit consists of the following components: 1) a 3D printed module with electrodes that can be attached to a paper cup, 2) an impedance analyzer to perform Electrical Impedance Spectroscopy (EIS) across two electrodes to acquire measurements such as a beverage’s real part of impedances, imaginary part of impedances, phase angles, and 3) a control module to configure the impedance analyzer and send measurements to a computer that has the deep learning framework to conduct the analysis. Two types of multi-task learning models (hard parameter sharing multi-task network and multi-task network cascade) and their variations (with principal component analysis and different combinations of features) were employed to develop a proof-of-concept prototype to recognize eight different beverage types with various volume levels and sugar concentrations: two types of black tea (LiptonTM and TwiningsTM English-Breakfast), two types of coffee (StarbucksTM dark roasted and medium roasted), and four types of soda (regular and diet coca-cola, and regular and diet Pepsi). Measurements were acquired from these beverages while changing volume levels and sugar concentrations to construct training and test datasets. Both types of networks were trained using the training dataset while validated with the test dataset. Results show that the multi-task network cascades outperformed the hard parameter sharing multi-task networks in discriminating against a limited number of drinks (accuracy = 96.32%), volumes (root mean square error = 13.74ml), and sugar content (root mean square error = 7.99gdm3). Future work will extend this approach to include additional beverage types and their attributes to improve the robustness and performance of the system and develop a methodology to recognize solid foods with their attributes. The findings of this thesis will contribute to enable a new avenue for human-food interactive technology developments, such as automatic food journaling, virtual flavors, and wearable devices for non-invasive quality assessment

Harnessing Evolution in-Materio as an Unconventional Computing Resource

This thesis illustrates the use and development of physical conductive analogue systems for unconventional computing using the Evolution in-Materio (EiM) paradigm. EiM uses an Evolutionary Algorithm to configure and exploit a physical material (or medium) for computation. While EiM processors show promise, fundamental questions and scaling issues remain. Additionally, their development is hindered by slow manufacturing and physical experimentation. This work addressed these issues by implementing simulated models to speed up research efforts, followed by investigations of physically implemented novel in-materio devices. Initial work leveraged simulated conductive networks as single substrate ‘monolithic’ EiM processors, performing classification by formulating the system as an optimisation problem, solved using Differential Evolution. Different material properties and algorithm parameters were isolated and investigated; which explained the capabilities of configurable parameters and showed ideal nanomaterial choice depended upon problem complexity. Subsequently, drawing from concepts in the wider Machine Learning field, several enhancements to monolithic EiM processors were proposed and investigated. These ensured more efficient use of training data, better classification decision boundary placement, an independently optimised readout layer, and a smoother search space. Finally, scalability and performance issues were addressed by constructing in-Materio Neural Networks (iM-NNs), where several EiM processors were stacked in parallel and operated as physical realisations of Hidden Layer neurons. Greater flexibility in system implementation was achieved by re-using a single physical substrate recursively as several virtual neurons, but this sacrificed faster parallelised execution. These novel iM-NNs were first implemented using Simulated in-Materio neurons, and trained for classification as Extreme Learning Machines, which were found to outperform artificial networks of a similar size. Physical iM-NN were then implemented using a Raspberry Pi, custom Hardware Interface and Lambda Diode based Physical in-Materio neurons, which were trained successfully with neuroevolution. A more complex AutoEncoder structure was then proposed and implemented physically to perform dimensionality reduction on a handwritten digits dataset, outperforming both Principal Component Analysis and artificial AutoEncoders. This work presents an approach to exploit systems with interesting physical dynamics, and leverage them as a computational resource. Such systems could become low power, high speed, unconventional computing assets in the future

Essays in Wine Economics

This thesis explores relevant topics in wine economics and viticulture from a multidisciplinary perspective. It is based on six main analytical chapters (i.e., published papers in scientific journals) and two other publications added as appendixes. The first two papers use econometric methods to quantify the potential impact of climate change in Australia. They show that climate change will likely have a negative impact on the country’s viticulture, mainly due to the deteriorating effect that higher temperatures could have on grape (and wine) quality. The third paper classifies and describes the world’s wine regions based on their climates. It also shows that for maintaining wine styles, winegrowers in many regions may need to source winegrapes from regions with more appropriate (usually cooler) climates or to plant alternative winegrape varieties that do better in their climates. This situation is not different in Australia, as suggested in the first two papers and discussed in the first two appendixes. The fourth paper shows that, far from becoming more diverse, the mix of winegrape varieties is becoming more similar across countries and more concentrated globally. While the main aim of the fifth paper is to analyse how globalisation has changed the impact of some key variables on wine trade flows, it also shows that countries with a more similar mix of winegrape varieties trade more wine (although this is not necessarily a causal relationship). Finally, the last paper estimates the impact of the European grapevine moth on grape production and justifies its eradication program.Thesis (Ph.D.) -- University of Adelaide, School of Economics and Public Policy, 202

Implementation of Digital Technologies on Beverage Fermentation

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. Fermented beverages have been one of the most researched industries to implement these technologies to assess product composition and improve production processes and product quality. This Special Issue (SI) is focused on the latest research on the application of digital technologies on beverage fermentation monitoring and the improvement of processing performance, product quality and sensory acceptability