Review: NIR Spectroscopy as a Suitable Tool for the Investigation of the Horticultural Field

The last 10 years of knowledge on near infrared (NIR) applications in the horticultural field are summarized. NIR spectroscopy is considered one of the most suitable technologies of investigation worldwide used as a nondestructive approach to monitoring raw materials and products in several fields. There are different types of approaches that can be employed for the study of key issues for horticultural products. In this paper, an update of the information collected from the main specific International Journals and Symposia was reported. Many papers showed the use of NIR spectroscopy in the horticultural field, and the literature data were grouped per year, per product, and per application, such as studies of direct (chemical composition) and indirect (physical and sensorial) properties (P), process control (PC), and authenticity and classification studies (AC). A mention was made of a recent innovative approach that considers the contribution of water absorption in the study of biological systems

A Review on Deep Learning Approaches for Spectral Imaging

Deep learning algorithms have revolutionized the computer vision field in the last decade. They can reduce tedious feature engineering and have opened new possibilities of automated visual inspection. With deep learning techniques, the availability of large amounts of qualitative labeled data became more important than ever. The main share of computer vision research focuses on RGB images. With the advances in sensor technologies multi- and hyperspectral cameras have become more cost effective and accessible in recent years, allowing this imaging technology to be applied to new fields of application. This article gives an overview of approaches to apply deep learning techniques to multi- or hyperspectral data. Several state-of-the-art methods will be reviewed and problems and difficulties will be discussed. An overview of a selection of available datasets is presented. To give a broad and diverse insight, research from different fields of application are considered, namely the remote sensing domain, the agricultural domain and the food industry

Thoughts for Foods: Imaging Technology Opportunities for Monitoring and Measuring Food Quality

In recent decades, the quality and safety of fruits, vegetables, cereals, meats, milk, and their derivatives from processed foods have become a serious issue for consumers in developed as well as developing countries. Undoubtedly, the traditional methods of inspecting and ensuring quality that depends on the human factor, some mechanical and chemical methods, have proven beyond any doubt their inability to achieve food quality and safety, and thus a failure to achieve food security. With growing attention on human health, the standards of food safety and quality are continuously being improved through advanced technology applications that depend on artificial intelligence tools to monitor the quality and safety of food. One of the most important of these applications is imaging technology. A brief discussion in this chapter on the utilize of multiple imaging systems based on all different bands of the electromagnetic spectrum as a principal source of various imaging systems. As well as methods of analyzing and reading images to build intelligence and non-destructive systems for monitoring and measuring the quality of foods

Quality changes of pear during shelf-life

The aims of our research work were the investigation of postharvest changes of pear samples (Pyrus communis cv. Bosc kobak) during combined cold storage and shelf-life (storage at room temperature), the determination of quality changes by mainly non-destructive methods, the modeling of the changes of the non-destructive parameters (acoustic, impact stiffness coefficient, chlorophyll fluorescence parameters [Fv/Fm, Fm/F0]), and multivariate statistical analysis of the measured and predicted data based on the data of the non-destructive texture analysis (acoustic and impact methods), chlorophyll fluorescence analysis and laser scattering measurement. Storage Time Equivalent Value (STEV) was calculated and introduced based on mass-loss analysis. The changes of the non-destructive parameters were analyzed vs. this virtual storage time (STEV). The changes of acoustic, impact stiffness coefficient and chlorophyll fluorescence parameters can be predicted by exponential function. The predicted time constants of the parameters were 21.0, 45.8, 47.1, 83.4, acoustic, impact stiffness coefficient, Fm/F0, Fv/Fm, respectively. The lower the time constant, the quicker is the change of the given parameter during storage, the higher is its sensitivity. By this point of view, the percentage mass loss related sensitivity to the characterization of textural changes, the predicted acoustic stiffness coefficient was found to be more sensitive than the impact stiffness coefficient. The Fm/F0 value characterized more sensibly the changes of the chlorophyll fluorescence than in the literature commonly used Fv/Fm. The non-contact laser scattering method based significant PLS models were constructed to predict the quality related pear characteristics (mechanical properties, chlorophyll fluorescence parameters)

OCM 2023 - Optical Characterization of Materials : Conference Proceedings

The state of the art in the optical characterization of materials is advancing rapidly. New insights have been gained into the theoretical foundations of this research and exciting developments have been made in practice, driven by new applications and innovative sensor technologies that are constantly evolving. The great success of past conferences proves the necessity of a platform for presentation, discussion and evaluation of the latest research results in this interdisciplinary field

Crop Disease Detection Using Remote Sensing Image Analysis

Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops

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