2,174 research outputs found
Multispectral images of peach related to firmness and maturity at harvest
wo multispectral maturity classifications for red soft-flesh peaches (‘Kingcrest’, ‘Rubyrich’ and ‘Richlady’ n = 260) are proposed and compared based on R (red) and R/IR (red divided by infrared) images obtained with a three CCD camera (800 nm, 675 nm and 450 nm). R/IR histograms were able to correct the effect of 3D shape on light reflectance and thus more Gaussian histograms were produced than R images. As fruits ripened, the R/IR histograms showed increasing levels of intensity. Reference measurements such as firmness and visible spectra also varied significantly as the fruit ripens, firmness decreased while reflectance at 680 nm increased (chlorophyll absorption peak)
A comprehensive review of fruit and vegetable classification techniques
Recent advancements in computer vision have enabled wide-ranging applications in every field of life. One such application area is fresh produce classification, but the classification of fruit and vegetable has proven to be a complex problem and needs to be further developed. Fruit and vegetable classification presents significant challenges due to interclass similarities and irregular intraclass characteristics. Selection of appropriate data acquisition sensors and feature representation approach is also crucial due to the huge diversity of the field. Fruit and vegetable classification methods have been developed for quality assessment and robotic harvesting but the current state-of-the-art has been developed for limited classes and small datasets. The problem is of a multi-dimensional nature and offers significantly hyperdimensional features, which is one of the major challenges with current machine learning approaches. Substantial research has been conducted for the design and analysis of classifiers for hyperdimensional features which require significant computational power to optimise with such features. In recent years numerous machine learning techniques for example, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Trees, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) have been exploited with many different feature description methods for fruit and vegetable classification in many real-life applications. This paper presents a critical comparison of different state-of-the-art computer vision methods proposed by researchers for classifying fruit and vegetable
Quantitative Ink Analysis: Estimating the Number of Inks in Documents through Hyperspectral Imaging
In the field of document forensics, ink analysis plays a crucial role in
determining the authenticity of legal and historic documents and detecting
forgery. Visual examination alone is insufficient for distinguishing visually
similar inks, necessitating the use of advanced scientific techniques. This
paper proposes an ink analysis technique based on hyperspectral imaging, which
enables the examination of documents in hundreds of narrowly spaced spectral
bands, revealing hidden details. The main objective of this study is to
identify the number of distinct inks used in a document. Three clustering
algorithms, namely k-means, Agglomerative, and c-means, are employed to
estimate the number of inks present. The methodology involves data extraction,
ink pixel segmentation, and ink number determination. The results demonstrate
the effectiveness of the proposed technique in identifying ink clusters and
distinguishing between different inks. The analysis of a hyperspectral cube
dataset reveals variations in spectral reflectance across different bands and
distinct spectral responses among the 12 lines, indicating the presence of
multiple inks. The clustering algorithms successfully identify ink clusters,
with k-means clustering showing superior classification performance. These
findings contribute to the development of reliable methodologies for ink
analysis using hyperspectral imaging, enhancing th
Sensores: De los biosensores a la nariz electrónica
The recent advances in sensor devices have allowed the developing of new applications in many technological fields. This review describes the current state-of-the-art of this sensor technology, placing special emphasis on the food applications. The design, technology and sensing mechanism of each type of sensor are analysed. A description of the main characteristics of
the electronic nose and electronic tongue (taste sensors) is also given. Finally, the applications of some statistical procedures in sensor systems are described briefly.Los recientes avances en los sistemas de sensores han permitido el desarrollo de nuevas aplicaciones en muchos campos tecnológicos. Este artículo de revisión describe el estado actual de esta nueva tecnología, con especial énfasis en las aplicaciones alimentarias. El diseño, la tecnología y el mecanismo sensorial de cada tipo de sensor son analizados en el artículo. También se describen las principales características de la nariz y la lengua electrónica (sensores de sabor). Finalmente, se describe brevemente el uso de algunos procedimientos estadísticos en sistemas de sensores.Peer reviewe
A Fuzzy-Wavelet Neural Network Model for the Detection of Meat Spoilage using an Electronic Nose
Food product safety is one of the most promising
areas for the application of electronic noses. The performance
of a portable electronic nose has been evaluated in monitoring
the spoilage of beef fillet stored aerobically at different storage
temperatures (0, 4, 8, 12, 16 and 20°C). This paper proposes a
fuzzy-wavelet neural network model which incorporates a
clustering pre-processing stage for the definition of fuzzy rules.
The dual purpose of the proposed modeling approach is not
only to classify beef samples in the respective quality class (i.e.
fresh, semi-fresh and spoiled), but also to predict their
associated microbiological population directly from volatile
compounds fingerprints. Comparison results indicated that the
proposed modeling scheme could be considered as a valuable
detection methodology in food microbiolog
Sensores: De los biosensores a la nariz electrónica
The recent advances in sensor devices have allowed the
developing of new applications in many technological fields. This
review describes the current state-of-the-art of this sensor
technology, placing special emphasis on the food applications.
The design, technology and sensing mechanism of each type of
sensor are analysed. A description of the main characteristics of
the electronic nose and electronic tongue (taste sensors) is also
given. Finally, the applications of some statistical procedures in
sensor systems are described briefly.Los recientes avances en los sistemas de sensores han permitido
el desarrollo de nuevas aplicaciones en muchos campos
tecnológicos. Este artículo de revisión describe el estado actual
de esta nueva tecnología, con especial énfasis en las aplicaciones
alimentarias. El diseño, la tecnología y el mecanismo sensorial
de cada tipo de sensor son analizados en el artículo. También
se describen las principales características de la nariz y la lengua
electrónica (sensores de sabor). Finalmente, se describe brevemente
el uso de algunos procedimientos estadísticos en sistemas
de sensores
Neuro-Fuzzy Based Intelligent Approaches to Nonlinear System Identification and Forecasting
Nearly three decades back nonlinear system identification consisted of several ad-hoc approaches, which were restricted to a very limited class of systems. However, with the advent of the various soft computing methodologies like neural networks and the fuzzy logic combined with optimization techniques, a wider class of systems can be handled at present. Complex systems may be of diverse characteristics and nature. These systems may be linear or nonlinear, continuous or discrete, time varying or time invariant, static or dynamic, short term or long term, central or distributed, predictable or unpredictable, ill or well defined. Neurofuzzy hybrid modelling approaches have been developed as an ideal technique for utilising linguistic values and numerical data. This Thesis is focused on the development of advanced neurofuzzy modelling architectures and their application to real case studies. Three potential requirements have been identified as desirable characteristics for such design: A model needs to have minimum number of rules; a model needs to be generic acting either as Multi-Input-Single-Output (MISO) or Multi-Input-Multi-Output (MIMO) identification model; a model needs to have a versatile nonlinear membership function.
Initially, a MIMO Adaptive Fuzzy Logic System (AFLS) model which incorporates a prototype defuzzification scheme, while utilising an efficient, compared to the Takagi–Sugeno–Kang (TSK) based systems, fuzzification layer has been developed for the detection of meat spoilage using Fourier transform infrared (FTIR) spectroscopy. The identification strategy involved not only the classification of beef fillet samples in their respective quality class (i.e. fresh, semi-fresh and spoiled), but also the simultaneous prediction of their associated microbiological population directly from FTIR spectra. In the case of AFLS, the number of memberships for each input variable was directly associated to the number of rules, hence, the “curse of dimensionality” problem was significantly reduced. Results confirmed the advantage of the proposed scheme against Adaptive Neurofuzzy Inference System (ANFIS), Multilayer Perceptron (MLP) and Partial Least Squares (PLS) techniques used in the same case study.
In the case of MISO systems, the TSK based structure, has been utilized in many neurofuzzy systems, like ANFIS. At the next stage of research, an Adaptive Fuzzy Inference Neural
Network (AFINN) has been developed for the monitoring the spoilage of minced beef utilising multispectral imaging information. This model, which follows the TSK structure,
incorporates a clustering pre-processing stage for the definition of fuzzy rules, while its final fuzzy rule base is determined by competitive learning. In this specific case study, AFINN model was also able to predict for the first time in the literature, the beef’s temperature directly from imaging information. Results again proved the superiority of the adopted model. By extending the line of research and adopting specific design concepts from the previous case studies, the Asymmetric Gaussian Fuzzy Inference Neural Network (AGFINN) architecture has been developed. This architecture has been designed based on the above design principles. A clustering preprocessing scheme has been applied to minimise the number of fuzzy rules. AGFINN incorporates features from the AFLS concept, by having the
same number of rules as well as fuzzy memberships. In spite of the extensive use of the standard symmetric Gaussian membership functions, AGFINN utilizes an asymmetric
function acting as input linguistic node. Since the asymmetric Gaussian membership function’s variability and flexibility are higher than the traditional one, it can partition the input space more effectively. AGFINN can be built either as an MISO or as an MIMO system. In the MISO case, a TSK defuzzification scheme has been implemented, while two different learning algorithms have been implemented. AGFINN has been tested on real datasets related to electricity price forecasting for the ISO New England Power Distribution System. Its performance was compared against a number of alternative models, including ANFIS, AFLS, MLP and Wavelet Neural Network (WNN), and proved to be superior. The concept of asymmetric functions proved to be a valid hypothesis and certainly it can find application to other architectures, such as in Fuzzy Wavelet Neural Network models, by designing a suitable flexible wavelet membership function. AGFINN’s MIMO characteristics also make the proposed architecture suitable for a larger range of applications/problems
Application of an electronic nose coupled with fuzzy-wavelet network for the detection of meat spoilage
Food product safety is one of the most promising areas for the application of electronic noses. During the last twenty years, these sensor-based systems have made odour analyses possible. Their application into the area of food is mainly focused on quality control, freshness evaluation, shelf-life analysis and authenticity assessment. In this paper, the performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillets stored either aerobically or under modified atmosphere packaging, at different storage temperatures. A novel multi-output fuzzy wavelet neural network model has been developed, which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the relevant quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population. Comparison results against advanced machine learning schemes indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology
Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression
Effectively cleaning equipment is essential for the safe production of food but requires a significant amount of time and resources such as water, energy, and chemicals. To optimize the cleaning of food production equipment, there is the need for innovative technologies to monitor the removal of fouling from equipment surfaces. In this work, optical and ultrasonic sensors are used to monitor the fouling removal of food materials with different physicochemical properties from a benchtop rig. Tailored signal and image processing procedures are developed to monitor the cleaning process, and a neural network regression model is developed to predict the amount of fouling remaining on the surface. The results show that the three dissimilar food fouling materials investigated were removed from the test section via different cleaning mechanisms, and the neural network models were able to predict the area and volume of fouling present during cleaning with accuracies as high as 98% and 97%, respectively. This work demonstrates that sensors and machine learning methods can be effectively combined to monitor cleaning processes
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