An Intelligent Decision Support System for the Detection of Meat Spoilage using Multispectral Images

In food industry, quality and safety are considered important issues worldwide that are directly related to health and social progress. The use of vision technology for quality testing of food production has the obvious advantage of being able to continuously monitor a production using non-destructive methods, thus increasing the quality and minimizing cost. The performance of an intelligent decision support system has been evaluated in monitoring the spoilage of minced beef stored either aerobically or under modified atmosphere packaging, at different storage temperatures (0, 5, 10, and 15 °C) utilising multispectral imaging information. This paper utilises a neuro-fuzzy model which incorporates a clustering pre-processing stage for the definition of fuzzy rules, while its final fuzzy rule base is determined by competitive learning. Initially, meat samples are classified according to their storage conditions, while identification models are then utilised for the prediction of the Total Viable Counts of bacteria. The innovation of the proposed approach is further extended to the identification of the temperature used for storage, utilizing only imaging spectral information. Results indicated that spectral information in combination with the proposed modelling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage

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

Non-destructive evaluation of white striping and microbial spoilage of Broiler Breast Meat using structured-illumination reflectance imaging

Manual inspection is a prevailing practice for quality assessment of poultry meat, but it is labor-intensive, tedious, and subjective. This thesis aims to assess the efficacy of an emerging structured illumination reflectance imaging (SIRI) technique with machine learning approaches for assessing WS and microbial spoilage in broiler breast meat. Broiler breast meat samples were imaged by an in house-assembled SIRI platform under sinusoidal illumination. In first experiment, handcrafted texture features were extracted from direct component (DC, corresponding to conventional uniform illumination) and amplitude component (AC, unique to the use of sinusoidal illumination) images retrieved from raw SIRI pattern images build linear discriminant analysis (LDA) models for classifying normal and defective samples. A further validation experiment was performed using deep learning as a feature extractor followed by LDA. The third experiment was on microbial spoilage assessment of broiler meat, deep learning models were used to extract features from DC and AC images builds on classifiers. Overall, this research has demonstrated consistent improvements of AC over DC images in assessing WS and spoilage of broiler meat and that SIRI is a promising tool for poultry meat quality detection

An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling

Over the past decade, analytical approaches based on vibrational spectroscopy, hyperspectral/multispectral imagining and biomimetic sensors started gaining popularity as rapid and efficient methods for assessing food quality, safety and authentication; as a sensible alternative to the expensive and time-consuming conventional microbiological techniques. Due to the multi-dimensional nature of the data generated from such analyses, the output needs to be coupled with a suitable statistical approach or machine-learning algorithms before the results can be interpreted. Choosing the optimum pattern recognition or machine learning approach for a given analytical platform is often challenging and involves a comparative analysis between various algorithms in order to achieve the best possible prediction accuracy. In this work, “MeatReg”, a web-based application is presented, able to automate the procedure of identifying the best machine learning method for comparing data from several analytical techniques, to predict the counts of microorganisms responsible of meat spoilage regardless of the packaging system applied. In particularly up to 7 regression methods were applied and these are ordinary least squares regression, stepwise linear regression, partial least square regression, principal component regression, support vector regression, random forest and k-nearest neighbours. MeatReg” was tested with minced beef samples stored under aerobic and modified atmosphere packaging and analysed with electronic nose, HPLC, FT-IR, GC–MS and Multispectral imaging instrument. Population of total viable count, lactic acid bacteria, pseudomonads, Enterobacteriaceae and B. thermosphacta, were predicted. As a result, recommendations of which analytical platforms are suitable to predict each type of bacteria and which machine learning methods to use in each case were obtained. The developed system is accessible via the link: http://elvis.misc.cranfield.ac.uk/SORF/

11th International Conference on Predictive Modelling in Food: book of abstracts

It is our great pleasure to welcome you in Bragança, Portugal, for the 11th International Conference of Predictive Modelling in Food (ICPMF11). Since 1992, ten ICPMF editions have taken place, providing a forum for the exchange of ideas, identification of research needs and novel approaches for the advancement of predictive modelling towards ensuring safety and quality of foods. Bragança is a typically-Portuguese old town (Romanic origin dates back to the 10th century), located by the Natural Park of Montesinho – one of the wildest forest zones of Europe – and the Douro Valley – the third oldest protected wine region in the world; and surrounded by traditional villages of a distinctive rustic beauty. Bragança houses several traditional industries producing a myriad of local foods, such as cheese, fermented meats, wine, chestnuts and honey, which provide substantial economic sustainability to the region. ICPMF11 reunites food researchers, stakeholders, risk assessors and users of predictive models to present recent developments and trends in modelling approaches for food quality, safety and sustainability. We succeeded to gather a significant number of delegates from over the world to participate in a comprehensive scientific programme that includes keynote lectures, oral communications and posters, allocated in sessions focusing on: . Advances in predictive microbiology modelling . Predictive modelling in innovative food processing and preservation technologies . Advances in microbial dynamics and interactions . Advances in software and database tools . Meta-analysis protocols and applications . Advances in risk assessment methods and integration of omics techniques . Advances in predictive modelling in food quality and safety . Predictive mycology . Individual cell and whole-cell modelling Apart from those, ICPMF11 features for the first time a special session dedicated to “Innovative approaches for ensuring safety of traditional foods” and the Round Table: “Assuring the Safety of Traditional Foods: A Scientific Contribution to Protecting our Cultural Heritage”. We, as food researchers based in a Mediterranean mountain region, are aware that the production of traditional foods plays a key role in the development of rural regions, since the agricultural commodities used as raw materials are generally produced locally, allowing and stimulating local commercialisation, thus contributing to a sustainable environment, and employment in rural populations. It was inspiring for us to have received many submissions from both developed and developing countries on the valorisation of traditional foods through the application of up-to-date modelling research. Besides that, one morning workshop and three afternoon tutorials were programmed during the day before the scientific programme. The workshop “How to benefit from the Risk Assessment Modelling and Knowledge Integration Platform (RAKIP)” was organised by Matthias Filter. The parallel tutorials “Towards an integrated predictive software map: Practical examples of use of predictive microbiology software tools for food safety and quality”; “Advanced methods in predictive microbiology” and “Topics in quantitative microbial risk assessment using R” were organised by Fernando Pérez-Rodríguez, Pablo Fernández, Alberto Garre and Mariem Ellouze; by Lihan Huang, Cheng-An Hwang and Vasco Cadavez; and by Patrick Njage and Ana Sofia Ribeiro Duarte, respectively. We thank these organisers for their proposals. Abstracts, reviewed by the ICPMF11 Scientific Committee, are published in the present Book of Abstracts while peer-reviewed original research articles will be invited to be published in ICPMF11 Special Issues in the International Journal of Food Microbiology and Microbial Risk Analysis. To stimulate the participation of postgraduate students and young researchers, two kinds of awards were arranged: the Young Researcher Best Oral Presentation prizes, sponsored by Elsevier; and the Developing Scientist Best Poster prizes, sponsored by the International Committee on Food Microbiology and Hygiene (ICFMH) of the International Union of Microbiological Societies (IUMS). For the first time, this ICPMF edition gives out two awards for the Senior Researcher Best Oral Presentation, sponsored by the open-access journal Foods – MDPI. In addition to the scientific programme, we prepared an exciting social programme for delegates to appreciate the rich culture, gastronomy and traditions of Bragança, w includes welcome reception, live music, tasting of regional food and a gala dinner in the Castle of Bragança. We look forward to lively discussions, and hope that this meeting will give you the opportunity to strengthen friendship and cooperation, and build new contacts for future research endeavours.info:eu-repo/semantics/publishedVersio

Recent Advances in Reducing Food Losses in the Supply Chain of Fresh Agricultural Produce

Fruits and vegetables are highly nutritious agricultural produce with tremendous human health benefits. They are also highly perishable and as such are easily susceptible to spoilage, leading to a reduction in quality attributes and induced food loss. Cold chain technologies have over the years been employed to reduce the quality loss of fruits and vegetables from farm to fork. However, a high amount of losses (≈50%) still occur during the packaging, transportation, and storage of these fresh agricultural produce. This study highlights the current state-of-the-art of various advanced tools employed to reducing the quality loss of fruits and vegetables during the packaging, storage, and transportation cold chain operations, including the application of imaging technology, spectroscopy, multi-sensors, electronic nose, radio frequency identification, printed sensors, acoustic impulse response, and mathematical models. It is shown that computer vision, hyperspectral imaging, multispectral imaging, spectroscopy, X-ray imaging, and mathematical models are well established in monitoring and optimizing process parameters that affect food quality attributes during cold chain operations. We also identified the Internet of Things (IoT) and virtual representation models of a particular fresh produce (digital twins) as emerging technologies that can help monitor and control the uncharted quality evolution during its postharvest life. These advances can help diagnose and take measures against potential problems affecting the quality of fresh produce in the supply chains. Plausible future pathways to further develop these emerging technologies and help in the significant reduction of food losses in the supply chain of fresh produce are discussed. Future research should be directed towards integrating IoT and digital twins in order to intensify real-time monitoring of the cold chain environmental conditions, and the eventual optimization of the postharvest supply chains. This study gives promising insight towards the use of advanced technologies in reducing losses in the postharvest supply chain of fruits and vegetables

A review of optical nondestructive visual and near-infrared methods for food quality and safety

This paper is a review of optical methods for online nondestructive food quality monitoring. The key spectral areas are the visual and near-infrared wavelengths. We have collected the information of over 260 papers published mainly during the last 20 years. Many of them use an analysis method called chemometrics which is shortly described in the paper. The main goal of this paper is to provide a general view of work done according to different FAO food classes. Hopefully using optical VIS/NIR spectroscopy gives an idea of how to better meet market and consumer needs for high-quality food stuff.©2013 the Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed

OCM 2017 - Optical Characterization of Materials - conference proceedings

Each material has its own specific spectral signature independent if it is food, plastics, or minerals. During the conference we will discuss new trends and developments in material characterization. You also will be informed about latest highlights to identify spectral footprints and their realizations in industry