A Method of Protein Model Classification and Retrieval Using Bag-of-Visual-Features

In this paper we propose a novel visual method for protein model classification and retrieval. Different from the conventional methods, the key idea of the proposed method is to extract image features of proteins and measure the visual similarity between proteins. Firstly, the multiview images are captured by vertices and planes of a given octahedron surrounding the protein. Secondly, the local features are extracted from each image of the different views by the SURF algorithm and are vector quantized into visual words using a visual codebook. Finally, KLD is employed to calculate the similarity distance between two feature vectors. Experimental results show that the proposed method has encouraging performances for protein retrieval and categorization as shown in the comparison with other methods

Dynamic spot analysis in the 2D electrophoresis gels images

Práce shrnuje faktory a parametry, které ovlivňují výsledky 2D elektroforézy, se zaměřením na zpracování obrazu jako jeden ze způsobů snížení nesprávné interpretace jejích výstupů. Proces zpracování obrazu využívá jako zdroj dat především obrazů z opakovaných provedení téhož pokusu, neboli víceplik. Pomocí analýzy obrazů víceplik je možno pozorovat nebo korigovat změny jednoho pokusu a také porovnávat je s výstupy jiných pokusů. Cílem práce je poskytnout podporu specialistovi, který má na starosti popsat vlastnosti struktur nacházejících se v elektroforetických obrazech.The text briefly describes factors and parameters which influence the results of 2D electrophoresis focusing on image processing as one manner to reduce incorrect interpretation of its outputs. As dataset, image processing performance uses images from repeated execution of one experiment also known as multiplicates. Using multiplicates analysis it is possible to observe or lower the changes of one experiment and to compare them with outputs of other experiments. The aim of this work is to provide support for specialist who takes care about describing the character patterns located in electrophoretic images.

SENSORY CHARACTERIZATION OF COMMERCIAL PLANT-BASED MEAT ALTERNATIVES

Consumers are increasingly interested in plant-based meat alternatives (PBMAs) as dietary protein sources, so the demand for products that closely mimic animal meat, especially in taste and texture, has grown. While few sensory studies on PBMAs exist, which are mostly focused on new formulations using functional ingredients or under-utilized plant proteins, there is little sensory information on different categories of commercial plant-based food products. This study aimed to evaluate the sensory attributes of a variety of commercially available plant protein alternatives with a focus on taste and texture. The descriptive-analytical method was used to identify differences among commercial plant-based protein alternatives. Nine samples (n=9) from two categories, ‘mildly processed and refined products’, were selected from a doctoral study and purchased from the Kuppitta city market in Turku, Finland. Trained panelists (N = 10) compared the samples' taste and texture attributes with references on a line scale from 0 to 10 (0 – no intensity, 10 – high intensity). Panel performance was investigated to ascertain the important attributes. The significant difference observed between the samples and their attributes was benchmarked at p<0.05. Principal component analysis revealed that umami, saltiness, and sweetness were positively correlated but all showed a negative correlation with softness. Similarly, a positive correlation was observed between rubbery and moistness while correlating negatively with crumbliness. The study revealed that the refined products were more meat-like in taste and texture than those of the mildly processed category. The outcome of this study establishes that plant-based food sensory characteristics are influenced by the manufacturers’ choice of processing techniques, ingredient formulations, and/or the type of plant protein utilized during processing

The importance of physicochemical characteristics and nonlinear classifiers in determining HIV-1 protease specificity

This paper reviews recent research relating to the application of bioinformatics approaches to determining HIV-1 protease specificity, outlines outstanding issues, and presents a new approach to addressing these issues. Leading machine learning theory for the problem currently suggests that the direct encoding of the physicochemical properties of the amino acid substrates is not required for optimal performance. A number of amino acid encoding approaches which incorporate potentially relevant physicochemical properties of the substrate are identified, and are evaluated using a nonlinear task decomposition based neuroevolution algorithm. The results are evaluated, and compared against a recent benchmark set on a nonlinear classifier using only amino acid sequence and identity information. Ensembles of these nonlinear classifiers using the physicochemical properties of the substrate are demonstrated to consistently outperform the recently published state-of-the-art linear support vector machine based approach in out-of-sample evaluations

Optimisation of plant protein and transglutaminase content in novel beef restructured steaks for older adults by central composite design

peer-reviewedWith the goal of optimising a protein-enriched restructured beef steak targeted at the nutritional and chemosensory requirements of older adults, technological performance of thirty formulations, containing plant-based ingredients, pea protein isolate (PPI), rice protein (RP) and lentil flour (LF) with transglutaminase (TG) to enhance binding of meat pieces, were analysed. Maximal protein content of 28% in cooked product was achieved with PPI, RP and LF. Binding strength was primarily affected by TG, while textural parameters were improved with LF inclusion. Optimal formulation (F) to obtain a protein-enriched steak with lowest hardness values was achieved with TG (2%), PPI (8%), RP (9.35%) and LF (4%). F, F1S (optimal formulation 1 with added seasoning) and control restructured products (not containing plant proteins or seasonings) were scored by 120 consumers' aged over-65 years. Controls were most preferred (P < .05), while F1S were least liked by the older consumers. Consumer testing suggests further refinement and optimisation of restructured products with plant proteins should be undertaken.This research was funded by the FIRM programme administered under the Irish Department of Agriculture, Food and the Marine, Meat4Vitality: Enhancement of texture, flavour and nutritional value of meat products for older adults and the Walsh Fellowship Scheme (11/F/045)

Production of a yeast-free focaccia with reduced salt content using a selected Leuconostoc citreum strain and seawater

Abstract A biotechnological protocol to produce a focaccia (a typical Italian flat bread) without bakers' yeast addition and with reduced salt was developed, to meet the current needs of the consumer. Based on its leavening capability, the Leuconostoc citreum strain C2.27 was selected to be used as a starter instead of the baker's yeast and inoculated in a liquid sourdough (type-II) for the production of the "yeast-free" focaccia. The addition of different NaCl concentrations and the replacement of the salt with food grade seawater were evaluated, and the capability of the selected strain to affect technological, nutritional and sensory features of the focaccia investigated. A significant improvement of the nutritional characteristics of the focaccia was observed compared to the control (leavened with bakers' yeast and added with NaCl 1.5 g/100 g) using 0.7 g/100 g of salt in the form of NaCl or seawater. Besides the reduced Na content (66% lower than the control), focaccia with seawater also showed a higher content of Ca2+ and Mg2+ (ca. 36% and 53%, respectively), and the lowest predicted glycemic index compared to the other experimental focaccia

An Empirical Study of Different Approaches for Protein Classification

Many domains would benefit from reliable and efficient systems for automatic protein classification. An area of particular interest in recent studies on automatic protein classification is the exploration of new methods for extracting features from a protein that work well for specific problems. These methods, however, are not generalizable and have proven useful in only a few domains. Our goal is to evaluate several feature extraction approaches for representing proteins by testing them across multiple datasets. Different types of protein representations are evaluated: those starting from the position specific scoring matrix of the proteins (PSSM), those derived from the amino-acid sequence, two matrix representations, and features taken from the 3D tertiary structure of the protein. We also test new variants of proteins descriptors. We develop our system experimentally by comparing and combining different descriptors taken from the protein representations. Each descriptor is used to train a separate support vector machine (SVM), and the results are combined by sum rule. Some stand-alone descriptors work well on some datasets but not on others. Through fusion, the different descriptors provide a performance that works well across all tested datasets, in some cases performing better than the state-of-the-art

Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment

In recent years, new and emerging digital technologies applied to food science have been gaining attention and increased interest from researchers and the food/beverage industries. In particular, those digital technologies that can be used throughout the food value chain are accurate, easy to implement, affordable, and user-friendly. Hence, this Special Issue (SI) is dedicated to novel technology based on sensor technology and machine/deep learning modeling strategies to implement artificial intelligence (AI) into food and beverage production and for consumer assessment. This SI published quality papers from researchers in Australia, New Zealand, the United States, Spain, and Mexico, including food and beverage products, such as grapes and wine, chocolate, honey, whiskey, avocado pulp, and a variety of other food products