Starter cultures used in probiotic dairy product preparation and popular probiotic dairy drinks

Dairy products that contain probiotic bacteria are those that are produced with various fermentation methods, especially lactic acid fermentation, by using starter cultures and those that have various textures and aromas. Fermented dairy products are popular due to their differences in taste and their favourable physiological effects. Today, fermented dairy beverages in general are produced locally by using traditional methods. Recently, due to the increased demand for natural nutrients and probiotic products, fermented dairy beverages have reached a different position and are considered to have an important impact on human health and nutrition. In this article, probiotic bacteria and functional dairy products that are produced by using probiotic bacteria are discussed.

Use of starter cultures isolated from native microbiota of artisanal sausage in the production of Italian sausage

The most promising microorganisms for use as starter cultures are those isolated from the native microbiota of traditional fermented products. The aim of this study was to evaluate the use of lactic acid bacteria selected from the native microbiota of sausages produced by spontaneous fermentation as starter cultures for the production of sausage. Strains of Lactobacillus plantarum 503 and 341 have the potential for use as starter cultures in the manufacture of Italian sausage type. The population of lactic acid bacteria in sausages was >8 log CFU.g-1 during fermentation, which caused the pH to decrease to <4.5. This decrease in pH and the water activity of < 0.90 of sausages ensures the safety and preservation of this product. Sausages produced with these lactic cultures fulfill the requirements for microbiological quality and composition of Italian sausage type. Our results suggest the possibility of using these starter cultures for the production of sausages with peculiar characteristics that contribute to the identity of the product.

Visual Customer Segmentation and Data Exploration with the Self-Organizing Map

Advancements in information technology have made it possible for organizations to gather and store vast amounts of data of their customers. Information stored in databases can be highly valuable for organizations. However, analyzing large databases has proven to be difficult in practice. For companies in the retail industry, customer intelligence can be used to identify profitable customers, their characteristics, and behavior. By clustering customers into homogeneous groups, companies can more effectively manage their customer base and target profitable customer segments. This thesis will study the use of the self-organizing map (SOM) as a method for analyzing large customer datasets, clustering customers, and discovering information about customer behavior. Aim of the thesis is to find out whether the SOM could be a practical tool for retail companies to analyze their customer data.

Multicultural Belonging. Individuals across cultures, languages and places

The article-based doctoral dissertation deals with adult individuals in Western societies who were born into multilingual and multicultural families and have parents of different nationalities. The study’s participants grew up outside their parents’ countries of origin and relate to a multitude of bonds that link them across various cultures, languages and places. The study explores the social dimension of cultural belonging and examines diverse approaches that enable the participants to create notions of belonging and identification despite possessing at times contradictory transnational allegiances. The works offers new perspectives on transnational belonging and makes a timely contribution to discussions in the fields of cultural heritage studies, ethnology and transnational studies. The dissertation combines qualitative research methods with an insider perspective. The empirical material is based on semi-structured interviews with fifteen participants, among which are also the author’s siblings. The study addresses the relevance of the author’s personal situatedness and her multi-faceted roles as well as ethical concerns related to the methodological approach of insider research. The social dimension of cultural identities affect both the participants’ identification with their multiple attachments and language use in everyday life. The key research findings present interrelated discussions of the participants’ notion of being a mixture, the importance of family bonds and multilingualism, a specific mixed family lifestyle, the notion of non-belonging and the study participants’ sense of otherness as a means of creating communality with others. The study discusses the participants’ various life strategies of flexible relativising, juggling with multiple affiliations, the approach of “blending in” and their sense of ironic nation-ness for constructing a coherent sense of belonging. The author argues that multicultural belonging is inextricably connected to an association with multiple languages, cultures and places. Multicultural belonging is relational and depends on the context, social relationships and locations. The study proposes that multicultural belonging creates a tolerant understanding of membership and enables experiences of cosmopolitanism and selected notions of allegiance.

Wood species identification by visual characterization of fibers

Kandidaatintyö tehtiin osana PulpVision-tutkimusprojektia, jonka tarkoituksena on kehittää kuvapohjaisia laskenta- ja luokittelumetodeja sellun laaduntarkkailuun paperin valmistuksessa. Tämän tutkimusprojektin osana on aiemmin kehitetty metodi, jolla etsittiin kaarevia rakenteita kuvista, ja tätä metodia hyödynnettiin kuitujen etsintään kuvista. Tätä metodia käytettiin lähtökohtana kandidaatintyölle. Työn tarkoituksena oli tutkia, voidaanko erilaisista kuitukuvista laskettujen piirteiden avulla tunnistaa kuvassa olevien kuitujen laji. Näissä kuitukuvissa oli kuituja neljästä eri puulajista ja yhdestä kasvista. Nämä lajit olivat akasia, koivu, mänty, eukalyptus ja vehnä. Jokaisesta lajista valittiin 100 kuitukuvaa ja nämä kuvat jaettiin kahteen ryhmään, joista ensimmäistä käytettiin opetusryhmänä ja toista testausryhmänä. Opetusryhmän avulla jokaiselle kuitulajille laskettiin näitä kuvaavia piirteitä, joiden avulla pyrittiin tunnistamaan testausryhmän kuvissa olevat kuitulajit. Nämä kuvat oli tuottanut CEMIS-Oulu (Center for Measurement and Information Systems), joka on mittaustekniikkaan keskittynyt yksikkö Oulun yliopistossa. Yksittäiselle opetusryhmän kuitukuvalle laskettiin keskiarvot ja keskihajonnat kolmesta eri piirteestä, jotka olivat pituus, leveys ja kaarevuus. Lisäksi laskettiin, kuinka monta kuitua kuvasta löydettiin. Näiden piirteiden eri yhdistelmien avulla testattiin tunnistamisen tarkkuutta käyttämällä k:n lähimmän naapurin menetelmää ja Naiivi Bayes -luokitinta testausryhmän kuville. Testeistä saatiin lupaavia tuloksia muun muassa pituuden ja leveyden keskiarvoja käytettäessä saavutettiin jopa noin 98 %:n tarkkuus molemmilla algoritmeilla. Tunnistuksessa kuitujen keskimäärinen pituus vaikutti olevan kuitukuvia parhaiten kuvaava piirre. Käytettyjen algoritmien välillä ei ollut suurta vaihtelua tarkkuudessa. Testeissä saatujen tulosten perusteella voidaan todeta, että kuitukuvien tunnistaminen on mahdollista. Testien perusteella kuitukuvista tarvitsee laskea vain kaksi piirrettä, joilla kuidut voidaan tunnistaa tarkasti. Käytetyt lajittelualgoritmit olivat hyvin yksinkertaisia, mutta ne toimivat testeissä hyvin.

Pretraining Convolutional Neural Networks for Visual Recognition

Convolutional Neural Networks (CNN) have become the state-of-the-art methods on many large scale visual recognition tasks. For a lot of practical applications, CNN architectures have a restrictive requirement: A huge amount of labeled data are needed for training. The idea of generative pretraining is to obtain initial weights of the network by training the network in a completely unsupervised way and then fine-tune the weights for the task at hand using supervised learning. In this thesis, a general introduction to Deep Neural Networks and algorithms are given and these methods are applied to classification tasks of handwritten digits and natural images for developing unsupervised feature learning. The goal of this thesis is to find out if the effect of pretraining is damped by recent practical advances in optimization and regularization of CNN. The experimental results show that pretraining is still a substantial regularizer, however, not a necessary step in training Convolutional Neural Networks with rectified activations. On handwritten digits, the proposed pretraining model achieved a classification accuracy comparable to the state-of-the-art methods.

Revue des cultures coloniales

1902/03/05 (A6,N96,T10).

Revue des cultures coloniales

1899/11/05 (A3,N40,T5).

Revue des cultures coloniales

1897/06/05 (A1,N1,T1).

Revue des cultures coloniales

1899/07/20 (A3,N33,T5).

Revue des cultures coloniales

1902/01/20 (A6,N93,T10).

Revue des cultures coloniales

1897/07/05 (A1,N2,T1).

Revue des cultures coloniales

1902/02/20 (A6,N95,T10).

Revue des cultures coloniales

1902/02/05 (A6,N94,T10).