scholarly journals Combined effect of water activity and pH on the growth of food-related ascospore-forming molds

2020 ◽  
Vol 70 (1) ◽  
Author(s):  
Irene Racchi ◽  
Nicoletta Scaramuzza ◽  
Alyssa Hidalgo ◽  
Elettra Berni
Keyword(s):  
Logistic Regression ◽  
Logistic Regression Model ◽  
Raw Materials ◽  
Growth Models ◽  
Combined Effect ◽  
Growth Data ◽  
Food Industries ◽  
Sucrose Solutions ◽  
Germination And Growth ◽  
Ordinary Logistic Regression

Abstract Purpose The contamination of raw materials, packaging, or processing environments by fungal ascospores is a real concern for food industries, where variable rates of spoilage can be reached in pasteurized acidic products such as fruit juices, fruit jams, or soft drinks. The aim of this work was to assess the combined effect of aw and pH on the growth of six isolates from three genera of ascospore-forming molds that may occur in raw materials and in food industrial environments, in order to determine the environmental conditions that prevent the spoilage of pasteurized foods and beverages. Methods Growth tests were carried out on 60-day-old ascospores from Aspergillus hiratsukae (≡Neosartorya hiratsukae), Aspergillus thermomutatus (≡Neosartorya pseudofischeri), Chaetomium flavoviride, Chaetomium globosum, Talaromyces bacillisporus, and Talaromyces trachyspermus. The tests were performed up to 90 days at 25 °C, using sucrose solutions at different aw (0.85, 0.88, 0.92, 0.95) and pH (3.20, 3.50, 3.80, 4.20, 4.60) values. Growth was characterized by fitting an ordinary logistic regression model to the collected growth data. Results The explained percentage of the growth/no growth models ranged between 81.0 and 99.3%: aw exerted the largest influence on the growth of all tested species, while pH was significant only for Chaetomium isolates. The minimum conditions for germination and growth were aw 0.92 and pH 3.50 or 3.80, respectively, for C. flavoviride (46 days) and C. globosum (39 days), aw 0.92 and pH 3.20 for T. trachyspermus (13 days), aw 0.88 and pH 3.20 for T. bacillisporus (39 days), and aw 0.88 and pH 3.20 for the two aspergilli (33 and 27 days, respectively, for A. hiratsukae and A. thermomutatus). Conclusions Most of the spoiling mycetes tested were well-adapted to the formulations considered; therefore, foods strategies aiming to inhibit their growth should explore also the hurdle effect exerted by other factors (e.g., antioxidants, organic acids, oxygen levels).

ANALISIS POLA KLASTER DAN ORIENTASI PASAR (STUDI KASUS PADA SENTRA INDUSTRI KRIPIK TEMPE SANAN KOTA MALANG, 2014)

2015 ◽  
Vol 1 (2) ◽  
pp. 257
Author(s):  
Zaim Mukaffis
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Market Orientation ◽  
Logistic Regression Model ◽  
Raw Materials ◽  
Binary Logistic Regression ◽  
Total Sample ◽  
Export Market ◽  
Binary Logistic Regression Model ◽  
Hub And Spoke

<p>The purpose of this study is to analyze cluster patterns and market orientation (Case study in center of keripik tempe industries in sanan Malang 2014). Biographical characteristics are consists of age, gender, religion, owners education level of the owners, labor education, and wealth. Total sample in the research are 38 respondents. For the purpose, this study uses Cluster Analysis to analyze cluster pattern refers to the variable in the Markussen model (1996) and Logistic Regression Model is used in this study to analyze the important factors that distinguish export-oriented industries and domestic-oriented industries. The results of the identification of the proposed cluster patterns Markusen, it can be concluded that the pattern of industrial district of keripik tempe industries in sanan Malang Regency follows the pattern of clusters Marshallian and the Hub and Spoke. The results of binary logistic regression model analysis in this study showed that of seven independent variables, there are two variables that significantly influence the export market orientation of the age of business and the active promotion. While the training, the labor, the buyer network, technology and network suppliers of raw materials has no effect on export market orientation</p><p>Keywords: Cluster Patterns, Market Orientation, Marshallian, Hub and Spoke</p>

Exploring the performance of logistic regression model types on growth/no growth data of Listeria monocytogenes

2007 ◽  
Vol 114 (3) ◽  
pp. 316-331 ◽  
Author(s):  
K.P.M. Gysemans ◽  
K. Bernaerts ◽  
A. Vermeulen ◽  
A.H. Geeraerd ◽  
J. Debevere ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Listeria Monocytogenes ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Growth Data

scholarly journals Pemodelan Status Usaha (Pengusaha dan Pekerja/Karyawan) Menggunakan Regresi Logistik Multilevel

2017 ◽  
Vol 3 (1) ◽  
pp. 30-38
Author(s):  
Eko Yulian
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Hierarchical Modeling ◽  
Adult Population ◽  
Population Based ◽  
The Status ◽  
Ideal Number ◽  
Ordinary Logistic Regression ◽  
The Ideal

The level of a country's economy is directly proportional to the number of entrepreneurs in the country. According to the World Bank standard number of entrepreneurs, the ideal of a country is at least 4% of the total population. Based on data from the Indonesian Young Entrepreneurs Association (HIPMI), the number of entrepreneurs in Indonesia is only about 1.5%. Of course not easy to achieve the ideal number of bank standards-based world that is 4%. This study aims to determine what factors are driving someone in determining a career as an entrepreneur or not (worker/employee). The data used is the Adult Population Survey (APS) in 2013 conducted by the Global Entrepreneurship Monitor (GEM). the survey conducted in 16 provinces, 51 districts/cities, and 176 subdistricts. Data generated hierarchical modeling that will be performed using multilevel logistic regression. The variables studied were the state variable effort (Y), variable knowent (X1), variable opport (X2), variable suskill (X3), variable fearfail (X4), the variable gender (X5) at level 1 and the variable sub-district at level 2. the analysis showed that the logistic regression model 2-level produce a better model than the ordinary logistic regression model. Based on modeling results we concluded that all predictor variables (knowent, opport, suskill, fearfail, gender, etc.) affect the status of one's business.  

The Effect of Sanctions on Cooperative Behavior: A Study on Medium Trust Individuals in the Context of Corruption

2018 ◽  
Vol 34 (1) ◽  
Author(s):  
Dewi Rosiana ◽  
Achmad Djunaidi ◽  
Indun Lestari Setyono ◽  
Wilis Srisayekti
Keyword(s):  
Logistic Regression ◽  
Public Interest ◽  
Logistic Regression Model ◽  
Cooperative Behavior ◽  
Research Subjects ◽  
Chi Square ◽  
Public Interests ◽  
The Public ◽  
Chi Square Analysis ◽  
Personal Interests

This study aims to describe the effect of sanctions (individual sanctions, collective sanctions, and absence of sanctions) on cooperative behavior of individuals with medium trust in the context of corruption. Both collective sanctions and individual sanctions, are systemic, which means sanctioning behavior is exercised not by each individual but by the system. Cooperative behavior in this context means choosing to obey rules, to reject acts of corruption and to prioritize public interests rather than the personal interests. Conversely, corruption is an uncooperative behavior to the rules, and ignores the public interest and prioritizes personal interests. Research subjects were 62 students. The Chi-Square Analysis was used to see the association between the variables and the logistic regression model was applied to describe the structure of this association. Individual sanction is recommended as punishment to medium trust individuals to promote cooperative behavior in the context of corruption. The results showed that individuals with medium trust had more cooperative behavior.

scholarly journals Seasonality of Non-SARS, Non-MERS Coronaviruses and the Impact of Meteorological Factors

Pathogens ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 187
Author(s):  
Olympia E. Anastasiou ◽  
Anika Hüsing ◽  
Johannes Korth ◽  
Fotis Theodoropoulos ◽  
Christian Taube ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Relative Humidity ◽  
Cloud Cover ◽  
Detection Rate ◽  
Logistic Regression Model ◽  
Meteorological Factors ◽  
Seasonal Pattern ◽  
Weather Factors ◽  
Immunosuppressed Patients ◽  
Immunocompetent Patients

Background: Seasonality is a characteristic of some respiratory viruses. The aim of our study was to evaluate the seasonality and the potential effects of different meteorological factors on the detection rate of the non-SARS coronavirus detection by PCR. Methods: We performed a retrospective analysis of 12,763 respiratory tract sample results (288 positive and 12,475 negative) for non-SARS, non-MERS coronaviruses (NL63, 229E, OC43, HKU1). The effect of seven single weather factors on the coronavirus detection rate was fitted in a logistic regression model with and without adjusting for other weather factors. Results: Coronavirus infections followed a seasonal pattern peaking from December to March and plunged from July to September. The seasonal effect was less pronounced in immunosuppressed patients compared to immunocompetent patients. Different automatic variable selection processes agreed on selecting the predictors temperature, relative humidity, cloud cover and precipitation as remaining predictors in the multivariable logistic regression model, including all weather factors, with low ambient temperature, low relative humidity, high cloud cover and high precipitation being linked to increased coronavirus detection rates. Conclusions: Coronavirus infections followed a seasonal pattern, which was more pronounced in immunocompetent patients compared to immunosuppressed patients. Several meteorological factors were associated with the coronavirus detection rate. However, when mutually adjusting for all weather factors, only temperature, relative humidity, precipitation and cloud cover contributed independently to predicting the coronavirus detection rate.

Sign in / Sign up

Export Citation Format

Share Document