Characteristics of the churning process in continuous butter manufacture and modelling using an artificial neural network

Funahashi, Haruyuki; Horiuchi, Jun‐ichi

doi:10.1016/j.idairyj.2007.08.001

Cited by 21 publications

(12 citation statements)

References 10 publications

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“…While, in case of cream fat levels, high cream fat gives low fat content. These results further supported by the findings of Funahashia and Horiuchi, (2008) who reported that in high fat cream small butter globules formation occur with high moisture and low fat content. The results further showed that more fat percentage was found at cream of 35% fat level and 10°C churning temperature.…”

Section: Fatsupporting

confidence: 89%

Development and optimization of sweet cream butter from buffaloes at cottage scale

Siddique¹,

Khan²,

Ahmad³

et al. 2011

Afr. J. Biotechnol.

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The research was intended to optimize and scientifically evaluate the traditional/ household butter making methods for getting good quality and yield of butter by standardizing cream fat levels and altering churning temperatures during butter making. In the present study, cream fat levels were standardized at 30, 35 and 40% and they were churned at different temperatures (8, 10 and 12°C) for making butter samples. The parameters used for quality assessment of butter were significantly influenced by cream fat levels and churning temperature and their interactions. Higher yield (88%) of butter with more total solids (89.17%) was obtained when cream standardized at 35% fat level and churned at 10°C while less non fat solids and moisture content was observed at these levels. A decreasing trend in pH was accompanied with an increase in fat level and churning temperature in all samples. Free fatty acids increased as a function of cream fat levels and churning temperatures. Most of the organoleptic parameters received higher scores when butter samples were prepared from the cream at 35% fat level and churned at 10°C. Using these conditions of fat levels and churning temperature, high quality butter can be produced at cottage scale.

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Section: Fatsupporting

confidence: 89%

Development and optimization of sweet cream butter from buffaloes at cottage scale

Siddique¹,

Khan²,

Ahmad³

et al. 2011

Afr. J. Biotechnol.

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show abstract

“…Artificial neural networks have been used successfully in the dairy industry to predict shelf life (as reviewed by Goyal and Goyal, 2012), physicochemical composition (Etzion et al, 2004;Khanmohammadi et al, 2009), and sensory characteristics (Singh et al, 2009;; to discriminate varieties, geographical origin, or seasonal variations (He et al, 2005;Cruz et al, 2009Cruz et al, , 2013Gori et al, 2012); to control milk quality (Hettinga et al, 2008;Souza et al, 2011); and to model operational parameters during product manufacture (Funahashi and Horiuchi, 2008). As far as cheese manufacture is concerned, ANN have been applied mainly for authentication, classification, or traceability purposes (Pillonel et al, 2005;Zeppa et al, 2005;Barile et al, 2006;Verdini et al, 2007;Cevoli et al, 2011Cevoli et al, , 2013 but also for predicting ripening (Soto-Barajas et al, 2013) or moisture (Jimenez-Marquez et al, 2003 or the optimization of the cheese-making process (Paquet et al, 2000;Horiuchi et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Estimation of somatic cell count levels of hard cheeses using physicochemical composition and artificial neural networks

Hernández-Ramos

Quintana

Revilla

2019

Journal of Dairy Science

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This study addresses the prediction of the somatic cell counts of the milk used in the production of sheep cheese using artificial neural networks. To achieve this objective, the neural network was designed using 33 parameters of the physicochemical composition of the cheeses obtained after they have been matured for 12 mo as input data. The physicochemical analysis of the cheeses revealed that the somatic cell count level of the cheese has a significant influence on the amount of protein, fat, dry extract, and fatty acids. When properly set up, the neural network allows the correct classification of the cheeses (100% of correct results in both training and test phases) and therefore their samples in each of the 3 nominal output variables (low, average, and high somatic cell counts). The fatty composition of the cheeses, individual fatty acids, and fat acidity are the variables that most affect the correct operation of the neural network.

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“…The results are in agreement with the findings reported by Siddique et al (2011). Funahashi and Horiuchi (2008) concluded that the moisture content of butter decreased with increasing shear rate on the beater.…”

Section: Effect Of Churning Temperature and Churn Speed On The Moistumentioning

confidence: 99%

“…The increase in yield with increase in churning temperature might be due to increase in the overrun of butter. Funahashi and Horiuchi (2008) reported that cream temperature is the important factor for deciding the characteristics of churning process. The yield had also increased with increase in churn speed.…”

Section: Effect Of Churning Temperature and Churn Speed On The Yield mentioning

confidence: 99%

Development and performance evaluation of frustum cone shaped churn for small scale production of butter

et al. 2016

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The present research was intended to develop a small scale butter churn and its performance by altering churning temperature and churn speed during butter making. In the present study, the cream was churned at different temperatures (8, 10 and 12°C) and churn speeds (35, 60 and 85 rpm). The optimum parameters of churning time (40 min), moisture content (16 %) and overrun (19.42 %) were obtained when cream was churned at churning temperature of 10°C and churn speed of 60 rpm. Using appropriate conditions of churning temperature and churn speed, high quality butter can be produced at cottage scale.

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Characteristics of the churning process in continuous butter manufacture and modelling using an artificial neural network

Cited by 21 publications

References 10 publications

Development and optimization of sweet cream butter from buffaloes at cottage scale

Development and optimization of sweet cream butter from buffaloes at cottage scale

Estimation of somatic cell count levels of hard cheeses using physicochemical composition and artificial neural networks

Development and performance evaluation of frustum cone shaped churn for small scale production of butter

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