Assessment of milk fat based on signal-to-ground voltage

Wu, Shilin; Zhang, Huang; Jin, Yamei; Yang, Na; Xu, Xueming; Xie, Zhengjun

doi:10.1007/s11694-020-00733-5

Cited by 2 publications

(1 citation statement)

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“…The milk with different amount of fat is described from its mien, texture, and taste (McCarthy et al, 2017;Soukoulis et al, 2010). Recently, different novel technologies and methods have been studied among milk fat evaluation and assessment, such as fluorescent sensor (Xu et al, 2014), Spectralsensitive Pulsed Photometry (Ragni et al, 2016), Dielectric (Zhu et al, 2015), molecular distillation (Berti et al, 2018), HPLC-UV detection (Abdellatif et al, 2020), electromagnetic (Wu et al, 2021) and Nuclear Magnetic Resonance (NMR) (Sacchi et al, 2018). In addition, long evaluation time, high instrumental 4 cost, complex pre-treatment of the samples and use of expertise for measurements and analysis are some limitations, defects, and disadvantages of previous studies for milk fat evaluation.…”

Section: Introductionmentioning

confidence: 99%

Rapid monitoring of milk fat using Image processing coupled with ANN and PSO methods

Nouri

Mohtasebi

Moghadam

et al. 2022

Preprint

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Monitoring the main compositions of milk content like fat, lactose, protein and total solids, has become a major challenge in dairy cattle farming. For quantitative determination of fat content in milk based on the relation of milk color features different methods have been used, but long time, high cost, and need for experts for analysis are some disadvantages of them. In this study, for rapid monitoring of milk fat content, novel technology of image processing coupled with artificial neural network (ANN) and Particle swarm optimization (PSO) methods has been applied. The estimated milk fat content of the best proposed method was extensively compared with the reference sample (R2=0.99, MAE=0.22, and MSE=0.05). Moreover, effect of water on color components of milk with different percentages of fat content have been investigated. Results approved the proposed method as a reliable, rapid and low-cost method for monitoring milk fat content.

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