A rapid method on identifying disqualified raw goat's milk based on total bacterial count by using dielectric spectra. (December 2018)

• Record Type: Journal Article
• Authors: Zhu, Zhuozhuo; Zhu, Xinhua; Kong, Fanrong; Guo, Wenchuan
• Is Part Of: Journal of food engineering. Volume 239(2018)
• Page Start: 40
• Page End: 51
• Publication Date: 2018-12
• Subjects: Goat's milk -- Total bacterial count -- Dielectric spectra -- Chemometrics -- Artificial neural network -- Qualitative analysis; Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Analyse -- Périodiques; Aliments -- Recherche -- Périodiques; 664.005
• DOI: 10.1016/j.jfoodeng.2018.06.020
• Languages: English
• ISSNs: 0260-8774

Abstract

Abstract: Chinese national food safety standard for raw milk regulates that the milk is regarded as hygiene disqualified milk if its total bacterial count (TBC) exceeds 2 × 10 6 CFU/ml. To provide a rapid method for identifying hygiene disqualified milk, the dielectric spectra of 150 raw goat's milk samples were obtained. The partial least squares discriminant analysis, support vector machine (SVM), and extreme learning machine algorithms were applied to build models to identify whether the milk was hygiene qualified or disqualified on TBC. The results showed that SVM based on principal component analysis was the best model with total identification accuracy rate of 100%. The research indicates that the dielectric spectra could be used to detect whether the TBC of milk exceeds the national standard or not, and provides useful information on developing a rapid detector to evaluate milk hygienic quality in-situ or on-line. Highlights: A novel method was developed to identify hygiene disqualified milk. 150 goat's milk samples with different total bacteria count were used. Models were built for identifying hygiene disqualified milk on total bacteria. SVM-PCA was the best model with total identification accuracy rate of 100%. Dielectric spectra have great potential in in-situ or on-line bacteria detection.