Identifikasi Kualitas Susu Sapi dengan Menggunakan Deret Sensor Gas dan Potensiometri dengan Metode Neural Network

Identifikasi kualitas susu merupakan sebuah upaya untuk menggolongkan kondisi susu sapi yang akan dikonsumsi. Dalam identifikasi kualitas susu membutuhkan proses pengecekan laboratorium dengan waktu lama. Pengenalan tersebut dapat diketahui dengan melihat mikroorganisme yang umum ditemukan dalam susu. Selain itu dapat juga langsung dideteksi dengan menggunakan hidung dan lidah. Namun, ini berbahaya karena dapat mempengaruhi kesehatan manusia. Selain itu, indra manusia memiliki sensitivitas yang berbeda dan tidak akurat dalam mendeteksi kualitas susu. Pada penelitian ini telah mengembangkan sensor untuk mengidentifikasi kualitas susu. Peran hidung manusia diganti dengan deret sensor gas yang bertujuan untuk identifikasi dari aroma susu. Sedangkan peran lidah diganti dengan deret sensor potensiometri untuk identifikasi rasa atau senyawa dalam susu. Output sensor gas dan potensiometri akan menjadi masukan bagi neural network. Fungsi neural network ini adalah untuk mengidentifikasi kualitas susu dengan cara dilatih terlebih dahulu. Hasil penelitian ini dapat menghasilkan pola yang berbeda terhadap sampel susu yaitu susu segar, basi, dan sangat basi. Hasil identifikasi menggunakan neural network memiliki tingkat keberhasilan 83%. Penelitian ini diharapkan dapat digunakan untuk menilai kualitas susu dengan cepat, mudah dan akurat. ======================= Identification of milk quality is an attempt to classify the condition of cow's milk to be consumed. Currently, the identification of milk quality requires laboratory tests which is time-consuming. This is due to the identification of milk quality by analyzing the microorganisms commonly found in milk. In addition, milk quality can be directly detected by using the human nose and tongue. However, this is harmful because it can affect the human health. In addition, the human senses have a different sensitivity that is not accurate in detecting the quality of milk. In this study has developed a sensor system to assess the quality of milk. The role of the human nose is replaced by gas sensor array for the identification of the smell or odor of milk. While the tongue is taken over by a potentiometric sensor array for identification of taste or compounds in the milk. The output of the gas sensors and potensiometric sensors become input for the neural network. The function of this neural network to identificaion of milk quality by way of being trained first. The experimental result shows that this sensor array can produce different patterns to the fresh, sour, and spoiled milk samples. The Neural Network can be used to assess the quality of milk with a success rate of 83%. This technique is expected to be used as a tool to assess the quality of milk quickly, easily, and accurately

Milk quality analysis based on a novel ultrasound spectroscopy method

In this paper, a promising technique for milk analysis based on an ultrasound spectroscopy is presented and tested. Particularly, it employ a wide-band random multi-sine as excitation signal to analyse the system responses aiming to obtain its transfer function. This signal assure a better utilization of the signal spectrum, compacting the energy in the frequency band of interest. An experimental setup has been developed to perform polydisperse system analysis with different ultrasonic techniques. Six different milk types were analysed, allowing to evaluate different fat, carbohydrate and protein concentrations. Results showed the transfer functions amplitude and phase variations for each type of milk studied