Rapid methods and sensors for milk quality monitoring and spoilage detection.

Monitoring of food quality, in particular, milk quality, is critical in order to maintain food safety and human health. To guarantee quality and safety of milk products and at the same time deliver those as soon as possible, rapid analysis methods as well as sensitive, reliable, cost-effective, easy-to-use devices and systems for process control and milk spoilage detection are needed. In this paper, we review different rapid methods, sensors and commercial systems for milk spoilage and microorganism detection. The main focus lies on chemical sensors and biosensors for detection/monitoring of the well-known indicators associated with bacterial growth and milk spoilage such as changes in pH value, conductivity/impedance, adenosine triphosphate level, concentration of dissolved oxygen and produced CO2. These sensors offer several advantages, like high sensitivity, fast response time, minimal sample preparation, miniaturization and ability for real-time monitoring of milk spoilage. In addition, electronic-nose- and electronic-tongue systems for the detection of characteristic volatile and non-volatile compounds related to microbial growth and milk spoilage are described. Finally, wireless sensors and color indicators for intelligent packaging are discussed.

A compact and low cost electronic nose for aroma detection.

This article explains the development of a prototype of a portable and a very low-cost electronic nose based on an mbed microcontroller. Mbeds are a series of ARM microcontroller development boards designed for fast, flexible and rapid prototyping. The electronic nose is comprised of an mbed, an LCD display, two small pumps, two electro-valves and a sensor chamber with four TGS Figaro gas sensors. The performance of the electronic nose has been tested by measuring the ethanol content of wine synthetic matrices and special attention has been paid to the reproducibility and repeatability of the measurements taken on different days. Results show that the electronic nose with a neural network classifier is able to discriminate wine samples with 10, 12 and 14% V/V alcohol content with a classification error of less than 1%.