Monitoring of Milk Quality With Disposable Taste Sensor

Indian Journal of Science and Technology, 2018

Objectives: The aim of this work is shows the elaboration of a device based on PSOC technology called electronic tongue, which is able to separate samples of raw milk. Methods/Statistical Analysis: The device developed consists of an electrochemical sensor array based on conductive polymers, a multi-channel system of electrochemical measurements, responsible for acquire and processing signals from substances trough sensors, and a data processing stage capable to discriminate different substances with multivariate statistical methods. Findings: The results show how this system was capable of offering unique signal front for each different samples of milk, the data obtained were discriminated by data processing stage thanks to the use of a multivariate statistical method called Principal Components Analysis. This work has been focused on the dairy sector, with the purpose to provide a device capable to make inspection of the raw milk in field. Application/Improvements: The device elaborated in our laboratory is compact and portable (347 g). The signals registered with the sensor array of the electronic tongue allowed to discriminate adulterated milk samples.

Sensors and Actuators B: Chemical

This paper reports the use of a semiconductor thin ®lms based electronic nose to recognise the rancidity of two different kinds of milk (UHT and pasteurised) during their ageing days. The employed sensor array consists of ®ve different SnO 2 thin ®lms prepared by means of the sol±gel technology. The data coming from the response of the sensors have been elaborated by principal component analysis (PCA) in order to obtain a classi®cation of the data clusters related to different milk ageing days and so track the dynamic evolution of milk rancidity. The obtained results could lead to industrial applications in dairying for the quality control of starting milk and the rancidity check during the different stages of production.

Mljekarstvo, 2009

AIP Conference Proceedings, 2016

An electronic tongue (e-tongue) consisted of a sensor array of the ion-selective electrode based on lipid/polymer membranes has been developed. The e-tongue consisted of 8 sensors with gold electrodes and equipped with an Arduino Mega 2560 Microcontroller-based data acquisition system. The lipid/polymer membranes were fabricated by combining polyvinyl chloride (PVC) as a matrix, 4 different kinds of plasticiser material (2-NPOE, bis(2ethylhexyl) sebacate, bis(2-ethylhexyl) phosphate, and cis(1-butyl pentyl) adipate), and 4 different kinds of lipids (octadecyl amine, oleoyl alcohol, methyl trioctylammonium chloride, and oleic acid) for an active material. Potentiometric measurements were carried out with a reference electrode Ag/AgCl. Data logger was equipped with a moving average filter for reducing the noise. From the score plot of principal component analysis (PCA), the first and second PCs contribute 90.5% of the total variance. It indicates that the developed e-tongue is able to classify dairy products according to the types and brands.

Sensors and Actuators B: Chemical, 2009

An electronic tongue with 36 cross-sensibility sensors was built allowing a successful recognition of the five basic taste standards, showing high sensibility to acid, salty and umami taste substances and lower performance to bitter and sweet tastes. The taste recognition capability was afterwards tested in the detection of goat milk adulteration with bovine milk, which is a problem for the dairy industry. This new methodology is an alternative to the classical analytical methods used to detect caprine milk adulterations with bovine milk, being a simpler, faster and economical procedure. The different signal profiles recorded by the e-tongue device together with linear discriminant analysis allowed the implementation of a model that could distinguish between raw skim milk groups (goat, cow and goat/cow) with an overall sensibility and specificity of 97% and 93%, respectively. Furthermore, cross-validation showed that the model was able to correct classify unknown milk samples with a sensibility and specificity of 87% and 70%, respectively. Additionally, the model robustness was confirmed since it correctly or incorrectly classified milk samples with, respectively, higher and lower probabilities than those that could be expected by chance.

International Journal of Current Research and Review, 2021

Introduction: Monitoring milk quality is very crucial to maintain food safety and human health. The identification or detection of milk spoilage holds a prominent role in the improvement of milk preservation methods. Objective: The focus of this work lies on rapid, reliable and cost-effective method of milk spoilage detection for milk quality determination. Methods: Various physio-chemical properties of milk including fat, acidity, pH, ultrasonic velocity, thermal conductivity were performed to identify the major changes associated with spoilage parameter responsible for spoilage. This method emphasized milk spoilage detection using impedance measurement by using chip AD5933. These spoilage parameters were correlated with microbial spoilage of milk. Results: The pH and acidity value of fresh raw milk was 6.68 and 0.17% respectively. These two key parameters to evaluate milk freshness. There was significant difference in ultrasonic velocity from 1504.41 + 0.12m/sec to 1692.24 + 0.16m/sec at 72 hours. An impedance measurement apparatus was set up using chip AD5933 that shows a higher impedance reading when the microbial count was high. There is a prominent correlation between impedance and microbial growth. Therefore, electrical impedance could be considered as an indicator for the determination of milk quality. Conclusion: This study demonstrated a rapid, cost-effective measurement technique to determine spoilage of milk using an apparatus set up for Impedance measurement.

World Academy of Science, Engineering and Technology, International Journal of Agricultural and Biosystems Engineering, 2014

The aim of the present study was to develop a rapid method for electronic nose for online quality control of oat milk. Analysis by electronic nose and bacteriological measurements were performed to analyze spoilage kinetics of oat milk samples stored at room temperature and refrigerated conditions for up to 15 days. Principal component analysis (PCA), Discriminant Factorial Analysis (DFA) and Soft Independent Modelling by Class Analogy (SIMCA) classification techniques were used to differentiate the samples of oat milk at different days. The total plate count (bacteriological method) was selected as the reference method to consistently train the electronic nose system. The e-nose was able to differentiate between the oat milk samples of varying microbial load. The results obtained by the bacteria total viable counts showed that the shelf-life of oat milk stored at room temperature and refrigerated conditions were 20hrs and 13 days, respectively. The models built classified oat milk ...

Milchwissenschaft, 2012

Food Research International, 2010

In this paper, studies were conducted to determine the odor profile feature of five commercial milk flavorings (three natural milk flavorings and two synthetic flavorings) and one self-made enzyme induced milk flavoring prepared by lipolyzed milk fat. The discrimination of different flavor analysis methods was also evaluated. The ability of an electronic nose consisting of 18 metal oxide semiconductor (MOS) sensors to correlate with the solid phase micro extraction gas chromatography mass spectrometry (SPME-GC-MS) analysis and the sensory test of different milk flavorings was determined. The principal component analysis (PCA) of electronic nose sensor data has been compared with the data provided by GC-MS and sensory analysis. The results indicate that the electronic nose sensors can clearly and rapidly distinguish the difference among the synthetic milk flavorings, the natural milk flavorings and the enzyme induced milk flavoring. And the discrimination among different natural milk flavorings, which were easily confused by sensory tests, was also well performed by the electronic nose.

2001

An electronic nose unit including 14 conducting polymer sensors, was used to detect the volatile pro®les produced by uninoculated skimmed milk media or that inoculated with bacteria (Pseudomonas aureofaciens, P.¯uorescens, Bacillus cereus) or yeasts (Candida pseudotropicalis, Kluyveromyces lactis) when grown for 5 h at 308C. Using discriminant function analyses (DFA) it was possible to separate unspoiled milk and that containing spoilage bacteria or yeasts. The sensor array used was a useful discriminator of microbial volatile pro®les. Quantitative differentiation between three different concentrations of P. aureofaciens (10 6 , 3X5 Â 10 8 , 8 Â 10 8 CFUs ml À1 ) was also investigated and showed that the system could effectively differentiate between treatments. Using an initial inoculum of about 10 3 ± 10 4 CFUs ml À1 it was possible to discriminate between unspoiled milk, yeasts and bacterial species (S. aureus, B. cereus and the Pseudomonas spp.) using principal component analyses (PCA), and also between the bacteria, the unspoiled milk, and the two yeasts C. pseudotropicalis and K. lactis with 85% of the data accounted for. The potential for differentiation between four of the ®ve individual bacterial and yeast species was analysed after 5 h growth at 258C by using a three-layer back propagation neural network (NN) of 46 input sensor parameters. This showed that it was possible to recognise, and differentiate, between species, the butanol and milk medium controls. Cross validation using labelled individual replicates of treatments as unknowns demonstrated that it was possible to differentiate between (a) butanol controls; (b) unspoiled milk medium; (c) S. aureus; (d) K. lactis; (e) C. psuedotropicalis; and (f) B. cereus. The potential for using an electronic nose system for early detection of microbial spoilage of milk-based products is discussed. #