Original papersDevelopment of two dielectric sensors coupled with computational techniques for detecting milk adulteration
Introduction
Milk, as the main and prevalent dairy product worldwide, has rapidly increasing consumption in community due to its high nutritional value and benefits to human safety leading to its high demands all over the world. Nonetheless, economic revenue has caused the fraudulent and adulterate motivations which threat the public health. It is worth mentioning milk fraud and milk adulteration are two different terms. Fraud in milk includes some illegal activities such as tampering, over run, theft, diversion, adulteration, mislabeling, and counterfeiting (Handford et al., 2016). Thus milk adulteration, as a subcategory of fraud, is the illegitimate adding foreign chemical compounds to milk for various aims such as increasing milk weight for sale by adding water, increasing milk shelf-life particularly in warm and hot seasons during transportation by adding detergent chemicals. Also, some chemicals can be added to enhance the cosmetic nature of milk like foamy appearance (Soomro et al., 2014). Therefore, dairies are facing a serious issue to characterize between authentic and adulterated milk samples because dairy industry has to confirm the raw milk quality supplied by farmers and ensure consumers for purchasing fresh milk from the market.
Traditionally, screening milk adulteration is carried out by chemical assay methods and chromatographic techniques such as HPLC and GC–MS. These approaches suffer from many drawbacks because they are expensive, time-consuming and labor-intensive (Rebechi et al., 2016). Even human based methods like sensory evaluation have some disadvantages because much time and attempt should be spent to choose and train the expert panelists. Furthermore, besides subjectivity and low reproducibility of this method, human senses are prone to fatigue leading to response lag and impact on detection accuracy. Therefore these drawbacks limit the applications of above mentioned methods to rapid and cost-effective screen of milk adulteration.
One of the ways to detect milk adulteration is the fabrication and development of reliable diagnosing tools and techniques and nowadays, there is an increasing interest in developing simple, non-expensive, fast, and on line instruments for this goal. Such trend has therefore become of interest with application of non-destructive measurement tools such as electronic nose and tongue, electrical admittance measurement, biosensors, frequency conductance measurement, spectroscopy, image processing and ultrasound for milk adulteration evaluation. In this context, readers are referred to the following more recent studies about instrumental detection of milk adulteration (Finete et al., 2013, Abernethy and Higgs, 2013, Motta et al., 2014).
Santos et al. (2016) conducted an experimental procedure to quantify different milk adulteration types involving urea, hydrogen peroxide, synthetic urine and synthetic milk. For this purpose, they employed time domain nuclear magnetic resonance (D NMR) and multivariate techniques such as principal component analysis (PCA), soft independent modelling of class analogy (SIMCA), k nearest neighbors (kNN), and partial least squares regression (PLSR) to classify different levels of constitutes added to milk. They concluded that the technique could predict the adulteration levels and classification models with success rates larger than 90%. In another study, Lim et al. (2016) evaluated the melamine-milk powder mixture samples with different concentration levels within 0.02–0.1 g/ml range using NIR hyperspectral imaging system. They employed regression coefficient of partial least squares regression (PLSR) for developing models to melamine particles detection. Although the method showed an effective tool to milk adulteration detection, this type of imaging system is expensive.
Detergent materials are also used to reduce milk microbial load especially in warm transportation conditions. The study by Kumar et al. (2016) dealt with the detection of aniodic detergent in milk using unmodified gold nanoparticles. They suggested the fabricated sensor does not need expensive instrumentation and is simple to use in dairy. However, their method was merely specific for detergent detection not for a variety of adulteration types.
In recent years, special attention has been paid to computerized methods in milk adulteration research. Since distinguishing among adulterated samples needs to consider many variables, so the instruments should be coupled with such computational approaches so called chemometrics to achieve high classification accuracy. Such techniques combined with rich-data instrumental systems are used to qualitatively classify unknown samples with similar attributes as well as to quantitatively determine the adulteration levels in samples (Moore et al., 2012).
Mendes et al. (2016) employed orthogonal partial least square (OPLD) and multiple layer regression (MLP) for analyzing the data gained by fatty acid (FA) profile method on adulterated milk samples. However, the method proposed by the authors is time consuming.
Measuring dielectric properties has been shown promising results for milk evaluation (Nunes et al., 2006, Guo et al., 2010) because such properties (dielectric constant, dielectric loss factor, loss tangent) mainly depend on the physicochemical changes in the food sample (Naderi-Boldaji et al., 2015, Mireei et al., 2016). The dielectric studies on milk are more dedicated to detection of milk freshness or compositions (e.g. fat content) and less to adulteration detection.
This study was thus aimed at the development and application of two different types of dielectric sensors for detection of some prevalent adulterations in milk. The measurement systems have been coupled with advanced chemometrics to screen the type and also the level of milk adulterations. This is the first study to develop and examine the dielectric sensors as a simple, cheap and non-destructive method to milk authentication. The low cost of the sensors is one of the major advantages of this method because it does not require any particular costly measuring system. Considering above novelty statements and to knowledge of the authors, the available literature lacks reports on the title of this study. Therefore the research idea in this paper to screen the type and the level of adulterant substances using dielectric sensors combined with computational techniques is quite original and novel.
Section snippets
Parallel-plate dielectric sensor
A parallel-plate capacitor (PPC) was constructed with dimensions of 50 ∗ 50 mm and 25 mm gap between the plates. The selected dimensions were based on a series of trial and errors which resulted in a more sensitive performance of the sensor with respect to the adulteration levels of milk. Larger dimensions (either larger area of plates or the gap between the plates) of the sensor showed poor sensitivity most likely because of insufficient power of the function generator to provide an effective
Dielectric spectra evaluation
The typical shapes of the dielectric spectra of the PPC (as affected by sodium bicarbonate) and CS sensors (as affected by water), are shown in Fig. 2a and b, respectively. It is indicated that the power of the parallel-plate capacitor increased with increasing the content of sodium bicarbonate, more clearly reflected in the frequency range of 10–70 MHz (Fig. 2a). It is interestingly found that the dielectric spectra show some spiky peaks and valleys with a meaningful trend with the percent of
Conclusion
Application of parallel plate and cylindrical dielectric sensors showed promising capability to demonstrate adulterant substance type and level in raw milk. By use of different data mining techniques, the information content of the variables from the second instrument (cylindrical sensor) is lower than those from the first instrument (parallel plate capacitor) in terms of classification performances. With the first instrument the prediction rates with SIMCA, MRM and TREE was respectively 76, 76
Acknowledgements
The technical and financial supports of Shahrekord University for this research are gratefully appreciated.
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