Fresh milk has a shelf life of a few hours atrefrigerated temperature of less than 4?, due to increase the shelf life ofmilk and saving the costs of cooling, milk adulterated with chemicals such aspenicillin, formalin and hydrogen peroxide. These additives can increase theshelf life of milk without cooling and these additions have the potential tocause serious health-related problems for human and also affect on processingof milk in dairy industry.
Several analytical techniques can be used to detectadulteration but they often require time-consuming, sample preparation,expensive laboratory equipment, and highly skilled personnel. We employed aportable Near Infrared spectroscopy (NIR) combined with multivariate analysiswas developed to detect adulteration as well as to quantify the level ofhydrogen peroxide in cow milk adulterated. In this study fresh cow milk sampleswere collected from three farms surrounding Beijing district and five farms inHebei province in China were investigated.
Those fresh cow milk samples werethen adulterated with hydrogen peroxide at ten different percentage levels: 0.5%,0.7%, 0.9%, 1%, 1.
5%, 1.7%, 1.9%, 2%, 2.5%, and 2.7% of hydrogen peroxide. Allsamples were scanned using NIR spectroscopy (JDSU, California, USA). TheMicroNIR 1700 spectrometer analyses the wavelength region is 870 – 1660 nm. Thechemometric tools like Principle component analysis (PCA), partial leastdiscriminant analysis (PLS-DA) and partial least squares regression (PLS) wasapplied for statistical analysis of the obtained NIR spectral data.
To checkthe discrimination between the pure and hydrogen peroxide adulterated milksamples, PLS-DA model was used. For PLSDA model the R-square value obtained was0.989 with 0.081 RMSE (Root mean square error). Furthermore, PLS regressionmodel was also built to quantify the levels hydrogen peroxide adulterant in cowmilk samples. The PLS regression model was obtained with the R-square 93% andwith 1.
38 RMSECV(Root mean square error of cross validation) value having goodprediction with RMSEP (Root mean square error of prediction) value 1.50 andcorrelation of 0.95. This recently developed method is non-destructive, low-cost,no need of much sample preparation and having sensitivity level less than 1%level of hydrogen peroxide adulteration. This recently developed method is fastand simple, being suitable for the control of raw milk in a dairy industry orfor the quality inspection of commercialized milk.Keywords: NIR-spectroscopy; hydrogen peroxide;Milk adulteration; PCA; PLS-DA; PLS regression 1.
Introduction Adulteration in food is a biger problem facesthe world, which has required more investigation, because it has attracted moreattention worldwide recently. (Spink and Moyer 2011). Milk is the fluid secreted by the female ofall mammalian species, to meet the essential nutritional requirement of theneonate, and it is very important component in human diets in the worldwide.Milk is most important source for essentialnutrients and health maintenance. Milk is perfect food. It is valuable sourceof fat, protein, carbohydrates, vitamins and minerals.
(Faraz et al., 2013).Milk is a balanced mixture and a perishablefood. It is one of few foods consumed in the natural form throughout the world.Milk contain 87% water, 3.
3% protein , 3.9% fats , 5% lactose and 0.7% ash .Milk provides us building protein for body, giving vitamins, minerals (calcium)for bone forming and energy supplying (lactose and milk fat). Besides providingcertain essential fatty acids it includes all essential amino acid. All theproperties of milk make it an important food for all ages. (Khan et al., 2005).
The low costs and high nutritional value ofmilk has made it form a significant part of the human diet. However, globalincreased demand made this foodstuff pone for more potential adulteration (Salih et al., 2017). In 2016, more than 816 milliontons of milk was produced around the world (including cow, buffalo, sheep, goatand camel milk).
India was the largest producer, with 155.2 millions of tons, USAwas the second major producer (96.3 millions of tons) and China was 43.
4millions of tons, table 1 and figure 1 (FAO, 2016). However, as fast as theproduction and demand for milk have grown, a larger frequency of sophisticatedmilk adulterations has been reported in different countries. year 2014 2015 2016 WORLD 771 803 816 Table 1.
World production milk figure1. World production milkAdulteration of milk and other dairy productshas existed from old times (Astrid et al., 2010), and it is done either forprofit margin or lack of hygienic status of storage, processing, transportationand marketing (Faraz et al., 2013).Cleanliness and purity of milk are indicatingfor good quality milk (Kanwal et al., 2004).
Forquality dairy items and better health of consumer, quality control of milk isrequired (Nirwal et al., 2013).Unfortunately, adulteration is very common indeveloping countries (Salih et al., 2017). Increase population, the urbanization of rural, andscattered colonization are the few main factors affected on increasing thedemand of milk production (Awan et al., 2014). In order to meet the shortage between demand andsupply of milk the unscrupulous producers are often found to involve in milkadulteration by adding adulterating substances in milk (Mustafa et al., 2014).
In fresh milk, chemical used as preservativeslike formalin, hydrogen peroxide, boric acid and antibiotics are added toincrease the shelf life (Chanda et al., 2012).The other kind of adulteration ofmilk by the additions of starch, rice flour, skim milk powder, reconstitutedmilk, urea, melamine, salt, glucose, vegetable oil, animal fat and whey powder.These additions is to increase the thickness and viscosity of the milk, and tomaintain the composition of fat, carbohydrate and protein (Campos Motta etal., 2014; Singuluri and Sukumaran 2014; Soomro et al.
, 2014).So to increase the shelf life, addition ofchemical preservatives in branded milk (Mohanan et al., 2002) is a very commonpractice. Sometimes hydrogen peroxide (H2O2) is used as apreservative (Paixao and Bertotti.2009). These additions have the potential tocause serious health-related problems.
2. Materials and methodsThe Figure2 show a Technical frame diagram of experiment design. 2.1. Cow milk samples preparationIn this study fresh cow milk samples werecollected from three farms surrounding Beijing district and five farms in Hebeiprovince in China were investigated. Those fresh cow milk samples were thenadulterated with hydrogen peroxide at ten different percentage levels: 0.
5%,0.7%, 0.9%, 1%, 1.5%, 1.7%, 1.9%, 2%, 2.5%, and 2.7% of hydrogen peroxide.
Thetotal number of samples used was 114: 34 pure cow milk samples, 80 adulteratedwith hydrogen peroxide. For PLS regression all the samples were joined togetherand split into two sets, a training set (70% of the samples) and a test set forvalidation (30% of the samples).Technical frame diagram: Milk samples Farms Milk directly from cow Adulteration milk samples Control H2O2 Unknown samples NIRS scan Scatter effect remove Calibration Robust NIR method with procedure and optimal models Validation Linear /non-linear analysis Pilot farms/ markets Application 2.2. NIR spectroscopic analysisAll samples were scanned using NIR spectroscopy(JDSU, California/ USA). The MicroNIR 1700 spectrometer analyses the wavelengthregion is 870 – 1660 nm. Prominent absorption peaks were appeared in the regionfrom 930 to 1350 nm wavelength.
2.3. Statistical analysisMicrosoft Excel 2010 and The Unscramblerversion 9.0 by Camo were used for statistical analysis. The PCA, PLS-DA and PLSregression models were built for both pure and adulterated cow milk samples.Spectral pretreatments, such as standard normal variate (SNV) and 1stderivative with Savitzky-Golay smoothing 5 points were carried. Full crossvalidation was used to validate the PLS-DA models. For PLS regression all thesamples were joined together and split into two sets, a training set (70% ofthe samples) and a test set for validation (30% of the samples).
External crossvalidation was used to validate the PLS regression models built with thetraining set. The Root Mean Square Error of Cross Validation (RMSECV) was usedas an internal indicator of the predictive ability of the models. RMSECV iscalculated using equation (1) ——————————————–(1) Where (yi ) is the measured value(actual % of adulteration), (?i) is the % of adulteration predictedby the model, and n is the number of segments left-out in the cross-validationprocedure, which is equal to the number of samples of the training set. Smallervalues of RMSECV are indicative of a better prediction ability of the model.The RMSEP is a statistical measure how wellthe model predicts new samples (not used when building the model). It iscalculated using equation (2) ——————————————– (2) Where yt,i is the measured value(actual % of adulteration), ?t,i is the % of adulteration predictedby the model, and nt is the number of samples in the test set. RMSEPexpresses the average error to be expected in future predictions when thecalibration model is applied to unknown samples. 3.
Results and Discussion3.1. Near infrared spectraFigure 3 shows the NIR spectra of all thesamples ranging from 870 – 1660 nm in term of wavelength while in term of wavenumbers ranging from 11494.
25 -6024.10 cm-1. Figure 3 NIR spectra both for pure and hydrogen peroxide adulterated milk The spectra in Figure 3 show a scatteringeffect due to milk and hydrogen peroxide solid particles and white colors.Spectral pretreatments, such as SNV were used to remove the scattering effectas shown in Figure 4. Figure4 NIR spectra both for pure and hydrogen peroxide adulterated milk samplesafter SNV pre-processing. Although the spectra appear to be verysimilar, the application of a 1st derivative function withSavitzky-Golay smoothing 5 points were applied at 2 polynomial order shows thatthere are clear differences in the spectral absorption regions as shown inFigure 5. Figure5: 1st derivative NIR spectra both for pure and hydrogen peroxide adulteratedmilk samples.
It can be seen from the spectra in Figures 5 that there are prominentabsorption peaks at wavelength 980 nm and 1200 nm for both pure and hydrogenperoxide adulterated milk samples. Figure 6:PCA score plot for four different types of cow milks samples In order to visualize the effect of variationamong the tow different places (Beijing & Hebei) of cow milk an alternativeapproach of principal components analysis (PCA), was applied in that a PCAmodel was built as shown in Figure 6. PCA is a standard multivariate dataanalysis exploratory tool. It is used to reduce the dimensionality of a complexdata set without much loss of information, to extract the most importantinformation from the data table, to identify noise and outlier in the data set.It is a way of identifying the underlying patterns in data for further analysisusing other techniques.
The procedure of PCA is like that it converts a set ofcorrelated variables into a new set of uncorrelated variables called principalcomponents. PCA redistributes the total variance of the data set in such a waythat the first principal component has maximum variance, followed by secondcomponent and so on.VariancePC1>Variance PC2>… Variance PCkTotalvariance=Variance PC1+Variance PC2+ … Variance PCkThe covariance of any of the principal componentwith any other principal component is zero (uncorrelated) and they areorthogonal to each other. It canbe seen from the PCA score plot that there is complete differentiation andseparation among those two different places of milk samples. They are spacedand grouped in the specific regions of the PCA score plot. The milk samplesthose are similar are clustered in one group than the different one. Useexisting PCA models to build a SIMCA classification model, and then classify new samples.
This table (2) shows the classification of each sample. Classes that are significant for a sample are marked with a star.The outcome of the classification depends on the significance limit; 0 .
5%. Lookfor samples that are not recognized by any of the classes, or those that areallocated to more than one class. Table 2.The classification of pure and hydrogen peroxide adulteratedmilk samples.
Sample Control PCA Adulterated PCA 1H hp3 * * 1H hp4 * * 1H hp5 * * 1H hp7 * * 1H hp8 * * 1H hp9 * * 1B hpl2 * * 1B hpl3 * * 1B hpl5 * * 1B hpl6 * * 1B hpl7 * * 1B hpl8 * * 1B hpl9 * * 2B col1 * 2B col2 * 2H col1 * 2H col2 * 2H col3 * 2H col5 * 2H hp10 2H hp2 * * 2H hp3 * * Similarly, Partial least-squares discriminantanalysis (PLS-DA) model was built for the spectral data between pure and with 2%hydrogen peroxide milk adulteration as shown in Figure 7. PLS-DA model can beused as an identification tool to check hydrogen peroxide adulteration in cow milk.If there is any amount of hydrogen peroxide in cow milk they will occupy thespace in between the pure and adulterated samples of the Figure 7. Figure 7: PLS-DA model for the pure cow milkand with 2%hydrogen peroxide adulteration It can be seen from Figure 7 that there is a clear discriminationbetween the pure cow milk samples as well as with 2% hydrogen peroxide milk adulteration.The RMSECV value for PLSDA model was found 0.081 with R square value of 0.
989.The PLSDA model with minimum error that is RMSECV and with highest correlationshipvalue i.e. R is the best one.
PLS regression resultsTo predict the level of hydrogen peroxideadulteration in cow milk samples PLS regression model was built by using 70% ofthe samples as a training set with hydrogen peroxide at ten differentpercentage levels: 0.5%, 0.7%, 0.9%, 1%, 1.5%, 1.
7%, 1.9%, 2%, 2.5%, and 2.7%of hydrogen peroxide. PLS regression models are shown in Figures 8 and 9.
PLSfinds a set of orthogonal components that maximize the level of explanation ofboth X and Y provides a predictive equation for Y in terms of the X’s