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NIR can act as a feasible technique for investigating the presence of detergent as an adulterant in milk. Present work has been carried out to formulate a chemometric model to evaluate the presence of adulterating detergent in milk, qualitatively as well as quantitatively using NIR Spectroscopy. Total 30 samples were prepared using three different varieties of milk, out of which three samples was free from adulterants and rest were having detergent present in them.
Those 27 adulterated milk samples were having detergents at nine disparate concentrations : 0.025%, 0.05%, 0.1%, 0.2%, 0.4%, 0.6%, 0.8%, 1.0% and 2.0% for each kind of milk.
The data conglomerated from NIRS instrument was analyzed using chemometric software (CAMOUnscrambler version X 10.3). The Principal Component Analysis (PCA) was run on the set of samples to know the relation between the different samples on the basis of the Near Infrared spectral data. It was noticed that the PCA score plot could classify the samples in three groups on the basis of their adulteration: low, medium and high adulteration.
Partial least square regression analysis (PLS-RA) was deployed to design a statistical model to envisage the percentage detergent content present in the adulterated milk samples by selecting vital wavelengths.
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It was analyzed that the regression model revealed quite good results for the prediction of detergent adulterated milk samples with coefficient of correlation higher than 0.9 and the root means square error of validation (RMSEV) was 0.013. Thus, it was agglomerated that NIR spectroscopy can bestow dairy industry a lucid, coherent, quick, green and flexible technique for determination and quantification of detergents in milk adulterated samples.
The value of milk is linked to its protein content, minerals for bone strength, health nourishing vitamins, milk fat and other fatty acids.
However, one of the common phenomenons that have been overlooked across the globe is the presence of spoilers, for instance, water, sugar, detergents, whey; melamine etc. creates threat to well being of an individual. There are various analytical methods that can be applied for the detection of milk composition that are quite time-consuming, demands hazardous chemicals, expensive, and are usually destructive and off-line by nature. The edge of NIRS in detection of contaminants in milk acts as a green and non-destructive approach in the wavelength region of 700 nm to 2500 nm.
PranitaJaiswaletal have reported the appearance of anionic detergent as degraders in milk using FTIR in the range of 1600-996 cm-1 and 3040-2851 cm-1. ManjunTayetal have aimed to investigate the infant milk for the detergent determination using Liquid Chromatogarphy (LC)-Qtrap and LC-hybrid quadrapole tie of flight mass spectroscopy (LC-QTOF-MS) with chemometrics. AlessandraBorinetal have revealed the feasibility of NIR along with least square vector machine for assessing some common adulterants like starch, whey, or sucrose in powdered milk samples. Most of the subjective methods for investigating the foul materials in milk are colorimetric but lacks due to limited range of concentrations and inaccuracy.
Most of the adulterants are added to revamp the taste of milk while most of them are toxic chemicals used to enhance the physical features and shelf-life of milk listed in Table 1. These are mostly harmful and results in fatal ailments to consumers. In this work, we have investigated the presence of detergents that are usually added to improve the appearance of milk but leads to harmful diseases.
Sugar
5ml of milk was mixed with 1ml of conc. HCl and 0.1g of resorcinol solution in a test tube and then placed in water bath for next 5 minutes.
Appearance of red color confirms the existence of sugar.
0.2% (w/v)
(Kamthaniaetal.2014); (ArvindS ngh etal. 2012)
Common Salt
1ml of 0.1 N silver nitrate solution was taken in test tube containing 5ml of milk sample. After stirring, 0.5 ml of 10% potassium chromate solution was also poured to it.
Emergence of yellow color confirms the appearance of salts while brick red color indicates sample is free from taint salt.
0.2% (w/v)
(Sharmaetal. 2012)
Urea
Add 5ml of of p-Dimethyl Amino Benzaldehyde to equal amount of milk sample.
Presence of yellow coloration indicates urea is present in milk.
0.2%(w/v)
(Sharmaetal. 1993);(Arvind Singh etal. 2012); (Bectoretal. 1998); (Kavita, 2000)
Detergent
Mix 1ml of methylene blue dye solution to 2ml chloroform, swirl and centrifugate at about 1100 rpm for 2-3 minutes
Intense blue coloration in the lower layer confirms the presence of detergent in milk.
0.0125%
(Rajput, Sharma, & Kaur)
Pulverized Soap
An equal amount of milk and hot water can be taken in 10 ml test tube and few drops of phenolphathalein indicator is added to it.
Pink color appears.
(Arvind Singh etal. 2012); (Kamthaniaetal. 2014); (Ghoedkar 1974)
Coloring matter
Add sodium bicarbonate to milk sample and then immerse a strip of filter paper for around 2 hours.
Presence of red color specifies annatto is present in milk samples.
(Lechner and Klostermeyer 1981)
Ammonium Sulphate
Take 10 ml of TCA solution in equal amount of milk in a stoppered test tube. After filtration, add 2-3 drops of barium chloride solution to it.
Milky white precipitates will appear.
0.05% (w/v)
(Sharmaetal. 2012)
Previous literature reveals that ample amount of work has been performed that detects the occurrence of harmful contaminates like urea, melamine, formalin etc. in milk and milk products still very few reports are there that reports satisfactory results of detection of toxic detergents in milk samples. There is a large gap that is needed to be bridged for both computable and qualitative determination of fraud chemicals in milk. In this communication, we have tried to link the gap between chemical tests and chemometrics by adopting NIR spectroscopic based method along with chemometrics using unscramble X software to ensure the safety of milk consumers in a green, non-destructive and fast way.
In this report, three distinct types of milk samples has been employed that were purchased from nearby area of Chandigarh, Punjab region and were investigated which includes:
These samples were then adulterated with detergent at nine distinct concentrations: 0.025%, 0.05%, 0.1%, 0.2%, 0.4%, 0.6%, 0.8%, 1.0% and 2.0% of detergent respectively for each milk. NIR spectra of samples were obtained using NIRSDS 2500 Spectrometer (Metrohm) in reflectance mode in the spectral span of 700-2500 nm with a gap of 0.5 nm with 4200 data points. The absorbance of the samples was calculated by employing VISION software.
For each sample, spectrometer gave an averaged spectrum of 3 scans. During experimentation and sample collection, all possible precautions were taken to avoid any external contamination. The experimentation and testing work was carried out in CSIO Research laboratory of Chandigarh. Total 27 samples were prepared and analyzed out of which 24 were adulterated with detergents and rests were pure samples.
The data collected was analyzed by employing chemometric software (CAMOUnscrambler version X 10.3). This tool promises a facile approach for any sort of multivariate data analysis. This has the benefits of finding variations, co-variations and in stating the key relationships in data matrices. This can also be utilized for developing prediction models for the real time analysis of spectroscopic materials. It was initially developed in 1986 by Harald Martens and later further designed by CAMO software.
This provides key features to evaluate the Principal Component Analysis (PCA), Principle Least Square Regression (PLS), Multivariate Curve Resolution and much more. In this study, we have used PCA using this software along with NIPLAS algorithm to verify the correlation between the samples (Scores) and the variables (Loadings). PCA is a mathematical strategy that transforms a number of correlated variables to a number of uncorrelated variables called principal components. It has benefits of reducing ascribe space to a smaller number of factors.
NIR spectrum of the samples was carried in reflectance mode and absorbance was calculated by the VISION software of NIRS DS2500 NIR spectrometer as shown in Fig.4.It can be observed that absorbance peaks for detergent maximum at (1498.50-1765.00) and minimum at (974.50-1099.00) wavelengths. According to literature these peaks corresponds Aromatic bonds with frequency (1600-1475) cm-1 of medium weak intensity, C=C Alkene bonds with frequency (1680-1600) cm-1 of medium weak intensity, Ketonic bonds with frequency (1725-1705) cm-1 of strong intensity, C=O bonds Aldehyde with frequency (1740-1720) cm-1 of strong intensity, COOH bonds with frequency (1760-1700)cm-1 of strong intensity, Anhydride bonds with frequency (1810 -1760) cm-1 of strong intensity, N=O, Nitro bonds with frequency (1550-1350) cm-1 of strong intensity,CONH2(1680-1630) cm-1 , COOR (750-1730) cm-1of strong intensity bonds.
The Principal Component Analysis (PCA) was performed for the spectral data to check the relation between prepared adulterated samples and the absorbance of the wavelengths from 700-2500 nm as shown in Fig 5. It was unfolded that the spectral data alone was sufficient to classify the adulterated samples in groups. It was observed that 100 % data was covered by the first two principal components.
PCA correlation loading plots (Fig. 6) showed that the important wavelengths responsible for the detergent adulteration in milk. Although the spectral data is for the wavelengths from 700-2500 nm However; some wavelengths are not important for a particular molecular bond. Thus, this is needed to be eradicated while building a statistical model to predict the adulteration for that molecule.
The correlation loading values varies from -1 to +1 (negative 100% to positive 100 correlations). The variable above 70% correlation (negative or positive) is considered important for the prediction of the respective parameter. It was observed that for detergent adulteration at (1825.50 -2128.00) nm wavelengths were important.
Partial Least Square regression was used to build the statistical mechanism to predict the adulteration of milk samples qualitatively and quantitatively it was observed that coefficient of correlation (r2) for the percentage of adulteration of prepared samples with the predicted adulteration percentage was more than 0.9 (greater than 90%) for both the set of adulterated samples. The root means square error of validation (RMSEV) was 0.013 for the detergent adulterated milk samples. The high values of correlation coefficient and low values of RMSEV showed that the PLS regression model can be used for the detection of adulteration in milk significantly and qualitatively. The build PLS model can be used for prediction of percentage of adulteration and type of adulterant for any milk samples.
In this study, we explored the application of NIR spectroscopy with principal component analysis (PCA) and partial least (PLS) regression to classify and quantify the detergent adulteration in milk. The study shows that many bonds present in adulterated milk samples can be detected as absorbance peak at 1400- 1750, 968-1091, 1498.50-1765 and 974-1099 nm respectively. The PCA method was performed to display the relation between prepared adulterated samples and the absorbance wavelength from 780 nm to 2500 nm. Thus, PCA plays an extremely prime role in examination of adulterated milk samples. The result revealed that the high adulteration found from group II of PCA and 100% data was covered by first two groups respectively. Also, the correlation values of loadings vary from -1 to +1 and the variables above 70% correlation are considered as important for predicted parameters.
Partial least square (PLS) regression model was employed to build statistical models for finding the adulterants in milk qualitatively and perceptibly. The result shows that coefficient of correlation (r2) for predicted as well as for prepared sample was more than 90% and the root mean square error of validation (RMSEV) was 0.013 for detergent. Thus, it is culminated that NIR spectroscopy can act as promising benefic for dairy industry with its various merits, for instance simple, convenient, rapid, green and non-destructive technique for discernment and quantification of adulterated milk samples.
Authors of this paper acknowledge Dr. SunitaMishra, Sr. Principal Scientist and Head of Ubiquitous Analytical Techniques and R & D Division, CSIR-Central Scientific Instruments and Organisation, Chandigarh for availing us NIR DS2500 Near Infrared Spectrometer for the collection of spectral data of the samples and for the use of licensed version of CAMOUnscrambler for the regression analysis.
Detection of Detergent Adulteration in Milk Using NIR Spectroscopy: A Chemometric Approach. (2024, Feb 21). Retrieved from https://studymoose.com/document/detection-of-detergent-adulteration-in-milk-using-nir-spectroscopy-a-chemometric-approach
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