Detection and Quantification of Formalin Adulteration in Cow Milk Using Near Infrared Spectroscopy Combined with Multivariate Analysis

Mabood, Fazal; Hussain, Javid; Moo, Al Nabhani; Gilani, Syed Amir; Farooq, Saima; Naureen, Zakira; Jabeen, Farah; Ahmed, Manzor; Hussain, Zahid; Al‐Harrasi, Ahmed

doi:10.4172/2329-888x.1000167

Cited by 26 publications

(12 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the observed peaks in the region of 4200, 4300, 5800, 8200, and 8600 cm −1 were attributed to the CH group. The absorption at 5030, 4850, and 4400 cm −1 were related to the NH group 53,54 . The spectral range of 6400–5500 cm −1 may also be attributed to the sulfonate group and other chemical components of SDS.…”

Section: Resultsmentioning

confidence: 88%

Application of genetic algorithm and multivariate methods for the detection and measurement of milk‐surfactant adulteration by attenuated total reflection and near‐infrared spectroscopy

et al. 2020

View full text Add to dashboard Cite

BACKGROUND The adulteration of milk by hazardous chemicals like surfactants has recently increased. It conceals the quality of the product to gain profit. As milk and milk‐based products are consumed by many people, novel analytical procedures are needed to detect these adulterants. This study focused on Fourier‐transform infrared (FTIR) spectroscopy equipped with an attenuated total reflection (ATR) accessory, and near‐infrared (NIR) spectroscopy for the determination of milk‐surfactant adulteration using a genetic algorithm (GA) coupled with multivariate methods. The model surfactant was sodium dodecyl sulfate (SDS), and its concentration varied from 1.94–19.4 gkg−1 in adulterated samples. RESULTS Prominent peaks in the spectral range of 5500–6400 cm−1, 1160–1260 cm−1 and 1049–1080 cm−1 may correspond to the sulfonate group in SDS. A genetic algorithm could significantly reduce the number of variables to almost one third by selecting the specific wavenumber region. Principal component analysis (PCA) for ATR and NIR data indicated separate clusters of samples in terms of the concentration level of SDS (P ≤ 0.05). Partial least squares regression (PLSR) was used to determine the maximum R2 value for ATR and NIR data for calibration, cross‐validation and prediction, which were 0.980, 0.972, 0.980, and 0.970, 0.937, and 0.956 respectively. The results showed apparent differences between unadulterated and adulterated samples using partial least squares‐discriminant analysis (PLS‐DA), which was validated by the permutation test. CONCLUSION The results clearly show the successful application of the proposed methods with multivariate analysis in the selection of variables, classification, clustering, and identification of the adulterant in amounts as low as 1.94 gkg−1 in milk. © 2020 Society of Chemical Industry

show abstract

Section: Resultsmentioning

confidence: 88%

Application of genetic algorithm and multivariate methods for the detection and measurement of milk‐surfactant adulteration by attenuated total reflection and near‐infrared spectroscopy

et al. 2020

View full text Add to dashboard Cite

show abstract

“…After that partial least regression model was built for the quantification of the level of formalin in cow milk and R-square was found to be 93%, Root mean square error of cross validation=1.38, root mean square error of prediction=1.50 and less 2% formalin can be quantified. From the obtained results, they conclude that this method was successfully used to quantify the lowest level of formalin (not more the 2%) present in milk with a non-destructive and cheaper manner and also sample preparation was not much needed [45] (Table 5).…”

Section: Formalin Adulteration In Cow Milkmentioning

confidence: 93%

Combination of FTIR Spectroscopy and Chemometric Method on Quantitative Approach - A Review

Verma¹,

MJ²,

Anchliya³

2021

AustinJAnalPharmChem

View full text Add to dashboard Cite

Fourier transform infrared spectroscopy is an effectual and noncritical approach for quantitative study of different types analytes present in pharmaceutical and foodstuffs. The present review describes the basic principles and the instrumentation of FTIR spectroscopy along with its sample preparation techniques, sample handling techniques and advancements. FTIR spectroscopy in combination with chemometrics techniques has been followed over long times. The main objective of this review is to assemble the data linked to application of FTIR spectroscopic and chemometrics techniques for the quantitative study of varieties of analytes like API, adulterants, caffeine, cocaine, lipids, fats & oils, sugar and others. The FTIR spectroscopy with chemometrics techniques proved to be a beneficial methodology for quantitative study to routine analysis of these analytes.

show abstract

“…The NIR spectroscopy combined with improved ν-Support vector machine produces output with maximum validation correct ratio ,which Correlates positively with the adulterant solution's content and discrimination adulteration equals or exceeds 5%. A new method of identifying formalin in raw cow milk was analyzed using NIR (700-2500 nm) combined with multivariate analysis [22]. The PLS (Partial Least Square) model obtained a good prediction with a root mean square error prediction value of 1.45% and quantified formalin adulteration with less than 2%.…”

Section: Related Workmentioning

confidence: 99%

Development of Spectroscopic Sensor System for an IoT Application of Adulteration Identification on Milk Using Machine Learning

Sowmya

Ponnusamy

2021

IEEE Access

View full text Add to dashboard Cite

Adulteration in milk is a common scenario for gaining extra profit, which may cause severe harmful effects on humans. The qualitative spectroscopic technique provides a better solution for detecting the toxic contents of milk and foodstuffs. All the available spectroscopic methods for milk adulterant detection are based on laboratory-based with costly equipment. This laboratory-based detection takes a long time and is more expensive, which may not be afforded by a common man. To overcome this issue, this research work involves the design and development of a low-cost,portable, multispectral, AI-based, non-destructive spectroscopic sensor system that can be used to detect the milk adulterant in real-time. The designed sensor system uses the spectroscopic method with wavelength ranges from (410-940nm) which consists of three different bands Ultraviolet(UV), visible, and Infra-Red(IR) spectrum to improve the accuracy of detection. The sensor system is connected to the internet via the developed IoT application module, which displays the detected adulterant results in a dedicated web page designed for this purpose. This IoT application enables the adulterant detected results published on the internet immediately with location information for bringing transparency. Adulterant detection problem is formulated as a classification problem and solved by machine learning algorithms of a decision tree, Naive Bayes, linear discriminant analysis, support vector machine and neural network model. The average accuracy of linear discriminant analysis, support vector machine, Naive Bayes, decision tree and neural network model are obtained as 88.1%,90%,90%,91.7% and 92.7% respectively. Genetic algorithm framework is formulated for hyperparameter tuning of neural network model which improved the accuracy from 92.7% to 100%. The model is trained for five different classes of four adulterants, namely Sodium Salicylate, Dextrose, Hydrogen Peroxide, Ammonium Sulphate, and one pure milk sample. INDEX TERMSBack propagation Algorithm , K-means clustering , Machine learning , Milk Adulteration , Multispectral spectroscopy , Neural network I. INTRODUCTION N OWADAYS,food adulteration is a common scenario among shopkeepers in gaining extra immediate profits.Adulteration is added in foods like ripening mangoes, adding chalk powder on turmeric, starch on curry powder, blending papaya seeds on black pepper, etc. This adulteration venture leads to harmful effects for humans on a long-term basis. The fluid cow milk consumption is about 77.68 million metric tons in India. In December 2019, statistics showed that India plays the best role in cow milk consumption [1]. Milk has powerful nutrients such as lactose, fat, proteins, minerals, and vitamins in a significant proportion, which helps to provide a better human diet [2]. Since the peoples consume the milk daily in their diet, detection of adulterants in the milk is one of the significant research to ensure the health safety of the humans. Spectroscopy-based adulteration detection is one of the met...

show abstract

Detection and Quantification of Formalin Adulteration in Cow Milk Using Near Infrared Spectroscopy Combined with Multivariate Analysis

Cited by 26 publications

References 10 publications

Application of genetic algorithm and multivariate methods for the detection and measurement of milk‐surfactant adulteration by attenuated total reflection and near‐infrared spectroscopy

Application of genetic algorithm and multivariate methods for the detection and measurement of milk‐surfactant adulteration by attenuated total reflection and near‐infrared spectroscopy

Combination of FTIR Spectroscopy and Chemometric Method on Quantitative Approach - A Review

Development of Spectroscopic Sensor System for an IoT Application of Adulteration Identification on Milk Using Machine Learning

Contact Info

Product

Resources

About