Application of chemometrics for detection and modeling of adulteration of fresh cow milk with reconstituted skim milk powder using voltammetric fingerpriting on a graphite/ SiO2 hybrid electrode

Nikolaou, Pavlos; Deskoulidis, Efstathios; Topoglidis, Emmanuel; Kakoulidou, Anna Tsantili; Tsopelas, Fotios

doi:10.1016/j.talanta.2019.120223

Cited by 22 publications

(13 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the case of the contribution of two or more electroactive compounds in an anodic current wave, the area under the wave is measured instead of the peak current, in units of charge ( Q = I × t ) . Multiple studies have shown the advantage of the CV method to quantify specific analytes in milk, or to obtain fingerprints for different milk samples, and to differentiate adulterated milk profiles from fresh milk or powder milk profiles …”

Section: Cyclic Voltammetrymentioning

confidence: 99%

“…80 Multiple studies have shown the advantage of the CV method to quantify specific analytes in milk, or to obtain fingerprints for different milk samples, and to differentiate adulterated milk profiles from fresh milk or powder milk profiles. 81 ■ DIFFERENTIAL PULSE VOLTAMMETRY Differential pulse voltammetry (DPV) is different from CV in that a differential current (Δi) is measured, and the current intensity before a potential pulse (I 1 ) is subtracted from the current value after the pulse (I 2 ). 67 A peak-shaped voltammogram is generated when the difference between these current values is plotted versus applied potential, while the base potential increases steadily between pulses (Figure 1B).…”

Section: ■ Cyclic Voltammetrymentioning

confidence: 99%

See 1 more Smart Citation

Electrochemical Methods for the Analysis of Milk

Motshakeri

Sharma

Phillips

et al. 2022

J. Agric. Food Chem.

View full text Add to dashboard Cite

The milk and dairy industries are some of the most profitable sectors in many countries. This business requires close control of product quality and continuous testing to ensure the safety of the consumers. The potential risk of contaminants or degradation products and undesirable chemicals necessitates the use of fast, reliable detection tools to make immediate production decisions. This review covers studies on the application of electrochemical methods to milk (i.e., voltammetric and amperometric) to quantify different analytes, as reported over the last 10 to 15 years. The review covers a wide range of analytes, including allergens, antioxidants, organic compounds, nitrogen-and aldehyde containing compounds, biochemicals, heavy metals, hydrogen peroxide, nitrite, and endocrine disruptors. The review also examines pretreatment procedures applied to milk samples and the use of novel sensor materials. Final perspectives are provided on the future of cost-effective and easy-to-use electrochemical sensors and their advantages over conventional methods.

show abstract

Section: Cyclic Voltammetrymentioning

confidence: 99%

Section: ■ Cyclic Voltammetrymentioning

confidence: 99%

Electrochemical Methods for the Analysis of Milk

Motshakeri

Sharma

Phillips

et al. 2022

J. Agric. Food Chem.

View full text Add to dashboard Cite

show abstract

“…Therefore, the detection of goat milk adulterated with cow milk in dairy products is imperative to ensure food safety and consumer con dence. When electrochemical techniques are combined with appropriate multivariate statistical methods, it could be an intriguing method to be employed for detecting and quantifying adulterations in dairy products, especially in milk (Tazi et al 2018;Nikolaou et al 2020;Minetto et al 2022). One technique in chemometrics analysis that has been intensively used in identifying food adulterants in food products is a partial least squares-discriminant analysis and orthogonal partial least square-discriminant analysis (PLS-DA and OPLS-DA) (Shi et al 2018;Texeira et al 2021).…”

Section: Introductionmentioning

confidence: 99%

The detection of goat milk adulteration with cow milk using a combination of voltammetric fingerprints and chemometrics analysis

et al. 2023

View full text Add to dashboard Cite

In this study, a novel analytical approach was developed for detecting and predicting adulteration of goat milk with cow milk using a combination of voltammetric ngerprints and chemometrics analysis. The fresh milk samples were obtained from local farmers and analyzed using cyclic voltammetry technique using a glassy carbon electrode as the working electrode and KClO 4 as the supporting electrolyte. The voltammetric ngerprint was obtained from both milk samples and showed an anodic peak between a potential range of 0.40 to 0.75 V vs. Ag/AgCl. This anodic peak is mainly attributed to several electroactive species contained in both milk samples. The current intensities at the potential range of 0 V to + 1 V vs Ag/AgCl were further selected due to the majority of electroactive components in the milk samples having their oxidation potential in this potential range. The current intensities were further pretreated using maximum normalization and submitted to the chemometric tools for multivariate analysis. Orthogonal partial least square-discriminant analysis provided clear discrimination between goat and cow milk. Meanwhile, the prediction of goat milk adulteration with cow milk was achieved using partial least squares regression analysis. These multivariate analysis enabled a satisfactory discrimination and successful model to predict the percentage of cow milk as adulterants in goat milk samples. The demonstrated results revealed that a combination of voltammetric ngerprints and chemometrics tools might offer a low-cost, simple, and rapid analysis which might be possible as a promising method to be developed further for the detection of adulterants.

show abstract

“…[14][15][16] Voltammetry is no exception to this, and pattern recognition algorithms such as linear discriminant analysis (LDA), principal component analysis (PCA), soft independent modelling of class analogy (SIMCA) and more recently machine learning (ML), are frequently encountered. [17][18][19][20] One reason pattern recognition methods have become such a success is their easy implementation in software. Programming languages, such as Python or R, have highly convenient packages that allow a user to quickly run e.g.…”

Section: Introductionmentioning

confidence: 99%