Rapid detection and quantification of milk adulteration using infrared microspectroscopy and chemometrics analysis

• Published in: Food chemistry Vol. 138; no. 1; pp. 19 - 24
• Main Authors: Santos, P.M., Pereira-Filho, E.R., Rodriguez-Saona, L.E.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.05.2013; Elsevier
• Subjects: Animals; Biological and medical sciences; Cattle; Chemistry Techniques, Analytical - methods; Chemometric analysis; Food Contamination - analysis; Food industries; Fundamental and applied biological sciences. Psychology; General aspects; Methods of analysis, processing and quality control, regulation, standards; Milk - chemistry; Milk adulteration; Milk and cheese industries. Ice creams; MIR-microspectroscopy spectra; Spectroscopy, Fourier Transform Infrared - methods; MIR-microspectroscopy spectra; Milk adulteration; Chemometric analysis; Analysis method; Dairy product; Control method; Detection; Adulteration; Chemometrics; Quantitative analysis; Milk
• ISSN: 0308-8146; 1873-7072
• DOI: 10.1016/j.foodchem.2012.10.024

► Milk was adulterated with five different adulterants in five levels. ► MIR spectra exhibited specific bands for each adulterant. ► SIMCA models allowing discrimination of control from adulterated milk. ► PLSR models showed a strong predicted ability with high and low errors (SEP). ► MIR-microspectroscopy is a simple and fast method to monitor milk authenticity. The application of attenuated total reflectance mid-infrared microspectroscopy (MIR-microspectroscopy) was evaluated as a rapid method for detection and quantification of milk adulteration. Milk samples were purchased from local grocery stores (Columbus, OH, USA) and spiked at different concentrations of whey, hydrogen peroxide, synthetic urine, urea and synthetic milk. Samples were place on a 192-well microarray slide, air-dried and spectra were collected by using MIR-microspectroscopy. Pattern recognition analysis by Soft Independent Modeling of Class Analogy (SIMCA) showed tight and well-separated clusters allowing discrimination of control samples from adulterated milk. Partial Least Squares Regression (PLSR) showed standard error of prediction (SEP) ∼2.33, 0.06, 0.41, 0.30 and 0.014g/L for estimation of levels of adulteration with whey, synthetic milk, synthetic urine, urea and hydrogen peroxide, respectively. Results showed that MIR-microspectroscopy can provide an alternative methodology to the dairy industry for screening potential fraudulent practice for economic adulteration of cow’s milk.