Detection and quantification of anionic detergent (lissapol) in milk using attenuated total reflectance-Fourier Transform Infrared spectroscopy

Rapid detection and quantification of melamine, urea, sucrose, water, and milk powder adulteration in pasteurized milk using Fourier transform infrared (FTIR) spectroscopy coupled with modern statistical machine learning algorithms

There is an evident requirement for a rapid, efficient, and simple method to screen the authenticity of milk products in the market. Fourier transform infrared (FTIR) spectroscopy stands out as a promising solution. This work employed FTIR spectroscopy and modern statistical machine learning algorithms for the identification and quantification of pasteurized milk adulteration. Comparative results demonstrate modern statistical machine learning algorithms will improve the ability of FTIR spectroscopy to predict milk adulteration compared to partial least square (PLS). To discern the types of substances utilized in milk adulteration, a top-performing multiclassification model was established using multi-layer perceptron (MLP) algorithm, delivering an impressive prediction accuracy of 97.4 %. For quantification purposes, bayesian regularized neural networks (BRNN) provided the best results for the determination of both melamine, urea and milk powder adulteration, while extreme gradient boosting (XGB) and projection pursuit regression (PPR) gave better results in predicting sucrose and water adulteration levels, respectively. The regression models provided suitable predictive accuracy with the ratio of performance to deviation (RPD) values higher than 3. The proposed methodology proved to be a cost-effective and fast tool for screening the authenticity of pasteurized milk in the market.

Application of ATR-FTIR Incorporated with Multivariate Data Analysis for Discrimination and Quantification of Urea as an Adulterant in UHT Milk

Urea is naturally present in milk, yet urea is added intentionally to increase milk's nitrogen content and shelf life. In this study, a total of 50 Ultra heat treatment (UHT) milk samples were spiked with known urea concentrations (0-5 w/v%). Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy with principal component analysis (PCA), discriminant analysis (DA), and multiple linear regression (MLR) were used for the discrimination and quantification of urea. The PCA was built using 387 variables with higher FL > 0.75 from the first PCA with cumulative variability (90.036%). Subsequently, the DA model was built using the same variables from PCA and demonstrated the good distinction between unadulterated and adulterated milk, with a correct classification rate of 98% for cross-validation. The MLR model used 48 variables with p-value < 0.05 from the DA model and gave R2 values greater than 0.90, with RMSE and MSE below 1 for cross-validation and prediction. The DA and MLR models were then validated externally using a test dataset, which shows 100% correct classification, and the t-test result (p > 0.05) indicated that the MLR could determine the percentage of urea in UHT milk within the permission limit (70 mg/mL). In short, the wavenumbers 1626.63, 1601.98, and 1585.5534 cm-1 are suitable as fingerprint regions for detecting urea in UHT milk.

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

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.

FTIR spectroscopy with chemometrics for determination of tylosin residues in milk

BACKGROUND

Contamination of milk by antibiotic residues represents risks to the health of consumers; therefore they should be monitored. The objective of this study was to propose a methodology for the determination of tylosin residues directly in fluid milk based on mid-infrared spectroscopy associated with chemometrics, using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy associated with multilayer perceptron network (MLP) and partial least squares (PLS).

RESULTS

MLP was shown to be adequate for the discrimination of milk samples contaminated with tylosin below or equal to or above the maximum residue limit (MRL), with an accuracy greater than 99%, using FTIR spectra data. PLS was shown to be appropriate for the prediction of the very low concentrations (0-100 μg L^-1 ) of tylosin residues in milk using FTIR spectra data. PLS models with high correlation coefficients (R > 0.99) were generated.

CONCLUSION

FTIR with chemometrics proved to be a non-destructive, efficient and low-cost method for the investigation and quantification of tylosin residues directly in fluid milk. © 2020 Society of Chemical Industry.

Rapid detection and quantification of sucrose adulteration in cow milk using Attenuated total reflectance-Fourier transform infrared spectroscopy coupled with multivariate analysis

Adulteration of milk to gain economic benefit has become a common practice in recent years. Sucrose is illegally added in milk to reconstitute its compositional requirement by improving the total solid contents. The present study is aimed to use FTIR spectroscopy in combination with multivariate chemometric modelling for the differentiation and quantification of sucrose in cow milk. Pure milk and adulterated milk spectra (0.5-7.5% w/v) were observed in the spectral region 4000-400 cm^-1. Principal component analysis (PCA) was used for the discrimination of pure milk and adulterated milk. Soft independent modelling of class analogy (SIMCA) was able to classify test samples with a classification efficiency of 100%. Partial least square regression (PLS-R) and principle component regression (PCR) models were established for normal spectra, 1st derivative and 2nd derivative for the quantification of sucrose in milk. PLS-R model (normal spectra) in the combined wavenumber range of 1070-980 cm^-1 showed the best prediction based on parameters like coefficient of determination (R²) (Cal: 0.996; Val: 0.993), RMSE (Cal: 0.15% w/v; Val: 0.20% w/v), RE% (Cal: 4.9% w/v; Val: 5.1% w/v) and RPD (13.40). This method has a detection level of 0.5% w/v sucrose adulteration.

Research on a Dual-Mode Infrared Liquid-Crystal Device for Simultaneous Electrically Adjusted Filtering and Zooming

A new dual-mode liquid-crystal (LC) micro-device constructed by incorporating a Fabry⁻Perot (FP) cavity and an arrayed LC micro-lens for performing simultaneous electrically adjusted filtering and zooming in infrared wavelength range is presented in this paper. The main micro-structure is a micro-cavity consisting of two parallel zinc selenide (ZnSe) substrates that are pre-coated with ~20-nm aluminum (Al) layers which served as their high-reflection films and electrodes. In particular, the top electrode of the device is patterned by 44 × 38 circular micro-holes of 120 μm diameter, which also means a 44 × 38 micro-lens array. The micro-cavity with a typical depth of ~12 μm is fully filled by LC materials. The experimental results show that the spectral component with needed frequency or wavelength can be selected effectively from incident micro-beams, and both the transmission spectrum and the point spread function can be adjusted simultaneously by simply varying the root-mean-square value of the signal voltage applied, so as to demonstrate a closely correlated feature of filtering and zooming. In addition, the maximum transmittance is already up to ~20% according the peak-to-valley value of the spectral transmittance curves, which exhibits nearly twice the increment compared with that of the ordinary LC-FP filtering without micro-lenses.