Assessment of geographical origin of virgin coconut oil using inductively coupled plasma mass spectrometry along with multivariate chemometrics

Recently, Virgin coconut oil (VCO) has emerged as one of the most favorable edible oils because of its application in cooking, frying as well as additive used in food, pharmaceuticals, and cosmetic goods. These qualities have established VCO in high consumer demand and there is a great need of establishing a reliable method for the identification of its geographical origin. Through this present study, for the first time, it has been established that Inductively Coupled Plasma-Mass-Spectrometry (ICP-MS) combined with multivariate chemometrics can be used for the identification of the geographical origin of the VCO samples of various provinces. Principal Component Analysis (PCA), and Linear Discriminant Analysis (LDA) were able to differentiate and classify the VCO samples of different geographical origins. Further, calibration models (Principal Component Regression and Partial Least Square Regression) were developed on the calibration dataset of the elemental concentration obtained from the ICP-MS analysis. An external dataset was used to develop the prediction model to predict the geographical origin of an unknown sample. Both PCR and PLS-R models were successfully able to predict the geographical origin with a high R² value (0.999) and low RMSEP value 0.074 and 0.075% v/v of prediction respectively. In conclusion, ICP-MS combined with regression modelling can be used as an excellent tool for the identification of the geographical origin of the VCO samples of various provinces. This whole technique is the most suitable as it has high sensitivity as well as provides easy multi-metal analysis for a single sample of edible oil.

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Virgin coconut oil plays an essential role in our routine life.

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High consumption demand of VCO leads to immense adulteration incidents.

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ICP-MS & chemometrics is an excellent tool for the VCO origin detection.

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PCA, and LDA classified the VCO samples of different geographical origins.

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PCR, and PLS-R models correctly predicted the geographical origin of VCO.

Rapid and non-destructive approach for the detection of fried mustard oil adulteration in pure mustard oil via ATR-FTIR spectroscopy-chemometrics

Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy integrated with chemometrics was effectively applied for the rapid detection and accurate quantification of fried mustard oil (FMO) adulteration in pure mustard oil (PMO). PMO was adulterated with FMO in the range of 0.5-50% v/v. Principal component analysis (PCA) elucidated the studied adulteration using two components with an explained variance of 97%. The linear discriminant analysis (LDA) was adopted to classify the adulterated PMO samples with FMO. LDA model showed 100% accuracy initially, as well as when cross-validated. To enhance the overall quality of models, characteristic spectral regions were optimized, and principal component regression (PCR) and partial least square regression (PLS-R) models were constructed with high accuracy and precision. PLS-R model for the 2nd derivative of the optimized spectral region 1260-1080 cm^-1 showed best results for prediction sample sets in terms of high R² and residual predictive deviation (RPD) value of 0.999 and 31.91 with low root mean square error (RMSE) and relative prediction error (RE %) of 0.53% v/v and 3.37% respectively. Thus, the suggested method can detect up to 0.5% v/v of adulterated FMO in PMO in a short time interval.

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.

Development of an FTIR based chemometric model for the qualitative and quantitative evaluation of cane sugar as an added sugar adulterant in apple fruit juices

A Fourier Transform Infrared Spectroscopy based chemometric model was evaluated for the rapid identification and estimation of cane sugar as an added sugar adulterant in apple fruit juices. For all the ninety samples, spectra were acquired in the mid-infrared range (4000 cm^-1-400 cm^-1). The spectral analysis provided information regarding the distinctive variable region, which lies in the range of 1200cm^-1 to 900cm^-1, designated as fingerprint region for the carbohydrates. A specific peak in the fingerprint region was observed at 997cm^-1 in all the adulterated samples and was undetectable in pure samples. Based on different levels of cane sugar adulteration (5, 10, 15, and 20%), principal component analysis showed the clustering of samples and further helped us in compression of data by selecting wavenumbers with maximum variability based on the loading line plot. Supervised classification methods (SIMCA and LDA) were evaluated based on their classification efficiencies for a test set. Though SIMCA showed 100% classification efficiency (Raw data set), LDA was able to classify the test set with an accuracy of only 96.67% (Raw as well as Transformed data set) between pure and 5% adulterated samples. For the quantitative estimation, calibration models were developed using partial least square regression (PLS-R) and principal component regression method (PCR) methods. PLS-1^st derivative showed a maximum coefficient of determination (R²) with a value of 0.991 for calibration and 0.992 for prediction. The RMSECV, RMSEP, LOD and LOQ observed for PLS-1^st derivative model were 0.75% w/v, 0.61% w/v, 1.28%w/v and 3.88%w/v, respectively. The coefficient of variation as a measure of precision (repeatability) was also determined for all models, and it ranged from 0.23% to 1.83% (interday), and 0.25% to 1.43% (intraday).