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This study aimed at developing a fast and low-cost detection method to discriminate between ice cream samples containing pork or non-pork gelatin by using Fourier Transform Near Infrared (FT-NIR) spectroscopy and Partial Least Squares Discriminant Analysis (PLS-DA). Forty two samples of ice cream were used, among which twenty three samples were adulterated with different levels i.e. 1%, 5%, 10%, and 20 % of pork gelatin (Non-Halal). Whereas, the remaining nineteen samples containing only cow gelatin (Halal) were used as a control. All the ice cream samples were measured with the FT-NIR spectrophotometer in the reflection mode. Spectra were collected in the wavenumber range from 10000 to 4000 cm−1 (1000 to 2500 nm). The results show that the PLS-DA model with Unit Vector Normalization (UVN) spectral transformations for 1% pork gelatin adulteration is the optimal one which was based on a compromise between the lowest value of root mean square error of cross validation (RMSECV) for the calibration set. The lowest value of root mean square error of prediction (RMSEP) for the test set, the least number of factors and the percentage of correctly classified samples, the Halal and Non-Halal, for both calibration and test sets. This newly developed method is fast, involves simple sample preparation and is low cost.

Daily consumption of caffeine in coffee, tea, chocolate, cocoa, and soft drinks has gained wide and plentiful public and scientific attention over the past few decades. The concentration of caffeine in vivo is a crucial indicator of some disorders-for example, kidney malfunction, heart disease, increase of blood pressure and alertness-and can cause some severe diseases including type 2 diabetes mellitus (DM), stroke risk, liver disease, and some cancers. In the present study, near-infrared spectroscopy (NIRS) coupled with partial least-squares regression (PLSR) was proposed as an alternative method for the quantification of caffeine in 25 commercially available tea samples consumed in Oman. This method is a fast, complementary technique to wet chemistry procedures as well as to high-performance liquid chromatography (HPLC) methods for the quantitative analysis of caffeine in tea samples because it is reagent-less and needs little or no pre-treatment of samples. In the current study, the partial least-squares (PLS) algorithm was built by using the near-infrared NIR spectra of caffeine standards prepared in tea samples scanned by a Frontier NIR spectrophotometer (L1280034) by PerkinElmer. Spectra were collected in the absorption mode in the wavenumber range of 10,000-4000 cm-1, using a 0.2 mm path length and CaF2 sealed cells with a resolution of 2 cm-1. The NIR results for the contents of caffeine in tea samples were also compared with results obtained by HPLC analysis. Both techniques provided good results for predicting the caffeine contents in commercially available tea samples. The results of the proposed study show that the suggested FT-NIRS coupled with PLS regression algorithun has a high potential to be routinely used for the quick and reproducible analysis of ca

© 2020 Elsevier Ltd This study aimed to develop a fast analytical method, combining near infrared reflectance spectroscopy and multivariate analysis, for detection and quantification of pork meat in other meat samples. A total of 5952 mixture samples from 39 types of meat were prepared in triplicate, with the inclusion of pork at 0%, 1%, 5%, 10%, 30%, 50%, 70%, 90% and 100%. Each sample was scanned using an FT-NIR spectrophotometer in the reflection mode. Spectra were collected in the wavenumber range from 10,000 to 4000 cm−1, at a resolution of 2 cm−1 and a total path length of 0.5 mm. Principal Component Analysis (PCA) revealed the similarities and differences among the various types of meat samples and Partial Least-Squares Discriminant Analysis (PLS-DA) showed a good discrimination between pure and pork-spiked meat samples. A Partial Least-Squares Regression (PLSR) model was built to predict the pork meat contents in other meats, which provided the R2 value of 0.9774 and RMSECV value of 1.08%. Additionally, an external validation was carried out using a test set, providing a rather good prediction error, with an RMSEP value of 1.84%.

Ethanol, as a result of its high octane rating of 108, is often added as adulterant to premium 91 gasoline fuels to boost up their octane ratings to 96 or more, but it does not provide the same power to the engine as that of superpremium 96 gasoline fuels. In this study, sensitive near-infrared spectroscopy (NIRS) coupled with chemometrics was proposed for analysis of the ethanol content in premium 91 gasoline fuels. Standard samples of premium 91 octane gasoline were collected from Oman Oil Refineries and Petroleum Industries Company commonly known as ORPIC. The premium 91 samples were then intentionally spiked with ethanol at various levels. NIRS was employed in the absorption mode to obtain the spectra of all samples scanning from 700 to 2500 nm. Then, partial least squares regression (PLSR), partial least squares discriminant analysis (PLS-DA), and principal component analysis (PCA) were applied to model and interpret the near-infrared (NIR) spectra. A PLS-DA model was developed to discriminate between the pristine gasoline samples and those intentionally mixed with ethanol, with excellent results [R2 = 98% and root-mean-square error (RMSE) = 0.049%] by random cross-validation. A PLSR model was established to determine the ethanol content in premium 91 gasoline samples, with values of R2 = 99% and root-mean-square error of cross-validation (RMSECV) = 1.88% and R2 = 99% and root-mean-square error of prediction (RMSEP) = 1.58 for cross-validation and test-set validation results, respectively. This newly developed method is simple and rapid and can quantify less than 2% ethanol adulteration in premium 91 gasolines.

New NIR spectroscopy combined with multivariate analysis for detection and quantification of camel milk adulteration with goat milk was investigated. Camel milk samples were collected from Aldhahira and Sharqia regions of Sultanate of Oman and were measured using NIR spectroscopy in absorption mode in the wavelength range from 700 to 2500 nm, at 2 cm−1 resolution and using a 0.2 mm path length CaF2 sealed cell. The multivariate methods like PCA, PLS-DA and PLS regression were used for interpretation of NIR spectral data. PLS-DA was used to detect the discrimination between the pure and adulterated milk samples. For PLSDA model the R-square value obtained was 0.974 with 0.08 RMSE. Furthermore, PLS regression model was used to quantify the levels of adulteration from, 0%, 2%, 5%, 10%, 15% and 20%. The PLS model showed the RMSEC = 1.10% with R2 = 94%. This method is simple, reproducible, having excellent sensitivity. The limit of detection was found 0.5%, while the limit of quantification was 2%.

Super premium 95 octane gasoline is a special blend of petrol with a higher octane rating that can produce higher engine power, as well as knock-free performance for cars with a high-octane requirement. Super premium grade gasoline 95 is often adulterated with cheaper Premium grade 91 that lowers the octane number of the Super premium gasoline. In the present study a new Near Infrared (NIR) spectroscopy combined with multivariate analysis was developed to detect as well as to quantify the level of Premium 91 gasoline adulteration in Super premium 95 octane gasolines. In this study standard samples of Premium 91 and Super premium 95 octane gasoline were collected from Oman Oil Refineries and Petroleum Industries Company SAOC (ORPIC) and were investigated. Super premium 95 samples were then adulterated with eighteen different percentage levels: 0%, 1%, 3%, 5%, 7%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, and 75% of Premium 91 gasoline. All samples were measured using NIR spectroscopy in absorption mode in the wavelength range from 700 to 2500 nm. The multivariate methods like PCA, PLSDA and PLS regression were applied for statistical analysis of the obtained NIR spectral data. Partial least-squares discriminant analysis (PLSDA) was used to check the discrimination between the pure and adulterated gasoline samples. For PLSDA model the R-square value obtained was 0.99 with 0.012 RMSE. Furthermore, PLS regression model was also built to quantify the levels of Premium 91 adulterant in Super Premium 95 gasoline samples. The PLS regression model was obtained with the R-square 0.99 and with 1.33 RMSECV value having good prediction with RMSEP value 1.35 and correlation of 0.99. This newly developed method is having lower limit of detection less than 1.5% l