Isotopic signatures and patterns of volatile compounds for discrimination of genuine lemon, genuine lime and adulterated lime juices

Isotopic signatures and patterns of volatile organic compounds (VOCs) are a useful set of markers for the authenticity assessment of fruit juices. In the present study, the carbon and nitrogen stable isotopes and VOCs fingerprinting of 16 genuine lemon and 16 genuine lime juices as well as 28 citric acid-adulterated lime juices were investigated to discriminate them and reveal the underlying mechanism for their differences. Samples were subjected to isotope ratio mass spectrometry (IRMS) and proton transfer reaction time of flight mass spectrometry (PTR-ToF-MS). Following δ13C and δ15N analysis, no significant difference between genuine lemon (δ13C: -24.50 ± 1.29; δ15N: 5.40 ± 2.06) and genuine lime juices (δ13C: -25.17 ± 0.7; δ15N: 5.30 ± 0.97) was observed due to the same photosynthetic pathway (C3 photosynthetic pathway) of lemon and lime trees. However, Adulterated lime juice samples had higher δ13C values (-14.99 ± 2.79) and lower δ15N (1.23 ± 2.36) values compared to the genuine lemon and genuine lime juices which could be related to the added exogenous commercially available citric acid manufactured by fermenting sugars that follow the C4 photosynthetic pathway. Besides, a positive correlation (r2 = 0.941) between citric acid to iso-citric acid ratios and δ13C values was found in the adulterated samples. No significant difference was observed in the total concentration of VOCs among the analyzed samples. However, for all samples, ions m/z 81 and 137 had the highest concentrations. Exploratory VOC pattern analysis by principal component analysis (PCA) and hierarchical cluster analysis (HCA) revealed the clustering of samples in different groups according to their nature. Besides, extremely adulterated samples were well distinguished from slightly adulterated samples following HCA analysis. The current study provided empirical evidence on the capability of IRMS and PTR-ToF-MS in the discrimination of lemon juice, lime juice, and adulterated lime juices. However, further investigation is required to confirm the promising results of this study

The feasibility of two handheld spectrometers for meat speciation combined with chemometric methods and its application for halal certification

Handheld visible-near-infrared (Vis-NIR) and near-infrared (NIR) spectroscopy can be cost-effective, rapid, non-destructive and transportable techniques for identifying meat species and may be valuable for enforcement authorities, retail and consumers. In this study, a handheld Vis-NIR (400–1000 nm) and a handheld NIR (900–1700 nm) spectrometer were applied to discriminate halal meat species from pork (halal certification), as well as speciation of intact and ground lamb, beef, chicken and pork (160 meat samples). Several types of class modeling multivariate approaches were applied. The presented one-class classification (OCC) approach, especially with the Vis-NIR sensor (95–100% correct classification rate), was found to be suitable for the application of halal from non-halal meat-species discrimination. In a discriminant approach, using the Vis-NIR data and support vector machine (SVM) classification, the four meat species tested could be classified with accuracies of 93.4% and 94.7% for ground and intact meat, respectively, while with partial least-squares discriminant analysis (PLS-DA), classification accuracies were 87.4% (ground) and 88.6% (intact). Using the NIR sensor, total accuracies of the SVM models were 88.2% and 81.5% for ground and intact meats, respectively, and PLS-DA classification accuracies were 88.3% (ground) and 80% (intact). We conclude that the Vis-NIR sensor was most successful in the halal certification (OCC approaches) and speciation (discriminant approaches) for both intact and ground meat using SVM

Novel application of near-infrared spectroscopy and chemometrics approach for detection of lime juice adulteration

The aim of this study is to investigate the novel application of a ‎handheld near infra-red spectrophotometer coupled with classification methodologies as a screening approach in detection of adulterated lime juices. For this purpose, a miniaturized near infra-red spectrophotometer (Tellspec®) in the spectral range of 900–1700 nm was used. Three diffuse reflectance spectra of 31 pure lime juices were collected from Jahrom, Iran and 25 adulterated juices were acquired. Principal component analysis was almost able to generate two clusters. Partial least square discriminant analysis and k-nearest neighbors algorithms with different spectral preprocessing techniques were applied as predictive models. In the partial least squares discriminant analysis, the most accurate prediction was obtained with SNV transforming. The generated model was able to classify juices with an accuracy of 88% and the Matthew’s correlation ‎coefficient ‎value of 0.75 in the external validation set. In the k-NN model, the highest accuracy and Matthew’s correlation ‎coefficient in the test set (88% and 0.76, respectively) was obtained with multiplicative signal correction followed by 2nd-order derivative and 5th nearest neighbor. The results of this preliminary study provided promising evidence of the potential of the handheld near infra-red spectrometer and machine learning methods for rapid detection of lime juice adulteration. Since a limited number of the samples were used in the current study, more lime juice samples from a wider range of variability need to be analyzed in order to increase the robustness of the generated models and to confirm the promising results achieved in this study.</p