Addition Of Adulterants Research Articles

Smart Framework for Quality Check and Determination of Adulterants in Saffron Using Sensors and AquaCrop

Saffron is a rare and valuable crop that is only cultivated in specific regions with suitable topographical conditions. To improve saffron cultivation, it is crucial to monitor and precisely control the crop’s agronomic variables over at least one growth cycle to create a fully automated environment. To this end, agronomic variables in the Punjab region of India were analyzed and set points were calculated using third-order polynomial equations through the application of image processing techniques. The relationship between canopy cover, growth percentage, and agronomic variables was also investigated for optimal yield and quality. The addition of adulterants, such as turmeric and artificial colorants, to saffron is a major concern due to the potential for quality compromise and fraud by supply chain vendors. Hence, there is a need for devising an easy, reliable, and user-friendly mechanism to help in the detection of adulterants added to the saffron stigmas. This paper proposes an automated IoT-based saffron cultivation environment using sensors for determining set points of agronomical variables. In addition, a sensor-based chamber has been proposed to provide quality and adulteration checks of saffron and to eliminate product counterfeiting. The AquaCrop simulator was employed to evaluate the proposed framework’s performance. The results of the simulation show improved biomass, yield, and harvest index compared with the existing solutions in precision agriculture. Given the high value and demand for saffron, ensuring its purity and quality is essential to sustain its cultivation and the economic viability of the market.

Detection of adulteration using statistical methods over carbon isotope ratios in carob, grape, fig and mulberry pekmez

Pekmez, produced from different fruits, such as grape, mulberry, fig, apple and date, is a traditional Turkish food and widely consumed in Turkey. However, pekmez is among the most adulterated food products. This study used carbon isotope values to detect adulteration of carob, fig, grape and mulberry pekmez samples with glucose and fructose syrups at the ratio of 0%, 10%, 20%, 30%, 40%, 50%, 60% and 70% (by weight). The EA-IRMS chromotograms were recorded and results were used to propose the statistical models. All of the pure pekmez samples were found to be more negative than −23.5‰ and the results decreased by the addition of adulterants. Parametric statistical analysis was used to test the distribution of pekmez and adulterants. Linear fit models were constructed and higher regression coefficient values were obtained for adulterated carob, fig and grape pekmez samples while adulterated mulberry pekmez samples showed lower values. It was determined that the proposed linear fit models can be used as a tool to elucidate the possible detection of varying adulteration levels of pekmez samples, even at low levels. These models may be an introductory step in the detection of adulteration of other Turkish traditional food products.

Amino Acid Profiling with Chemometric Analysis as a Feasible Tool for the Discrimination of Marine-Derived Peptide Powders.

Marine-derived peptide powders have suffered from adulteration via the substitution of lower-price peptides or the addition of adulterants in the market. This study aims to establish an effective approach for the discrimination and detection of adulterants for four representative categories of marine-derived peptide powders, namely, oyster peptides, sea cucumber peptides, Antarctic krill peptides, and fish skin peptides, based on amino acid profiling alongside chemometric analysis. The principal component analysis and orthogonal partial least squares discriminant analysis results indicate that four categories of marine-derived peptides could be distinctly classified into four clusters and aggregated with the respective raw materials. Taurine, glycine, lysine, and protein contents were the major discriminants. A reliable classification model was constructed and validated by the prediction dataset, mixture sample dataset, and unclassified sample dataset with accuracy values of 100%, 100%, and 100%, respectively.

NMR-Based Analysis of Pomegranate Juice Using Untargeted Metabolomics Coupled with Nested and Quantitative Approaches.

Pomegranate juice is a complex mixture of structurally diverse compounds appearing in various concentrations. The composition of the final product depends on several factors, such as the fruit variety and the addition of adulterants. Its diverse composition makes pomegranate juice an excellent system for assessing the potential of an analytical method for targeted and untargeted analysis. Here, we tested the ability of 1D and 2D NMR spectroscopy techniques for the targeted and untargeted metabolite analysis of pomegranate juice. The NMR spectra assignment was performed using the novel NOAH sequences and spiking with model compounds. Several metabolites, including sugars, organic acids, and amino acids, were identified and quantified in a rapid and simultaneous manner. Five internal standards were tested, with potassium hydrogen phthalate and dimethylmalonic acid found to be the most appropriate, based on their shorter T1 relaxation times and spectral simplicity, while MnCl2 was successfully applied as a relaxation agent for the reduction of the experimental time. Among the pulse sequences that were tested for their quantitative potential, the Carr-Purcell-Meiboom-Gill gave the best results. The quantitative, QEC-HSQC experiment was also found to be very promising for mixture analysis. Additionally, the potential of 1D/2D NMR-based untargeted analysis was successfully tested on two cases, namely, differentiation between cultivars and detection of adulteration with apple juice. This study demonstrates the proof of concept for 1D and 2D NMR methods in the targeted and untargeted analysis of pomegranate juice and can be extended to other complex matrices.

Detection of adulteration in raw milk and its impact on quality

The present study was conducted to evaluate the physical and chemical quality of milk, as well as detection of milk adulteration by addition of water, partial skimming of fat, addition of preservatives or adulterants (urea) in raw milk samples collected from different sources in Dakahlia governorate, Egypt. Milk samples were analyzed by using automatic milk analyzer (to determine added water % , freezing point, fat %, SNF %, protein% and pH ), specific tests to detect prevalence of preservatives (Formalin, H2O2, Boric acid, and Salicylic acid) and addition of some adulterants (Urea).The obtained results revealed that, 72.4% of samples were adulterated by added water in different percentages. The mean value of freezing point, fat, SNF%, protein and pH were detected as -0.469, 2.74%, 8.21%, 3.25%, and 6.8, respectively. Boric acid was detected in 40% and 34% of street vendors and dairy shops respectively. The extent of urea was 100% with mean concentration of 18.4 mg/dl. Considering these results, it could be inferred that the examined milk samples collected from different sources in Dakahlia government were not comply with national and international standards and adulteration is still in practice by all various ways.

A comprehensive approach for milk adulteration detection using inherent bio-physical properties as ‘Universal Markers’: Towards a miniaturized adulteration detection platform

This paper proposes a novel milk quality detection approach based on utilization of inherent biophysical properties as ‘markers’ for adulteration. Unlike the traditional adulterant-specific approaches, this method is generic and universal. It exploits the change in innate milk properties, such as electrical conductivity and pH, upon addition of adulterants as a transduction mechanism for detecting milk adulteration. In this work, adulteration with more than 10 commercially known hazardous adulterants is detected by monitoring the changes in milk electrical conductivity and pH. The electrical parameters for pure milk were standardized using AC impedance-spectroscopy with glassy carbon working electrode and platinum counter/reference electrode at a potential of 0.3V and in the frequency range of 1Hz–1MHz. The experiments were repeated using gold-electrodes fabricated on glass-substrate as a first step towards developing a miniaturized platform. The concept of a ‘unified-universal-marker’ for successful prediction of adulteration is accentuated in this work.

Food Adulteration: From Vulnerability Assessment to New Analytical Solutions

Crises related to the presence of melamine in milk or horse meat in beef have been a wake-up call to the whole food industry showing that adulteration of food raw materials is a complex issue. By analysing the situation, it became clear that the risk-based approach applied to ensure the safety related to chemical contaminants in food is not adequate for food fraud. Therefore, a specific approach has been developed to evaluate adulteration vulnerabilities within the food chain. Vulnerabilities will require the development of new analytical solutions. Fingerprinting methodologies can be very powerful in determining the status of a raw material without knowing the identity of each constituent. Milk adulterated by addition of adulterants with very different chemical properties could be detected rapidly by Fourier-transformed mid-infrared spectroscopy (FT-mid-IR) fingerprinting technology. In parallel, a fast and simple multi-analytes liquid-chromatography tandem mass-spectrometry (LC/MS-MS) method has been developed to detect either high levels of nitrogen-rich compounds resulting from adulteration or low levels due to accidental contamination either in milk or in other sensitive food matrices. To verify meat species authenticity, DNA-based methods are preferred for both raw ingredients and processed food. DNA macro-array, and more specifically the Meat LCD Array have showed efficient and reliable meat identification, allowing the simultaneous detection of 32 meat species. While the Meat LCD Array is still a targeted approach, DNA sequencing is a significant step towards an untargeted one.

Steady, dynamic and creep rheological analysis as a novel approach to detect honey adulteration by fructose and saccharose syrups: Correlations with HPLC-RID results

In this study, natural honey was adulterated with the addition of adulterants, namely saccharose and fructose syrups at a ratio of 0%, 10%, 20%, 30%, 40% and 50% by weight. Steady, dynamic and creep tests were conducted, revealing that the changes in the flow, viscoelastic and creep behavior of natural honey were clear and remarkable. Syrup addition decreased viscosity (η), storage (G′) and loss modulus (G″) values of the control honey samples. Deformation represented by the compliance (J(t)) values was more prominent in the adulterated honey samples. In addition, HPLC-RID analysis was conducted to determine major sugar composition of the adulterated samples. Pearson's correlation test indicated that there were significant (P<0.05; 0.01) correlations between sugar composition and rheology parameters, η (viscosity), K″, K⁎ (intercepts for G″ and complex modulus (G⁎), respectively) and η0 (viscosity of Maxwell dashpot), suggesting that K′, K″, K⁎ and η0 could be prominent indicators for presence of saccharose or fructose syrups added in natural honey within the studied concentration levels. These results suggested that use of steady, dynamic and creep analysis would be a novel and potential approach to detect honey adulteration by fructose and saccharose syrups.

Investigation of trace inorganic elements in street doses of heroin

Sixteen trace elements found in 309 street heroin samples, piped water and contaminated water were determined using inductively coupled plasma-mass spectrometry. All the street heroin samples were found to contain high levels of sodium, a reflection of the use of sodium bicarbonate during heroin synthesis. Additionally, this element was also found to be one of the potential contaminants acquired from the piped water. Calcium could be derived from lime while iron, aluminum and zinc could have come from the metallic container used in the processing/cutting stage. The levels of these elements remained low in the heroin and it could be due to the dilution effects from the addition of adulterants. Statistical validation was performed with six links of related heroin samples using principal component analysis to find the best pretreatment for sample classification. It was obtained that normalization followed by fourth root showed promising results with 8% errors in the sample clustering. The technique was then applied to the case samples. Finally, the result suggested that the case samples could have originated from at least two major groups respectively showing unique elemental profiles at the street level.