Quantification of the adulteration concentration of palm kernel oil in virgin coconut oil using near-infrared hyperspectral imaging

The adulteration concentration of palm kernel oil(PKO)in virgin coconut oil(VCO)was quantified using near-infrared(NIR)hyperspectral imaging.Nowadays,some VCO is adulterated with lower-priced PKO to reduce production costs,which diminishes the quality of the VCO.This study used NIR hyperspectral imaging in the wavelength region 900-1,650 nm to create a quantitative model for the detection of PKO contaminants(0-100%)in VCO and to develop predictive mapping.The prediction equation for the adulteration of VCO with PKO was constructed using the partial least squares regression method.The best predictive model was pre-processed using the standard normal variate method,and the coefficient of determination of prediction was 0.991,the root mean square error of prediction was 2.93%,and the residual prediction deviation was 10.37.The results showed that this model could be applied for quantifying the adulteration concentration of PKO in VCO.The prediction adulteration concentration mapping of VCO with PKO was created from a calibration model that showed the color level according to the adulteration concentration in the range of 0-100%.NIR hyperspectral imaging could be clearly used to quantify the adulteration of VCO with a color level map that provides a quick,accurate,and non-destructive detection method.

Drug adulteration analysis based on complexation with cyclodextrin and metal ions using ion mobility spectrometry

Drug adulteration and contamination are serious threats to human health therefore,their accurate monitoring is very important.Allopurinol(Alp)and theophylline(Thp)are commonly used drugs for the treatment of gout and bronchitis,while their isomers hypoxanthine(Hyt)and theobromine(Thm)have no effect and affect the efficacy of the drug.In this work,the drug isomers of Alp/Hyt and Thp/Thm are simply mixed withα-,β-,γ-cyclodextrin(CD)and metal ions and separated using trapped ion mobility spectrometry-mass spectrometry(TIMS-MS).TIMS-MS results showed that Alp/Hyt and Thp/Thm isomers could interact with CD and metal ions and form corresponding binary or ternary complexes to achieve their TIMS separation.Different metal ions and CDs showed different separation effect for the isomers,among which Alp and Hyt could be successfully distinguished from the complexes of[Alp/Hyt+γ-CD+Cu–H]^(+)with separation resolution(RP–P)of 1.51;whereas Thp and Thm could be baseline separated by[Thp/Thm+γ-CD+Ca–H]^(+)with RP–P of 1.96.Besides,chemical calculations revealed that the complexes were in the inclusion forms,and microscopic interactions were somewhat different,making their mobility separation.Moreover,relative and absolute quantification was investigated with an internal standard to determine the precise isomers content,and good linearity(R^(2)>0.99)was obtained.Finally,the method was applied for the adulteration detection where different drugs and urine were analyzed.In addition,due to the advantages of fast speed,simple operation,high sensitivity,and no chromatographic separation required,the proposed method provides an effective strategy for the drug adulteration detection of isomers.

Use of fluorometry for determination of skim milk powder adulteration in fresh milk

A FAST (fluorescence of advanced Maillard products and Soluble Tryptophan) method for identification of recon- stituted milk made from skim milk powder in the fresh milk was developed. Considering milk and skim milk powders variations from different seasons and countries, milk was collected from different dairy farms in different seasons and skim milk powders were collected from different countries to measure the Tryptophan (Trp), advanced Maillard products (AMP) fluorescence values. The results showed that there were differences (P<0.01) between raw and reconstituted milk. The plot of values in each mixed level of raw and reconstituted milk had a correlation coefficient >0.97. The FAST method is a simple, rapid, low-cost and sensitive method enabling the detection of 5% reconstituted milk in fresh milk. The measurement of the Trp, AMP fluorescence values and calculation of the FAST index is a suitable method for large-scale monitoring of fresh milk samples.

Identifying camellia oil adulteration with selected vegetable oils by characteristic near-infrared spectral regions

In this paper,a methodology based on characteristic spectral bands of near infrared spectroscopy(1000-2500 nm)and multivariate analysis was proposed to identify camellia oil adulteration withvegetable oils,Sunflower,peanut and corn oils were selected to conduct the test.Pure camlia oiland that adulterated with varying concentrations(1-10%with the gradient of 1%,10-40%withthe gradient of 5%,40-100%with the gradient of 10%)of each type of the three vegetable oilswere prepared,respectively.For each type of adulterated oil,full-spectrum partial least squarespartial least squares(PLS)models and synergy interval partial least squares(SI-PLS)modelswere developed.Parameters of these models were optimized simultaneously by cross-validation,The SI-PLS models were proved to be better than the full-spectrum PLS models.In SI-PLSmodels,the correlation coefficients of predition set(Rp)were 0.9992,0.9998 and 0.9999 foradulteration with sunflower oil,peanut oiloil seperately;the corresponding root meansquare errors of prediction set(RMSEP).66nd 0.37.Furthermore,a new genericPLS model was built based on the chalselected from the intervals of thethree SI-PLS models to identify the oil adulterantsardless of the adultrated oil types.Themodel achieved with Rp=0.9988 and RMSEP==1.52,These results indicated that the charac-teristic near infrared spectral regions could determine the level of adulteration in the camllia oil.

Identification of sea snake meat adulteration in meat products using PCR-RFLP of mitochondrial DNA

PCR-RFLP based technique for identification of sea snakes in Thai waters was achieved by developing species-specific markers.To distinguish between sea snake species,the PCR products of cytochrome b(Cyt b),12S and 16S rRNA were sequenced and cut with different restriction endonuclease,Alu I and Hinf I.Each enzyme generated different-sized fragments which specific to Cyt b of eight sea snake species.However,the identical pattern was found among Hydrophis group.This result could be resolved by using these enzymes 12S rRNA digestion.This technique was successfully applied to blood,shed skin,raw meat,cooked meat,sea snake-fish binary admixture,and sea snake-pork binary admixture.Hence,it could be applied for identification when sea snake meat adulteration in meat products and sold as meatballs to reduce production costs.Hopefully,this technique would improve sea snake species identification when morphological examination is no longer possible because the animals are already processed.This is very important to track when sea snake species are being hunted and also used to assess the conservation and management of the sea snakes in Thai waters,especially the Gulf of Thailand.

Development and Optimization of an Alternative Methodology for Detection of Milk Adulteration by Water

This paper presents a process which allows the determination of milk adulteration by water. Considering the limitations of the reference method （cryoscopy）, the proposed methodology matches electrical conductivity and cryoscopy index measurements. Based on statistical procedures, a linear descriptive model is achieved to quantify adulteration of milk with water. It is also shown that electrical conductivity measurements allow the detection of adulteration with water and sodium chloride.

Visible and Near-Infrared Spectroscopic Discriminant Analysis Applied to Identification of Soy Sauce Adulteration

The identification of soy sauce adulteration can avoid fraud, and protect the rights and interests of producers and consumers. Based on two measurement models (1 mm, 10 mm), the visible and near-infrared (Vis-NIR) spectroscopy combined with standard normal variate-partial least squares-discriminant analysis (SNV-PLS-DA) was used to establish the discriminant analysis models for adulterated and brewed soy sauces. Chubang soy sauce was selected as an identification brand (negative, 70). The adulteration samples (positive, 72) were prepared by mixing Chubang soy sauce and blended soy sauce with different adulteration rates. Among them, the “blended soy sauce” sample was concocted of salt water (NaCl), monosodium glutamate (C5H10NNaO5) and caramel color (C6H8O3). The rigorous calibration-prediction-validation sample design was adopted. For the case of 1 mm, five waveband models (visible, short-NIR, long-NIR, whole NIR and whole scanning regions) were established respectively;in the case of 10 mm, three waveband models (visible, short-NIR and visible-short-NIR regions) for unsaturated absorption were also established respectively. In independent validation, the models of all wavebands in the cases of 1 mm and 10 mm have achieved good discrimination effects. For the case of 1 mm, the visible model achieved the optimal validation effect, the validation recognition-accuracy rate (RARV) was 99.6%;while in the case of 10 mm, both the visible and visible-short-NIR models achieved the optimal validation effect (RARV = 100%). The detection method does not require reagents and is fast and simple, which is easy to promote the application. The results can provide valuable reference for designing small dedicated spectrometers with different measurement modals and different spectral regions.

Study of Calcium and Sodium Behavior to Identify Milk Adulteration Using Flame Atomic Absorption Spectrometry

A fast and direct method for determination of milk adulteration by monitoring of calcium and sodium concentrations variations was described. Milk samples were furnished by a dairy company located at S?o Carlos (S?o Paulo State, Brazil) and and spiked with tap-water, whey, hydrogen peroxide, synthetic urine, urea and synthetic milk in the ranged from 5% to 50% (v/v), expect for caustic soda. Caustic soda was added in the milk until establish the original pH. The milk samples were analyzed by using flame atomic absorption spectrometry (FAAS) and no acid digestion process was required. Results showed a significant decrease in the Na and Ca concentrations with addition of synthetic milk and tap-water, a nonlinear variation with addition of synthetic urine, whey and hydrogen peroxide and a largest increase in the Na concentration with addition of NaOH. Correlation between Na and Ca concentrations in pure and adulterated milk were evaluated by paired t-test at a 95% confidence level. Results showed that the method proposed is efficient to identify samples adulterated with tap-water, caustic soda, synthetics milk and urine.

Adulteration detection of edible oils with castor oil by gas chromatography

Developing a method of adulteration detection is critical for protecting customers＂ rights which is a particular concern in food quality. In this study, fatty acid profiles of castor oils were estab-lished by GC and employed to classify 4 types of edible oils and castor oil with multivariate statistical methods. The results indicated that fatty acid profiles of edible oils could be used to classify the 5 kinds of oils. Meanwhile, simulated data test indicated that fatty acid profiles could be used to detect adultera-ted by 5% . Finally, a RF model was built to detect adulteration of edible oils with castor oils by fatty acid composition. The results from cross validation indicated that the oils adulterated by castor oil at low levels （5% 7V/V） could be completely separated from 4 kinds of edible oils. Therefore this model could be used to detect adulteration of 4 kinds of edible oil with castor oils.

Determination of Adulteration Dye Auramine O in Gegen Qinlian Tablets by HPLC-MS/MS

[Objectives]This study aimed to establish a method for determination of adulteration dye auramine O in Gegen Qinlian tablets by HPLC-MS/MS.[Methods]The instrument conditions were as follows:column,Agilent Eclipse plus C18(2.1 mm×100 mm,1.8μm);mobile phase,acetonitrile-0.01 mol/L ammonium acetate;gradient elution;flow rate,0.3 mL/min;column temperature,35℃;injection volume,2μL;ion source,electrospray ionization source(ESI);and scanning mode,multiple reaction monitoring(MRM).[Results]Methodological validation shows that auramine O had a good linear relationship in the range of 0.0934-1.869 pg(r=0.9999),and the average recovery rate was 94.38%(RSD=3.60%,n=9).[Conclusions]The method established is accurate,convenient,precise,reproducible,and stable,with good linear relationship,and it can be used determine the content of adulteration dye auramine O in Gegen Qinlian tablets.

Detection of Fuel Adulteration Using Wave Optical with Machine Learning Algorithms

Fuel is a very important factor and has considerable influence on the air quality in the environment,which is the heart of the world.The increase of vehi-cles in lived-in areas results in greater emission of carbon particles in the envir-onment.Adulterated fuel causes more contaminated particles to mix with breathing air and becomes the main source of dangerous pollution.Adulteration is the mixing of foreign substances in fuel,which damages vehicles and causes more health problems in living beings such as humans,birds,aquatic life,and even water resources by emitting high levels of hydrocarbons,nitrogen oxides,and carbon monoxide.Most frequent blending liquids are lubricants and kerosene in the petrol,and its adulteration is a considerable problem that adds to environ-mental pollution.This study focuses on detecting the adulteration in petrol using sensors and machine learning algorithms.A modified evanescent wave opticalfiber sensor with discrete wavelet transform is proposed for classification of adult-erated data from the samples.Furthermore,support vector machine classifier is used for accurate categorization.The sensor isfirst tested with fuel and numerical data is classified based on machine learning algorithms.Finally,the result is eval-uated with less error and high accuracy of 99.9%,which is higher than all existing techniques.

A Smart Fluorescent Light Spectroscope to Identify the Pork Adulteration for Halal Authentication

The complication of adulterated ingredients in processed food items is widely observed in the food industry and remains a continuous disquiet for end users. This problem may affect consumers’ spiritual beliefs, likewise with their fitness and diet. Hence commercial foods should be scrutinized for the precision of the avowed ingredients. This study is dedicated to developing a Fluorescent light Spectroscope to identify the pork adulteration. A simple way of DNA extraction process has been introduced to make the system more convenient. The spectral bands linked with pork fat (PF), beef fat (BF) and their combinations in different food formulation were skimmed, and recognized by correlating them to those spectroscopically illustrative to clean Pork or PF and other different items. Every material has the properties to absorb some light of specific wavelength, and our activity is to determine thus wavelength range at which are absorbed or make any change by the target material. The findings have revealed that spectroscopy can be used as one of the procedures to detect and quantify of pork in different foods and beverages formulation for Halal verification purposes. Special laborious procedures and equipment both are essential for the existing testing methods named RT-PCR (Reverse transcription-polymerase chain reaction) and ELISA (enzyme-linked immunosorbent assay). Most of the food processors and dealers are not skillful to conduct sufficient testing for their products with all these sample preparation, extraction, analysis, and obtaining results which can be overcome by our proposed setup.

Rheological Modeling of the Effects of Adulteration on Nigerian Honey

Honey was serially diluted with different percentages of glucose, fructose and water and each was analysed rheologically at room temperature of 27°C. Pure honey exhibits thixotropic time-dependent rheological behaviour, the behaviour of glucose and fructose solutions in water (a Newtonian solvent) tends towards near Newtonian. The rheological profiles of pure and adulterated honey samples were determined using RV DV-III Ultra Programmable Rheometer at low rates of share. A Structural Kinetic Model was developed which provided good correlations with the rheological data. The new model was used to classify samples using their average molecular weights as one of the distinguishing parameters. Also the order of the kinetics in the new model suggests the number of active components in the “honey” undergoing deformation as 3. Carreau-Yasuda model was also improved upon to provide an independent assessment of average molecular weight of samples.

Adulteration detection of plant protein beverages by UPLC-MS/MS based on signature peptide of allergen

Plant protein beverage adulteration occurs frequently,which may cause health problems for consumers due to the hidden allergens.Hence,a novel method was developed for authentication by ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS).Almond,peanut,walnut and soybean were hydrolyzed,followed by separation by NanoLC-Triple TOF MS.The obtained fingerprints were identified by ProteinPilotTM combined with Uniprot,and 16 signature peptides were selected.Afterwards,plant protein beverages treated by trypsin hydrolysis were analyzed with UPLC-MS/MS.This method showed a good linear relationship with R2>0.99403.The limit of quantification(LOQ)were 0.015,0.01,0.5 and 0.05 g/L for almond,peanut,walnut and soybean,respectively.Mean recoveries ranged from 84.77%to 110.44%with RSDs<15%.The developed method was successfully applied to the adulteration detection of 31 plant protein beverages to reveal adulteration and false labeling.Conclusively,this method could provide technical support for authentication of plant protein beverages to protect the rights and health of consumers.

Graphene-and Multi-Walled Carbon Nanotubes-Coated Tapered Plastic Optical Fiber for Detection of Lard Adulteration in Olive Oil

A rapid and high-performance sensor for lard adulterant in edible oil was developed using the tapered plastic optical fiber(POF)coated with graphene and multi-walled carbon nanotubes.The coating material was deposited onto a tapered POF with a taper waist diameter and a taper length of 0.45 mm and 1 cm,respectively.The addition of the coating material was used to increase the sensitivity and selectivity coefficient of the tapered POF toward the lard substance.The sensing mechanism is based on a simultaneous interaction of lard substance and an evanescent wave of tapered POF with the coating layers.The results showed that graphene coating on the tapered POF increased the selectivity coefficient of the tapered POF towards lard substance from 33.54 to 324.19,and it gave a sensitivity of 0.427 dBm/%.In comparison,multi-walled carbon nanotubes coating increased the selectivity coefficient to 71.65 and increased its sensitivity to 1.189 dBm/%.Thus,the proposed configuration of the tapered POF with the coating material offered a simple configuration for a rapid,high sensitivity and selectivity detection of lard adulterant in edible oils.

Automated recognition and classification of adulteration levels from bulk paddy grain samples

Fraudulent labeling and adulteration are the major concerns in the global rice industry.Almost all the paddy varieties being sold in the market are prone to adulteration.It is very difficult to differentiate paddy grains of various varieties in the mixed bulk sample based on visual observation.Currently,there is no sophisticated appearance-based commercial scale technology to reliably detect and quantify adulteration in bulk paddy grain samples.The paper presents a cost-effective image processing technique for the recognition of adulteration and classification of adulteration levels(%)from the images of adulterated bulk paddy samples using state-of-the-art color and texture features.In this work,seven adulterated bulk paddy samples are considered and each of the samples is prepared by mixing a premium paddy variety with the identical looking and commercially inferior paddy variety at five different adulteration levels(weight ratios)of 10%,15%,20%,25%and 30%.The study compares the performances of three different classification models,namely,Multilayer Back Propagation Neural Network(BPNN),Support Vector Machine(SVM)and k-Nearest Neighbor(k-NN).The Principal Component Analysis(PCA)and Sequential Forward Floating Selection(SFFS)methods have been employed separately for the automatic selection of optimal feature subsets from the combined color and texture features.The maximum average adulteration level classification accuracy of 93.31%is obtained using the BPNN classification model trained with PCA-based reduced features.The proposed technique can be used as an economic,rapid,non-destructive and quantitative technique for testing adulteration,authenticity,and quality of bulk paddy grain samples.

Detection of the adulteration of extra virgin olive oil by near-infrared spectroscopy and chemometric techniques

Introduction and Objectives:Due to the value of extra virgin olive oil(EVOO),adulteration has become an important issue in the industry,which has created demand for quick and inexpensive fraud detection testing.In contrast to many current food fraud detection methods,near-infrared spectroscopy(NIRS)can be inexpensive and convenient by minimizing sample preparation and measurement times.In this study,we developed a method using NIRS and chemometrics to detect adulteration of EVOO with other edible oil types that does not require sample preparation and can be completed in less than 10 min.Methods,Results,and Discussions:First,a single EVOO was adulterated with corn oil from 2.7%to 25%w/w.Spectra for the unadulterated sample and its adulterated counterparts were measured.A principal component analysis(PCA)scores plot showed separation between the adulterated mixtures and the unadulterated sample,which demonstrated that the developed method could detect as low as 2.7%w/w adulteration if an unadulterated sample of the oil in question is provided.To study adulteration detection without an unadulterated sample for reference,the spectra of unadulterated samples and samples adulterated with corn,sunflower,soybean,and canola oils were measured.A PCA with soft independent modelling of class analogy was used for adulteration detection.Lower limits of adulteration detection for corn,sunflower,soybean,and canola oils were found to be approximately 20%,20%,15%,and 10%,respectively.Conclusions:These results demonstrate that the developed method can be used to rapidly screen for adulterated olive oils.

Dielectric spectroscopy coupled with artificial neural network for classification and quantification of sesame oil adulteration

Adulteration using cheap vegetable oils into expensive oils such as sesame oil is a considerable challenge in the edible oil market. To discriminate pure and adulterated sesame oilwith sunflower and canola oils (commonly used as an adulterant to the high-price oils),dielectric spectroscopy was applied in the range of 40 kHz–20 MHz. The principal component analysis (PCA) plots were able to distinguish the pure sesame oil, while it was impossible to separate the adulterated oils based on the kind of adulteration. The correlationbased feature selection (CFS) method was used to select the more relevant dielectric datawithin the spectrum and to reduce the dimensionality of the input vector belongs to theartificial neural network (ANN). The ANN classifier with topology of 19-5-4 structureshowed a perfect accuracy of 100% in detecting the authentic and the adulterated sesameoil. The regression ANN with the topology of 15-5-1, 21-8-1 and 10-11-1 were the mostrobust models in quantifying the amount of adulteration in sesame oil generated by sun-flower oil, canola oil and sunflower + canola oils, with R2Test of 1, 1 and 0.999 9, respectively.The proposed technique is a powerful and simple method to detect and quantify adulteration of sesame oil. The novelty of this research is capability of used system for authentication of adulterated sesame oil using low frequency. Furthermore, the developed systemhas a good capability for other types of sesame oil adulterations as well as to detect adulteration in other expensive edible oils.

Validation of the Methods for Detection the Non-Milk Fat in a Mixture of Milk Fat and Palm Oil

Milk fat contains a variety of nutritive and health-promoting compounds that guard against some disease. In the current system of global competition, when the quality of milk and milk products is not an option but rather a requirement, therefore, determining the purity of milk fat is critical. This study aims to validate analytical methods for detecting palm oil in a mixture of milk fat and palm oil. Methods of this study was involved detection of non-milk fat in fat blinders by determining the saponification value, iodine number, refractive index, butyro refractometer reading, Gas chromatography, Reverse Phase High-performance liquid chromatography, and Fourier transforms Infrared. The results of this study revealed that the saponification value, Iodine number, refractive index, and Butyro Reading could be used to detect the addition of palm oil by a level of 10% - 20% or more to the milk. The level of some fatty acids in the milk as determined by GC, such as myristic acid (C14:0), palmitic acid (C16:0), and stearic acid (C18:0), is correlated well with the level of adding palm oil to milk fat. The determination of cholesterol and β-sito-sterol content by RP-HPLC could be used for the detection of the addition of palm oil to milk fat. The spectrum behavior produced by FTIR spectroscopy in this adulterated sample is almost the same, so this technique could not be used to detect the palm oil in milk fat.

Advances in Research of Identification of Cnidii Fructus and Its Adulterants

In this paper,the traditional identification and modern identification technology of Cnidii Fructus and its adulterant were reviewed,and their advantages,disadvantages and practicability were summarized,to provide a reference for the rapid and accurate identification and quality evaluation of Cnidii Fructus.