Detection Of Adulteration Research Articles

Fusing one-class and two-class classification – A case study on the detection of pepper fraud

Black pepper is a commercially important commodity, which is susceptible for fraudulent additions. Analytical tools are capable of detection of specific additions, but in most published cases these tools and associated mathematical models are suitable for only one or a few predetermined adulterants. There is a need for methodology that can detect any addition without having to know the type of adulterant a priori . We analyzed a dataset of 200 authentic black pepper samples and a total of 210 adulterated samples consisting of mixtures of black pepper and oil-dress pepper, spent pepper, coffee husk, coffee skin and papaya seeds, respectively. A small, non-destructive spectral tool, a visible-near infrared spectrophotometer, (VIS/NIR) and a slower and more expensive mass spectrometric tool, direct analysis in real time-mass spectrometer (DART-MS), were evaluated according to their performances in terms of adulteration detection for a number of machine learning modes. The often-used approach where an ‘optimal’ model is selected and employed yielded for VIS/NIR very reasonable results for most of the adulterants used, but no single model performed well for all adulterants in the dataset. However, high-level fusion modelling of both one- and two-class models developed for different adulterant types using a penalized excess scoring system led to a performance of typically >75% correct classification, regardless of the nature of the adulterant. DART-MS outperformed VIS/NIR but also led to no single model that was able to detect all adulterants present. From the small number of tested fusion strategies, again the penalized excess score outperformed the other fusion options and yielded perfect classification scores for all but one of the adulterants tested. This shows that this type of modelling for cases where the nature of a target is unknown is a promising approach. It is even speculated that this modelling approach is likely to be suitable for types of adulterant that were not used in the model development phase. • Detection of adulterants in black pepper powder using spectroscopy and DART. • Application of VIS/NIR in a fused prototype device. • Simultaneous detection of multiple adulterants using chemometrics. • Investigation into different fusion approaches of VIS/NIR and DART data. • Fusing of chemometric models enhances and broadens the detection power.

A comprehensive review on unethical honey: Validation by emerging techniques

Globally, honey is consumed as natural functional food, featuring high economic value, related to its authenticity and purity. Due to the high demand for honey, it has been wildly targeted for food adulteration with substandard honey or low-price syrups, and emerging absorbent resin technology has also affected the honey market. Researchers worldwide have been working persistently to invent and innovate advanced technologies for detecting honey adulteration and assuring its authenticity. The compositional intricacy of pure and unauthentic honey could be easily detangled by a combined approach of chemometrics and instrument. In this palimpsest, various detection methods like ISCIRA, NMR, AT-FTIR, Sensors, PCR based assay united with an appropriate Multivariate approach that provides accurate and acceptable results for the determination of honey authentication and adulteration were described. The botanical origin authentication of honey was determined with the application of δ13C-EA-IRMS and δ13C-LC-IRMS coupled SVM, which discriminate samples based on specific markers. LIBS, NMR, HPTLC, UHPLC, GC, and real-time PCR, generated data and processed with LDA, OPLS, PCA, ANN, CNN etc. The generated data discriminate adulterated honey from pure, as NMR clubbed with PCA can detect 1% of adulterants in honey. Hence, various methodologies in chemometrics have manifested their proficiency during ground application. Perhaps, there is a long way to go in this field to develop a universal technology for honey authentication and adulteration detection.

Adulteration of Milk Identification Using Ionic Polymer Metal Composite as Sensor

Milk adulteration is found as one of the social problems throughout the globe. Consumption of adulterated milk causes various diseases in humans and great concern to the food industry. Adulteration is generally done by adding chemicals to the milk, especially water. Inappropriately, milk is being very easily adulterated throughout the world and significantly worse in developing and underdeveloped countries due to the absence of adequate monitoring and lack of proper law implementation. The international and national research surveys presented the use of various electrochemical, nanomaterial, and thermal sensing mechanisms for the detection of adulteration in milk (example: spectroscopic methods, thermal sensors, chitosan-multiwalled carbon nanotubes, etc.). However, the ionic polymer metal composite (IPMC) is used as a sensor or actuator; for this application, IPMC is explored as a sensor for detecting the impurities in milk. IPMC used in this experiment is Nafion base, which is a sulfonated tetrafluoroethylene based fluoropolymer-copolymer. In this letter, the authors took the opportunity to use the electroactive polymer material IPMC as a taste sensor.

Non-targeted metabolomics and chemometrics for saffron (Crocus sativus L.) authentication and adulteration detection in relation to its anticholinesterase activity

Saffron (Crocus sativus L.), is a spice of vulnerable medical importance. It is often adulterated to lower its quality and efficacy. The present study sought to detect the level of adulteration in five traded saffron collected from different localities using a high-performance liquid chromatography coupled with high resolution tandem mass spectrometry (UPLC-HR-MS/MS) technique. In total, 62 metabolites were identified in the five extracts namely from monoterpene aldehydes, flavonoid derivatives, apocarotenoids and carotenoids classes as well as chemical markers of adulteration. One common adulterant; Gardenia jasminoides Ellis was detected through the simultaneous determination of its specific constituents including geniposide, shanzhiside and major jasminosides. Also, one synthetic dye; auramine-O was detected in one marketed saffron product. The anticholinesterase activity of the five marketed saffron was also evaluated. Furthermore, saffron samples that contain high crocin levels possessed a promising anticholinesterase activity. It was detected for the first time that auramine-O dye is sometimes used as a potential adulterant of saffron. In spite of the grand researches on saffron, it still lacks an accurate evaluation of the commercial saffron products. The results of our study showed that high adulteration rate of trade saffron not only reduces saffron efficacy as a herbal remedy but may also simultaneously represent a health risk.

Development of a paper strip test for colorimetric detection of urea in raw materials for animal feed.

Aiming at developing an easily implementable method for on-site analysis to detect urea adulteration in feed ingredients, a simple and inexpensive paper strip for urea detection via colorimetric assay is herein presented. The paper strip can be simply fabricated by immobilizing urease with bromothymol blue (BTB) as a pH indicator on cellulose fiber. Upon dipping the paper strip into the target sample, the release of ammonia during the reaction between urea in the sample and urease on the paper strip causes a pH change that results in the development of a blue color, thus indicating the presence of urea. A semiquantitative detection method was developed on the basis of the color change on the paper strip, which can be detected by naked eyes and compared with a color chart made by spiking urea at concentrations varying from 0.10% to 1.0% (w/w) in animal protein and fishmeal samples. Moreover, quantitative data were obtained by taking a picture with a smartphone camera and measuring the color intensity using ImageJ software. A comparison between BTB and phenol red as pH indicators revealed that the former afforded better results in terms of resolution. Under optimal conditions, good linear responses of blue intensity were obtained in a concentration range of 0.10%-1.0% (w/w). The recovery was determined to range between 98.1% and 118.3% with a relative standard deviation of <5%. The developed paper strip assay was applied to determine urea in animal protein and fishmeal, finding good agreement with the official AOAC method (No. 967.07). The present paper strip is rapid and requires neither sophisticated devices nor skilled personnel, allowing its use by quality controllers for the routine on-site detection of the urea adulteration of raw materials.

Quantitative assessment of adulteration of coconut oil using transmittance multispectral imaging.

Economical to a fault, coconut oil is a commodity related to fraudulent activities such as oil adulteration for undue profits. Unfortunately, the conventional methods used in the detection of adulteration and toxicants are laborious, destructive, and time-consuming. Hence, it is imperative to engineer a non-destructive and rapid screening test with sufficient accuracy. To that end, the proposed work has an in-house developed imaging system hardware and a method to estimate relevant quality parameters from multispectral imagery. Multispectral images of adulterated coconut oil were analyzed through a cascade of statistical algorithms: Fisher Discriminant Analysis and Bhattacharyya distance respectively. In this work, a functional relationship was developed for the estimation of adulteration level that recorded an R2 of 0.9876 for the training samples and an MSE of 0.0029 for the testing samples. Besides, the proposed imaging system offers flexibility on post-processing of raw measurements as the algorithm is designed to operate from raw multispectral images. In addition, the developed imaging system is economical in its capacity to estimate the adulteration of coconut oil with remarkable accuracy considering the low cost of production. Moreover, the proposed work validates the use of multispectral imagery as an initial screening technique instead of expensive spectroscopy methods.

Portable Spectroscopy for Food Safety and Adulteration Screening

In this review we discuss various applications of portable vibrational spectroscopy in food adulteration analyses and authentication confirmation. These applications generally involve the adulteration of a food product with materials which may be of lesser monetary value and may also be detrimental to human health. Portable spectroscopic testing for food authentication opens potential for rapid and on-site analyses of food products at various stages in the food production chain. These portable techniques also require the development of data analysis methods for each individual application, which will also be discussed in this review. Specific food safety applications for portable spectroscopy to be discussed include adulteration detection and evaluation of authenticity. Adulteration detection examples focus on the commonly adulterated milk powder and spice commodities, as well as the potential for component substitution (e.g., artificial sweeteners). For authentication, examples from marine oil dietary supplements, olive oils, honeys, coffees, and grains are presented along with examples of speciation (e.g. fish) and growth conditions (bell peppers).

Multiplex Surface-Enhanced Raman Scattering: An Emerging Tool for Multicomponent Detection of Food Contaminants.

For survival and quality of human life, the search for better ways to ensure food safety is constant. However, food contaminants still threaten human health throughout the food chain. In particular, food systems are often polluted with multiple contaminants simultaneously, which can cause synergistic effects and greatly increase food toxicity. Therefore, the establishment of multiple food contaminant detection methods is significant in food safety control. The surface-enhanced Raman scattering (SERS) technique has emerged as a potent candidate for the detection of multicomponents simultaneously. The current review focuses on the SERS-based strategies in multicomponent detection, including the combination of chromatography methods, chemometrics, and microfluidic engineering with the SERS technique. Furthermore, recent applications of SERS in the detection of multiple foodborne bacteria, pesticides, veterinary drugs, food adulterants, mycotoxins and polycyclic aromatic hydrocarbons are summarized. Finally, challenges and future prospects for the SERS-based detection of multiple food contaminants are discussed to provide research orientation for further.

Post-heating Fluorescence-based Alteration and Adulteration Detection of Extra Virgin Olive Oil

Olive oils are more expensive compared with other vegetable oils. Therefore, adulterating such expensive oil is prevalent. The traditional methods for olive oil adulteration detection are complex and require pre-analysis sample preparation. Therefore, simple and precise alternative techniques are required. In the present study, the Laser-induced fluorescence (LIF) technique was implemented for detecting alteration and adulteration of olive oil mixed with sunflower or corn oil based on the post-heating emission characteristics. Diode-pumped solid-state laser (DPSS, λ = 405 nm) was employed for excitation and the fluorescence emission was detected via an optical fiber connected to a compact spectrometer. The obtained results revealed alterations in the recorded chlorophyll peak intensity due to olive oil heating and adulteration. The correlation of the experimental measurements was evaluated via partial least-squares regression (PLSR) with an R-squared value of 0.95. Moreover, the system performance was evaluated using receiver operating characteristics (ROC) with a maximum sensitivity of 93%.

Rapid detection of sunset yellow adulteration in tea powder with variable selection coupled to machine learning tools using spectral data.

The online version contains supplementary material available at 10.1007/s13197-023-05694-3.

Honey authentication and adulteration detection using emission – excitation spectra combined with chemometrics

The aim of this study was to evaluate the usefulness of emission-excitation matrices for honey authentication and adulteration detection. For this purpose, 4 types of authentic honeys (tilia, sunflower, acacia and rape) and samples adulterated with different adulteration agents (agave, maple, inverted sugar, corn and rice in different percentages – 5%, 10% and 20%) were analysed. Each honey type and each adulteration agent exhibit unique emission-excitation spectra that can be used for the classification according to the botanical origin and for the detection of adulteration. The principal component analysis clearly separated the rape, sunflower and acacia honeys. The partial least squares – discriminant analysis (PLS-DA) and support vector machines (SVM) were used in a binary mode to separate the authentic honeys from the adulterated ones, and the SVM proved to separate much better than PLS-DA.

Non-destructive detection of adulteration of weight-loss drugs in the field of spectral feature fusion

To crack down on criminals using the delivery channel to transport weight-loss drugs doped with toxic and harmful nonfood raw materials, a pattern recognition method of weight-loss drugs based on terahertz time-domain spectroscopy was proposed. Compared with traditional methods, terahertz spectrum had high signal-to-noise ratio in time-domain spectrum, which was fast, time-saving and lossless. In this study, seven kinds of weight-loss drugs were selected as experimental samples. The terahertz time-domain spectra of the samples were collected. Three characteristic frequency intervals of 0–0.19, 1.75–2.14 and 2.23–2.5 (THz) were found by automatic peak finder. The characteristic frequency intervals were processed by Hilbert transform, Butterworth low-pass filter, fast Fourier transform low-pass filter and the first-order derivatives after standard normal transform, the feature data was fused with the original spectra, and the original data and the data fused by the four methods were classified and recognized by particle swarm optimization least squares support vector machine and extreme learning machine model optimized by Cuckoo algorithm. The experimental results showed that the particle swarm optimization least squares support vector machine model had the best recognition effect on the spectral feature fusion data after Hilbert transform, and the accuracy can reach 100 %. It had a certain reference significance for the identification of weight-loss drugs in forensic science.

Quality Control and Evaluation of Herbal Product

Abstract: Standardization of herbal formulation is important in order to assess of quality drug. The quality of herbal drug is the considerate all the factor which contribute directly or indirectly to the safety, effectiveness of the product Medicinal plant have been used to improve human health , increasing popularity global as drug , complementary and alternative medicines, food supplement, cosmetic as medical devices. The complexity of herbs and extracts supplied to such a wide range of markets and in different requlatory environment, raise major quality issues, increasing the need for appropriate analytical method for their identification, standardization &amp;also for the detection of adulterants and contamination. Major limitation factors, such as herbal product naming, sampling and sample preparation are also discussed. The use of products of natural origin has remarkably increased in the global market, because of consumer becoming more health-conscious herbal medicines are considered safe by consumers and these product are generally less expensive than allopathic medicines.

Artificial Intelligence Aided Adulteration Detection and Quantification for Red Chilli Powder

Artificial Intelligence Aided Adulteration Detection and Quantification for Red Chilli Powder

Application of portable near‐infrared spectroscopy for rapid detection and quantification of adulterants in baobab fruit pulp

SummaryBaobab fruit pulp powder (BFPP) is susceptible to economically driven adulteration owing to its incredible nutrient density and rapidly expanding demand worldwide. In this study, a portable NIR spectroscopy (wavelength 900–1700 nm) coupled with chemometrics was used for the detection of BFPP adulteration. BFPP samples separately adulterated with rice flour (RF), wheat flour (WF), and maize flour (MF) at 0%, 1%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 40%, 50% and 60% concentrations were subjected to NIR spectroscopy. Two‐class models proved to be reliable with sensitivity and specificity of above 0.98 and an error of below 0.01. Four‐class models attained sensitivity and specificity of above 0.68 and an error of below 0.276. The correlation coefficient (R2) and root mean square error (RMSE) of the prediction set were above 0.88 and below 6.20% respectively for PLSR models. The LODs were also below 13.79%. Therefore, NIR spectroscopy has a promising potential for rapid screening of BFPP adulterations.

Novel method for the detection of adulterants in coffee and the determination of a coffee's geographical origin using near infrared spectroscopy complemented by an autoencoder

SummaryCoffee authenticity is a foundational aspect of quality when considering coffee's market value. This has become important given frequent adulteration and mislabelling for economic gains. Therefore, this research aimed to investigate the ability of a deep autoencoder neural network to detect adulterants in roasted coffee and to determine a coffee's geographical origin (roasted) using near infrared (NIR) spectroscopy. Arabica coffee was adulterated with robusta coffee or chicory at adulteration levels ranging from 2.5% to 30% in increments of 2.5% at light, medium and dark roast levels. First, the autoencoder was trained using pure arabica coffee before being used to detect the presence of adulterants in the samples. Furthermore, it was used to determine the geographical origin of coffee. All samples adulterated with chicory were detectable by the autoencoder at all roast levels. In the case of robusta‐adulterated samples, detection was possible at adulteration levels above 7.5% at medium and dark roasts. Additionally, it was possible to differentiate coffee samples from different geographical origins. PCA analysis of adulterated samples showed grouping based on the type and concentration of the adulterant. In conclusion, using an autoencoder neural network in conjunction with NIR spectroscopy could be a reliable technique to ensure coffee authenticity.

Detection of Low-Level Adulteration of Hungarian Honey Using near Infrared Spectroscopy

Honey adulteration is a worldwide problem; however, its detection is a challenge for researchers and authorities. There are numerous ways of honey counterfeiting; amongst them, direct adulteration is one of the most common methods. Correlative techniques, such as near-infrared spectroscopy (NIRS), are useful tools in the detection of honey adulteration; however, this method has not been applied to Hungarian honeys. The aim of this research was to investigate the performance of NIRS for the detection of sugar syrup addition to Hungarian honeys at lower concentration levels (&lt;10% w/w). Acacia, rape, forest, sunflower, and linden honeys were mixed with high-fructose-content sugar syrup, rice syrup, or self-made glucose fructose syrup in 3%, 5%, and 10% w/w. NIRS analysis was performed in the spectral range of 950–1650 nm. Principal component analysis was coupled with linear discriminant analysis and partial least square regression models were built for the classification and prediction of adulteration levels, respectively. Our results showed that the performance of NIRS highly depends on both type of syrup and honey. PCA-LDA models provided the 100% correct classification of control in the case of all the models, while PLSR results could predict the added sugar syrup content in the case of rice and F40 syrup models, obtaining &gt;2.2 RPDCV value.

Detection of adulterants and evaluate the quality of selected commercial pickles and chutneys collected from various local markets in Jashore, Bangladesh

In Bangladesh, pickles and chutneys are very popular appetizers that give main dishes more taste. In this present study quality of selected commercial pickles and chutneys were evaluated by conducting physicochemical parameters and analyzing the adulterant. A total 19 samples were used for this experiment. The pickle and chutney samples were evaluated for physicochemical parameters including moisture content, acidity as citric acid, sodium benzoate, and arsenic in laboratory. The results suggested that, highest concentration of moisture content (49.80±0.21%), acidity (1.85±0.02 mg/Kg) and sodium benzoate (363.30±0.26 mg/Kg) were found in Tamarind chutney (C-1), Jujube chutney (B-3) and Mixed chutney (E-1) respectively. However, all of the samples estimated arsenic (As) levels remained within the corresponding guideline limit. It is concluded that samples of moisture content (57.89%), acidity (68.42%) and sodium benzoate (15.79%) were higher than BSTI standard which is not suitable for food safety. However, pickles and chutneys are available in both good and bad quality in the market. Asian J. Med. Biol. Res. 2023, 9 (1), 9-13

Non-destructive detection of Tieguanyin adulteration based on fluorescence hyperspectral technique

Non-destructive detection of Tieguanyin adulteration based on fluorescence hyperspectral technique

Integrating microneedle DNA extraction to hand-held microfluidic colorimetric LAMP chip system for meat adulteration detection

Most microfluidic-based “sample-in-result-out” systems suffer sophisticated microfluidic production processes, high-cost chips, and expensive instruments. They cannot be used in the meat market as well as farmer's markets in rural areas. Here, we developed a hand-held microfluidic chip system for on-site meat species qualitative authentication detection which integrated a simple microneedle DNA extraction and a visual loop-mediated isothermal amplification (LAMP). The chip can be used by easily pricking meat samples, simply hand-shaking the chip, and readily available isothermal heating instead of a complicated DNA extraction process and microfluidic control device. The system demonstrates high specificity and sensitivity for selected six species of meat samples and low to 1% simulated adulteration could be detected within 60 min. Besides, the whole cost was less than 1 dollar. The integrated hand-held microfluidic detection system offers a simple, fast, low-cost “sample-in-result-out” point-of-care device which could be extended to medical diagnosis and animal/plant disease identification.