Detection Of Adulteration Research Articles

Ensuring authenticity of cinnamon powder: Detection of adulteration with coffee husk and corn meal using NIR, MIR spectroscopy and chemometrics

Cinnamon (Cinnamomum sp.) is a commonly used spice that is at risk of being counterfeited due to its intricate production process, which can compromise its integrity. This study aimed to develop a rapid detection method using Near Infrared (NIR) and Mid Infrared (MIR) spectroscopy, along with chemometrics, to identify fraud in genuine cinnamon powder. Various techniques, including exploratory, non-targeted, and targeted methods, were used to detect the presence of adulterants such as coffee husks and corn meal in cinnamon, with concentrations ranging from 10% to 50%. The exploratory and non-targeted models successfully identified the potential occurrence of fraud as a result of the addition of coffee husks and corn meal. The models' effectiveness was tested on commercial cinnamon samples, exposing a high proportion of potentially fraudulent samples. Adulteration percentages exceeded 60% for the presence of coffee husks and 50% for corn meal. The results show that NIR and MIR spectroscopy, when used with chemometrics, are effective for monitoring the quality of cinnamon powder.

Detection of adulterations in native stingless bees honey from Argentina using UV–Vis spectroscopy coupled with chemometrics

Detection of adulterations in native stingless bees honey from Argentina using UV–Vis spectroscopy coupled with chemometrics

ATR-FTIR spectroscopy and machine/deep learning models for detecting adulteration in coconut water with sugars, sugar alcohols, and artificial sweeteners

Packaged coconut water offers various options, from pure to those with added sugars and other additives. While the purity of coconut water is esteemed for its health benefits, its popularity also exposes it to potential adulteration and misrepresentation. To address this concern, our study combines Fourier transform infrared spectroscopy (FTIR) and machine learning techniques to detect potential adulterants in coconut water through classification models. The dataset comprises infrared spectra from coconut water samples spiked with 15 different types of potential sugar substitutes, including: sugars, artificial sweeteners, and sugar alcohols. The interaction of infrared light with molecular bonds generates unique molecular fingerprints, forming the basis of our analysis. Departing from previous research predominantly reliant on linear-based chemometrics for adulterant detection, our study explored linear, non-linear, and combined feature extraction models. By developing an interactive application utilizing principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), non-targeted sugar adulterant detection was streamlined through enhanced visualization and pattern recognition. Targeted analysis using ensemble learning random forest (RF) and deep learning 1-dimensional convolutional neural network (1D CNN) achieved higher classification accuracies (95% and 96%, respectively) compared to sparse partial least squares discriminant analysis (sPLS-DA) at 77% and support vector machine (SVM) at 88% on the same dataset. The CNN’s demonstrated classification accuracy is complemented by exceptional efficiency through its ability to train and test on raw data.

Rapid quantitative and qualitative analysis of talcum powder in wheat flour by microwave detection combined with multivariate analysis

Rapid and accurate monitoring of talcum powder in wheat flour is of great practical importance for maintaining market order, public health and food safety. In this study, the potential of a home-made microwave detection system for rapid quantitative and qualitative detection of talcum powder adulteration in wheat flour at 2.5–11.5 GHz was investigated. Useful microwave spectral information is selected using three feature selection strategies, the bootstrapping soft shrinkage (BOSS), the multiple feature spaces ensemble with least absolute shrinkage and selection operator (MEF-LASSO), and the competitive adaptive reweighted sampling (CARS). The eXtreme Gradient Boosting (XGBoost) classification and regression models are constructed and evaluated, and three nature-inspired optimization algorithms (NIOA) are used for parameter optimization: the Osprey Optimization Algorithm (OOA), the Crested Porcupine Optimizer (CPO), and the Sparrow Search Algorithm (SSA). The experimental results show that both feature selection strategies and NIOA algorithms can effectively improve the model performance. The SSA-XGBoost classification model has the highest prediction accuracy, and its prediction accuracy and F1-score can reach 100 % and 1, respectively. The MEF-LASSO-SSA-XGBoost regression model has strong robustness. The root mean square error (RMSE) was 1.76 %, the prediction coefficient of determination (R2) was 0.98, and the ratio of performance to deviation (RPD) was 8.66. The results of this study showed that the combination of microwave technology and machine learning model with multivariate analysis method can realize the rapid and accurate determination of qualitative and quantitative talcum powder in wheat flour.

Detection of coconut oil adulteration with palm oil through NMR spectroscopic method

Coconut oil is a costly commodity in the food and traditional medicinal sectors and its adulteration with cheap palm oil is a serious issue. The present study evaluates the application of 1H NMR spectroscopy to authenticate coconut oil, and to monitor its adulteration with the cheap palm oil substitute. Various parameters such as average chain length (14.25), saponification index (244.66 mg KOH/100 g), molecular weight (652.12), iodine value (8.27 mg/100 g), peroxide value (0.02 meqO2/kg) and percentage of unsaturation (7.81%) were calculated through the NMR technique, and were found to be in concurrence with the values obtained from wet lab experiments. The extent of palm oil adulteration can be detected through NMR by evaluating the chemical shift values for olefinic protons. The findings have a significant impact on both the food and traditional medicine sectors, as NMR spectroscopy can replace the conventional wet lab methods as a reliable and precise method for analysis.

Detection of different adulteration in cinnamon powder using hyperspectral imaging and artificial neural network method

Cinnamon is one of the medicinal spices that is important in point of economic and human health. The goal of the present research was to classify the levels of different adulterations in the spice using hyperspectral imaging technology. In the present research, three adulterants were investigated including sea foam powder and chickpea and wheat flour with 0, 5, 15, 30, and 50% adulteration levels. After sample preparation, the hyperspectral images of them were acquired using a line scan imaging system. The effective wavelengths were selected and image features were extracted. The effective features were selected and classified using the artificial neural network method. The classification accuracies of the classifier to identify sea foam powder and chickpea and wheat flour adulterants were equal to 98.9, 100, and 100%, respectively. The results showed the high ability of hyperspectral imaging combined with artificial neural networks method to detect adulteration in cinnamon with high reliability.

Analysis of milk adulteration by means of a potentiometric electronic tongue

The adulteration of milk presents significant challenges in the food industry, promoting the need for efficient detection methods. This study introduces a potentiometric electronic tongue for rapid and accurate milk adulteration detection. Utilizing polymeric membranes integrated with various additives, the electronic tongue distinguishes between different milk types and detects common adulterants. Experimental results demonstrate its effectiveness in discriminating raw, pasteurized, and medicated cow milk, as well as goat milk. Moreover, it successfully identifies adulterants like water and bovine milk in goat milk samples. Chemometric analyses, including Principal Component Analysis and Partial Least Squares regression, correlate sensor responses with traditional milk parameters such as fat, protein, and lactose content with up to a 0.97 R2 on the validation step. Strong correlations validate the electronic tongue's potential for rapid milk quality assessment. This innovative approach offers a cost-effective, reliable solution for detecting milk adulteration in contrast with current techniques that require numerous, time consuming experiments.

Detection of adulteration of cattle and buffalo meat through NIRS and chemometric analysis

The objective of this study was to evaluate the efficacy of near-infrared (NIR) reflectance spectroscopy in detecting adulteration in cattle and buffalo meat. A total of 16 samples were tested, 2 of which were pure and 14 were adulterated. The beef samples were adulterated by mixing buffalo meat in the range of 0-28% (w/w) at approximately 2% increments according to weight. To detect adulteration, DLP® NIRscan™ Nano Software was used to gather spectra. The Unscrambler X program was used to develop calibration and validation models utilizing principal component regression and partial least squares. Root mean square error of calibration (RMSEc), root mean square error of cross-validation (RMSEcv), coefficient of calibration (R2 c ), and coefficient of cross-validation (R2 cv) were used to assess the accuracy of the calibration models. The R2 value of 0.90 or above indicates that the regression model is excellent. For the PCR model, the predicted R2 cv value was 0.73 and for the PLSR model, the predicted R2 cv value was 0.98 through leverage correction. In cross-validation, the R2 cv value was 0.65 for both the PCR and PLSR models. According to the findings, it is suggested that NIR spectroscopy is a reasonably efficient method for detecting adulteration in cattle meat with buffalo meat.

Mid-infrared spectroscopy and chemometrics in the detection of adulteration in chia oil (Salvia hispanica L) and α-linolenic acid content prediction

Chia oil has high commercial value due to polyunsaturated fatty acids (PUFAs), especially α-linolenic acid (ALA), and suffers from tampering. Traditional adulterant detection in oils applies gas chromatography, but this approach has disadvantages such as time consumption. The development of fast analytical methods like infrared spectroscopy is important to detect oil fraud. The study aims to employ mid-infrared (FTIR) and chemometrics to detect adulteration in chia oil. Chia oil was extracted by cold pressing and adulterated with sunflower, corn, and soybean oils. FTIR-ATR spectra were obtained using a Fourier transform infrared spectrophotometer and horizontal attenuated reflectance accessory (HATR). Partial least square (PLS) models were adjusted to predict the adulteration content in chia oil and to predict the fatty acid content, including ALA. Gas chromatography was the reference method for the fatty acid content, and the adulteration content was known. The model obtained for adulteration content in chia oil had a high predictive capacity with r2 = 0.9868 for the prediction set and a low limit of detection (1.47%) and limit of quantification (4.40%). The models for fatty acid content also had good prediction capabilities (0.90 < r2, RMSE <21 mg g−1, RSD <6.5%, LOD <12 mg g−1, and LOQ <36 mg g−1). The results indicate that it is possible to quantify fraud in chia oil even using different adulterants when analyzing FTIR-ATR spectra in tandem with PLS. The proposed method is an important, fast, low-cost alternative for monitoring adulterations in vegetable oils.

Prospects of nanotechnology advances in food adulterant detection, spoilage detection, packaging and preservation applications

Abstract Food is essential for the development and essential functions of our lives. Our typical dietary intake consists of vegetables, fruits, legumes, pulses, grains, and other similar foods. These ingredients can be consumed in their raw form or transformed into delicious recipes. Food adulteration refers to the deliberate introduction of potentially hazardous substances into food or food products to contaminate or adulterate them. Prolonged storage of food without sufficient preservation leads to spoilage, which poses a health risk. Food undergoes degradation over an extended duration of storage, leading to the proliferation of germs on its surface. Nanotechnology revolutionizes food safety by employing precise detection and preservation methods. Nanoparticles play a crucial role in expediting the detection of adulterants, enabling the identification of even minute amounts of contaminants like pesticides or hazardous compounds. This, in turn, ensures the preservation of the quality and safety of food items. Nanosensors are used to identify spoilage by monitoring microbial activity or changes in pH levels, to prevent the intake of spoiled food. Nano-emulsions and coatings can extend the shelf life of products by establishing protective barriers that hinder the ingress of oxygen, moisture, and microbial growth. This process effectively preserves the freshness and nutritional value of the products. In this review, we have discussed the role of nanotechnology in detecting food adulteration, food spoilage, food preservation and food packaging techniques.

Highly sensitive adulteration detection in kelulut honey utilizing fiber Bragg grating technology with Q-switched pulse erbium-doped fiber laser and innovative spider silk substrate

The growing field of photonics has seen significant developments in optical pulse fiber-based sensing technologies. In this research, a novel fiber Bragg grating using a Q-switched erbium-doped fiber laser source and innovative spider silk as a base material is presented, specifically designed for the detection of adulteration in pure kelulut honey. Kelulut honey has attracted much attention from Malaysian researchers as it is a highly prized, versatile local health remedy. During the experimental setup, concentrations of the adulterant isoglucose ranging from 0 % to 8 % and 10–50 % were intentionally added to the pure kelulut honey. The sensor showed remarkable sensitivity and detected subtle shifts in both the wavelength and radio frequency spectrum. The experimental results obtained emphasize the effectiveness of the proposed sensor and demonstrate its exceptional sensitivity in detecting adulteration in pure kelulut honey. This inherent property makes the derived pulse fiber laser sensor a promising tool for various applications in clinical detection and diagnosis as well as in the food industry. The results presented here not only contribute to the field of photonics but also have the potential to advance the quality control and authenticity assessment of kelulut honey in various fields.

The detection of adulteration of olive oil with various vegetable oils – A case study using high-resolution 700 MHz NMR spectroscopy coupled with multivariate data analysis

Olive oil, being very salubrious, is one of the main ingredients of the Mediterranean diet. However, high oil prices combined with high consumption have increasingly subjected olive oils to fraudulent practices. The large rise in numbers of producers engaging in this illicit practice has made it necessary to develop a swift, convenient, and consistent analytical methodology to detect adulterating oil products and ensure product quality. In this study, we have merged multivariate statistical analysis with one-dimensional (1D) 700 MHz 1H nuclear magnetic resonance (NMR) to analyze different types of vegetable and olive oil.Additionally, we have spiked virgin olive oil with varying percentages (1%–75% (v/v)) of other vegetable oils to obtain the spectra of adulterated olive oil. As a result, the method applied in our study can not only detect adulterated olive oils but also identify mixed adulterants. Ease of sample preparation, quick sample analysis, and straightforward data comprehension distinguishes this method from the currently published ones.

DEVELOPMENT OF A PAPER-BASED DEVICE USING INDOXYL ACETATE AS A CHROMOGENIC SUBSTRATE FOR RAPID QUALITATIVE DETERMINATION OF LIPASE INHIBITOR

An improved paper-based device using indoxyl acetate as a chromogenic substrate was developed for rapid qualitative determination of lipase inhibitory activity. Colorless indoxyl acetate can be catalytically hydrolyzed by lipase to produce the blue-colored indigo dimer on the paper device. In the presence of a lipase inhibitor, enzyme activity diminishes, resulting in reduced hydrolysis of indoxyl acetate. Consequently, this leads to a decrease in the intensity of the blue coloration, which can be visually observed across a range of blue shades and analyzed using image analysis. Orlistat was used as the representative lipase inhibitor in the assay. The LODs obtained from visual detection and the image analysis were comparable. The paper-based device using indoxyl acetate for lipase inhibition assay was simple and convenient. It could be useful as a simple screening method for the detection of orlistat adulteration in weight loss supplements.

A proposed two-level classification approach for forensic detection of diesel adulteration using NMR spectroscopy and machine learning.

Adulteration of diesel fuel poses a major concern in most developing countries including Ghana despite the many regulatory schemes adopted. The solvent tracer analysis approach currently used in Ghana has over the years suffered several limitations which affect the overall implementation of the scheme. There is therefore a need for alternative or supplementary tools to help detect adulteration of automotive fuel. Herein we describe a two-level classification method that combines NMR spectroscopy and machine learning algorithms to detect adulteration in diesel fuel. The training sets used in training the machine learning algorithms contained 20-40% w/w adulterant, a level typically found in Ghana. At the first level, a classification model is built to classify diesel samples as neat or adulterated. Adulterated samples are passed on to the second stage where a second classification model identifies the type of adulterant (kerosene, naphtha, or premix) present. Samples were analyzed by 1H NMR spectroscopy and the data obtained were used to build and validate support vector machine (SVM) classification models at both levels. At level 1, the SVM model classified all 200 samples with only 2.5% classification errors after validation. The level 2 classification model developed had no classification errors for kerosene and premix in diesel. However, 2.5% classification error was recorded for samples adulterated with naphtha. Despite the great performance of the proposed schemes, it showed significantly erratic predictions with adulterant levels below 20% w/w as the training sets for both models contained adulterants above 20% w/w. The proposed method, nevertheless, proved to be a potential tool that could serve as an alternative to the marking system in Ghana for the fast detection of adulterants in diesel.

Image-Based Detection of Adulterants in Milk Using Convolutional Neural Network.

Adulteration of milk poses a severe human health hazard. Existing methods for detecting adulterants such as water, urea, ammonium sulfate (AmS), oils, and surfactants in milk are selective, expensive, and often challenging to implement in rural areas. The present work shows the potential of machine learning to detect milk adulterants using patterns of evaporative milk deposits. The final deposit patterns obtained after evaporation of the adulterated milk droplets are used to create an image data set. This data set is used to develop a deep learning model that deploys a convolutional neural network (CNN/ConvNet) to classify the distinct evaporation patterns obtained for different types and concentrations of adulterants. Further, we apply implicit and explicit regularization and compare their accuracies. The models trained with different regularization optimization schemes demonstrate that a CNN can be successfully implemented to detect adulterants in milk. Additionally, we experimentally determine how the type and concentration of milk adulterants, including ammonium sulfate (AmS), urea, oil, and surfactants, affect milk evaporative deposition. Added AmS and urea in milk crystallizes during evaporation to produce recognizable patterns that can be used for their detection. The method is capable of detecting AmS added in excess of 2.4% and urea in excess of 5% in diluted milk (20 wt %) due to the crystallization of AmS and urea, respectively. In the case of milk adulterated with vegetable oil, evaporation leads to the separation and accumulation of oil at the top of the deposit, leading to the detection of oil present in excess of 2% in 20% diluted milk. Furthermore, a minimum individual amount of 5% urea, 2.4% AmS, and 2% oil concentration in diluted milk (20%) is shown to be individually detected by evaporation pattern-based technique when milk is adulterated with all the adulterants (water, urea, AmS, and oil + surfactant) together. When subjected to different regularization optimization schemes, the CNN gives varying degrees of accuracy for successful detection. The use of implicit regularization in the form of data augmentation gives the best results with a testing average accuracy of 98%, showing that a CNN can be successfully deployed to classify and detect adulterants in milk.

Detection of sheep butter adulteration with cow butter and margarine by employing Raman spectroscopy and multivariate data analysis

The adulteration of sheep butter with cow butter and margarine is a significant challenge in the food industry, impacting product authenticity and consumer confidence. In this research article, a comprehensive and accurate approach is proposed for detecting sheep butter adulteration by employing Raman spectroscopy in conjunction with the combined method of DD-SIMCA and PLS-DA.Initially, Raman spectroscopy employed to collect spectral data from pure butter and margarine samples, enabling the modeling of target objects by DD-SIMCA to exclude adulterated samples. Subsequently, PLS-DA was utilized for the discrimination of sample classes.Raman spectroscopy, combined with the classification and discrimination strategy, is a powerful tool for quickly and non-destructively detecting sheep butter adulteration, ensuring food quality control. The combined strategy successfully discriminated between pure samples, achieving high performances based on the figures of merit (sensitivity: 77.78–100% and specificity: 88.23–100%) for detecting and identifying the type of adulteration in sheep butter. These outcomes underscore the efficacy of the proposed approach in not only detecting but also identifying the specific type of adulteration present in sheep butter, thereby addressing a critical need in the food industry. This research contributes significantly to advancing the field of food quality assurance and holds great implications for support consumer confidence in product authenticity.

Detection of adulterants in rice grains by the characteristics of reflection coefficient of the incident microwave signal

Detection of food quality by identifying different adulterants in food products is a grave requirement for the food industry. Various methods of food analysis have been evolved to identify the quality and quantity of impurities but those methods are time-consuming, expensive and laborious. Our research aims at reducing the challenges experienced in these existing methods. Our procedure is to detect food adulterants using a microwave signal and observe the change in reflection coefficient and resonant frequency of the reflected microwave signal. The property of modification of reflection coefficient and resonant frequency of reflected microwave signal from pure rice grains in the presence of adulterants like small stones/pebbles and broken rice is exploited. In this work, the detection and proportion of adulterants, such as stones and low-quality broken rice in fresh rice in varying proportions, have been determined. Adulterants as low as 2% weight of rice are highly detectable utilizing microwave.

Real-time quantitative detection of hydrocolloid adulteration in meat based on Swin Transformer and smartphone.

Hydrocolloids are widely used in meat products as common food additives. However, research has indicated that excessive consumption of these hydrocolloids may have potential health implications. Currently, consumers mainly rely on sensory evaluation to identify hydrocolloid adulteration in meat products. Although many studies on quantitative detection of hydrocolloids have been conducted by biochemical methods in laboratory environments, there is currently a lack of effective tools for consumers and regulators to obtain real-time and reliable information on hydrocolloid adulteration. To address this challenge, a smartphone-based computer vision method was developed to quantitatively detect carrageenan adulteration in beef in this work. Specifically, Swin Transformer models, along with pre-training and fine-tuning techniques, were used to successfully automate the classification of beef into nine different levels of carrageenan adulteration, ranging from 0% to 20%. Among the tested models, Swin-Tiny (Swin-T) achieved the highest trade-off performance, with a Top-1 accuracy of 0.997, a detection speed of 3.2ms, and a model size of 103.45 Mb. Compared to computer vision, the electrochemical impedance spectroscopy achieved a lower accuracy of 0.792 and required a constant temperature environment and a waiting time of around 30 min for data stabilization. In addition, Swin-T model was also capable of distinguishing between different types of hydrocolloids with a Top-1 accuracy of 0.975. This study provides consumers and regulators with a valuable tool to obtain real-time quantitative information about meat adulteration anytime, anywhere. PRACTICAL APPLICATION: This research provides a practical solution for regulators and consumers to non-destructively and quantitatively detect the content and type of hydrocolloids in beef in real-time using smartphones. This innovation has the potential to significantly reduce the costs associated with meat quality testing, such as the use of chemical reagents and expensive instruments.

Magnetic-gold nanoparticle-mediated paper-based biosensor for highly sensitive colorimetric detection of food adulteration

Food adulteration presents a significant challenge due to the evasion of legal oversight and the difficulty of identification. Addressing this issue, there is an urgent need for on-site, rapid, visually based small-scale equipment, along with large-scale screening technology, to enable prompt results without providing opportunities for dishonest traders to react. Colorimetric reactions offer advantages in terms of speed, visualization, and miniaturization. However, there is a scarcity of suitable colorimetric reactions for food adulteration detection, and interference from colored food impurities and easily comparable color results affects accuracy. To overcome limitations, this study introduces a novel approach utilizing polydopamine magnetic nanoparticles to enrich DNA in food samples, effectively eliminating interfering components. By employing gold nanoparticles to generate magnetic-gold nanoparticles, a single magnetic bead achieves simultaneous enrichment, impurity removal, and detection. The use of paper-based biosensors and visualization equipment allows for the visualization and digital analysis of results, achieving a low detection limit of 4.59 nmol mL−1. The method also exhibits high accuracy and repeatability, with a RSD ranging from 1.6 % to 4.0 %. This innovative colorimetric method addresses the need for rapid, miniaturized, and large-scale detection, thus providing a solution for food adulteration challenges.

Label-free detection of thiram in trace quantity in fruits and vegetables using chemically made SERS-active substrate

Surface-enhanced Raman spectroscopy (SERS) provides a sensitive, rapid, and accurate mechanism to detect and classify the analytes in trace concentrations. Numerically optimized design and its accurate fabrication plays an important role in realization of a SERS-active substrates, used to enhance the Raman signals. Thiram (tetramethyl-thiuram disulfide) is a fungicide that is used to protect the crops from fungal infections. However, excessive use of thiram can cause several health-related complications and must be avoided. In this study, we have laid out a simple but fast and sensitive SERS technique and demonstrated its application for the detection of thiram residues in vegetable and fruit peels. This study employs silver nanoparticles decorated with reduced graphene as SERS-active substrate to detect R6G and thiram in trace amount in tomato and apple peels. The proposed substrate yielded an enhancement factor of about 1016 for the most prominent Raman mode. We were able to achieve the detection limit of 1000 pM and 100 pM concentrations of thiram in tomato and apple peels respectively, using the proposed SERS-active substrate. It was also able to detect PNBA, an explosive analyte, up to nanomolar concentration, demonstrating the versatility of the proposed substrate. The proposed scheme has a great potential for detection of adulterants in fruits and vegetables and can also be implemented for trace detection in various other fields such as narco-analysis, defence and medicine etc.