The application of Near-Infrared Reflectance Spectroscopy (NIRS) to detect melamine adulteration of soya bean meal

Soya bean products are used widely in the animal feed industry as a protein based feed ingredient and
have been found to be adulterated with melamine. This was highlighted in the Chinese scandal of
2008. Dehulled soya (GM and non-GM), soya hulls and toasted soya were contaminated with melamine
and spectra were generated using Near Infrared Reflectance Spectroscopy (NIRS). By applying chemometrics
to the spectral data, excellent calibration models and prediction statistics were obtained. The coefficients
of determination (R2) were found to be 0.89–0.99 depending on the mathematical algorithm used,
the data pre-processing applied and the sample type used. The corresponding values for the root mean
square error of calibration and prediction were found to be 0.081–0.276% and 0.134–0.368%, respectively,
again depending on the chemometric treatment applied to the data and sample type. In addition, adopting
a qualitative approach with the spectral data and applying PCA, it was possible to discriminate
between the four samples types and also, by generation of Cooman’s plots, possible to distinguish
between adulterated and non-adulterated samples.

The feasibility of using near infrared and Raman spectroscopic techniques to detect fraudulent adulteration of chili powders with Sudan dye

Chili powder is a globally traded commodity which has been found to be adulterated with Sudan dyes from 2003 onwards. In this study, chili powders were adulterated with varying quantities of Sudan I dye (0.1-5%) and spectra were generated using near infrared reflectance spectroscopy (NIRS) and Raman
spectroscopy (on a spectrometer with a sample compartment modified as part of the study). Chemometrics were applied to the spectral data to produce quantitative and qualitative calibration models and prediction statistics. For the quantitative models coefficients of determination (R2) were found to be
0.891-0.994 depending on which spectral data (NIRS/Raman) was processed, the mathematical algorithm used and the data pre-processing applied. The corresponding values for the root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were found to be 0.208-0.851%
and 0.141-0.831% respectively, once again depending on the spectral data and the chemometric treatment applied to the data. Indications are that the NIR spectroscopy based models are superior to the models produced from Raman spectral data based on a comparison of the values of the chemometric
parameters. The limit of detection (LOD) based on analysis of 20 blank chili powders against each calibration model gave 0.25% and 0.88% for the NIR and Raman data, respectively. In addition, adopting a qualitative approach with the spectral data and applying PCA or PLS-DA, it was possible to discriminate
between adulterated chili powders from non-adulterated chili powders.

The use of handheld near-infrared reflectance spectroscopy (NIRS) for the proximate analysis of poultry feed and to detect melamine adulteration of soya bean meal

The use of handheld near infrared (NIR) instrumentation, as a tool for rapid analysis, has the potential to be used widely in the animal feed sector. A comparison was made between handheld NIR and benchtop instruments in terms of proximate analysis of poultry feed using off-the-shelf calibration models and including statistical analysis. Additionally, melamine adulterated soya bean products were used to develop qualitative and quantitative calibration models from the NIRS spectral data with excellent calibration models and prediction statistics obtained. With regards to the quantitative approach, the coefficients of determination (R2) were found to be 0.94-0.99 with the corresponding values for the root mean square error of calibration and prediction were found to be 0.081-0.215 % and 0.095-0.288 % respectively. In addition, cross validation was used to further validate the models with the root mean square error of cross validation found to be 0.101-0.212 %. Furthermore, by adopting a qualitative approach with the spectral data and applying Principal Component Analysis, it was possible to discriminate between adulterated and pure samples.

Development of a novel continuous statistical modelling technique for detecting the adulteration of extra virgin olive oil with hazelnut oil by using spectroscopic data

The adulteration of extra virgin olive oil with other vegetable oils is a certain problem with economic and health consequences. Current official methods have been proved insufficient to detect such adulterations. One of the most concerning and undetectable adulterations with other vegetable oils is the addition of hazelnut oil. The main objective of this work was to develop a novel dimensionality reduction technique able to model oil mixtures as a part of an integrated pattern recognition solution. This final solution attempts to identify hazelnut oil adulterants in extra virgin olive oil at low percentages based on spectroscopic chemical fingerprints. The proposed Continuous Locality Preserving Projections (CLPP) technique allows the modelling of the continuous nature of the produced in house admixtures as data series instead of discrete points. This methodology has potential to be extended to other mixtures and adulterations of food products. The maintenance of the continuous structure of the data manifold lets the better visualization of this examined classification problem and facilitates a more accurate utilisation of the manifold for detecting the adulterants.

A comprehensive strategy to detect the fraudulent adulteration of herbs: The oregano approach

Fraud in the global food supply chain is becoming increasingly common due to the huge profits associated with this type of criminal activity. Food commodities and ingredients that are expensive and are part of complex supply chains are particularly vulnerable. Both herbs and spices fit these criteria perfectly and yet strategies to detect fraudulent adulteration are still far from robust. An FT-IR screening method coupled to data analysis using chemometrics and a second method using LC-HRMS were developed, with the latter detecting commonly used adulterants by biomarker identification. The two tier testing strategy was applied to 78 samples obtained from a variety of retail and on-line sources. There was 100% agreement between the two tests that over 24% of all samples tested had some form of adulterants present. The innovative strategy devised could potentially be used for testing the global supply chains for fraud in many different forms of herbs.

Adulteration and Poor Quality of Ginkgo biloba Supplements

Adulteration of Ginkgo products sold as unregistered supplements within the very large market of Ginkgo products (reputedly £650 million annually) through the post-extraction addition of cheaper (e.g. buckwheat derived) rutin is suspected to allow sub-standard products to appear satisfactory to third parties, e.g. secondary buyers along the value chain or any regulatory authorities. This study was therefore carried out to identify products that did not conform to their label specification and may have been actively adulterated to enable access to the global markets. 500 MHz Bruker NMR spectroscopy instrumentation combined with Topspin version 3.2 and a CAMAG HPTLC system (HPTLC Association for the analysis of Ginkgo biloba leaf) were used to generate NMR spectra (focusing on the 6–8 ppm region for analysis) and chromatograms, respectively. Out of the 35 samples of Ginkgo biloba analysed, 33 were found to contain elevated levels of rutin and/or quercetin, or low levels of Ginkgo metabolites when compared with the reference samples. Samples with disproportional levels of rutin or quercetin compared with other gingko metabolites are likely to be adulterated, either by accident or intentionally, and those samples with low or non-existent gingko metabolite content may have been produced using poor extraction techniques. Only two of the investigated samples were found to match with the High-Performance Thin-Layer Chromatography (HPTLC) fingerprint of the selected reference material. All others deviated significantly. One product contained a 5-hydroxytryptophan derivative, which is not a natural constituent of Ginkgo biloba. Overall, these examples either suggest a poor extraction technique or deliberate adulteration along the value chain. Investigating the ratio of different flavonoids e.g. quercetin and kaempferol using NMR spectroscopy and HPTLC will provide further evidence as to the degree and kind of adulteration of Gingko supplements. From a consumer perspective the equivalence in identity and overall quality of the products needs to be guaranteed for supplements too and not only for products produced according to a quality standard or pharmacopoeial monograph.

Fabrication of disposable voltammetric electronic tongues by using Prussian Blue films electrodeposited onto CD-R gold surfaces and recognition of milk adulteration

A new approach to fabricate a disposable electronic tongue is reported. The fabrication of the disposable sensor aimed the integration of all electrodes necessary for measurement in the same device. The disposable device was constructed with gold CD-R and copper sheets substrates and the sensing elements were gold, copper and a gold surface modified with a layer of Prussian Blue. The relative standard deviation for signals obtained from 20 different disposable gold and 10 different disposable copper electrodes was below 3.5%. The performance, electrode materials and the capability of the device to differentiate samples were evaluated for taste substances model, milk with different pasteurization processes (homogenized/pasteurized, ultra high temperature (UHT) pasteurized and UHT pasteurized with low fat content) and adulterated with hydrogen peroxide. In all analysed cases, a good separation between different samples was noticed in the score plots obtained from the principal component analysis (PCA). Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.

Coffee adulteration: more than two decades of research

Abstract Coffee is a ubiquitous food product of considerable economic importance to the countries that produce and export it. The adulteration of roasted coffee is a strategy used to reduce costs. Conventional methods employed to identify adulteration in roasted and ground coffee involve optical and electron microscopy, which require pretreatment of samples and are time-consuming and subjective. Other analytical techniques have been studied that might be more reliable, reproducible, and widely applicable. The present review provides an overview of three analytical approaches (physical, chemical, and biological) to the identification of coffee adulteration. A total of 30 published papers are considered. It is concluded that despite the existence of a number of excellent studies in this area, there still remains a lack of a suitably sensitive and widely applicable methodology able to take into account the various different aspects of adulteration, considering coffee varieties, defective beans, and external agents.

Solvent-Resistant Polymeric Sensors for Adulteration Detection in Liquid Fuels

Polymeric sensors with improved resistance to organic solvents were produced via the layer-by-layer thin film deposition followed by chemical cross-linking. According to UV-vis spectroscopy, the mass loss of polyaniline/poly(vinyl alcohol) and polyaniline/novolac-type resin based films deposited onto glass slides was less than 20% when they were submitted to successive immersions (up to 3,000 immersion cycles) into commercially available ethanol and gasoline fuel samples. Polyallylamine hydrochloride/nickel tetrasulfonated phthalocyanine films presented similar stability. The electrical responses assessed by impedance spectroscopy of films deposited onto Au-interdigitated microelectrodes were relatively unaffected after continuous or cyclic immersions into both fuels. After these studies, an array including these polymeric sensors was employed to detect adulteration in ethanol and gasoline samples. After principal component analysis, it was possible to conclude that the proposed sensor array is capable to discriminate with remarkable reproducibility ethanol samples containing different amounts of water or else gasoline samples containing different amounts of ethanol. In both examples, more than 90% of data variance was retained in the first principal component. For each type of sample, ethanol and gasoline, it was found a linear correlation between one of the principal components and the sample's composition. These findings allow one to conclude that these films present great potential for the development of reliable and low-cost sensors for fuel analysis in liquid phase.

Application of Independent Components Analysis with the JADE algorithm and NIR hyperspectral imaging for revealing food adulteration

In recent years, Independent Components Analysis (ICA) has proven itself to be a powerful signal-processing technique for solving the Blind-Source Separation (BSS) problems in different scientific domains. In the present work, an application of ICA for processing NIR hyperspectral images to detect traces of peanut in wheat flour is presented. Processing was performed without a priori knowledge of the chemical composition of the two food materials. The aim was to extract the source signals of the different chemical components from the initial data set and to use them in order to determine the distribution of peanut traces in the hyperspectral images. To determine the optimal number of independent component to be extracted, the Random ICA by blocks method was used. This method is based on the repeated calculation of several models using an increasing number of independent components after randomly segmenting the matrix data into two blocks and then calculating the correlations between the signals extracted from the two blocks. The extracted ICA signals were interpreted and their ability to classify peanut and wheat flour was studied. Finally, all the extracted ICs were used to construct a single synthetic signal that could be used directly with the hyperspectral images to enhance the contrast between the peanut and the wheat flours in a real multi-use industrial environment. Furthermore, feature extraction methods (connected components labelling algorithm followed by flood fill method to extract object contours) were applied in order to target the spatial location of the presence of peanut traces. A good visualization of the distributions of peanut traces was thus obtained

Adulteration of food. Statutes and cases dealing with coffee, tea, bread, seeds, food and drugs, margarine, milk-blended butter, fertilisers and feeding stuffs, & c, & c,

Mode of access: Internet.

The sale of food & drugs acts, 1875-1907 : and forms, regulations, orders and notices issued thereunder, with notes and cases together with an appendix containing the other acts relating to adulteration, chemical notes, etc., etc. /

Mode of access: Internet.

Beverages and their adulteration; origin, composition, manufacture, natural, artificial, fermented, distilled, alkaloidal and fruit juices,

Mode of access: Internet.