A review of vibrational spectroscopic techniques for the detection of food authenticity and adulteration

Optical screening for presence of banned Sudan III and Sudan IV dyes in edible palm oils

Due to the proven carcinogenicity of Sudan III and IV dyes, they are considered global public health issues. They are banned in all forms as food colourants. We propose the monitoring of simple and easy-to-measure optical properties of palm oils, such as the refractive indices and spectrophotometric properties, as efficient indicators to detect adulteration. Coupling these results with principal component analysis, excess refractive index, and integration of transmittance introduces a novel detection tool for the authentication of edible palm oil. This opens a new opportunity for accurate handheld devices to detect adulteration and provide control in the field. This work assessed in total of 49 samples, some collected from different parts of Ghana and others, in-house adulterated samples. The Ghana Food and Drugs Authority, who performed a complex and expensive chemical analysis of the samples, confirmed our results with good agreement.

Development of an FTIR based chemometric model for the qualitative and quantitative evaluation of cane sugar as an added sugar adulterant in apple fruit juices

A Fourier Transform Infrared Spectroscopy based chemometric model was evaluated for the rapid identification and estimation of cane sugar as an added sugar adulterant in apple fruit juices. For all the ninety samples, spectra were acquired in the mid-infrared range (4000 cm^-1-400 cm^-1). The spectral analysis provided information regarding the distinctive variable region, which lies in the range of 1200cm^-1 to 900cm^-1, designated as fingerprint region for the carbohydrates. A specific peak in the fingerprint region was observed at 997cm^-1 in all the adulterated samples and was undetectable in pure samples. Based on different levels of cane sugar adulteration (5, 10, 15, and 20%), principal component analysis showed the clustering of samples and further helped us in compression of data by selecting wavenumbers with maximum variability based on the loading line plot. Supervised classification methods (SIMCA and LDA) were evaluated based on their classification efficiencies for a test set. Though SIMCA showed 100% classification efficiency (Raw data set), LDA was able to classify the test set with an accuracy of only 96.67% (Raw as well as Transformed data set) between pure and 5% adulterated samples. For the quantitative estimation, calibration models were developed using partial least square regression (PLS-R) and principal component regression method (PCR) methods. PLS-1^st derivative showed a maximum coefficient of determination (R²) with a value of 0.991 for calibration and 0.992 for prediction. The RMSECV, RMSEP, LOD and LOQ observed for PLS-1^st derivative model were 0.75% w/v, 0.61% w/v, 1.28%w/v and 3.88%w/v, respectively. The coefficient of variation as a measure of precision (repeatability) was also determined for all models, and it ranged from 0.23% to 1.83% (interday), and 0.25% to 1.43% (intraday).

Comparison and quantitative analysis of wild and cultivated Macrohyporia cocos using attenuated total refection-Fourier transform infrared spectroscopy combined with ultra-fast liquid chromatography

Dried sclerotium of Macrohyporia cocos is a well-known and widely-consumed traditional Chinese medicine and is also used as dietary supplement. According to the differential treatment between cultivation and wild habitats in the market, the comparison and quantitative analysis of wild and cultivated M. cocos were performed using attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy and ultra-fast liquid chromatography combined with partial least squares discriminant analysis and partial least squares regression (PLSR). 636 samples were used for the spectral scan and chromatographic analysis. Results indicated that contents of dehydrotumulosic acid, poricoic acid A and dehydrotrametenolic acid in cultivated samples were significantly different from wild samples in two medicinal parts. Differences of dehydropachymic acid and pachymic acid just existed in inner part samples (P < 0.05). Wild M. cocos samples could be discriminated with cultivated samples with >95.14% efficiency using spectral data. ATR-FTIR combined with PLSR provided satisfactory performance for content predictions of poricoic acid A and dehydrotrametenolic acid. This study demonstrated that growth patterns could affect the quality of inner part and epidermis of M. cocos, and ATR-FTIR was a promising technique for the identification of wild and cultivated M. cocos and the rapid determination of triterpene acids contents.

Rapid visible-near infrared (Vis-NIR) spectroscopic detection and quantification of unripe banana flour adulteration with wheat flour

Unripe banana flour is a premium nutritious product with a potential to curb degenerative diseases through resistant starch and gluten free traits, however, with scant techniques to monitor adulteration practices. The objective of the present study was to determine the efficacy of visible-near infrared spectroscopy (Vis-NIR) spectroscopy (Vis-NIRS) in the detection and quantification of unripe banana flour adulteration with wheat flour. To do this, simulated adulteration of a composite banana flour was performed with different levels of wheat flour, in intervals of 20 g kg-1, ranging from 0 to 800 g kg-1. Each level was acquired in duplicate giving a total of 82 samples. Vis-NIR spectral data was acquired using a portable F-750 spectrometer in the range 447-1005 nm. Spectral data was analysed chemometrically using principle components analysis and partial least squares regression (PLSR), with 41 samples used as a calibration set and 41 for validation. The first two principal components accounted for 95% of spectral data variation, revealing five distinct clusters related to 0 g kg-1, 20-200 g kg-1, 220-400 g kg-1, 420-600 g kg-1 and 620-800 g kg-1 adulterated samples. The best PLSR model to predict wheat flour adulteration degrees in unripe banana flour was obtained using 2nd derivative Savitzky-Golay (19-point smoothing, 2nd order polynomial), showing the highest Rc 2 (0.991); Rp 2 (0.993); RPD (12.021) and the lowest RMSEC (2.226 g kg-1) and RMSEP (1.993 g kg-1) values. The obtained Vis-NIRS PLSR models therefore demonstrated the technology novelty in monitoring unripe banana flour quality by the processing industries and in retail markets during product verification.

Evidence on the discrimination of quinoa grains with a combination of FT-MIR and FT-NIR spectroscopy

Quinoa is considered as a valuable re-emergent crop due to its nutritional composition. In this study, five quinoa grains from different geographical origin (Real, CHEN 252, Regalona, BO25 and UDc9) were discriminated using a combination of FT-MIR and FT-NIR spectra as input for principal component analysis (PCA), cluster analysis (CA) and soft independent modelling class analogy (SIMCA). The results obtained from PCA and CA show a great power of discrimination, with an average silhouette width value of 0.96. Moreover, SIMCA showed an error rate and accuracy values of 0 and 1 respectively with only 4% misclassified samples. A relationship between each principal component and the most important variables for the discrimination were mainly due to vibrations of several oleofins groups (C-H, C-H₂, C-H₃), alkene group (-CH=CH-), hydroxyl group (O-H) and Amides I and II vibrational modes.

Fast, Spectroscopy-Based Prediction of In Vitro Dissolution Profile of Extended Release Tablets Using Artificial Neural Networks

The pharmaceutical industry has never seen such a vast development in process analytical methods as in the last decade. The application of near-infrared (NIR) and Raman spectroscopy in monitoring production lines has also become widespread. This work aims to utilize the large amount of information collected by these methods by building an artificial neural network (ANN) model that can predict the dissolution profile of the scanned tablets. An extended release formulation containing drotaverine (DR) as a model drug was developed and tablets were produced with 37 different settings, with the variables being the DR content, the hydroxypropyl methylcellulose (HPMC) content and compression force. NIR and Raman spectra of the tablets were recorded in both the transmission and reflection method. The spectra were used to build a partial least squares prediction model for the DR and HPMC content. The ANN model used these predicted values, along with the measured compression force, as input data. It was found that models based on both NIR and Raman spectra were capable of predicting the dissolution profile of the test tablets within the acceptance limit of the f2 difference factor. The performance of these ANN models was compared to PLS models using the same data as input, and the prediction of the ANN models was found to be more accurate. The proposed method accomplishes the prediction of the dissolution profile of extended release tablets using either NIR or Raman spectra.

Opportunities and Challenges Using Non-targeted Methods for Food Fraud Detection

In recent years, non-targeted methods have been a popular "buzz" phrase in food fraud detection. Using analytical instrumentation techniques, non-targeted methods have been developed and applied in many food and agricultural situations. However, confusion and misstatements remain regarding how the methods are used. This perspective will discuss the definitions related to non-targeted testing, the procedure of developing and validating methods, the techniques and data analysis, and opportunities and challenges regarding the use of this class of analytical methods. The perspective seeks to provide readers with the latest information regarding recent advances in the use of non-targeted methods.

Lighting the Ivory Track: Are Near-Infrared and Chemometrics Up to the Job? A Proof of Concept

A rapid tool to discriminate rhino horn and ivory samples from different mammalian species based on the combination of near-infrared reflection (NIR) spectroscopy and chemometrics was evaluated. In this study, samples from the Australian Museum mammalogy collection were scanned between 950 nm and 1650 nm using a handheld spectrophotometer and analyzed using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). An overall correct classification rate of 73.5% was obtained for the classification of all samples. This study demonstrates the potential of NIR spectroscopy coupled with chemometrics as a means of a rapid, nondestructive classification technique of horn and ivory samples sourced from a museum. Near-infrared spectroscopy can be used as an alternative or complementary method in the detection of horn and ivory assisting in the combat of illegal trade and aiding the preservation of at-risk species.

Rapid and Non-Destructive Detection of Water-Injected Pork Using Low-Field Nuclear Magnetic Resonance (LF-NMR) and Magnetic Resonance Imaging (MRI)

The challenges of food adulteration such as illegal production of water-injected meat remain serious in many areas of the world. This study investigated the feasibility of using LF-NMR and MRI to identify water-injected pork. Longissimus dorsi muscles were injected with 0 %, 5 %, 10 %, 15 %, 20 % and 25 % content of deionized water, respectively. The CPMG decay curves of water-injected pork decayed slower than that of the normal. The peak area proportion of immobilized water of water-injected pork decreased while relaxation time and peak area proportion of free water increased significantly (p < 0.05). The first two principal components (PCs) of PCA accounted for 54.54 % and 32.06 % of the observed variance, respectively. Based on the two PCs, the water-injected pork could be differentiated from the normal. Furthermore, the accumulation location of the injected-water in pork could be visualized by MRI. Therefore, LF-NMR combined with MRI offers an effective method for the detection of water-injected pork.

Rapid Determination of Nutritional Parameters of Pasta/Sauce Blends by Handheld Near-Infrared Spectroscopy

Nowadays, near infrared (NIR) spectroscopy has experienced a rapid progress in miniaturization (instruments < 100 g are presently available), and the price for handheld systems has reached the < $500 level for high lot sizes. Thus, the stage is set for NIR spectroscopy to become the technique of choice for food and beverage testing, not only in industry but also as a consumer application. However, contrary to the (in our opinion) exaggerated claims of some direct-to-consumer companies regarding the performance of their "food scanners" with "cloud evaluation of big data", the present publication will demonstrate realistic analytical data derived from the development of partial least squares (PLS) calibration models for six different nutritional parameters (energy, protein, fat, carbohydrates, sugar, and fiber) based on the NIR spectra of a broad range of different pasta/sauce blends recorded with a handheld instrument. The prediction performance of the PLS calibration models for the individual parameters was double-checked by cross-validation (CV) and test-set validation. The results obtained suggest that in the near future consumers will be able to predict the nutritional parameters of their meals by using handheld NIR spectroscopy under every-day life conditions.

Approaching Authenticity Issues in Fish and Seafood Products by Qualitative Spectroscopy and Chemometrics

The intrinsically complex nature of fish and seafood, as well as the complicated organisation of the international fish supply and market, make struggle against counterfeiting and falsification of fish and seafood products very difficult. The development of fast and reliable omics strategies based on spectroscopy in conjunction with multivariate data analysis has been attracting great interest from food scientists, so that the studies linked to fish and seafood authenticity have increased considerably in recent years. The present work has been designed to review the most promising studies dealing with the use of qualitative spectroscopy and chemometrics for the resolution of the key authenticity issues of fish and seafood products, with a focus on species substitution, geographical origin falsification, production method or farming system misrepresentation, and fresh for frozen/thawed product substitution. Within this framework, the potential of fluorescence, vibrational, nuclear magnetic resonance, and hyperspectral imaging spectroscopies, combined with both unsupervised and supervised chemometric techniques, has been highlighted, each time pointing out the trends in using one or another analytical approach and the performances achieved.

Random forest as one-class classifier and infrared spectroscopy for food adulteration detection

This paper proposes the use of random forest for adulteration detection purposes, combining the random forest algorithm with the artificial generation of outliers from the authentic samples. This proposal was applied in two food adulteration studies: evening primrose oils using ATR-FTIR spectroscopy and ground nutmeg using NIR diffuse reflectance spectroscopy. The primrose oil was adulterated with soybean, corn and sunflower oils, and the model was validated using these adulterated oils and other different oils, such as rosehip and andiroba, in pure and adulterated forms. The ground nutmeg was adulterated with cumin, commercial monosodium glutamate, soil, roasted coffee husks and wood sawdust. For the primrose oil, the proposed method presented superior performance than PLS-DA and similar performance to SIMCA and for the ground nutmeg, the random forest was superior to PLS-DA and SIMCA. Also, in both applications using the random forest, no sample was excluded from the external validation set.

PTR-QiToF-MS and HSI for the characterization of fermented cocoa beans from different origins

The wide range of geographical cocoa production areas and the increasing consumption trend towards single origin products induced the necessity to verify and certify cocoa beans origin for quality assurance purposes. In this study cocoa beans of various origins were examined by machine olfaction and machine vision techniques. Fifty-nine fermented and dried Forastero cocoa beans from 23 different geographical origins (Africa, Americas, Southeast Asia) were investigated using Proton Transfer Reaction-Quadrupole interface-Time of Flight-Mass Spectrometry and Hyperspectral Imaging to elucidate the geographical information in the beans. The volatile and spectral fingerprints showed the same tendency in clustering samples from Africa separate from those from the Americas. High variability was observed for the Southeast Asian samples, which is most likely related to differences in fermentation. Machine olfaction and machine vision characterization provided a similar degree of separation but are complementary rapid techniques, which may be further developed for use in practical settings.

Application of invasive weed optimization and least square support vector machine for prediction of beef adulteration with spoiled beef based on visible near-infrared (Vis-NIR) hyperspectral imaging

Different multivariate data analysis methods were investigated and compared to optimize rapid and non-destructive quantitative detection of beef adulteration with spoiled beef based on visible near-infrared hyperspectral imaging. Four multivariate statistical analysis methods including partial least squares regression (PLSR), support vector machine (SVM), least squares support vector machine (LS-SVM) and extreme learning machine (ELM) were carried out in developing full wavelength models. Good prediction was obtained by applying LS-SVM in the spectral range of 496-1000 nm with coefficients of determination (R²) of 0.94 and 0.94 as well as root-mean-squared errors (RMSEs) of 5.39% and 6.29% for calibration and prediction, respectively. To reduce the high dimensionality of hyperspectral data and to establish simplified models, a novel method named invasive weed optimization (IWO) was developed to select key wavelengths and it was compared with competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA). Among the four multivariate analysis models based on important wavelengths determined by IWO, the LS-SVM simplified model performed best where R² of 0.97 and 0.95 as well as RMSEs of 4.74% and 5.67% were attained for calibration and prediction, respectively. The optimum simplified model was applied to hyperspectral images in pixel-wise to visualize the distribution of spoiled beef adulterant in fresh minced beef. The current study demonstrated that it was feasible to use Vis-NIR hyperspectral imaging to detect homologous adulterant in beef.

Advanced Analysis of Roots and Tubers by Hyperspectral Techniques

Hyperspectral techniques in terms of spectroscopy and hyperspectral imaging have become reliable analytical tools to effectively describe quality attributes of roots and tubers (such as potato, sweet potato, cassava, yam, taro, and sugar beet). In addition to the ability for obtaining rapid information about food external or internal defects including sprout, bruise, and hollow heart, and identifying different grades of food quality, such techniques have also been implemented to determine physical properties (such as color, texture, and specific gravity) and chemical constituents (such as protein, vitamins, and carotenoids) in root and tuber products with avoidance of extensive sample preparation. Developments of related quality evaluation systems based on hyperspectral data that determine food quality parameters would bring about economic and technical values to the food industry. Consequently, a comprehensive review of hyperspectral literature is carried out in this chapter. The spectral data acquired, the multivariate statistical methods used, and the main breakthroughs of recent studies on quality determinations of root and tuber products are discussed and summarized. The conclusion elaborates the promise of how hyperspectral techniques can be applied for non-invasive and rapid evaluations of tuber quality properties.

Organizational and Technological Aspects of a Platform for Collective Food Awareness

Can Internet-of-food technologies foster collective food awareness within a food consumer community? The paper contributes to answer this question in a fourfold aspect. Firstly, we model a cooperative process for generating and sharing reliable food information that is derived from food instrumental measurements performed by consumers via smart food things. Secondly, we outline the functional architecture of a platform capable to support such a process and to let a consumer community share reliable food information. Thirdly, we identify main entities and their attributes necessary to model the contextualized interaction between a consumer and the platform. Lastly, we review articles reviewing technologies capable of acquiring and quantifying food characteristics for food performances assessment. The purpose is to give an insight into current research directions on technologies employable in a platform for collective food awareness.

An additional data fusion strategy for the discrimination of porcini mushrooms from different species and origins in combination with four mathematical algorithms

Porcini are a source of popular food products with many beneficial functions and the internal quality of these mushrooms is largely determined by many factors. An additional data fusion strategy based on low-level data fusion for two portions (cap and stipe) and mid-level data fusion for two spectroscopic techniques (UV and FTIR) was developed to discriminate porcini mushrooms from different species and origins. Based on a finally obtained data array, four mathematical algorithms including PLS-DA, k-NN, SVM and RF were comparatively applied to build classification models. Each calibrated model was developed after selecting the best debug parameters and then a test set was used to validate the established model. The results showed that the SVM algorithm based on a GA procedure searching for parameters had the best performance for discriminating different porcini samples with the highest cross-validation, specificity, sensitivity and accuracy of 100.00%. Our study proved the feasibility of two spectroscopic techniques for the discrimination of porcini mushrooms originated from different species and origins. This proposed method can be used as an alternative strategy for the quality detection of porcini mushrooms.

Authenticity and geographic origin of global honeys determined using carbon isotope ratios and trace elements

Honey is the world's third most adulterated food. The addition of cane sugar or corn syrup and the mislabelling of geographic origin are common fraudulent practices in honey markets. This study examined 100 honey samples from Australia (mainland and Tasmania) along with 18 other countries covering Africa, Asia, Europe, North America and Oceania. Carbon isotopic analyses of honey and protein showed that 27% of commercial honey samples tested were of questionable authenticity. The remaining 69 authentic samples were subject to trace element analysis for geographic determination. One-way ANOVA analysis showed a statistical difference (p < 0.05) in trace element concentrations of honey from Australian regions and different continents. Principal component analysis (PCA) and canonical discriminant analysis (CDA) coupled with C5.0 classification modelling of honey carbon isotopes and trace element concentrations showed distinct clusters according to their geographic origin. The C5.0 model revealed trace elements Sr, P, Mn and K can be used to differentiate honey according to its geographic origin. The findings show the common and prevalent issues of honey authenticity and the mislabelling of its geographic origin can be identified using a combination of stable carbon isotopes and trace element concentrations.

From the Field to the Bottle—An Integrated Strategy for Wine Authenticity

The wine sector is one of the most economically important agro-food businesses. The wine market value is largely associated to terroir, in some cases resulting in highly expensive wines that attract fraudulent practices. The existent wine traceability system has some limitations that can be overcome with the development of new technological approaches that can tackle this problem with several means. This review aims to call attention to the problem and to present several strategies that can assure a more reliable and authentic wine system, identifying existent technologies developed for the sector, which can be incorporated into the current traceability system.

Integrated analytical assets aid botanical authenticity and adulteration management

This article reviews and develops a perspective for the meaning of authenticity in the context of quality assessment of botanical materials and the challenges associated with discerning adulterations vs. contaminations vs. impurities. Authentic botanicals are by definition non-adulterated, a mutually exclusive relationship that is confirmed through the application of a multilayered set of analytical methods designed to validate the (chemo)taxonomic identity of a botanical and certify that it is devoid of any adulteration. In practice, the ever-increasing sophistication in the process of intentional adulteration, as well as the growing number of botanicals entering the market, altogether necessitate a constant adaptation and reinforcement of authentication methods with new approaches, especially new technologies. This article summarizes the set of analytical methods - classical and contemporary - that can be employed in the authentication of botanicals. Particular emphasis is placed on the application of untargeted metabolomics and chemometrics. An NMR-based untargeted metabolomic model is proposed as a rapid, systematic, and complementary screening for the discrimination of authentic vs. potentially adulterated botanicals. Such analytical model can help advance the evaluation of botanical integrity in natural product research.