Rapid quantification of the adulteration of fresh coconut water by dilution and sugars using Raman spectroscopy and chemometrics

Rapid analysis technologies with chemometrics for food authenticity field: A review

In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.

Rapid quantification of the adulteration of pomegranate juices by Raman spectroscopy and chemometrics

The juice drink industry has repeatedly been exposed to adulteration. Unscrupulous producers, for example, use cheap juice for substitution in the pursuit of more significant economic benefits, which presents a tremendous challenge for the control of the quality of drinks. The objective of this study was to apply Raman spectroscopy combined with chemometrics to rapidly quantify the adulteration concentration of apple juice or grape juice in pomegranate juice. Two supervised learning algorithms: partial least squares regression (PLSR) and support vector machine regression (SVR) were used to analyze the Raman spectra of 114 samples. The coefficient of determination (R2), root mean square error (RMSE), and residual prediction deviation (RPD) of the prediction set when using PLSR and SVR to predict the adulterated concentration of apple juice in pomegranate juice were 0.9357 and 0.9465, 6.446% and 5.974%, 3.945 and 4.322, respectively. The R2, RMSE, and RPD of the prediction set when using PLSR and SVR to predict the adulteration concentration of grape juice in pomegranate juice were 0.9501 and 0.9502, 6.334% and 5.571%, and 4.475 and 4.481, respectively. It was concluded that Raman spectroscopy combined with chemometrics has excellent potential for application as a rapid quantitative method to detect adulterated concentrations of pomegranate juice.

Hybrid method for accurate starch estimation in adulterated turmeric using Vis-NIR spectroscopy

Turmeric is widely used as a health supplement and foodstuff in South East Asian countries because of its medicinal benefits. Like several other plants and peppers, turmeric is prone to exploitation because of its economic value, rising consumer need, and essential food element that adds colour and flavour. Due to this, quick and comprehensive testing processes are needed to detect adulterants in turmeric. In this study, pure turmeric powders were mixed with starch in proportions ranging from 0 to 50% with a 1% variation to obtain different combinations. Reflectance spectra of pure turmeric and starch mixed samples were recorded using a JASCO-V770 spectrometer from 400 to 2050 nm. The recorded spectra were pre-processed using a Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV). The Savitzky-Golay (SG) filter was initially applied to these original (X), MSC, and SNV-corrected spectra. Secondly, the Extra Tree Regressor (ETR) feature selection method was employed to select the best features. Finally, principal component analysis (PCA) was used to reduce the dimension of the selected features. The stacked generalization method was applied to improve the performance of this work. Both regressors and classifier stacking techniques have been tested with different classification and regression methods. The K-Nearest Neighbours (KNN), Decision Tree (DT), and Random Forest (RF) models were used as base learners, and Logistic Regression (LRC) was used as a meta-model for classification and Linear Regression (LR) for regression analysis. The proposed method achieved the best regression performance with r2 of 0.999, Root Mean Square Error (RMSE) of 0.206, Ratio of Performance to Deviation (RPD) of 73.73, and Range Error Ratio (RER) of 480.58, whereas 100% F1 score and Matthew's Correlation Coefficient (MCC) classification performance.

Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches

In today's era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.

Polarimetric and fluorescence spectroscopic based classification of mono and disaccharide solutions

In this article, we demonstrate the potential application of polarimetry and fluorescence spectroscopy for classifying mono and disaccharides (sugar) both qualitatively and quantitatively. A phase lock-in rotating analyzer (PLRA) polarimeter has been designed and developed for real time quantification of sugar concentration in a solution. Polarization rotation in the form of phase shift in sinusoidal photovoltages of reference and sample beams occurred when incident on the two spatially distinct photodetectors. Monosaccharide (fructose and glucose) and disaccharide (sucrose) have been quantitatively determined with sensitivities of 122.06 deg ml g^-1, 272.84 deg ml g^-1 and 163.41 deg ml g^-1 respectively. Calibration equations have been obtained from the respective fitting functions to estimate the concentration of each individual dissolved in deionized (DI) water. In comparison to the predicted results, the absolute average errors of 1.47 %, 1.63 % and 1.71 % are calculated for the readings of sucrose, glucose and fructose, respectively. Furthermore, the performance of the PLRA polarimeter has been compared with fluorescence emission results acquired from the same set of samples. The Limit of detections (LODs) attained from both experimental setups are comparable for mono and disaccharides. A linear detection response is observed by both polarimeter and fluorescence spectrometer in a wide range 0-0.28 g/ml of sugar. These results depict that PLRA polarimeter is novel, remote, precise and cost-effective for quantitative determination of optically active ingredient in the host solution.

Rapid qualitative detection of titanium dioxide adulteration in persimmon icing using portable Raman spectrometer and Machine learning

Persimmon icing is the white crystalline powder that adheres to the surface of persimmon cakes when the sugar in the persimmon spills over during processing, which is considered the essence of persimmon. Titanium dioxide is a food additive that is commonly added to the surface of persimmon cakes to impersonate high-quality persimmon cakes. However, excessive titanium dioxide can be harmful to humans, so a quick method is needed to identify persimmon cakes as adulterated. Raman spectroscopy with distinctive advantages of water-insensitivity, real-time, field-deployable, label-free, and fingerprinting-identification has been rapidly developed and used in food quality assurance and safety monitoring. In this study, we investigated Raman spectroscopy integrated with machine learning to assess titanium dioxide adulteration in dried persimmon icing. The adaptive iterative reweighting partial least squares (air-PLS) algorithm as an effective algorithm was used to remove fluorescent background signals in raw Raman spectroscopy. Principal components analysis (PCA) was employed to analyze the spectral data and determine the class memberships, and results showed that 99.9% of information could be explained by PC-1 and PC-2. Compared with extreme learning machine (ELM), support vector machine (SVM), back propagation artificial neural network (BP-ANN), and random forest (RF) models, one-dimensional stack auto encoder convolutional neural network (1D-SAE-CNN) could provide the highest detection accuracy of 0.9825, precision of 0.9824, recall of 0.9825, and f1-score of 0.9824. This study shows that Raman spectroscopy coupled with 1D-SAE-CNN is a promising method to detect titanium dioxide adulteration in persimmon icing.

Rapid quantification of goat milk adulteration with cow milk using Raman spectroscopy and chemometrics

As goat milk has a higher economic value compared to cow milk, the phenomenon of adulterating goat milk with cow milk appears in the market. In this study, the potential of Raman spectroscopy along with chemometrics was investigated for the authentication and quantitation of liquid goat milk adulterated with cow milk. First, the results of principal component analysis (PCA) showed that there were differences between the Raman spectra of cow and goat milk, which made quantitative experiments possible. For quantification, three different brands of cow milk and goat milk were selected randomly and adulterated goat milk with cow milk at the proportion of 5-95%. 342 samples were used for the construction of the partial least squares regression (PLSR) model with 80% for the calibration set and 20% for the prediction set. The PLSR model showed excellent performance in quantifying the level of adulteration, for the prediction set, with a coefficient of determination (R²) of 0.9781, root mean square error (RMSE) of 3.82%, and a ratio of prediction to deviation (RPD) of 6.8. The results demonstrated the potential of Raman spectroscopy as a rapid, low cost and non-destructive analytical tool for detecting adulteration in goat milk.

Analysis of urine using electronic tongue towards non-invasive cancer diagnosis

Electronic tongues (e-tongues) have been broadly employed in monitoring the quality of food, beverage, cosmetics, and pharmaceutical products, and in diagnosis of diseases, as the e-tongues can discriminate samples of high complexity, reduce interference of the matrix, offer rapid response. Compared to other analytical approaches using expensive and complex instrumentation as well as required sample preparation, the e-tongue is non-destructive, miniaturizable and on-site method with little or no preparation of samples. Even though e-tongues are successfully commercialized, their application in cancer diagnosis from urine samples is underestimated. In this review, we would like to highlight the various analytical techniques such as Raman spectroscopy, infrared spectroscopy, fluorescence spectroscopy, and electrochemical methods (potentiometry and voltammetry) used as e-tongues for urine analysis towards non-invasive cancer diagnosis. Besides, different machine learning approaches, for instance, supervised and unsupervised learning algorithms are introduced to analyze extracted chemical data. Finally, capabilities of e-tongues in distinguishing between patients diagnosed with cancer and healthy controls are highlighted.

Simultaneous Multiplexed Quantification of Banned Sudan Dyes Using Surface Enhanced Raman Scattering and Chemometrics

Azo compounds such as the Sudan dyes I-IV are frequently used illegally as colorants and added to a wide range of foods. These compounds have been linked to a number of food safety hazards. Several methods have been proposed to detect food contamination by azo compounds and most of these are laboratory based; however, the development of reliable and portable methods for the detection and quantification of food contaminated by these chemicals in low concentration is still needed due to their potentially carcinogenic properties. In this study, we investigated the ability of surface enhanced Raman scattering (SERS) combined with chemometrics to quantify Sudan I-IV dyes. SERS spectra were acquired using a portable Raman device and gold nanoparticles were employed as the SERS substrate. As these dyes are hydrophobic, they were first dissolved in water: acetonitrile (1:10, v/v) as single Sudan dyes (I-IV) at varying concentrations. SERS was performed at 785 nm and the spectra were analyzed by using partial least squares regression (PLS-R) with double cross-validations. The coefficient of determination (Q²) were 0.9286, 0.9206, 0.8676 and 0.9705 for Sudan I to IV, respectively; the corresponding limits of detection (LOD) for these dyes were estimated to be 6.27 × 10^-6, 5.35 × 10^-5, 9.40 × 10^-6 and 1.84 × 10^-6 M. Next, quadruplex mixtures were made containing all four Sudan dyes. As the number of possible combinations needed to cover the full concentration range at 5% intervals would have meant collecting SERS spectra from 194,481 samples (21⁴ combinations) we used a sustainable solution based on Latin hypercubic sampling and reduced the number of mixtures to be analyzed to just 90. After collecting SERS spectra from these mixture PLS-R models with bootstrapping validations were employed. The results were slightly worse in which the Q² for Sudan I to IV were 0.8593, 0.7255, 0.5207 and 0.5940 when PLS1 models (i.e., one model for one dye) was employed and they changed to 0.8329, 0.7288, 0.5032 and 0.5459 when PLS2 models were employed (i.e., four dyes were modelled simultaneously). These results showed the potential of SERS to be used as a high-throughput, low-cost, and reliable methods for detecting and quantifying multiple Sudan dyes in low concentration from illegally adulterated samples.

Contemporary Developments and Emerging Trends in the Application of Spectroscopy Techniques: A Particular Reference to Coconut (Cocos nucifera L.)

The number of food frauds in coconut-based products is increasing due to higher consumer demands for these products. Rising health consciousness, public awareness and increased concerns about food safety and quality have made authorities and various other certifying agencies focus more on the authentication of coconut products. As the conventional techniques for determining the quality attributes of coconut are destructive and time-consuming, non-destructive testing methods which are accurate, rapid, and easy to perform with no detrimental sampling methods are currently gaining importance. Spectroscopic methods such as nuclear magnetic resonance (NMR), infrared (IR)spectroscopy, mid-infrared (MIR)spectroscopy, near-infrared (NIR) spectroscopy, ultraviolet-visible (UV-VIS) spectroscopy, fluorescence spectroscopy, Fourier-transform infrared spectroscopy (FTIR), and Raman spectroscopy (RS) are gaining in importance for determining the oxidative stability of coconut oil, the adulteration of oils, and the detection of harmful additives, pathogens, and toxins in coconut products and are also employed in deducing the interactions in food constituents, and microbial contaminations. The objective of this review is to provide a comprehensive analysis on the various spectroscopic techniques along with different chemometric approaches for the successful authentication and quality determination of coconut products. The manuscript was prepared by analyzing and compiling the articles that were collected from various databases such as PubMed, Google Scholar, Scopus and ScienceDirect. The spectroscopic techniques in combination with chemometrics were shown to be successful in the authentication of coconut products. RS and NMR spectroscopy techniques proved their utility and accuracy in assessing the changes in coconut oil’s chemical and viscosity profile. FTIR spectroscopy was successfully utilized to analyze the oxidation levels and determine the authenticity of coconut oils. An FT-NIR-based analysis of various coconut samples confirmed the acceptable levels of accuracy in prediction. These non-destructive methods of spectroscopy offer a broad spectrum of applications in food processing industries to detect adulterants. Moreover, the combined chemometrics and spectroscopy detection method is a versatile and accurate measurement for adulterant identification.

Alcoholic Fermentation Monitoring and pH Prediction in Red and White Wine by Combining Spontaneous Raman Spectroscopy and Machine Learning Algorithms

In the following study, total sugar concentrations before and during alcoholic fermentation, as well as ethanol concentrations and pH levels after fermentation, of red and white wine grapes were successfully predicted using Raman spectroscopy. Fluorescing compounds such as anthocyanins and pigmented phenolics found in red wine present one of the primary limitations of enological analysis using Raman spectroscopy. Unlike the spontaneous Raman effect, fluorescence is a highly efficient process and consequently emits a much stronger signal than spontaneous Raman scattering. For this reason, many enological applications of Raman spectroscopy are impractical as the more subtle Raman spectrum of any red wine sample is in large part masked by fluorescing compounds present in the wine. This work employs a simple extraction method to mitigate fluorescence in finished red wines. Ethanol and total sugars (fructose plus glucose) of wines made from red (Cabernet Sauvignon) and white (Chardonnay, Sauvignon Blanc, and Gruner Veltliner) varieties were modeled using support vector regression (SVR), partial least squares regression (PLSR) and Ridge regression (RR). The results, which compared the predicted to measured total sugar concentrations before and during fermentation, were excellent (R2SVR = 0.96, R2PLSR = 0.95, R2RR = 0.95, RMSESVR = 1.59, RMSEPLSR = 1.57, RMSERR = 1.57), as were the ethanol and pH predictions for finished wines after phenolic stripping with polyvinylpolypyrrolidone (R2SVR = 0.98, R2PLSR = 0.99, R2RR = 0.99, RMSESVR = 0.23, RMSEPLSR = 0.21, RMSERR = 0.23). The results suggest that Raman spectroscopy is a viable tool for rapid and trustworthy fermentation monitoring.

Portable through Bottle SORS for the Authentication of Extra Virgin Olive Oil

The authenticity of olive oil has been a significant long-term challenge. Extra virgin olive oil (EVOO) is the most desirable of these products and commands a high price, thus unscrupulous individuals often alter its quality by adulteration with a lower grade oil. Most analytical methods employed for the detection of food adulteration require sample collection and transportation to a central laboratory for analysis. We explore the use of portable conventional Raman and spatially-offset Raman spectroscopy (SORS) technologies as non-destructive approaches to assess the adulteration status of EVOO quantitatively and for SORS directly through the original container, which means that after analysis the bottle is intact and the oil would still be fit for use. Three sample sets were generated, each with a different adulterant and varying levels of chemical similarity to EVOO. These included EVOO mixed with sunflower oil, pomace olive oil, or refined olive oil. Authentic EVOO samples were stretched/diluted from 0% to 100% with these adulterants and measured using two handheld Raman spectrometers (excitation at 785 or 1064 nm) and handheld SORS (830 nm). The PCA scores plots displayed clear trends which could be related to the level of adulteration for all three mixtures. Conventional Raman (at 785 or 1064 nm) and SORS (at 830 nm with a single spatial offset) conducted in sample vial mode resulted in prediction errors for the test set data ranging from 1.9–4.2% for sunflower oil, 6.5–10.7% for pomace olive oil and 8.0–12.8% for refined olive oil; with the limit of detection (LOD) typically being 3–12% of the adulterant. Container analysis using SORS produced very similar results: 1.4% for sunflower, 4.9% for pomace, and 10.1% for refined olive oil, with similar LODs ranging from 2–14%. It can be concluded that Raman spectroscopy, including through-container analysis using SORS, has significant potential as a rapid and accurate analytical method for the non-destructive detection of adulteration of extra virgin olive oil.

Authenticity and the Potability of Coconut Water - a Critical Review

BACKGROUND

The content of the endosperm of the coconut (Cocos nucifera L.) contains "coconut water". This practically sterile liquid which is prized for its delicate, albeit labile, flavor when fresh, has had a recent dramatic increase in global demand. The organoleptic superiority of water from young coconuts means that degree of maturity at harvesting is the most influential factor in yield and composition.

OBJECTIVE

To provide a guide to establishing the authenticity and the potability of samples of coconut water.

METHOD

Review and evaluate the literature on the factors that determine the composition and stability of coconut water.

RESULTS

Data is presented on the variances in natural composition, maturity, processing-induced compositional changes, adulterations, product recalls, classical and instrumental methods of analysis and on the available composition standards of coconut water.

CONCLUSIONS

Advice is provided for official food analysts, and others, on prudent approaches as how to ascertain the authenticity and potability, or otherwise, of coconut water samples.

Selected Instrumental Techniques Applied in Food and Feed: Quality, Safety and Adulteration Analysis

This review presents an overall glance at selected instrumental analytical techniques and methods used in food analysis, focusing on their primary food science research applications. The methods described represent approaches that have already been developed or are currently being implemented in our laboratories. Some techniques are widespread and well known and hence we will focus only in very specific examples, whilst the relatively less common techniques applied in food science are covered in a wider fashion. We made a particular emphasis on the works published on this topic in the last five years. When appropriate, we referred the reader to specialized reports highlighting each technique's principle and focused on said technologies' applications in the food analysis field. Each example forwarded will consider the advantages and limitations of the application. Certain study cases will typify that several of the techniques mentioned are used simultaneously to resolve an issue, support novel data, or gather further information from the food sample.

Hybrid variable selection strategy coupled with random forest (RF) for quantitative analysis of methanol in methanol-gasoline via Raman spectroscopy

With the trend of portable and miniaturization, Raman spectrometer requires more advanced analytical methods providing more rapid and accurate analysis performance for in-situ analysis. In this work, a hybrid variable selection method based on V-WSP and variable importance measurement (VIM) coupled with random forest (RF) was used to improve the quantitative analysis performance of portable laser Raman instruments for quantitative analysis of methanol content in methanol gasoline. First, five preprocessing methods were applied to reduce the infection information in the raw spectra, respectively. Based on the spectra data processed by multivariate scattering correction (MSC), V-WSP was employed to filter the infection or redundant information in Raman spectroscopy, and 579 variables were obtained when the correlation threshold is 0.9600. Then, the variables were further eliminated by VIM. Finally, 43 variables were obtained by the V-WSP-VIM method. In data processing, out of bag (OOB) error estimation and 10-flod cross validation (CV) were applied to optimize the parameters of preprocessing methods, V-WSP, VIM and RF model. The results fully demonstrated that compared with the RF model based on raw spectra, the RF model based on V-WSP-VIM method can achieve a better prediction performance for the quantitative analysis of methanol content in methanol-gasoline, with the coefficients of determination of cross-validation (R²_CV) improving from 0.9100 to 0.9662, the root mean square error of cross-validation (RMSE_CV) reducing from 0.0572 to 0.0365%, the coefficients of determination of prediction set (R²_P) improving from 0.9214 to 0.9407, the root mean square error of prediction set (RMSE_P) reducing from 0.0420 to 0.0382%, the variables reducing from 1044 to 43 and the modeling time reducing from 72.94 to 6.41 s. The results indicates that V-WSP-VIM coupled with RF is an effective method to improve the performance of portable laser Raman spectrometer for quantitative analysis of methanol content in methanol gasoline.

Attenuated Total Reflectance-Fourier transform infrared spectroscopy coupled with chemometrics for the rapid detection of coconut water adulteration

Attenuated Total Reflectance-Fourier Transform Infrared spectroscopy (ATR-FTIR), in combination with chemometrics, was explored as a rapid method of detecting sugar adulteration in coconut water. In a simulated experiment, coconut water was substituted with binary sugars, mixed sugars, and high fructose corn syrup and discriminated using the fingerprint infrared band region between 1200 and 950 cm^-1. Principal component analysis (PCA) performed on data pre-processed by the Savitzky-Golay smoothing and gap-segment derivative, revealed data clusters discernible by the type and level of substituted sugars, enabling visual diagnosis of the similarity and anomalous features in the dataset. Statistical performance metrics following a cross-validated partial least square (PLS) regression indicated the prediction of adulterant sugars at single-digit percent substitutions. A parallel exploratory analysis of 31 different commercial coconut water samples showed a distinct PCA clustering for samples bearing the label "added sugar", suggesting the potential use of the methods to screening samples for undeclared sugar additions.

Detection of adulteration in food based on nondestructive analysis techniques: a review

In recent years, people pay more and more attention to food quality and safety, which are significantly relating to human health. Food adulteration is a world-wide concerned issue relating to food quality and safety, and it is difficult to be detected. Modern detection techniques (high performance liquid chromatography, gas chromatography-mass spectrometer, etc.) can accurately identify the types and concentrations of adulterants in different food types. However, the characteristics as expensive, low efficient and complex sample preparation and operation limit the use of these techniques. The rapid, nondestructive and accurate detection techniques of food adulteration is of great and urgent demand. This paper introduced the principles, advantages and disadvantages of the nondestructive analysis techniques and reviewed the applications of these techniques in food adulteration screen in recent years. Differences among these techniques, differences on data interpretation and future prospects were also discussed.

Detection of the adulteration of fresh coconut water via NMR spectroscopy and chemometrics

Here, we applied NMR spectroscopy in combination with chemometrics to quantify the adulteration of fresh coconut water, stretched with water-sugar mixtures. Coconut water was extracted from young Costa Rican coconuts and adulterated with concentrations of various sugar solutions. A total of 45 samples were analysed by 1D proton NMR spectroscopy and chemometrics. Results showed highly sensitive quantification, with a limit of detection of adulteration with sugars of 1.3% and a root-mean-squared error of prediction of 0.58%. Interestingly, we identified a regular drift in the chemical shift and a change in the lineshape of malic acid signals concomitant with increasing levels of adulteration. On further investigation, this was found to originate from changes in the concentration of divalent cations, such as magnesium, within the samples. It can be concluded that 1H NMR spectroscopy enables accurate quantification for the degree of adulteration in this product, with the added discovery finding that the shift and lineshape of the malic acid signal can be utilised as a potential diagnostic marker for partial substitution of fresh coconut water with extrinsic components such as sugar mixtures.

Rapid Detection of Adulterants in Whey Protein Supplement by Raman Spectroscopy Combined with Multivariate Analysis

The growing demand for whey protein supplements has made them the target of adulteration with cheap substances. Therefore, Raman spectroscopy in tandem with chemometrics was proposed to simultaneously detect and quantify three common adulterants (creatine, l-glutamine and taurine) in whey protein concentrate (WPC) powder. Soft independent modeling class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA) models were built based on two spectral regions (400-1800 cm-1 and 500-1100 cm-1) to classify different types of adulterated samples. The most effective was the SIMCA model in 500-1100 cm-1 with an accuracy of 96.9% and an error rate of 5%. Partial least squares regression (PLSR) models for each adulterant were developed using two different Raman spectral ranges (400-1800 cm-1 and selected specific region) and data pretreatment methods. The determination coefficients (R2) of all models were higher than 0.96. PLSR models based on typical Raman regions (500-1100 cm-1 for creatine and taurine, the combination of range 800-1000 cm-1 and 1300-1500 cm-1 for glutamine) were superior to models in the full spectrum. The lowest root mean squared error of prediction (RMSEP) was 0.21%, 0.33%, 0.42% for creatine, taurine and glutamine, and the corresponding limit of detection (LOD) values for them were 0.53%, 0.71% and 1.13%, respectively. This proves that Raman spectroscopy with the help of multivariate approaches is a powerful method to detect adulterants in WPC.

Rapid through-container detection of fake spirits and methanol quantification with handheld Raman spectroscopy

The spirits drinks industry is of significant global economic importance and a major employer worldwide, and the ability to ensure product authenticity and maintain consumer confidence in these high-value products is absolutely essential. Spirit drinks counterfeiting is a worldwide problem, with counterfeiting and adulteration of spirit drinks taking many forms, such as substitution, stretching with lower-grade products, or creation of counterfeits with industrial, surrogate, or locally produced alcohols. Methanol for example, which has been used as a substitute alcohol for ethanol, has a high toxicity in humans. The counterfeiting of spirit drinks is consequently one of the few leading reported types of food fraud which can be directly and unequivocally linked to food safety and health concerns. Here, for the first time, we use handheld Raman spectroscopy with excitation in the near IR (1064 nm) for the through-container differentiation of multiple spirit drinks, detection of multiple chemical markers of counterfeit alcohol, and for the quantification of methanol. We established the limits of detection (LOD) of methanol in the analysed samples from four different spirit types (between 0.23-0.39%), which were considerably lower than a quoted maximum tolerable concentration (MTC) of 2% (v/v) methanol for humans in a 40% alcohol by volume (ABV) spirit drink, and even lower than the general EU limit for naturally occurring methanol in fruit spirits of 0.5% v/v (10 g methanol per L ethanol). We believe that Raman spectroscopy has considerable practicable potential for the rapid in situ through-container detection of counterfeit spirits drinks, as well as for the analysis and protection of other beverages and liquid samples.

Tackling Fraudsters with Global Strategies to Expose Fraud in the Food Chain

Deliberate adulteration of food products is as old as food processing and production systems. Food adulteration is occurring increasingly often today. With globalization and complex distribution systems, adulteration may have a far-reaching impact and even adverse consequences on well-being. The means of the international community to confront and solve food fraud today are scattered and largely ineffective. A collective approach is needed to identify all stakeholders in the food supply chain, certify and qualify them, exclude those failing to meet applicable standards, and track food in a real time. This review provides some background into the drivers of fraudulent practices (economically motivated adulteration, food-industry perspectives, and consumers' perceptions of fraud) and discusses a wide range of the currently available technologies for detecting food adulteration followed by multivariate pattern recognition tools. Food chain integrity policies are discussed. Future directions in research, concerned not only with food adulterers but also with food safety and climate change, may be useful for researchers in developing interdisciplinary approaches to contemporary problems.