Chemometrics Methods for Specificity, Authenticity and Traceability Analysis of Olive Oils: Principles, Classifications and Applications

Machine learning-enabled high-throughput industry screening of edible oils

Long-term consumption of mixed fraudulent edible oils increases the risk of developing of chronic diseases which has been a threat to the public health globally. The complicated global supply-chain is making the industry malpractices had often gone undetected. In order to restore the confidence of consumers, traceability (and accountability) of every level in the supply chain is vital. In this work, we shown that machine learning (ML) assisted windowed spectroscopy (e.g., visible-band, infra-red band) produces high-throughput, non-destructive, and label-free authentication of edible oils (e.g., olive oils, sunflower oils), offers the feasibility for rapid analysis of large-scale industrial screening. We report achieving high-level of discriminant (AUC > 0.96) in the large-scale (n ≈ 11,500) of adulteration in olive oils. Notably, high clustering fidelity of 'spectral fingerprints' achieved created opportunity for (hypothesis-free) self-sustaining large database compilation which was never possible without machine learning. (137 words).

Elemental and spectral chemometric analyses of Octopus vulgaris beaks as reliable markers of capture location

The high demand and economic relevance of cephalopods make them prone to food fraud, including related to harvest location. Therefore, there is a growing need to develop tools to unequivocally confirm their capture location. Cephalopod beaks are nonedible, making this material ideal for traceability studies as it can also be removed without a loss of commodity economic value. Within this context, common octopus (Octopus vulgaris) specimens were captured in five fishing areas along the Portuguese coast. Untargeted multi-elemental total X-ray fluorescence analysis of the octopus beaks revealed a high abundance of Ca, Cl, K, Na, S, and P, concomitant with the keratin and calcium phosphate nature of the material. We tested a suite of discrimination models on both elemental and spectral data, where the elements contributing most to discriminate capture location were typically associated with diet (As), human-related pressures (Zn, Se, and Mn), or geological features (P, S, Mn, and Zn). Among the six different chemometrics approaches used to classify individuals to their capture location according to their beaks' element concentration, classification trees attained a classification accuracy of 76.7%, whilst reducing the number of explanatory variables for sample classification and highlighting variable importance for group discrimination. However, using X-ray spectral features of the octopus beaks further improved classification accuracy, with the highest classification of 87.3% found with partial least-squares discriminant analysis. Ultimately, element and spectral analyses of nonedible structures such as octopus beaks can provide an important, complementary, and easily accessible means to support seafood provenance and traceability, whilst integrating anthropogenic and/or geological gradients.

Trusted Data Analysis and Consensus Mechanism of Product Traceability Based on Blockchain

As a decentralized, distributed system between functional and benefit management functions, blockchain is effective for financial transaction data security, data tracking and antitampering, product tracking, and access control. In this context, we have conducted experimental research on the blockchain product traceability trusted data analysis consensus mechanism and reached the following conclusions. (1) There are decentralization, irreversible tampering, traceability, and openness through the blockchain's own information and other functions so that a series of processes from raw material production, transportation, and logistics sales are well documented. (2) Under the same network environment, if the number of matches in the system increases, the average matching time consumed by the original engine is greater than the average matching time on the optimized engine. For example, taking 10% of Byzantine nodes in the system, the number of consensus increases, the average ITPBFT consensus time is about 5.74 s, and the average consensus time of the PBFT consensus mechanism is about 6.13 s. As a decentralized distributed data management system through nodes, blockchain is widely used in financial transactions, copyright protection, and product areas such as tracking and access control. In this regard, we conducted an experimental study of the consensus mechanism to analyze reliable data on the traceability of blockchain products and came to the conclusion of the experiment.

The Application of FTIR Spectroscopy and Chemometrics for the Authentication Analysis of Horse Milk

Expensive milk such as horse's milk (HM) may be the target of adulteration by other milk such as goat's milk (GM) and cow's milk (CM). FTIR spectroscopy in combination with chemometrics of linear discriminant analysis (LDA) and multivariate calibrations of partial least square regression (PLSR) and principal component regression (PCR) was used for authentication of HM from GM and CM. Milk was directly subjected to attenuated total reflectance (ATR) spectral measurement at midinfrared regions (4000-650 cm-1). Results showed that LDA could make clear discrimination between HM and HM adulterated with CM and GM without any misclassification observed. PLSR using 2nd derivative spectra at 3200-2800 and 1300-1000 cm-1 provided the best model for the relationship between actual values of GM and FTIR predicted values than PCR. At this condition, R 2 values for calibration and validation models obtained were 0.9995 and 0.9612 with RMSEC and RMSEP values of 0.0093 and 0.0794. PLSR using normal FTIR spectra at 3800-3000 and 1500-1000 cm-1 offered R 2 for the relationship between actual values of CM and FTIR predicted values of >0.99 in calibration and validation models with low errors of RMSEC of 0.0164 and RMSEP of 0.0336 during authentication of HM from CM. Therefore, FTIR spectroscopy in combination with LDA and PLSR is an effective method for authentication of HM from GM and CM.

Phytochemical Profile of Eight Categories of Functional Edible Oils: A Metabolomic Approach Based on Chromatography Coupled with Mass Spectrometry

Functional vegetable oils are highly considered not only for their nutritional value, but also for their health benefits. The profile of phytochemicals responsible for their quality is useful also for the identification of possible mislabeling or adulteration. The comparative composition of eight categories (sunflower, pumpkin, hempseed, linseed, soybean, walnut, sea buckthorn and olive) of commercial vs. authentic oils was determined. Fatty acids, volatiles, carotenoids, tocopherols, and phenolic components were analyzed by gas- and liquid chromatography-based techniques coupled with diode array, mass spectrometry, or fluorescence detection. Classification models, commonly used in metabolomics, e.g., principal component analysis, partial least squares discriminant analysis, hierarchical clusters and heatmaps have been applied to discriminate each category and individual samples. Carotenoids, tocopherols, and phenolics contributed mostly, qualitatively, and quantitatively to the discrimination between the eight categories of oils, as well as between the authentic and the commercial ones. This metabolomic approach can be easily implemented and the heatmaps can be considered as “identity” cards of each oil category and the quality of commercial oils, comparative to the authentic ones of the same botanical and geographical origin.

Projection to latent correlative structures, a dimension reduction strategy for spectral-based classification

Latent variables are used in chemometrics to reduce the dimension of the data. It is a crucial step with spectroscopic data where the number of explanatory variables can be very high. Principal component analysis (PCA) and partial least squares (PLS) are the most common. However, the resulting latent variables are mathematical constructs that do not always have a physicochemical interpretation. A new data reduction strategy, named projection to latent correlative structures (PLCS), is introduced in this manuscript. This approach requires a set of model spectra that will be used as references. Each latent variable is the relative similarity of a given spectrum to a pair of reference spectra. The latent structure is obtained using every possible combination of reference pairing. The approach has been validated using more than 500 FTIR-ATR spectra from cool-season culinary grain legumes assembled from germplasm banks and breeders' working collections. PLCS has been combined with soft discriminant analysis to detect outliers that could be particularly suitable for a deeper analysis.

Different representation of the data by selecting the latent variables.

Gold nanorod arrays enable highly sensitive bacterial detection via surface-enhanced infrared absorption (SEIRA) spectroscopy

Infrared (IR) spectroscopy is a unique and powerful method in the identification, characterization, and classification of chemical and biological molecules. However, the low absorbance of biological molecules has arisen as a major bottleneck and inhibits the application of IR in practical applications. To overcome this limitation, in the last four decades, surface-enhanced IR absorption (SEIRA) spectroscopy has been proposed and has become the focus of interest in various applications. In this study, for the first time, we proposed the employment of 3D anisotropic gold nanorod arrays (GNAs) as a highly active SEIRA platform in bacterial detection. For this, GNA platforms were fabricated through an oblique angle deposition (OAD) approach by using a physical vapor deposition (PVD) system. OAD of gold at proper deposition angle (10°) created closely-packed and columnar gold nanorod structures onto the glass slides in a well-controlled manner. GNA platform was tested as a SEIRA system in three different species of bacteria (Escherichia coli, Staphylococcus aureus, and Bacillus subtilis) by collecting IR spectra of each bacteria from different parts of GNA. The employment of GNA provided robust IR spectra with high reproducibility and signal-to-noise ratio. For the comparison, IR spectra of each bacteria were collected from aluminum foil and a smooth gold surface (SGS). No or very low IR spectra were observed in comparison to the GNA platform for these substrates. Unsupervised (PCA, HCA) and supervised (SIMCA, LDA, and SVM classification) machine learning analysis of bacteria spectra obtained from GNA substrate indicated that all bacteria samples can be detected and identified without using a label-containing biosensor, in a fast and simple manner.

Discrimination of botanical origin of olive oil from selected Greek cultivars by SPME-GC-MS and ATR-FTIR spectroscopy combined with chemometrics

BACKGROUND

Consumers today wish to know the botanical origin of the olive oil they purchase. The objective of the present study was the development of robust chemometric models based on gas chromatography-mass spectrometry (GC-MS) and attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) for the purpose of botanical differentiation of three commercial Greek olive oil cultivars.

RESULTS

Using the solid-phase microextraction technique (SPME), volatile compounds (VC) were obtained and analyzed by GC-MS. Five hydrocarbons and one ester were selected by the forward stepwise algorithm, which best discriminated the olive oil samples. From ATR-FTIR analysis, the spectral regions chosen from the forward stepwise algorithm were associated with CO stretching vibration of the esters of triglycerides and the CH bending vibrations of the CH₂ aliphatic group and double bonds. Application of the supervised methods of linear and quadratic discriminant cross-validation analysis, based on VC data, provided a correct classification score of 97.4% and 100.0%, respectively. Corresponding statistical analyses were used in the mid-infrared spectra, by which 96.1% of samples were discriminated correctly.

CONCLUSION

ATR-FTIR and SPME-GC-MS techniques in conjunction with the appropriate feature selection algorithm and classification methods proved to be powerful tools for the authentication of Greek olive oil. The proposed methodology could be used in an industrial setting for determination of the botanical origin of Greek olive oil. © 2020 Society of Chemical Industry.

Real wine or not? Protecting wine with traceability and authenticity for consumers: chemical and technical basis, technique applications, challenge, and perspectives

Wine is a high-value alcoholic beverage welcomed by consumers because of its flavor and nutritional value. The key information on wine bottle label is the basis of consumers' choice, which also becomes a target for manufacturers to adulterate, including geographical origin, grape variety and vintage. With the improvement of wine adulteration technology, modern technological means are needed to solve the above mentioned problems. The chemical basis of wine determines the type of technique used. Detection technology can be subdivided into four groups: mass spectrometry techniques, spectroscopic techniques, chromatography techniques, and other techniques. Multivariate statistical analysis of the data was performed by means of chemometrics methods. This paper outlines a series of procedures for wine classification and identification, and classified the analytical techniques and data processing methods used in recent years with listing their principles, advantages and disadvantages to help wine researchers choose appropriate methods to meet the challenge and ensure wine traceability and authenticity.

Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR) Coupled with Chemometrics, to Control the Botanical Authenticity and Quality of Cold-Pressed Functional Oils Commercialized in Romania

Attenuated total reflectance-Fourier transform infrared ppectroscopy (ATR-FTIR) proved to be a reliable, rapid, and easy-to-use technique to evaluate vegetable oils quality and authenticity. The spectral range of the middle infrared region (MIR) of FTIR spectra, from 4000 to 600 cm−1, has been commonly used to fingerprint specific functional groups of lipids and their modified forms induced by oxidation of thermal treatment. The applicability of FTIR-MIR spectroscopy in assessing oil fingerprinting and quality parameters is crucially dependent on the chemometric methods, including calibrations with authentic samples. We report here the evaluation of seven types of cold-pressed functional oils (sunflower, pumpkin, hempseed, soybean, walnut, linseed, sea buckthorn) produced in Romania, provided directly from small enterprises (as genuine, process-controlled authentic samples) comparative to commercialized samples. Concomitantly, olive oils of similar claimed quality were investigated. The ATR-FTIR-MIR data were complemented by UV–Vis spectral fingerprints and multivariate analysis using Unscrambler X.10.4 and Metaboanalyst 4.0 software (e.g., PCA, PLSDA, cluster analysis, heatmap, Random forest analysis) and ANOVA post-hoc analysis using Fischer’s least significant difference. The integration of spectral and chemometric analysis proved to offer valuable criteria for their botanical group recognition, individual authenticity, and quality, easy to be applied for large cohorts of commercialized oils.

1H-NMR Determination of Organic Compounds in Municipal Wastewaters and the Receiving Surface Waters in Eastern Cape Province of South Africa

Surface water is the recipient of pollutants from various sources, including improperly treated wastewater. Comprehensive knowledge of the composition of water is necessary to make it reusable in water-scarce environments. In this work, proton nuclear magnetic resonance (¹H-NMR) was combined with multivariate analysis to study the metabolites in four rivers and four wastewater treatment plants releasing treated effluents into the rivers. ¹H-NMR chemical shifts of the extracts in CDCl were acquired with Bruker 400. Chemical shifts of ¹H-NMR in chlorinated alkanes, amino compounds and fluorinated hydrocarbons were common to samples of wastewater and lower reaches or the rivers. ¹H-NMR chemical shifts of carbonyl compounds and alkyl phosphates were restricted to wastewater samples. Chemical shifts of phenolic compounds were associated with treated effluent samples. This study showed that the sources of these metabolites in the rivers were not only from improperly treated effluents but also from runoffs. Multivariate analyses showed that some of the freshwater samples were not of better quality than wastewater and treated effluents. Observations show the need for constant monitoring of rivers and effluent for the safety of the aquatic environment.

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.

Fast spectroscopic monitoring of inhibitors in the 2G ethanol process

One of the main challenges of second generation (2G) ethanol production is the high quantities of phenolic compounds and furan derivatives generated in the pretreatment of the lignocellulosic biomass, which inhibit the enzymatic hydrolysis and fermentation steps. Fast monitoring of these inhibitory compounds could provide better control of the pretreatment, hydrolysis, and fermentation processes by enabling the implementation of strategic process control actions. We investigated the feasibility of monitoring these inhibitory compounds by ultraviolet-visible (UV-Vis) spectroscopy associated with partial least squares (PLS) regression. Hydroxymethylfurfural, furfural, vanillin, and ferulic and p-coumaric acids generated during different severities of liquid hot water pretreatment of sugarcane bagasse were quantified with highly accuracy. In cross-validation (leave-one-out), the PLS-UV-Vis method presented root mean square error of prediction (RMSECV) of around only 5.0%. The results demonstrated that the monitoring performance achieved with PLS-UV-Vis could support future studies of optimization and control protocols for application in industrial processes.