Authentication of edible fats and oils by non-targeted 13 C INEPT NMR spectroscopy

Raman spectroscopy and multivariate analysis for the waste and edible vegetable oil classification

Twelve samples of waste cooking oil (WCO) were prepared by four different deep-frying procedures. The edible and the waste oil samples were characterised by Raman spectroscopy, revealing few and almost negligible differences between them. Therefore, the possibility of classifying the different groups of samples by extracting valuable data from the Raman spectra through statistical multivariate analysis was explored. Even if the number of samples was not enough to draw definitive conclusions, unsupervised principal component analysis (PCA) and supervised partial least square discriminant analysis (PLS-DA) conducted on the raw Raman signals, allowed to distinguish within edible and waste vegetable oil, and to select the most relevant combination of variables associated with each family. Using sparse partial least square discriminant analysis (S-PLS-DA), we determined a chemical fingerprint characteristic of each sample by creating a Variable In Projection (VIP) plot. The methodology herein presented could find relevant application in the detection of waste adulteration in vegetable oils sold for industrial purposes other than food.

Food authentication, current issues, analytical techniques, and future challenges: A comprehensive review

Food authentication and contamination are significant concerns, especially for consumers with unique nutritional, cultural, lifestyle, and religious needs. Food authenticity involves identifying food contamination for many purposes, such as adherence to religious beliefs, safeguarding health, and consuming sanitary and organic food products. This review article examines the issues related to food authentication and food fraud in recent periods. Furthermore, the development and innovations in analytical techniques employed to authenticate various food products are comprehensively focused. Food products derived from animals are susceptible to deceptive practices, which can undermine customer confidence and pose potential health hazards due to the transmission of diseases from animals to humans. Therefore, it is necessary to employ suitable and robust analytical techniques for complex and high-risk animal-derived goods, in which molecular biomarker-based (genomics, proteomics, and metabolomics) techniques are covered. Various analytical methods have been employed to ascertain the geographical provenance of food items that exhibit rapid response times, low cost, nondestructiveness, and condensability.

Quantitative comparison and rapid discrimination of Panax notoginseng powder and Caulis clematidis armandii using NMR combined with pattern recognition

BACKGROUND

The market demand for Panax notoginseng (P. notoginseng) is growing rapidly because of its useful properties in food and medicine. However, the frequent adulteration of P. notoginseng seriously affects the health of consumers and is a great challenge to food safety. In this study, low- and high-field nuclear magnetic resonance (LF/HF-NMR) were applied to detect the transverse relaxation distribution of P. notoginseng contaminated with different ratios of Caulis clematidis armandii (CCA) and the components in P. notoginseng and CCA, respectively.

RESULTS

Fifty-seven kinds of major and minor components in P. notoginseng and CCA were identified and quantified from their high-resolution NMR spectra, and there were significant differences in ginsenosides, sucrose, and glucose between P. notoginseng and CCA. Furthermore, the partial least squares regression analysis results indicated that LF-NMR parameters (T₂₁ and S₂₁ ) changed linearly as the ratio of CCA increased, and these changes were attributed to the variations in polysaccharide and sucrose in adulterated P. notoginseng.

CONCLUSION

In the relaxation time-based pattern recognition models, the authentic P. notoginseng powder could be classified with 100% accuracy from adulterated P. notoginseng when the adulteration ratio was greater than 30%, demonstrating the possibility of LF-NMR, in combination with pattern recognition, for rapid discrimination of food authenticity. © 2022 Society of Chemical Industry.

NMR-Based Approaches in the Study of Foods

In this review, the three different NMR-based approaches usually used to study foodstuffs are described, reporting specific examples. The first approach starts with the food of interest that can be investigated using different complementary NMR methodologies to obtain a comprehensive picture of food composition and structure; another approach starts with the specific problem related to a given food (frauds, safety, traceability, geographical and botanical origin, farming methods, food processing, maturation and ageing, etc.) that can be addressed by choosing the most suitable NMR methodology; finally, it is possible to start from a single NMR methodology, developing a broad range of applications to tackle common food-related challenges and different aspects related to foods.

Progress in nanostructure understanding of edible crystalline fats and their application in nano-delivery systems: Cocoa butter as a model

Nucleation and crystal growth of edible fats at the nanoscale have received little attention due to analytical limitations. A key factor to modify the properties of edible fats is nanostructure understanding. Progress within the last years will be presented, including analytical techniques used to investigate fat crystallization. Cocoa butter has been the subject of several works due to its properties and its high impact on chocolate manufacturing. Moreover, this vegetable fat has been used as the solid lipid component in nano delivery systems. Since nanoplatelet is the smallest unit in crystalline fats, and the nanoscale is influenced by supersaturation, temperature, shear fields, and surfactants, nanostructure engineering is possible. On its part, cocoa butter has been included in innovative delivery systems along the last years. This review will highlight main results and challenges on these topics.

Advanced techniques in edible oil authentication: A systematic review and critical analysis

Adulteration of edible substances is a potent contemporary food safety issue. Perhaps the overt concern derives from the fact that adulterants pose serious ill effects on human health. Edible oils are one of the most adulterated food products. Perpetrators are adopting ways and means that effectively masks the presence of the adulterants from human organoleptic limits and traditional oil adulteration detection techniques. This review embodies a detailed account of chemical, biosensors, chromatography, spectroscopy, differential scanning calorimetry, non-thermal plasma, dielectric spectroscopy research carried out in the area of falsification assessment of edible oils for the past three decades and a collection of patented oil adulteration detection techniques. The detection techniques reviewed have some advantages and certain limitations, chemical tests are simple; biosensors and nuclear magnetic resonance are rapid but have a low sensitivity; chromatography and spectroscopy are highly accurate with a deterring price tag; dielectric spectroscopy is rapid can be portable and has on-line compatibility; however, the results are susceptible to variation of electric current frequency and intrinsic factors (moisture, temperature, structural composition). This review paper can be useful for scientists or for knowledge seekers eager to be abreast with edible oil adulteration detection techniques.

Stepwise strategy based on 1H-NMR fingerprinting in combination with chemometrics to determine the content of vegetable oils in olive oil mixtures

¹H NMR fingerprinting of edible oils and a set of multivariate classification and regression models organised in a decision tree is proposed as a stepwise strategy to assure the authenticity and traceability of olive oils and their declared blends with other vegetable oils (VOs). Oils of the 'virgin olive oil' and 'olive oil' categories and their mixtures with the most common VOs, i.e. sunflower, high oleic sunflower, hazelnut, avocado, soybean, corn, refined palm olein and desterolized high oleic sunflower oils, were studied. Partial least squares (PLS) discriminant analysis provided stable and robust binary classification models to identify the olive oil type and the VO in the blend. PLS regression afforded models with excellent precisions and acceptable accuracies to determine the percentage of VO in the mixture. The satisfactory performance of this approach, tested with blind samples, confirm its potential to support regulations and control bodies.

When detection of dairy food fraud fails: An alternative approach through proton nuclear magnetic resonance spectroscopy

This paper investigated the limits of the current approach for the determination of the fatty acids profile of milk fats from proton nuclear magnetic resonance data based on the hypothesis that the signal at 0.96 ppm, currently assigned in the literature as a marker for the "short chain fatty acids," is generated only by the butyric moiety (not by all of the short-chain fatty acids, which also include C6:0-caproic acid). The hypothesis was tested and experimentally confirmed. Moreover, the triplet at 0.96 ppm can also be due to n-3 fatty acids such as linolenic acid (C18:3); therefore, a previously reported methodology for the fatty acids profiling of dairy products-considered as general in the literature-cannot be used in fraud-detection approaches because it allows linolenic acid to be mistaken for butyric acid, consequently leading to misclassification of adulterated samples as nonadulterated. To support our opinion, we have applied the current literature approach for the determination of the fatty acids composition of 3 synthetic nondairy fat blends and have obtained fatty acid compositions similar to milk fats, allowing for their misclassification as genuine milk fats. However, in reality, the blends had very different compositions, as confirmed by gas chromatography. Consequently, we have highlighted the weaknesses of the existing methodology for the detection of dairy food adulteration. In return, new proton nuclear magnetic resonance descriptors based on various integral ratios of signals associated with CH₂ moiety versus signals associated with butyric and n-3 fatty acids were proposed to detect adulterations.

Potentialities of Rapid Analytical Strategies for the Identification of the Botanical Species of Several "Specialty" or "Gourmet" Oils

A comprehensive data collection of authentic "specialty" or "gourmet" oils, namely cold-pressed industrial virgin oils, was performed. Eight different botanical species, i.e., Almond, Apricot, Avocado, Hazelnut, Mosqueta rose, Rosehip, Sunflower, and Walnut oils were studied plus Olive oil as the gold standard of cold-pressed virgin oils. Two different analytical approaches are proposed to rapidly verify the botanical species of the oil-based raw material. The first approach is based on a multivariate statistical analysis of conventional analytical data, namely their fatty acid composition. These data have been re-elaborated in a multivariate way by Principal Component Analysis (PCA) and classification methods. The second approach proposes a fast and non-destructive spectrophotometric analysis to determine the color of these oils to discriminate among different species. In this regard, the raw diffuse reflectance spectra (380-780 nm) obtained by a UV-Vis spectrophotometer with an integrating sphere was considered and elaborated by chemometrics. This information was compared with the results obtained by the most common approach based on the CIELab parameters. A data fusion of chromatographic and spectral data was also investigated. Either fatty acid composition or color of these oils demonstrated to be two promising markers of their botanical authenticity.

ROC curves for the optimization of one-class model parameters. A case study: Authenticating extra virgin olive oil from a Catalan protected designation of origin

This paper proposes a ROC curve-based methodology to find optimal classification model parameters. ROC curves are implemented to set the optimal number of PCs to build a one-class SIMCA model and to set the threshold class value that optimizes both the sensitivity and specificity of the model. The authentication of the geographical origin of extra-virgin olive oils of Arbequina botanical variety is presented. The model was developed for samples from Les Garrigues, target class, Samples from Siurana were used as the non-target class. Samples were measured by FT-Raman with no pretreatment. PCA was used as exploratory technique. Spectra underwent pre-treatment and variables were selected based on their VIP score values. ROC curve and others already known criteria were applied to set the threshold class value. The results were better when the ROC curve was used, obtaining performance values higher than 82%, 75% and 77% for sensitivity, specificity and efficiency, respectively.

Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years

Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance. Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud. It is the aim of the present manuscript to review the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions will also be discussed.

Chemometric tools for the authentication of cod liver oil based on nuclear magnetic resonance and infrared spectroscopy data

Cod liver oil is a popular dietary supplement marketed as a rich source of omega-3 fatty acids as well as vitamins A and D. Due to its high market price, cod liver oil is vulnerable to adulteration with lower priced vegetable oils. In this study, ¹H and ¹³C nuclear magnetic resonance spectroscopy, Fourier transform infrared spectroscopy, and gas chromatography (coupled to a flame ionization detector) were used in combination with multivariate statistics to determine cod liver oil adulteration with common vegetable oils (sunflower and canola oils). Artificial neural networks (ANN) were able to differentiate adulteration levels based on infrared spectra with a detection limit of 0.22% and a root mean square error of prediction (RMSEP) of 0.86%. ANN models using ¹H NMR and ¹³C NMR data yielded detection limits of 3.0% and 1.8% and RMSEPs of 2.7% and 1.1%, respectively. In comparison, the ANN model based on fatty acid profiles determined by gas chromatography achieved a detection limit of 0.81% and an RMSEP of 1.1%. The approach of using spectroscopic techniques in combination with multivariate statistics can be regarded as a promising tool for the authentication of cod liver oil and may pave the way for a holistic quality assessment of fish oils. Graphical abstract.

¹H-NMR Profiling and Carbon Isotope Discrimination as Tools for the Comparative Assessment of Walnut (Juglans regia L.) Cultivars with Various Geographical and Genetic Origins-A Preliminary Study

The aim of the study was to investigate the differences between walnut genotypes of various geographical and genetic origins grown under the same or different environmental conditions. The biological material analyzed consisted in walnut kernels of 34 cultivars, nine advanced selections, and six hybrids harvested in 2015 and 2016, summing up to a total of 64 samples. The walnuts, walnut oil, and residue were characterized in respect to their chemical (proximate composition—fat, protein, nutritional value, fatty acids profile by ¹H-NMR) and carbon-13 isotopic composition. The data was used to statistically discriminate the cultivars according to composition, geographical area of origin, and year of harvest, comparing the Romanian cultivars, selections, and hybrids with the internationally available ones.