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

Origin tracing and adulteration identification of bird's nest by high- and low-field NMR combined with pattern recognition

Edible bird's nest (EBN) is a high-value health food with various nutrients and bioactive components. With increasing demand for EBN, they are often adulterated with cheaper ingredients or falsely labeled by the origin information, thus harming consumer interests. In this study, high- and low-field nuclear magnetic resonance (HF/LF-NMR) technology combined with multivariate statistical analysis was used to identify the geographical marker of EBN from different origins and authenticate the adulterated EBN with various adulterants at different adulteration rates. Authentic EBN samples from Malaysia were used to simulate adulteration using gelatin (GL), agar (AG) and starch (ST) at 10 %, 20 %, 40 %, 60 %, 80 %, and 100 % w/w, respectively. The results showed significant differences in composition among EBN from different origins, with isocaproate and citric acid serving as geographical markers for Malaysia and Vietnam, respectively. Leucine, glutamic acid, and N-acetylglycoprotein serving as geographical markers for Indonesia. In addition, PLS model further verified the accuracy of origin identification of EBN. The LF-NMR results of adulteration EBN showed a linear correlation between the transverse relaxation (T2, S2) and the adulterated ratio. The OPLS-DA based on T2 spectra could accurately identify authentic EBN from adulterated with GL, AG and ST at 40 %, 20 %, and 20 %, respectively. Fisher discrimination model was able to differentiate at 20 %, 20 %, and 40 %, respectively. These results show that the 1H NMR combined with multivariate statistical analysis method could be a potential tool for the detection of origin and adulteration of EBN.

Research progress on Walnut oil: Bioactive compounds, health benefits, extraction methods, and medicinal uses

Walnut oil is extracted from walnut kernels (Juglans regia Linne) or iron walnut kernels (Juhlans sigillata Dode). The percentage of oil in walnuts is 52%-70%. The main constituents in oil are fatty acids, phenols, sterols, squalene, melatonin, vitamins, and minerals. Many extraction methods such as supercritical carbon dioxide extraction, maceration, modified "bligh and dyer extraction," aqueous enzymatic extraction, ultrasonic extraction, soxhlet extraction, and cold-press extraction methods are reported in the literature. Walnut oil showed anti-inflammatory, antitumor, antioxidant, immunomodulatory, neuroprotective, cardioprotective, antidiabetic, and antihyperlipidemic activities. The reported data in the literature suggest that walnut oil has many health benefits. This review summarizes the extraction methods, bioactive constituents, health benefits, and pharmacological actions of walnut oil. PRACTICAL APPLICATIONS: Walnut oil is a natural vegetable oil of significant importance due to their nutritional, and intelligence-boosting benefits. Several factors, including the processing parameters and the phytochemical profile, affect walnut oil products' flavor and color. In addition, storage environment of walnut oil can also affect walnut oil quality. Apart from the predominant ingredient fatty acids, the chemical composition of walnut oil comprises phenols, sterols, squalene, melatonin, vitamins, and minerals. These bioactive compounds are of potential value owing to their health-promoting benefits, including antioxidant, antitumor, and cholesterol-lowering effects. Many chemical constituents were isolated from walnut oil; however, all the compounds are not explored for their possible medicinal value. Thus, clinical studies, exploration of the therapeutic potential and the molecular mechanisms of all the compounds, and development of convenient dosage forms either for therapeutic or functional food purposes are warranted.

Classification of Prunus Genus by Botanical Origin and Harvest Year Based on Carbohydrates Profile

The 1H-NMR carbohydrates profiling was used to discriminate fruits from Rosaceae family in terms of botanical origin and harvest year. The classification was possible by application of multivariate data analysis, such as principal component analysis (PCA), linear discriminant analysis (LDA) and Pearson analysis. Prior, a heat map was created based on 1H-NMR signals which offered an overview of the content of individual carbohydrates in plum, apricot, cherry and sour cherry, highlighting the similarities. Although, the PCA results were almost satisfactory, based only on carbohydrates signals, the LDA reached 94.39% and 100% classification of fruits according to their botanical origin and growing season, respectively. Additionally, a potential association with the relevant climatic data was explored by applying the Pearson analysis. These findings are intended to create an efficient NMR-based solution capable of differentiating fruit juices based on their basic sugar profile.

Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with Chemometrics

Chinese walnuts have extraordinary nutritional and organoleptic qualities, and counterfeit Chinese walnut products are pervasive in the market. The aim of this study was to investigate the feasibility of hyperspectral imaging (HSI) technique to accurately identify and visualize Chinese walnut varieties. Hyperspectral images of 400 Chinese walnuts including 200 samples of Ningguo variety and 200 samples of Lin’an variety were acquired in range of 400–1000 nm. Spectra were extracted from representative regions of interest (ROIs), and principal component analysis (PCA) of spectra showed that the characteristic second principal component (PC2) was potentially effective in variety identification. The PC transformation was also conducted to hyperspectral images to make an exploratory visualization according to pixel-wise PC scores. Three different modeling methods including partial least squares-discriminant analysis (PLS-DA), k-nearest neighbor (KNN), and support vector machine (SVM) were individually employed to develop classification models. Results indicated that raw full spectra constructed PLS-DA model performed best with correct classification rates (CCRs) of 97.33%, 95.33%, and 92.00% in calibration, cross-validation, and prediction sets, respectively. Successful projects algorithm (SPA), competitive adaptive reweighted sampling (CARS), and PC loadings were individually used for effective wavelengths selection. Subsequently, simplified PLS-DA model based on wavelengths selected by CARS yielded the best 96.33%, 95.67% and 91.00% CCRs in the three sets. This optimal CARS-PLS-DA model acquired a sensitivity of 93.62%, a specificity of 88.68%, the area under the receiver operating characteristic curve (AUC) value of 0.91, and Kappa coefficient of 0.82 in prediction set. Classification maps were finally generated by classifying the varieties of each pixel in multispectral images at CARS-selected wavelengths, and the general variety was then readily discernible. These results demonstrated that features extracted from HSI had outstanding ability, and could be applied as a reliable tool for the further development of an on-line identification system for Chinese walnut variety.

Fatty Acid Stable Carbon Isotope Ratios Combined with Oxidation Kinetics for Characterization and Authentication of Walnut Oils

Walnut oil is vulnerable to oxidation due to its high content of polyunsaturated fatty acids and adulteration due to its high price. This study investigated 12 typical walnut oils from six main walnut-producing areas in China, using differential scanning calorimetry (DSC), Rancimat test, gas chromatography (GC), elemental analyzer-isotope ratio mass spectrometry (EA-IRMS) and GC-IRMS combined with oxidation kinetic analysis, Pearson correlation analysis, and principal component analysis (PCA). The melting and crystallization profiles using DSC indicated that walnut oils with a relatively high crystal onset temperature tended to be more stable against oxidation. Oleic acid was found to be the most characteristic fatty acid in walnut oil, with a content ranging from 13.84 to 35.08%. Two walnut oils with the highest oleic acid contents of 35.08 and 32.78% had the highest activation energies in nonisothermal DSC. Their predicted shelf lives based on the Rancimat test were 3.5-4.0 times longer than that of the oil with the highest α-linolenic acid at 4 °C and 3.1-3.5 times longer at 25 °C. The δ¹³C values of walnut oils were determined by EA-IRMS, and the δ¹³C values of fatty acids were determined by GC-IRMS. Fatty acid stable carbon isotope ratios combined with PCA were successfully applied to intuitively discriminate different walnut oils. The results suggested that fatty acid δ¹³C values determined by IRMS combined with chemometrics and lipid compositions are promising as a powerful means of vegetable oil authentication and discrimination.

Comparison of Regular, Pure Shift, and Fast 2D NMR Experiments for Determination of the Geographical Origin of Walnuts

¹H NMR spectroscopy, in combination with chemometric methods, was used to analyze the methanol/acetonitrile (1:1) extract of walnut (Juglans Regia L.) regarding the geographical origin of 128 authentic samples from different countries (France, Germany, China) and harvest years (2016–2019). Due to the large number of different metabolites within the acetonitrile/methanol extract, the one-dimensional (1D) ¹H NOESY (nuclear Overhauser effect spectroscopy) spectra suffer from strongly overlapping signals. The identification of specific metabolites and statistical analysis are complicated. The use of pure shift ¹H NMR spectra such as PSYCHE (pure shift yielded by chirp excitation) or two-dimensional ASAP-HSQC (acceleration by sharing adjacent polarization-heteronuclear single quantum correlation) spectra for multivariate analysis to determine the geographical origin of foods may be a promising method. Different types of NMR spectra (1D ¹H NOESY, PSYCHE, and ASAP-HSQC) were acquired for each of the 128 walnut samples and the results of the statistical analysis were compared. A support vector machine classifier was applied for differentiation of samples from Germany/China, France/Germany, and France/China. The models obtained by conduction of a repeated nested cross-validation showed accuracies from 58.9% (±1.3%) to 95.9% (±0.8%). The potential of the ¹H-¹³C HSQC as a 2D NMR experiment for metabolomics studies was shown.

Food Profiling: Determination of the Geographical Origin of Walnuts by 1H NMR Spectroscopy Using the Polar Extract

Walnuts, with their health-promoting ingredients, are among the most popular nuts, and practicable methods for determining their geographical origin are needed to tackle food fraud. Authentic walnut samples (235, Juglans Regia L.) from different harvest years (2016-2019) and countries were analyzed by ¹H NMR spectroscopy in combination with chemometric methods to determine their geographical origin. Two sample groups were analyzed at a time with a support vector machine algorithm to obtain two-class classifier models. In total, nine two-class models were built (e.g., Germany/China, France/Germany, and USA/Switzerland), and a repeated nested cross-validation was performed. The models obtained showed high accuracies from 78.0% (±2.3%) to 96.6% (±0.6%). Furthermore, identification of potential chemical markers in the walnut extract was performed.

Determination of the Geographical Origin of Walnuts (Juglans regia L.) Using Near-Infrared Spectroscopy and Chemometrics

The prices of walnuts vary according to their geographical origin and, therefore, offer a financial incentive for adulteration. A reliable analysis method is required to quickly detect possible misdeclarations and thus prevent food fraud. In this study, a method to distinguish between seven geographical origins of walnuts using Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics as a fast, versatile, and easy to handle analytical tool was developed. NIR spectra of 212 ground and afterwards freeze-dried walnut samples, harvested in three consecutive years (2017-2019), were collected. We optimized the data pre-processing by applying and evaluating 50,545 different pre-processing combinations, followed by linear discriminant analysis (LDA) which was confirmed by nested cross-validation. The results show that in the scope of our research minimal pre-processing led to the best results: By applying just multiplicative scatter correction (MSC) and median centering, a classification accuracy of 77.00% ± 1.60% was achieved. Consequently, this complex model can be used to answer economically relevant questions e.g., to distinguish between European and Chinese walnuts. Furthermore, the great influence of the applied pre-processing methods, e.g., the selected wavenumber range, on the achieved classification accuracy is shown which underlines the importance of optimization of the pre-processing strategy.

Origin Determination of Walnuts (Juglans regia L.) on a Worldwide and Regional Level by Inductively Coupled Plasma Mass Spectrometry and Chemometrics

To counteract food fraud, this study aimed at the differentiation of walnuts on a global and regional level using an isotopolomics approach. Thus, the multi-elemental profiles of 237 walnut samples from ten countries and three years of harvest were analyzed with inductively coupled plasma mass spectrometry (ICP-MS), and the resulting element profiles were evaluated with chemometrics. Using support vector machine (SVM) for classification, validated by stratified nested cross validation, a prediction accuracy of 73% could be achieved. Leave-one-out cross validation was also applied for comparison and led to less satisfactory results because of the higher variations in sensitivity for distinct classes. Prediction was still possible using only elemental ratios instead of the absolute element concentrations; consequently, a drying step is not mandatory. In addition, the isotopolomics approach provided the classification of walnut samples on a regional level in France, Germany, and Italy, with accuracies of 91%, 77%, and 94%, respectively. The ratio of the model's accuracy to a random sample distribution was calculated, providing a new parameter with which to evaluate and compare the performance of classification models. The walnut cultivar and harvest year had no observable influence on the origin differentiation. Our results show the high potential of element profiling for the origin authentication of walnuts.

Comparative Study of the Bioactive Properties and Elemental Composition of Red Clover (Trifolium pratense) and Alfalfa (Medicago sativa) Sprouts during Germination

Considering the growing interest in functional foods, the identification of the individual species of elements is of great importance in understanding specific nutraceutical properties. The present study aims to compare the dynamic of the elemental content (K, Na, Ca, Mg, Fe, Cu, Zn, Mn, Ni, and Se), total polyphenols, and antioxidant activity of Trifolium pratense L. and Medicago sativa L. sprouts in different germination stages. The elemental profile was established by atomic absorption spectroscopy after the microwave acid digestion of the samples, while total polyphenols and anti-radical activity were evaluated by UV-Vis spectroscopic methods. Phenolic compounds and anti-radical activity of both alfalfa and red clover sprouts varied with germination stages. Germination can significantly increase the anti-radical activity in the first 3 days of germination, followed by a decline in the following days. An increase in total polyphenols was noticed, starting from the second day of germination in both plant species. There were significant (p < 0.05) differences for Ca, Na, Fe, K, Zn, and Mg contents among the sprouts, depending on the germination stage and plant species. The calcium contents of alfalfa ranged between 200.74 µg/g DW (raw seeds) and 2765.31 µg/g DW (sprouted), while in red clover between 250.83 µg/g DW and 601.59 µg/g DW. Maximum selenium content in alfalfa sprouts, reached in the 3rd day of germination (11.42 µg/g DW), exceeded the maximum value measured in red clover (9.42 µg/g DW). The data were subject to statistical processing using analysis of variance (ANOVA), multivariate analysis (PCA) and hierarchical clustering analysis (HCA).

Morphological and Compositional Analysis of Two Walnut (Juglans regia L.) Cultivars Growing in China

Two selected walnut cultivars (Xiangling and Jizhaomian) growing in China were analyzed in terms of proximate and mineral composition, fatty acid and amino acid profile. According to the higher kernel rate (61.22%), thinner shell (1.03 mm), higher content of fat (66.93%), protein (20.97%), essential amino acids (29.31% of total amino acids) and minerals such as manganese, zinc and copper, Xiangling would be the more nutritive cultivar with more commercial value. However, Jizhaomian would be the more healthful cultivar based on the higher content of polyunsaturated fatty acids (79.39% of total fatty acids), and it would be more sensitive to rancidity. Jizhaomian also showed higher content in potassium, calcium, magnesium and iron. Genotype is the major source of variability in morphology and chemical compositions. Regardless of the cultivar, consumption of walnuts is desirably encouraged, since all of them contain compounds potentially beneficial to health.

Amino Acid Profile of Fruits as Potential Fingerprints of Varietal Origin

This study aims to assess the capability of the ¹H-NMR profiling of fruits from different genera in combination with multivariate data analysis to provide feasible information for fruit juices’ authenticity in terms of botanical origin. Nine fruit varieties from four genera were selected for the experimental plan. The juice obtained from the fruits was characterized using the ¹H-NMR technique, selecting the obtained amino acid profile of fruits as a potential specific fingerprint. Due to the complex information provided by the NMR spectra, a chemometric approach of the data was further applied to enable the differentiation of the fruit samples, highlighting thus its suitability as a discrimination tool for the varietal origin. The advantage of this analytical approach is given by the relatively simple working procedure, which consists of an easy, fast, and accessible preparation stage while providing complex information on fruit composition.

High-Throughput 1H-Nuclear Magnetic Resonance-Based Screening for the Identification and Quantification of Heartwood Diterpenic Acids in Four Black Pine (Pinus nigra Arn.) Marginal Provenances in Greece

A high-throughput quantitative Nuclear Magnetic Resonance ¹H-NMR method was developed and applied to screen the quantity of the diterpenic resin acids in the heartwood of black pine, due to the renewed scientific interest in their medicinal properties and use in various diseases treatment. The 260 samples were taken from Pinus nigra clones, selected from four provenances of the Peloponnese (Greece), participating in a 35-year-old clonal seed orchard. Total resin acids per dry heartwood weight (dhw) varied greatly, ranging from 30.05 to 424.70 mg/g_dhw (average 219.98 mg/g_dhw). Abietic was the predominant acid (76.77 mg/g_dhw), followed by palustric acid (47.94 mg/g_dhw), neoabietic acid (39.34 mg/g_dhw), and pimaric acid (22.54 mg/g_dhw). Dehydroabietic acid was at moderate levels (11.69 mg/g_dhw), while levopimaric, isopimaric, and sandaracopimaric acids were in lower concentrations. The resin acid fraction accounted for 72.33% of the total acetone extractives. Stilbenes were presented in significant quantities (19.70%). The resin acid content was composed mainly of the abietane type resin acids (83.56%). Peloponnesian Pinus nigra heartwood was found to be the richest source of resin acids identified to date and is considered the best natural source for the production of such bioactive extracts. The results indicate a high potential for effective selection and advanced breeding of pharmaceutical and high economic value bioactive substances from Pinus nigra clones.