Geographical origin of durum wheat studied by 1H-NMR profiling

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.

Spatially Correlated Nuclear Magnetic Resonance Profiles as a Tool for Precision Agriculture

Nuclear magnetic resonance (NMR) profiling, sample georeferentiaton, and geostatistics are applied to evaluate the spatial variability of metabolic expression of durum wheat in fields managed by precision agriculture. Durum wheat at three different vegetation stages, grown in two different places of the Basilicata region, in Italy, is analyzed by NMR. The spatial variability, within each field, of metabolites, quantified by NMR, is evidenced by appropriate geostatistic tools through the definition of a suitable metabolic index. Metabolic maps are compared to highlight the effects of soil and farming strategies.

NMR Tracing of Food Geographical Origin: The Impact of Seasonality, Cultivar and Production Year on Data Analysis

The traceability of typical foodstuffs is necessary to protect high quality of traditional products. It is well-known that several factors could influence metabolites content in certified foods, but soil composition, altitude, latitude and coded production protocols constitute the territorial conditions responsible for the peculiar organoleptic and nutritional properties of labelled foods. Instead, regardless of origin, seasonality, cultivar, collection year can affect all agricultural products, so it is appropriate to include them in data analysis in order to obtain a correct interpretation of the differences linked to growing areas alone. Therefore, it is useful to use a flexible all-round technique, and NMR spectroscopy coupled with multivariate statistical analysis is considered a powerful means of assessing food authenticity. The purpose of this review is to investigate the relevance of year, cultivar, and seasonal period in the determination of food geographical origin using NMR spectroscopy. The strategy for testing these three factors may differ from author to author, but a preliminary study of cultivar or collection year effects on NMR spectra is the most popular method before starting the geographical characterization of samples. In summary, based on the available literature, the most significant influence is due to cultivar, followed by harvesting year, however seasonality is not considered a source of variability in data analysis.

An energy dispersive X-ray fluorescence spectrometry approach for the identification of geographical origin of wheat flour

In this work, a method was established for discriminating geographical origins of wheat flour based on energy dispersive X-ray fluorescence spectrometry (ED-XRF) and chemometrics. 68 wheat flour samples from three different origins were collected and analyzed using ED-XRF technology. Firstly, the principal component analysis method was applied to analyze the feasibility of discrimination and reduce data dimensionality. Then, Competitive Adaptive Reweighted Sampling (CARS) was used to further extract feature variables, and 12 energy variables (corresponding to mineral elements) were identified and selected to characterize the geographical attributes of wheat flour samples. Finally, a non-linear model was constructed using principal component analysis and quadratic discriminant analysis (QDA). The CARS-PCA-QDA model showed that the accuracy of five-fold cross-validation was 84.25%. The results showed that the established method was able to select important energy channel variables effectively and wheat flour could be classified based on geographical origins with chemometrics, which could provide a theoretical basis for unveiling the relationship between mineral element composition and wheat origin.

Nontargeted 1 H NMR fingerprinting and multivariate statistical analysis for traceability of Greek PDO Vostizza currants

In this study, non-targeted ¹ H NMR fingerprinting was used in combination with multivariate statistical analyses for the classification of Greek currants based on their geographical origins (Aeghion, Nemea, Kalamata, Zante, and Amaliada). As classification techniques, Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were carried out. To elucidate different components according to PDO (Protected Designation of Origin), products from Aeghion (Vostizza) were statistically compared with each one of the four other regions. PLS-DA plots ensure that currants from Kalamata, Nemea, Zante, and Amaliada are well classified with respect to the PDO currants, according to differences observed in metabolites. Results suggest that composition differences in carbohydrates, amino, and organic acids of currants are sufficient to discriminate them in correlation to their geographical origin. In conclusion, currants metabolites which mostly contribute to classification performance of such discriminant analysis model present a suitable alternative technique for currants traceability. The study results contribute information to the currants' metabolite fingerprinting by NMR spectroscopy and their geographical origin. PRACTICAL APPLICATION: This study presents an analytical approach for a high nutritional value Greek PDO product, Vostizza currant. A further research and implementation of this method in food industry, can be the key to food fraud incidents. Thus, application of this work opens up posibilities to "farm to table" mission.

Wheat authentication：An overview on different techniques and chemometric methods

Wheat (Triticum aestivum L.) is one of the most important cereal crops and is consumed as a staple food around the globe. Wheat authentication has become a crucial issue over the last decades. Recently, many techniques have been applied in wheat authentication including the authentication of wheat geographical origin, wheat variety, organic wheat, and wheat flour from other cereals. This paper collected related literature in the last ten years, and attempted to highlight the recent studies on the discrimination and authentication of wheat using different determination techniques and chemometric methods. The stable isotope analysis and elemental profile of wheat are promising tools to obtain information regarding the origin, and variety, and to differentiate organic from conventional farming of wheat. Image analysis, genetic parameters, and omics analysis can provide solutions for wheat variety, organic wheat, and wheat adulteration. Vibrational spectroscopy analyses, such as NIR, FTIR, and HIS, in combination with multivariate data analysis methods, such as PCA, LDA, and PLS-DA, show great potential in wheat authenticity and offer many advantages such as user-friendly, cost-effective, time-saving, and environment friendly. In conclusion, analytical techniques combining with appropriate multivariate analysis are very effective to discriminate geographical origin, cultivar classification, and adulterant detection of wheat.

Relaxometric learning: a pattern recognition method for T2 relaxation curves based on machine learning supported by an analytical framework

Nuclear magnetic resonance (NMR)-based relaxometry is widely used in various fields of research because of its advantages such as simple sample preparation, easy handling, and relatively low cost compared with metabolomics approaches. However, there have been no reports on the application of the T₂ relaxation curves in metabolomics studies involving the evaluation of metabolic mixtures, such as geographical origin determination and feature extraction by pattern recognition and data mining. In this study, we describe a data mining method for relaxometric data (i.e., relaxometric learning). This method is based on a machine learning algorithm supported by the analytical framework optimized for the relaxation curve analyses. In the analytical framework, we incorporated a variable optimization approach and bootstrap resampling-based matrixing to enhance the classification performance and balance the sample size between groups, respectively. The relaxometric learning enabled the extraction of features related to the physical properties of fish muscle and the determination of the geographical origin of the fish by improving the classification performance. Our results suggest that relaxometric learning is a powerful and versatile alternative to conventional metabolomics approaches for evaluating fleshiness of chemical mixtures in food and for other biological and chemical research requiring a nondestructive, cost-effective, and time-saving method.

Discrimination of the quality of Brazilian wheat genotypes and their use as whole-grains in human nutrition

Thirteen wheat genotypes were grown in four regions in Brazil: Cachoeira do Sul (CDS), Santo Augusto (STA), São Gabriel (SAG), and Vacaria (VAC). The principal component analysis explained between 88.3% and 99% of the growing location separation, depending on genotype. Among the 13 genotypes analyzed, TW ranged from 73.75 to 79.83 kg/hL. Final viscosity, gluten strength, extensibility, and falling number exhibited the highest influence on growing region discrimination. STA wheat was selected for the second stage of the study to evaluate the quality of cooked grains. The genotype TBIO Toruk had the longest cooking time of 36.92 min. For most of the studied genotypes, the yellowness reduced after cooking. Damaged grains exhibited values varying from 3.6% to 25.29%. At the end of in vitro digestion, the genotypes were divided into two groups: five genotypes had digestibility in the range of 51.93-58.13%, and eight presented 72.74-84.54% of starch hydrolysis.

Metabolomics Provides Valuable Insight for the Study of Durum Wheat: A Review

Metabolomics is increasingly being applied in various fields offering a highly informative tool for high-throughput diagnostics. However, in plant sciences, metabolomics is underused, even though plant studies are relatively easy and cheap when compared to those on humans and animals. Despite their importance for human nutrition, cereals, and especially wheat, remain understudied from a metabolomics point of view. The metabolomics of durum wheat has been essentially neglected, although its genetic structure allows the inference of common mechanisms that can be extended to other wheat and cereal species. This review covers the present achievements in durum wheat metabolomics highlighting the connections with the metabolomics of other cereal species (especially bread wheat). We discuss the metabolomics data from various studies and their relationships to other "-omics" sciences, in terms of wheat genetics, abiotic and biotic stresses, beneficial microbes, and the characterization and use of durum wheat as feed, food, and food ingredient.

Nuclear Magnetic Resonance (NMR) Spectroscopy in Food Science: A Comprehensive Review

Nuclear magnetic resonance (NMR) spectroscopy is a robust method, which can rapidly analyze mixtures at the molecular level without requiring separation and/or purification steps, making it ideal for applications in food science. Despite its increasing popularity among food scientists, NMR is still an underutilized methodology in this area, mainly due to its high cost, relatively low sensitivity, and the lack of NMR expertise by many food scientists. The aim of this review is to help bridge the knowledge gap that may exist when attempting to apply NMR methodologies to the field of food science. We begin by covering the basic principles required to apply NMR to the study of foods and nutrients. A description of the discipline of chemometrics is provided, as the combination of NMR with multivariate statistical analysis is a powerful approach for addressing modern challenges in food science. Furthermore, a comprehensive overview of recent and key applications in the areas of compositional analysis, food authentication, quality control, and human nutrition is provided. In addition to standard NMR techniques, more sophisticated NMR applications are also presented, although limitations, gaps, and potentials are discussed. We hope this review will help scientists gain some of the knowledge required to apply the powerful methodology of NMR to the rich and diverse field of food science.

Environmental metabolomics with data science for investigating ecosystem homeostasis

A natural ecosystem can be viewed as the interconnections between complex metabolic reactions and environments. Humans, a part of these ecosystems, and their activities strongly affect the environments. To account for human effects within ecosystems, understanding what benefits humans receive by facilitating the maintenance of environmental homeostasis is important. This review describes recent applications of several NMR approaches to the evaluation of environmental homeostasis by metabolic profiling and data science. The basic NMR strategy used to evaluate homeostasis using big data collection is similar to that used in human health studies. Sophisticated metabolomic approaches (metabolic profiling) are widely reported in the literature. Further challenges include the analysis of complex macromolecular structures, and of the compositions and interactions of plant biomass, soil humic substances, and aqueous particulate organic matter. To support the study of these topics, we also discuss sample preparation techniques and solid-state NMR approaches. Because NMR approaches can produce a number of data with high reproducibility and inter-institution compatibility, further analysis of such data using machine learning approaches is often worthwhile. We also describe methods for data pretreatment in solid-state NMR and for environmental feature extraction from heterogeneously-measured spectroscopic data by machine learning approaches.

Territorial origin of olive oil: representing georeferenced maps of olive oils by NMR profiling

Proton NMR profiling is nowadays a consolidated technique for the identification of geographical origin of food samples. The common approach consists in correlating NMR spectra of food samples to their territorial origin by multivariate classification statistical algorithms. In the present work, we illustrate an alternative perspective to exploit territorial information, contained in the NMR spectra, which is based on the implementation of a geographic information system (GIS). Nuclear magnetic resonance spectra are used to build a GIS map permitting the identification of territorial regions having strong similarities in the chemical content of the produced food (terroir units). These terroir units can, in turn, be used as input for labeling samples to be analyzed by traditional classification methods. In this work, we describe the methods and the algorithms that permit to produce GIS maps from NMR profiles and apply the described method to the analysis of the geographical distribution of olive oils in an Italian region. In particular, we analyzed by ¹ H NMR up to 98 georeferenced olive oil samples produced in the Abruzzo Italian region. By using the first principal component of the NMR variables selected according to the Moran test, we produced a GIS map, in which we identified two regions incidentally corresponding to the provinces of Teramo and Pescara. We then labeled the samples according to the province of provenience and built an LDA model that provides a classification ability up to 99% . A comparison between the variables selected in the geostatistics and classification steps is finally performed. Copyright © 2016 John Wiley & Sons, Ltd.

Multi-isotopic signatures of organic and conventional Italian pasta along the production chain

The variability of stable isotope ratios (δ(2) H, δ(13) C, δ(15) N, δ(18) O and δ(34) S) along the production chain of pasta (durum wheat, flour and pasta) produced by using both conventional and organic farming systems in four Italian regions in 2 years was investigated. The aim was to evaluate if and how the farming system and geographical origin affect stable isotope ratios determined along the production chain. Irrespective of the processing technology, 65% of the samples were correctly classified according to the farming system and 98% were correctly classified regarding the geographical region. When considering both farming system and geographical region simultaneously, 80% of the samples were correctly classified. The measured isotope parameters were thus primarily affected by the geographical origin. In conclusion, it is expected that the use of these parameters will allow the development of analytical control procedures that can be used to check the geographical origin of Italian organic and conventional pasta and its raw materials. Copyright © 2016 John Wiley & Sons, Ltd.

Structural and Nutritional Properties of Pasta from Triticum monococcum and Triticum durum Species. A Combined ¹H NMR, MRI, and Digestibility Study

The aim of the present study was to characterize the structure of two different types of pasta, namely Triticum turgidum ssp. durum (cv. Saragolla) and Triticum monococcum ssp. monococcum (cv. Monlis), under different processing conditions. MRI analysis and NMR spectroscopy (i.e., T1 and T2 NMR relaxation times and diffusion parameters) were conducted on pasta, and (1)H NMR spectroscopic analysis of the chemical compounds released by pasta samples during the cooking process was performed. In addition, starch digestibility (enzimatically determined) was also investigated. The NMR results indicated that Saragolla pasta has a more compact structure, ascribed to pasta network and in particular to different technological gluten properties, that mainly determine the lower ability of Monlis pasta in binding water. These results correlate well with the lower rate of starch hydrolysis measured for Monlis pasta compared to Saragolla when both are dried at high temperature.

Comprehensive multiphase NMR spectroscopy of intact ¹³C-labeled seeds

Seeds are complex entities composed of liquids, gels, and solids. NMR spectroscopy is a powerful tool for studying molecular structure but has evolved into two fields, solution and solid state. Comprehensive multiphase (CMP) NMR spectroscopy is capable of liquid-, gel-, and solid-state experiments for studying intact samples where all organic components are studied and differentiated in situ. Herein, intact (13)C-labeled seeds were studied by a variety of 1D/2D (1)H/(13)C experiments. In the mobile phase, an assortment of metabolites in a single (13)C-labeled wheat seed were identified; the gel phase was dominated by triacylglycerides; the semisolid phase was composed largely of carbohydrate biopolymers, and the solid phase was greatly influenced by starchy endosperm signals. Subsequently, the seeds were compared and relative similarities and differences between seed types discussed. This study represents the first application of CMP-NMR to food chemistry and demonstrates its general utility and feasibility for studying intact heterogeneous samples.

Identification of milk mixtures by 1H NMR profiling

Nuclear magnetic resonance profiling, combined with a single-layer artificial neural network, is used for the evaluation of the content of mixtures of different kinds of milk. In particular, aqueous fractions of cow and sheep milk mixtures are analyzed by (1) H NMR. The spectral differences are highlighted by an analysis of the variance and a principal component analysis. The species classification problem is solved by a linear discriminant analysis. The quantification of the relative amount of the milk of two different species is then achieved by solving the appropriate multilinear problem.

Nuclear magnetic resonance profiling of wine blends

Nuclear magnetic resonance (NMR) profiling is used for characterization of monocultivar binary wine mixtures. Classification and quantification of the relative amount of wine in the mixture are made in two steps. First, each sample is classified as a mixture of a determined type by solving the appropriate classification problem using NMR profiles. The relative amount of the two corresponding monovarietal wines is then evaluated by multilinear regression of a selected set of NMR variables. Linear discriminant analysis (LDA), used in the classification step, gives a very good separation among the different mixture classes. On the other hand, a single layer artificial neural network, used to solve the multilinear problem, gives the relative amount of wine type in the mixture with a precision of about 10%.