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Review

Techniques and Methods for Fatty Acid Analysis in Lipidomics: Exploring Pinus cembroides Kernels as a Sustainable Food Resource

by
Luis Ricardo León-Herrera
1,*,
Luis Miguel Contreras-Medina
2,*,
Ana Angélica Feregrino-Pérez
3,
Christopher Cedillo
2,
Genaro Martín Soto-Zarazúa
3,
Miguel Angel Ramos-López
4,
Samuel Tejeda
5,
Eduardo Amador-Enríquez
6 and
Enrique Montoya-Morado
1
1
Faculty of Engineering, Campus Pinal de Amoles, Autonomous University of Queretaro, Calle de los Cultivos S/N, Fracción III, Predio “El Potrero”, Barrio “La Loma”, Pinal de Amoles 76300, Mexico
2
Center for Applied Biosystems Research, Universidad Autónoma de Querétaro, Cerro de las Campanas S/N, Queretaro 76010, Mexico
3
Faculty of Engineering, Campus Amazcala, Autonomus University of Queretaro, Carretera Chichimequillas, km 1 S/N, El Marques 76275, Mexico
4
Faculty of Chemistry, Autonomous University of Queretaro, Centro Universitario S/N, Colonia Las Campanas, Querétaro 76010, Mexico
5
Department of Environmental Studies, National Institute of Nuclear Research, Carretera Mexico-Toluca S/N, Ocoyoacac 52750, Mexico
6
Faculty of Engineering, Campus Conca, Autonomous University of Queretaro, Valle Agrícola S/N, Concá, Arroyo Seco 76410, Mexico
*
Authors to whom correspondence should be addressed.
Separations 2025, 12(2), 41; https://doi.org/10.3390/separations12020041
Submission received: 21 December 2024 / Revised: 1 February 2025 / Accepted: 4 February 2025 / Published: 6 February 2025
(This article belongs to the Special Issue Extraction and Analysis of Chemical Composition from Natural Products and Metabolites)
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Abstract

:
The large-scale conversion of forests to agriculture has caused biodiversity loss, climate change, and disrupted dietary fatty acid balances, with adverse public health effects. Wild edibles like pine nuts, especially Pinus cembroides, provide sustainable solutions by supporting ecosystems and offering economic value. However, variability in seed quality limits market potential, and lipidomic studies on P. cembroides remain sparse. This paper underscores the ecological, social, and nutritional value of P. cembroides while advocating for advanced research to enhance its use as a non-timber forest resource in Mexico’s communal areas. It explores various analytical techniques, such as nuclear magnetic resonances (NMR), chromatography coupled with mass spectrometry (HPLC-MS, GC-MS) and GC coupled with flame ionization detector (GC-FID), highlighting extraction methods like derivatization, purification, and thin-layer chromatography. Likewise, some considerations are addressed for the treatment of data obtained in the detection of fatty acids from bioformatics and the evaluation of the data through statistical methods and artificial intelligence and deep learning. These approaches aim to improve fatty acid profiling and seed quality assessments, fostering the species economic viability and supporting sustainable livelihoods in rural communities, encouraging researchers across the country to explore the fatty acid composition of different P. cembroides populations can drive valuable insights into its nutritional and ecological significance. Such efforts can enhance understanding of regional variations, promote sustainable use, and elevate the specie’s economic and scientific value.

1. Introduction

The conversion of forested land for agriculture, particularly for monoculture crops and livestock farming, has been one of the major’s drivers of global warming, biodiversity loss, and increased greenhouse gas emissions. Since 1990, approximately 420 million hectares of forests have been deforested or degraded worldwide due to these practices [1,2]. Agriculture has traditionally depended on crops from the Poaceae family, which form the foundation of the human diet [3]. However, modern lifestyle changes, such as increased food consumption and shifting dietary patterns, have disrupted the balance of essential fatty acid intake, adversely affecting cardiovascular health [3,4].
Given these ecological and public health challenges, there is growing interest in alternative food sources like wild edible plants [5]. Pine nuts, for example, offer a promising solution by promoting sustainable food systems and healthier dietary choices [6]. P. cembroides Zucc., commonly known as the Mexican piñon [7,8], is endemic to Mexico and parts of the southern United States [9,10]. This species is ecologically and economically significant and inhabits arid and semi-arid mountainous regions with shallow soils [11,12]. It is vital in preventing soil erosion, enhancing water infiltration, and sequestering carbon dioxide in these fragile ecosystems. Its seeds, known as pine nuts, also serve as an important food source for wildlife and humans [13,14,15,16,17].
Pine nuts are highly valued globally for their culinary uses. Between 2015 and 2017, international production was approximately 20,000 tons, rising to 34,000 tons by 2019, reflecting growing demand [18,19]. In recent years, they have represented a market worth approximately 100 million USD, with prices in Europe ranging from 45,000 to 60,000 €/t [20]. Beyond their culinary appeal, pine nuts are celebrated for their exceptional nutraceutical properties. Their rich chemical composition—including antioxidants, fatty acids, phospholipids, tocopherols, and proteins [18,20,21,22]—makes them a valuable component in functional foods [23], being the fatty acids the principal components of these effects [24].
Given these health benefits, the detailed characterization, quantification, dynamics and interaction of fatty acids in various organs and plant tissues has become a critical area of study within lipidomic [25,26,27], a specialized subfield of omics sciences in plants [28]. The composition and concentration of fatty acids in pine nuts, including those from P. cembroides, are significantly influenced by environmental conditions, plant species or subspecies, geographic location, and origin [13,18,29,30]. As P. cembroides inhabits ecologically diverse regions with considerable environmental variability [14,31], the quality of its seeds—encompassing agronomic traits, flavor, nutrient content, and vitamin levels [32]—varies widely across its range [13]. This variability challenges competing with other commercial seed species [20,29,33] and restricts collectors in Mexico’s access to the international market despite having good acceptance in the foreign market [34].
P. cembroides forests are primarily found in rural and marginalized forestry regions, often within communal landholdings known as “Ejidos”. These “Ejidos” represent 50% to 80% of Mexico’s forested areas [35,36]. A significant obstacle to the sustainable utilization of P. cembroides kernels is the lack of technical support and effective community organisation [37,38]. This challenge is particularly pronounced in seed quality assessments and lipidomics analyses across regional seed groups, which are crucial for generating representative data on seed quality and understanding environmental variables specific to each area.
Unfortunately, many seed collectors lack access to the advanced analyses of P. cembroides fatty acids kernels [36]. Public universities, research institutes, and specialized centres can conduct these studies, although disparities in resources and equipment remain significant challenges. This review outlines state-of-the-art lipidomics techniques for oil and fatty acid extraction and analysis, providing practical guidance for various laboratory settings. As well as the processing of data using bioinformatics and statistical analysis, as well as artificial intelligence and deep learning. By fostering collaborations and considering environmental factors, these efforts aim to increase the economic value of P. cembroides seeds, enhance market competitiveness, and promote sustainable livelihoods in local communities.

2. The Biogeographic and Physiographic Context of P. cembroides

P. cembroides, is a group of pines comprising at least 15 clades, extending from the southwestern United States to central Mexico. It is the primary pinyon pine species found in Mexico, covering around 2.5 million hectares [9,10,15,16,31,39,40], locating in the estates of Sonora, Chihuahua, Coahuila, Durango, Zacatecas, Nuevo Leon, Tamaulipas, San Luis Potosi, Aguascalientes, Jalisco, Guanajuato, Queretaro, Hidalgo, Veracruz, Tlaxcala and Puebla [41]. These pines inhabit mountainous, arid, and semi-arid regions, often characterized by steep slopes, rocky terrains, poor deep soil, and pronounced inclines [13].
In astrobiology, habitability refers to an environment’s capacity to sustain one or more species. However, from an ecological perspective, habitability can also encompass evolutionary aspects, focusing on how species adapt and thrive within specific environmental conditions over time [42,43]. The ecological capacity to support Mexican pinyon is intricately tied to its biogeography. These species occupy a transition zone between the Nearctic and Neotropical biogeographical realms, between xeric and relatively mesic forest communities at higher elevations [12,31,44,45], with their distribution beginning in the Oaxaca-Tehuacan (OAX-TEH) and Gulf physiographic provinces. The highest concentration occurs in the Trans-Mexican Volcanic Belt (TMVB), extending through the Zacatecan (ZAC), Eastern Sierra Madre (ESM), Western Sierra Madre (WSM), and Coahuilan (COA) provinces. Populations also spread further north into the Chihuahuan (CHI) and Sonoran (SON) provinces, reaching into the southern United States [31,46].
The physiographic conditions of the Mexican pinyon habitat contribute to the formation of distinct wind patterns and irregular precipitation, with annual rainfall ranging from 350 to 700 mm in temperate dry to temperate sub-humid zones. These pines thrive at altitudes between 1350 and 2800 m [10,44], occupying various ecotones and coexisting with diverse biodiversity, often in Juniperus, Quercus, and Pinus communities. [16,31,47]. Figure 1 illustrates the distribution of P. cembroides, highlighting its biogeographical and physiographic context.
The diverse environmental conditions across Mexico significantly impact the methods for analyzing and monitoring P. cembroides. These include assessing specific ecological factors and biochemical pathways involved in fatty acid production. Variability in growth conditions leads to differences in nutrient composition and fatty acid profiles across the distribution [12]. The following section reviews current studies, summarizing the lipidomic profiles of pine nut species, mainly focusing on P. cembroides kernels and emphasizing the characterization of their fatty acid content.

3. Chemical and Fatty Acids Composition of Pine Nuts and P. cembroides Kernels

An alternative to replacing animal-derived products is the development of plant-based sources rich in proteins, lipids, and phenolic compounds, which are widely used in the pharmaceutical, cosmetic, and food industries. Pine nuts present a viable option in this regard [19,49]. These edible seeds, encased within pine cones, are essentially tree embryos protected by a hard shell known as a sclerotesta [50]. Pine nuts are universally popular as snacks, key ingredients in various processed foods [51], and essential components of Indigenous culinary traditions [7]. Table 1 shows the different nutritional compositions of pine nuts and their locations worldwide.
Pine nuts are well-known for their antioxidant properties, which are attributed to their high phenolic compound content. Additionally, consuming pine almonds is associated with a reduced risk of cardiovascular diseases due to their cardioprotective properties, driven by a high concentration of unsaturated fatty acids [13,18,33]. The monounsaturated and polyunsaturated fatty acids (MUFA and PUFA) found in pine nuts are particularly noteworthy for significantly reducing the risk of coronary heart disease. Fatty acids also perform a range of critical biological functions, such as acting as essential components of cell membranes, serving as a primary energy source, and regulating enzyme activity and inflammatory responses [13,18,33,55,56,57]. Pine nut’s nutritional benefits and commercial value are closely linked to their high fatty acid content, which also plays a crucial role in the seed’s germination process [29].
Fatty acids, a fundamental class of lipids, are categorized into four groups [55,58,59] based on the length of their aliphatic carbon chains: short (C2–C6), medium (C6–C12), long (C12–C18), and very long (C18+). This classification is helpful because fatty acid molecules consist primarily of two parts: an aliphatic chain and a carboxyl group [29]. Fatty acids are fatty acyl characterized by repeating methylene groups formed sequentially by adding malonyl-CoA or methyl malonyl-CoA to an acetyl-CoA primer. This chain elongation process is crucial for fatty acid synthesis, determining the chain length and degree of saturation [60].
The structural diversity of fatty acids arises from variations in the number and positions of double bonds within their aliphatic chains, leading to a wide range of isomers, including geometric and structural variants [61]. Identifying the precise positions of C=C bonds remains a critical challenge in lipidomics [62].
Common fatty acids found in pine almonds include linoleic and oleic acids, which are unsaturated fatty acids, and palmitic, stearic, and lignoceric acids, which are saturated fatty acids [30,49]. Figure 2 shows the chemical structure of three unsaturated fatty acids commonly found in pine nuts and P. cembroides kernels.
Fatty acids commonly found in edible pine nuts and P. cembroides kernels (Figure 2).
Pine nuts are typically beige or white; however, the kernels of P. cembroides species can also exhibit a pink almond [63]. The chemical composition of almonds varies significantly depending on the species or variety, their geographical distribution, and the environmental conditions in which they grow [13,18,25,26].
For example, principal component analysis (PCA) demonstrated that similar fatty FA profiles cluster various Pinus taxa into subsections, highlighting distinct activities of ∆ 5 and ∆ 9 desaturase enzymes. Additionally, PNLA was identified in the seed’s oils of twenty-one Pinus taxa [64]. Lipid content varies between 23% and 75% across pine nut species, with P. cembroides, P. edulis, P. sibirica, and P. koraiensis exhibiting higher concentrations [22]. In contrast, P. maximartinezii (subsection Cembroides) has a lower fat content (42%) compared to P. cembroides (65%). Its FA composition includes palmitic (8.74%), stearic (3.97%), oleic (31.35%), linoleic (52.27%), arachidic (0.47%), behenic (0.40%), and >C20 fatty acids (2.80%) [54].
In contrast, three distinct phenotypes of P. cembroides studied, which showed variations in fatty acid composition within the following ranges: lauric acid (0–4.8%), myristic acid (3.4–9.1%), palmitic acid (6.0–7.8%), stearic acid (3.1–5.5%), oleic acid (36.7–47.2%), and linoleic acid (32.9–44.5%). Statistically significant differences are evident among the phenotypes in the concentrations of myristic, oleic, and linoleic acids [65].
This contextualization provides a general overview of the fatty acid complexity of pine nuts and P. cembroides almonds. Below are some techniques for extracting oils and characterizing fatty acids in pine kernels and P. cembroides kernels. Table 2 shows the different types of pine nuts and the contents of their fatty acids.

4. Oils Extraction and Fatty Acids Analyses in Pine Nuts and P. cembroides spp. Kernel

The metabolome of cells or tissues includes a wide array of molecules, such as sugars, organic acids, amino acids, and lipids [62,71]. Lipidomics, a specialised branch of omics sciences, focuses on the large-scale profiling and quantification of biogenic lipids to study their biology and metabolism. Lipids are generally defined as amphiphilic organic molecules poorly soluble in water but readily dissolve in organic solvents. This dual nature enables them to play essential roles in biological systems, including forming membranes and storing energy [60]. A critical aspect of lipidomics is the characterisation of fatty acids. This process typically involves five key steps: (1) fatty acid extraction, (2) sample processing and separation techniques, (3) spectroscopy data acquisition, (4) post-acquisition data processing, and (5) clarification of physiological significance using bioinformatics or computational tools to analyse lipid pathways [28,61,62].
This review focuses on the first three steps in the Section 4.1 and Section 4.2, while the latter two are included in the Section 4.3. Notably, multiple studies have analysed fatty acids in pine nuts, with species such as P. koraiensis, P. pinea, P. gerardiana and P. sibirica receiving the most attention [64]. However, further investigation is needed into the fatty acid composition of P. cembroides and its subspecies.

4.1. Fatty Acids Extraction in Pine Nuts and P. Cembroides spp. Kernel

Lipids are typically embedded within a complex matrix rather than in a free state. Therefore, lipid extraction processes are designed to simplify this matrix, enhancing the efficiency and accuracy of spectrometry methods for lipid detection [62]. Before extraction, a pre-treatment step is carried out. This step involves drying the kernel using methods such as air-drying [18], oven-drying [29], industrial drying [51], cold pressed [22] or lyophilization which is a standard method used for stabilizing biological samples, followed by grinding and homogenisation to prepare the sample for subsequent analysis [29].
Lipidomics and metabolomics differ primarily in the types of molecules they target. Lipidomics focuses on lipophilic molecules, while metabolomics is centred on hydrophilic compounds. Fatty acids are the simplest lipids and can be extracted using various methods, including liquid-liquid extraction (LLE), Soxhlet extraction, and vapour distillation. These methods often employ non-polar solvents such as chloroform, methanol, hexane, toluene, or methyl-ter-butyl ether [28], with or without mechanical assistance (e.g., vertexing, microwaving, or using ultrasound) [60]. Table 2 highlights various solvents used in different pine nut species for fatty acid extraction.
The three most universally applied lipid extraction techniques are LLE, single organic solvent extraction (SOSE), and solid-phase extraction (SPE), which are widely regarded as traditional methods. These include processes like cold-press extraction. However, in food science, more advanced techniques have also been employed to extract fatty acids, such as microwave-assisted extraction (MAE), ultrasonic-assisted extraction (UAE), and supercritical fluid extraction (SFE) [55].
In addition to traditional methods, several green extraction techniques have emerged for nuts, including bio-based and ionic liquid solvents, subcritical fluid extraction, and enzyme-assisted methods for obtaining nut oil [72]. One noteworthy example is homogenate-circulating ultrasound combined with aqueous enzymatic extraction, which has been applied to P. pumila kernels. This method is efficient, environmentally friendly, and effective for analysing edible oils and quantifying unsaturated fatty acids, demonstrating its potential for sustainable oil extraction and fatty acid profiling [73].
In pine nuts from P. sibirica and P. koraiensis, glyceride oil extraction has been commonly performed using Soxhlet extraction with n-hexane [18]. Other studies have employed n-hexane-isopropanol (3:2) as a solvent for oil extraction in P. pinea [29]. For P. cembroides, Soxhlet extraction with ethyl ether over six hours has been utilized to determine lipid content [13]. The sample size varies, with some studies using 10 g from 20 ± 5 g of powdered seeds [74].

4.2. Processing, Separation and Detection Techniques of Fatty Acids in Pine Nuts and P. cembroides Kernels

After extraction, further steps such as vertexing, centrifugation, and supernatant collection are often required. Concentrated extracts are typically analyzed using instrumental techniques. Depending on the targets, samples may undergo additional processing, such as derivatization through transesterification to fatty acid methyl esters (FAMEs) or fatty acid enrichment to detect trace compounds [62].
There is a lack of standardized protocols for sample preparation in lipidomics, which remains a critical challenge. Comparative studies evaluating different isolation techniques are necessary. These studies provide insights into optimizing lipid extraction and establish criteria for consistency and reproducibility [60]. The choice of processing and separation methods for fatty acid analysis depends on the available spectral equipment analysis and separation techniques. In lipidomics analysis, the extracts obtained are customised to the research target before the extracted shotgun is sent to the spectral detector or technique separation. This involves solid-phase extraction, acid/alkaline hydrolysis, derivatisation, and purification using thin-layer chromatography (TLC) and liquid chromatography (LC). Advanced methods such as high-performance (HPLC), ultra-high-performance liquid chromatography (UHPLC), supercritical fluid chromatography (SFC), and gas chromatography (GC) have also been developed [26,28,75]. Therefore, the principles on which some fatty acid processing and separation techniques are based and their advantages and disadvantages are described below.

4.2.1. Derivatization

Derivatization enhances analysis by improving the molecule’s physicochemical properties, facilitating better electrospray ionization (ESI) efficiency. This leads to increased sensitivity and more reliable detection of target compounds [76], is essential for fatty acid analysis using GC-MS and LC-MS, particularly for long-chain fatty acids (over ten carbons). Fatty acids are commonly converted to fatty acid methyl esters (FAMEs) for detection. Methods for derivatization include acid—and base-catalyzed approaches and agents like trimethyl sulfonium hydroxide, pentafluorobenzyl bromide, or BF3. The choice depends on the analysis goals; for example, acid derivatization works for free and esterified fatty acids, while base derivatization is limited to esterified fatty acids [61,77].
Research on fatty acids in P. cembroides seeds has been limited and primarily conducted in the 1990s. For instance, Sagrero [65] employed the Mason and Waller method, using 2,2-dimethoxypropane for transesterification at low temperatures [78]. Conversely, Wolff and Marpeau [66] applied the Morrison and Smith technique, utilizing boron fluoride-methanol for methanolysis, benefiting from its strong electronegativity, stability, and ease of handling [79]. In 2001, fatty acid derivatization in P. maximartinezzi seeds followed the AOAC method, where esterification was catalyzed using either acidic or basic [54].
Although there is little research regarding P. cembroides kernels for fatty acid analysis, a large number of derivatization methods have now emerged, among which are: Paternò–Büchi (PB) reaction, ozone-induced dissociation (OzID), ultraviolet photodissociation (UVPD), and epoxidation reaction [80].

4.2.2. TLC

These techniques utilize preparative column chromatography to separate lipid components, visualized as clear spots 1.5–2 cm above the plate’s base. The plate develops in a solvent chamber, where capillary action separates components based on polarity and interactions with the adsorbent [77]. However, TLC suffers from low separation efficiency and sensitivity, requiring larger sample sizes and limiting its utility for small biological samples. While coupling TLC with MS, particularly matrix-assisted laser desorption/ionization coupled mass spectrometry (MALDI-MS), can enhance sensitivity and provide rapid, cost-effective lipid analysis, compatibility issues and resolution challenges remain significant drawbacks [62]. TLC has been applied to separate and confirms the presence of α -linoleic acid in Linum usitatissimum seeds, techniques which could be applied to P. cembroides fatty acids targeted research [81].

4.2.3. LC and GC

Liquid chromatography (LC) separates lipids based on their physicochemical properties, such as polar head group, chain length, and degree of unsaturation. Reverse-phase LC, commonly used in lipidomics, separates lipids by hydrophobicity with standard C8, C18, and C30 columns. Techniques like HPLC and UHPLC provide excellent resolution and speed, while specialized methods like Ag-LC and chiral LC improve the separation of isomers. Advances in UHPLC enhance sensitivity and throughput. Non-aqueous capillary electrophoresis and supercritical fluid chromatography further extend LC’s versatility for lipidomic analysis [62,75,77]. On the other hand, GC is a highly efficient method for separating volatile compounds, with its accuracy enhanced when paired with mass spectrometry (MS). Key factors like column selection are crucial, with high-polarity columns (e.g., HP-88, DB-FFAP) used for fatty acids of varying chain lengths and ionic liquid columns ideal for fatty acid isomers and methyl esters (FAMEs). Advanced multidimensional GC techniques, such as GC×GC, improve resolution, sensitivity, and analysis speed, especially for complex matrices. Coupling GC with detectors like MS or FID enables precise fatty acid profiling tailored to study goals and equipment [28,61]. Moreover, GC-IMS could also improve structural insights through ion mobilities [76]. Although LC and GC are widely used techniques for separating organic molecules, LC is principally used for analyzing vitamins and phenolic compounds. At the same time, GC is the most widely used technique for identifying and quantifying fatty acids in pine kernels [82,83], where coupling to MS and FID are the most common methods. Table 2 observes this tendency.

4.2.4. Fatty Acids Detections: FID, NMR and MS

Lipidomics studies are classified as either targeted or non-targeted. Targeted studies focus on specific lipid molecules or groups, while non-targeted (global lipidomics) aim to profile the entire lipidome [62]. Spectrometric techniques like NMR and MS dominate fatty acids detection, with MS preferred for its exceptional sensitivity, resolution, and molecular specificity, often in combination with chromatography [58,60,62]. However, GC can analyse lipids without needing MS or NMR. This is achieved using specific detectors, such as the flame ionization detector (FID). The use of GC-FID has been demonstrated to be effective in the analysis of fatty acids in walnut and biological samples [84,85]. For example, studies using GC-FID revealed that dehulled P. cembroides seeds are rich in oil, comprising 64% of their weight. The oil primarily comprises oleic acid (~47%) and linoleic acid (~41%), with only about 10% saturated fatty acids. Additionally, notable levels of ∆5-olefinic acids were identified, highlighting their unique lipid profile [66].
Lipidome analysis using NMR spectroscopy is supported by detecting the magnetic spin of nuclei such as 1H, 13C, 15N, and 31P present in lipids. NMR can accurately quantify the density, size, and particle numbers and determine their total lipid content [75]. This technique provides precise and non-destructive measurements and high sensitivity, revealing structural details in lipid molecules. Two main types of NMR are employed: high-resolution NMR (HR-NMR) and low-resolution NMR (LR-NMR). HR-NMR, which operates at frequencies above 100 MHz, delivers significantly more precise information about the molecular structures of samples, capturing multiple signals with assigned chemical shifts, coupling constants, splitting patterns, and areas [77]. Solid-state NMR is advantageous and technical for studying the structure of solid materials and provides high-resolution data with the benefit of the non-destructive technique, but it has limitations in sensitivity and dynamic studies. Liquid-state NMR of Dynamic Nuclear Polarisation excels in sensitivity and the ability to study dynamic processes but may struggle with complex structures and aggregation issues. The choice between solid-state and liquid-state NMR often depends on the specific research question and the nature of the sample being studied [86]. In targeted lipidomics of pine nuts, 1HNMR spectroscopy in the liquid state allows for the rapid and efficient determination of the ratio between ∆ 5-fatty acids ( ∆ 5-FA) and other FA in pine nut oils. Applied directly to crude oils, this method quickly quantities ∆ 5-unsafurated polyunsaturated fatty acids ( ∆ 5-UPIFA) relative to total FA content, offering valuable insights into lipid composition and streamlining analysis processes. This technique is employed because linoleic acid (LA, all cis-9, 12–18:2) and PNLA can produce overlapping peaks in gas chromatography due to their similar chromatographic properties, such as comparable retention times, making it challenging to distinguish between them in the resulting chromatograms [64], similar evaluations had been previously carried out on P. simbrica and P. silvestris [87]. In vivo studies on six conifer species using 13C NMR spectroscopy provided detailed insights into major storage lipids, total seed oil content, and fatty acid composition. This non-destructive technique allows for precise lipid analysis without compromising the seed’s integrity, making it an efficient tool for evaluating conifer seed oils [88].
By the way, MS is often coupled with chromatographic techniques such as TLC, GC, and LC or supercritical fluid chromatography, direct infusion (shotgun MS) and advanced methods like mass spectrometry imaging (MSI). These integrations form the cornerstone of lipidomics, with GC-MS being particularly popular [58,77]. MS utilize various ionization techniques, such as electrospray ionization (ESI), electron ionization (EI), and desorption electrospray ionization (DESI), as well as methods like matrix-assisted laser desorption/ionization (MALDI). Each ionization principle offers unique capabilities for analyzing different types of compounds, enabling comprehensive detection and characterization of molecules in lipidomics and metabolomics studies [89]. MS offers exceptional sensitivity and precision in lipid analysis by measuring mass-to-charge ratios (m/z). This method separates ions based on their size, shape, and charge under an electric field in the gas phase, allowing the distinction of isomers with identical mass but differing spatial configurations. MS achieves picomolar detection limits, which can be enhanced further with derivatization techniques, making it a powerful tool for detailed lipid characterization [60,75,76]. Fatty acids analysis in P. halepensis, P. pinea, P. pinaster, and P. canariensis performed by GC-MS, revealed that the major unsaturated are linoleic acid (30–59%) and oleic acid (17.4–34.6%). Palmitic acid (5–29%) was identified as the predominant saturated fatty acid [49].
Moreover, tandem mass spectrometry (MS/MS) offers detailed structural data but has limitations in resolving certain features like fatty acyl positions or double bond geometry. Techniques like ion mobility spectrometry (IMS) complement MS, offering separation of isomers and enhanced structural insights through ion mobilities (K0) and collision cross-section (CCS) calculations [76]. Although these approaches have been successfully applied to biological samples, their development and application in pine nuts, particularly P. cembroides seeds, present a significant opportunity for further research and innovation.

4.3. Post-Acquisition Data Processing, Bioinformatics and Computational Tools to Analyse Lipid Pathways of Fatty Acids in Pine Nuts and P. cembroides Kernels

Interpreting lipidomics is complex due to the dynamic nature of metabolic products across various reactions and cellular contexts. Targeted and non-targeted approaches differ in data acquisition methods and processing workflows. Techniques like Data-Dependent Acquisition (DDA), Information-Dependent Acquisition (IDA), and Data-Independent Acquisition (DIA) enable rapid, detailed analyses. Advanced data processing, bioinformatics tools, and statistical analyses are essential for converting large datasets into meaningful metabolic maps, identifying altered metabolites, and uncovering biological pathways. These efforts provide deeper insights into lipid functions and metabolic processes [28].
Lipidomic data analysis involves three key stages: raw data processing, statistical analysis, and visualization [90]. Pre-processing includes noise reduction, retention time correction, peak detection, integration, and chromatographic alignment [91,92]. Molecule quantification in MS often relies on precursor spectra or MS2 fragments, with advanced MSn providing detailed identification. Data is stored in vendor-specific formats, including chromatographic retention times, drift times, NMR spectra [93], and metadata. Standardized formats (e.g., CSV, mzXML, mzTab-M) and open-source tools ensure reproducibility, collaborative development, and accessibility. For further details, see Hoffmann et al. [89].
Next, data processing is essential for quality control, ensuring samples are consistent and comparable to standards. It also identifies metabolites with high variance, which are excluded from analysis. Statistical methods such as one-way and multiple analysis of variance (ANOVA), principal component analysis (PCA), non-parametric tests, partial least squares discriminant analysis (PLS), multiple regression, and cluster analysis are applied to interpret results and link metabolites to pathways. Additionally, machine learning and deep learning models, like random forests, support vector machines (SVMs), and convolutional neural networks (CNNs), enhance regression and classification tasks [28,91].
Finally, statistical and machine learning approaches used in lipidomics can integrate with other omics fields, such as metabolomics, phenomics, proteomics, transcriptomics, and genomics, providing a comprehensive research framework [28,62]. This integration is facilitated by open-access platforms like Google Colab and Jupyter Notebook, leveraging libraries such as Pandas, NumPy, scikit-learn, TensorFlow, and Keras. Programming languages like Python, C++, and JavaScript enhance data management and analysis, often relying on tensors and cloud computing for flexibility [90,94,95]. While multi-omics integration is well-studied, research on P. cembroides almonds remains underexplored, highlighting a significant research gap.
As examples of other omics approaches, Montes et al. conducted a genomic study on the phylogenetics of P. cembroides using low-copy nuclear gene sequences, offering critical insights into the relationships between P. cembroides varieties and their chemical compositions [9]. Additionally, phenomics studies focus on regional and ecological dimensions, leveraging remote sensing data from passive sensors such as Unmanageable Aerial Vehicles (UAV), satellites, such as Landsat and Sentinel and active sensors such as Light Detection and Ranging (LiDAR) [95,96]. Spectral and multispectral pixel data from images can capture phenotypes related to plants’ biochemical and physiological traits, stress levels, phenology, and overall health [97,98,99,100]. Typical RGB bands and spectral indices like the Normalized Difference Vegetation Index (NDVI), Green Chlorophyll Vegetation Index (GCVI), and Enhanced Vegetation Index (EVI) are used to analyse these characteristics [98]. These methods can also be applied as time-series analyses to track changes. In P. cembroides, such approaches have linked forest pest infestations with NDVI values [101] and mapped varieties like P. monophyla [102]. Figure 3 depicts vegetation and physiographic patterns in the Sierra Gorda Biosphere Reserve, Mexico, where these species are found. This transitional region includes four “Ejidos”: Camargo and Maguey Verde in the Peñamiller municipality, as well as El Tejamanil and Madroño in Pinal de Amoles.
Technological support for obtaining large amounts of metabolite data has been valuable in hypothesizing and analyzing the underlying molecular mechanisms, enabling a better understanding of complex gene-to-metabolite networks [103]. Lipidomics, as a subset of metabolomic analysis, is considered particularly relevant for integration into multiomics approaches to advance the understanding of Pinus systems.
Maldonado-Alconada et al. [104] studied Quercus ilex using a multiomics approach over 20 years. In their review, different methodologies were summarized alongside their contributions to identifying molecular markers for resilience and nutritional value. Transcriptional regulation of desiccation tolerance, hormone synthesis and signalling, metabolism, and oxidative stress were highlighted as factors potentially associated with seed recalcitrance. A total of 55 bioactive peptides were identified, including an angiotensin-converting enzyme inhibitor with potential antihypertensive activity. Metabolites were annotated as antimicrobial, insecticidal, allelochemical, antioxidant, antitumor, and proangiogenic. Altogether, this work has resulted in a network comprising 62,629 transcripts, 2380 protein species, and 62 metabolites, which is being further developed to understand resilience mechanisms and seed germination better.
Q. ilex is a perennial tree with renewed interest as a sustainable food source for local consumers, similar to P. cembroides. However, publicly available datasets, such as reference genomes and transcriptomes for this species, are described as “anecdotal compared with that of herbaceous models and crop species” [104]. While this presents an opportunity to contribute with new research and data, it also highlights a significant lack of information, limiting the potential for analyses based on existing published data.

5. Conclusions

The complexity and diversity of lipids in biological matrices make relying on a single analytical method impractical. Instead, complementary techniques are essential and tailored to specific research goals. Current chromatographic methods effectively separate fatty acids by class, chain length, and unsaturation degree but often fail to resolve isomers, a limitation that advanced techniques like IMS and NMR could address. Innovations such as SCF, cold-press extraction, and capillary electrophoresis demonstrate promising environmental techniques for extracting and analyzing fatty acids. Moreover, data processing and format standardization to multi-omics approaches are two essential steps to link information and analyses statically to understand the pathways in the fatty acids origin in P. cembroides kernels.
From an environmental and socio-economic perspective, promoting pine nuts as sustainable food sources can support forest conservation, biodiversity, and local economies. While P. koraiensis, P. pinea, and P. sibirica are well-studied, P. cembroides remains underexplored, presenting a significant research opportunity. Mexican pine nut studies could enhance governance, create communal jobs in ejidos, and provide a sustainable alternative to monocultures; technical advice regarding the nutritional quality of the seed is a primary requirement. Comparing P. cembroides with commercial seeds like P. koraiensis highlights its potential to effectively combat food insecurity, conserve biodiversity, and address climate change challenges.

6. Future Directions

To deepen the understanding of fatty acid origins and processes in pine nuts, the following perspectives are proposed:
  • Integration of different techniques and methods: the integration of High-Resolution Techniques and Non-Destructive techniques, as advanced tools like UHPLC, HPLC, or MDGC are employed for improved separation and resolution; non-invasive techniques, such as NMR spectroscopy, to assess fatty acid content without damaging seeds; and GC-FID to improve the resolution of different fatty acid isomers [76,105].
  • Multi-omics integration and environmental impact studies: combining lipidomics with other omics studies to understand broader metabolic pathways affecting fatty acid synthesis [62]. Investigating how environmental factors influence fatty acid profiles across Pinus species. This could be achieved through open-access platforms that analyze the territories on a regional scale [106].
  • Bioinformatics and Machine Learning: Using computational tools to analyze complex datasets and predict fatty acid compositions [26].
These perspectives may serve as a foundation for fostering cultural innovations in the sustainable management and utilization of P. cembroides within communal areas, promoting ecological and economic benefits.

Author Contributions

L.R.L.-H.: Conceptualization, Writing—original draft preparation; L.M.C.-M.: Writing—original draft preparation, Visualization, Supervision; A.A.F.-P.: Writing—original draft preparation; C.C.: Writing—review and editing; G.M.S.-Z.: Visualization, Supervision; M.A.R.-L.: Visualization, Supervision; S.T.: Writing—original draft preparation; E.A.-E.: Writing—original draft preparation; E.M.-M.: Writing—original draft preparation. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by SECRETARÍA DE DESARROLLO SUSTENTABLE (SEDESU), grant number FIN202306.

Acknowledgments

We are grateful to the Autonomous University of Queretaro for supporting our project in collaboration with SEDESU.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The biogeographic and physiographic context of P. cembroides is illustrated as follows: (a) the locations of P. cembroides are mapped based on their distribution across physiographic provinces and biogeographic realms; (b) their distribution is shown relative to altitude above sea level. These visualizations were created using QGIS 3.36.3 software, utilizing metadata from the CONABIO Geoportal and RESOLVE Ecoregions 2017 [46,48].
Figure 1. The biogeographic and physiographic context of P. cembroides is illustrated as follows: (a) the locations of P. cembroides are mapped based on their distribution across physiographic provinces and biogeographic realms; (b) their distribution is shown relative to altitude above sea level. These visualizations were created using QGIS 3.36.3 software, utilizing metadata from the CONABIO Geoportal and RESOLVE Ecoregions 2017 [46,48].
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Figure 2. This figure shows three fatty acids: a monounsaturated fatty acid, oleic acid, and two poly-unsaturated fatty acids, linoleic acid, with two double bounds, as well as pinoleic acid (PNLA), with 3 double bounds.
Figure 2. This figure shows three fatty acids: a monounsaturated fatty acid, oleic acid, and two poly-unsaturated fatty acids, linoleic acid, with two double bounds, as well as pinoleic acid (PNLA), with 3 double bounds.
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Figure 3. This figure presents various traits analyzed using physiographic features and vegetation indices. (a) Location of the piñon zone within the Sierra Gorda Biosphere Reserve; (b) RGB image showing visual colour representation; (c) Normalized Difference Vegetation Index (NDVI) image; (d) Enhanced Vegetation Index (EVI) image. (e) Green Chlorophyll Vegetation Index (GCVI) image (f) Digital Elevation Model (DEM) of the area (g) Relief map of the zone; (h) Contour lines of the region; The distinct patterns observed in these images result from the reflectance characteristics of the analyzed spectral bands. The images were generated using QGIS 3.36.3 and Google Earth Engine, utilizing harmonized Sentinel-2 imagery.
Figure 3. This figure presents various traits analyzed using physiographic features and vegetation indices. (a) Location of the piñon zone within the Sierra Gorda Biosphere Reserve; (b) RGB image showing visual colour representation; (c) Normalized Difference Vegetation Index (NDVI) image; (d) Enhanced Vegetation Index (EVI) image. (e) Green Chlorophyll Vegetation Index (GCVI) image (f) Digital Elevation Model (DEM) of the area (g) Relief map of the zone; (h) Contour lines of the region; The distinct patterns observed in these images result from the reflectance characteristics of the analyzed spectral bands. The images were generated using QGIS 3.36.3 and Google Earth Engine, utilizing harmonized Sentinel-2 imagery.
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Table 1. Percentage content of different pine nut species and locations.
Table 1. Percentage content of different pine nut species and locations.
SpeciesLocationMoisture (%) Protein (%)Fat (%)Carbohydrates (%)Ash (%)Ref.
P. pineaMediterranean Europe and Near East5 32–3445–487–145[30]
P. halepensisMediterranean Basin8273767[49]
P. pinasterWestern Mediterranean countries8162425[49,52]
P. canariensisCanary Islands91723.945[49]
P. gerardianaHimalayas of India-145123-[20]
P. edulis *Southwestern US
and Northern Mexico		-1462–7118-[20]
P. sibricaChina, Russia and Mongolia-1951–7512-[20,53]
P. monophyla *Southwestern US
and Northern Mexico		-102354-[20]
P. koraiensisAsia31564122[18,20]
P. sabinianaCalifornia (United States)-28569-[20]
P. cembraSwiss 17–1850–5917-[54]
P. cembroidesCentral and North America1516–1948–5819–323[13]
P. maximartinezii *Central and North America5314224[54]
The values presented in the table were rounded to handle whole numbers. Likewise, each reference’s analysis methods may differ, and circumstances must be considered. * Represent the species below to sub-section Cembroides.
Table 2. Methods to extract and detect fatty acids in P. cembroides and different pine nuts species.
Table 2. Methods to extract and detect fatty acids in P. cembroides and different pine nuts species.
SpeciesSolvents (Extraction)/Detection and SeparationFatty Acids (%)Reference
* P. maximartinneziiAOAC method/GC-FIDLinoleic (52), oleic (31), palmitic (9)[54]
* P. cembroides (phenotype brown)Hexane/GC-FIDLinoleic (45), oleic (37), palmitic (7)[65]
* P. cembroides (phenotype fawn)Hexane/GC-FIDLinoleic (43), oleic (42), palmitic (6)[65]
* P. cembroides (phenotype black)Hexane/GC-FIDLinoleic (33), oleic (47), palmitic (8)[65]
* P. cembroides edulisCH-Cl3-MeOH/GC-MS/LC-MSOleic (47), linoleic (41)[66]
P. cembraCH-Cl3-MeOH/GC-FIDLinoleic (45), oleic (23), pinoleic (19)[67]
P. gerardiana-Palmitic (11), oleic (52), linoleic (43)[68]
P. sibirican-hexane/GC-FIDOleic (24), linoleic (43), pinoleic (16)[18]
P. koraiensisn-hexane/GC-FIDOleic (33), linoleic (41), pinoleic (18)[18]
CH-Cl3-MeOH/GC-FIDOleic (24), linoleic (48), pinoleic (15)[69]
P. pineaAOAC method/GC-FIDPalmitic (6), oleic (39), linoleic (48)[30]
Hydro-distilled/GC-MSOleic (35), linoleic (53), palmitic (7)[49]
P. halepensisHydro-distilled/GC-MSOleic (25), linoleic (59), palmitic (5)[49]
P. pinasterHydro-distilled/GC-MSPalmitic (30), oleic (18), linoleic (52[49]
P. canariensisHydro-distilled/GC-MSLinoleic (65), oleic (17), arachirid (6)[49]
P. sylvestrisHeptane/GC-FID [70]
The values presented in the table were rounded to handle whole numbers. * Indicates which species are below P. cembroides.
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León-Herrera, L.R.; Contreras-Medina, L.M.; Feregrino-Pérez, A.A.; Cedillo, C.; Soto-Zarazúa, G.M.; Ramos-López, M.A.; Tejeda, S.; Amador-Enríquez, E.; Montoya-Morado, E. Techniques and Methods for Fatty Acid Analysis in Lipidomics: Exploring Pinus cembroides Kernels as a Sustainable Food Resource. Separations 2025, 12, 41. https://doi.org/10.3390/separations12020041

AMA Style

León-Herrera LR, Contreras-Medina LM, Feregrino-Pérez AA, Cedillo C, Soto-Zarazúa GM, Ramos-López MA, Tejeda S, Amador-Enríquez E, Montoya-Morado E. Techniques and Methods for Fatty Acid Analysis in Lipidomics: Exploring Pinus cembroides Kernels as a Sustainable Food Resource. Separations. 2025; 12(2):41. https://doi.org/10.3390/separations12020041

Chicago/Turabian Style

León-Herrera, Luis Ricardo, Luis Miguel Contreras-Medina, Ana Angélica Feregrino-Pérez, Christopher Cedillo, Genaro Martín Soto-Zarazúa, Miguel Angel Ramos-López, Samuel Tejeda, Eduardo Amador-Enríquez, and Enrique Montoya-Morado. 2025. "Techniques and Methods for Fatty Acid Analysis in Lipidomics: Exploring Pinus cembroides Kernels as a Sustainable Food Resource" Separations 12, no. 2: 41. https://doi.org/10.3390/separations12020041

APA Style

León-Herrera, L. R., Contreras-Medina, L. M., Feregrino-Pérez, A. A., Cedillo, C., Soto-Zarazúa, G. M., Ramos-López, M. A., Tejeda, S., Amador-Enríquez, E., & Montoya-Morado, E. (2025). Techniques and Methods for Fatty Acid Analysis in Lipidomics: Exploring Pinus cembroides Kernels as a Sustainable Food Resource. Separations, 12(2), 41. https://doi.org/10.3390/separations12020041

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