An overview on lipids in nuts and oily fruits: oil content, lipid composition, health effects, lipidomic fingerprinting and new biotechnological applications of their by-products

Abstract

Tree nuts and oily fruits are used as a diet complement and are highly consumed worldwide. The production and consumption of these foods have been increasing, and an enormous global market value is forecasted for 2023. Besides their high nutritional value and lipid content, they provide health benefits to fat metabolism, heart, skin, and brain. The industrial by-products of these oily foods represent promising raw materials for many industries. However, the lipidomic analysis of nuts and oily fruits is still in its early stages. State-of-the-art analytical approaches for the lipid profiling and fingerprinting of nuts and oily fruits have been developed using high-performance liquid chromatography and high-resolution mass spectrometry for the accurate identification and structural characterization at the molecular species level. It is expected to bring a new understanding of these everyday foods’ nutritional and functional value. This review comprises the oil content and lipid composition of various nuts and oily fruits, particularly those mostly consumed worldwide and having recognized beneficial health effects, biological activities associated with the lipids from different oily foodstuffs, analytical methodologies to analyze lipids in nuts and oily fruits, and the potential biotechnological applications of their industrial by-products for a lipid-based commercial valorization.

Keywords:

• Biological activity
• fatty acid
• glycolipid
• phospholipid
• sterol
• triacylglycerol

Introduction

Tree nuts and oily fruits are nutritious, tasty, fitting, and easy snacks that complement a healthy diet in the busy lifestyles of worldwide populations. They are commonly consumed as whole nuts (raw, roasted or salted) or fruits (table olives or fresh avocado). In addition, these foodstuffs are also typically consumed in the form of oil (e.g. hazelnut, walnut, and olive), flour (e.g. almond and hazelnut) and butter (e.g. walnut, peanut, and cashew). Furthermore, they are used as ingredients in various processed foods, especially in spreads, bakery, and pastry products. In 2019, the world tree nut estimated consumption reached 4.4 million tons, with almonds and walnuts representing together half of the global consumption (30% and 20%, respectively). Also, in 2019, Europe was the leading consumer (30%), followed by Asia (25%), North America (24%) and the Middle East (13%). The world production of tree nuts has progressively increased, reaching over 5.3 million tons in season 2020/2021, with almonds being were the most produced nut (ca. 31% of the global nut production), followed by walnuts (19%), pistachios (19%), cashews (16%) and hazelnuts (10%). Macadamias, pine nuts, pecan nuts and Brazil nuts represented the remaining 5% (International Nut and Dried Fruit Council 2021). The global market cap for nuts and seeds forecast for 2023 is 1422.1 trillion U.S. dollars, a considerable growth compared to 1301.4 billion U.S. dollars in 2018 (Shahbandeh 2020).

Over the last two decades, the production of oily fruits also increased significantly. The world’s avocado production increased 164.9% (Shahbandeh 2022) and table olives and olive oil’s production grew 141.7% (2.90 million tons in 2018/2019) and 35.8% (3.26 million tons in 2018/2019), respectively (International Olive Council, 2021a). The consumption of avocado, olive oil and table olives increased around 450% (USA), 29% (worldwide) and 143% (worldwide), respectively, in the same period (International Olive Council, 2021a; Buchholz 2019).

There was a perception that food sources rich in lipids, such as almonds, hazelnuts, walnuts, or olives, should not be eaten regularly due to their high-fat content. However, numerous epidemiologic and clinical studies have shown that tree nuts and oily fruits are health-promoting foods, now considered essential components of a healthy diet and key food categories in the Mediterranean diet (Widmer et al. 2015). In fact, in agreement with the Mediterranean pyramid guidelines, olives, nuts, and seeds should be consumed daily in moderate amounts, such as a handful (Bach-Faig et al. 2011). Furthermore, studies reveal an inverse relationship between the consumption of these foodstuffs and the incidence of cardiovascular diseases (CVD) and coronary heart diseases (CHD), and a positive effect on cholesterol levels, oxidative stress, inflammation, blood pressure, visceral adiposity, metabolic syndrome, insulin sensitivity, and cancer, among others (Becerra-Tomás et al. 2019; Fulgoni et al. 2013; Guasch-Ferré et al. 2014; Massaro et al. 2020; Peou et al. 2016; Rocha et al. 2020; Ros 2015). Additionally, although nuts are fat- and energy-dense foods, adding nuts to the usual diets does not result in weight gain. On the contrary, reports from large prospective cohorts reveal that the frequency of nut consumption and weight gain over time were inversely related (Ros 2015).

The health benefits of consuming oily fruits and nuts are predominantly associated with the lipid composition of these food matrices. These are rich sources of polyunsaturated fatty acids (PUFA) and or monounsaturated fatty acids (MUFA), compounds widely acclaimed for their proven positive impact on human health. C18:2n-6 and C18:3n-3, abundant PUFA in nuts and oily fruits, are essential fatty acids (FA) that the body cannot synthesize, so the diet must provide them. In addition to MUFA and PUFA, mainly esterified to triacylglycerols (TG), these matrices contain other lipids that have been associated with health benefits, such as phytosterols (sterols and stanols), tocochromanols and polar lipids (e.g. phospholipids – PL and sphingolipids – SL). Nevertheless, since most lack complete lipid profiles, information is limited regarding the bioaccessibility, bioavailability, and metabolism of tree nuts and oily fruits. Thus, further research and discussion on this topic are needed to understand the real impact of consuming these foodstuffs on human health and nutrition, gain knowledge to support their health claims and inform consumers to promote the consumption of these foodstuffs.

This review aims to provide an overview of the most representative lipids in tree nuts and oily fruits, from the chemical (oil content and lipid composition) to the methodological (lipidomic fingerprinting approaches) and biological (health effects) standpoints (Figure 1).

Figure 1. Infographic of the actual state-of-the-art on lipids and lipidomics of nuts and oily fruits.

Oily fruits and nuts

Olive

Olive is the fruit of the olive tree (Olea europaea L.), a subtropical evergreen plant indigenous to the Mediterranean Basin and the only edible species belonging to the Oleaceae family. This species is one of the oldest cultivated trees worldwide, with indications of its cultivation dating, at least, from the Early Bronze Age (3100–2000 BC). It is grown worldwide in Southern Africa, China, Japan, and Australia (International Olive Council 2021b; Liphschitz et al. 1991). The intercrosses between wild and domesticated O. europaea resulted in many different cultivars generating almost 1,000 recorded varieties among the immensity olive trees around the globe (e.g. Cobrançosa, Arbequina, Picual, and Leccino) (Al-Ruqaie et al. 2016; Luchetti 2002). The olive fruit is commonly divided into three distinct anatomical parts: peel, pulp, and seed. The pulp or mesocarp represents a significant proportion of the fruit (71.5–80.5% dry weight) (Bianchi and Vlahov 1994; Food and Agriculture Organization 1985). Olives are consumed as table olives and olive oil. Table olives must be processed before human consumption due to their bitter-solid flavor. The use of a particular olive variety for the production of table olives or olive oil is closely related to its chemical composition, oil content and size (Ghanbari et al. 2012). Some olive cultivars are more appropriate for table olives production in the existing diversity of varieties, while others are used to extract the oil, and others can serve both purposes (Ghanbari et al. 2012; Vinha et al. 2005).

Avocado

Avocado (Persea americana Mill.) is an evergreen dicotyledonous plant from the Lauraceae family. It grows in tropical or subtropical regions and has been cultivated in Central America since the pre-Columbian period. This oily fruit comprises more than 500 identified varieties (e.g. Hass, Reed, Fuerte, and Ryan), and the fruiting seasons depend on the cultivar (Yahia and Woolf 2011). As for the olive fruit, the avocado fruit is divided into the peel, pulp and seed, with the edible pulp representing most of the fruit (ca. 65%) (Gómez-López, 2002). The avocado pulp has a smooth and butter-like consistency, distinct from other fruits with acidic and or sweet tastes. There are many ways to eat avocados, and they can be added to many recipes to give meals a nutritional boost. Avocados can be eaten raw, seasoned, and stuffed in guacamole, salads, soups, smoothies, desserts, drinks, and sauces, among several others.

Nuts

Tree nuts are defined as dry fruits with commonly one seed in which the overall wall becomes hard at maturity. The most consumed tree nuts include almond (Prunus spp.), Brazil nut (Bertholletia excelsa), cashew (Anacardium occidentale), hazelnut (Corylus avellana), macadamia nut (Macadamia spp.), pecan nut (Carya illinoinensis), pine nut (Pinus spp.), pistachio (Pistacia vera), and walnut (Juglans regia). From the point of view of nutritionists and consumers, this food group also includes peanuts. Although peanuts are legumes, they present a nutrient profile like tree nuts and share beneficial health effects. Therefore, in this review, the term ‘nuts’ will comprise all standard tree nuts mentioned above, plus peanuts. Nuts can be eaten raw (dried) or roasted. Also, oil can be extracted from several nuts such as almonds, Brazil nuts, cashew nuts, hazelnuts, macadamia nuts, pine nuts, pecan nuts, pistachios, and walnuts for cooking or seasoning.

The oil content of oily fruits and nuts

Oily fruits and nuts are healthy food sources composed of lipids, water, proteins, carbohydrates, ashes, minerals, and fibers. These matrices’ chemical composition, size, and shape always vary and depend on environmental, genetic, and agronomic factors. Notwithstanding, they are excellent sources of lipids. On a dry basis, avocados have an oil content ranging from 30.3% to 69.4% (Barros et al. 2016; Corzzini et al. 2017; Reddy et al. 2012; Santana et al. 2015; Tan et al. 2017; Yanty et al. 2011) and olives between 44.3% and 86.7% (Moussaoui et al. 2008; Ninot et al. 2018; del Rio et al. 2002) (Table 1). Likewise, the lipid content in most nuts represents, on average, close to two-thirds of their dry weight (Table 1). The water content is significantly different between them, representing more than half of the fruit weight in oily fruits like olives and avocados, having little expression in the case of nuts (Bora et al. 2001; Boskou 2006; Venkatachalam and Sathe 2006).

Table 1. Oil content of different oily fruits and nuts.

Source	Botanical name	Oil content (g/ 100 g)	References
Almond	Prunus spp.	D: 16.0–69.2 F: 30.0–54.1	(Abdallah et al. 1998; Alizadeh-Salte et al. 2018; Barreira et al. 2012; Font i Forcada et al. 2015; Houmy et al. 2016; Kodad et al. 2014; Kornsteiner-Krenn, 2013; Kornsteiner et al. 2006; Matthäus et al. 2018b; Moayedi et al. 2011; Momchilova and Nikolova-Damyanova 2007; Ojeda-Amador et al. 2019; Soler et al. 1988; Vecka et al. 2019; Wang et al. 2019; Zhu et al. 2017) (Kalogeropoulos et al. 2013; Maguire et al. 2004; Miraliakbari and Shahidi 2008; Robbins et al. 2011; Sena-Moreno et al. 2016; Song et al. 2018; Tokusoglu, 2011; Venkatachalam and Sathe 2006; Yada et al. 2013)
Avocado	Persea americana	D: 30.3–69.4 F: 8.8–32.3	(Barros et al. 2016; Corzzini et al. 2017; Reddy et al. 2012; Santana et al. 2015; Tan et al. 2017; Yanty et al. 2011) (Bora et al. 2001; Corrales-García et al. 2019; Gómez-López, 2002; Mardigan et al. 2019; Salgado et al. 2008; Takenaga et al. 2008; Villa-Rodríguez et al. 2011)
Brazil nut	Bertholetia excelsa	D: 61.2–72.5 F: 60.8–72.5	(Cornelio-Santiago et al. 2019; Derewiaka et al. 2014; Kornsteiner-Krenn, 2013; Kornsteiner et al. 2006; Vecka et al. 2019; Zanqui et al. 2020a) (Chunhieng et al. 2008; Miraliakbari and Shahidi 2008; Robbins et al. 2011; Ryan et al. 2006; Venkatachalam and Sathe 2006)
Cashew nut	Anacardium occidentale	D: 21.3–49.1 F: 40.4–61.2	(Derewiaka et al. 2014; Kornsteiner-Krenn, 2013; Kornsteiner et al. 2006; Vecka et al. 2019; Wang et al. 2019; Zanqui et al. 2020b) (Ryan et al. 2006; Song et al. 2018; Venkatachalam and Sathe 2006)
Hazelnut	Corylus avellana	D: 53.4–74.1 F: 49.2–69.0	(Alasalvar et al. 2003; Balta et al. 2006; Cittadini et al. 2020; Derewiaka et al. 2014; Kanbur et al. 2013; Kornsteiner-Krenn, 2013; Kornsteiner et al. 2006; Momchilova and Nikolova-Damyanova 2007; Ojeda-Amador et al. 2019; Tanriverdi, 2016; Vecka et al. 2019; Wang et al. 2019) (Amaral et al. 2006; Bada et al. 2004; Kalogeropoulos et al. 2013; Maguire et al. 2004; Miraliakbari and Shahidi 2008; Robbins et al. 2011; Venkatachalam and Sathe 2006)
Macadamia	Macadamia integrifolia	D: 69.1–78.4 F: 59.2–66.1	(Derewiaka et al. 2014; Kaijser et al. 2000; Koaze et al. 2002; Kornsteiner-Krenn, 2013; Kornsteiner et al. 2006; Wang et al. 2019) (Maguire et al. 2004; Venkatachalam and Sathe 2006)
Peanut	Arachis hypogaea	D: 45.8–53.8 F: 37.9–67.1	(Kornsteiner-Krenn, 2013; Kornsteiner et al. 2006; Vecka et al. 2019; Wang et al. 2019) (Campos-Mondragón et al. 2009; Jonnala et al. 2005; Maguire et al. 2004; Song et al. 2018; Venkatachalam and Sathe 2006)
Pecan nut	Carya illinoinensis	D: 67.9–73.6 F: 58.1–80.1	(Derewiaka et al. 2014; Kornsteiner-Krenn, 2013; Kornsteiner et al. 2006; Vecka et al. 2019) (Miraliakbari and Shahidi 2008; Robbins et al. 2011; Ryan et al. 2006; Song et al. 2018; Venkatachalam and Sathe 2006)
Pine nut	Pinus spp.	D: 52.3–70.8 F: 58.8–75.1	(Derewiaka et al. 2014; Kornsteiner-Krenn, 2013; Kornsteiner et al. 2006; Vecka et al. 2019; Wang et al. 2019) (Miraliakbari and Shahidi 2008; Robbins et al. 2011; Ryan et al. 2006; Venkatachalam and Sathe 2006)
Pistachio	Pistacia vera	D: 43.3–78.9 F: 32.0–64.9	(Derewiaka et al. 2014; Ghrab et al. 2010; Kornsteiner-Krenn, 2013; Kornsteiner et al. 2006; Küçüköner and Yurt 2003; Okay 2002; Seferoglu et al. 2006; Tsantili et al. 2010; Vecka et al. 2019; Wang et al. 2019; Yıldız et al. 1998) (Agar et al. 1998; Kalogeropoulos et al. 2013; Miraliakbari and Shahidi 2008; Ryan et al. 2006; Sena-Moreno et al. 2016; Song et al. 2018; Tokusoglu, 2011; Venkatachalam and Sathe 2006)
Olive	Olea europaea	D: 44.3–86.7 F: 23.0	(Moussaoui et al. 2008; Ninot et al. 2018; del Rio et al. 2002) (Bianchi and Vlahov 1994)
Walnut	Juglans regia	D: 39.1–80.3 F: 49.0–72.5	(Cittadini et al. 2020; Derewiaka et al. 2014; Gao et al. 2019b; Kornsteiner-Krenn, 2013; Kornsteiner et al. 2006; Martínez et al. 2006; Matthäus et al. 2018b; Momchilova and Nikolova-Damyanova 2007; Vecka et al. 2019; Wang et al. 2019) (Kalogeropoulos et al. 2013; Maguire et al. 2004; Miraliakbari and Shahidi 2008; Robbins et al. 2011; Sena-Moreno et al. 2016; Song et al. 2018; Venkatachalam and Sathe 2006)

Note: D: oil content expressed in g/100 g of dry matter; F: oil content expressed in g/100 g of fresh matter.

Moreover, considerable differences in the lipid content were observed in studies that were based on different varieties from the same food source, as in almonds (Abdallah et al. 1998; Font i Forcada et al. 2015; Houmy et al. 2016; Kodad et al. 2014; Moayedi et al. 2011; Ojeda-Amador et al. 2019; Wang et al. 2019), avocados (Amado et al. 2019; Gómez-López, 2002; Mardigan et al. 2019; Takenaga et al. 2008; Yanty et al. 2011), hazelnuts (Amaral et al. 2006; Cittadini et al. 2020; Kanbur et al. 2013; Ojeda-Amador et al. 2019; Tanriverdi 2016; Taş and Gökmen 2015), macadamias (Kaijser et al. 2000), peanuts (Campos-Mondragón et al. 2009), pecan nuts (Poletto et al. 2020), pine nuts (Matthäus et al. 2018a), pistachios (Agar et al. 1998; Ghrab et al. 2010; Küçüköner and Yurt 2003; Okay 2002; Tsantili et al. 2010; Yıldız et al. 1998) and walnuts (Cittadini et al. 2020; Martínez et al. 2006). Since lipids are one of the most abundant nutrients in oily fruits and the most representative in seeds and nuts, it is essential to deepen the knowledge of the lipid profile of these food sources to understand better the positive impact of their consumption on human health and nutrition.

Lipid composition of the oily fruits and nuts

Oily fruits and nuts oils are mainly composed of TG. Thus, TG and total FA are the most representative lipid components of these matrices. Nevertheless, there are also phytosterols and polar lipids. Next, each of these four groups will be discussed in more detail.

Fatty acids

FA are aliphatic monocarboxylic acids. There are hundreds of FA with distinct configurations, chain lengths, unsaturation types, and substituents along the carbon chain (Rustan 2005). Complex lipids’ properties and nutritional value are closely related to their FA composition, so it is crucial to assess the FA profile.

The oils from nuts, avocados, and olives are mainly composed of oleic (18:1n-9), linoleic (18:2n-6), and palmitic (16:0) acids. Stearic (18:0), palmitoleic (16:1), and linolenic (18:3n-3) acids are also present in lower amounts (Table 2). In these foodstuffs, the FA chain lengths range between 6 and 26 carbons, with up to 6 double bonds, being oleic acid (up to 87.4%) and linoleic acid (up to 69.0%) the most abundant FA by far (Table 2). Unique and distinguished contents can be pointed out. A considerable content of FA16:1 was found in macadamia nuts (15.0-33.75%) and avocados (0.19-17.87%) (Table 2). The latter also has a different content of FA 16:0 (13.56-34.48%). It is interesting to note that, in contrast to other nuts and oily fruits, walnuts presented exceptional values of FA 14:0 (up to 14.4%), 18:2n-6 (up to 69.0%), and mainly 18:3n-3 (up to 15.6%) (Table 2). Pine nuts showed a considerable content of FA 20:0, reaching 15.8% of total FA, and were the only oily food source with a reported content of FA18:4 (0.02–0.50%) (Table 2). Minor FA were found in various nuts and oily fruits, while some were particular to a certain matrix. The FA18:4 was found in pine nuts, the FA 7:0, 9:0, 15:1, 25:0 and 26:0 were found in avocados, 22:2 and 22:3 in pistachios, and 23:0 in almonds (Table 2).

Table 2. Fatty acid profile (expressed in % of total FA) of oily fruits and nuts.

Fatty acid	Almond	Avocado	Brazil nut	Cashew nut	Hazelnut	Macadamia nut	Peanut	Pecan nut	Pine nut	Pistachio	Olive	Walnut
References	(Barreira et al. 2012; Fernandes et al. 2017; Givianrad et al. 2013; Houmy et al. 2016; Kalogeropoulos et al. 2013; Kodad et al. 2014; Maguire et al. 2004; Matthäus et al. 2018b; Mericli et al. 2017; Miraliakbari and Shahidi 2008; Moayedi et al. 2011; Momchilova and Nikolova-Damyanova 2007; Özcan et al. 2020; Sathe et al. 2008; Sena-Moreno et al. 2016; Soler et al. 1988; Tokusoglu, 2011; Vecka et al. 2019; Venkatachalam and Sathe 2006; Zamany et al. 2017; Zhu et al. 2017)	(Abou-Aziz, et al. 1973; Alkhalaf et al. 2019; Bora et al. 2001; Corrales-García et al. 2019; Lísa and Holčapek 2008; Méndez-Zúñiga et al. 2019; Reddy et al. 2012; Salgado et al. 2008; Santana et al. 2015; Takenaga et al. 2008; Tan et al. 2018, 2017; Yanty et al. 2011)	(Chunhieng et al. 2008; Cornelio-Santiago et al. 2019; Derewiaka et al. 2014; Miraliakbari and Shahidi 2008; Ryan et al. 2006; Vecka et al. 2019; Venkatachalam and Sathe 2006)	(Derewiaka et al. 2014; Rico et al. 2016; Ryan et al. 2006; Vecka et al. 2019; Venkatachalam and Sathe 2006)	(Alasalvar et al. 2003, 2006; Bada et al. 2004; Balta et al. 2006; Cittadini et al. 2020; Derewiaka et al. 2014; Fernandes et al. 2017; Ji et al. 2019; Kalogeropoulos et al. 2013; Kanbur et al. 2013; Maguire et al. 2004; Miraliakbari and Shahidi 2008; Momchilova and Nikolova-Damyanova 2007; Tanriverdi 2016; Taş and Gökmen 2015; Vecka et al. 2019; Venkatachalam and Sathe 2006)	(Derewiaka et al. 2014; Kaijser et al. 2000; Koaze et al. 2002; Ma et al. 2022; Maguire et al. 2004; Vecka et al. 2019; Venkatachalam and Sathe 2006)	(Campos-Mondragón et al. 2009; Dong et al. 2015; Maguire et al. 2004; Vecka et al. 2019; Veličkovska et al. 2016; Venkatachalam and Sathe 2006)	(Derewiaka et al. 2014; Fernandes et al. 2017; Miraliakbari and Shahidi 2008; Ryan et al. 2006; Vecka et al. 2019; Venkatachalam and Sathe 2006)	(Derewiaka et al. 2014; Fardin-Kia et al. 2012; Matthäus et al. 2018a; Miraliakbari and Shahidi 2008; Ryan et al. 2006; Vecka et al. 2019; Venkatachalam and Sathe 2006)	(Agar et al. 1998; Ballistreri et al. 2011; Chahed et al. 2008; Derewiaka et al. 2014; Ghrab et al. 2010; Givianrad et al. 2013; Kalogeropoulos et al. 2013; Küçüköner and Yurt 2003; Miraliakbari and Shahidi 2008; Okay 2002; Rabadán et al. 2018; Ryan et al. 2006; Seferoglu et al. 2006; Sena-Moreno et al. 2015, 2016; Tokusoglu, 2011; Tsantili et al. 2010; Vecka et al. 2019; Venkatachalam and Sathe 2006; Yıldız et al. 1998)	(Kammoun and Zarrouk 2012; Ranalli et al. 2002a; Sakouhi et al. 2010)	(Cittadini et al. 2020; Derewiaka et al. 2014; Gao et al. 2018, 2019b; Kalogeropoulos et al. 2013; Maguire et al. 2004; Martínez et al. 2006; Matthäus et al. 2018b; Miraliakbari and Shahidi 2008; Momchilova and Nikolova-Damyanova 2007; Sena-Moreno et al. 2016; Vecka et al. 2019; Venkatachalam and Sathe 2006; Verardo et al. 2013)
6:0		0.12	0.14	0.02					0.15	0.03		<0.1 + 0.32
7:0		<0.06
8:0		0.08			0.05–0.11
9:0		<0.06
10:0			0.05		0.05–0.47				0.07	0.02		0.05
11:0			0.11	0.05					0.15	0.08		0.24
12:0	0.02	<0.06		0.25	0.03–0.04	0.09
13:0	0.31–0.70	0.28
14:0	<0.2 + 0.03–0.06	0.1–1.58	0.01–0.08	0.03–0.07	<0.1 + 0.01–0.4	0.4–1.84	0.03	<0.2 + 0.04–0.11	<0.2	<0.2 + 0.01–2.55		0.03–14.44
14:1		0.12–0.27	0.05							0.07–0.08
15:0	0.01–0.03	0.06			0.01–0.09			0.01	0.5	0.02		0.02
15:1		0.07
16:0	5.04–8.41	13.56–34.48	12.0–18.43	8.30–10.85	3.11–10.1	5.26–12.1	6.20–13.36	4.28–8.3	2.3–7.05	7.1–15.7	8.86–19.22	3.05–9.70
16:1	0.25–1.50	0.19–17.87	0.03–0.6	0.36–0.54	0.09–0.4	15.0–33.75	<0.2 + 0.07–0.15	<0.2 + 0.10–0.14	<0.2 + 0.04–0.70	0.4–2.5	0.3–2.58	<0.2 + 0.06–0.23
17:0	0.04–0.10	0.01–0.03	0.08–0.22	0.10–0.12	0.04–0.06		0.07	0.05–0.09	0.07–0.16	0.04–0.1	0.03–0.16	0.04–0.06
17:1	0.09–0.19	0.01–0.11			0.05–0.08			0.06		0.07–0.19	0.06–0.30	0.1
18:0	0.2–3.30	0.07–12.80	9.51–18.60	7.60–10.35	0.44–4.61	1.01–5.2	2.06–5.36	1.80–3.2	0.86–3.7	0.2–3.85	1.6–3.02	1.0–5.79
18:1	59.52–81.57	40.73–75.16	28.75–41.2	57.24–64.30	74.46–87.38	43.84–65.15	35.68–81.28	40.63–69.13	15.3–49.0	50.29–80.7	55.7–77.5	12.0–28.44
18:2 n–6	8.17–29.58	3.37–20.87	28.09–45.43	16.3–19.15	4.29–15.99	1.72–5.2	3.87–37.60	20.67–37.71	35.2–59.60	11.56–33.43	5.8–24.3	49.55–68.97
18:3 n–3	<0.10 + 0.04–0.29	0.47–3.57	0.01–0.7	<0.2 + 0.50–0.70	<0.2 + 0.10–0.82	<0.2 + 0.23	<0.2	0.80–1.59	0.06–2.9	0.30–0.56	0.50–1.1	10.37–15.59
18:3 n–6	0.02					<0.2	0.24–0.53		4.9–18.75	0.02		0.06
18:4									0.02–0.50
20:0	<0.2 + 0.04–0.23	0.07–0.15	0.02–0.6	0.50–0.97	<0.2 + 0.09–0.20	0.93–3.23	0.10–2.06	<0.2 + 0.12–0.34	0.22–15.75	<0.2 + 0.04–0.6	0.26–0.5	<0.2 + 0.04–0.20
20:1	<0.2 + 0.06–0.12	0.19–0.5	<0.2 + 0.21–0.28	<0.2 + 0.25	<0.2 + 0.13–0.17	0.41–2.63	0.6–1.7	0.29–1.21	0.4–1.5	0.29–0.94	0.14–0.5	0.04–0.54
20:2	0.09–0.1		<0.2	<0.2	<0.2 + 0.02	<0.2	0.16	<0.2	0.01–2.2	<0.2		<0.2 + 0.02–0.24
20:3						0.01	0.02		0.11–7.9
20:5							0.02					0.06
21:0	0.14–0.29								0.09	0.04
22:0	<0.2 + 0.02–0.47	0.06	<0.2 + 0.06–0.12	<0.2 + 0.10–0.39	0.02–0.25	0.2–0.82	0.1–3.0	0.02–0.16	<0.2 + 0.08–0.50	<0.2 + 0.03–0.35	0.21	<0.2 + 0.02–0.07
22:1	0.02	0.07	0.34	0.28	0.02–0.03	0.07–0.24	0.14	0.25	0.40	0.02–0.57
22:2										0.03
22:4										0.05
22:6						0.27	0.75
23:0	0.06–0.28
24:0	0.05–0.45	0.07–0.10		<0.2 + 0.13–0.20	0.01	<0.2 + 0.11–0.33	1.27–3.93			0.04–0.1		0.02
24:1					0.02					0.03
25:0		0.02
26:0		0.02

Abbreviations: 6:0, caproic acid; 7:0, enanthic acid; 8:0, caprylic acid; 9:0, pelargonic acid; 10:0, capric acid; 11:0, undecylic acid; 12:0, lauric acid; 13:0, tridecylic acid; 14:0, myristic acid; 14:1, myristoleic acid; 15:0, pentadecanoic acid; 15:1, pentadecenoic acid; 16:0, palmitic acid; 16:1, palmitoleic acid; 17:0, margaric acid; 17:1, heptadecenoic acid; 18:0, stearic acid; 18:1, oleic acid; 18:2, linoleic acid; 18:3, linolenic acid; 18:4, stearidonic acid; 19:0, nonadecanoic acid; 20:0, arachidic acid; 20:1, eicosenoic acid; 21:0, heneicosanoic acid; 20:2, eicosadioic acid; 20:3, eicosatrienoic acid; 20:4, arachidonic acid; 22:0, behenic acid; 22:1, erucic acid; 24:0, tetracosanoic acid; n–6, omega–6 fatty acid; n-3, omega–3 fatty acid; tr, trace.

Triacyclglycerols

The major portion of the FA in nuts and oily fruits is esterified to other lipids, mainly in TG. TG have three FA esterified to a glycerol molecule, wherein the FA may be the same, two may be different, or all may be different (Scrimgeour and Harwood 2007). TG provide energy reserve and are a source of essential FA. The stereospecific number (sn) of the carbon atoms in the glycerol backbone is a determinant for characterizing acylglycerols (tri-, di- and monoacylglycerols). The “sn” prefix indicates the FA position in the glycerol backbone and is mentioned before the compound’s name (Cyberlipid Center 2021a). With some exceptions, the distribution of the FA in the TG molecule is characteristic of vegetable oils, with saturated FA occupying the sn-1 and or sn-3 positions and unsaturated FA occupying the sn-2 position (Amaral et al. 2006).

Overall, nuts and oily fruits have around five main TG species corresponding to over 60% of the total TG, usually bearing the FA 18:1, 18:2, and 16:0. In olives, the most abundant TG are POO, OLP, OOO, OLO, and OLL (Table 3). Letters P, O, L, and Po meaning palmitic, oleic, linoleic and palmitoleic acid, respectively. Avocados have the same TG as olives and other relevant ones like POP, POPo, and PLP (Table 3). In nuts like almonds and hazelnuts, the most abundant TG are also POO, OLP, OOO, OLO, and OLL (Table 3). In certain nuts, other TG become relevant, like POP (pistachios), SOO (e.g. cashew nuts, hazelnuts, and pistachios), OLLn (peanuts and walnuts), PLL, LnLP, LLL, LLLn, and LnLLn (walnuts) (Table 3). Moreover, compared with other nuts, walnuts showed a very distinct TG profile, where POO and OOO are not major TG, and the set PLL + LnLP + LLL + OLL + OLLn + LLLn + LnLLn could easily reach up to more than 90% of the total TG (Table 3). As mentioned above, walnuts showed remarkable contents of FA 18:2n-6 (L) and 18:3n-3 (Ln), mirrored in the high abundance of the TG species bearing these essential FA. Finally, also worth mentioning, comparing all these food sources, peanuts were the only ones where considerable amounts of TG bearing FA with longer acyl chains were reported, like FA 20:0, 20:1, and 24:0 (Table 3).

Table 3. Triacylglycerol profile (relative abundance, in %) of oily fruits and nuts.

TG	C:N	Almond	Avocado	Cashew nut	Hazelnut	Peanut	Pecan nut	Pistachio	Olive	Walnut
Reference		(Barreira et al. 2012; Fernandes et al. 2017; Givianrad et al. 2013; Holčapek et al. 2005)	(Lísa and Holčapek 2008; Tan et al. 2018, 2017; Yanty et al. 2011)	(Holčapek et al. 2005)	(Alasalvar et al. 2009, 2006;Amaral et al. 2006; Bada et al. 2004; Fernandes et al. 2017; Holčapek et al. 2005; Parcerisa et al. 1999)	(Cherif et al. 2013; Dong et al. 2015; Singleton and Pattee 1987)	(Fernandes et al. 2017)	(Arena et al. 2013; Ballistreri et al. 2011; Givianrad et al. 2013)	(Bianchi and Vlahov 1994; Kammoun and Zarrouk 2012; Ranalli et al. 2002a; Sakouhi et al. 2010)	(Amaral et al. 2004; Gao et al. 2019a, 2018; Holčapek et al. 2005)
PPP	48:0		0.13–1.88		Tr-0.1			0.2	1.0
PPoP	48:1		0.19–1.57	Tr
PPoPo	48:2		0.4
PPS	50:0		0.11–0.18	Tr
POP	50:1	Tr-0.5	2.36–12.43	4.3	Tr-1.8	Tr-2.7	0.5	0.3–13.4	2.2–4.51	0.1–0.6
POPo	50:2		4.2–11.54	Tr	0.1
PLP	50:2	Tr-3.2	1.3–9.61	1.8	Tr-0.4	0.5–4.2		0.5–2.3	0.4–1.9	0.2–0.8
PoOPo	50:3		0.7
PLPo	50:3		1.07–5.03
PLnP	50:3		0.2
PoLPo	50:4		0.4
PLnPo	50:4		0.2
MoOP	51:2	0.2	Tr	Tr	0.1
MaOP	51:2			Tr
MoLP	51:3	Tr
POS	52:1	0.2	0.06–0.75	5.3	Tr-1.3	0.6–4.0		0.2–2.4	0.6–1.3	Tr-0.3
POO	52:2	5.0–11.5	19.65–27.41	11.8	6.4–18.5	5.6–17.5	8.1	5.7–30.8	16.5–25.96	0.3–4.3
SLP	52:2	0.1		1.7	0.5	0.8–1.1
OOPo	52:3		6.9	0.1	0.3
OLP	52:3	2.8–11.8	6.31–19.29	5.4	0.4–7.0	5.2–15.8		4.2–10.7	3.4–13.9	2.6–5.0
PLL	52:4	0.4–4.0	1.23–4.21	4.3	Tr-2.0	Tr-8.4		2.5–7.9	0.1–1.42	6.0–17.6
OLPo	52:4		3.4	0.1	0.1
LnOP	52:4		1.0	0.1	0.6–0.9	1.2–1.8		0.4–2.0
LLPo	52:5		0.3	0.1	0.1
LnOPo	52:5		0.4
LnLP	52:5	0.11	0.4–1.09			Tr-0.5	0.1	0.2–1.6	0.2–0.3	0.7–6.4
LnLnP	52:6									1.4
OOMa	53:2	0.1	Tr	0.1	0.3
OOMo	53:3	0.3			0.4
OLMa	53:3	0.1		Tr	0.1
OLMo	53:4	0.1		Tr	0.1			0.4–3.0
LLMa	53:4	Tr		Tr
LLMo	53:5	0.1								0.1
LnLMa	53:5									0.1
SSO	54:1								0.4
AOP	54:1		Tr	0.1	Tr	0.4
SOS	54:1		Tr	2.6	0.1	0.1–0.4		Tr		1.2–4.3
SOO	54:2	0.4–4.9	0.19–1.27	11.2	0.8–8.6	2.7–5.0	4.1	2.1–13.7	2.7–4.9
ALP	54:2					0.2
GOP	54:2		0.3			0.2–0.3
OOO	54:3	28.5–55.0	11.00–34.69	15.6	31.8–77.5	4.5–25.4		14.1–31.9	21.0–47.4	0.2–1.0
SLO	54:3	1.4–2.0		6.4	1.2–13.4	3.9–5.0	2.6
GLP	54:3					0.3–0.6
OLO	54:4	20.1–33.0	7.71–20.22	12.9	10.5–22.8	6.7–21.5		8.3–18.7	11.9–22.1	1.6–12.0
SLL	54:4			3.6		0.3–0.5
SLS	54:4			0.7
SOLn	54:4					2.6-3.3
OLL	54:5	7.0–18.1	2.32–6.44	7.6	0.9–15.8	6.4–26.1	9.7	6.3–14.5	1.1–11.1	6.40–27.8
SLLn	54:5								0.4
OOLn	54:5		1.2	0.1	0.3	2.6–3.5			1.9–4.5
LLL	54:6	0.4–3.3	0.16–1.77	2.4	Tr-2.0	Tr-5.82	2.6	3.4–9.1	0.1–2.5	16.3–44.3
OLLn	54:6				0.1–0.3	0.7–13.2		0.1–3.1	0.1–0.9	3.7–25.0
SLnLn	54:6									0.3
OLLn	54:6		0.6
LLLn	54:7		0.49–1.87					0.3–1.5		5.95–23.5
LnOLn	54:7									1.6
LnLLn	54:8									0.61–4.2
LnLnLn	54:9									0.8
AOS	56:1					Tr-0.1
BOP	56:1					0.4–0.5
AOO	56:2	0.1	0.2	1.1	0.4	0.5–4.2
ALS	56:2			Tr		Tr-0.1
BLP	56:2					0.5–0.6
GOO	56:3	0.1	0.3	0.3	0.1–0.4	0.2–5.4		0.5–1.7
ALO	56:3	0.1			0.2	1.3–1.7			0.6–0.9
GLO	56:4	0.3	Tr	Tr	0.1	0.2–0.3		Tr-0.8
ALL	56:4					0.5–1.0
AOLn	56:4					0.3–0.5
GLL	56:5			Tr
LgOP	58:1		Tr			0.1–0.2
BOS	58:1					Tr-0.1
LgLP	58:2					0.2–0.4
BOO	58:2		0.1			1.4–2.2
BLS	58:2					0.1
BLO	58:3		0.1	0.1		1.1–1.7
BLL	58:4			Tr		0.4–2.3
BOLn	58:4					0.3–0.5
LgOO	60:2		0.1
LgLO	60:3		0.1			1.0–4.3
GLB	60:3					Tr
LgLL	60:4					0.1–1.8
LgLnO	60:4					0.8–1.3
C25:0OO	61:2		Tr
C25:0LO	61:3		Tr
C26:0OO	62:2		0.1
PLL + OOLn					3.3–4.9				2.0
OLL + SLL									12.2
SOL + OOO									50.8
OLLn + PoLL		0.3			Tr		0.7
OOLn + PoOL		0.5			0.6		1.0
PLL + PoPoO		2.5			0.6		1.7
POLn + PPoPo + PPoL		0.4					0.3
OOL + LnPP		26.3			14.0		23.2
PoOO + SLL + PLO		10.8			3.6		6.0
PoOP + SpoL + POLn + SPoPo		0.3			0.4		0.2
PLP + OOO + PoPP		27.4			55.6		38.8
POS + SSL		0.4			0.5		0.4	4.3
LLL + PoLL		0.1						0.6
OLL + PoOL		1.2						7.0
OOLn + PLL		1.4						15.7
PLnP + OOL		10.7						5.8
SLL + PLO		4.7						21.8
SOL + POO		26.3			12.4–13.5			16.7
PLP + POP		3.0
OLO + POO					14.6–18.6
PLS + POP					0.5–0.9
AOL + SOO					4.4–6.1

Note: TG, triacylglycerol; C:N, number of carbons: number of double bonds; Tr, traces (<0.1); Cl, caprylic acid, 8:0; La, lauric acid, 12:0; M, myristic acid, 14:0; P, palmitic acid, 16:0; Po, palmitoleic acid, 16:1; Ma, heptadecanoic acid, 17:0; Mo, heptadecenoic acid, 17:1; S, stearic acid, 18:0; O, oleic acid, 18:1; L, linoleic acid, 18:2; Ln, linolenic acid, 18:3; A, arachidic acid, 20:0; G, gadoleic acid, 20:1; B, behenic acid, 22:0; Lg, lignoceric acid, 24:0; C25:0, pentacosylic acid, 25:0; C26:0, cerotic acid, 26:0.

Sterols

Plant sterols, also called phytosterols, are steroidal alcohols belonging to the group of triterpenoids and represent essential components of the membranes of all eukaryotic organisms. Phytosterols are composed of a tetracyclic cyclopenta[α]phenantrene ring, a hydroxyl group on C3 in β-stereochemistry, a flexible side chain on C17, and two methyl groups at C10 and C13 (Piironen et al. 2000). Structurally, depending on the number of methyl groups on C4, sterols are categorized as 4-desmethyl sterols, 4α-monomethyl sterols, and 4,4′-dimethyl sterols. The 4-desmethyl sterols, such as sitosterol and campesterol, are predominant in plants and may be classified according to the number and position of the double bond(s) in the B-ring in Δ⁵-sterols, Δ⁷-sterols, Δ^5,7-sterols (Piironen et al. 2000). Besides 4α-monomethyl sterols and 4,4′-dimethyl sterols, stanols, fully saturated forms of sterols, are also present in lower amounts in plants (Piironen et al. 2000). Sterols may also be present in plants in low amount in other forms, in ascending order of polarity: sterol esters (esterified sterols), sterolglycosides (SG, sterols linked to sugars), and acylsterolglycosides (ASG, sterols linked to a sugar that can be esterified to a FA).

Small amounts of phytosterols are present in nuts and oily fruits (ca. 0.8–5.9 g Kg⁻¹ of oil, Table 4). As expected, the most common phytosterols in these foodstuffs are 4-desmethyl sterols, wherein β-sitosterol is the most representative, reaching up to 82.3% of the total phytosterols (Table 4). On the other hand, Δ⁵-avenasterol (up to 56.9 mg 100 g⁻¹ oil in walnuts), stigmasterol (up to 60.0 mg 100 g⁻¹ oil in pecan nuts), and campesterol (up to 32.1 mg 100 g⁻¹ oil in peanuts) have a lower but remarkable presence in almost all the oily matrices studied (Table 4). Moreover, other relevant phytosterols can be highlighted: Δ^5,24-stigmastadienol in walnuts (up to 28.2 mg 100 g⁻¹ oil), Δ⁷-avenasterol in avocados (up to 13.8 mg 100 g⁻¹ oil), cholesterol in Brazil nuts and hazelnuts (up to 18.0 mg 100 g⁻¹ oil), cycloartenol in walnuts (27.9 mg 100 g⁻¹ oil), ergosterol in almonds and hazelnuts (up to 26.6 mg 100 g⁻¹ oil), lanosterol in peanuts (30.0 mg 100 g⁻¹ oil), and sitostanol in hazelnuts and peanuts (up to 41.4 mg 100 g⁻¹ oil) (Table 4). Even though stanols and cholesterol are usually found in trace amounts in plants, some exceptions have been observed in some nuts, such as hazelnuts, peanuts, and Brazil nuts.

Table 4. Sterol and stanol profile (mg 100 g⁻¹ of oil) of nuts and oily fruits.

Sterol	Almond	Avocado	Brazil nut	Cashew nut	Hazelnut	Macadamia	Peanut	Pecan nut	Pine nut	Pistachio	Olive	Walnut
Reference	(Dulf et al. 2010; Fernandes et al. 2017; Kornsteiner-Krenn, 2013; Maguire et al. 2004; Matthäus et al. 2018b; Miraliakbari and Shahidi 2008; Özcan et al. 2020; Sena-Moreno et al. 2016; Vecka et al. 2019)	(Jorge et al. 2015; Madawala et al. 2012)	(Derewiaka et al. 2014; Kornsteiner-Krenn, 2013; Miraliakbari and Shahidi 2008; Vecka et al. 2019)	(Derewiaka et al. 2014; Kornsteiner-Krenn, 2013; Rico et al. 2016; Vecka et al. 2019)	(Alasalvar et al. 2009; Derewiaka et al. 2014; Fernandes et al. 2017; Ji et al. 2019; Kornsteiner-Krenn, 2013; Maguire et al. 2004; Miraliakbari and Shahidi 2008; Vecka et al. 2019; Yorulmaz et al. 2009)	(Derewiaka et al. 2014; Kaijser et al. 2000; Kornsteiner-Krenn, 2013; Maguire et al. 2004; Vecka et al. 2019)	(Jonnala et al. 2006; Kornsteiner-Krenn, 2013; Maguire et al. 2004; Vecka et al. 2019; Veličkovska et al. 2016)	(Derewiaka et al. 2014; Fernandes et al. 2017; Kornsteiner-Krenn, 2013; Miraliakbari and Shahidi 2008; Vecka et al. 2019)	(Derewiaka et al. 2014; Kornsteiner-Krenn, 2013; Miraliakbari and Shahidi 2008; Vecka et al. 2019)	(Derewiaka et al. 2014; Kornsteiner-Krenn, 2013; Massimo et al. 2020; Miraliakbari and Shahidi 2008; Rabadán et al. 2018; Sena-Moreno et al. 2016; Vecka et al. 2019)	(Fernández-Cuesta et al. 2013; Kammoun and Zarrouk 2012; Ninot et al. 2018; Ranalli et al. 2002b)	(Derewiaka et al. 2014; Gao et al. 2019a, 2018; Kornsteiner-Krenn, 2013; Maguire et al. 2004; Matthäus et al. 2018b; Miraliakbari and Shahidi 2008; Sena-Moreno et al. 2016; Vecka et al. 2019; Verardo et al. 2013)
24-Methylene-cholesterol	0.4–0.5		12.0–13.0		1.0		1.5	5.0			0.1-0.8
24-Ethylene-cholesterol	0.9–1.3
Δ 5-Avenasterol	10.0–36.1	1.1–1.4	2.0–11.0	13.6	2.3–10.6	8.2–20.7	23.6	10.0–22.0	6.0–52.3	13.0–23.1	12.3–30.0	1.7–56.9
Δ 5,23-Stigmastadienol	0.4–4.5						0.1					1.6–4.8
Δ 5,24-Stigmastadienol	3.3–4.4				0.5–1.3		1.5	2.5			0.2–1.0	0.5–28.2
Δ 5,25- Stigmastadienol	0.9–3.5
Δ 7-Avenasterol	1.1–3.1	10.1–13.8			0.5–4.4	1.5	2.4				0.3–1.2
Δ 7-Campesterol	2.6–4.4				0.2–1.5						0.5–0.6	1.0
Δ 7-Campestanol							0.6
Δ 7-Stigmastenol	1.3–2.0				0.5–3.7		0.9				0.2–0.8	0.4-23.7
Δ 7-Stigmasterol	2.9–5.6										0.4–1.3
β-Sitosterol	2.3–257.8	62.4–82.3	53.2–112.0	104.3–141.4	79.5–193.2	90.1–150.7	84.5–181.2	97.3–175.0	104.5–224.1	202.6–474.8	119.0–208.9	45.4–225.0
Brassicasterol							0.8
Campesterol	2.0–22.6	0.8–1.9	2.9–15.0	11.4	4.0–17.0	5.0–11.2	11.5–32.1	6.5–24.0	10.6–26.5	10.5–24.5	3.8–7.3	1.6–30.2
Campestanol	3.8				0.3–0.6		3.3				0.1–0.5
Cholestanol			2.0		8.0–9.0				1.0
Cholesterol	0.6–3.0		12.0–18.0		0.6–13.0		0.4	7.0–8.0	2.0	3.0–4.0	Tr-1.2	0.4–10.0
Clerosterol	1.0–3.7				0.6–1.6		1.4				0.1–1.4	10.6
Cycloartenol			3.3	7.6				9.4		13.0		27.9
Ergosterol	10.6–19.3				19.4–26.6
Lanosterol							30.0
Sitostanol	4.2–5.4				3.0–41.4		3.0–27.0			8.0–10.0	0.3–1.8	1.0–1.2
Stigmasterol	Tr-19.7		7.9–23.0		Tr-39.0	0.8–3.8	8.0–16.3	2.6–60.0	13.0–15.0	Tr-11.0	0.6–6.4	Tr-35.0
Stigmastanol	4.2–6.4	0.8–1.1
Total sterols	199.9–377.0	75.6–100.0	134.9–206.0	180.8–317.0	118.0–230.1	111.7–246.0	225.0–265.9	194.0–276.0	129.0–438.0	152.0–586.0	118.8–184.7	144.8–299.0

Note: Tr; traces (<0.1)

Polar lipids

Unlike FA, TG, and sterols, polar lipids have been much less studied in nuts, oily fruits, and their oils. Polar lipids comprise many molecules and may be divided into several classes and subclasses. Depending on their structure, the polar head group is the main differentiator. In this paper, for simplicity, these minor lipids will be described considering the four groups: phospholipids (PL), glycolipids (GL), sphingolipids (SL), and betaine lipids (BL).

The PL represent a significant group of lipids in cell membranes, being structural elements forming the cells’ lipid bilayers, and are commonly found in foodstuffs, including cereals and nuts. PL may be divided into two sub-groups: glycerophospholipids (GPL), with a glycerol backbone, and sphingophospholipids (SPL), with a sphingosyl backbone (Pasini et al. 2013; Scrimgeour and Harwood 2007). GPL structures are sn-1,2-diacylglycerols with a 3-sn-phosphatidic acid linked to a head group. The polar head group (a carbohydrate, an amino acid, an amino alcohol, or another functional moiety) is attached to the phosphate residue at the sn-3 position. The SPL contain sphingosine or a related amino alcohol instead of glycerol, like sphingomyelins (Scrimgeour and Harwood 2007).

In nuts and oily fruit oils, PL were found in low amounts. In nuts, these minor compounds represent 0.30–3.81% (w/w) of the nuts’ total lipids, and in oily fruits, such as avocados and olives, they represent 0.7–2.1% (w/w) of the pulp’s total lipids (Table 5). Several PL classes were identified in nuts and oily fruits. The most abundant are phosphatidylcholines (PC), phosphatidylethanolamines (PE), phosphatidylinositols (PI), phosphatidic acids (PA), and phosphatidylglycerols (PG) (Table 5). Less abundant there are lysoPL, with only one fatty acyl chain (e.g. lysoPC and lysoPE), and other PL like cardiolipins (CL), phosphatidylserines (PS), and sphingomyelins (SM) (Table 5). Worth noticing that N-acyl phosphatidylethanolamines [PE-N(FA)] and sphingoid bases (SPB) were also identified in hazelnuts (Table 5).

Table 5. Polar lipid content and composition in nuts and oily fruits, based on various studies.

Sample of interest	Polar lipid content	Polar lipid classes	References
Almond	PL: 1.67% (w/w)a SL: 0.53–0.63% (w/w)a	PE (33.1%), PC (31.7%), PI (19.1%), PA (7.8%), PG (7.1%), PS (0.1%), LPC (1.2%)b Cer (149.4–189.6 pmol/g)c, HexCer (0.068 mg/g)d	(Fang et al. 2005; Miraliakbari and Shahidi 2008; Momchilova and Nikolova-Damyanova 2007; Paroni et al. 2019; Song et al. 2018)
Avocado	Polar lipids: 5.2% (w/w)e PL: 0.7–2.1% (w/w)e GL: 2.5–3.2% (w/w)e	PE (39.3%), PC (35.9%), PA (10.4%), PG (7.1%), PI (1.0%)a, LPC MGDG, DGDG HexCer (11.4%)a	(Pacetti et al. 2007; Takenaga et al. 2008)
Brazil nut	SL: 0.83–0.91% (w/w)a	PC (0.34–0.78%), PI (0.09–0.10%), PS (0.26–0.32%)a, PA, PE	(Chunhieng et al. 2008; Miraliakbari and Shahidi 2008)
Cashew nut	PL: 2.95% (w/w)a	PI (32.1%), PE (23.7%), PC (16.6%), PG (10.9%), PA (9.2%), PS (0.3%), LPC (4.6%), LPG (0.3%), LPE (1.7%), LPI (0.6%)b Cer, HexCer (0.039 mg/g)d	(Fang et al. 2005; Song et al. 2018)
Hazelnut	Polar lipids: 0.7–1.2% (w/w)a PL: 0.5–0.9% (w/w)a SL: 0.26–0.32% (w/w)a	PC (40.7–41.7%), PI (19.6–21.7%), PE (13.8–15.7%) PS (5.0%), PA (5.0%), CL (5.0%), LPE (1.0-5.0%), LPC (1.0–5.0%), SM (1.0–5.0%)b, LPI, PE-N(FA), SPB Cer, HexCer (0.021 mg/g)d	(Alasalvar et al. 2003; Angelova-Romova et al. 2013; Fang et al. 2005; Miraliakbari and Shahidi 2008; Momchilova and Nikolova-Damyanova 2007; Napolitano et al. 2018)
Peanut	PL: 1.62–1.82% (w/w)a	PI (34.7%), PC (22.7%), PE (22.0%), PG (6.5%), PA (3.3%), PS (0.2%), LPC (8.6%), LPE (1.6%), LPI (0.3%)b Cer, HexCer (0.026 mg/g)d	(Fang et al. 2005; Song et al. 2018; Yoshida et al. 2005)
Pecan nut	PL: 1.19% (w/w)a SL: 0.48–0.55% (w/w)a	PA (24.6%), PI (24.5%), PC (18.1%), PE (17.1%), PG (10.3%), PS (0.1%), LPC (4.4%), LPE (0.8%)b	(Miraliakbari and Shahidi 2008; Song et al. 2018)
Pine nut	SL: 0.45–0.57% (w/w)a	PC (0.19–0.37%), PS (0.23–0.33%), PI (0.14–0.19%)a Cer, HexCer (0.042 mg/g)d	(Fang et al. 2005; Miraliakbari and Shahidi 2008)
Pistachio	PL: 3.81% (w/w)a SL: 0.73–0.82% (w/w)a	PE (13.4%), PC (19.3%), PI (26.1%), PA (28.1%), PG (5.0%), PS (0.6%), LPC (6.1%), LPG (0.7%), LPE (0.5%), LPI (0.3%)b Cer (202.3–463.4 pmol/g)f	(Miraliakbari and Shahidi 2008; Paroni et al. 2019; Song et al. 2018)
Olive	PL: 0.12% (w/w)e, 71.69%g GL: 25.44%g SL: 2.33%g BL: 0.54%g	PC (69.30%), LPC (0.02%), PE (<0.01%), PG (<0.01%), SM (<0.01%)g, PA, PI, LPE, LPA MGDG (0.13%), DGDG (0.12%) and DGMG (<0.01%)g HexCer (2.33%)g DGTS (<0.01%) and MGTS (<0.01%)g	(Alves et al. 2019; Moussaoui et al. 2008)
Walnut	PL: 0.30–2.52% (w/w)a SL: 0.54–0.68% (w/w)a	PC (19.1–42.5%), PI (23.4–32.5%), PE (12.5–26.2%), PA (5.0–14.8%), CL (5.0–10.0%), PG (4.1%), PS (0.7–5.0%), SM (1.0–5.0%), LPC (0.1–2.3%), LPE (0.1–0.4%)b Cer, HexCer (0.025 mg/g)d	(Angelova-Romova et al. 2013; Fang et al. 2005; Miraliakbari and Shahidi 2008; Momchilova and Nikolova-Damyanova 2007; Song et al. 2018)

Abbreviations: w/w, weight per weight; BL, betaine lipids; Cer, ceramides; CL, cardiolipins; DGDG, diglycosyldiacylglycerols; DGMG, diglycosylmonoacylglycerols; DGTS, diacylglyceryl-N,N,N-trimethylhomoserines; GL, glycolipids; HexCer, hexosylceramides; LPA, lysophosphatidic acids; LPC, lysophosphatidylcholines; LPE, lysophosphatidylethanolamines; LPG, lysophosphatidylglycerols; LPI, lysophosphatidylinositols; MGDG, monoglycosyldiacylglycerols; MGTS, monoacylglyceryl-N,N,N-trimethylhomoserines; PA, phosphatidic acids; PC, phosphatidylcholines; PE, phosphatidylethanolamines; PE-N(FA), N-acyl phosphatidylethanolamines; PG, phosphatidylglycerols; PI, phosphatidylinositols; PL, phospholipids; PS, phosphatidylserines; SL, sphingolipids; SM, sphingomyelins; SPB, sphingoid bases.

^aRelative abundance regarding the whole fruit/nut’s total lipids.

^bRelative abundance regarding whole fruit/nut’s total phospholipids.

^cRelative abundance regarding the whole fruit/nut, in both dry and fresh basis.

^dRelative abundance regarding the whole fruit/nut, in a fresh basis.

^eRelative abundance regarding pulp’s total lipids.

^fRelative abundance regarding the whole fruit/nut, in a dry basis.

^gRelative abundance regarding pulp’s total polar lipids.

SL, besides PL, also include other compounds such as ceramides and glycosphingolipids. Ceramides (Cer) consist of sphingosine or other related amino alcohol with a FA attached to the amino group. Neutral glycosphingolipids are SL with sugar residues. These sugars, either glucose or galactose, are linked to the primary alcohol of the sphingosyl moiety by an O-ester linkage. These structures are called hexosylceramides (HexCer) or cerebrosides (Scrimgeour and Harwood 2007). SL are a significant class of eukaryotic cell membranes’ constituents, considered building blocks of cell architecture and regulators of vital biological functions (Gangoiti et al. 2010).

In nuts, SL represent 0.26–0.91% (w/w) of the total lipids, and in olives, they comprise around 2.3% of the pulp’s total polar lipids (Table 5). Cer were identified in almost all nuts, comprising 149.4–189.6 pmol g⁻¹ in almonds and 202.3-463.4 pmol g⁻¹ in pistachios (Table 5). HexCer were reported in both nuts and oily fruits. HexCer ranged from 0.021 mg g⁻¹ in hazelnuts to 0.068 mg g⁻¹ in almonds on a fresh basis (Table 5). They comprised 11.4% of the avocado’s total lipids (Table 5).

GL, such as glycosylglycerides, consist of acylglycerols attached to a carbohydrate moiety by a glycosidic linkage at the sn-3 position (Scrimgeour and Harwood 2007). They are membrane components that influence several biological processes like cell-cell communication or signal transduction and appear in very distinct organisms, such as bacteria, plants, and animals, including humans (Ishida and Kiso 2008).

GL were poorly identified and quantified in oily fruits. The amount of GL reported in avocados was 2.5–3.2% (w/w) of the pulp’s total lipids (Table 5). GL represent about 25.4% of the olive pulp’s total polar lipids (Table 5). The most common GL were monoglycosyldiacylglycerols (MGDG) and diglycosyldiacylglycerols (DGDG) (Table 5). In olives, diglycosylmonoacylglycerols (DGMG) were also found (Table 5). To our knowledge, no studies have explored the GL profile in nuts.

BL are glycerolipids containing a betaine moiety linked to the sn-3 position of the glycerol backbone and FA esterified in the sn-1 and sn-2 positions (Scrimgeour and Harwood 2007). The most common betaine lipids are diacylglyceryl-N,N,N trimethylhomoserines (DGTS). DGTS are commonly found in lower plants, algae, marine eukaryotic phytoplankton, fungi, and bacteria (Canãvate et al. 2016; Cañavate et al. 2017; Dembitsky, 1996). Recently, DGTS and monoacylglyceryl-N,N,N-trimethylhomoserines (MGTS) have been reported in olives and olive oil (Alves et al. 2019, 2016). The chemical structures of the main polar lipid classes found in nuts and oily fruits are depicted in Figure 2.

Figure 2. Polar lipid classes found in nuts and oily fruits. Phospholipids (PC, PE, PI, and PA), glycolipids (MGDG and DGDG), sphingolipids (Cer and HexCer) and betaine lipids (DGTS).

Health effects of neutral lipids, sterols, and polar lipids from oily fruits and nuts

The epidemiological, clinical, and experimental knowledge derived from decades of research evidenced that dietary patterns, namely the Mediterranean diet and consumption of specific foods, profoundly influence health outcomes and prevent chronic diseases like CVD. The lipids from foodstuffs have been a target of several studies investigating the biological activity of food consumption and the benefits of the Mediterranean diet. TG, FA, sterols, and polar lipids from diverse food sources have been explored as bioactive compounds (Table 6). Nevertheless, the real impact of these compounds on human health is not yet fully understood. High-level scientific proof is needed before formulating nutritional recommendations based on guidelines from evidence-based medicine and personalized medicine.

Table 6. Biological activity of fatty acids, triacylglycerols, sterols, and polar lipids from various nuts and oily fruits and other foods.

Source*	Lipids	Health effects	References
NA	MUFA (as n-9)	Improvement of several cardiovascular and diabetic risk factors (e.g. HDL-cholesterol and glycosylated haemoglobin)	(Covas et al. 2015; Fehily et al. 1992; Gumbiner et al. 1998; Hammad et al. 2016; Schwingshackl and Hoffmann 2012)
NA	PUFA (as n-6 and n-3)	Anti-inflammatory activity, maintenance of homeostasis and membrane fluidity, inhibition of LDL cholesterol synthesis, positive effects on the liver, kidney, and heart metabolism, reduction in blood pressure, essential for normal eye and brain function and beneficial involvement in several diseases such as CVD, diabetes, asthma, arthritis, and multiple sclerosis	(Abedi and Sahari 2014; Choo et al. 2010; Enot et al. 2015; Fehily et al. 1992; Fonolla-Joya et al. 2016; Gill and Valivety 1997; Gonçalves-De-Albuquerque et al. 2016; Hammad et al. 2016; Lai et al. 2013; Nettleton 1995; Perdomo et al. 2015; Steinmann et al. 2012; Wang et al. 2014; Wiktorowska-Owczarek et al. 2015; Wu et al. 2014; Zhao et al. 2013)
Olive oil and seed oils (soybean, corn, sunflower, and sesame)	NL (as TG), polar lipids	Antithrombotic activity (PAF antagonists)	(Karantonis et al. 2002)
NA, Pinus spp.	Sterols	Total and LDL serum cholesterol-lowering effect, positive effects on CVD risk, immune stimulation, prostate conditions, cutaneous wound healing, and connection to anti-ageing development by involvement in hypothalamus function	(Andersson et al. 2004; Brüll et al. 2010; Calpe-Berdiel et al. 2007; Escurriol et al. 2010; Esposito et al. 2013; Klippel et al. 1997; Kopylov et al. 2021; Pinedo et al. 2007; Shi et al. 2015; Trautwein et al. 2018)
Dicentrarchus labrax (olive pomace-fed sea bass)	FA, GPL	Cardioprotective activity (influence in PAF activities)	(Nasopoulou et al. 2014)
Spinacia oleracea (spinach), Chlorella vulgaris (green alga), Rosa canina (rosehip), and Crassocephalum rubens	GL	Cancer chemoprevention (suppression of inflammatory mediators), antitumoral activity (human gastric and pancreatic cancers), and anti-inflammatory effects in arthritis and osteoarthritis	(Akasaka et al. 2016; Hou et al. 2007; Larsen et al. 2003; Morimoto et al. 1995; Murakami et al. 2003; Nagatsu et al. 1994; Wang et al. 2002)
Salmon oil, seed oils (corn, cottonseed, and soybean), milk, supplementation, egg yolk, fish, krill, shellfish, meat, grains, algae	PL	Antioxidant activity (improving oxidative stability), antimicrobial activity, prevention of atherosclerosis, cancers (e.g. colon, breast, and lung), hepatopathies and CVD, positive effects in cognitive function, memory, handling stress, immunity, inflammation, physical activity, and exercise (e.g. positive cortisol effects, lower muscular pain, and faster recovery), reduction of plasma levels of LDL-cholesterol and TG	(Castro-Gómez et al. 2015; Hidalgo et al. 2005; King et al. 1992; Küllenberg et al. 2012; List and Friedrich 1989; Sun et al. 2018; Vesper et al. 1999)
Rice bran, maize seeds, soybeans, rye and wheat grains, milk, supplementation, meat, fish	SL	Connection to cell growth, differentiation, and apoptosis in most types of cells (e.g. colon and skin cells) from several origins (e.g. mice and human), cancer inhibitory action, improvement of the skin’s barrier function, prevention of atherosclerosis, reduction of plasma levels of LDL-cholesterol and TG, promotion of plasma levels of HDL-cholesterol, antimicrobial activity	(Aida et al. 2003; Castro-Gómez et al. 2015; Duivenvoorden et al. 2006; Gangoiti et al. 2010; Küllenberg et al. 2012; Vesper et al. 1999)

Note: Studies that explored the effects of a whole matrix (e.g. olive oil or hazelnut kernel) and did not refer to the impact of specific groups, classes, or molecular species from the neutral lipid (TG, FA), intermediate polarity (sterols) and or polar lipid (PL, GL, and SL) fractions are not mentioned in this table.

Abbreviations: NA, not applicable; MUFA, monounsaturated fatty acids; HDL, high-density lipoprotein; CVD, cardiovascular disease; GL, glycolipids; MGDG, monoglycosyldiacylglycerols; DGDG, diglycosyldiacylglycerols; FA, fatty acids; GPL, glycerophospholipids; PAF, platelet-activating factor; NL, neutral lipids; PL, phospholipids, PUFA, polyunsaturated fatty acids; LDL, low-density lipoprotein; SL, sphingolipids; TG, triacylglycerols.

*Lipid source used in the study, if applicable.

Although it is not fully clear yet how lipid compounds act on some biological processes, lipids are essential compounds involved in distinct biological functions. Each lipid class can have more than one biological function or activity. MUFA and PUFA’s bioactivities are associated with a beneficial impact on several organs like the liver, kidney, brain, and heart, and pathological conditions such as CVD, diabetes, and arthritis (Abedi and Sahari 2014; Choo et al. 2010; Covas et al. 2015; Enot et al. 2015; Fehily et al. 1992; Fonolla-Joya et al. 2016; Gill and Valivety 1997; Gonçalves-De-Albuquerque et al. 2016; Gumbiner et al. 1998; Hammad et al. 2016; Lai et al. 2013; Nettleton 1995; Perdomo et al. 2015; Schwingshackl and Hoffmann 2012; Steinmann et al. 2012; Wang et al. 2014; Wiktorowska-Owczarek et al. 2015; Wu et al. 2014; Zhao et al. 2013). Based on the FA content of a food or dietary pattern, dietary lipid indices can be calculated, such as the index of atherogenicity or the index of thrombogenicity, as a measure of the propensity of the diet to influence the risk of coronary heart disease. These indices consider MUFA, PUFA, and SFA ratios in their formulas (Fehily et al. 1992; Kalogeropoulos et al. 2013). The effect of designer lipids (also known as structured lipids or tailor-made fats) reveal great physiological activity and numerous benefits to the human body in comparison with plant or animal lipids. Designer lipids consist of chemically/enzymatically modified forms of common lipids which is key for increasing functionality and nutritional value of lipids by changing the composition and/or position of FA on the glycerol backbone (Jadhav and Annapure 2021).

Moreover, sterols have shown several positive effects as reducing total and low-density lipoprotein (LDL) serum cholesterol levels and risk of developing CVD, and positive influence on immune stimulation, and anti-ageing development, among others (Andersson et al. 2004; Brüll et al. 2010; Calpe-Berdiel et al. 2007; Escurriol et al. 2010; Esposito et al. 2013; Klippel et al. 1997; Kopylov et al. 2021; Pinedo et al. 2007; Shi et al. 2015; Trautwein et al. 2018). Finally, studies about dietary polar lipids have shown various beneficial health effects. PL have antioxidant, anti-inflammatory, antimicrobial, cancer chemoprevention, and cardioprotective activities and positively influence cognitive function, memory, and physical activity (Castro-Gómez et al. 2015; Hidalgo et al. 2005; King et al. 1992; Küllenberg et al. 2012; List and Friedrich 1989; Sun et al. 2018; Vesper et al. 1999). GL present anti-tumour and anti-inflammatory activity in arthritis, osteoarthritis, and cancer prevention (Akasaka et al. 2016; Hou et al. 2007; Larsen et al. 2003; Morimoto et al. 1995; Murakami et al. 2003; Nagatsu et al. 1994; Wang et al. 2002). SL are intrinsically involved in different organisms’ parts such as skin, plasma, colon, and brain. The evidence suggests they also have cancer prevention and antimicrobial activities (Aida et al. 2003; Castro-Gómez et al. 2015; Duivenvoorden et al. 2006; Gangoiti et al. 2010; Küllenberg et al. 2012; Vesper et al. 1999). Studies indicate that betaines have antioxidant activity and beneficial effects on lipid metabolism, glucose homeostasis/insulin resistance and influence different organs such as the brain, liver, kidneys, and heart (Airaksinen et al. 2018; Chai et al. 2013; Du et al. 2018; Ejaz et al. 2016; Ganesan et al. 2010; Hagar and Al 2014; Hoffman et al. 2009; Tiihonen et al. 2016). However, no studies have reported the biological activity and health effects of BL as far as is known (Table 6).

Methodologies to analyze lipids in oily fruits and nuts

Lipidomics

Lipidomics, a rapidly growing field of research, describes and quantifies all lipid molecular species and tries to comprehend their biological functions (O'Donnell et al. 2020). To ensure that lipid structural identification and quantitation are correctly performed and to compare published papers, standardized approaches and guidelines are needed, as recognized by the lipidomics research community (O'Donnell et al. 2020). Some guidelines provided by the Lipidomics Standards Initiative (LSI), for example, cover several analytical steps like extraction, analysis, processing, identification, quantification and data reporting. This is to improve the knowledge about lipid biology and analytical chemistry like mass spectrometry (MS) analysis, which is especially useful for new researchers in lipidomics (Lipidomics Standards Initiative Consortium 2019; O'Donnell et al. 2020).

The lipidomics workflow, either for targeted or untargeted approaches, is essentially built into four steps after preparing the biological sample (Figure 3). The workflow begins with total lipid extraction, followed by fractionation, data analysis and data processing (Maciel et al. 2018; Lipidomics Standards Initiative Consortium 2019). The untargeted analysis is used to identify and quantify as many lipid species as possible and, consequently, find a specific lipid signature of the biological sample. Analyzing the lipid extracts using liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS), for example, can generate a high amount of data that needs to be interpreted and validated. On the other hand, with the information acquired from untargeted strategies, it is possible to design targeted lipidomic approaches where the goal is to detect and quantify a set of specific lipids (Maciel et al. 2018). Although there is a certain consensus about the workflow steps, relatively to the techniques used in each step, harmony between studies reveals itself hard to achieve due to the absence of standard procedures with the current use of distinct approaches to analyze neutral, intermediate polarity or polar lipids.

Figure 3. Brief overview of a lipidomics workflow.

Extraction

The most used method for extracting oil from oily fruits and nuts and determining the oil yield is solvent extraction with petroleum ether or n-hexane in a Soxhlet apparatus and cold pressing (Table 7). However, Soxhlet extraction uses high temperatures, which degrades bioactive compounds and promotes lipid oxidation, and cold pressing, although a simple, ecological and not much costly method, often leads to low oil yields. As such, lipid extraction in lipidomics usually consists of a solid/liquid extraction and or a liquid/liquid extraction and or a solid-phase extraction (SPE) with organic solvents. Oil extraction from oily fruits and nuts has also been done by these methods (Table 7). The most utilized methods to extract total lipids use organic solvents with distinct polarities, namely the Folch method (Folch et al. 1957) and the Bligh and Dyer method (Bligh and Dyer 1959). The former uses chloroform, or a mixture of dichloromethane/methanol (2:1, by volume). The latter uses chloroform, or a mixture of dichloromethane/methanol (1:2, by volume), and an additional volume of chloroform or dichloromethane. Lipids are recovered from the lower phase, in the organic fraction, in these methods, after adding water to the mixture and centrifuging. Although these methods are widely used, allowing higher oil yields and extractions in parallel, they have disadvantages, such as costly and high-purity solvents, toxic and flammable organic solvents, and a clean-up step to remove solvent residues from the extracts (Danlami et al. 2014).

Table 7. Procedures and methods for extracting and analyzing lipids in nuts and oily fruits.

Procedure	Method		Food source [references]
Oil extraction	Soxhlet	with petroleum ether	Olives (Ranalli et al. 2002b; Vlahov et al. 1999), avocados (Tan et al. 2017; Yanty et al. 2011), several nuts (Kornsteiner-Krenn, 2013; Venkatachalam and Sathe 2006), walnuts (Abdallah et al. 2015), almonds (Kodad et al. 2014; Özcan et al. 2020; Sathe et al. 2008), pistachios (Seferoglu et al. 2006)
		with n-hexane	Walnuts (Cittadini et al. 2020), hazelnuts (Bada et al. 2004; Cittadini et al. 2020; Taş and Gökmen 2015), several nuts (Derewiaka et al. 2014), Brazil nuts (Chunhieng et al. 2008)
	Cold pressing		Walnuts (Gao et al. 2019b, 2019a, 2018), several nuts (Fernandes et al. 2017; Sena-Moreno et al. 2016), pistachios (Givianrad et al. 2013; Rabadán et al. 2018), almonds (Ojeda-Amador et al. 2019; Özcan et al. 2020), hazelnuts (Ojeda-Amador et al. 2019), peanuts (Dong et al. 2015)
	Organic solvents	Bligh & Dyer (or modified)	Peanuts (Cherif et al. 2014, 2013), olives (Alves et al. 2019; Shen, Dong, Yang, Baibado, et al. 2013), several nuts (Robbins et al. 2011), almond (Boukhchina et al. 2004; Shen, Dong, Yang, Li, et al. 2013; Zamany et al. 2017), avocados (Shen et al. 2014), pistachios (Chahed et al. 2008)
		Folch (or modified)	Olives (Montealegre et al. 2013), avocados (Takenaga et al. 2008), several nuts (Kalogeropoulos et al. 2013; Song et al. 2018; Vecka et al. 2019), walnuts (Angelova-Romova et al. 2013; Gao et al. 2019b), hazelnuts (Angelova-Romova et al. 2013), peanuts (Yoshida et al. 2005), pistachios (Tsantili et al. 2010)
		Hexane	Several nuts and oily fruits (Derewiaka et al. 2014; Lísa and Holčapek 2008; Miraliakbari and Shahidi 2008), avocados (Bora et al. 2001), walnuts (Gao et al. 2019b), almonds (Givianrad et al. 2013), pistachios (Arena et al. 2013; Massimo et al. 2020)
		Others	Hexane/isopropanol in several nuts (Maguire et al. 2004; Ryan et al. 2006; Tokusoglu 2011), ethyl acetate, petroleum ether and acetone in walnuts (Gao et al. 2019b), methanol in hazelnuts (Napolitano et al. 2018)
	Others		Supercritical fluid extraction using CO2 in avocados (Abaide et al. 2017; Tan et al. 2018), walnuts (Oliveira et al. 2002) and almonds (Mericli et al. 2017), subcritical fluid extraction using n-propane in cashew nuts (Zanqui et al. 2020b) and Brazil nuts (Zanqui et al. 2020a), ultrasound-assisted extraction in avocados (Tan et al. 2018), compressed liquefied petroleum gas in avocados (Abaide et al. 2017), aqueous enzymatic process assisted microwave extraction in macadamia nuts (Ma et al. 2022), supercritical fluid extraction using isopropyl alcohol and ethyl alcohol in Brazil nuts (Cornelio-Santiago et al. 2019)
Total fatty acid profile	Derivatisation	Transmethylation (alkaline conditions)	Olives (Kammoun and Zarrouk 2012; Sakouhi et al. 2010), avocados (Tan et al. 2018, 2017; Yanty et al. 2011), several nuts (Fernandes et al. 2017; Holčapek et al. 2005; Sena-Moreno et al. 2016), walnuts (Gao et al. 2019a, 2018; Verardo et al. 2013), almonds (Barreira et al. 2012; Kodad et al. 2014; Ojeda-Amador et al. 2019), hazelnuts (Bada et al. 2004; Ojeda-Amador et al. 2019; Taş and Gökmen 2015), peanuts (Dong et al. 2015), pistachios (Chahed et al. 2008; Ghrab et al. 2010; Rabadán et al. 2018)
		Acid hydrolysis or transmethylation/methylation procedure under sequential alkaline and acid conditions	Avocados (Alkhalaf et al. 2019; Amado et al. 2019; Bora et al. 2001; Corrales-García et al. 2019; Mardigan et al. 2019; Méndez-Zúñiga et al. 2019; Pacetti et al. 2007; Takenaga et al. 2008; Villa-Rodríguez et al. 2011), several nuts (Cittadini et al. 2020; Derewiaka et al. 2014; Kalogeropoulos et al. 2013; Maguire et al. 2004; Miraliakbari and Shahidi 2008; Momchilova and Nikolova-Damyanova 2007; Robbins et al. 2011; Ryan et al. 2006; Song et al. 2018; Vecka et al. 2019; Venkatachalan and Sathe 2006), almonds (Askin et al. 2007; Houmy et al. 2016; Özcan et al. 2020; Sathe et al. 2008; Soler et al. 1988; Tokusoglu, 2011; Zamany et al. 2017; Zhu et al. 2017), hazelnuts (Alasalvar et al. 2006; Balta et al. 2006; Tanriverdi 2016), peanuts (Veličkovska et al. 2016; Yoshida et al. 2005), cashew nuts (Rico et al. 2016; Zanqui et al. 2020b), pistachios (Küçüköner and Yurt 2003; Seferoglu et al. 2006; Tsantili et al. 2010), Brazil nuts (Chunhieng et al. 2008; Zanqui et al. 2020a), macadamia nuts (Koaze et al. 2002), olives (Ranalli et al. 2002a), walnuts (Martínez et al. 2006), pine nuts (Fardin-Kia et al. 2012; Matthäus et al. 2018a)
	Analysis	GC-FID	Olives (Ranalli et al. 2002a), avocados (Alkhalaf et al. 2019; Bora et al. 2001; Mardigan et al. 2019; Pacetti et al. 2007; Takenaga et al. 2008; Yanty et al. 2011), several nuts (Fernandes et al. 2017; Kornsteiner-Krenn, 2013; Maguire et al. 2004; Miraliakbari and Shahidi 2008; Robbins et al. 2011; Ryan et al. 2006; Sena-Moreno et al. 2016; Vecka et al. 2019; Venkatachalan and Sathe 2006) walnuts (Gao et al. 2019a, 2018; Martínez et al. 2006), almonds (Barreira et al. 2012; Givianrad et al. 2013; Kodad et al. 2014; Ojeda-Amador et al. 2019; Tokusoglu, 2011; Zamany et al. 2017), pistachios (Givianrad et al. 2013; Küçüköner and Yurt 2003; Rabadán et al. 2018; Seferoglu et al. 2006; Tokusoglu, 2011; Tsantili et al. 2010), hazelnuts (Alasalvar et al. 2006; Ojeda-Amador et al. 2019; Parcerisa et al. 1999; Taş and Gökmen 2015), peanuts (Dong et al. 2015; Veličkovska et al. 2016), cashew nuts (Rico et al. 2016; Zanqui et al. 2020b), pine nuts (Fardin-Kia et al. 2012), macadamia nuts (Koaze et al. 2002; Ma et al. 2022), Brazil nuts (Chunhieng et al. 2008)
		GC-MS	Several nuts (Derewiaka et al. 2014; Kalogeropoulos et al. 2013; Song et al. 2018), walnuts and hazelnuts (Cittadini et al. 2020), almonds (Mericli et al. 2017; Sathe et al. 2008)
		Others
Oil fractionation	Solid-phase extraction (SPE)	with silica bounding columns	Avocados (Pacetti et al. 2007), walnuts and hazelnuts (Angelova-Romova et al. 2013), several nuts (Fang et al. 2005; Fernandes et al. 2017; Wang et al. 2019)
		Others	With aminopropyl cartridges in olives (Alves et al. 2019), with TiO2 nanoparticles packed cartridges in almonds (Shen, Dong, Yang, Li, et al. 2013) and olives (Shen, Dong, Yang, Baibado, et al. 2013), with graphene/TiO2 nanocomposite filled cartridges in avocados (Shen et al. 2014)
	Thin layer chromatography (TLC)		Avocados (Takenaga et al. 2008), several nuts (Miraliakbari and Shahidi 2008; Momchilova and Nikolova-Damyanova 2007), walnuts (Abdallah et al. 2015; Martínez et al. 2006), almonds (Givianrad et al. 2013; Özcan et al. 2020), pistachios (Chahed et al. 2008; Givianrad et al. 2013), hazelnuts (Bada et al. 2004; Parcerisa et al. 1999), peanuts (Veličkovska et al. 2016; Yoshida et al. 2005), cashew nuts (Rico et al. 2016), macadamia nuts (Koaze et al. 2002), Brazil nuts (Chunhieng et al. 2008)
Sterol profile	Derivatisation with silylation		Several nuts (Derewiaka et al. 2014; Kalogeropoulos et al. 2013; Normén et al. 2007; Phillips et al. 2005; Robbins et al. 2011), walnuts (Abdallah et al. 2015; Gao et al. 2019a, 2018), almonds and pistachios (Givianrad et al. 2013; Tokusoglu, 2011), hazelnuts (Bada et al. 2004; Yorulmaz et al. 2009), peanuts (Shin et al. 2010), cashew nuts (Zanqui et al. 2020b), Brazil nuts (Chunhieng et al. 2008), pistachios (Massimo et al. 2020)
	Hydrolysis, extraction and purification with no derivatisation		Several nuts (Kornsteiner-Krenn, 2013; Maguire et al. 2004; Ryan et al. 2006; Vecka et al. 2019; Wang et al. 2019), walnuts (Martínez et al. 2006), almonds (Özcan et al. 2020), peanuts (Campos-Mondragón et al. 2009; Veličkovska et al. 2016), cashew nuts (Rico et al. 2016), Brazil nuts (Costa et al. 2010)
	Analysis	GC-FID	Several nuts (Kalogeropoulos et al. 2013; Miraliakbari and Shahidi 2008; Phillips et al. 2005; Robbins et al. 2011), walnuts (Abdallah et al. 2015; Gao et al. 2018), almonds (Givianrad et al. 2013; Tokusoglu, 2011), pistachios (Chahed et al. 2008; Givianrad et al. 2013; Massimo et al. 2020; Tokusoglu, 2011), hazelnuts (Alasalvar et al. 2009, 2006; Bada et al. 2004; Yorulmaz et al. 2009), peanuts (Shin et al. 2010), cashew nuts (Rico et al. 2016), Brazil nuts (Costa et al. 2010)
		GC–MS	Several nuts (Derewiaka et al. 2014; Miraliakbari and Shahidi 2008; Normén et al. 2007; Phillips et al. 2005; Wang et al. 2019), walnuts (Gao et al. 2019a; Martínez et al. 2006), almonds (Özcan et al. 2020), peanuts (Shin et al. 2010; Veličkovska et al. 2016), cashew nuts (Zanqui et al. 2020b), pistachios (Massimo et al. 2020)
		Others	HPLC-ESI-MS and MS^2 in several nuts (Vecka et al. 2019), HPLC-PDA in several nuts (Maguire et al. 2004; Ryan et al. 2006), HPLC-UV/VIS in peanuts (Campos-Mondragón et al. 2009) and several nuts (Kornsteiner-Krenn, 2013)
Triacylglycerol profile	Ag-NARP-HPLC-ESI-MS^n with silver cationization in peanuts (Cherif et al. 2014, 2013), RP-HPLC-ESI-MS and MS^2 in olives (Alves et al. 2019), NARP-HPLC-APCI-MS in avocados, olives and several nuts (Lísa and Holčapek 2008), RP-HPLC and ^13C NMR spectroscopy in olives (Vlahov et al. 1999), HPLC-RID in avocados (Tan et al. 2017; Yanty et al. 2011), hazelnuts (Bada et al. 2004), pistachios (Rabadán et al. 2018) and several nuts (Fernandes et al. 2017), HPLC-APCI-MS, HPLC-ELSD and HPLC-UV in several nuts (Holčapek et al. 2005), GC-FID in walnuts (Gao et al. 2019a, 2018), HPLC-ELSD in walnuts (Amaral et al. 2004) and hazelnuts (Alasalvar et al. 2009, 2006; Parcerisa et al. 1999), TLC and scanning densitometry in walnuts, hazelnuts and almonds (Momchilova and Nikolova-Damyanova 2007), HPLC-ELSD in almonds (Barreira et al. 2012), HPLC-PDA in hazelnuts (Taş and Gökmen 2015), HPLC-APCI-MS in peanuts (Dong et al. 2015), EASI-MS in cashew nuts (Zanqui et al. 2020b) and Brazil nuts (Zanqui et al. 2020a)
Phospholipid profile	HILIC-ESI-MS and MS^2 in several nuts and oily fruits (Alves et al. 2019; Song et al. 2018), NACE-ESI-MS and MS^2 in olives (Montealegre et al. 2013), TLC in avocados (Takenaga et al. 2008) and peanuts (Yoshida et al. 2005), NP-HPLC-ESI-MS and MS^2 in avocados (Pacetti et al. 2007), almonds (Boukhchina et al. 2004) and hazelnuts (Napolitano et al. 2018), TLC-FID-Iatroscan in several nuts (Miraliakbari and Shahidi 2008), 2D-TLC and colorimetric tests in walnuts and hazelnuts (Angelova-Romova et al. 2013), MALDI-TOF/MS in almonds (Shen, Dong, Yang, Li, et al. 2013), olives (Shen, Dong, Yang, Baibado, et al. 2013) and avocados (Shen et al. 2014), HPLC-ELSD in hazelnuts (Parcerisa et al. 1999), TLC and colorimetric tests in Brazil nuts (Chunhieng et al. 2008)
Glycolipid profile	HILIC-ESI-MS and MS^2 in olives (Alves et al. 2019), TLC in avocados (Takenaga et al. 2008), HPLC-ESI-MS and MS^2 in avocados (Pacetti et al. 2007) and hazelnuts (Napolitano et al. 2018)
Sphingolipid profile	HILIC-ESI-MS and MS^2 in olives (Alves et al. 2019), TLC in avocados (Takenaga et al. 2008), TLC-FID-Iatroscan in several nuts (Miraliakbari and Shahidi 2008), HPLC-APCI-MS in several nuts (Fang et al. 2005), HPLC-ESI-MS and MS^2 in hazelnuts (Napolitano et al. 2018), almonds and pistachios (Paroni et al. 2019)

Abbreviations: SPE, solid-phase extraction; TLC, thin layer chromatography; GC-FID, gas chromatography-flame ionization detector; GC-MS, gas chromatography-mass spectrometry; Ag-NARP-HPLC-ESI-MSⁿ, silver cationization-non-aqueous reversed-phase high-performance liquid chromatography-electrospray ionization-mass spectrometry; TG, triacylglycerols; RP-HPLC-ESI-MS, reversed-phase high-performance liquid chromatography-electrospray ionization-mass spectrometry; MS/MS, tandem mass spectrometry; EASI-MS, easy ambient sonic-spray ionization-mass spectrometry; HILIC-ESI-MS, hydrophilic interaction liquid chromatography-electrospray ionization-mass spectrometry; NARP-HPLC-APCI-MS, non-aqueous reversed-phase high-performance liquid chromatography-atmospheric pressure chemical ionization-mass spectrometry; NACE-ESI-MS, non-aqueous capillary electrophoresis-electrospray ionization-mass spectrometry; NMR, nuclear magnetic resonance; HPLC-RID, high-performance liquid chromatography-refractive index detector; RP-HPLC, reversed-phase high-performance liquid chromatography; HPLC, high-performance liquid chromatography; NP-HPLC-ESI-MS, normal-phased liquid chromatography-electrospray ionization-mass spectrometry; HPLC-ESI-MS, high-performance liquid chromatography-electrospray ionization-mass spectrometry; HPLC-PDA, high-performance liquid chromatography-photodiode array detector; HPLC-UV/VIS, high-performance liquid chromatography-ultraviolet/visible detector; HPLC-APCI-MS, high-performance liquid chromatography-atmospheric pressure chemical ionization-mass spectrometry; HPLC-ELSD, high-performance liquid chromatography-evaporative light-scattering detection; HPLC-UV, high-performance liquid chromatography-ultraviolet detection; TLC-FID-Iatroscan, thin layer chromatography-flame ionization detector-iatroscan analyzer; 2D-TLC, two-dimensional thin layer chromatography; MALDI-TOF/MS, matrix-assisted laser desorption ionization − time-of-flight-mass spectrometry; HPLC-APCI-MS, high-performance liquid chromatography-atmospheric pressure chemical ionization-mass spectrometry

Advances in lipidomics methodologies allowed cleaner and more environmentally friendly methods to avoid using chlorinated and toxic solvents (Table 7). These include the use of greener and food-grade systems, like isopropyl alcohol and ethyl alcohol (Abaide et al. 2017; Cornelio-Santiago et al. 2019; Mericli et al. 2017; Oliveira et al. 2002; Tan et al. 2018), combined with different mechanical or other methods to increase extraction efficiencies, such as ultrasound-assisted extraction (Tan et al. 2018), supercritical fluid extraction using CO₂, subcritical fluid extraction using n-propane (Zanqui et al. 2020a; Zanqui et al. 2020b), and aqueous enzymatic process assisted with microwave extraction (Ma et al. 2022).

Fractionation of lipid extracts

Fractionation is not mandatory but may be necessary after total lipid extraction when a high purity of interest compounds is required. The sample must be concentrated if these components exist in low concentration. SPE allows fractionating lipids from the total extract or recovering different or enriched fractions from a total lipid extract. Silica cartridges are used to recover neutral lipids, intermediate polarity lipids and polar lipids or fractionate the lipid extract into neutral lipid classes (free FA, sterols, and TG) and a polar lipid-rich fraction (Table 7). These silica cartridges have been commonly used in foodstuffs like avocados, walnuts, and almonds (Angelova-Romova et al. 2013; Fang et al. 2005; Fernandes et al. 2017; Pacetti et al. 2007; Wang et al. 2019). More recently, SPE using alternative cartridges has shown promising results (Table 7), as aminopropyl cartridges in olives (Alves et al. 2019), TiO₂ nanoparticles packed cartridges in almonds (Shen, Dong, Yang, Li, et al. 2013), and graphene/TiO₂ nanocomposite packed cartridges in avocados (Shen et al. 2014). However, in these cases, procedures were not yet optimized for recovering the complex polar lipidome of these matrices. SPE is a crucial step in obtaining the polar lipid-rich fraction from oils or oily matrices to allow their identification and structural characterization, especially in the case of oil where polar lipids are in low abundance.

Thin-layer chromatography (TLC) can be used for the fractionation of total extracts and can also be used for monitoring the efficiency of an extraction procedure and determining the purity of the extract after a previous extraction or separation step. Acylglycerols can be fractionated using n-hexane/diethyl ether/acetic acid (80:20:1, by volume). Polar lipid classes, such as PL, can be fractionated by one-dimension TLC using a solvent system composed of chloroform/ethanol/water/triethylamine (35:30:7:35, by volume) (Cyberlipid Center 2021b). Spraying with a revealing agent as a solution of primuline in acetone/water and visualization using an ultraviolet lamp allows for detecting the lipid spots in the TLC plate (Cyberlipid Center 2021b). It is possible to identify the lipid classes by comparing their retention factors (R_f) with commercial lipid standards applied to the same plate. The relative content of each lipid class of PL or GL in the total lipid extract is also reachable after TLC separation by scrapping off the individual lipid spots and quantifying phosphorus or glucose in each spot, respectively. Additionally, the lipids from each TLC spot can be further analyzed regarding lipid class identification and molecular characterization by MS or FA analysis after recovery by scraping the spots and using proper extraction methods (Alves et al. 2018; Maciel et al. 2018). TLC has been performed for oil fractionation and further identification of lipid classes in several nuts and oily fruits (Table 7).

Analysis and identification of lipids

After SPE or TLC, the lipid fractions may be analyzed through several techniques like gas chromatography - flame ionization detector (GC-FID) or GC-MS, high-performance liquid chromatography (HPLC)-MS, direct injection (DI)-MS, nuclear magnetic resonance (NMR) spectroscopy or non-aqueous capillary electrophoresis (NACE) (Alves et al. 2018; Maciel et al. 2018).

GC can be used coupled to flame ionization detectors (GC-FID), MS analyzers (GC-MS) or both for the identification and quantification of fatty acid methyl esters (FAME) (Maciel et al. 2018; Pacchiarotta et al. 2010). One of the challenges of GC relies on the need for volatilization of the compounds on samples that contain nonvolatile compounds, such as FA, that can be achieved by derivatisation using acid or alkaline hydrolysis or transmethylation (Pacchiarotta et al. 2010). The transesterification of FA with methanol in alkaline conditions allowed the derivatisation into fatty acid methyl esters (FAME), (Sekora et al. 2009). GC-MS offers more accurate characterization than GC-FID because the identification is based on the retention time (RT) and the mass spectrum of each FAME. Nevertheless, GC-FID has been the elected method for identifying the FA profile of oily fruits and nuts oils (Table 7), most probably because GC-FID usually provides results with higher accuracy for the quantification of compounds while GC-MS gives more robust results in the identification of all compounds in a sample.

Sterols represent a significant portion of the unsaponifiable matter of oily fruits and nuts. They are predominantly in the form of free sterols, sterol esters, SG and ASG. Sterol esters, SG, and ASG, unlike free sterols, must be hydrolyzed to be analyzed by GC-MS or GC-FID as free sterol moieties. On the other hand, sterol esters, SG, and ASG can be analyzed by LC-MS without requiring the hydrolytic step. These lipids are often extracted from the oils using non-polar solvents, and then both phases (saponifiable and non-saponifiable) are subsequently separated, evaporated, and redissolved in a proper solvent. Generally, these phases may be cleaned up by TLC or column chromatography and derivatised, usually by silylation. GC-FID has been the most employed method for analyzing the sterol profile in oily fruits and nuts oils (Abdallah et al. 2015; Alasalvar et al. 2006, 2009; Bada et al. 2004; Chahed et al. 2008; Costa et al. 2010; Gao et al. 2018; Givianrad et al. 2013; Kalogeropoulos et al. 2013; Massimo et al. 2020; Miraliakbari and Shahidi, 2008; Phillips et al. 2005; Rico et al. 2016; Robbins et al. 2011; Shin et al. 2010; Tokusoglu, 2011; Yorulmaz et al. 2009) (Table 7). However, alternative methods such as GC–MS (Derewiaka et al. 2014; Gao et al. 2019a; Martínez et al. 2006; Massimo et al. 2020; Miraliakbari and Shahidi 2008; Normén et al. 2007; Özcan et al. 2020; Phillips et al. 2005; Shin et al. 2010; Veličkovska et al. 2016; Wang et al. 2019; Zanqui et al. 2020a, 2020b) and HPLC (Campos-Mondragón et al. 2009; Kornsteiner-Krenn, 2013; Maguire et al. 2004; Ryan et al. 2006; Vecka et al. 2019) have also been applied in this analysis (Table 7).

After TLC or SPE, the lipid classes or lipids species can be separated by HPLC, which allows for the separation of complex mixtures with multiple components. HPLC is a separation technique often coupled to different detectors, like ultraviolet detectors (HPLC–UV) or mass spectrometers (HPLC–MS). These hyphenated techniques allow for the detection and molecular characterization of TG and polar lipids or esterified glycosylated sterols. The LC coupled to electrospray ionization mass spectrometry (LC-ESI-MS) is the most popular way to analyze total lipid extracts or lipid fractions nowadays. More recently, HPLC-HRMS and HPLC-HRMS/MS have provided detailed TG (Alves et al. 2019; Cherif et al. 2014, 2013; Dong et al. 2015; Holčapek et al. 2005) and polar lipid (Alves et al. 2019; Boukhchina et al. 2004; Fang et al. 2005; Napolitano et al. 2018; Pacetti et al. 2007; Paroni et al. 2019; Song et al. 2018) profiles of several nuts and oily fruits (Table 7). In fact, on the one hand, MS allows molecular identification through the MS spectrum (full MS) and, on the other hand, the structural characterization of the individual components with high molecular specificity and detection sensitivity through the tandem MS spectra (MS/MS). MS data gives information on the polar head of polar lipids such as PL, GL and SL and the fatty acyl composition of polar lipids and TG, representing a powerful tool for lipid fingerprinting. Regarding the foodstuffs under study, other less used techniques have also been employed to characterize TG, and the polar lipidome (Table 7), such as HPLC coupled to evaporative light-scattering detection (ELSD) (Alasalvar et al. 2009, 2006; Amaral et al. 2004; Barreira et al. 2012; Holčapek et al. 2005; Parcerisa et al. 1999), photodiode array detectors (HPLC-PDA) (Taş and Gökmen 2015) and refractive index detectors (HPLC-RID) (Bada et al. 2004; Fernandes et al. 2017; Rabadán et al. 2018; Tan et al. 2017; Yanty et al. 2011), and also NACE (Montealegre et al. 2013), GC-FID (Gao et al. 2018, 2019a), NMR (Vlahov et al. 1999) and TLC (Angelova-Romova et al. 2013; Chunhieng et al. 2008; Miraliakbari and Shahidi 2008; Momchilova and Nikolova-Damyanova 2007; Takenaga et al. 2008; Yoshida et al. 2005).

Quantification

In addition to analysis and identification, the relative and absolute quantification of lipid groups, classes, and species is also possible in lipid studies. For example, TG analysis cis normally carried out by HPLC coupled to different types of detectors (Table 7). For lipids such as sterols, PL, and GL, colourimetric tests are often employed after fractionation by TLC or SPE (Angelova-Romova et al. 2013; Chunhieng et al. 2008; Takenaga et al. 2008). These approaches are generally used to quantify groups and or classes of lipids. On the other hand, HPLC-MS has proven to be an effective tool for the relative or absolute quantification of lipids at the molecular level in foodstuffs like oily fruits and nuts, allowing a more in-depth characterization of the lipid profile (Alves et al. 2019, Song et al. 2018, Pacetti et al. 2007). The relative quantification of lipid classes is determined by calculating the peak area of each ion of the respective class and the normalization per the peak area of the respective internal standard. For instance, in the lipidomic profiling of the olive fruit pulp, PC(14:0_14:0) and PE(14:0_14:0) were used as internal standards for the quantification of phosphatidylcholine and phosphatidylethanolamine lipid species, respectively (Alves et al. 2019). Internal standards should not be present in the sample’s lipidome. However, some limitation exists since there are not available standards for all lipid classes.

Regarding each molecular species within a lipid class, the relative abundance is obtained by dividing its peak area by the sum of the peak areas of all ions identified for that specific class. Moreover, if the objective is to quantify each molecular species in absolute terms, it will be necessary to use internal standards preferentially from the same classes but with unusual fatty acyl chains and create calibration curves (Khoury et al. 2018). However, the lack of standards from some lipid classes impairs the proper quantification of some lipids.

Lipidomic studies toward oily fruits and nuts’ oils authenticity and traceability

Food authenticity and traceability remain on the agenda for food safety regulatory authorities. In the case of oily fruits and nuts, these questions become essential to assess their botanical and geographical origin and detect fraud or adulteration in oils (e.g. olive oil, avocado oil, hazelnut, and peanut oils). Fraud refers to selling a lower-quality product (e.g. extra virgin olive oil for virgin olive oil). Adulteration involves mixing other oils or substances similar to the components of the product (e.g. mixing other vegetable oils with a similar chemical profile and adding dyes). Such practices are illegal and stem primarily from economic reasons. They reduce the quality of food, can cause harm to human health in the case of food allergens, and lead to consumer distrust. In the case of nuts and oily fruits, it is imperative to obtain analytical methods that guarantee their quality and authenticity, as they are foods in great demand.

In addition, food fraud and adulteration have led to the need to establish efficient traceability monitoring systems as an essential means for food safety management and control, such as the latest blockchain technology (Feng et al. 2020). However, sensitive and robust analytical tools are needed to determine products’ quality and authenticity.

Lipidomics offers a formidable solution for tracking lipids in food and raw materials. Food lipidomics leads to a better exploration of the lipid profile and fingerprinting of oilseed fruits, nuts and their oils, using, for example, state-of-the-art analysis tools such as orbitrap mass spectrometers and their coupling to reversed-phase HPLC (C₁₈ columns) that allows analyzing the entire lipidome in a short timeframe. However, although software for analyzing lipidomics data is evolving, the analysis is still essentially manual. Moreover, it must be careful because of isobaric and unknown compounds, especially among minor lipids such as polar lipids.

Several studies show lipid profiles serve as markers of authenticity, identity, and traceability of nuts and oily fruits using lipidomics. Some examples are given based on chromatographic techniques coupled with MS or other types of detectors.

The analysis of the TG profile by HPLC-ELSD coupled with multivariate statistical analyses proved effective in discriminating protected designation of origin (PDO) and non-PDO almond cultivars independently of the harvest year (Barreira et al. 2012). These differences identified classification parameters, providing an important tool for authenticity (Barreira et al. 2012). The lipid fractions of fourteen hazelnut varieties from two harvest seasons were comprehensively analyzed for their TG, FA, and tocopherol profiles by HPLC-DAD. Significant statistical differences in most TG were found considering hazelnut varieties and harvest year (Taş and Gökmen 2015). Also, significant differences in the TG composition were found by HPLC-ELSD between walnut cultivars. These differences were also significant when cultivars were grouped by year of production, showing that the TG composition could be strongly influenced by environmental factors (Amaral et al. 2004). It was also possible to differentiate commercial oil extracted from almonds, hazelnuts, and pecan nuts using the TG profiles analyzed by HPLC-RID (Fernandes et al. 2017). TG profiles established by HPLC–UV–vis detection predicted the botanical origin of vegetable oils (olive, hazelnut, peanut) with assignment probabilities higher than 95% (Lerma-García et al. 2011). Distinct TG compositions were found among the Tunisian peanut varieties detected by LC–ESI–MS (Cherif et al. 2013). Another study on high-oleic and regular peanut oils revealed different TG profiles by HPLC-MS, mainly in the contents of OOO, OPO, and POL. This finding provided a theoretical foundation for detecting edible oils’ adulteration and analyzing the nutritional function of high-oleic peanut oils (Dong et al. 2015). HPLC analysis of Malaysian avocado varieties found several differences in the TG profile. While the major TG of local avocado cultivars were POO, POL, OOO and PPO, the dominant TG of the Hass variety were OOO, PPO, OOL and POL. Due to these differences in FA and TG positional patterns, the oils of local avocado cultivars possessed utterly different characteristics in several quality parameters from those of the Hass variety (Yanty et al. 2011).

Other lipids have also been evaluated in these foodstuffs. For instance, significant differences were found in the TG, polar lipids, DAG, free FA, and neutral lipid contents between two Tunisian pistachio varieties from four different regions (Chahed et al. 2008). Furthermore, PL were found as potential lipid markers of geographical origin for the chemical fingerprint of almonds (Prunus dulcis L. cv Nonpareil) using TiO₂ nanoparticle-based matrix solid-phase dispersion and matrix-assisted laser desorption ionization − time-of-flight-mass spectrometry (MALDI-TOF/MS) (Shen, Dong, Yang, Li, et al. 2013). Furthermore, a characteristic PL composition was also found by LC-MS and MS/MS in different vegetable oils, including olive and almond oils, which may be helpful in the identification of the oil source in commercial applications (Boukhchina et al. 2004). Also, a few differences in the amounts of some PL classes were reported in hazelnuts and walnuts from Bulgaria as analyzed by SPE and two-dimensional TLC when compared with the same fruits from other countries (Angelova-Romova et al. 2013). Furthermore, unique PL profiles were revealed by hydrophilic interaction liquid chromatography-electrospray ionization-ion trap-time-of-flight-mass spectrometry (HILIC-ESI-IT-TOF-MS) in almonds, walnuts, peanuts, pistachios, cashews, and pecans (Song et al. 2018). Finally, lipid profiling by ESI-MS was helpful for the successful evaluation of fraud in avocado oil-based products with soybean oil (Rydlewski et al. 2020).

Although there are few studies of lipid phenotyping at the molecular level by lipidomics approaches in oily fruits and nuts, the ongoing research is promising. Therefore, much is expected for years to come regarding the contribution of lipidomics toward the quality, authenticity, and traceability of these agri-food products.

Potential industrial applications of oily fruits and nuts’ by-products

Alongside the health benefits of lipids that suggest the beneficial consumption of oily fruits, nuts, and products thereof, the by-products deriving from these fruits’ industrial processing have been associated with other relevant uses.

For instance, using an avocado paste made with avocado waste for finishing pigs (pigs being fed to market weight) seems to benefit meat’s quality and shelf life (Hernández-López et al. 2016). Dietary avocado significantly impacted intramuscular fat content and composition, reducing the lipid content in specific muscles and increasing the unsaturation degree. Muscles from treated pigs had significantly lower lipid and protein oxidation rates during chilled storage, and the color of the muscles was preserved from oxidation (Hernández-López et al. 2016). Conversely, avocado PL are usually unwanted by the oil industry. Still, they have excellent potential for producing emulsions in the food, cosmetics, and medical industries, showing gel-like behavior and strong intermolecular forces (Züge et al. 2017).

Olive mill processing by-products (e.g. olive leaf, olive pomace, and deodorization distillate), which are residues representing a considerable environmental burden of the olive oil industry, have been valorized in many applications like alternatives for energetic production (biodiesel, biogas, bioethanol, biohydrogen, and biofuel), animal feed, agricultural applications (soil regulator), food (gelling agents, functional foods, and preservatives), drugs, nutraceuticals, cosmetics (natural moisturizers and sunscreen agents), and other biotechnological applications (bioplastic/biopolymer, biosurfactant, and lipase production) (Seçmeler and Galanakis 2019).

Olive pomace and stoned olive cakes, being rich in FA and polar lipids, may be helpful to produce animal feeds, such as functional fish feeds or as an ingredient to include in animal feedstocks, improving the nutritional and nutraceutical properties of various foods such as mammal’s meat, milk and cheese (Castellani et al. 2017; Cibik and Keles 2016; Nasopoulou and Zabetakis 2013; Terramoccia et al. 2013). Furthermore, olive seed oil, that is, the oil retrieved from pressing the seed that is inside the olive’s stone, has a lipid content of 27.3–53.0 mg 100 mg⁻¹ biomass which is comprised by 80–97% TG, 8% free FA, 2.3–4.1% sterols with up to 90% corresponding to β-sitosterol. The average PL content in olive seed oil was estimated to be 1.25% and the GL content was 1.49% on average (Seçmeler and Galanakis 2019; Alves et al. 2022). The polar lipid fingerprint carried out by hydrophilic interaction liquid chromatography (HILIC)-HR-ESI-MS, and MS/MS revealed ten polar lipid classes and 94 lipid species including 56 PL, 17 GL, 16 SL, and 5 ASG (Alves et al. 2022). Olive stones, pomace, and leaves have a potential role in skincare products and cosmetics due to the positive effect of the FA at distinct levels, such as the composition of the lipid film in the skin surface, the protective barrier, and the maintenance of hydration (Lin and Khnykin 2014; Rodrigues et al. 2015). Worth noticing that avocado stone oil presents significant higher PUFA/SFA ratios than avocado pulp or peel oils (Amado et al. 2019). Moreover, the polar lipid (PL and GL) content in avocado stone oil (28.3 ± 8.7 mg 100 mg⁻¹ lipids) was about five-fold the content found in the pulp (5.2 ± 2.0 mg 100 mg⁻¹) (Pacetti et al. 2007). These contents may be responsible for the significant anti-oxidant, anti-inflammatory and anti-cancer effects of the avocado stone oil compared to the avocado pulp oil (Alkhalaf et al. 2019). These results point out to an underrated potential and use of the waste parts of some fruits like avocado in phytomedicine.

Hazelnut skin, an agro-food by-product rich in FA and sterols, has revealed blood lipid-lowering effects, decreasing the circulating levels of total and LDL-cholesterol and TG in hamsters (Caimari et al. 2015). Pecan nut cake, a good source of lipids, carbohydrates, and proteins, may represent a potential ingredient for producing foods such as desserts, jelly, yoghurts, sausage, pasta, processed cheese, and bakery and confectionery products (Maciel et al. 2020).

Conclusion

Nuts and oily fruits have a high lipid content provided by the TG concentration. These foods have a wide range of FA (between 6 and 26 carbons, with up to 6 double bonds), being a good source of n-9 MUFA and n-6 PUFA. Besides TG, nuts and oily fruits contain other minor important lipids, such as phytosterols and polar lipids, which have high chemical diversity and complexity. Although these minor lipids represent a source of high value-added phytochemicals with beneficial effects on human health, they have been scarcely studied.

The real impact of lipids from nuts and oily fruits on human health in the different populations’ dietary patterns is still far from being fully understood. However, scientific research has revealed that lipids (sterols, MUFA, PUFA, phospholipids, glycolipids, and sphingolipids) from these foodstuff are positively involved in lipid metabolism, cardiovascular and diabetic risk factors, blood pressure, cholesterol and TG levels, cell growth, skin barrier function, and other important biological activities. Likewise, lipids from nuts, oily fruits and their industry by-products serve as valuable ingredients or raw materials for several industries, such as the food, nutraceutical, pharmaceutical, cosmetic, and animal feed industries.

Keeping in mind the considerable lack of information about oily fruits and nuts’ oil profile and the real impact of these lipids on human health, lipidomics has become a rapidly growing field of research that can provide a critical analytical approach for lipidome fingerprinting and discovering new compounds with potential biological activity, especially from plant-derived foodstuffs. Among the distinct approaches to extract total lipids from oleaginous fruits, the most widely used is solvent extraction using the reference method in a Soxhlet apparatus with n-hexane, while the Bligh and Dyer method, the Folch method or SPE have been less used yet. GC-FID and HPLC-MS have been the main approaches to study the FA and TG profile, respectively. Regarding the sterol analysis, the selected approaches are GC-FID and GC-MS. LC–MS approaches have been the chosen analytical strategy for polar lipid profiling in oily fruits and nuts, with still scarce but promising results. Standardized (un)targeted approaches and guidelines are still needed, but with the recent advances on the detailed lipidomic profiling carried out by high-resolution LC–MS analysis, a long road has been opened that will deepen the knowledge of these foodstuff at the molecular level. These analytical tools will help better understand the influence of several parameters, such as the geographical and botanical origin in the oily fruits ant nuts’ composition in further studies. Lipidomics offers a formidable solution for tracking lipids in food and raw materials, being a potential helpful tool to detect fraud or adulteration in the oils from nuts and oily fruits.

Nevertheless, there is still a research gap on the comprehensive lipid composition of nuts, oily fruits, their by-products and their beneficial effects. Further studies regarding lipidomic phenotypes, bioaccessibility and bioavailability should be carried out to contribute to the valorization of nuts, oily fruits and their industry by-products.

Abbreviations
APCI	=	atmospheric pressure chemical ionization
ASG	=	acylsterolglycoside
BL	=	betaine lipid
C:N	=	number of carbons: number of double bond
Cer	=	ceramide
CHD	=	coronary heart disease
CL	=	cardiolipin
CVD	=	cardiovascular disease
DAD	=	diode-array detection
DGDG	=	diglycosyldiacylglycerol
DGMG	=	diglycosylmonoacylglycerol
DGTS	=	diacylglyceryl-N,N,N-trimethylhomoserine
EASI	=	easy ambient sonic-spray ionization
ELSD	=	evaporative light-scattering detection
ESI	=	electrospray ionization
FA	=	fatty acid
FAME	=	fatty acid methyl ester
FID	=	flame ionization detector
GC	=	gas chromatography
GL	=	glycolipid
GPL	=	glycerophospholipid
HDL	=	high-density lipoprotein
HexCer	=	hexosylceramide
HILIC	=	hydrophilic interaction liquid chromatography
HPLC	=	high-performance liquid chromatography
HRMS	=	high-resolution mass spectrometry
LC	=	liquid chromatography
LDL	=	low-density lipoprotein
LPA	=	lysophosphatidic acid
LPC	=	lysophosphatidylcholine
LPE	=	lysophosphatidylethanolamine
LPG	=	lysophosphatidylglycerol
LPI	=	lysophosphatidylinositol
LSI	=	Lipidomics Standards Initiative
MALDI	=	matrix-assisted laser desorption ionization
MGDG	=	monoglycosyldiacylglycerol
MGTS	=	monoacylglyceryl-N,N,N-trimethylhomoserine
MS	=	mass spectrometry
MS/MS	=	tandem mass spectrometry
MUFA	=	monounsaturated fatty acid
NACE	=	non-aqueous capillary electrophoresis
NARP	=	non-aqueous reversed-phase
NL	=	neutral lipid
NMR	=	nuclear magnetic resonance
NP	=	normal-phased
PA	=	phosphatidic acid
PAF	=	platelet-activating factor
PC	=	phosphatidylcholine
PDA	=	photodiode array detector
PDO	=	protected designation of origin
PE	=	phosphatidylethanolamine
PE-N(FA)	=	N-acyl phosphatidylethanolamine
PG	=	phosphatidylglycerol
PI	=	phosphatidylinositol
PL	=	phospholipid
PS	=	phosphatidylserine
PUFA	=	polyunsaturated fatty acid
Rf	=	retention factor
RID	=	refractive index detector
RP	=	reversed-phase
RT	=	retention time
SG	=	sterolglycoside
SL	=	sphingolipid
SM	=	sphingomyelin
sn	=	stereospecific number
SPB	=	sphingoid base
SPE	=	solid-phase extraction
SP	=	sphingophospholipid
TG	=	triacylglycerol
TLC	=	thin-layer chromatography
TOF	=	time-of-flight
Tr	=	trace
UV	=	ultraviolet
VIS	=	visible
w/w	=	weight per weight.

Acknowledgments

The authors acknowledge the University of Aveiro, Fundação para a Ciência e a Tecnologia, I. P. (FCT, Portugal) and Ministério da Ciência e Tecnologia (MCT) for the financial support for the LAQV-REQUIMTE (FCT UIDB/50006/2020) and CESAM (UIDP/50017/2020 + UIDB/50017/2020 + LA/P/0094/2020) through national funds and, where applicable, co-financed by the European Regional Development Fund (FEDER), within the Portugal 2020 Partnership Agreement, and to the Portuguese Mass Spectrometry Network (RNEM, LISBOA-01-0145-FEDER-402-022125). Thanks are also due to FCT/MCT through national funds (PIDDAC) and the co-funding by FEDER, within the Portugal 2020 Partnership Agreement and Programa Operacional Temático Factores de Competitividade (COMPETE) 2020, and to the COST Action EpiLipidNET, CA19105 – Pan-European Network in Lipidomics and EpiLipidomics. This work was also funded by national funds through FCT in the scope of the Individual Call to Scientific Employment Stimulus 2017 with a Junior Researcher contract to Eliana Alves (reference CEEC-IND/00971/2017).

Disclosure statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.