Fruit-Vegetable Food Industry Waste as Biocomponents of Liquid Fuels

Abstract

This study explores the potential of seeds from fruit and vegetable processing waste as feedstocks for biodiesel biocomponent production. Fatty acid methyl esters (FAMEs), obtained through a transesterification reaction, were extracted using ultrasound-assisted extraction and identified by gas chromatography–mass spectrometry (GC–MS) in selected ion monitoring (SIM) mode. A total of 31 to 34 individual FAME compounds were identified across all samples. The fatty acid profiles varied both quantitatively and qualitatively depending on the raw material; however, unsaturated FAMEs predominated over saturated ones in all cases. The highest proportions were observed for γ-linolenic acid (C18:3n6), particularly in apple, melon, lemon, and pumpkin seeds. Substantial contributions of oleic acid (C18:1n9c) were found in apple, quince, cherry, and melon seeds, while linolelaidic acid (C18:2n6t) dominated in melon, pumpkin, quince, and cherry seeds. The highest total FAME contents were recorded for apple, melon, lemon, and pumpkin seeds, while the lowest values were observed in apricot and pepper seeds. Among the materials studied, grape seeds proved to be the most promising feedstock, exhibiting a favorable cetane number and a beneficial fatty acid profile characterized by high monounsaturated fatty acid (MUFA) content, low polyunsaturated fatty acid (PUFA) content, and a moderate fraction of saturated fatty acids (SFAs). Plum and peach seeds also showed significant potential, but their higher PUFA levels may compromise oxidative stability and could require antioxidant supplementation or blending with MUFA-rich feedstocks.

1. Introduction

In the face of escalating challenges associated with the depletion of fossil fuel resources and the urgent need to mitigate greenhouse gas emissions, the transformation of the energy sector toward renewable sources has become a cornerstone of global climate and energy policy. Consequently, the search for renewable, low-emission energy carriers has emerged as one of the most critical areas of contemporary scientific and technological research. Among alternative fuels, biodiesel—defined as fatty acid methyl esters (FAMEs)—represents one of the most promising liquid biofuels capable of partially substituting conventional diesel fuel. Biodiesel offers several advantages, including biodegradability and reduced emissions of sulfur oxides and particulate matter, thereby contributing to the mitigation of greenhouse gas emissions [1,2].

Conventional biodiesel production relies predominantly on first-generation feedstocks (e.g., vegetable oils) and second-generation feedstocks (e.g., waste oils and animal fats). However, large-scale commercialization remains constrained by economic challenges, particularly those associated with feedstock availability and cost [3]. In response, increasing attention has been directed toward the utilization of agri-food industry residues—including waste cooking oils, residual fats, and other organic fractions—as low-cost substrates for liquid biofuel production, in alignment with circular economy principles [4].

Lipids derived from food waste and used cooking oils have demonstrated considerable potential as substrates for biodiesel production. Karmee et al. [5] reported that lipids extracted from food waste could be efficiently transesterified to FAMEs with yields approaching 100% using an alkaline catalyst (KOH), while biocatalytic systems employing the lipase Novozyme 435 achieved conversion efficiencies of approximately 90%. Comparable results have been reported for used cooking oils, where methyl ester contents of approximately 95.3% and overall process yields of about 92% were achieved, confirming the feasibility of large-scale biodiesel production from such waste streams [6]. Although the quality of used frying fats is highly variable, detailed analyses have demonstrated that FAME composition and polymeric degradation products can be effectively characterized, which is essential for evaluating their industrial applicability [7].

Biodiesel research focuses not only on traditional sources, such as used cooking oils, but also on unconventional waste streams like sewage sludge and dairy industry residues. Lipids extracted from sewage sludge generated during dairy wastewater treatment have been successfully recovered using solvent extraction methods and converted into biodiesel, while studies using green solvents for lipid extraction showed high recovery efficiency and effective FAME production, maintaining the biodegradability of the residual sludge [8,9]. Seasonal variations also affect lipid extraction efficiency and biodiesel yield, which is an important aspect in designing large-scale processes [10].

Expanding research to microbial lipid sources, the potential of microorganisms and microalgae cultivated in wastewater treatment systems as biodiesel feedstocks has been demonstrated. Although lipid yields from these sources may be lower than those from conventional waste oils, they still represent a promising alternative [11]. Oleaginous microorganisms, such as Rhodosporidium toruloides, can utilize food waste hydrolysates for lipid accumulation. Chen et al. [12] demonstrated that microbial lipids obtained through this method, after transesterification, comply with BS EN 14214 [13] and ASTM D6751 [14] biodiesel standards, confirming their suitability for biodiesel production. The fatty acid profile analyses of microalgae show the predominance of palmitic acid (C16:0), oleic acid (C18:1), and linoleic acid (C18:2) [15].

In addition to conventional catalytic transesterification, innovative conversion methods such as biocatalysis are being explored, where enzymes or microorganisms catalyze transesterification reactions, which can improve process efficiency and selectivity while reducing the need for traditional catalysts [16].

Specific agri-food waste streams, such as herbal processing residues, have also become an interesting source of lipids for biodiesel production. Studies have shown that the fatty acid profiles of FAMEs from these residues meet established biodiesel quality standards, supporting their role in sustainable biofuel systems [17].

Comprehensive valorization of agri-food residues is essential for assessing their suitability for biofuel conversion processes. Mukherjee and Gupta [18] demonstrated that converting agri-food waste into biodiesel and bioethanol can simultaneously address waste management challenges, enhance energy security, and support the development of a circular economy. Numerous studies further confirm that food waste can serve as a versatile feedstock for the production of biofuels and biochemicals, thereby contributing to environmental protection and resource efficiency [19]. The conversion of these residues into liquid biofuel components aligns with the principles of a circular economy, zero-waste bioeconomy, and sustainable resource management, while reducing landfill disposal, lowering greenhouse gas emissions, and enabling the development of local, cost-effective biofuel sources [20,21].

Despite extensive research on biodiesel production from food processing residues, a knowledge gap remains regarding the comprehensive evaluation of diverse waste fractions—such as peels, pulp, seeds, and pomace—with respect to their fatty acid composition, transesterification susceptibility, and compliance of the resulting FAMEs with applicable fuel standards. Among these fractions, seeds and pomace are of particular interest, as they represent lipid-rich materials with significant potential for the production of high-quality biofuel components.

For instance, previous studies have shown that seeds of fruits such as watermelon (Citrullus lanatus) and honeydew melon (Cucumis melo) contain substantial amounts of oil that can be converted via transesterification into biodiesel exhibiting fuel properties comparable to those specified in the ASTM D6751 standard [14,22]. Furthermore, analyses of seed oils from nine industrial fruit species—including sea buckthorn (Hippophae rhamnoides), red currant (Ribes rubrum), pomegranate (Punica granatum), Japanese quince (Chaenomeles japonica), grape (Vitis vinifera), gooseberry (Ribes uva-crispa), and apple (Malus domestica)—have reported lipid contents ranging from approximately 11.8% to 28.5%. The fatty acid profiles of these oils, characterized by high proportions of unsaturated fatty acids, indicate their promising potential as alternative feedstocks for FAME production [23].

These findings suggest that fruit seed fractions generated by the fruit and vegetable processing industry represent valuable and underutilized feedstocks for biodiesel production. At the same time, they highlight the need for detailed valorization studies and comprehensive evaluation of the fuel properties of the resulting FAMEs to fully assess their industrial applicability and compliance with established quality standards.

Accordingly, the aim of the present study was to quantitatively determine the FAME content of seeds derived from selected fruit and vegetable processing residues, identify the dominant fatty acids, estimate the cetane number, and compare their composition with literature data for other plant-based feedstocks, in order to evaluate their suitability as biofuel components for biodiesel production.

2. Materials and Methods

2.1. Seed Materials

Samples of sixteen seed materials derived from fruits and vegetables—sour cherry (Prunus cerasus), European plum (Prunus domestica), common quince (Cydonia oblonga), common pumpkin (Cucurbita pepo), peach (Prunus persica), melon (Cucumis melo), grape (Vitis vinifera), watermelon (Citrullus lanatus), common apricot (Prunus armeniaca), common lemon (Citrus limon), avocado (Persea americana), apple (Malus domestica), common pear (Pyrus communis), bell pepper (Capsicum annuum), sweet cherry (Prunus avium), and sweet orange (Citrus sinensis)—were obtained from food-processing companies located in the Podlaskie Voivodeship, Poland.

2.2. Fatty Acid Methyl Ester (FAME) Analysis

All reactants used in the transesterification reaction were purchased from Avantor Performance Materials (Gliwice, Poland). Solvents for gas chromatography–mass spectrometry (GC–MS) analysis, as well as the Supelco 37 Component FAME Mix standard, were obtained from Merck (Darmstadt, Germany) (

Table 1. FAMEs standards used in the GC-MS analysis of seed samples.

Type of FAME	Systematic Name	The Common Name of FAME	Abbreviation
MUFA	Myristoleic acid methyl ester	Myristoleic acid	C14:1
	cis-10-Pentadecanoic acid methyl ester	Pentadecanoic acid	C15:1
	9-Hexadecenoic acid methyl ester	Palmitoleic acid	C16:1
	cis-10-Heptadecenoic acid methyl ester	Heptadecenoic acid	C17:1
	trans-9-Octadecenoic acid methyl ester (Z)	Elaidic acid	C18:1n9t
	9-Octadecenoic acid methyl ester (E)	Oleic acid	C18:1n9c
	cis-11-Eicosenoic acid methyl ester	Gondoic acid	C20:1
	13-Docosenoic acid methyl ester (Z)	Erucic acid	C22:1n9
	15-Tetracosenoic acid methyl ester (Z)	Nervonic acid	C24:1n9
PUFA	9,12-Octadecadienoic acid methyl ester (E, E)	Linolelaidic acid	C18:2n6t
	9,12-Octadecadienoic acid methyl ester (Z, Z)	Linoleic acid	C18:2n6c
	all-cis-6,9,12-Octadecatrienoic acid	γ-Linolenic acid	C18:3n6
	9,12,15-Octadecatrienoic acid methyl ester (Z, Z, Z)	Linolenic acid	C18:3n3
	cis-11,14-Eicosadienoic acid methyl ester	Eicosadienoic acid	C20:2
	cis-11,14,17-Eicosatrienoic acid methyl ester	Eicosatrienoic acid	C20:3n3
	cis-8,11,14-Eicosatrienoic acid methyl ester	Dihomo-γ-linolenic acid	C20:3n6
	5,8,11,14-Eicosatetraenoic acid methyl ester (all-Z)	Arachidonic acid	C20:4n6
	cis-5,8,11,14,17-Eicosapentaenoic acid methyl ester	Eicosapentaenoic acid	C20:5n3
	cis-13,16-Docasadienoic acid methyl ester	Docosadienoic acid	C22:2n6
	4,7,10,13,16,19-Docosahexaenoic acid methyl ester (all-Z)	Cervonic acid	C22:6n3
SFA	Butyric acid methyl ester	Butyric acid	C4:0
	Hexanoic acid methyl ester	Caproic acid	C6:0
	Octanoic acid methyl ester	Caprylic acid	C8:0
	Decanoic acid methyl ester	Capric acid	C10:0
	Undecanoic acid methyl ester	Undecylic acid	C11:0
	Dodecanoic acid methyl ester	Lauric acid	C12:0
	Tridecanoic acid methyl ester	Tridecylic acid	C13:0
	Tetradecanoic acid methyl ester	Myristic acid	C14:0
	Pentadecanoic acid methyl ester	Pentadecylic acid	C15:0
	Hexadecanoic acid methyl ester	Palmitic acid	C16:0
	Heptadecanoic acid methyl ester	Margaric acid	C17:0
	Octadecanoic acid methyl ester	Stearic acid	C18:0
	Eicosanoic acid methyl ester	Arachidic acid	C20:0
	Heneicosanoic acid methyl ester	Heneicosylic acid	C21:0
	Docosanoic acid methyl ester	Behenic acid	C22:0
	Tricosanoic acid methyl ester	Tricosylic acid	C23:0
	Tetracosanoic acid methyl ester	Lignoceric acid	C24:0

MUFA—monounsaturated fatty acid, PUFA—polyunsaturated fatty acid, SFA—saturated fatty acid.

).

For FAME analysis, a 0.5 g seed sample was subjected to extraction with hexane in the presence of a methanol–potassium hydroxide (KOH) solution acting as a catalyst. The reaction mixture was prepared by adding hexane to initiate the transesterification process. A detailed description of the transesterification procedure, including optimized extraction conditions and analytical parameters, is provided in the study by Sienkiewicz et al. [17].

2.3. Statistical Analysis

All results are expressed as mean values ± standard deviations (SDs) calculated from four independent replicates. Differences between mean values were evaluated using Tukey’s honest significant difference (HSD) post hoc test at a significance level of p < 0.05.

Hierarchical cluster analysis (HCA) was applied to classify the samples and visualize similarities among them in the form of a dendrogram. Clustering was performed using Euclidean distance as the measure of dissimilarity and Ward’s method as the agglomerative criterion. The optimal number of clusters was determined using Sneath’s criterion at 66% of the maximum linkage distance.

Subsequently, principal component analysis (PCA) was employed to explore the relationships among the analyzed variables. The first fifteen principal components were initially considered in the biplot analysis. The final biplot was constructed using the first two principal components (PC1 and PC2), which together accounted for 52.86% of the total variance in the dataset.

All statistical analyses were conducted using Statistica 13.3 software (TIBCO Software Inc., Palo Alto, CA, USA).

2.4. Cetane Number Prediction

To estimate the approximate cetane number (CN) of biodiesel derived from seed wastes based on their FAME compositions, a multiple linear regression model equation, proposed by Gopinath et al. [24], was applied:

$$\begin{gathered} \mathrm{C}\mathrm{N}=62.2+0.017\times \mathrm{P}{\mathrm{C}}_{12:0}+0.074\times \mathrm{P}{\mathrm{C}}_{14:0}+0.115\times \mathrm{P}{\mathrm{C}}_{16:0}+0.177\times \mathrm{P}{\mathrm{C}}_{18:0} \\ -0.103\times \mathrm{P}{\mathrm{C}}_{18:1\mathrm{n}9\mathrm{c}}-0.279\times \mathrm{P}{\mathrm{C}}_{18:2\mathrm{n}6\mathrm{c}}-0.366\times \mathrm{P}{\mathrm{C}}_{18:3\mathrm{n}3}, \end{gathered}$$

(1)

where $\mathrm{P}n$ represents the weight percentage of the corresponding FAME component $n$.

It is important to note that Equation (1) can be used to predict the CN of both pure FAMEs and biodiesel blends. Additionally, the approximated error in the CN was calculated using the transformed relative error Equation (2):

$$\mathrm{C}{\mathrm{N}}_{\mathsf{\epsilon }}={\displaystyle \frac{\mathrm{C}\mathrm{N}}{1\pm \mathsf{\epsilon }}},$$

(2)

where $\mathsf{\epsilon }$ represents the assumed percentage error, for instance, 8%, which is the maximum relative estimation error of the model developed by Gopinath et al. [24].

3. Results and Discussion

Identification and Composition of Fatty Acid Methyl Esters

Analysis of the fatty acid composition isolated from seeds constituting waste from the fruit and vegetable processing industry, performed using gas chromatography coupled with mass spectrometry operating in selected ion monitoring mode (GC–MS/SIM), revealed the presence of 34 out of the 37 targeted fatty acid methyl esters (FAMEs). The detected compounds comprised 15 saturated fatty acids (SFAs), 9 monounsaturated fatty acids (MUFAs), and 10 polyunsaturated fatty acids (PUFAs).

The monounsaturated fatty acid content of the investigated seeds exhibited pronounced species-dependent variability (

Table 2. The content of monounsaturated fatty acids (MUFA) (mean ± SD, n = 4) in fruit and vegetable waste seeds. Means with the same letters are not significantly different (p ≥ 0.05) according to Tukey’s post hoc test.

Fruit and Vegetable Waste Seeds	[µg g−1dw]
	C14:1	C15:1	C16:1	C17:1	C18:1n9t	C18:1n9c	C20:1	C22:1n9	C24:1n9
Cherry	37.89 ± 0.39 e	1611.34 ± 27.34 c	23.82 ± 0.88 gh	146.28 ± 2.01 e	28,701.90 ± 522.90 a	32,927.03 ± 547.85 c	49.22 ± 0.93 g	21.07 ± 0.90 c	46.01 ± 8.33 b
Plum	34.98 ± 0.25 ef	763.44 ± 9.09 f	191.60 ± 3.14 d	18.97 ± 0.14 hi	24,216.43 ± 328.01 b	10,399.22 ± 130.82 g	34.37 ± 0.62 g	4.60 ± 5.88 ef	36.49 ± 2.38 bc
Quince	34.89 ± 0.34 ef	864.42 ± 3.56 f	14.15 ± 4.79 h	157.41 ± 1.79 de	8131.43 ± 218.41 f	39,037.22 ± 167.14 b	49.27 ± 8.71 g	91.81 ± 2.66 b	16.94 ± 0.40 ef
Pumpkin	33.53 ± 0.05 f	1145.81 ± 12.09 e	65.83 ± 1.59 f	538.60 ± 24.29 b	9063.43 ± 87.76 e	14,439.76 ± 31.39 f	2531.67 ± 48.16 c	11.97 ± 1.55 de	16.18 ± 0.76 ef
Peach	42.08 ± 0.21 d	828.30 ± 15.61 f	8.77 ± 0.27 h	249.52 ± 5.53 d	2759.78 ± 50.91 h	4460.79 ± 83.09 h	61.29 ± 0.82 g	10.36 ± 1.35 de	16.98 ± 0.39 ef
Melon	37.91 ± 0.35 e	3505.51 ± 36.76 b	55.40 ± 1.86 f	1330.54 ± 13.79 a	13,455.68 ± 312.83 d	21,508.22 ± 208.66 d	143.88 ± 7.41 f	16.05 ± 4.17 cd	25.24 ± 0.30 de
Grape	59.39 ± 0.24 c	268.28 ± 2.96 g	13.57 ± 0.16 h	134.08 ± 1.78 e	1675.45 ± 9.28 i	2734.63 ± 18.86 i	416.41 ± 12.12 e	14.58 ± 1.09 cd	27.50 ± 0.06 cd
Watermelon	35.23 ± 0.07 ef	2935.51 ± 209.64 b	5.27 ± 0.24 h	11.63 ± 0.54 i	3403.56 ± 0.54 g	3265.34 ± 40.65 hi	50,612.58 ± 579.99 a	2.79 ± 0.03 f	57.68 ± 9.41 a
Apricot	ND	3.63 ± 0.08 h	2.54 ± 0.73 h	0.70 ± 0.06 i	7.10 ± 0.43 j	35.81 ± 5.56 k	123.93 ± 0.94 f	1.74 ± 0.88 f	14.10 ± 0.41 f
Lemon	217.86 ± 3.64 b	4016.31 ± 305.33 a	3611.56 ± 66.27 b	16.85 ± 0.37 hi	9914.07 ± 82.26 e	15,991.39 ± 120.02 e	5241.87 ± 38.61 b	21.39 ± 0.87 c	30.64 ± 2.60 cd
Avocado	1666.77 ± 14.12 a	249.09 ± 11.21 g	289.20 ± 10.22 c	3239.54 ± 292.68 a	84.71 ± 0.52 j	370.57 ± 1.23 k	2684.01 ± 15.20 c	161.54 ± 1.57 a	31.20 ± 0.46 cd
Apple	176.65 ± 0.20 b	29.50 ± 0.29 h	6592.61 ± 66.12 a	78.13 ± 0.51 fg	1054.36 ± 7.39 i	97,987.14 ± 336.12 a	13,604.24 ± 33.78 b	106.37 ± 1.17 b	114.57 ± 0.80 a
Pear	86.95 ± 1.11 c	ND	360.32 ± 9.41 c	50.48 ± 3.26 gh	29.79 ± 2.57 j	1544.62 ± 14.46 j	599.45 ± 36.45 e	8.53 ± 0.32 ef	34.77 ± 0.18 bc
Pepper	36.47 ± 1.22 ef	9.11 ± 1.98 h	165.44 ± 5.99 d	4.64 ± 0.25 i	32.48 ± 9.41 j	613.61 ± 22.09 jk	353.83 ± 13.88 e	3.19 ± 0.40 f	13.31 ± 0.02 f
Sweet cherry	211.32 ± 1.06 b	8.13 ± 0.18 h	2791.15 ± 29.12 b	8.09 ± 0.61 i	39.07 ± 6.23 j	14,002.33 ± 400.95 f	4543.25 ± 64.06 b	14.95 ± 0.22 cd	14.14 ± 0.47 f
Orange	35.41 ± 0.15 ef	79.24 ± 3.36 h	233.35 ± 13.52 cd	48.25 ± 1.44 gh	14.03 ± 0.56 j	1264.73 ± 40.10 j	848.96 ± 30.33 d	4.24 ± 0.20 ef	13.70 ± 3.05 f

ND, not detected.

). Oleic acid (C18:1n9c) was the dominant MUFA in most samples, with the highest concentrations observed in apple (97.99 mg g⁻¹dry weight, dw), quince (39.04 mg g⁻¹dw), cherry (32.93 mg g⁻¹dw), and melon (21.51 mg g⁻¹dw) seeds. A high proportion of oleic acid is particularly advantageous for biodiesel production, as it enhances both the cetane number and oxidative stability of the resulting methyl esters. Notably, significant amounts of the trans isomer C18:1n9t were detected in cherry (28.70 mg g⁻¹dw), plum (24.22 mg g⁻¹dw), and melon (13.46 mg g⁻¹dw) seeds, suggesting species-specific lipid metabolism pathways.

Long-chain MUFAs (C20:1, C22:1, and C24:1) were generally present at low levels; however, exceptionally high concentrations of C20:1 were recorded in watermelon seeds (50.61 mg g⁻¹dw), which may positively influence the cetane number of biodiesel. In contrast, apricot and pepper seeds exhibited the lowest total MUFA contents, indicating their limited suitability as feedstocks for producing oxidatively stable biofuel components.

The PUFA fraction was dominated by linoleic (C18:2n6) and linolenic (C18:3) acids (

Table 3. The content of polyunsaturated fatty acids (PUFA) (mean ± SD, n = 4) in fruit and vegetable waste seeds. Means with the same letters are not significantly different (p ≥ 0.05) according to Tukey’s post hoc test.

Fruit and Vegetable Waste Seeds	[µg g−1dw]
	C18:2n6t	C18:2n6c	C18:3n6	C18:3n3	C20:2	C20:3n6	C20:4n6	C20:5n3	C22:2n6	C22:6n3
Cherry	15,552.89 ± 268.82 c	15.56 ± 0.29 cd	115,106.67 ± 1845.13 e	48.37 ± 1.64 f	6.08 ± 0.35 f	5.46 ± 1.76 b	23.66 ± 1.10 bc	664.41 ± 8.02 a	32.31 ± 1.38 e	20.92 ± 0.29 d
Plum	4769.94 ± 59.64 e	14.53 ± 0.49 d	35,517.38 ± 464.96 g	28.55 ± 0.23 f	17.48 ± 4.84 ef	3.00 ± 0.82 b	22.74 ± 0.16 bc	6.04 ± 0.25 f	76.97 ± 12.63 cd	24.03 ± 0.77 cd
Quince	18,947.23 ± 324.07 b	17.05 ± 0.85 c	136,179.13 ± 2355.71 d	ND	25.54 ± 0.53 de	5.35 ± 1.04 b	18.16 ± 0.86 cd	34.60 ± 6.67 d	59.57 ± 14.44 d	30.72 ± 3.14 bc
Pumpkin	24,488.10 ± 40.23 a	13.30 ± 0.42 de	181,102.07 ± 262.10 c	8.85 ± 0.29 g	2.67 ± 2.74 f	5.17 ± 1.28 b	13.56 ± 4.06 de	102.88 ± 17.02 c	27.29 ± 2.88 ef	21.34 ± 1.28 d
Peach	1248.40 ± 25.83 g	15.82 ± 0.71 cd	9234.66 ± 195.54 h	9.80 ± 3.55 g	2.24 ± 0.20 f	5.02 ± 1.17 b	16.86 ± 0.39 cd	1.83 ± 0.10 f	14.27 ± 0.50 f	20.51 ± 0.18 d
Melon	42,887.93 ± 424.35 a	14.23 ± 0.38 d	313,475.37 ± 312.83 b	0.88 ± 0.08 g	7.34 ± 2.36 f	4.86 ± 0.95 b	24.22 ± 0.64 bc	175.06 ± 2.57 b	148.18 ± 2.71 b	21.12 ± 0.30 d
Grape	95.89 ± 3.58 h	2.93 ± 0.05 f	641.02 ± 4.48 i	1.38 ± 0.04 g	2.55 ± 0.55 f	5.61 ± 0.06 b	27.01 ± 0.06 b	3.76 ± 0.19 f	23.05 ± 0.01 ef	40.37 ± 0.08 b
Watermelon	4030.26 ± 88.74 e	15.74 ± 0.12 cd	2149.72 ± 123.66 i	26,241.21 ± 579.99 a	ND	2.13 ± 0.64 b	11.55 ± 3.42 e	66.72 ± 12.21 cd	10.07 ± 2.99 f	20.27 ± 0.44 d
Apricot	11.89 ± 2.31 h	1.71 ± 0.04 f	731.85 ± 3.23 i	37.43 ± 0.39 f	1.49 ± 0.04 f	2.03 ± 0.60 b	15.53 ± 3.89 de	1.35 ± 0.40 f	12.40 ± 3.10 f	19.78 ± 0.07 d
Lemon	2921.05 ± 107.47 f	18.41 ± 0.40 c	224,738.00 ± 125.20 c	13,310.98 ± 589.01 c	1.05 ± 0.56 f	7.80 ± 0.50 b	25.69 ± 0.43 b	20.42 ± 4.52 e	20.75 ± 0.09 f	42.77 ± 2.33 b
Avocado	344.30 ± 17.31 h	16.79 ± 0.38 c	27,739.90 ± 238.33 g	1868.39 ± 5.12 e	266.96 ± 3.88 a	2882.83 ± 285.47 a	2370.34 ± 29.15 a	10.43 ± 3.31 ef	269.75 ± 1.93 a	29.05 ± 6.01 bc
Apple	10,904.87 ± 97.71 d	31.55 ± 0.19 b	508,488.33 ± 554.41 a	20,548.58 ± 671.51 b	70.57 ± 2.64 b	36.80 ± 5.77 b	101.78 ± 1.27 b	18.88 ± 0.29 e	68.73 ± 0.59 cd	91.14 ± 0.09 a
Pear	187.11 ± 1.72 h	27.14 ± 0.19 b	2153.35 ± 133.84 i	156.00 ± 10.07 f	30.81 ± 2.10 cd	5.12 ± 0.58 b	ND	3.00 ± 0.35 f	31.69 ± 0.10 e	50.13 ± 0.05 b
Pepper	65.82 ± 1.86 h	12.19 ± 0.36 e	1384.74 ± 44.11 i	93.05 ± 5.60 f	2.62 ± 0.57 f	2.64 ± 0.01 b	15.96 ± 0.35 de	1.49 ± 0.04 f	12.53 ± 0.10 f	19.88 ± 0.11 d
Sweet cherry	164.87 ± 3.78 h	1095.44 ± 44.40 a	3467.61 ± 82.24 i	12,931.00 ± 258.56 c	2.04 ± 0.07 f	13.65 ± 2.47 b	15.98 ± 0.59 de	31.25 ± 0.37 de	12.36 ± 0.12 f	19.79 ± 0.29 d
Orange	173.65 ± 4.41 h	14.38 ± 0.23 d	3968.63 ± 106.55 i	280.84 ± 9.30 f	1.04 ± 0.32 f	2.90 ± 0.49 b	15.75 ± 0.45 de	1.85 ± 0.09 f	12.53 ± 0.08 f	19.26 ± 0.05 d

ND, not detected.

). The highest total PUFA concentrations were found in apple (540.36 mg g⁻¹dw), melon (356.76 mg g⁻¹dw), lemon (241.11 mg g⁻¹dw), and pumpkin (205.79 mg g⁻¹dw) seeds. Elevated PUFA levels are associated with improved low-temperature properties of biodiesel; however, they also imply reduced oxidative stability, in accordance with the relationships defined in the BS EN 14214 standard [13]. Particularly high concentrations of γ-linolenic acid (C18:3n6) were observed in apple, melon, lemon, and pumpkin seeds, indicating the predominance of PUFA biosynthesis pathways typical of oilseed plants.

In contrast, grape seeds were characterized by a low total PUFA content; the value of 641.02 µg g⁻¹dw refers exclusively to C18:3n6. Although the cumulative PUFA content was higher, it remained substantially lower than that observed in most other seeds, suggesting improved oxidative stability of the resulting fuel. Long-chain PUFAs (C20–C22) were detected only in trace amounts in all samples and therefore did not significantly affect fuel properties, although elevated concentrations of these compounds are known to adversely influence oxidative stability [25].

Among the saturated fatty acids, palmitic acid (C16:0) was the predominant component (

Table 4. The content of saturated fatty acids (SFA) (mean ± SD, n = 4) in fruit and vegetable waste seeds. Means with the same letters are not significantly different (p ≥ 0.05) according to Tukey’s post hoc test.

Fruit and Vegetable Waste Seeds	[µg g−1dw]
	C8:0	C10:0	C11:0	C12:0	C13:0	C14:0	C15:0	C16:0	C17:0	C18:0	C20:0	C21:0	C22:0	C23:0	C24:0
Cherry	97.80 ± 1.99 c	26.10 ± 0.27cd	11.90 ± 0.10 fg	23.93 ± 0.21 de	9.79 ± 0.09 c	37.57 ± 0.45 ef	11.53 ± 0.24 c	3041.21 ± 51.72 e	131.95 ± 2.51 f	821.02 ± 13.71 c	300.53 ± 4.17 e	16.38 ± 0.17 cd	77.86 ± 0.96 b	35.49 ± 0.49 b	473.49 ± 5.41 a
Plum	ND	25.23 ± 0.04 de	11.13 ± 0.03 g	22.27 ± 0.06 e	9.59 ± 0.03 c	28.35 ± 0.18 f	4.53 ± 0.08 e	1428.20 ± 16.58 g	44.70 ± 1.15 h	312.77 ± 3.49 e	54.57 ± 0.26 h	18.33 ± 0.35 bc	18.81 ± 0.11 de	6.91 ± 0.90 de	5.41 ± 1.75 f
Quince	ND	24.47 ± 0.09 e	10.94 ± 0.04 g	21.88 ± 0.08 e	9.44 ± 0.01 c	39.88 ± 0.86 e	10.40 ± 0.46 cd	4064.16 ± 562.21 cd	97.72 ± 4.59 g	301.33 ± 15.59 e	984.55 ± 15.12 d	ND	34.02 ± 3.50 c	2.16 ± 0.01 f	1.39 ± 1.23 f
Pumpkin	2.67 ± 0.11 d	25.97 ± 0.05 cd	13.68 ± 0.02 ef	27.37 ± 0.04 cd	9.58 ± 0.04 c	101.27 ± 0.86 b	23.02 ± 0.10 b	2218.74 ± 26.73 f	35.82 ± 2.88 h	977.26 ± 35.58 b	1274.71 ± 3.61 c	11.89 ± 3.46 e	23.51 ± 1.96 d	62.00 ± 2.52 a	95.24 ± 4.30 c
Peach	64.61 ± 1.61 c	25.39 ± 0.06 de	15.11 ± 0.08 e	30.22 ± 0.17 c	9.56 ± 0.04 c	32.26 ± 0.29 ef	9.43 ± 0.20 d	1584.46 ± 201.80 g	58.00 ± 0.45 h	368.43 ± 3.50 e	97.35 ± 1.35 h	16.58 ± 0.29 cd	12.91 ± 0.19 e	1.51 ± 0.08 f	23.46 ± 0.82 e
Melon	32.66 ± 0.36 cd	24.59 ± 0.25 e	12.10 ± 0.12 fg	24.20 ± 0.24 de	9.45 ± 0.09 c	59.90 ± 0.65 d	1.27 ± 0.04 gh	6711.92 ± 71.55 a	32.67 ± 0.35 h	933.57 ± 26.71 b	2151.38 ± 208.66 b	15.86 ± 0.60 cd	18.23 ± 2.37 de	5.50 ± 0.30 e	150.13 ± 0.65 b
Grape	ND	42.92 ± 0.07 b	19.09 ± 0.04 d	38.18 ± 0.08 b	16.47 ± 0.04 b	44.76 ± 0.10 e	7.79 ± 0.94 de	681.77 ± 4.44 h	47.29 ± 0.63 h	175.57 ± 0.99 f	63.54 ± 0.10 h	27.16 ± 0.07 a	22.93 ± 0.90 d	4.15 ± 0.06 ef	23.35 ± 0.80 e
Watermelon	ND	25.49 ± 0.03 de	12.93 ± 0.05 f	25.85 ± 0.10 d	9.58 ± 0.02 c	69.94 ± 7.92 c	2.23 ± 0.05 fg	4000.04 ± 111.63 d	17,885.03 ± 542.38 b	4.83 ± 1.11 g	2363.15 ± 197.35 b	126.85 ± 8.44 a	1.73 ± 0.54 f	1.42 ± 0.46 f	8.02 ± 1.56 f
Apricot	ND	24.48 ± 0.04 e	10.37 ± 0.01 g	20.74 ± 0.02 e	7.11 ± 2.06 d	19.09 ± 0.03 f	0.03 ± 0.01 h	6.44 ± 3.22 i	54.54 ± 15.08 h	ND	35.25 ± 0.18 h	15.46 ± 0.03 cd	6.64 ± 0.01 f	0.90 ± 0.02 f	3.70 ± 0.14 f
Lemon	80.94 ± 2.39 c	42.50 ± 0.13 b	21.51 ± 0.12 c	43.01 ± 0.23 b	15.61 ± 1.21 b	101.06 ± 1.64 b	14.86 ± 0.21 c	4460.98 ± 306.21 c	25,116.34 ± 407.64 a	5.52 ± 0.11 g	265.94 ± 38.48 e	300.50 ± 6.72 a	12.28 ± 0.05 e	6.68 ± 0.66 de	23.20 ± 0.85 e
Avocado	ND	26.35 ± 1.76c	11.19 ± 0.06 g	22.37 ± 0.13 e	11.79 ± 0.71 bc	38.20 ± 0.17 ef	1.51 ± 0.07 gh	249.07 ± 26.63 hi	1111.28 ± 6.37 e	2.07 ± 0.03 g	73.52 ± 0.42 h	17.46 ± 1.59 cd	7.33 ± 0.22 f	0.67 ± 0.02 f	12.90 ± 3.95 f
Apple	162.75 ± 1.79 a	120.71 ± 0.09 a	52.49 ± 0.38 a	104.98 ± 0.76 a	44.79 ± 0.08 a	211.68 ± 0.94 a	14.64 ± 0.04 c	60.87 ± 3.37 i	471.96 ± 13.06 f	36.20 ± 0.37 g	2175.28 ± 44.86 b	249.32 ± 9.72 b	704.86 ± 33.46 a	28.98 ± 0.11 c	21.63 ± 0.58 e
Pear	ND	61.69 ± 5.28 b	27.99 ± 0.09 b	55.98 ± 0.17 b	24.60 ± 0.17 b	49.19 ± 2.68 e	2.35 ± 0.16 fg	2.95 ± 0.35 i	1721.97 ± 73.10 d	15.78 ± 0.69 g	128.09 ± 4.27 g	51.97 ± 2.20 c	17.13 ± 0.07 de	2.42 ± 0.02 f	61.30 ± 4.46 d
Pepper	ND	ND	12.67 ± 0.65 f	25.34 ± 1.31 d	12.56 ± 1.38 bc	31.17 ± 0.46 ef	16.83 ± 0.57 bc	4.78 ± 0.66 i	950.28 ± 35.24 e	5.36 ± 0.60 g	42.69 ± 0.31 h	21.75 ± 1.66 c	18.69 ± 4.06 de	1.19 ± 0.40 f	5.25 ± 0.88 f
Sweet cherry	22.29 ± 0.34 d	25.18 ± 0.42 de	13.16 ± 0.92 f	26.32 ± 1.84 d	17.53 ± 1.81 b	31.78 ± 10.68 ef	133.16 ± 29.84 a	6.71 ± 2.23 i	2238.83 ± 96.41 c	47.66 ± 4.27 g	1094.59 ± 28.04 d	205.92 ± 0.93 b	10.14 ± 2.80 ef	1.68 ± 0.22 f	17.70 ± 0.27 ef
Orange	ND	25.61 ± 1.27 de	18.91 ± 7.72 d	20.73 ± 0.30 e	9.43 ± 0.16 c	19.41 ± 0.41 f	0.28 ± 0.02 h	6.42 ± 0.17 i	656.57 ± 21.76 f	3.86 ± 0.08 g	190.66 ± 3.83 f	31.12 ± 0.45 c	6.62 ± 1.22 f	2.65 ± 0.35 f	6.15 ± 0.78 f

ND, not detected.

), reaching the highest concentrations in melon (6.71 mg g⁻¹dw), lemon (4.46 mg g⁻¹dw), quince (4.06 mg g⁻¹dw), and watermelon (4.00 mg g⁻¹dw) seeds. Stearic acid (C18:0) constituted the second most abundant SFA, particularly in pumpkin (977.26 µg g⁻¹dw) and melon (933.57 µg g⁻¹dw) seeds.

Additionally, unusually high concentrations of heptadecanoic acid (C17:0) were detected in watermelon (17.89 mg g⁻¹dw) and lemon (25.12 mg g⁻¹dw) seeds, representing a distinctive feature of the lipid profiles of these materials. Although increased SFA content generally enhances the cetane number of biodiesel, it may simultaneously deteriorate low-temperature properties by increasing the cloud point [26]. Overall, however, the SFA content in most analyzed seeds was lower than that of MUFAs and PUFAs, indicating a favorable fatty acid composition for biodiesel applications.

Additionally, quantitative analysis demonstrated a clear predominance of unsaturated fatty acids (MUFAs and PUFAs) over saturated fatty acids (SFAs) in all analyzed samples. The MUFA content ranged from 9.84% in melon seeds to 72.19% in grape seeds, while the PUFA fraction varied from 11.40% in grape seeds to 87.65% in melon seeds (Figure 1). In contrast, the SFA content was relatively low, ranging from 0.67% in apple seeds to 29.32% in pear seeds.

From the perspective of biodiesel quality, as defined by the BS EN 14214 standard [13], key parameters such as oxidative stability, cetane number, and low-temperature properties are directly influenced by the fatty acid profile of the methyl esters. A high content of monounsaturated fatty acids (MUFAs), particularly C18:1, contributes to enhanced oxidative stability and an increased cetane number, both crucial for optimal biodiesel performance. For example, grape seeds, with their high MUFA content (72.19%), are expected to produce biodiesel with superior oxidative stability and a higher cetane number, making them an ideal feedstock for high-quality biodiesel production. Conversely, a high polyunsaturated fatty acid (PUFA) content, such as that found in melon seeds (87.65%), improves low-temperature fluidity but reduces oxidative stability, making these feedstocks more susceptible to degradation over time. While melon and other high-PUFA seeds may enhance biodiesel’s cold-weather performance, their use may require antioxidant additives or blending with more stable feedstocks, such as those rich in MUFAs, to improve oxidative stability and overall fuel performance. The low saturated fatty acid (SFA) content in most of the seeds analyzed, particularly in apple seeds (0.67%), is consistent with biodiesel standards, as lower SFA content typically correlates with improved fuel properties, such as reduced cloud points and enhanced fuel flow characteristics. However, higher SFA levels, as seen in pear seeds (29.32%), could negatively affect biodiesel performance by increasing the cloud point and decreasing fuel flowability at lower temperatures.

Górnaś and Rudzińska [23] analyzed the fatty acid and phytosterol profiles of oils obtained from the seeds of nine species constituting by-products of the fruit and vegetable processing industry, including watermelon, melon, sea buckthorn, red currant, pomegranate, Japanese quince, grape, gooseberry, and apple. The oil yield varied markedly, ranging from 11.8% in sea buckthorn seeds to 28.5% in watermelon seeds, indicating substantial diversity in the potential of these raw materials. In all analyzed oils, β-sitosterol was the dominant phytosterol, with concentrations ranging from 0.5 to 3.1 mg g⁻¹ of oil. Fatty acid profiles were species-specific, with linoleic acid predominating in most samples (38.0–70.7%). A distinctive feature of pomegranate seed oil was its exceptionally high punicic acid content (86.2%), which confers particularly high biological activity. Oil obtained from Japanese quince seeds was identified as the most promising for biodiesel production due to a favourable balance of mono- and polyunsaturated fatty acids, supporting the formation of methyl esters with good oxidative stability. In contrast, pomegranate seed oil, owing to its unique bioactive lipid composition, was considered more suitable for cosmetic and pharmaceutical applications.

According to Muthai et al. [27], baobab seed oil contains 17–22% saturated fatty acids (SFAs), 32–38% monounsaturated fatty acids (MUFAs), and 22–26% polyunsaturated fatty acids (PUFAs). Palmitic acid (C16:0) was reported as the predominant SFA, while oleic acid (C18:1) and linoleic acid (C18:2) were the dominant MUFA and PUFA, respectively.

Ferreira et al. [28] identified grape pomace (72.7% PUFA), grape stems (42.5% PUFA), grape bunches (54.3% PUFA), and brewer’s spent grain (59.0% PUFA) as the most promising agri-food by-products due to their high polyunsaturated fatty acid content. In contrast, white wine lees (74.1% SFA), red wine lees (50.8% SFA), and potato peels (77.2% SFA) were considered less favourable feedstocks because of the predominance of saturated fatty acids.

Table 5. Sum of mean FAMEs content ± standard deviation (SD) in fruit and vegetable waste seeds both with and without distinction for MUFAs, PUFAs, and SFAs.

Fruit and Vegetable Waste Seeds	∑MUFAs ± SD	∑PUFAs ± SD	∑SFAs ± SD	∑FAMEs ± SD
	[µg g−1dw]
Cherry	63,564.56 ± 12,743.95	131,476.33 ± 34,297.60	5116.55 ± 754.09	200,157.44 ± 51,634.37
Plum	35,700.10 ± 7842.85	40,480.64 ± 10,584.78	1990.80 ± 364.73	78,171.55 ± 17,129.05
Quince	48,397.54 ± 12,158.91	155,317.34 ± 40,609.62	5602.33 ± 1047.08	209,317.21 ± 62,961.94
Pumpkin	27,846.78 ± 4859.25	205,785.26 ± 54,002.67	4902.70 ± 628.63	238,534.77 ± 89,778.94
Peach	8437.88 ± 1504.01	10,569.41 ± 2750.77	2349.27 ± 391.52	21,356.56 ± 3483.06
Melon	40,078.41 ± 7297.40	356,759.20 ± 93,476.48	10,183.41 ± 1706.20	407,021.03 ± 156,806.75
Grape	5343.89 ± 905.56	843.56 ± 187.59	1214.97 ± 165.60	7402.43 ± 2039.58
Watermelon	60,329.60 ± 15,592.36	32,547.67 ± 7768.76	24,537.09 ± 4482.14	117,414.36 ± 15,337.35
Apricot	189.55 ± 37.89	835.46 ± 216.37	204.74 ± 14.86	1229.75 ± 300.97
Lemon	39,061.96 ± 5176.62	241,106.93 ± 66,992.26	30,510.95 ± 6265.53	310,679.84 ± 97,323.03
Avocado	8776.63 ± 1165.96	35,798.73 ± 8118.07	1585.70 ± 275.46	46,161.07 ± 14,728.79
Apple	119,743.56 ± 30,254.52	540,361.24 ± 151,625.38	4461.13 ± 535.10	664,565.94 ± 230,313.36
Pear	2714.91 ± 479.39	2644.41 ± 632.63	2223.41 ± 421.88	7582.72 ± 217.00
Pepper	1232.07 ± 200.85	1610.92 ± 408.90	1148.55 ± 233.81	3991.55 ± 201.19
Sweet cherry	21,632.42 ± 4978.58	17,753.98 ± 3859.32	3892.66 ± 592.54	43,279.06 ± 7614.88
Orange	2541.91 ± 430.38	4490.81 ± 1176.57	998.40 ± 164.11	8031.12 ± 1428.97

presents the total contents of monounsaturated and polyunsaturated fatty acids, as well as the overall content of fatty acid methyl esters (FAMEs), in seeds derived from fruit and vegetable by-products. Considerable variability in lipid content was observed among the analyzed raw materials. The highest fat contents, expressed as FAME, were recorded for apple (664.57 mg g⁻¹dw), melon (407.02 mg g⁻¹dw), lemon (310.68 mg g⁻¹dw), and pumpkin (238.53 mg g⁻¹dw) seeds, whereas the lowest values were observed for apricot (1.23 mg g⁻¹dw) and pepper (3.99 mg g⁻¹dw) seeds, indicating limited oil potential for these by-products.

In most samples, polyunsaturated fatty acids constituted the dominant fraction, particularly in apple, melon, lemon, and pumpkin seeds, reflecting a high degree of lipid unsaturation and potentially elevated iodine values for oils derived from these materials. The MUFA content was generally lower; high MUFA proportions were observed in watermelon and grape seeds, while cherry seeds exhibited a moderate share. In contrast, melon and lemon seeds were characterised by low MUFA proportions. Notably, in watermelon and grape seeds, the MUFA fraction was comparable to or higher than the PUFA fraction, indicating a more oleic character of the lipids and potentially enhanced oxidative stability.

The FAME profile of cherry seed by-products was characterised by a high ratio of unsaturated to saturated fatty acids [29]. A similar pattern has been reported for by-products derived from herbal plants, in which the content of unsaturated fatty acid methyl esters exceeded that of saturated ones; linoleic acid was identified as the dominant PUFA, while palmitic acid remained the main SFA [17].

Cluster analysis based on the fatty acid methyl ester (FAME) composition of seeds derived from fruit and vegetable processing waste enabled the identification of four distinct clusters (A–D) (Figure 2). Cluster A, represented exclusively by avocado seeds, is characterised by a moderate proportion of monounsaturated fatty acids (MUFAs), a high content of polyunsaturated fatty acids (PUFAs), and a low share of saturated fatty acids (SFAs). The predominance of PUFAs in this cluster may positively influence fuel fluidity and calorific value; however, the oxidative stability of the resulting biodiesel may be limited due to the high degree of unsaturation.

Cluster B, comprising apple seeds, is likewise dominated by polyunsaturated fatty acids. The MUFA fraction remains at a moderate level, whereas saturated fatty acids are nearly absent. This lipid profile ensures excellent cold-flow properties and a high energy value. Nevertheless, the very high PUFA content (>80%) indicates that biodiesel produced solely from these seeds would be highly susceptible to rapid oxidation. Therefore, blending with MUFA-rich oils is recommended to enhance oxidative stability and improve overall fuel performance.

Cluster C includes pear, grape, pepper, orange, apricot, peach, quince, and plum seeds and exhibits a more diversified fatty acid composition. The MUFA content in this group ranges from 23.12% in quince seeds to 45.67% in plum seeds, while the PUFA content varies between 49.49% in peach seeds and 74.20% in quince seeds. The proportion of saturated fatty acids spans from a low level (2.55% in plum seeds) to a relatively high value (29.32% in pear seeds). The balanced fatty acid profile observed in this cluster provides a favourable compromise between oxidative stability, fluidity, and energy value, indicating that seeds within this group represent the most promising biocomponents for biodiesel production.

Cluster D, comprising cherry, lemon, watermelon, melon, pumpkin, and sweet cherry seeds, is characterised by a high proportion of polyunsaturated fatty acids, particularly in pumpkin (86.27%), melon (87.65%), and lemon (77.61%) seeds. The MUFA fraction is relatively low (e.g., 11.67% in pumpkin seeds and 9.85% in melon seeds), while saturated fatty acids account for only a minor share of the total composition, confirming the dominance of PUFAs in this cluster. Such a lipid profile favours the production of biodiesel with excellent cold-flow properties and low cloud and pour points. However, the high PUFA content is associated with reduced oxidative stability. Consequently, seeds from Cluster D are best utilised in blends with MUFA-rich oils to obtain a more oxidation-resistant and technologically stable biofuel component.

Cluster analysis, complemented by visualisation in the form of a heat map (Figure 3), enabled the grouping of seeds derived from the analyzed fruit and vegetable processing waste according to the contents of individual fatty acid methyl esters (FAMEs). The heat map revealed four principal clusters of samples, reflecting distinct fatty acid distribution patterns.

Avocado seeds formed a separate cluster owing to their highest concentrations of unsaturated fatty acids (indicated by red coloration), including cis-13,16-docosadienoic acid (C22:2n6), dihomo-γ-linolenic acid (C20:3n6), arachidonic acid (C20:4n6), and eicosadienoic acid (C20:2), accompanied by relatively low levels of short-chain saturated fatty acids (C8:0–C12:0) (green coloration). This distinctive fatty acid profile explains their isolated position in the hierarchical structure.

Apple seeds constituted another distinct group, characterised by the predominance of unsaturated fatty acids, particularly oleic acid (C18:1n9c) and nervonic acid (C24:1n9), together with a low contribution of short-chain saturated fatty acids. The dominance of long-chain unsaturated compounds clearly differentiates this cluster from the remaining groups.

The third cluster comprised pear, grape, pepper, orange, apricot, peach, quince, and plum seeds, which exhibited moderate concentrations of saturated and monounsaturated fatty acids. A notable feature within this group was the pronounced presence of caprylic acid (C8:0) in peach seeds, alongside moderate levels of polyunsaturated fatty acids, such as linoleic acid (C18:2n6c) and linolenic acid (C18:3n3). The relatively balanced fatty acid distribution observed in this cluster supports its previously identified suitability for biodiesel production.

The fourth cluster included cherry, lemon, watermelon, melon, pumpkin, and sweet cherry seeds. These samples were characterised by relatively high contents of polyunsaturated fatty acids, particularly linoleic acid (C18:2n6c) and linolenic acid (C18:3n3), combined with low concentrations of saturated and monounsaturated fatty acids. The strong predominance of PUFAs explains their grouping and suggests favourable cold-flow properties of the derived biodiesel, albeit with potentially limited oxidative stability.

Overall, the heat map analysis confirms the differentiation observed in the cluster analysis and provides a comprehensive visualisation of the variability in FAME composition among seed fractions derived from fruit and vegetable processing residues.

Principal component analysis indicated that the first two principal axes jointly explained 52.86% of the total variability in the dataset (Factor 1: 33.06%; Factor 2: 19.80%) (Figure 4). Factor 1 represents the primary gradient in fatty acid composition, ranging from PUFA-dominated profiles to those relatively enriched in SFAs and MUFAs. Negative loadings along Factor 1 are therefore associated with a high degree of polyunsaturation, whereas positive loadings reflect a greater contribution of saturated and monounsaturated fatty acids. Factor 2 differentiates the samples according to the contribution of long-chain MUFAs and selected less abundant fractions (vectors located in the upper half-plane), in contrast to profiles more strongly associated with lower-carbon-number saturated fractions and/or n-6 and n-3 PUFAs (lower half-plane).

The spatial distribution of individual samples is consistent with this interpretation. Apple seeds are positioned on the negative side of Factor 1, reflecting their high PUFA content and low SFA contribution. Avocado seeds are distinctly shifted towards positive values of Factor 2, indicating a relatively higher contribution of long-chain MUFAs. Most of the remaining seeds, including pear, grape, pepper, orange, apricot, peach, quince, and plum, cluster near the origin of the coordinate system, suggesting a balanced distribution of SFAs, MUFAs, and PUFAs without a single dominant fraction.

In contrast, pumpkin, melon, watermelon, cherry, and sweet cherry seeds are located in the lower-right quadrant of the plot, indicating a moderate SFA contribution combined with a lower share of long-chain MUFAs along the Factor 2 dimension. From the perspective of their application as liquid biofuel components, the distribution of samples along Factor 1 confirms that PUFA-rich profiles (left side of the plot) favour improved fluidity and enhanced low-temperature properties, but may compromise oxidative stability. Conversely, profiles with higher MUFA and SFA contributions (right side of the plot) ensure greater oxidative resistance, albeit with slightly reduced fluidity.

The position of avocado seeds along the positive direction of Factor 2 suggests their potential suitability as blending components due to their contribution of long-chain MUFAs. Meanwhile, the clustering of numerous seed types near the centre of the plot confirms their intermediate and balanced fatty acid composition, which is advantageous for the formulation of biodiesel fuels combining adequate stability with favourable performance characteristics.

Based on the calculated cetane number (CN) and its comparison with the requirements of ASTM D6751 (CN ≥ 47) [14] and BS EN 14214 (CN ≥ 51) [13], all analyzed feedstocks—except for cherry and watermelon seeds—may be considered suitable biocomponents for biodiesel production (Figure 5). The watermelon sample meets the standard requirements only at its nominal CN value; however, when model uncertainty is taken into account (CN_min), the value falls below the threshold of 51, classifying it as a borderline feedstock. Cherry seeds exhibit the lowest CN and do not meet the quality requirements for biodiesel.

Among the analyzed materials, grape seeds appear to be the most promising biodiesel biocomponent. They combine a favourable cetane number (CN ≈ 59.91) with a particularly advantageous fatty acid distribution, characterised by a very high MUFA content (72.19%), low PUFA content (11.40%), and a moderate SFA share (16.41%). Such proportions are beneficial for both ignition quality and oxidative stability, making this waste stream a highly promising candidate for fuel applications.

Plum and peach seeds also demonstrate considerable potential owing to their favourable CN values (61.10 and 61.18, respectively). However, their applicability may be constrained by a markedly higher PUFA content (51.78% and 49.49%, respectively). This elevated degree of polyunsaturation may reduce the oxidative stability of the resulting biodiesel, necessitating the use of stabilising additives or blending with MUFA-rich fractions to obtain a fuel that fully complies with quality standards.

4. Conclusions

This study demonstrates that seeds derived from fruit and vegetable processing waste possess fatty acid profiles suitable for biodiesel production. The wide variation in the content of monounsaturated fatty acids (MUFAs), polyunsaturated fatty acids (PUFAs), and saturated fatty acids (SFAs) indicates that these raw materials offer diverse feedstock options for biodiesel production. Specifically, the study identifies both monounsaturated-rich materials, similar to rapeseed oil, and polyunsaturated materials, comparable to sunflower oil, which can be strategically utilized depending on the desired biodiesel properties. The generally low SFA content in most seeds further enhances the low-temperature properties of the resulting methyl esters.

The physicochemical analysis of fruit seed oils highlighted grape seeds as the most promising raw material for biodiesel production, owing to their favorable cetane number and beneficial fatty acid composition, which ensures good ignition quality and oxidative stability. Other seeds, such as those from plum and peach, also show potential, but their higher PUFA content suggests that antioxidant additives or blending with MUFA-rich feedstocks may be necessary to mitigate potential oxidative stability issues.

The broader significance of these findings lies in their potential for contributing to the development of more sustainable and diverse feedstocks for biodiesel production. By utilizing food industry waste, this approach not only helps reduce environmental impact but also supports the circular economy by turning waste into a valuable resource.

Future research should focus on a comprehensive characterization of the physicochemical properties of FAME mixtures derived from these feedstocks, including acid value, carbon residue, cloud point, density, flash point, viscosity, and methanol content, and on their comparison with conventional petroleum diesel. Additionally, efforts should be directed toward developing efficient and scalable extraction methods that can be implemented at an industrial scale. This includes optimizing processes to reduce energy consumption, minimize waste, and lower production costs, which are crucial for the economic feasibility of using waste-derived feedstocks in biodiesel production. Furthermore, long-term stability studies are needed to evaluate the oxidative stability and storage conditions of biodiesel produced from these alternative feedstocks. It will also be important to understand how seasonal variations in raw material composition affect biodiesel performance, ensuring consistent fuel quality year-round. Lastly, future research should explore the potential for blending these FAME mixtures with conventional diesel to enhance fuel properties, particularly in terms of cold-weather performance and oxidative stability, thus broadening their practical applications in the transportation and energy sectors. Addressing these challenges will be pivotal for the large-scale adoption of biodiesel produced from fruit-vegetable food industry waste, contributing to a more sustainable and circular biofuels industry.