A comprehensive insight into peanut storage: patterns of quality changes, pathways of quality deterioration, and storage strategies

Abstract

Peanut (Arachis hypogaea L.) is a major oilseed, possessing a high nutritional significance and economic importance but is very susceptible to postharvest spoilage. Despite considerable research, the multifaceted mechanisms underlying quality deterioration remain fragmented, lacking comprehensive integration across deterioration pathways, detection technologies, and storage strategies. This review systematically synthesizes contemporary progress in peanut storage science: (i) quality dynamics (sensory attributes, lipid oxidation, protein structure, and enzymatic activity) under environmental variation; (ii) key deterioration pathways (oxidative rancidity, fungal contamination, and pest infection); (iii) quality detection technologies (conventional and nondestructive, assessed by sensitivity, specificity, and practicality); and (iv) storage strategies, such as controlled-temperature storage, modified atmosphere storage, fumigation, and packaging, with attention to their mechanistic efficacy, operational feasibility, and environmental sustainability. By integrating mechanisms and technologies, valuable theoretical guidance and practical implications are proposed to enhance the safety, quality, and economic value of peanuts throughout global postharvest supply chains.

Highlights

• •Systematic Integration of Peanut Quality Deterioration Pathways.
• •Bridging Knowledge Gaps on Peanut Storage Pests and Damage.
• •Multidimensional Evaluation of Non-Destructive Detection Technologies for Peanut Quality.
• •Evaluating Peanut Storage Strategies: Efficiency, Feasibility, and Sustainability in Practice.

1. Introduction

Peanut (Arachis hypogaea L.), cultivated in more than 100 countries, serves as both an oilseed crop and a food ingredient, and it plays a vital role in agricultural economies, food security systems, and nutritional strategies worldwide (Seidu et al., 2024). Recent estimates from the Food and Agriculture Organization (FAO) (https://www.fao.org/faostat/zh/#data/QCL) indicate that the global production of in-shell groundnuts reached approximately 54.27 million metric tons in 2023, with country-specific contributions illustrated in Fig. 1. Peanuts have demonstrated substantial potential as an economical dietary supplement for combating malnutrition, owing to its exceptional nutritional composition characterized by 26 % protein, 48 % lipids, 3 % dietary fiber, and high levels of calcium, thiamine, and niacin (Ma, Li, et al., 2024). Furthermore, peanuts are rich in bioactive compounds such as resveratrol, procyanidins, phytosterols, phenolic acids, flavonoids, and triterpenoids (Li et al., 2022), which possess health-promoting effects including anti-inflammatory, antioxidant, antimicrobial, and cardioprotective activities.

Fig. 1.

Global Peanut Production (t) in 2023.

Beyond direct consumption, peanuts hold significant industrial importance as raw materials for processed products through primary processing (roasting and oil extraction) and secondary processing (peanut butter and confectionery). The value-added byproducts generated during processing are particularly noteworthy. Defatted peanut meal (an oil extraction residue) and peanut skins serve as rich sources of procyanidins for functional food development, while peanut shells show promise for non-food applications, including biofuel production, activated nanocarbon synthesis, and environmental adsorbents (Chute et al., 2024).

However, peanuts are particularly prone to oxidative deterioration due to their high lipid content (44–56 %), which is dominated by unsaturated fatty acids (UFAs) such as oleic (41 %), linoleic (37 %), and linolenic acid (0.7 %) (Li, Deng, et al., 2023; Martín, Asensio, et al., 2018). The lipid oxidative cascade proceeds via three primary pathways (Liu et al., 2019; Ma, Wen, et al., 2024; Martín, Grosso, et al., 2018): (1) free radical-mediated chain reactions generating hydroperoxides and secondary aldehydes, leading to sensory degradation (off-flavors) and nutrient loss; (2) carbonyl-amine crosslinking inducing protein denaturation, manifested as seed coat browning and cotyledon oil leakage; (3) compromised activity of key lipid metabolism enzymes (lipases and lipoxygenases), reducing seed germination rates. Concurrently, fungal contamination (e.g., Aspergillus spp. producing aflatoxins) and insect infection exacerbate quality losses, causing economic damage.

Although numerous studies have investigated the storage stability of oil-rich crops, current research on the critical aspects of peanut storage remains limited. Insights into how storage conditions dynamically influence key quality parameters are scarce. Moreover, deterioration pathways are rarely integrated with targeted detection strategies, impeding responsive quality assessment frameworks. Evaluations of storage strategies are also fragmented, often lacking comparative analyses of long-term effectiveness, environmental sustainability, and cross-context adaptability.

This review bridges these gaps through a systematic synthesis of peanut storage science. First, it explores the dynamic quality changes in sensory, lipid, protein, and enzymatic activity under various storage conditions. Second, crucial deterioration pathways, including lipid oxidation, fungal contamination, pest infection, and their interactions are elucidated. Furthermore, this work critically evaluates the advantages and limitations of quality detection techniques. Finally, four categories of storage strategies are systematically assessed, namely controlled-temperature storage, modified atmosphere storage, fumigation (chemical and biological), and packaging, with emphasis on effectiveness and constraints. The overall framework is illustrated in Fig. 2. By integrating mechanistic insights into deterioration pathways, advanced detection technologies, and storage strategies, this review seeks to establish a foundation for developing intelligent, sustainable, and scientifically grounded peanut storage systems.

Fig. 2.

Review Framework and Major Contents.

2. Patterns of quality changes in Peanut storage

Peanut quality deterioration during storage is driven by environmental factors (temperature, humidity, and light), leading to irreversible sensory, chemical, and nutritional alterations. This section reviews key changes under various storage conditions, focusing on sensory traits, lipid oxidation, protein integrity, and enzyme activity (summarized in Table 1).

Table 1.

Changes in Peanut Quality Under Different Storage Conditions.

Sample	Storage conditions	Indicator	Key Findings
Peanut seeds(Bhattacharya & Raha, 2002) (procured immediately after harvest from a local market in Santiniketan, West Bengal, India)	stored in 10-kg gunny bags in a local godown under fluctuating natural conditions for 365 days	Fungal Infection	A. niger (55 % → 40 %, down) A. flavus (40 % → 78 %, up)
		Germinability	79 % → 5 % (down)
		Carbohydrate content	17 % → 12.7 % (down)
		Protein content	26 % → 21.8 % (down)
		Oil content	46 % → 43 % (down)
		Free fatty acid content	1.2 % → 2.8 % (up)
Peanuts in pods(Passone et al., 2010) (from a storage company located in the south of Córdoba Province, Argentina)	stored in “big bag” manufactured of polypropylene raffia under fluctuating natural conditions for 4 months (from July to November 2008)	Fungal density	0.92 ± 0.01 aw, reduction percentages = 35.2 % 0.88 ± 0.01 aw, reduction percentages = 65.1 % 0.84 ± 0.01 aw, reduction percentages = 33.6 % 0.76 ± 0.02 aw, reduction percentages = 100 %
		Aflatoxin levels (ng/g)	0.92 ± 0.01 aw, 86.8 ± 27.8 → 200.4 ± 69.1 (up) 0.88 ± 0.01 aw, 3.9 ± 0.2 → 140.9 ± 14.9 (up) 0.84 ± 0.01 aw, 1.1 ± 1.1 → 12.5 ± 3.0 (up) 0.76 ± 0.02 aw, not detected
peanut seeds(Martín, Asensio, et al., 2018) (type Runner, Granoleico, from the Lorenzati, Ruetsch & Cia company, Córdoba Province, Argentina)	stored in polypropylene (PP) and ethylene vinyl alcohol (EVOH) bags in the dark at 25 ± 2 °C and 10 ± 2 °C for 720 days	PV (meq O2/kg)	PP-25: 0.38 → 5.3 (up) PP-10: 0.38 → 2.8 (up) EVOH-25: 0.38 → 4.5 (up) EVOH-10: 0.38 → 1.9 (up)
		γ-tocopherol (mg/100 g)	PP-25: 21.5 ± 0.12 → 18.0 ± 0.41 (down) PP-10: 21.5 ± 0.12 → 18.2 ± 0.23 (down) EVOH-25: 21.5 ± 0.12 → 19.1 ± 0.26 (down) EVOH-10: 21.5 ± 0.12 → 19.5 ± 0.51 (down)
		Total alkane content (Electronic counts/g peanut (millions))	PP-25: 0 → 13.5 (up) PP-10: 0 → 11 (up) EVOH-25: 0 → 6.3 (up) EVOH-10: 0 → 6 (up)
Defatted peanut flour(Sun et al., 2018) (produced commercially from Qingdao, Shandong, China)	stored in vacuum-sealed polyethylene bags at −20 °C, 4 °C and 37 °C for 10 weeks	Free Sulfhydryl content (μmol/g protein)	-20 °C: 15.96 ± 0.15 → 10.54 ± 0.24 (down) 4 °C: 15.96 ± 0.15 → 7.97 ± 0.27 (down) 37 °C: 15.96 ± 0.15 → 6.22 ± 0.18 (down)
		α-helix content	-20 °C: 14.76 ± 0.04 % → 14.35 ± 0.05 % (down) 4 °C: 14.76 ± 0.04 % → 14.31 ± 0.07 % (down) 37 °C: 14.76 ± 0.04 % → 14.29 ± 0.03 % (down)
		β-sheet content	-20 °C: 36.76 ± 0.07 % → 35.60 ± 0.08 % (down) 4 °C: 36.76 ± 0.07 % → 35.51 ± 0.06 % (down) 37 °C: 36.76 ± 0.07 % → 35.31 ± 0.04 % (down)
		β-turn content	-20 °C: 34.23 ± 0.07 % → 35.37 ± 0.08 % (down) 4 °C: 34.23 ± 0.07 % → 35.41 ± 0.10 % (down) 37 °C: 34.23 ± 0.07 % → 35.58 ± 0.08 % (down)
		Fluorescence spectroscopy	-20 °C: decreased by 8.2 % 4 °C: decreased by 9.1 % 37 °C: decreased by 13.9 %
Shelled peanut seeds(Liu et al., 2019) (with name of YuHua-9326, harvested from Xingyang, China)	stored in cloth bags at 15 °C, 25 °C, and 35 °C for 320 days, keeping humidity of 70 %	PV (meq O2/kg)	15 °C: 2 → 4 (up) 25 °C: 2 → 10 (up) 35 °C: 2 → 12 (up)
		MDA (μmol/L)	15 °C: 0.01 → 0.015 (up) 25 °C: 0.01 → 0.05 (up) 35 °C: 0.01 → 0.06 (up)
		Carbonyl value (meq/kg)	15 °C: 2 → 5 (up) 25 °C: 2 → 7 (up) 35 °C: 2 → 8 (up)
Peanuts in pods(Sá et al., 2020) (from a family farmer in the municipality of Campo Mourão, PR)	stored as a complete pod with four water contents (dry basis 8, 10, 12 and 14 %), in polyethylene bags under uncontrolled temperature and humidity for 12 months	Seed water content	8 %db: 4.47 % → 4.19 % (up) 10 %db: 5.41 % → 6.49 % (up) 12 %db: 6.64 % → 6.67 % (up) 14 %db: 8.07 % → 12.26 % (up)
		Shell water content	8 %db: 6.88 % → 13.38 % (up) 10 %db: 9.07 % → 11.58 % (up) 12 %db: 11.82 % → 12.94 % (up) 14 %db: 12.30 % → 13.79 % (up)
		Electrical conductivity (μS cm−1 g−1)	8 %db: 72.64 → 55.78 (down) 10 %db: 86.65 → 101.34 (up) 12 %db: 92.35 → 138.54 (up) 14 %db: 217.92 → 414.24 (up)
		Germination Speed Index	8 %db: 26.29 → 23.09 (down) 10 %db: 13.37 → 10.57 (down) 12 %db: 6.23 → 2.34 (down) 14 %db: 0.29 → 0.00 (down)
Shelled peanuts(Weaver et al., 2021) (the cultivar Georgia-06G seeds, from Georgia)	stored in paper bags under (a) 9–12 °C and 43–51 % RH, LtLRH; (b) 18–27 °C and 42–54 % RH, MtLRH.; (c) 12–37 °C and 48–74 % RH, MtHRH; (d) 24–46 °C and 33–85 % RH, HtHRH; for 72 days	Maximum Germination Rate (b0)	LtLRH: b0 = 82.5 % MtLRH: b0 = 82.5 % MtHRH: b0 = 62.4 % HtHRH: b0 = 50.4 %
		Required to Achieve 80 % Germination (Cumulative Growing Degree Days, GDD)	LtLRH: 46 MtLRH: 51 MtHRH: not achieved HtHRH: not achieved
Shelled peanuts(Guo et al., 2021) (with name of YuHua-22, cultivated by the Henan Academy of Agricultural Sciences in China)	stored in cotton bags at 50 % RH, 65 % RH, and 80 % RH for 320 days, keeping 25 °C	PV (meq O2/kg)	50 % RH: 2.90 → 4.5 (up) 65 % RH: 2.90 → 6 (up) 80 % RH: 2.90 → 14 (up)
		MDA content (μmol/L)	50 % RH: 0.00824 → 0.012 (up) 65 % RH: 0.00824 → 0.018 (up) 80 % RH: 0.00824 → 0.039 (up)
		Carbonyl Value (meq /kg))	50 % RH: 0.4 → 3 (up) 65 % RH: 0.4 → 4.6 (up) 80 % RH: 0.4 → 11 (up)
Shelled peanuts(Floriano et al., 2023) (the cultivar Granoleico, from São Paulo State, Brazil)	stored in paper bags at 18 °C, 64 % RH and 25 °C, 52 % RH for 180 days	β-carotene content (mg/100 g)	18 °C: 8.3 → 8 (down) 25 °C: 8.3 → 6 (down)
Shelled peanuts(Yuan et al., 2016) (From farmers' market in Ningbo City, Zhejiang Province)	stored at (a) 4 °C, 45% – 65 % RH; (b) normal temperature, 20 % RH; (c) normal temperature, 45 % - 65 % RH for 360 days	Oleic acid / Linoleic acid	(a): 0.94 → 0.96 (up) (b): 0.94 → 1.25 (up) (c): 0.94 → 1.54 (up)
		Amino acid content (mg/g)	(a): 233.42 → 215.41 (down) (b): 233.42 → 207.73 (down) (c): 233.42 → 1197.85 (down)

The lipid composition of peanuts is primarily categorized into three major classes: glycerides, phospholipids, and glycolipids. Among these, glycerides—particularly triacylglycerols (TAGs) such as TG 18:2/18:2/18:2, TG 24:0/18:2/18:3, TG 20:5/14:1/18:2, TG 18:2/14:1/18:2, and TG 10:0/10:1/18:1—constitute approximately 95 % of the total glyceride mass and serve as the main energy storage molecules in peanuts (Huang et al., 2022). During storage, TAGs are hydrolyzed by lipase enzymes, producing diacylglycerols (DAGs), monoacylglycerols (MAGs), and eventually free fatty acids (FFAs). These FFAs first undergo lipoxygenase-catalyzed oxygenation to form fatty acid hydroperoxides; subsequent β-oxidation and secondary reactions generate shorter chain products. The resulting hydroperoxides can further participate in Strecker degradation; this generates volatile aldehydes such as hexanal, which are commonly associated with oxidative rancidity (Wang et al., 2019). Phospholipids, although structurally diverse, are present in significantly lower quantities than glycerides in peanut kernels. In contrast to phospholipids, glycolipids lack phosphate groups and nitrogen-containing choline derivatives in their structures (Ishibashi, 2022). During storage, the degradation of both phospholipids and glycolipids is enzymatically driven. Specifically, phospholipases (e.g., phospholipase D) catalyze the cleavage of ester bonds in phospholipids, whereas glycosyl hydrolases (e.g., β-glucosidase) break glycosidic linkages in glycolipids (Frankel, 2012b; Stahelin, 2016). The fatty acid profile of peanuts is characterized by a high degree of unsaturation. Oleic acid (C18:1) and linoleic acid (C18:2), the predominant unsaturated fatty acids, collectively account for approximately 80 % of the total fatty acid content. Saturated fatty acids, including palmitic acid (C16:0, approximately 10 %), stearic acid (C18:0, approximately 3.5 %), behenic acid (C22:0, approximately 2 %), and arachidic acid (C20:0, approximately 1.5 %), contribute to a relatively small proportion of the fatty acid profile (Nader et al., 2021). This distinctive fatty acid composition not only influences the structural properties of peanut lipids but also serves as a primary determinant of their oxidative susceptibility and storage stability.

Peanut protein, which is the second-largest nutritional component in peanut seeds after lipids, is susceptible to oxidative stress factors (e.g., reactive oxygen species, ROS; lipid peroxidation product MDA) during storage. This exposure induces post-translational oxidative modifications, leading to significant deterioration in its structural and functional properties. Key manifestations include: secondary structural transitions, sulfhydryl-disulfide homeostasis imbalance, and protein aggregation (Martín et al., 2019). The natural conformation of peanut proteins is compromised throughout the course of extended storage, which may precipitate the formation of substantial protein aggregates. These transformations attenuate the nutritional value of peanut proteins and could impair their viability as components in food formulations. Additionally, the interaction between lipids and proteins during lipid oxidation is critical in the deterioration process. Lipid oxidation-derived reactive intermediates (e.g., lipid peroxyl radicals, LOO•; lipid hydroperoxides, LOOH) and their secondary degradation products are critical factors that induce conformational rearrangement and functional impairment of proteins (Geng et al., 2023; Hu, 2016).

Enzyme activity, particularly that of lipase (EC 3.1.1.3) and lipoxygenase (LOX, EC 1.13.11.12), plays a pivotal role in the deterioration of peanut quality during storage. These enzymes are involved in lipid metabolism and are directly linked to the deterioration of peanut lipids, which in turn affects the sensory and nutritional quality of peanuts. Lipases are water-soluble enzymes that catalyze the hydrolysis of water-insoluble lipid molecules, including triglycerides and phospholipids (Lim et al., 2022). Li, Liu, et al. (2023) systematically investigated the dynamic changes in lipase activity of shelled peanuts stored at 15 °C, 25 °C, and 35 °C under constant relative humidity (RH) (50 %) over 30 weeks. The 15 °C storage group exhibited peak lipase activity at week 3 (41.60 ± 1.23 U/g), followed by stable levels. The 25 °C group displayed a bimodal activity pattern with peaks at week 3 (40.39 ± 1.12 U/g) and week 9 (40.82 ± 0.70 U/g), followed by a significant increase to 47.12 ± 1.34 U/g at week 12. The 35 °C group demonstrated an initial peak at week 3 (44.93 ± 1.03 U/g), followed by a continuous increase in activity, reaching 59.00 ± 1.70 U/g at the study endpoint. These results suggest that low temperature slows lipid deterioration by inhibiting both enzyme conformational dynamics and microbial proliferation, whereas high temperature not only activates seed lipases but may also induce thermotolerant microbial strains to secrete extracellular enzymes, thereby accelerating lipid decomposition. Functionally, lipases represent the first enzymes involved in lipolytic catabolism, specifically recognizing and hydrolyzing ester bonds in fatty acid chains. This hydrolytic process is critical for peanut seed germination, as it releases energy and nutrients essential for embryogenesis. Huang and Moreau (1978) reported that peanut lipase exhibits optimal activity at alkaline pH and is localized primarily in glyoxysomes, with minor distributions in the mitochondria and membrane fractions. Notably, glyoxysomal lipase alone cannot hydrolyze diglycerides or triglycerides, indicating that reserve triglyceride breakdown requires the cooperative actions of multiple lipase isoforms in different subcellular compartments. In peanuts, LOX-mediated peroxidation preferentially converts linoleic acid (18,2Δ9,12) and α-linolenic acid (18,3Δ9,12,15) into 13(S)-hydroperoxy derivatives through regiospecific catalysis, which subsequently undergo secondary transformations via allene oxide synthase or hydroperoxide lyase pathways (Mou et al., 2022).

3. Deterioration pathways and detection techniques in Peanut storage

Peanut, a key global source of oil and protein, suffers significant deterioration in storage due to synergistic deterioration involving lipid oxidation, fungal growth, and pest infection. The synergistic effect of the three deterioration pathways is illustrated in Fig. 3. This process begins with free radical-mediated peroxidation of unsaturated fatty acids, causing rancidity through the production of volatile aldehydes, ketones, and free fatty acids. Crucially, these oxidation products and the altered microenvironment facilitate fungal proliferation. These fungi secrete lipases and oxidoreductases that further exacerbate lipid oxidation while their by-products are metabolized. Concurrently, fungal activity increases substrate water activity and compromises seed coat and kernel integrity, creating entry points and favorable conditions for pest infection. In turn, infesting pests inflict physical damage that increases the surface area of the exposed lipid to atmospheric oxygen and microbial enzymes. Additionally, pests actively propagate fungal spores through their movement and frass, thereby reinforcing the cycle of oxidation and contamination.

Fig. 3.

Synergistic Effect of Deterioration Pathways.

3.1. Lipid oxidation

Peanut kernels exhibit accelerated triacylglycerol hydrolysis and FFAs are released due to reduced intermolecular hydrogen bonding in the lipid matrix (Butts & Ward, 2022). The autoxidation of UFAs in peanuts represents the predominant pathway of lipid oxidative deterioration during storage, governed by a free radical chain reaction mechanism. This molecular cascade comprises initiation, propagation, and termination (Shahidi & Zhong, 2010), as described in Fig. 4.

Fig. 4.

Free Radical Chain Reaction of Peanut Lipid Oxidation.

Initiation Phase (Formation of Allylic Radicals): The autoxidation susceptibility of UFAs originates from the reduced bond dissociation energy of allylic C—H bonds adjacent to double bonds (C=C), compared with that of nonallylic C—H bonds in saturated fatty acids. Using linoleic (18:2 Δ9,12) and oleic acids (18:1 Δ9) as exemplars, initiators (I; e.g., Fe²⁺, UV irradiation, or thermal stress) induce homolytic cleavage of these C—H bonds, generating alkyl radicals (L^·) through hydrogen abstraction. Notably, linoleic acid has a lower activation energy for radical formation due to its bis-allylic hydrogen at C11, conferring higher reactivity than oleic acid does (Shahidi & Zhong, 2010).

Propagation Phase (Radical Chain Amplification): The alkyl radical (L^·) derived from unsaturated lipids containing labile hydrogen undergoes a rapid reaction with molecular oxygen (rate constant k₀) to form peroxyl radicals (LOO•). This oxygen addition step predominates over the subsequent hydrogen abstraction reaction (k_p), wherein LOO• attacks allylic hydrogens of adjacent UFAs to generate hydroperoxides (LOOH). The marked disparity between k₀ and k_p results in the transient accumulation of LOO• intermediates, thereby driving the exponential propagation of radical chain reactions through continuous oxygen activation and lipid substrate oxidation (Frankel, 2012a). For the reaction of alkyl radicals at most carbon centers with oxygen, the rate constant k₀ typically ranges from 10⁸ to 10⁹ M⁻¹ s⁻¹. The k₀ values for oleic acid and linoleic acid are both large and similar. Linoleic acid, with its conjugated allylic hydrogens, forms initiating radicals more easily, making its oxidative stability much lower than that of oleic acid.

Termination Phase (Product Formation and Radical Quenching): At elevated concentrations, peroxyl radicals (LOO•) interact via termination reactions to form non-radical products through the following pathways: a) Dimerization: Two lipid-derived peroxyl radicals (LOO•; e.g., linoleate radicals at C9 or C13 positions) couple to form a tetroxide intermediate (LOO–OOL), which decomposes via the Russell mechanism to yield hexanal. b) β-Scission: Hydroperoxides (e.g., 13–OOH) undergo homolytic cleavage to generate alkoxyl radicals (LO•), which subsequently undergo β-scission at the C9–C10 position, which produces hexanal and propagating radicals (R^·) that sustain chain reactions. Notably, hexanal generated through pathways a) and b) serves as the primary contributor to the “cardboard-like off-flavor” in peanuts (Lopez et al., 2024). c) Other termination reactions: Peroxyl (LOO•), alkoxyl (LO•), and alkyl (L^·) radicals undergo coupling reactions under specific conditions. Under low-temperature conditions, peroxyl radicals combine to form peroxide-bridged dimers (LOOLs) with concomitant oxygen (O₂) release. Under oxygen-limited high-temperature conditions, alkoxyl and alkyl radicals dimerize into ether-linked (LO–L) or carbon–carbon bonded (L–L) structures. d) Antioxidant Quenching: Antioxidant mechanisms involve Hydrogen Atom Transfer (HAT), where direct hydrogen donation to LOO• yields stable hydroperoxides (LOOH), and Single Electron Transfer (SET), wherein electron transfer to LOO• generates low-reactivity antioxidant-derived radicals (Shanooba et al., 2020).

The oxidative cascade generates primary oxidation products (hydroperoxides) that subsequently decompose into secondary products including low-molecular-weight aldehydes and ketones, which collectively contribute to organoleptic deterioration through color fading, rancid flavor development, and promote protein denaturation or aggregation, thereby disrupting structural integrity and nutritional value. Free radicals that can affect human health and increase the risk of cardiovascular diseases and cancer cannot be ignored (Hashempour-Baltork et al., 2016). Furthermore, oxidation disrupts cell membranes and storage lipids, leading to cell damage and ultimately cell death as well as reduced seed germination (Tonnis et al., 2021).

3.2. Fungal contamination

Peanuts are susceptible to many pathogens. Microbial succession takes place on grains during storage. Physicochemical factors and biological conditions affect this process. Examples of the physicochemical factors are moisture, temperature, pH, oxygen concentration and the use of chemical additives. Biological conditions mean interaction with other microorganisms, the presence of insects and rodents. These factors drive temporary or lasting shifts in fungal communities. The species Penicillium, Aspergillus, Eurotium, and Fusarium are some of the most common fungi that grow in the peanut seeds that are kept in different conditions (Girardi et al., 2017). Khodavaisy et al. (2012) investigated fungal contamination in peanut kernels sold at retail stores in Sanandaj Province, Iran, and reported that approximately 72 % of the samples tested positive for fungi, with A. flavus being the predominant isolate (19 %). Ding et al. (2015) also explored the variation in the fungal microbiome in stored in-shell peanuts across four different regions of China (Liaoning (LN), Shandong (SD), Hubei (HB), and Guangdong (GD)). The dominant fungal genera varied among the peanuts stored in different regions. In the SD region, the predominant genera were Rhizopus, Emericella, and Clonostachys; in the LN region, Penicillium, Eurotium, and Clonostachys were more abundant. In the HB region, the abundances of Penicillium, Eurotium, and Aspergillus were relatively high. In the GD region, Eurotium, Aspergillus, and Emericella were the main genera observed. Moreover, the abundance of the Aspergillus genus differed among the peanuts from the four regions, ranging from 0.53 % in LN to 25.75 % in GD. The increase in Aspergillus abundance from north to south suggested that higher temperatures and RH may increase the risk of Aspergillus and aflatoxin contamination in peanuts. Additionally, in a study of peanut seed storage conditions in the Insana Barat region of Indonesia, four main types of fungal contamination were found to affect stored peanut seeds (Kono, 2021). These fungi include Fusarium sp., Aspergillus sp., Melanospora sp., and Rhizopus sp., which cause varying degrees of damage to the seeds during storage. For instance, Fusarium sp. can infect seeds during storage, potentially leading to discoloration of the seeds, inhibited germination, and diseases in the field.

When raw peanuts are infected with mold during storage, the mold consumes the nutrients in the peanuts progressively. The mold expands in size and mold content inside the peanut rises. Meanwhile, the mold is constantly excreting metabolites. It alters the smell and spectral properties of the peanut. Furthermore, among oilseeds, peanuts are the most susceptible to aflatoxin contamination. Aflatoxins (AFs), which are toxic secondary metabolites produced by A. flavus and A. parasiticus, have been classified as Class-Ӏ carcinogen by the International Agency for Research on Cancer due to the significant health risks posed by AFs (Dou et al., 2023; Tian et al., 2023). It should not be overlooked that the aflatoxins in peanuts can enter the human food chain through direct consumption of peanuts or related products such as oil, or through consumption of aflatoxins M₁ and M₂ containing milk or milk products, which originate from aflatoxins B₁ and B₂ contaminated feeds such as peanut meal ingested by lactating livestock (Jallow et al., 2021).

3.3. Pest infection

Peanuts in storage are also susceptible to pests. The pests not only eat the seeds directly, damaging them, but also contaminate the peanuts with activities such as spinning, netting and draining. These activities adversely affect eating quality and the commercial viability of peanuts (Diagne et al., 2019). Moreover, the presence of these contaminants often facilitates the growth of fungi, which may produce mycotoxins such as aflatoxins, toxic to humans and affect food quality and safety (Mir et al., 2021). The primary storage pests affecting peanuts, as detailed in TABLE 2, are predominantly Coleoptera (beetles) and Lepidoptera (moths).

Table 2.

Major Pests in Stored Peanut and Their Damage.

Common Name	Scientific Name	Family (order)	Destructive Stage and Behavior	Main Countries with Pest Infestations
Bruchid beetle	Caryedon serratus (Oliver)	Chrysomelidae (Coleoptera)	Larvae Bore into pods and feed on seeds	India(Tewari et al., 2024), Burkina Faso(Ouedraogo et al., 2017), Mexico(Orozco-Santos et al., 2012), Niger(Baoua et al., 2015), Senegal(Diagne et al., 2019), Togo(Banla et al., 2018), Nigeria(Ekesi et al., 2001)
Khapra beetle	Trogoderma granarium (Everts)	Dermestidae (Coleoptera)	Larvae Attack most seed parts, favoring the germ; facultative diapause for up to 8 years	India(Farsani et al., 2024), China(Lu et al., 2024), Senegal(Musa & Dike, 2011), Iran(Mohammadzadeh & Izadi, 2018), Niger(Baoua et al., 2015), Nigeria(Egwurube et al., 2010)
Rusty grain beetle	Cryptolestes ferrugineus (Stephens)	Laemophloeidae (Coleoptera)	Larvae and Adult Feed on germ and endosperm	Argentina(Nesci et al., 2011), Niger(Baributsa et al., 2017), United States(Mbata et al., 2024), China(Gu et al., 2023), UK(Collins et al., 2007)
Red flour beetle	Tribolium castaneum (Herbst)	Tenebrionidae (Coleoptera)	Larvae and Adult Feed on broken grains; result in dust formation; infested seeds emit sour and pungent smell	India(Swathi et al., 2024), Niger(Baributsa et al., 2017), United States(Perez et al., 2020), Pakistan(Sarwar et al., 2021), China(Xiong et al., 2018), Argentine(Garcia et al., 2019)
Peanut bettle	Ulomoides dermestoides (Fairmaire)	Tenebrionidae (Coleoptera)	Larvae Bore into seeds, causing the seeds to rot or fail to germinate	Spain(Morillo-Garcia et al., 2016), Argentina(Crespo et al., 2011), Brazil(Plata-Rueda et al., 2020), Colombia(Caballero-Gallardo et al., 2021)
Sawtoothed grain beetle	Oryzaephilus surinamensis (Linnaeus)	Silvanidae (Coleoptera)	Larvae and Adult Feed on externally on pods and seed	India (Source: Centre for Indian Knowledge Systems, Chennail), Argentina(Wilches et al., 2019), UK(Collins et al., 2007)
Merchant Grain Beetle	Oryzaephilus Mercator (Fauvel)	Silvanidae (Coleoptera)	Larvae only harm damaged kernels	Indonesia(Astuti et al., 2018), India(Dick, 1987)
Cadelle beetle	Tenebroides mauritanicus (Linnaeus)	Trogossitidae (Coleoptera)	Larvae Feed on kernels, especially embryo, forming irregular wormholes	Benin(Adjou et al., 2013)
Indian meal moth	Plodia interpunctella (Hübner)	Pyralidae (Lepidoptera)	Larvae Form massive silk that accumulate faecal pellets, cast skins and egg shells	United States(Perez et al., 2020), India(Dick, 1987), China(Wang et al., 2024), Botswana(Masukujane et al., 2020), Iran(Borzoui et al., 2018)
Rice moth	Corcyra cephalonica (Stainton)	Pyralidae (Lepidoptera)	Larvae Attack all areas of the seed and often penetrate into interior	Brazil: State of São Paulo(Dos Santos et al., 2016), India(Kaur et al., 2024), Niger(Baributsa et al., 2017)

The primary coleopteran pests in stored peanuts include Caryedon serratus (Olivier, 1795) (Coleoptera: Chrysomelidae), Trogoderma granarium (Everts, 1881) (Coleoptera: Dermestidae), and Cryptolestes ferrugineus (Stephens, 1831) (Coleoptera: Laemophloeidae). Out of 100 species of insect pests that attack the stored peanut, C. serratus is a major cosmopolitan pest of economic importance and is the only pest that infests both the pods and the kernels of groundnut, causing heavy losses in quality and quantity (Jenne et al., 2018). Under storage conditions of 38 °C and 45 % RH, over a period of approximately 48 to 52 days, peanut pods and kernels were severely infested by C. serratus (Kumari et al., 2002). The overall damage levels were 77.1 % for pods and 67.8 % for kernels, with pods being damaged 9.3 % more than kernels. In terms of weight loss, the pods lost 55.1 % of their weight, while kernels lost 52.3 %. The weight loss in pods was 2.8 % higher than in kernels. As for the qualitative losses, the oil content in the infested pods and kernels decreased by 2.9 % and 3.5 %, respectively. Infested pods and kernels had higher acid value (AV), which is an indicator of free fatty acid levels, than the uninfected pods. The increase in acid value was 0.201 % for pods and 3.088 % for kernels. These results highlight the negative impact of pest infection on the weight and oil quality of stored peanuts and that, compared with kernels, peanut pods are more susceptible to damage. Sreedhar et al. (2020) also performed a study on “biochemical changes in groundnut pods due to infection of C. serratu under stored conditions” and assessed weight loss due to insect infection at 30, 90, and 150 days. The study was carried out with groundnut pods stored in an environment maintained at 70 ± 5 % RH and a temperature of 28 ± 1 °C. The findings indicated that after 30 days of storage, weight loss varied from 3.55 % for the K1621 variety to 8.55 % for the K1715 variety. At 90 days, the weight loss ranged from 7.90 % for K1621 to 17.73 % for K1715. After 150 days, the loss extended from 13.42 % for K1621 to 23.80 % for K1715. These results revealed a significant increase in the weight loss of groundnut pods due to C. serratus infection with increasing storage period, and they also highlight the different sensitivities of various peanut varieties to infection. The khapra beetle (T. granarium) is among the most destructive pests. It can have an impact on any stored food such as peanuts, cereals, and other food items (Eliopoulos, 2013). The primary characteristic of T. granarium is that mature larvae of this beetle can go into facultative diapause. Although not all individuals enter this state, those that continue to occasionally feed and molt but may remain in diapause for more than 3 years before terminating diapause, pupating, and emerging as adults. Furthermore, under biotic and abiotic conditions adverse to larval development (e.g., temperatures below 30 °C, overcrowding, food scarcity), larvae may enter facultative diapause for up to 8 years (Boukouvala & Kavallieratos, 2020).

The predominant lepidopteran pest in stored peanuts is Plodia interpunctella (Hübner, 1813) (Lepidoptera: Pyralidae), which is considered the most important pest of storage products worldwide. Its larvae produce silk that can web food particles together, and the webbing contains larval excreta (frass) and exuvia (cast skins), thus creating an unpleasant odor in infested groundnuts (Masukujane et al., 2020). Infestation by P. interpunctella not only results in direct product attrition but also triggers a flow of economic losses, encompassing the financial burden of pest control measures, the deterioration of product quality, and the ensuing discontent and complaints among consumers. Kaliyan, Carrillo, et al. (2007) investigated the impact of varying low-temperature conditions on the survival rate of P. interpunctella larvae. The experimental results demonstrate that under extremely low-temperature conditions, such as −30 °C and − 24.0 °C, the survival duration of the larvae was exceedingly brief, amounting to 0.017 h and 1.0 h, respectively. This phenomenon suggests that ultra-low temperatures cause a fatal physiological stress on Indian meal moth larvae, precipitating a rapid cessation of their vital activities. As the temperature increased to −10.0 °C, the tolerance of the larvae is markedly enhanced, with the time required to achieve 100 % mortality extending to 312 h, or more than 13 days. These findings indicate that, in low-temperature environments close to the freezing point, the physiological mechanisms of the larvae can adapt and prolong their survival period. Furthermore, when the temperature increased to −0.5 °C, the larval tolerance reached its maximum, which necessitated 1992 h, or more than 83 days, to achieve complete mortality. These results reveal that P. interpunctella larvae exhibit significant cold resistance in sub-zero environments near the freezing point and are maintaining vital activities over an extended period through physiological regulatory mechanisms. Overall, these findings revealed that the mortality rate of P. interpunctella larvae can be effectively modulated by precisely controlling the low-temperature environment, thereby offering a potential non-chemical approach for pest control in stored peanuts.

3.4. Detection techniques

Oxidative rancidity is a primary cause of quality deterioration during peanut storage. AV and PV are two widely recognized indicators for assessing the degree of lipid oxidation. AV quantifies free fatty acids formed due to enzymatic hydrolysis and serves as an early marker of spoilage, whereas PV reflects the accumulation of primary oxidation products. Traditionally, AV and PV are determined using titration-based methods, such as those specified in the Chinese National Standard and the American Oil Chemists' Society protocols. Currently, a range of advanced analytical techniques have emerged, including high-performance liquid chromatography–mass spectrometry (Gao et al., 2022), multiple chemiluminescence flow injection analysis (Lara-Ortega et al., 2018), and mass spectrometry (Alberici et al., 2016). While these methods offer high sensitivity and accuracy, they also involve complex procedures and are time-consuming, thus limiting their suitability for rapid or online detection applications.

Detection techniques for fungal contamination and mycotoxins are generally categorized into morphological, chromatographic, and immunological methods (Abdolmaleki et al., 2021). However, traditional methods face significant limitations. They are not sufficiently sensitive to early-stage contamination, and they are unable to differentiate between viable and non-viable fungi. They also involve multi step practices and destructive sampling is required, which precludes on-site, rapid implementation.

To address these limitations, nondestructive spectroscopic methods have been developed to detect oxidative rancidity, mold growth, and mycotoxin contamination. As summarized in Table 3, these techniques preserve the physical integrity of peanut kernels and enable real-time online quality monitoring during processing, thereby supporting product safety and sustainable industry development. Nevertheless, several challenges hinder practical deployment. Such techniques are highly sensitive to environmental factors (e.g., temperature, humidity, and ambient light), limited by penetration depth and spatial resolution, and often require complex calibration to maintain accuracy across instruments or batches. Currently, the lack of standardized protocols and large-scale validation further constrains industrial adoption, while multimodal fusion systems can improve accuracy but increase system complexity and cost. Collectively, these factors limit the widespread application of nondestructive detection technologies and underscore the need for advances in calibration transfer, data fusion, and standardization to achieve reliable, large-scale monitoring.

Table 3.

Applications of Nondestructive Testing Technologies in Quality and Safety Detection of Peanuts.

Specific Technique	Target	Model	Performance Metrics
Near Infrared Spectroscopy	A. spp. contamination levels in peanut kernels(Shen et al., 2018)	Classification: LDA	accuracy = 92.11 %
		Quantification of colony: PLS Regression	Rp2 = 0.886, RPD = 3.0, LOD =0.578 log CFU/g
	aflatoxin B1 of peanut kernels(Li et al., 2023)	Support Vector Machine (optimization: penalty parameter = 2.8284 and kernel function parameter = 0.03536)	RMSEP = 24.6322, Rp = 0.9761, RPD = 4.6999
	antioxidant activity of peanut seeds(Bilal et al., 2021)	Genetic Algorithm - PLS	Rc = 0.82–0.96, Rp = 0.77–0.95, RPD = 2.66–3.61
	acid and peroxide in crude peanut oil(Haruna et al., 2022)	Competitive Adaptive Reweighted Sampling - PLS	AV: Rp = 0.9503, RMSEP = 0.00874, RPD = 3.14
			PV: Rp = 0.9637, RMSEP = 0.5650, RPD = 3.64
	AV of peanut oil(Rao et al., 2009)	Quantification: PLS	R2 = 0.9725, RMSECV = 0.308, SEP = 0.333
		Discriminant: PLS	accuracy = 96.55 %
	aflatoxin B1 in moldy peanut kernels(Jiang et al., 2023)	Two-Dimensional Convolutional Neural Network (base on Gramian Angular Summation)	RMSEP = 2.0, R2 = 0.99, RPD = 8.3 ratio of performance to interquartile range = 9.3
Fourier transform infrared spectroscopy	aflatoxin-contaminated peanut oil(Yang et al., 2018)	Multivariate Decision Tree	true-positive and true-negative rate of calibration and validation could both reach up to 100 %
Attenuated Total Reflection Fourier Transform Infrared Spectroscopy	aflatoxin B1 in peanut oil(Song et al., 2021)	Support Vector Machine (optimization: penalty parameter = 16; kernel function parameter = 0.0359)	the sensitivity = 95.83 % the specificity = 100 % the accuracy = 98.21 %
Portable Mid-Infrared Spectroscopy	aflatoxins in peanuts kernels(Yao et al., 2025)	Classification: Soft Independent Modeling of Class Analogy	the sensitivity = 94.7 % the specificity = 80.0 %, the accuracy = 89.6 %
		Quantitative: PLS Regression	Rc = 0.91, Rp = 0.85, RMSECV = 95.51, RMSEP = 96.02, RPD = 6.18
	acidity index of peanut kernels(Liu et al., 2022)	Variable Combination Population Analysis-Support Vector Machine	RMSEP = 0.61, Rp2 = 0.95, RPD = 4.31
Fourier Transform Near-Infrared Transmission and Diffuse Reflection Spectroscopy	aflatoxin B1 in peanuts kernels(Yao et al., 2022)	Classification: LDA	accuracy = 100 %
		Quantification: PLS Regression (Preprocessing Method: First Derivative combined with Norris Derivative Filter Smoothing)	Transmission Module R2 = 0.984, RMSEC = 1.28 %, RPD = 7.91, Rp2 = 0.936, RMSEP = 2.11 % Diffuse Reflection Module R2 = 0.937, RMSEC = 2.51 %, RPD = 3.98, Rp2 = 0.944, RMSEP = 2.61 %
Mid-Infrared Spectroscopy	A. spp. contamination in peanut kernels(Kaya-Celiker et al., 2015)	PLS Regression (preprocessing: mean-centering + first derivative smoothing)	Classification: percent variability = 74.1 % (Log CFU/g ≤ 2.5); percent variability = 76.5 % (Log CFU/g = 3.0–4.0); percent variability = 77.6 % (Log CFU/g = 4.5–5.0); percent variability = 79.6 % (Log CFU/g ≥ 5.5)
			Quantitative analysis: Rc = 96.20% – 99.98 %, RMSECV = 0.014–0.153, RMSEP = 0.120–0.235
Hyperspectral Imaging Techniques	moldy peanut kernels(Liu et al., 2021)	ShuffleNet V2 (based on band influence algorithm)	the average accuracy = 97.66 % F1 score = 0.977 kappa coefficient = 0.963
	moldy peanut kernels(Sun et al., 2021)	PLS Discriminant Analysis (based on successive projection algorithm)	calibration set accuracy = 100 % prediction set accuracy = 100 %
	peanut seed vigor(Zou et al., 2022)	Median Filtering-Light Gradient Boosting Machine-Random Forest	prediction accuracy = 92.59 % fitting time = 1.77 s
	peanut seeds storage period(Zou et al., 2022)	Decision Tree – Median Filter - Catboost	accuracy = 97.53 %.
	aflatoxins in peanut kernels(Wang et al., 2025)	Convolutional Neural Network-Bidirectional Long Short-Term Memory Network	validation accuracy = 94.92 % recall rate = 95.59 %.
Hyperspectral Imaging Techniques (in the near-infrared region)	moisture content of peanut kernels(Rabanera et al., 2021)	PLS Regression (optimization: selecting wavelengths with the highest absolute weighted regression coefficients)	Rc2 = 0.9357, Rv2 = 0.9133, Rp2 = 0.9445, RMSEC = 1.6822, RMSECV = 1.8316, RMSEP = 1.9519
Hyperspectral Microscopic Imaging	A. flavus contamination in peanut kernels(Guo et al., 2025)	Hybrid Convolutional Transformer-Feature Fusion Network	test accuracy = 100.00 %
	Aflatoxin B1 in single peanut kernels(Zhang et al., 2022)	Second-Order Derivative with a Support Vector Regression (using competitive adaptive reweighted sampling)	Rc2 = 0.95, Rv2 = 0.93, RMSEC = 1.950, RMSECV = 2.637, RPD = 3.32
Dual-Aspect Attention Spatial-Spectral Transformer-Hyperspectral Imaging	A. flavus contamination in peanut kernels(Guo et al., 2024)	Machine Learning Methods	accuracy = 99.40 % accuracy of distinguishing between different contamination times = 100 %
Line-Scan Raman Hyperspectral Imaging	peanut kernels infected with multiple A. flavus(Yang et al., 2024)	Binary Classification	accuracy >99 %
Terahertz imaging technology	peanut seed quality(Jiang et al., 2024)	Convolutional Neural Network	accuracy = 98.7 %

Abbreviations and threshold values that reflect good model performance: LDA (Linear Discriminant Analysis); PLS (Partial Least Squares); R² (coefficient of determination; > 0.90 excellent, 0.80–0.90 good, 0.65–0.80 acceptable, < 0.65 poor); Rc² (coefficient of determination for calibration set; thresholds similar to R²); Rv² (coefficient of determination for validation set; > 0.85 indicates strong predictive reliability); Rp² (coefficient of determination for prediction set; > 0.85 indicates strong performance); Rp (prediction correlation coefficient; > 0.90 excellent, 0.80–0.90 good, 0.65–0.80 acceptable); Rc (calibration correlation coefficient; thresholds similar to Rp); RMSEC (root mean square error of calibration; lower values indicate better fit); RMSECV (root mean square error of cross-validation; lower values indicate more robust models); RMSEP (root mean square error of prediction; lower values indicate stronger predictive ability); RPD (Residual Predictive Deviation; > 3.0 excellent, 2.5–3.0 very good, 2.0–2.5 approximate quantitative prediction, 1.5–2.0 rough screening, < 1.5 unreliable); SEP (standard error of prediction; lower values indicate higher precision); LOD (limit of detection; lower values indicate higher sensitivity).

4. Advances in peanut storage strategies

Peanuts, an economically important oilseed crop and food ingredient, require advanced storage strategies to ensure supply chain stability and enhance value-added processing potential. Contemporary storage methods for peanuts predominantly encompass four technical domains: controlled-temperature storage, modified atmosphere storage, fumigation treatments, and packaging innovations.

4.1. Controlled-temperature storage

Temperature is a critical abiotic factor in stored-grain ecosystems and profoundly influences both the oxidative stability and nutritional quality of peanuts during storage. For instance, raw peanuts packaged in polypropylene bags and stored at 25 ± 2  °C and 10 ± 2  °C for 720 days under 60–80 % RH demonstrated significant temperature-dependent lipid oxidation (Martín, Grosso, et al., 2018). At the end of the storage period, the PV reached 5.0 meq O₂/kg at 25  °C, compared with 3.0 meq O₂/kg at 10  °C, with the higher temperature consistently accelerating PV accumulation. Similarly, Floriano et al. (2023) monitored the changes in the lipid acidity (primary (K₂₃₂) and secondary (K₂₇₀) products of lipid oxidation) of shelled peanut grain stored at 18 and 25 ± 1 °C, respectively, over a period of 180 days. At 18 °C, the K₂₃₂ value increased by 28.81 % and the K₂₇₀ value did not significantly change after 180 days. In contrast, the K₂₃₂ value increased by 29.05 % and the K₂₇₀ value increased by 27.58 % at 25 °C, indicating accelerated formation of both primary and secondary oxidation products at higher temperatures. Additionally, Liu et al. (2019) reported that storage at 35  °C for 320 days resulted in dramatic losses of free amino acids: the concentrations of aspartic acid and serine decreased from 0.55 to 0.13 mg/g, 0.47 to 0.27 mg/g, and that of essential amino acids decreased from 0.89 to 0.39 mg/g. This degradation is primarily attributed to their involvement in non-enzymatic browning reactions, particularly the Maillard reaction, which occurs between the amino groups of amino acids and the carbonyl groups of reducing sugars or reactive aldehydes generated from lipid oxidation (Hans-Dieter et al., 2009). Elevated temperatures significantly accelerate these reactions, leading to the formation of melanoidins and other complex compounds, thereby reducing the bioavailability and nutritional value of amino acids. These results highlight the dual detrimental effects of higher temperature: intensified lipid oxidation and significant nutrient degradation through amino acid loss.

Controlled-temperature storage strategies utilize either passive (e.g., ambient winter air) or active (mechanical refrigeration) cooling systems to preserve peanut quality over extended periods. Successful implementation requires infrastructural adaptations such as thermal insulation, air-tight sealing, and environmental monitoring systems. Preconditioning peanuts to a moisture content ≤  8 % is also essential (Zhou et al., 2022). Natural cooling methods, which capitalize on regional climatic conditions, offer cost-effective and sustainable advantages. However, their efficacy is constrained in large-scale storehouses due to the limited temperature uniformity in central and lower grain layers. To address this spatial thermal heterogeneity, mechanical ventilation or refrigeration systems are often integrated as auxiliary cooling measures. Kaliyan, Morey, et al. (2007) conducted a series of studies in the north-central and east-central regions of the United States, aiming to utilize cold winter air to control the larvae of P. interpunctella. The study indicated that larval mortality was present with constant exposure to temperatures less than −10 °C and the larval mortality was 100 %. Based on the Cumulative Lethality Index (CLI) model, a downward aeration strategy was predicted to begin at the depth of 0.4 m of the grain pile top, which could be an effective way to enhance heat exchange when the grain temperature exceeds the surrounding air temperature. The strategy resulted in uniform larval mortality across the 12 test sites, with aeration fans operating only 10 % of the time from December to February, reducing energy consumption significantly. Economically, this approach incurred an average electrical cost of US$1.3 per ton of grain, highlighting its operational efficiency. In the tropics, Guzman (2019) designed and developed an automated intelligent bulk storage system for storing dried peanut pods, enabling six-month storage through adaptive aeration. The aeration fan operates at 0.2 m³min⁻¹ton⁻¹ when the RH is 65 %–70 %; when the RH is 71 %–75 %, the fan operates at 0.3 m³min⁻¹ton⁻¹; and when the RH is 76 %–80 %, the fan operates at 0.4 m³min⁻¹ton⁻¹. At the end of the storage period, the moisture content of the stored peanut pods in the developed system was 9.8 %, the seed viability was 80 %, the moldy seed content was 13.33 %, and the aflatoxin content was 0.3 ppb (meeting safety standards). Economic modeling revealed a low operational cost of ₱1.56 per kilogram per month, a 157 % internal rate of return by year five, and a payback period of less than 1 year, underscoring its dual benefits in terms of quality preservation and economic viability. Despite its demonstrated efficacy, the widespread adoption of low-temperature storage remains limited among smallholder farmers due to substantial initial capital investment and high operational costs.

4.2. Modified atmosphere storage

Modified Atmosphere Storage (MAS) is a sustainable grain preservation technique that primarily inhibits microbial and insect activity by reducing oxygen levels (Cao et al., 2019). MAS offers significant benefits for peanut storage by suppressing lipid rancidity, minimizing nutrient degradation, and inhibiting pest and mold growth, thereby reducing the dependence on chemical treatments while maintaining sensory and nutritional qualities. A study examining peanuts with 7.0 % and 8.0 % moisture content stored in a 99 % CO₂ atmosphere at 30  ±  1  °C for three months revealed substantially lower free fatty acid (FFA) levels (≤  0.77 %) in sealed samples, even with 3 % kernel damage (Navarro et al., 2011). In contrast, ventilated samples (especially those with higher moisture contents) presented FFA levels exceeding 2.57 %, surpassing market acceptability thresholds. The proliferation of the mold colonies was also effectively curtailed (≤ 9.7 × 10 CFU), in stark contrast to the ventilated samples, the proliferation of which was markedly increased to 4 × 10⁴ ± 3 × 10³ CFU (7 % moisture content) and 7.6 × 10⁵ ± 5 × 10⁴ CFU (8 % moisture content), respectively. From a lipidomic perspective, Ma, Zhang, et al. (2024) reported that nitrogen-enriched storage (NS, 98 ± 0.5 % N₂) effectively retarded lipid oxidation in peanut kernels. NS modulated both phospholipid and glycerophospholipid metabolic pathways by suppressing diacylglycerol degradation and upregulating the levels of protective lipids such as phosphatidylinositol and phosphatidylcholine after 300 days of storage. Similarly, Li et al. (2024) reported that compared with conventional storage, NS also mitigated protein oxidation, as evidenced by higher free sulfhydryl content and lower carbonyl accumulation. After 10 months, protein solubility in peanuts decreased to 121.0  ±  0.8 mg N/100 g (mg N/100 g indicates the amount of soluble nitrogen per 100 g of peanut sample, serving as a proxy for soluble protein content.) under conventional storage but remained high at 134.0  ±  0.8 mg N/100 g under NS conditions.

Notably, to improve the storage stability of packaged peanuts, modified atmosphere packaging (MAP) technology, which involves regulating the internal gas environment through specific packaging materials, has emerged as a promising strategy (Wang et al., 2014). Wu et al. (2022) systematically evaluated MAP using a nitrogen–oxygen mixture (N₂:O₂ = 9:1) and compared it to that of fresh edible peanuts stored at 4 °C for 120 days. The results revealed that MAP significantly delayed browning and inhibited kernel softening. Specifically, the control group exhibited obvious surface browning after 40 days, whereas the MAP-treated samples maintained lower browning indices throughout the storage period. Concurrently, compared with that in the control group, the kernel firmness in the MAP group remained consistently greater, reaching approximately 1.20-fold on Day 120. These findings highlight the efficacy of MAP in preserving the sensory and textural attributes of fresh peanuts. Further research by Opio and Photchanachai (2018) investigated the effects of MAP on microbial growth and enzymatic activity. Under 100 % CO₂, the growth of A. niger decreased. Simultaneously, lower lipase activity (0.90 ± 0.3 U/mg) was observed under the 100 % N₂ packaging atmosphere with inoculation, indicating that this atmosphere may effectively inhibit the biochemical changes caused by lipase. While various gas compositions are effective for peanut preservation, practical deployment requires considerations beyond efficacy. High CO₂ concentrations (e.g., 99 %) effectively suppress mold growth and insect activity and can rapidly establish the target atmosphere. However, CO₂ can corrode certain storage materials in the presence of moisture and may require specialized handling and monitoring equipment, increasing upfront costs. By contrast, N₂ enrichment effectively retards lipid and protein oxidation owing to its inert, oxygen-displacing properties. N₂ is generally easier to manage than high CO₂ systems and is widely available, making it suitable for large-scale storage; nevertheless, achieving and maintaining high N₂ purity demands tight sealing and continuous monitoring to prevent oxygen ingress. Mixed atmospheres—such as N₂:O₂ (e.g., 9:1)—offer a balanced approach: they inhibit spoilage while preserving limited aerobic metabolism that may benefit certain quality attributes and help avoid anaerobic off-flavors. The optimal choice among CO₂, N₂, or mixed atmospheres depends on specific objectives (e.g., mold control vs. oxidation control), scale of operation, available infrastructure, and a rigorous cost–benefit analysis.

MAS technology may be combined with intelligent monitoring systems to achieve more precise and automated control of the storage environment in the future. Research efforts are anticipated to focus on optimizing gas compositions, improving cost-efficiency, and developing novel eco-friendly materials to promote safe, economical, and sustainable peanut storage solutions.

4.3. Fumigation

4.3.1. Chemical fumigants

Chemical fumigants remain indispensable in peanut storage management, primarily for controlling insect pests and mitigating fungal contamination. Such fumigants are effective at penetrating structures and commodities (Sabarwal et al., 2018). Unfortunately, the intensive application of certain synthetic pesticides for several decades has had adverse effects on non-target organisms and the environment. For instance, the use of methyl bromide has led to the depletion of the ozone layer (Chaudhari et al., 2021). Additionally, persistent chemical application has led to the emergence of resistant pest populations, complicating pest management strategies. Resistance to phosphine has been documented in multiple populations of O. surinamensis, with resistance levels ranging from 2 % to 100 % across different developmental stages (Gautam et al., 2020).

To address these challenges, researchers are exploring alternative chemical fumigants with lower environmental persistence and reduced residual toxicity. Myers et al. (2021) reported that a combination of sulfuryl fluoride (SF) and propylene oxide (PPO) effectively controlled all life stages of T. granarium, a major pest in stored peanuts. Two effective procedures were proposed: 96 mg/L SF + 40 mg/L PPO at 10  °C, and 80 mg/L SF + 40 mg/L PPO at 20  °C. This combined fumigation strategy presents a promising alternative amidst increasing global restrictions on methyl bromide use. Moreover, chlorine dioxide (ClO₂) fumigation has also shown industrial potential for reducing aflatoxin contamination in peanuts (Liu et al., 2022). In small-scale experiments, analysis of peanut kernels treated with ClO₂ fumigation at 20 °C for two days revealed that the content of AFB₁ decreased to near the detection limit, achieving a degradation rate of 99.87 %. Under scaled-up experimental conditions, the degradation rate of AFB₁ was approximately 73 %, that of AFB₂ around 69 %, and the degradation rates of both AFG₁ and AFG₂ were 100 %, with the overall aflatoxin degradation rate exceeding 94 %. Further analysis of the crude peanut oil and meal obtained after the ClO₂-fumigated peanut kernels were pressed showed that the residual amount of AFB₁ in the oil was reduced to 4.05 μg/kg, and that in the meal was reduced to 3.52 μg/kg. In contrast, the AFB₁ concentrations in the untreated peanut materials were 6.91 μg/kg for oil and 15.12 μg/kg for meal. Additionally, Pan et al. (2020) elucidated the inhibitory mechanism of Dimethylformamide (DMF) on the growth of A. flavus and the biosynthesis of AFB₁. Initially, DMF targets the fungal cell wall, leading to the disruption of cell wall integrity and the perturbation of cell membrane-associated proteins. DMF subsequently downregulates the expression of the key regulatory genes aflS and aflR, resulting in the downregulation of structural genes within the AFs biosynthetic cluster. Furthermore, DMF interferes with glucose metabolic pathways, reducing the production of acetyl-CoA and NADPH and disrupting the process of oxidative phosphorylation, thus decreasing mitochondrial function and ATP generation. Meanwhile, it affects the biosynthesis and metabolism of amino acids, inhibiting the synthesis of essential amino acids and increasing the stress on fungal cells.

4.3.2. Biofumigants

Volatile organic compounds (VOCs) derived from plants and microbes have emerged as promising biofumigants, offering broad-spectrum antifungal activity, environmental safety, and minimal residue concerns (Wei et al., 2024). These compounds act through vapor-phase activity, enabling non-contact inhibition of storage pests and pathogens—a highly desirable feature for peanut preservation.

The integration of plant-derived natural fumigants into peanut storage systems has enhanced conventional management strategies through the introduction of targeted, eco-compatible solutions for suppressing pest infection and mycotoxin-producing fungal colonization. It has been reported that Methyl Benzoate (MBe), as a potential natural fumigant, shows significant fumigant toxicity against adult stages of P. interpunctella (Mostafiz et al., 2021). In fumigation experiments conducted within sealed glass bottles of one-liter volume, as the concentration of MBe increased, the mortality rate (after a 4 h exposure) of Indian meal moth adults correspondingly increased, reaching 56 %, 70 %, 90 %, and 100 % for concentrations of 0.1, 0.3, 0.5, and 1 μL/L air respectively. In a larger-scale experiment within a cardboard box of 96,000 cm³, MBe achieved a 100 % mortality rate even at a lower concentration (0.01 μL/cm³) after a 24 h exposure period. Trans-2-Hexenal has also demonstrated broad insecticidal efficacy against multiple developmental stages of T. castaneum, with the greatest susceptibility observed in eggs (LC₅₀ = 14.3 μL/L) (Cui et al., 2021). Moreover, Ma and Johnson (2021) reported that the use of Sorbic acid derivatives, including sorbinaldehyde and (E,E)-2,4-heptadienal, suppressed A. flavus growth in a dose-dependent manner. Specifically, at a concentration of 12.5 mg/mL air, the infection rate of sorbaldehyde reached 100 % by the fifth day, which then stabilized at approximately 60 % and 20 % at air concentrations of 25 mg/mL and 75 mg/mL, respectively. With respect to (E, E)-2,4-heptadienal, the infection rate also peaked at 100 % on the fifth day at a concentration of 6.25 mg/mL air, and subsequently decreased to approximately 40 % and 21 % at air concentrations of 12.5 mg/mL and 50 mg/mL, respectively. Notably, when the fumigation concentration was increased to 100 mg/mL air for sorbaldehyde and 75 mg/mL air for (E, E)-2,4-heptadienal, both compounds were able to completely (100 %) prevent A. flavus infection in stored peanut seeds.

Essential oils (EOs) and their active components are being increasingly investigated as botanical fumigants, which hold promise in meeting consumer demands for chemical additive-free food (Niu et al., 2022). The effects of EOs and their bioactive compounds on peanut storage are shown in TABLE 4 and primarily include insecticidal activity, antifungal activity, and toxin synthesis (especially AFB₁). Moreover, related studies have reported the development of inhibitory models against A. flavus and mycotoxins. Song et al. (2024) reported that thymol, the major bioactive component of M. didyma EO, disrupts energy metabolism in A. flavus, inducing intracellular peroxidation, cell membrane damage, and apoptosis. Concurrently, this compound downregulates the biosynthesis of O-methylsterigmatocystin (OMST), a critical intermediate in aflatoxin synthesis, thereby reducing mycotoxin accumulation. Yi et al. (2025) revealed that Peppermint EO (PEO) exerts antifungal activity against A. flavus by compromising cell wall/membrane integrity and permeability while disrupting antioxidant homeostasis to trigger excessive reactive oxygen species (ROS) accumulation. Furthermore, Liang et al. (2023) systematically expounded the inhibitory mechanism of estragole on A. flavus growth and aflatoxin biosynthesis. They identified its multi-target effects, which involve inducing ROS overproduction, suppressing antioxidant defense enzymes, blocking energy and secondary metabolic pathways, and disrupting the expression of genes related to cell development. Although the application of EOs in peanut storage is promising, it also has several limitations. EOs have relatively poor chemical stability and are susceptible to the influence of environmental factors such as light, high temperature, and oxygen (Hosseini & Jafari, 2020).

Table 4.

The effects of EOs and their Bioactive Compounds on Peanut Storage.

Target	EO / Compound	Main Composition / Source	Effects	Reference
Insecticidal Activity
C. serratus	Neem EO	Azadirachta indica	MR: 10 %v/w, 88.2 % (24 h) No. eggs laid: 10 %v/w, 2.3 eggs per 100 g No. adult emergence: 10 %v/w, no	(Harish et al., 2014)
	Pongamia EO	Pongamia pinnata	MR: 10 %v/w, 88.2 % (24 h) No. eggs laid: 10 %v/w, 7.0 eggs per 100 g No. adult emergence: 10 %v/w, no
	Mentha arvensis EO	30.43 % Menthol	No. eggs laid: 1.0 %v/w, 34.67 ± 0.58, p < 0.01, compared to control 67 ± 1.5 (24 h) Rep: 5 μL, 70 %, female (0.5 h)	(Tewari et al., 2024)
	Mentha piperita EO	30.18 % Menthol	No. eggs laid: 1.5 %v/w, 21 ± 1.53, p < 0.01, compared to control 67 ± 1.53 (24 h) Rep: 5 μL, 86 %, female (0.5 h)
	Mentha spicata EO	65.58 % Carvon	No. eggs laid: 2.0 %v/w, 0 ± 0, p < 0.01 (24 h) Rep: 5 μL, 90 %, male (0.5 h)
U. dermestoides	Lemongrass (Cymbopogon citratus) EO	24.6 %neral, 18.7 %citral, 12.4 %geranyl acetate, 12.3 %geranial, 7.5 %limonene	LD50 = 5.17 μg insect−1 (24 h)	(Plata-Rueda et al., 2020)
	Rosemary (Rosmarinus officinalis) EO	24.6 % 1,8-cineole, 17.7 % α-pinene, 12.4 % camphor, and 11.3 % camphene	Rep: 16 μL/mL, 100 % (4 h) No. eggs laid: 4 μL/mL (48 h), 9, p < 0.01	(Caballero-Gallardo et al., 2021)
	Citronella (Cymbopogon nardus) EO	25.3 % citronellal, 17.9 % citronellol, 11.6 % geraniol	Rep: 16 μL/mL, 100 % (4 h) No. eggs laid: 4 μL/mL (48 h), 8, p < 0.01
P. interpunctella	Ajania potaninii EO	22.19 % 1,8-Cineole, 13.84 % (−)-Verbenol, 12.84 % (+)-Camphor, 12.67 % Borneol	LC50 = 1.78 mg/L air (24 h)	(Shao et al., 2021)
	Ajania fruticulose EO	41.4 % 1,8-Cineole, 32.1 % (+)-Camphor)	LC50 = 1.70 mg/L air (24 h)
O. surinamensis	Thymus vulgaris EO	thyme	MR: 2000 ppm, 120 d exposure, 100 %	(Nesci et al., 2011)
Antifungal Activity
A. flavus	Cinnamon-Litsea Combined EO	49.33 % Cinnamaldehyde, 34.77 % Citral	MIC = 0.0313 μL/mL air (24 h)	(Liu et al., 2022)
	Cinnamaldehyde- calcium alginate encapsulation EO	cinnamon tree	at ≥75 μg/mL: significant inhibitory growth at 100 μg/mL: complete inhibition growth	(Zhang et al., 2023)
	Monarda didyma EO	92.3 % thymol	IC50 = 14.5 μg/mL at 50 μg/mL: a significant decline in the dry weight of mycelium with almost complete suppression	(Song et al., 2024)
	Cinnamomum burmannii Leaf EO	25.70 % Eucalypto, 18.09 % Borneol, 9.58 % p-Cymene, 8.61 % Bornyl acetate, 7.30 % α-Phellandrene, 6.99 % α-Terpineol	Colony diameter inhibition: 80.94 % (30 μL/disc, 7d) Mycelia dry weight: 8 mg (30 μL/disc, 7d; CK, 145 mg)	(Liang et al., 2025)
	Peppermint EO	peppermint stems and leaves	MIC = 0.343 μL/mL (5d) Moldy rates: significantly reduced by 63.33 % (1 × MIC, 10d); no visible mold infection (2 × MIC, 10d) Biocontrol efficacy: 67.67 % (1 × MIC, 10d)	(Yi et al., 2025)
	p-anisaldehyde	Pimpinella anisum L.	MIC = 1.0 μL/mL (48 h) Colony diameters: reduced by 50 % (0.5 μL/mL, 72 h) and 100 % (1.0 μL/mL, 72 h) Conidium production: reduced by 80 % (1.0 μL/mL, 5d), 87 % (2.0 μL/mL, 5d), and 95.5 % (4.0 μL/mL, 5d)	(Xin et al., 2024)
	(E)-2-hexenal	green leaf volatile	Vapor inhibitory: MIC = 0.032 μL/mL air (7d) Contact inhibitory: MIC = 1.6 μL/mL (7d) Fumigation inhibitory: MIC = 0.32 μL/mL air (7d)	(Ma et al., 2017)
A. parasiticus	Ocimum gratissimum EO	26.9 % thymol, 20 % gamma-terpinene, 17.6 % p-cymene, 8.2 % alpha-thujene, 6.4 % myrcene	MIC = 7.5 μL/mL r (8d) MFC = 8.0 μL/mL air (8d)	(Adjou et al., 2013)
A. ochraceus			MIC = 5.5 μL/mL air (8d) MFC = 6.5 μL/mL air (8d)
F. oxysporium			MIC = 5.5 μL/mL air (8d) MFC = 6.0 μL/mL air (8d)
A. section Flavi	Peumus boldus EO	30.61 % α-Terpinene, 28.49 % σ-cymene, 12.85 % eucalyptol	Lag phases (from 2.8 days to more than 30 days, 2 and 3 μL/g; > 30 days without visible mycelium, 3 μL/g)	(Passone and Etcheverry, 2014)
	Lippia turbinata EO	48.83 % Limonene, 18.06 % β-caryophyllene epoxide, 7.67 % piperitenone	Lag phases (from 0.9 days to 3.7 days, 5 μL/g)
P. griseofulvum and A. niger	Eucalyptus citriodora EO	58.214 % Citronellal, 13.805 % Citronellol, 9.790 % 2,6-Octadiene,2,6-dimethyl, 8.271 % Caryophyllene, 4.610 % Eucalyptol	MIC = 3.125 μL/mL (7d)	(Khan et al., 2025)
Toxin synthesis
AFB1	Cinnamaldehyde- calcium alginate encapsulation EO	cinnamon tree	AFB1 content:, below 0.5 μg/kg (75 μg/mL)	(Zhang et al., 2023)
	Cinnamomum burmannii Leaf EO	25.70 % Eucalypto, 18.09 % Borneol, 9.58 % p-Cymene, 8.61 % Bornyl acetate, 7.30 % α-Phellandrene, 6.99 % α-Terpineol	AFB1 content: not detected (30 μL/disc,7d)	(Liang et al., 2025)
	p-anisaldehyde	Pimpinella anisum L.	Inhibition rate: 69.4 % (4 μL/mL, 5d)	(Xin et al., 2024)

Abbreviations: MR (mortality rate); No. eggs laid (number of eggs laid); No. adult emergence (number of adult emergence); Rep (percentage of repellency); LD₅₀ (Lethal Dose, 50 %); LC₅₀ (Lethal Concentration, 50 %); MIC (Minimum Inhibitory Concentration); IC₅₀ (50 % Inhibitory Concentration); MFC (Minimum Fungicidal Concentration).

Recent advances have also highlighted microbial VOCs as innovative biocontrol agents. Gong et al. (2019) reported two antifungal volatiles, 1-pentanol and phenylethyl alcohol (PA), produced by Enterobacter asburiae Vt-7. They inhibited the growth of A. flavus and downregulated key aflatoxin biosynthesis genes (norB, ordB, and NorM) by up to 13.86-fold. Moreover, Lyu et al. (2020) highlighted that Streptomyces yanglinensis 3–10-derived methyl 2-methylbutyrate (M2M) potently inhibited A. flavus and A. parasiticus. The IC50 values of M2M for mycelial growth were 7.2 μL/mL (against A. flavus) and 8.0 μL/mL (against A. parasiticus), whereas the IC50 values for spore germination were even lower (0.7 μL/mL and 1.2 μL/mL, respectively). Critically, M2M reduced the concentrations of AFB₁ (from 39.9 μg/g to 4.1 μg/g) and AFB₂ (from 0.1 μg/g to 0.002 μg/g) in treated peanuts, directly enhancing safety. Additionally, Boukaew et al. (2024) evaluated acetophenone, a key VOC from strain SKRU-01. Acetophenone completely inhibited A. flavus (PSRDC-4) and A. parasiticus (TISTR 3276) mycelial growth at 1.0 μL/mL MIC, although 2.0 μL/mL (2MIC) was needed for full TISTR 3276 protections during peanut storage. The antifungal mechanisms of action of acetophenone were also investigated. On the one hand, acetophenone was found to disrupt the structural integrity and functionality of the fungal cell membrane by interfering with ergosterol biosynthesis; on the other hand, it induced oxidative stress by reducing the intracellular levels of methylglyoxal.

In conclusion, the application of plant- and microbial-derived VOCs as biofumigants presents a novel, environmentally sound, and highly effective strategy for peanut preservation. Future research should focus on improving compound stability, refining release systems, and optimizing formulations to fully realize the potential of biofumigants as next-generation storage interventions.

4.4. Packaging

The selection of appropriate packaging materials is critical for peanut storage, providing protection against environmental factors, insect infection, and microbial contamination and ultimately ensuring the food safety and commercial value of the product. Currently, polymer-based film bags are widely utilized for long-term storage and bulk transportation of peanuts. Common materials such as polypropylene (PP) and ethylene vinyl alcohol (EVOH) offer several desirable properties, including resistance to oil and environmental stress, low oxygen and moisture permeability, and protection against off-odor absorption and pest invasion (Taheri et al., 2024). Martín et al. (2016) demonstrated that, compared with ventilated PP films, EVOH film packaging better preserved raw peanut quality during high-temperature storage (40  °C for 60 days). EVOH-packaged samples exhibited lower peroxide values (0.63 vs. 0.95 meq O₂/kg), reduced sensory deterioration, and maintained favorable oleic/linoleic ratios and iodine values. In contrast, the PP samples developed pronounced cardboard flavors and lost their roasted peanut aroma. Furthermore, Swathi et al. (2024) triple-layer hermetic bags suppress lipid oxidation during storage, thereby extending shelf-life. At a moisture level of 10 %, the content of oleic acid in peanuts stored in hermetic bags increased from 53.3 % to 55.9 %, whereas that of linoleic acid decreased from 37.5 % to 32.1 %. The oil content slightly decreased from 55.4 % to 52.6 %, and the protein content decreased from 30.6 % to 24.5 %. At a moisture level of 14 %, the oleic acid content remained stable, the linoleic acid content decreased from 38.1 % to 28.0 %, the oil content dwindled from 53.8 % to 49.6 %, and the protein content reduced from 29.5 % to 20.7 %. During storage, PICS bags limited oxygen ingress and moisture exchange, inhibiting lipid oxidation. As a result, the relative content of oleic acid increased, while linoleic acid decreased. The change in protein content was particularly influenced by moisture levels, with higher moisture content promoting protease activity and protein degradation, while moisture also contributed to protein denaturation, leading to a reduction in protein content. The slight decrease in oil content could be attributed to minor leaching or volatilization losses, as well as microbial or enzymatic metabolic activities induced by changes in moisture. These findings suggest that optimizing the storage environment (low oxygen, low moisture) helps maintain the stability of both oil and protein content in peanuts, thereby extending their shelf-life. Additionally, the content of aflatoxins in hermetically stored peanuts increased from 12 μg/kg to 2445 μg/kg (at 10 % moisture) and 4259 μg/kg (at 14 % moisture) after six months and still significantly lower than that in conventional storage methods. While hermetic storage bags performed excellently in controlling aflatoxin accumulation, the initial moisture level of peanuts should be controlled below 10 % to ensure safe storage. However, not all packaging solutions perform equally under varying environmental conditions. Butts and Ward (2022) found that grain bags were unsuitable for in-shell peanut storage in Georgia because of substantial moisture migration, leading to a 63 % reduction in product value over 15–20 days. Although shelled peanuts performed better in sealed grain bags, proper unloading equipment would be necessary to facilitate their use.

Notably, active packaging (AP) technology has been developed and applied in the field of peanut storage and preservation. AP agents can be synthesized from synthetic or natural polymers depending on the desired characteristics and ecological implications. Despite the wide use of synthetic polymers, their environmental impact and high carbon footprint have prompted interest in biodegradable alternatives such as protein-, lipid-, or carbohydrate-based materials (Asgher et al., 2020). Biobased raw materials such as proteins, lipids, and carbohydrates are abundantly available on Earth. Their non-toxicity, renewability, eco-friendliness, and sustainability have made them a potential alternative to synthetic polymer packaging with no chemical intervention (Deshmukh et al., 2022). One promising example is sodium ascorbate (SA)-infused low-density polyethylene (LDPE) film (Sangatash et al., 2016). Peanuts packaged with 10 % SA-LDPE film reached a maximum peroxide value of 7.5 meq/kg on the 10th day of storage, whereas the control sample (packaged with pure LDPE film) reached a maximum peroxide value of 9.3 meq/kg on the 16th day of storage. In terms of barrier properties, the oxygen transmission rate of the 10 % SA film was 26 % lower than that of the control, indicating enhanced oxygen barrier properties and effective mitigation of peanut oxidation. Additional work (Modaresi & Niazmand, 2021) confirmed that SA films mitigated both primary and secondary lipid oxidation, with 10 % SA being optimal for PV reduction and 15 % SA being most effective for thiobarbituric acid (TBA) inhibition. Other active materials have shown potential in fungal and mycotoxin control. Otoni et al. (2014) demonstrated that allyl isothiocyanate (AITC)-releasing packaging inhibited A. flavus growth completely at 0.215 ppb, both through direct contact and volatile action. In practice, these AITC-based composite bags reduced fungal viability by 4.81 log cycles over 90 days, with a tenfold reduction in spore counts within the first week. However, volatile AITC concentrations decreased by 92.4 % after 15 days and became undetectable after 30 days, underscoring the importance of controlled-release technologies.

In summary, the evolution of peanut packaging is trending toward multifunctional, high-barrier, and environmentally sustainable materials. Multi-layer co-extruded films, bioactive additives, and intelligent packaging systems offer new opportunities to enhance shelf-life and quality retention. Future innovations will likely emphasize eco-friendly solutions that combine superior barrier function with real-time deterioration detection and pathogen suppression.

4.5. Comparative analysis of storage strategies

To provide practical guidance, a multidimensional evaluation framework was constructed (TABLE S1) to assess these strategies in terms of technological maturity, cost-effectiveness, safety, environmental sustainability, and scalability. This comparative perspective shows that controlled-temperature storage and chemical fumigants are mature and effective but face challenges in cost and sustainability. MAS and packaging strike a better balance between safety and environmental performance. Biofumigants show promising potential for sustainable peanut storage, although it remains at an early stage of development.

5. Conclusion and prospects

The deterioration of peanut quality during storage is governed by complex and interrelated mechanisms, particularly lipid oxidation, fungal contamination, and pest infection. Through synergistic interactions, these pathways degrade peanut composition and safety and undermine the feasibility of safe, long-term storage. Despite notable technological advances, a unified, mechanistically grounded storage strategy remains elusive.

Looking ahead, research should prioritize standardized, robust, nondestructive detection technologies that retain accuracy across diverse storage conditions. Integrating intelligent packaging and modified-atmosphere systems with real-time monitoring and data-driven decision-making will be central to dynamic quality management. In parallel, cost-effective and environmentally sustainable solutions are needed to enable adoption at multiple scales—from industrial facilities to smallholder operations. Meeting these needs will require interdisciplinary collaboration across food chemistry, materials science, and agricultural engineering, ultimately enabling next-generation storage systems that ensure peanut safety, extend shelf-life, and strengthen global supply-chain resilience.

CRediT authorship contribution statement

Yuxia Yang: Writing – review & editing, Writing – original draft, Validation, Resources, Investigation, Formal analysis. Jiahua Chen: Writing – review & editing, Resources, Investigation, Formal analysis. Liang Chen: Writing – review & editing, Visualization, Supervision, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

Supplementary material Table s1  Multidimensional Evaluation of Storage Strategies for Peanuts.

Data availability

No data was used for the research described in the article.