Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2024 Mar 27;89(5):2730–2746. doi: 10.1111/1750-3841.17042

Physical and chemical characteristics of walnut (Juglans regia L.) kernels with different skin lightness

Zatil A Athaillah 1, Selina C Wang 1,✉
PMCID: PMC13281142  PMID: 38534189

Abstract

Walnuts undergo rigorous grading before being sold to customers. There are multiple parameters used for the grading, including skin lightness. Walnuts with light skin receive superior grades while walnuts with dark skin are given poor grades or even rejected. However, information on the quality and physicochemical properties of walnuts with varying skin lightness levels is minimal. Therefore, we studied the quality of kernels of varying skin lightness from three common cultivars grown in California, USA (Chandler, Howard, and Tulare). The samples were subjected to size and weight, fat content, free fatty acid, peroxide value, oxidative stability, volatiles, tocopherols, fatty acid profile, and phenol measurements. The dark kernels had significantly lower weight and fat content, higher oxidative stability, and more volatiles than their light counterparts. The dark kernels had higher concentrations of some phenolics but low procyanidin B1 and non‐existent epicatechin gallate, compared to the light kernels, indicating that these two phenolics were likely involved in an antioxidant mechanism. Oxidation and depletion of epicatechin gallate likely contributed to the darkening of walnut color.

Keywords: metabolites, quality, skin lightness, walnuts

1. INTRODUCTION

Walnut is an important crop for many producers and handlers in various countries, including China, the United States, and India. Walnut fruits consist of husk, shell, kernel skin, and kernel (without skin). After harvest, walnuts are typically dehusked, dried, and sorted based on in‐shell size. The dried walnuts are later shelled, followed by separation of kernels from the broken shells (Liu et al., 2021). Subsequently, they are graded using multiple factors, including skin lightness, size, cleanliness, proper drying, and appearance (free of shell, foreign material, insect injury, shriveling, mold, and kernel discoloration) (USDA, 2017). Walnuts with light skin are commonly preferred by consumers; therefore, they are given superior grades and priced higher. On the other hand, walnuts with darker skin are less preferred; therefore, they are given poor grades or sometimes rejected. The consequence of having a large number of walnuts with dark skin includes profit reduction or even loss and increased waste, in the case the dark walnuts are rejected by consumers. California walnut industry experienced this issue in the harvest season in 2022 and the California Walnut Commission estimated the subsequent loss to be 1 billion USD (California Farm Bureau, 2022).

There are multiple reasons for this skin darkening. It can be induced by pre‐harvest and post‐harvest conditions. The pre‐harvest condition includes genetics; climate, namely excessive sun exposure and heat stress; and late harvest. Genetics have some role in kernel appearance, including skin lightness. A study shows that Chandler has lighter skin than the variety Jamal (Pakrah et al., 2021). Late harvest also leads to kernel skin browning, as evidenced in both Chandler and Jamal walnut varieties harvested at three different time points relative to packing tissue browning (Pakrah et al., 2021). Walnuts that are directly exposed to sunlight had darker skin than walnuts covered with kaolin, a material that can reflect sunlight (Mahmoudian et al., 2021). High fruit surface temperature can also lead to sunburn browning, as in the case of apples (Trandafir et al., 2017).

After harvest, kernel skin darkening can occur during extended storage. Kernel skin contains various phenolic compounds, and they can undergo oxidation when oxygen is present. The reaction is accelerated by the polyphenol oxidase (PPO) enzyme that is regulated by the jrPPO1 gene (Escobar et al., 2008). Polyphenol oxidase catalyzes the oxidation of phenols into quinones. These quinones are highly reactive and later transform into phytomelanins (Araji et al., 2014). The oxidation reaction is favored at high oxygen pressure (Adiletta et al., 2020), high humidity (Yuan et al., 2019), and moderate temperatures (25–50°C) (Taranto et al., 2017), even though it can also occur at cold temperatures when accompanied by high oxygen pressure and high humidity (Yuan et al., 2019).

Pre‐ or post‐harvest insect/pest infestation and pathogen infection can also trigger the activity of PPO enzymes, and this process is one of the plant adaptation strategies against biotic stress, for instance in defense against walnut blight‐inducing bacteria (Jiang et al., 2019). In this study, the skin darkening was likely a consequence of preharvest heat stress because a few weeks prior to the harvest time, extreme heat occurred in California, USA, where all the samples were sourced from. Multiple walnut growing locations in the region experienced increased temperatures up to 46°C. The darkening was noted in all cultivars commonly grown in California; the fruits were harvested at their normal harvest time; the darkening was noticed right after harvest so it was not developed during storage; and samples that had signs of pest/microbial attack were excluded from the study.

To understand if the kernel skin darkening is linked with changes in quality and physicochemical characteristics of walnut kernels, multiple parameters were assessed, and the values were compared between the light and dark walnuts. Size and weight were evaluated to understand whether disruption occurred during the filling phase of the kernels, whereas total fat is an indicator of whether the skin darkening is linked to changes in lipid metabolism and production.

Free fatty acids (FFAs) and peroxide value (PV) are often used to predict the susceptibility or extent of lipid oxidation in various matrices because FFAs are substrates whereas hydroxy peroxides are primary products of the oxidation reaction. Oxidative stability is an accelerated aging method to predict the shelf‐life of a sample by exposing it to high temperatures and high oxygen flow. Volatile compounds can be used to understand walnut oxidation because many of these compounds are generated through the process (Adkison et al., 2021; Frankel, 1980). The fatty acid profile was analyzed to determine if fatty acid metabolism is altered. Tocopherols are antioxidants that have been known to mitigate oxidation in foods (Kumar et al., 2013), and evaluating the concentrations can help understand if skin darkening is associated with oxidation. Lastly, another potential alteration is modified phenol metabolism. The skin darkening is likely associated with phenol oxidation, so various phenolic compounds would be determined.

Various cultivars may have different degrees of skin darkening, as indicated in the earlier study (Mahmoudian et al., 2021). However, very little information on other cultivars or other physical and chemical features is available. The three commonly grown cultivars in California, Chandler, Howard, and Tulare, were included in this study to help determine future cultivar selection for the growers.

2. MATERIALS AND METHODS

2.1. Sampling

Walnuts (±2 kg) were obtained from multiple locations in California during September and October 2022. A completely randomized design was selected for the sampling. Chandler, Howard, and Tulare were sourced from eight, three, and three orchards or handlers, respectively. The locations of the orchards where the samples were sourced from are provided in Table 1, along with the positions of the nearest weather stations. Temperature and precipitation were recorded for each weather station from The US National Weather Service website. Samples were stored at ±5°C prior to analysis.

TABLE 1.

Location of orchards where the samples were grown and locations of their nearest weather stations.

No Sample code Orchard location Location of the nearest weather station Maximum temperature in September 2022 (°C) Mean of maximum temperature in September 2003–2021 (°C)
1 Chandler 1 Visalia, CA Visalia, CA 36 37.16
2 Chandler 2 Colusa, CA Marysville AP, CA 46 39.26
3 Chandler 3 Glenn, CA Red Bluff, CA 46 40.84
4 Chandler 4 Lemoore/Greater Hanford, CA Hanford area, CA 46 39.32
5 Chandler 5 Lemoore/Greater Hanford, CA Hanford area, CA 46 39.32
6 Chandler 6 Lemoore/Greater Hanford, CA Hanford area, CA 46 39.32
7 Chandler 7 Hanford, CA Hanford 1S, CA 44 38.95
8 Chandler 8 Los Malinos, CA Red Bluff, CA 46 40.84
9 Tulare 1 Woodlake, CA Visalia, CA 36 37.16
10 Tulare 2 Tehama, CA Red Bluff, CA 46 40.84
11 Tulare 3 Hanford, CA Hanford 1S, CA 44 38.95
12 Howard 1 na1 na
13 Howard 2 Colusa, CA Marysville AP, CA 46 39.26
14 Howard 3 Yuba City, CA Marysville AP, CA 46 39.26
Open in a new tab

Abbreviation: na, not available.

The lightness (L*) values of the kernel skin were measured using a colorimeter (ColorReader Pro, Data Color, Lawrenceville, NJ, USA). Values from 40 to 59.9 were defined as “light” whereas values from 20 to 39.9 were grouped into “dark.” The light group covered colors extra light and light, while the dark group corresponded to light amber and amber. These four colors are widely used for walnut grading (USDA, 2017). A completely randomized design was used when subsets of samples were selected for each analysis.

2.2. Oil extraction

Walnut kernels (±400 g) were fed into an expeller/cold‐press machine (KK Oil Prince F Universal, Reut, Germany). The expeller screw was rotated at 22 rpm and a die number 7 was used. The resultant slurry was centrifuged at 10,000 g for 15 min to separate the oil from solid particles. The oil samples were kept at –20°C until analysis.

2.3. Weight and size

Ten half kernels of each sample were subjected to this analysis. Each of them was measured for their weight, width, and length.

2.4. Fat content

Approximately 15 walnut halves were ground using a food processor and 3 ± 0.1 g of them was added into a cellulose thimble (33 × 150 mm, Whatman 603 Cellulose thimbles). The thimbles were placed in a Buchi Universal Extractor E‐800 (Buchi Corporation, New Castle, USA). The fat was extracted 30 times using n‐hexane. The residual hexane was evaporated to obtain the fat mass. Three replicates were analyzed for each sample.

2.5. Free fatty acid content

FFA analysis was conducted following European Union Commission regulation (EEC) no 2568/91. Approximately 10 g of oil was used for the analysis. Three replicates were measured.

2.6. Peroxide value

The PV analysis was performed as illustrated in the AOCS official method Cd 8b‐90. Approximately 5 g of oil was used for the analysis. Duplicates were analyzed for each sample.

2.7. Oxidative stability

Oxidative stability was measured following a published study on walnuts (Grilo & Wang, 2021) using a Rancimat apparatus 617 (Metrohm AG, Herisau, Switzerland). Five kernel halves were randomly selected and then ground. Ground samples (0.5 g) were used for the analysis. Three replicates were analyzed.

2.8. Volatile organic compounds

Volatile organic compounds were detected and quantified using a headspace–solid phase microextraction (HS‐SPME) connected to a gas chromatography‐mass spectrometry (GC‐MS), following a published method (Grilo & Wang, 2021). Around 15 kernel halves were randomly selected and ground. A subset (±3 g) was used for the analysis. Three replicates were measured.

2.9. Tocopherols

The cold‐pressed oil from each sample (0.2 g) was added with 0.9 mL of isopropanol and 0.1 mL of internal standard (α‐tocopherol acetate), and then mixed thoroughly using a vortex. The mixture was then centrifuged at 10,000 rpm for 10 min. The supernatant was collected and filtered using a nylon filter (0.45 µm). The samples were then analyzed using high‐performance liquid chromatography coupled to a diode array detector (Agilent 1290 Infinity II LC System, Agilent Technologies, California, USA). Reverse phase LC followed a published method (Górnaś, 2015) with modifications in the dilution factor, HPLC detector type, and quantification method. The injection volume was 5 µL. Separation was done using a PFP column (150 mm × 4.6 mm, 3 µm) with methanol:water [93:7, v/v] as the mobile phase). The flow followed isocratic mode at 1 mL/min. The DAD wavelength was set at 290 nm. This analysis was done in triplicates.

2.10. Fatty acid profile

Walnut oil (±20 µL) was placed in a tube. Hexane (3 mL) was added to dissolve the oil sample. Methanolic potassium hydroxide (0.2 mL, 2 M) was added to derivatize the fatty acids in the oil to fatty acid methyl esters (FAMEs). The mixture was vortexed and then left to stratify until the upper solution became clear. The upper phase was then subjected to a gas chromatography‐flame ionization detector (GC‐FID, Agilent 7890A GC, Agilent Technologies, California, USA). The GC‐FID analysis was done following this method (Green & Wang, 2020). Peak identification was performed using a 37‐FAME mix, certified reference material (TraceCERT, Millipore Sigma, USA). This analysis was done in triplicates.

2.11. Phenols

Fifteen kernel halves were randomly selected and ground. Ground walnut kernel (±3 g) was placed in a centrifuge tube. First, the lipid portion was removed through hexane extraction. Hexane (25 mL) was added, and the mixture was vortexed for 5 s and sonicated for 5 min at room temperature. The mixture was centrifuged at 2000 g for 5 min, and later, the hexane phase was removed. This lipid extraction was done one more time with 15 mL of hexane. Afterward, 5 mL of aqueous acetone (acetone: water, 7:3, v/v) was added to the defatted sample for phenol extraction. The mixture was vortexed for 2 min and sonicated for 30 min. The mixture was centrifuged at 10,000 g for 5 min. The aqueous acetone phase was transferred into a centrifuge tube. Phenol extraction was repeated, and the aqueous acetone layer was combined with that of the first extraction. The solution was then filtered using a nylon filter (45 µm), and 2 µL of it was injected into a liquid chromatography coupled to tandem mass spectrometry.

Compound separation was done in the LC (Agilent 1260 Infinity II, Agilent Technologies Inc., California, USA) using a Poroshell 120 EC‐C18 column (100 × 2.1 mm, 1.9 µm). The flow rate was 0.30 mL/min. Mobile phase A was 0.5% formic acid in water and mobile phase B was a mixture of acetonitrile and methanol (1:1, v/v). The gradient conditions were as follows: 0–4 min/15% B; 8 min/17% B; 10 min/25% B; 15 min/50% B; 17 min/75% B; 20 min/95% B; and 23 min/15% B. The column temperature was 40°C. The ion source of the MS was Agilent Jet Stream electrospray ionization (AJS ESI). The mass spectrometer (6470 triple quadrupole, Agilent Technologies Inc., California, USA) parameters included gas temperature 350°C; gas flow 10 L/min; nebulizer pressure 275.79 kPa; sheath gas temperature 350°C; sheath gas flow 8.5 L/min; capillary voltage 3500 V (positive) and 2500 V (negative); and nozzle voltage 500 V (positive) and 1000 V (negative). Additional information on MS/MS setting for compound identification is provided in Table S1. This analysis was done in duplicate.

2.12. Statistical analysis

Analysis of variance (ANOVA) was conducted to detect if there was any significant difference between the two skin lightness levels of the whole dataset (data from the three cultivars combined), the three varieties (Chandler, Howard, and Tulare), and if the interaction between lightness and cultivar was significant. Tukey HSD was used as the pairwise test. Statistical significance was set at p < 0.05. All the analysis was performed using R Studio 2022.07.01 (Posit software, PBC, Boston, MA, USA) and R 4.2.2 (The R Foundation for Statistical Computing, Vienna, Austria).

3. RESULTS AND DISCUSSION

3.1. Weight, length, and width

The light kernels of Tulare had significantly higher weight than the dark kernels of Chandler and Howard (p‐value < 0.05). When comparing the weight of the dark kernels of Chandler, Howard, and Tulare, however, they were not significantly different from the weight of their respective light kernels (p‐values were > 0.05) (Table 3). Because of no significant interaction between skin lightness and cultivar, there was not enough evidence suggesting that the three cultivars had varying reductions in weight that could be linked to the skin darkening.

TABLE 3.

Weight, length, and width of half kernels of light and dark walnuts (cv. Chandler, Howard, Tulare, and the whole dataset).

Cultivar Lightness Weight (g) Length (cm) Width (cm)
Chandler Light 2.75 ± 0.21ab 2.83 ± 0.10a 2.55 ± 0.15a
Dark 2.32 ± 0.29b 2.79 ± 0.12a 2.51 ± 0.14a
Howard Light 2.78 ± 0.36ab 2.83 ± 0.06a 2.67 ± 0.15a
Dark 2.19 ± 0.33b 2.73 ± 0.15a 2.47 ± 0.06a
Tulare Light 3.16 ± 0.42a 2.83 ± 0.06a 2.53 ± 0.06a
Dark 2.67 ± 0.12ab 2.80 ± 0.00a 2.73 ± 0.12a
Whole dataset Light 2.84 ± 0.32A 2.83 ± 0.08A 2.57 ± 0.14A
Dark 2.37 ± 0.31B 2.78 ± 0.11A 2.36 ± 0.31A
Open in a new tab

Note: The values were expressed as average ± standard deviation. Different lowercase letters indicate significant differences (p < 0.05) between the combination of the skin lightness and cultivar. Different uppercase letters show significant differences (p < 0.05) between the light and dark groups for the whole dataset. Whole dataset means that the data from the three cultivars were combined, and they were only grouped based on the lightness index.

When we considered data of the three cultivars combined, the light kernels had a significantly larger value for weight than the dark kernels (p‐value 0.0002). Generally, only a small portion of recently harvested walnuts had dark kernel skin. Because the maximum temperature in September 2022 was higher than that of previous years (NOAA, 2022) (Table 1 and Figure S1), it was reasonable to link the weather event to the skin darkening. Kernel weight reduction due to heat stress was shown earlier in olives (Benlloch‑González et al., 2019) and lentils (Sita et al., 2018). Olive trees planted inside a temperature‐controlled open‐top‐chamber set at 4°C higher than the ambient temperature had smaller fruits, compared to the plants grown at ambient temperature. In lentils, heat stress reduced the seed growth rate by 30%–44% and shortened the seed filling period, decreasing the seed weight and ultimately the yield (Sita et al., 2018). This reduction could be due to several reasons: severe transpiration; impaired photosynthesis (Benlloch‑González et al., 2019); and decreased sucrose metabolism and transport (Kaushal et al., 2013).

Even though zero precipitation (Figure S2) was recovrded during the heat wave event that occurred in early September 2022 or around the 122nd day after walnuts were pollinated (Figure S3), the water content of the light and dark kernels was not significantly different (Figure S4). It was likely because the orchards were irrigated to minimize the damage. Another possibility was the rehydration of the kernels because approximately 2 weeks after the maximum temperature was recorded, the areas near Marysville, Red Bluff, and Hanford, where some samples were sourced from, experienced a spike in precipitation level (Figure S2).

Statistical analysis using ANOVA demonstrated significant differences in weight for either lightness levels of the kernel skin or cultivar and no interaction between them was observed (Tables 2 and 3). Considering both light and dark kernels together, Tulare had a significantly higher value for weight (p‐value 0.021), which is consistent with previous findings of Tulare kernels being heavier than Chandler and Howard kernels (UC, 2020).

TABLE 2.

p‐Value of the analysis of variance (ANOVA) results for the effects of lightness for the whole dataset, cultivar, or interaction between lightness and cultivar for various quality and metabolite parameters.

Parameter Lightness Cultivar Lightness * Cultivar
Weight 0.0002 0.0208 0.858
Length 0.222 0.851 0.807
Width 0.6685 0.2823 0.0449
Fat content <0.0001 <0.0001 0.0001
FFA 0.275 0.445 0.724
PV 0.656 0.530 0.841
Oxidative stability 0.0174 0.0028 0.9979
1‐Octen‐3‐ol 0.1160 0.0126 0.0430
1‐Hexanol 0.0247 0.0629 0.3751
3‐Buten‐2‐ol‐2‐methyl 0.627 0.114 0.204
1‐Penten‐3‐ol 0.0101 0.0134 0.0660
1‐Pentanol 0.0001 0.0003 0.0007
Hexanal 0.150 0.103 0.116
Pentanal 0.111 0.116 0.116
Butanal 2‐methyl 0.0020 0.2656 0.1914
Butanal, 3‐methyl 0.0028 0.1443 0.4996
Ethyl acetate 0.9520 0.0542 0.9477
Acetic acid methyl ester 0.0594 0.4150 0.9784
Butyrolactone 0.3439 0.0027 0.7611
Acetone 0.0058 0.9266 0.7036
2‐Heptanone 0.0376 0.0991 0.4415
Delta‐tocopherol 0.263 0.618 0.368
Gamma‐tocopherol 0.773 0.027 0.631
Alpha‐tocopherol 0.1614 0.0343 0.3621
Total tocopherol 0.5243 0.0131 0.6668
C16.0 0.573 <0.0001 0.887
C16.1 0.2785 0.3132 0.0637
C17.0 0.925 0.691 0.642
C18.0 0.3436 0.0024 0.7829
C18.1 0.737 0.590 0.998
C18.2 0.818 0.463 0.900
C18.3 0.330 <0.0001 0.539
C20.0 0.831 <0.0001 0.781
C20.1 0.2518 0.0001 0.1339
SFA 0.796 <0.0001 0.963
MUFA 0.736 0.555 0.998
PUFA 0.8404 0.0434 0.9957
MUFA to PUFA 0.905 0.725 0.918
C18.1 / C18.2 0.640 0.690 0.963
C18.2 / C18.3 0.461 <0.0001 0.546
Gallic acid 0.0107 0.2984 0.5599
3,4 Dihydroxybenzoic acid 0.0998 0.5718 0.0407
Catechin 0.177 0.819 0.837
Chlorogenic acid 0.856 <0.0001 0.516
Procyanidin B1 0.0153 0.2069 0.9499
Vanillic acid 0.0054 0.0964 0.3490
Caffeic acid 0.798 0.617 0.563
Epicatechin 0.085 0.608 0.983
Epicatechin gallate <0.0001 0.168 0.247
p‐Coumaric acid 0.126 0.915 0.464
Ferulic acid 0.105 0.096 0.516
Sinapic acid 0.845 0.217 0.239
Ellagic acid 0.0494 0.1754 0.3024
Quercetin 0.0001 0.0233 0.0350
Naringenin 0.0002 0.0021 0.2624
Open in a new tab

Size reduction as an effect of heat stress is less studied than weight decrease, though it has been observed in wheat (Ullah et al., 2022) and rice(Cao et al., 2008). In our study, there was no significant difference in the length and width between the two groups of varying lightness, either when the cultivar was considered or not (p‐value > 0.05) (Tables 2 and 3).

3.2. Fat content

Fat is the main component of walnut kernels and it generally accounts for 64%–72% by weight (Yi et al., 2017). There was an interaction between the cultivar and lightness index (p‐value of 0.0002). When the total fat content of the light and dark kernels of each cultivar was examined, we found a significant reduction for Howard, but not for Chandler and Tulare (Tables 2 and 4). When cultivar variation was excluded and the whole dataset was used for comparison of the light and dark groups, a significant reduction in the fat content of the dark kernels was observed (p‐value < 0.0001). Higher fat content in extra light walnuts than the light, amber, and dark amber walnuts was found in a previous study but the reason behind it was not discussed (Li et al., 2022). In the current study, the reason for the darkening was highly likely pre‐harvest high temperature. Reduction of fat content because of heat stress has been found in olives (Benlloch‑González et al., 2019; García‐Inza et al., 2014), canolas (Rivelli et al., 2023), and lentils (Sita et al., 2018). An increase in temperature by 4°C led to a significant decrease in oil content in olives because fruit maturation was forwarded (Benlloch‑González et al., 2019). Another study in olive demonstrated that oil concentration decreased linearly by 1.1% when the temperature was increased within the range of 16–32°C for 4 months (García‑Inza et al., 2014). The same study also showed that increasing temperature by 7°C for 1 month resulted in significantly lower oil content (García‑Inza et al., 2014). A similar trend was observed in canola, in which heat stress to 35°C resulted in a reduction of seed oil, from 42.9% to 39.3% (Rivelli et al., 2023). These studies were all conducted in a controlled setting and a comparison was made between heat‐treated plants to control. Ours was an observational study, and the comparison was made between the light and dark kernels. The light kernels likely had less heat radiation, for example, from shaded branches, but were from the same plants or orchards as the dark kernels. This may be a source of non‐significant difference between the light and dark groups for Chandler and Tulare; nonetheless, a decrease in fat content in the dark kernels of Howard or all samples from the three cultivars was observed in our study, suggesting some changes in lipid metabolism.

TABLE 4.

Fat content of light and dark walnuts (cv. Chandler, Howard, Tulare, and the whole dataset).

Cultivar Lightness Fat content (g/100 g)
Chandler Light 68.31 ± 2.62ab
Dark 65.81 ± 2.78b
Howard Light 67.01 ± 1.20ab
Dark 52.22 ± 0.78c
Tulare Light 71.22 ± 2.15a
Dark 66.13 ± 1.85ab
Whole dataset Light 68.71 ± 2.61A
Dark 62.49 ± 6.53B
Open in a new tab

Note: The values were expressed as average ± standard deviation. Different lowercase letters indicate significant differences (p < 0.05) between the combination of the skin lightness and cultivar. Different uppercase letters show significant differences (p < 0.05) between the light and dark groups for the whole dataset. Whole dataset means that the data from the three cultivars were combined, and they were only grouped based on the lightness index.

Another factor to consider was the timing of the extreme heat in relation to walnut maturation periods. A previous study showed that lipid synthesis in walnuts occurred rapidly between 74 and 88 days after pollination. From 88 to 144 days (full maturation), the process occurred at a steadier rate (Jin et al., 2023). The heat wave incidence in the present study occurred during the later stage of lipogenesis (around 122 days after pollination) (Figure S3) at which the lipid formation likely occurred steadily; therefore, it was reasonable that when the process was impaired, only a slight reduction in the total fat content was observed in Chandler and Tulare. It is proposed that had the heat event occurred between 74 and 88 days after pollination, the fat reduction could have been more severe.

3.3. Free fatty acid

FFAs are products of triacylglycerol hydrolysis, and this process is influenced by moisture content, water activity, temperature, and enzyme activity (Yang et al., 2019). FFA values of our kernels were like those reported previously in recently harvested walnuts (Ortiz et al., 2019; Wang et al., 2006).

There was no significant difference in FFA values between the samples of the two lightness indexes for each cultivar or for the whole dataset (all the samples from the three cultivars combined) (p‐value > 0.05). The interaction effect between lightness and cultivar was also not detected (p‐value > 0.05) (Tables 2 and 5). There was no evidence of the link between kernel skin darkening and lipid degradation, particularly fatty acid release.

TABLE 5.

Free fatty acid (FFA, % oleic acid), peroxide value (PV, mEq O2/kg), and induction time (h) of light and dark walnuts (cv. Chandler, Howard, Tulare, and the whole dataset).

Cultivar Lightness FFA (% oleic acid) PV (mEq O2/kg) Induction time (h)
Chandler Light 0.04 ± 0.01a 0.33 ± 0.05a 11.49 ± 1.07b
Dark 0.05 ± 0.02a 0.35 ± 0.05a 12.85 ± 1.43ab
Howard Light 0.05 ± 0.01a 0.28 ± 0.04a 13.70 ± 2.41ab
Dark 0.05 ± 0.00a 0.30 ± 0.18a 15.10 ± 1.34a
Tulare Light 0.04 ± 0.01a 0.32 ± 0.19a 13.47 ± 0.47ab
Dark 0.05 ± 0.01a 0.26 ± 0.09a 14.77 ± 0.62a
Whole dataset Light 0.04 ± 0.01A 0.32 ± 0.10A 12.46 ± 1.66B
Dark 0.05 ± 0.01A 0.31 ± 0.09A 13,81 ± 1.61A
Open in a new tab

Note: The values were expressed as average ± standard deviation. Different lowercase letters indicate significant differences (p < 0.05) between the combination of the skin lightness and cultivar. Different uppercase letters show significant differences (p < 0.05) between the light and dark groups for the whole dataset. Whole dataset means that the data from the three cultivars were combined, and they were only grouped based on the lightness index.

3.4. Peroxide value

Hydroperoxides are primary products of oxidation and are generated through auto‐oxidation or enzymatic oxidation. Auto‐oxidation generally occurs at a slow rate. Meanwhile, enzymatic oxidation is more rapid and mediated by lipoxygenases (Buranasompob et al., 2007; Salcedo & Nazareno, 2015). For both mechanisms, polyunsaturated fatty acids, which are abundant in walnuts (Salcedo & Nazareno, 2015), act as the main substrate. Hydroperoxides are later transformed into volatiles and this process is assisted by hydroperoxide lyases (Piccirillo et al., 2005).

The concentrations of PVs were not significantly different among Chandler, Howard, and Tulare or between the light and dark kernels, either for each cultivar or for the whole dataset (p‐value > 0.05) (Tables 2 and 5). The interaction between the lightness of the kernel skin and cultivar was also not found (p‐value > 0.05). Similar to FFA, previous studies on this topic are limited. It seems that the kernel skin darkening was not linked to considerable alterations in the primary lipid oxidation products.

3.5. Oxidative stability

Oxidative stability measurement using a Rancimat apparatus is a rapid technique to understand the stability of products that are prone to lipid oxidation. The measurement output is commonly expressed as induction time, which is the time when samples start to produce volatiles, that is, volatile acids, when exposed to high temperature and oxygen pressure.

The light kernels of Chandler had significantly lower induction time than the dark kernels of Howard and Tulare. However, there was no interaction effect between kernel skin lightness and cultivar (p‐value > 0.05), showing no considerable variation of the induction time changes among Chandler, Howard, and Tulare. This demonstrated that the change in oxidative stability was not cultivar‐specific.

Surprisingly, the induction time was significantly higher in the dark kernels when the data from all cultivars were compared (p‐value 0.0174; Table 5). This might be because the dark kernels had lower fat content, thus less substrates for the oxidation reaction. Another possible reason for the larger induction time of the dark kernels was that they had higher concentrations of some phenols (Table 8) that might generate some antioxidant capacity during the accelerated aging condition.

TABLE 8.

Phenol profile of the light and dark walnuts (cv. Chandler, Howard, Tulare, and the whole dataset).

Phenol Concentration (µg/g) kernel
Chandler Howard Tulare Whole dataset
Light Dark Light Dark Light Dark Light Dark
Ellagic acid 731.88 ± 236.48a 739.47 ± 218.90a 552.14 ± 95.01b 852.72 ± 67.12a 729.11 ± 231.97a 1254.54 ± 480.42a 671.35 ± 194.80 960.93 ± 376.28
Catechin 63.90 ± 32.84a 43.80 ± 13.34a 65.71 ± 7.53a 37.19 ± 13.86a 45.63 ± 40.34a 39.09 ± 41.82a 60.44 ± 26.33A 40.38 ± 24.22A
Procyanidin B1 30.39 ± 16.27a 18.82 ± 5.17a 25.08 ± 1.00a 11.12 ± 2.49b 19.31 ± 2.14a 9.13 ± 6.42a 26.16 ± 11.01A 13.26 ± 6.49B
Gallic acid 8.70 ± 3.20b 19.90 ± 6.00a 9.08 ± 1.39b 18.52 ± 2.62a 11.34 ± 8.17a 34.15 ± 22.41a 9.41 ± 3.73B 24.90 ± 14.62A
Epicatechin 8.31 ± 7.50a 4.31 ± 1.90a 8.72 ± 4.59a 4.64 ± 0.18a 5.52 ± 1.37a 2.39 ± 1.48a 7.82 ± 5.32A 3.67 ± 1.68A
Epicatechin gallate 0.43 ± 0.12a ndb 0.56 ± 0.03a ndb 0.42 ± 0.07a ndb 0.47 ± 0.10A ndB
3,4 Dihydroxybenzoic acid 0.09 ± 0.02a 0.08 ± 0.03a 0.09 ± 0.02a 0.10 ± 0.02a 0.03 ± 0.03a 0.11 ± 0.03a 0.07 ± 0.03A 0.10 ± 0.03A
Vanillic acid 1.08 ± 0.35b 2.53 ± 0.86a 1.04 ± 0.35a 2.04 ± 0.72a 2.45 ± 1.41a 2.70 ± 0.27a 1.37 ± 0.83B 2.47 ± 0.62A
Chlorogenic acid nd nd 1.84 ± 0.83a 2.32 ± 0.39a nd nd 0.61 ± 1.01A 0.58 ± 1.08A
Caffeic acid 0.09 ± 0.01a 0.07 ± 0.06a 0.07 ± 0.01a 0.10 ± 0.01a 0.10 ± 0.04a 0.10 ± 0.03a 0.08 ± 0.02A 0.09 ± 0.04A
p‐Coumaric acid 0.12 ± 0.02b 0.22 ± 0.07a 0.19 ± 0.16a 0.18 ± 0.05a 0.13 ± 0.01a 0.23 ± 0.06a 0.15 ± 0.09A 0.21 ± 0.06A
Ferulic acid 0.28 ± 0.04a 0.34 ± 0.09a 0.31 ± 0.10a 0.30 ± 0.10a 0.36 ± 0.18a 0.49 ± 0.07a 0.31 ± 0.09A 0.39 ± 0.11A
Sinapic acid 0.33 ± 0.08a 0.36 ± 0.02a 0.53 ± 0.14a 0.37 ± 0.08a 0.36 ± 0.24a 0.44 ± 0.06a 0.40 ± 0.15A 0.39 ± 0.06A
Quercetin 0.37 ± 0.06b 0.65 ± 0.06a 0.38 ± 0.03a 0.45 ± 0.05a 0.32 ± 0.11a 0.47 ± 0.05a 0.36 ± 0.06B 0.53 ± 0.11A
Naringenin 0.22 ± 0.04b 0.47 ± 0.11a 0.12 ± 0.03a 0.25 ± 0.08a 0.12 ± 0.02b 0.26 ± 0.06a 0.16 ± 0.06B 0.34 ± 0.13A
Open in a new tab

Note: The values were expressed as average ± standard deviation. Different lowercase letters indicate significant differences (p < 0.05) between the combination of the skin lightness and cultivar. Different uppercase letters show significant differences (p < 0.05) between the light and dark groups for the whole dataset. Whole dataset means that the data from the three cultivars were combined, and they were only grouped based on the lightness index.

While this accelerated aging technique tremendously shortens the analysis time compared to real‐time shelf stability tests, it has limitations and should be used with caution (Mancebo‑Campos et al., 2007). Real‐time shelf‐life testing for light and dark kernels would be necessary to confirm how the induction time translates into shelf life.

3.6. Volatile organic compounds

To assess the extent of lipid oxidation, amino acid degradation, and carbohydrate catabolism (Schwab et al., 2008), HS‐SPME combined with GC‐MS was used to detect multiple groups of volatile compounds, including alcohols, aldehydes, esters, furans, and ketones. The list of the compounds, along with their sensory descriptors (Acree, 2004) and peak areas, is shown in Table 6.

TABLE 6.

Volatile organic compounds of light and dark walnuts (cv. Chandler, Howard, Tulare, and the whole dataset).

Group Compound Sensory descriptor Peak area (10*3)
Chandler Howard Tulare Whole dataset
Light Dark Light Dark Light Dark Light Dark
Alcohol 1‐Octen‐3‐ol Mushroom 2.81 ± 1.43a 2.56 ± 0.32a 4.20 ± 0.82a 5.58 ± 0.19a 1.83 ± 0.14a 4.63 ± 2.30a 2.62 ± 1.24A 7.12 ± 12.11A
1‐Hexanol Resin, flower, green 4.75 ± 1.94a 8.27 ± 5.75a 9.88 ± 7.08b 17.59 ± 8.08a 7.20 ± 4.23a 20.91 ± 15.14a 6.65 ± 4.36B 13.76 ± 10.15A
3‐Buten‐2‐ol Sweet 10.33 ± 3.38a 10.70 ± 3.64a 30.21 ± 30.22a 14.35 ± 5.90a 8.93 ± 4.11b 15.29 ± 5.80a 10.75 ± 13.93A 15.75 ± 11.57A
1‐Penten‐3‐ol Banana, cucumber, melon 6.59 ± 2.37a 7.79 ± 4.03a 8.50 ± 1.90a 14.47 ± 3.67a 7.83 ± 2.05b 18.27 ± 6.43a 7.35 ± 2.08B 28.51 ± 58.00A
1‐Pentanol Fruit, balsamic 2.10 ± 1.04a 3.00 ± 1.43a 4.43 ± 2.19b 10.49 ± 3.49a 1.84 ± 0.75a 12.89 ± 4.37a 2.54 ± 1.45B 8.15 ± 5.58A
Aldehyde Hexanal Grass, tallow, fat 58.14 ± 20.10a 84.99 ± 48.40a 94.12 ± 33.76b 165.82 ± 23.68a 84.58 ± 32.36b 259.83 ± 97.79a 72.34 ± 26.62A 149.57 ± 95.95A
Pentanal Almond, malt, pungent nd nd nd 6.83 ± 4.69 nd 12.85 ± 6.25 nd 4.79 ± 6.40
Butanal, 2‐methyl Cocoa, nut 10.68 ± 5.49b 16.13 ± 8.66a 9.90 ± 2.60b 27.91 ± 11.41a 7.40 ± 2.26a 19.20 ± 7.26a 9.81 ± 4.46B 19.31 ± 9.58A
Butanal, 3‐methyl Fruit, cocoa, nut, leaf 2.09 ± 2.07a 4.68 ± 2.89a 3.71 ± 1.55a 8.97 ± 3.30a 1.56 ± 1.37b 7.10 ± 4.42a 2.39 ± 1.83B 6.51 ± 3.61A
Ester Ethyl acetate Pineapple 74.29 ± 43.05a 101.04 ± 33.48a 256.34 ± 145.27a 200.41 ± 94.77a 78.42 ± 15.48b 140.43 ± 23.80a 164.30 ± 188.58A 156.43 ± 106.40A
Acetic acid methyl ester Fruit, wine, green, ether 31.59 ± 17.48a 52.20 ± 38.90a 38.74 ± 19.16b 54.55 ± 11.10a 49.37 ± 6.75a 67.12 ± 10.34a 36.93 ± 16.79A 55.90 ± 29.80A
Furan Butyrolactone Caramel, sweet 1.31 ± 0.48a 1.50 ± 0.65a 4.31 ± 3.57a 5.71 ± 1.74a 1.90 ± 1.05b 2.89 ± 1.38a 2.33 ± 2.23A 3.04 ± 1.15A
Ketone Acetone Ether, apple, pear 1833.98 ± 925.16b 2607.78 ± 674.06a 1893.97 ± 1078.07a 2660.34 ± 79.15a 1676.17 ± 792.08a 3056.01 ± 573.15a 1813.01 ± 861.57A 2715.09 ± 575.12B
2‐Heptanone Fruit, banana 1.29 ± 0.34a 2.15 ± 1.95a 2.24 ± 1.16a 5.56 ± 4.97a 1.63 ± 0.53b 3.82 ± 1.14a 1.59 ± 0.70A 3.32 ± 2.89B
Open in a new tab

Note: The values were expressed as average ± standard deviation. Different lowercase letters indicate significant differences ( p < 0.05) between the combination of the skin lightness and cultivar. Different uppercase letters show significant differences (p < 0.05) between the light and dark groups for the whole dataset. Whole dataset means that the data from the three cultivars were combined, and they were only grouped based on the lightness index.

Abbreviation: nd, not detected.

1‐Octen‐3‐ol, 1‐hexanol, 3‐buten‐2‐ol, 1‐penten‐3‐ol, and 1‐pentanol were among the alcohols that were identified. The light Tulare kernels had less 1‐octen‐3‐ol than the dark kernels of Howard. The light Chandler samples had a lower concentration of 1‐hexanol than the dark Tulare. The concentration of 3‐buten‐2‐ol‐2‐methyl was similar across the skin lightness levels and cultivars. The light and dark kernels of Chandler and the light kernels of Tulare had significantly lower concentrations of 1‐penten‐3‐ol than the dark kernels of Tulare. The light and dark kernels of Chandler, the light kernel of Howard, and the light kernel of Tulare had lower concentrations of 1‐pentanol than the dark kernel of Tulare. The light kernels of Chandler and Tulare also had less amount of 1‐pentanol than the dark Howard sample. These data suggested that Tulare dark kernels were more often to contain higher concentrations of alcoholic volatiles. A higher concentration of alcohol is associated with increased ripeness levels (Tietel et al., 2010). A significant difference between the dark and light kernels of the whole dataset was observed for 1‐hexanol, 1‐penten‐3‐ol, and 1‐pentanol. Significant difference among Chandler, Howard, and Tulare was noted for 1‐octen‐3‐ol, 1‐penten‐3‐ol, and 1‐pentanol. A significant interaction effect between the two parameters was shown for 1‐octen‐3‐ol and 1‐pentanol (Table 2).

The light kernels of Chandler and Tulare had the lowest concentrations of butanal, 2‐methyl and butanal, 3‐methyl, whereas the dark kernels of Howard had the highest. Significant differences between the dark and light kernels of the whole dataset were noted for the two compounds. Differences between the cultivars were not found for any aldehydes, and no interaction effect between skin lightness and cultivar was apparent (p‐value > 0.05).

There was no significant difference for the two esters that were identified (ethyl acetate and acetic acid methyl ester) among the varying skin lightness and cultivar. There was also no interaction effect between the two parameters. In addition to alcohols, aldehydes, and esters, butyrolactone (a furan) and two ketones were also found. The Howard dark kernels had a higher concentration of butyrolactone than the other combinations of variety and skin lightness level. Butyrolactone concentration was only significantly different among the cultivars but not between the light and dark kernels, including for the whole dataset.

Two ketones, acetone and 2‐heptanone, were identified. Our data showed no significant difference for all pairwise comparisons for the combination of varying cultivars and skin lightness for both acetone and 2‐heptanone. However, significant variation was observed when the whole dataset was used to compare the light and dark groups.

Methyl‐branched alcohols, aldehydes, ketones, and esters are generated through the degradation of amino acids, whereas aliphatic alcohols, aldehydes, acids, lactones, and esters are produced from the lipoxygenase oxidation pathway of fatty acids. Pentanal is likely produced from the oxidation of linoleic acid as well (Cao et al., 2014). It was not clear how butanal, 2‐methyl and butanal, 3‐methyl are produced in plants. Several studies suggested that they were produced by fungi to promote plant growth (Jiang et al., 2021) or released during the heating of protein and fat (Bhatti et al., 2014). The higher concentration of pentanal in the Howard and Tulare dark kernels, compared to their respective light kernels, and butanal, 2‐methyl and butanal, 3‐methyl in all the dark kernels, indicated oxidation of lipid and possibly protein as well. It was not clear how butyrolactone was synthesized. However, furanones are most likely synthesized from carbohydrates (Schwab et al., 2008).

Acetone, a ketone that was present in very large amounts in our samples, may be synthesized from the oxidation of unsaturated fatty acids, likely linolenic acid (Cao et al., 2014), to peroxyl radicals followed by cyclization to form epidioxide radicals. Epidioxide radicals were converted to acetone through fragmentation (Fruehwirth et al., 2021). 2‐Heptanone is likely produced through the oxidation of linoleic acid (Li et al., 2020). The higher concentration of acetone and 2‐heptanone in the dark kernels suggested oxidation of polyunsaturated fatty acids.

3.7. Tocopherols

Tocopherols, mostly in the form of alpha, gamma, and delta, are bioactive in walnuts and help prolong the shelf‐life of food by donating hydrogen to suppress oxidation (Balakrishnan & Schneider, 2023). They are also important antioxidants for biological systems by preventing various diseases in vitro or in mice (Gysin et al., 2002; Juretić et al., 2021).

Tocopherols concentration is commonly presented as amounts per oil mass. Some studies do not clearly describe whether the values are based on oil or kernel mass (Abdallah et al., 2015; Amaral et al., 2005). In this study, the data were presented as tocopherol concentration in the kernels, as the focus was the effects on kernels and to account for variability in oil content between samples.

Beta tocopherol was not discussed because it was lower than the quantification limit. Among the three types of tocopherols, gamma was the most abundant (Figure 1), similar to previous studies (Abdallah et al., 2015; Amaral et al., 2005). Delta‐ and gamma‐tocopherol concentrations in our samples were comparable to a previous study except for alpha isomer, which may be attribute to cultivar variation.

FIGURE 1.

FIGURE 1

Open in a new tab

Concentration of tocopherol: (a) delta, (b) gamma, (c) alpha, and (d) total in the light and dark walnuts (cv. Chandler, Howard, Tulare, and the whole dataset). The values were expressed as average ± standard deviation. Different lowercase letters indicate significant differences (p < 0.05) between the combination of the skin lightness and cultivar. Different uppercase letters show significant differences (p < 0.05) between the light and dark groups for the whole dataset. Whole dataset means that the data from the three cultivars were combined, and they were only grouped based on the lightness index. Different uppercase and italic letters show significant differences (p < 0.05) among Chandler, Howard, and Tulare.

Concentrations of delta, gamma, alpha, and total tocopherols were not affected by the skin lightness level or the interaction between skin lightness and cultivar (p‐value > 0.05). The comparison between the light and dark kernels was made with two scenarios: cultivar parameter included and excluded. Except for delta‐tocopherol, they were affected by cultivar. Chandler had significantly higher gamma, alpha, and total tocopherols than Tulare but neither Chandler nor Tulare was significantly different from Howard, in that regard (Figure 1).

Our finding suggested that the different kernel skin lightness was not associated with significant changes in tocopherol concentrations in walnuts. This disagreed with a similar study that showed that amber walnuts had significantly less gamma‐tocopherol than extra light, light, and light amber walnuts grown in China. The discrepancy could have resulted from variations in cultivar, agricultural practices, geographical factors, and more importantly the reason underlying the variation in skin color. It was not clear what factors caused the kernel skin darkness in that study (Li et al., 2022).

3.8. Fatty acid profile

Fatty acids in walnuts were dominated by linoleic acid, followed by linolenic or oleic acid, palmitic acid, and stearic acid. Some minor components included palmitoleic, margaric, eicosanoic, and eicosenoic acids. This profile was similar to those reported previously (Gecgel et al., 2011; Verardo et al., 2013). Our data showed that the kernel skin lightness was not associated with any changes in the fatty acid profile of walnuts (p‐value > 0.05), either when the cultivar was included or excluded from the statistical analysis (Tables 2 and 7). This is different from the finding of Li et al. and the disagreement could be due to variations in genetics, environment, agricultural practices, and post‐harvest handling (Li et al., 2022).

TABLE 7.

Fatty acid profile of the light and dark walnuts (cv. Chandler, Howard, Tulare, and the whole dataset).

Compound Fatty acid (%)
Chandler Howard Tulare Whole dataset
Light Dark Light Dark Light Dark Light Dark
C16:0 6.06 ± 0.19b 6.14 ± 0.33b 6.25 ± 0.23b 6.39 ± 0.22b 7.41 ± 0.24a 7.39 ± 0.31a 6.43 ± 0.62A 6.49 ± 0.62A
C16:1 0.06 ± 0.00a 0.06 ± 0.01a 0.04 ± 0.02a 0.07 ± 0.00a 0.06 ± 0.01a 0.05 ± 0.01a 0.06 ± 0.01A 0.06 ± 0.01A
C17:0 0.03 ± 0.02a 0.03 ± 0.02a 0.02 ± 0.03a 0.04 ± 0.02a 0.04 ± 0.02a 0.04 ± 0.01a 0.03 ± 0.02A 0.03 ± 0.02A
C18:0 2.62 ± 0.32a 2.54 ± 0.23a 2.60 ± 0.08a 2.37 ± 0.22a 3.03 ± 0.05a 2.99 ± 0.06a 2.72 ± 0.30A 2.63 ± 0.30A
C18:1 14.1 ± 0.71a 14.3 ± 0.53a 13.7 ± 1.33a 13.7 ± 1.57a 13.8 ± 1.22a 13.9 ± 1.77a 13.97 ± 0.87A 14.11 ± 1.00A
C18:2 61.7 ± 0.78a 62.0 ± 1.00a 61.4 ± 2.26a 61.7 ± 3.10a 62.8 ± 1.37a 62.5 ± 1.96a 61.94 ± 1.18A 62.08 ± 1.47A
C18:3 15.0 ± 0.58a 14.6 ± 0.56a 15.7 ± 0.70a 15.4 ± 1.05a 12.6 ± 0.19b 12.8 ± 0.26b 14.51 ± 1.28A 14.28 ± 1.07A
C20:0 0.09 ± 0.00bc 0.09 ± 0.00c 0.08 ± 0.01c 0.08 ± 0.04c 0.11 ± 0.00ab 0.11 ± 0.01a 0.09 ± 0.01A 0.09 ± 0.02A
C20:1 0.24 ± 0.01a 0.23 ± 0.01a 0.24 ± 0.00a 0.22 ± 0.04ab 0.21 ± 0.01b 0.21 ± 0.01b 0.23 ± 0.01A 0.22 ± 0.02A
SFA 8.81 ± 0.32b 8.80 ± 0.21b 8.96 ± 0.26b 8.86 ± 0.49b 10.59 ± 0.30a 10.53 ± 0.38a 9.28 ± 0.84A 9.24 ± 0.82A
MUFA 14.44 ± 0.71a 14.58 ± 0.53a 13.94 ± 1.30a 14.03 ± 1.53a 14.03 ± 1.22a 14.20 ± 1.77a 14.25 ± 0.87A 14.39 ± 1.00A
PUFA 76.74 ± 0.52a 76.62 ± 0.65a 77.10 ± 1.56a 77.10 ± 2.05a 75.37 ± 1.47a 75.27 ± 2.13a 76.46 ± 1.10A 76.36 ± 1.38A
MUFA/PUFA 0.19 ± 0.01a 0.19 ± 0.01a 0.19 ± 0.02a 0.18 ± 0.03a 0.19 ± 0.02a 0.19 ± 0.03a 0.19 ± 0.01A 0.19 ± 0.02A
C18:1/C18:2 0.23 ± 0.02a 0.23 ± 0.01a 0.22 ± 0.03a 0.23 ± 0.04a 0.22 ± 0.03a 0.22 ± 0.04a 0.22 ± 0.02A 0.23 ± 0.02A
C18:2/C18:3 4.12 ± 0.20b 4.25 ± 0.22b 3.92 ± 0.32b 4.04 ± 0.48b 4.98 ± 0.10a 4.87 ± 0.12a 4.30 ± 0.46A 4.37 ± 0.38A
Open in a new tab

Note: The values were expressed as average ± standard deviation. Different lowercase letters indicate significant differences (p < 0.05) between the combination of the skin lightness and cultivar. Different uppercase letters show significant differences (p < 0.05) between the light and dark groups for the whole dataset. Whole dataset means that the data from the three cultivars were combined, and they were only grouped based on the lightness index. MUFA/PUFA: ratio of MUFA to PUFA; C18:1/C18:2: ratio of oleic to linoleic acid; C18:2/C18:3: ratio of linoleic to linolenic acid.

Abbreviations: SFA, saturated fatty acids; MUFA, monounsaturated fatty acid; PUFA, polyunsaturated fatty acid.

Cultivar affected some fatty acids. The significant difference in the fatty acid profile due to cultivar variation was noted for palmitic acid (p‐value < 0.0001), stearic acid (p‐value 0.0024), linolenic acid (p‐value < 0.0001), eicosanoic acid (p‐value < 0.0001), and eicosenoic acid (p‐value 0.0001). Concentrations of palmitic, stearic, and eicosanoic acids were higher in Tulare than in Howard and Chandler. Meanwhile, Tulare had a lower concentration of linolenic and eicosenoic acids than Howard and Chandler. There was no significant difference in the fatty acid profile between Chandler and Howard. In addition, there was no significant interaction between cultivar and lightness (p‐value > 0.05).

3.9. Phenols

Twenty‐four free phenolic compounds were analyzed in walnut kernels. Epigallocatechin gallate, vanillin, rutin, myricetin, trans‐cinnamic acid, procyanidin B2, syringic acid, luteolin, and kaempferol were below their limit of detection in all the samples. Other compounds are shown in Table 8. Our data demonstrated that the dark and light kernels differed in concentrations of several phenols. Chandler, Howard, and Tulare similarly had no detectable epicatechin gallate in the dark kernels. Significant interaction between skin lightness index and cultivar was only found on 3,4 dihydroxybenzoic acid and quercetin. Both Chandler and Tulare, but not Howard, had higher quercetin in their dark kernels than their respective light kernels. A significant change in 3,4 dihydroxybenzoic acid was only found in Tulare, while a significant change in naringenin content was only shown in Chandler. Chandler was more adaptive, and Howard was the least adaptive among them.

When the whole dataset (cultivar variation was not considered) was used for comparison, the concentrations of gallic acid, procyanidin B1, vanillic acid, epicatechin gallate, ellagic acid, quercetin, and naringenin were different between the light and dark kernels. The dark kernels had significantly higher concentrations of gallic acid, vanillic acid, ellagic acid, quercetin, and naringenin, but less procyanidin B1 and epicatechin gallate (Table 8).

Increased concentrations of some free phenols in the dark‐colored walnuts had been demonstrated in an earlier study (Wang et al., 2022) and it agreed with our findings. Because the mechanism was not explained in that study, we looked for a plausible reason in studies on other matrices. The mechanism could be similar to what occurred in tomatoes exposed to heat stress in which the phenol level increase was accompanied by a higher activity of phenylalanine ammonia‐lyase, an enzyme involved in phenol synthesis (Rivero et al., 2001). The increase in soluble phenolics likely prevented extensive oxidative damage and maintained osmotic balance (Wahid, 2007). Walnut plants responded to stress by synthesizing more phenolics. While the increased concentrations were apparent in some phenols, the level of some others (i.e., procyanidin B1 and epicatechin gallate) reduced, likely because they performed their antioxidant function.

The lower amount of procyanidin B1, a catechin oligomer, in the dark kernels, could be attributed to oxidation and formation of procyanidin A (Kondo et al., 2000). Another possibility for the reduction of procyanidin B1 was oxidation and through that process, the compound was converted into phlobaphenes or quinones. These two compounds were likely involved in the browning of pecan skin (Zhang et al., 2024). There were several possibilities for the oxidation types: autooxidation, enzymatic oxidation, and quinone‐mediated oxidation (Liu et al., 2022). Procyanidin B1 had high values for all of the following antioxidant assays: Trolox equivalent antioxidant capacity, ferric reducing antioxidant power, and deoxyribose assay that could be attributed to its multiple hydroxyl groups that were potent hydrogen donors and its conjugated double bonds that could delocalize electrons to stabilize phenoxy radical (Soobrattee et al., 2005).

Epicatechin gallate was only found in the light kernels of Chandler, Howard, and Tulare, but not in their dark kernels. A higher concentration of epicatechin gallate in walnut kernels with lighter skin was also noted by Wang et al. (2022). The reduction of its concentration as walnut skin became darker was likely due to auto‐oxidation of that compound, resulting in δ‐ and γ‐dehydrodicatechins that gave brown color (Tan et al., 2023).

4. CONCLUSIONS

Overall, the quality, in regards to FFA and PV, of dark and light kernels was similar. There were no changes in the fatty acid profile and tocopherols between the two groups on Chandler, Howard, and Tulare. However, several physical and chemical attributes varied between the two skin‐lightness groups. The dark kernels had less weight and fat content but higher oxidative stability. The concentration of eight volatile compounds (i.e., 1‐octen‐3‐ol; 1‐hexanol; 1‐penten‐3‐ol; 1‐pentanol; butanal, 2‐methyl; butanal, 3‐methyl; acetone; and 2‐heptanone) were higher in the dark kernels. There were no significant changes in the concentration of tocopherols and fatty acid profile. On the other hand, concentrations of several phenols increased while epicatechin gallate and procyanidin B2 decreased likely because they were transformed into other compounds that gave the brown color to the kernel skin. Our data suggested that skin lightness was linked to some changes in the physical (weight) and chemical (fat content, oxidative stability, volatiles, and phenols) properties of the kernels.

AUTHOR CONTRIBUTIONS

Zatil A. Athaillah: Methodology; software; data curation; formal analysis; visualization; writing—original draft; writing—review and editing; investigation; validation. Selina C. Wang: Conceptualization; methodology; supervision; resources; project administration; writing—review and editing; formal analysis; funding acquisition; investigation.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest in this study.

Supporting information

Table S1. Precursor ion (m/z), product ion (m/z), dwell time (ms), fragmentor voltage (V), collision energy (V), and cell accelerator voltage (V), and polarity for each phenolic compound analyzed.

Figure S1. Maximum temperature (°C) recorded for September of 2003‐2022 in weather stations located in Hanford 1S, Hanford area, Marysville, Red Bluff, and Visalia in California, USA.

Figure S2. Precipitation (mm) recorded during 74‐144 days after walnuts were generally pollinated (20th July – 28th September 2022) in weather stations located in Hanford 1S, Hanford area, Marysville, Red Bluff, and Visalia in California, USA.

Figure S3. Maximum temperature (°C) recorded during 74‐144 days after walnuts were generally pollinated (20th July – 28th September 2022) in weather stations located in Hanford 1S, Hanford area, Marysville, Red Bluff, and Visalia in California, USA.

Figure S4. Moisture content of light and dark walnuts (cv. Chandler, Howard, and Tulare). The values were expressed as average ± standard deviation.

JFDS-89-2730-s001.docx (13.4MB, docx)

ACKNOWLEDGMENTS

The works described herein were supported by the California Walnut Board, award number A23‐3761‐001. The authors are grateful to Joshua Rahm for collecting the samples and Shannon Yi for her assistance in preparing and analyzing the samples.

REFERENCES

  1. Abdallah, I. B. , Tlili, N. , Martinez‐Force, E. , Rubio, A. G. P. , Perez‐Camino, M. C. , Albouchi, A. , & Boukhchina, S. (2015). Content of carotenoids, tocopherols, sterols, triterpenic and aliphatic alcohols, and volatile compounds in six walnuts (Juglans regia L.) varieties. Food Chemistry, 173, 972–978. 10.1016/j.foodchem.2014.10.095 [DOI] [PubMed] [Google Scholar]
  2. Acree, T . (2004). Flavornet (H. Arn, Trans.). Flavornet and human odor space. http://flavornet.org/flavornet.html
  3. Adiletta, G. , Magri, A. , Albanese, D. , Liguori, L. , Sodo, M. , Di Matteo, M. , & Petriccione, M. (2020). Overall quality and oxidative damage in packaged freshly shelled walnut kernels during cold storage. Journal of Food Measurement and Characterization, 14(6), 3483–3492. 10.1007/s11694-020-00589-9 [DOI] [Google Scholar]
  4. Adkison, C. , Richmond, K. , Lingga, N. , Bikoba, V. , & Mitcham, E. (2021). Optimizing walnut storage conditions: Effects of relative humidity, temperature, and shelling on quality after storage. Hortscience, 56(10), 1–9. 10.21273/HORTSCI15881-21 [DOI] [Google Scholar]
  5. Amaral, J. S. , Alves, M. R. , Seabra, R. M. , & Oliveira, B. P. P. (2005). Vitamin E composition of walnuts (Juglans regia L.): A 3‐year comparative study of different cultivars. Journal of Agricultural and Food Chemistry, 53(13), 5467–5472. 10.1021/jf050342u [DOI] [PubMed] [Google Scholar]
  6. Araji, S. , Grammer, T. A. , Gertzen, R. , Anderson, S. D. , Mikulic‐Petkovsek, M. , Veberic, R. , Phu, M. L. , Solar, A. , Leslie, C. A. , Dandekar, A. M. , & Escobar, M. A. (2014). Novel roles for the polyphenol oxidase enzyme in secondary metabolism and the regulation of cell death in walnut. Plant Physiology, 164(3), 1191–1203. 10.1104/pp.113.228593 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Balakrishnan, G. , & Schneider, R. G. (2023). Tocopherol degradation and lipid oxidation during storage of Chenopodium quinoa . Journal of Food Composition and Analysis, 123, 105549. 10.1016/j.jfca.2023.105549 [DOI] [Google Scholar]
  8. Benlloch‐González, M. , Sánchez‐Lucas, R. , Bejaoui, M. A. , Benlloch, M. , & Fernández‐Escobar, R. (2019). Global warming effects on yield and fruit maturation of olive trees growing under field conditions. Scientia Horticulturae, 249, 162–167. 10.1016/j.scienta.2019.01.046 [DOI] [Google Scholar]
  9. Bhatti, Z. A. , Maqbool, F. , & Langenhove, H. V. (2014). Rendering plant emissions of volatile organic compounds during sterilization and cooking processes. Environmental Technology, 35(9–12), 1321–1327. 10.1080/09593330.2013.867364 [DOI] [PubMed] [Google Scholar]
  10. Buranasompob, A. , Tang, J. , Powers, J. R. , Reyes, J. , Clark, S. , & Swanson, B. G. (2007). Lipoxygenase activity in walnuts and almonds. LWT—Food Science and Technology, 40(5), 893–899. 10.1016/j.lwt.2006.05.003 [DOI] [Google Scholar]
  11. >California Farm Bureau . (2022). High Heat, Low Demand Hurt Walnut Crop. https://californiaagtoday.com/high‐heat‐low‐demand‐hurt‐walnut‐crop/
  12. Cao, J. , Zou, X. G. , Deng, L. , Fan, Y. W. , Li, H. , Li, J. , & Deng, Z. Y. (2014). Analysis of nonpolar lipophilic aldehydes/ketones in oxidized edible oils using HPLC‐QqQ‐MS for the evaluation of their parent fatty acids. Food Research International, 64, 901–907. 10.1016/j.foodres.2014.08.042 [DOI] [PubMed] [Google Scholar]
  13. Cao, Y. Y. , Duan, H. , Yang, L. N. , Wang, Z. Q. , Zhou, S. C. , & Yang, J. C. (2008). Effect of heat stress during meiosis on grain yield of rice cultivars differing in heat tolerance and its physiological mechanism. Acta Agronomica Sinica, 34(12), 2134–2142. 10.1016/S1875-2780(09)60022-5 [DOI] [Google Scholar]
  14. Escobar, M. A. , Shilling, A. , Higgins, P. , Uratsu, S. L. , & Dandekar, A. M. (2008). Characterization of polyphenol oxidase from walnut. Journal of the American Society for Horticultural Science, 133(6), 852–858. 10.21273/JASHS.133.6.852 [DOI] [Google Scholar]
  15. Frankel, E. N. (1980). Lipid oxidation. Progress in Lipid Research, 19(1–2), 1–22. 10.1533/9780857097927 [DOI] [PubMed] [Google Scholar]
  16. Fruehwirth, S. , Egger, S. , Flecker, T. , Ressler, M. , Firat, N. , & Pignitter, M. (2021). Acetone as indicator of lipid oxidation in stored margarine. Antioxidants, 10(1), 59. 10.3390/antiox10010059 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. García‐Inza, G. P. , Castro, D. N. , Hall, A. J. , & Rousseaux, M. C. (2014). Responses to temperature of fruit dry weight, oil concentration, and oil fatty acid composition in olive (Olea europaea L. var. ‘Arauco’). European Journal of Agronomy, 54, 107–115. 10.1016/j.eja.2013.12.005 [DOI] [Google Scholar]
  18. Gecgel, U. , Gumus, T. , Tasan, M. , Daglioglu, O. , & Arici, M. (2011). Determination of fatty acid composition of γ‐irradiated hazelnuts, walnuts, almonds, and pistachios. Radiation Physics and Chemistry, 80(4), 578–581. 10.1016/j.radphyschem.2010.12.004 [DOI] [Google Scholar]
  19. Górnaś, P. (2015). Unique variability of tocopherol composition in various seed oils recovered from by‐products of apple industry: Rapid and simple determination of all four homologues (α, β, γ and δ) by RP‐HPLC/FLD. Food Chemistry, 172, 129–134. 10.1016/j.foodchem.2014.09.051 [DOI] [PubMed] [Google Scholar]
  20. Green, H. S. , & Wang, S. C. (2020). First report on quality and purity evaluations of avocado oil sold in the US. Food Control, 116, 107328. 10.1016/j.foodcont.2020.107328 [DOI] [Google Scholar]
  21. Grilo, F. S. , & Wang, S. C. (2021). Walnut (Juglans regia L.) volatile compounds indicate kernel and oil oxidation. Foods, 10(2), 329. 10.3390/foods10020329 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Gysin, R. , Azzi, A. , & Visarius, T. (2002). Gamma‐tocopherol inhibits human cancer cell cycle progression and cell proliferation by down‐regulation of cyclins. The Faseb Journal, 16(14), 1952–1954. 10.1096/fj.02-0362fje [DOI] [PubMed] [Google Scholar]
  23. Jiang, L. , Lee, M. H. , Kim, C. Y. , Kim, S. W. , Kim, P. I. , Min, S. R. , & Lee, J. (2021). Plant growth promotion by two volatile organic compounds emitted from the fungus Cladosporium halotolerans NGPF1. Frontiers in Plant Science, 12, 794349. 10.3389/fpls.2021.794349 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Jiang, S. , Han, S. , He, D. , Cao, G. , Fang, K. , Xiao, X. , Yi, J. , & Wan, X. (2019). The accumulation of phenolic compounds and increased activities of related enzymes contribute to early defense against walnut blight. Physiological and Molecular Plant Pathology, 108, 101433. 10.1016/j.pmpp.2019.101433 [DOI] [Google Scholar]
  25. Jin, F. , Zhou, Y. , Zhang, P. , Huang, R. , Fan, W. , Li, B. , Li, G. , Song, X. , & Pei, D. (2023). Identification of key lipogenesis stages and proteins involved in walnut kernel development. Journal of Agricultural and Food Chemistry, 71(10), 4306–4318. 10.1021/acs.jafc.2c08680 [DOI] [PubMed] [Google Scholar]
  26. Juretić, N. , Sepúlveda, R. , D'Espessailles, A. , Vera, D. B. , Cadagan, C. , de Miguel, M. , González‐Mañán, D. , & Tapia, G. (2021). Dietary alpha‐ and gamma‐tocopherol (1:5 ratio) supplementation attenuates adipose tissue expansion, hepatic steatosis, and expression of inflammatory markers in a high‐fat‐diet‐fed murine model. Nutrition (Burbank, Los Angeles County, Calif.), 85, 111139. 10.1016/j.nut.2021.111139 [DOI] [PubMed] [Google Scholar]
  27. Kaushal, N. , Awasthi, R. , Gupta, K. , Gaur, P. , Siddique, K. H. M. , & Nayyar, H. (2013). Heat‐stress‐induced reproductive failures in chickpea (Cicer arietinum) are associated with impaired sucrose metabolism in leaves and anthers. Functional Plant Biology, 40(12), 1334–1349. 10.1071/FP13082 [DOI] [PubMed] [Google Scholar]
  28. Kondo, K. , Kurihara, M. , Fukuhara, K. , Tanaka, T. , Suzuki, T. , Miyata, N. , & Toyoda, M. (2000). Conversion of procyanidin B‐type (catechin dimer) to A‐type: Evidence for abstraction of C‐2 hydrogen in catechin during radical oxidation. Tetrahedron Letters, 41(4), 485–488. 10.1016/S0040-4039(99)02097-3 [DOI] [Google Scholar]
  29. Kumar, S. , Singh, R. , & Nayyar, H. (2013). α‐Tocopherol application modulates the response of wheat (Triticum aestivum L.) seedlings to elevated temperatures by mitigation of stress injury and enhancement of antioxidants. Journal of Plant Growth Regulation, 32(2), 307–314. 10.1007/s00344-012-9299-z [DOI] [Google Scholar]
  30. Li, P. , Zhu, Y. , Li, S. , Zhang, A. , Zhao, W. , Zhang, J. , Chen, Q. , Ren, S. , Liu, J. , & Wang, H. (2020). Variation patterns of the volatiles during germination of the foxtail millet (Setaria italic): The relationship between the volatiles and fatty acids in model experiments. Molecules (Basel, Switzerland), 25(5), 1238. 10.3390/molecules25051238 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Li, Q. , Mo, R. , Wang, R. , Ni, Z. , Zheng, Y. , Han, Y. , Wu, S. , Tang, F. , Yuan, X. , & Liu, Y. (2022). Characterization and assessment of chemical components in walnuts with various appearances. Journal of Food Composition and Analysis, 107, 104361. 10.1016/j.jfca.2021.104361 [DOI] [Google Scholar]
  32. Liu, M. , Li, C. , Cao, C. , Wang, L. , Li, X. , Che, J. , Yang, H. , Zhang, X. , Zhao, H. , He, G. , & Liu, X. (2021). Walnut fruit processing equipment: Academic insights and perspectives. Food Engineering Reviews, 1–36. 10.1007/s12393-020-09273-6 [DOI] [Google Scholar]
  33. Liu, X. , Chen, D. , Yang, Z. , Chen, F. , Hu, X. , Xiao, Z. , Ji, J. , & Ma, L. (2022). Non‐enzymatic browning induced by chlorogenic acid quinone mediated catechin oxidation. Food Research International, 156, 111297. 10.1016/j.foodres.2022.111297 [DOI] [PubMed] [Google Scholar]
  34. Mahmoudian, M. , Rahemi, M. , Karimi, S. , Yazdani, N. , Tajdini, Z. , Sarikhani, S. , & Vahdati, K. (2021). Role of kaolin on drought tolerance and nut quality of Persian walnut. Journal of the Saudi Society of Agricultural Sciences, 20(6), 409–416. 10.1016/j.jssas.2021.05.002 [DOI] [Google Scholar]
  35. Mancebo‐Campos, V. , Salvador, M. D. , & Fregapane, G. (2007). Comparative study of virgin olive oil behavior under Rancimat accelerated oxidation conditions and long‐term room temperature storage. Journal of Agricultural and Food Chemistry, 55(20), 8231–8236. 10.1021/jf070915y [DOI] [PubMed] [Google Scholar]
  36. NOAA, N. W. S . (2022). NOAA online weather data . https://www.weather.gov/wrh/climate?wfo=hnx
  37. Ortiz, C. M. , Vicente, A. R. , Fields, R. P. , Grilo, F. , Labavitch, J. M. , Donis‐Gonzalez, I. , & Crisosto, C. H. (2019). Walnut (Juglans regia L.) kernel postharvest deterioration as affected by pellicle integrity, cultivar and oxygen concentration. Postharvest Biology and Technology, 156, 110948. 10.1016/j.postharvbio.2019.110948 [DOI] [Google Scholar]
  38. Pakrah, S. , Rahemi, M. , Nabipour, A. , Zahedzadeh, F. , Kakavand, F. , & Vahdati, K. (2021). Sensory and nutritional attributes of Persian walnut kernel influenced by maturity stage, drying method, and cultivar. Journal of Food Processing and Preservation, 45(6), e15513. 10.1111/jfpp.15513 [DOI] [Google Scholar]
  39. Piccirillo, P. , Fasano, P. , Mita, G. , De Paolis, A. , & Santino, A. (2005). Exploring the role of lipoxygenases on walnut quality and shelf‐life. Acta Horticulturae, 705, 543–545. 10.17660/ActaHortic.2005.705.79 [DOI] [Google Scholar]
  40. Rivelli, G. M. , Gomez, N. V. , Mantese, A. I. , Miralles, D. J. , Abeledo, L. G. , & Rondanini, D. P. (2023). Photothermal quotient describes the combined effects of heat and shade stresses on canola seed productivity. Seeds (New York, N.Y.), 2(1), 149–164. 10.3390/seeds2010012 [DOI] [Google Scholar]
  41. Rivero, R. M. , Ruiz, J. M. , García, P. C. , López‐Lefebre, L. R. , Sánchez, E. , & Romero, L. (2001). Resistance to cold and heat stress: Accumulation of phenolic compounds in tomato and watermelon plants. Plant Science, 160(2), 315–321. 10.1016/S0168-9452(00)00395-2 [DOI] [PubMed] [Google Scholar]
  42. Salcedo, C. L. , & Nazareno, M. A. (2015). Effect of phenolic compounds on the oxidative stability of ground walnuts and almonds. RSC Advances, 5(57), 45878–45887. 10.1039/C5RA00245A [DOI] [Google Scholar]
  43. Schwab, W. , Davidovich‐Rikanati, R. , & Lewinsohn, E. (2008). Biosynthesis of plant‐derived flavor compounds. The Plant Journal, 54(4), 712–732. 10.1111/j.1365-313X.2008.03446.x [DOI] [PubMed] [Google Scholar]
  44. Sita, K. , Sehgal, A. , Bhandari, K. , Kumar, J. , Kumar, S. , Singh, S. , Siddique, K. H. , & Nayyar, H. (2018). Impact of heat stress during seed filling on seed quality and seed yield in lentil (Lens culinaris Medikus) genotypes. Journal of the Science of Food and Agriculture, 98(13), 5134–5141. 10.1002/jsfa.9054 [DOI] [PubMed] [Google Scholar]
  45. Soobrattee, M. A. , Neergheen, V. S. , Luximon‐Ramma, A. , Aruoma, O. I. , & Bahorun, T. (2005). Phenolics as potential antioxidant therapeutic agents: Mechanism and actions. Mutation Research, 579(1–2), 200–213. 10.1016/j.mrfmmm.2005.03.023 [DOI] [PubMed] [Google Scholar]
  46. Tan, J. , Vincken, J.‐P. , van Zadelhoff, A. , Hilgers, R. , Lin, Z. , & de Bruijn, W. J. C. (2023). Presence of free gallic acid and gallate moieties reduces auto‐oxidative browning of epicatechin (EC) and epicatechin gallate (ECg). Food Chemistry, 425, 136446. 10.1016/j.foodchem.2023.136446 [DOI] [PubMed] [Google Scholar]
  47. Taranto, F. , Pasqualone, A. , Mangini, G. , Tripodi, P. , Miazzi, M. M. , Pavan, S. , & Montemurro, C. (2017). Polyphenol oxidases in crops: Biochemical, physiological and genetic aspects. International Journal of Molecular Sciences, 18(2), 377. 10.3390/ijms1802037 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Tietel, Z. , Bar, E. , Lewinsohn, E. , Feldmesser, E. , Fallik, E. , & Porat, R. (2010). Effects of wax coatings and postharvest storage on sensory quality and aroma volatile composition of “Mor” mandarins. Journal of the Science of Food and Agriculture, 90(6), 995–1007. 10.1002/jsfa.3909 [DOI] [PubMed] [Google Scholar]
  49. Trandafir, I. , Cosmulescu, S. , & Nour, V. (2017). Phenolic profile and antioxidant capacity of walnut extract as influenced by the extraction method and solvent. International Journal of Food Engineering, 13(1), 20150284. 10.1515/ijfe-2015-0284 [DOI] [Google Scholar]
  50. UC, A. N. R . (2020). Walnut cultivar table . Sacramento Valley Orchard Source. https://www.sacvalleyorchards.com/walnuts/orchard‐development/cultivars/
  51. Ullah, A. , Nadeem, F. , Nawaz, A. , Siddique, K. H. M. , & Farooq, M. (2022). Heat stress effects on the reproductive physiology and yield of wheat. Journal of Agronomy and Crop Science, 208(1), 1–17. 10.1111/jac.12572 [DOI] [Google Scholar]
  52. USDA, A. M. S . (2017). Shelled walnuts grades and standards . USDA Agricultural Marketing Service. https://www.ams.usda.gov/grades‐standards/shelled‐walnuts‐grades‐and‐standards
  53. Verardo, V. , Riciputi, Y. , Sorrenti, G. , Ornaghi, P. , Marangoni, B. , & Caboni, M. F. (2013). Effect of nitrogen fertilisation rates on the content of fatty acids, sterols, tocopherols and phenolic compounds, and on the oxidative stability of walnuts. LWT—Food Science and Technology, 50(2), 732–738. 10.1016/j.lwt.2012.07.018 [DOI] [Google Scholar]
  54. Wahid, A. (2007). Physiological implications of metabolite biosynthesis for net assimilation and heat‐stress tolerance of sugarcane (Saccharum officinarum) sprouts. Journal of Plant Research, 120(2), 219–228. 10.1007/s10265-006-0040-5 [DOI] [PubMed] [Google Scholar]
  55. Wang, R. , Tian, X. , Li, Q. , Liao, L. , Wu, S. , Tang, F. , Shen, D. , & Liu, Y. (2022). Walnut pellicle color affects its phenolic composition: Free, esterified and bound phenolic compounds in various colored‐pellicle walnuts. Journal of Food Composition and Analysis, 109, 104470. 10.1016/j.jfca.2022.104470 [DOI] [Google Scholar]
  56. Wang, S. , Tang, J. , Sun, T. , Mitcham, E. J. , Koral, T. , & Birla, S. L. (2006). Considerations in design of commercial radio frequency treatments for postharvest pest control in in‐shell walnuts. Journal of Food Engineering, 77(2), 304–312. 10.1016/j.jfoodeng.2005.06.037 [DOI] [Google Scholar]
  57. Yang, G. , Tong, T. , Yang, Y. , Liu, W. , & Wang, X. (2019). Amano lipase ps‐catalyzed hydrolysis of pine nut oil for the fatty acids production using deep eutectic solvent as co‐solvent. Journal of Oleo Science, 68(10), 977–988. 10.5650/jos.ess19069 [DOI] [PubMed] [Google Scholar]
  58. Yi, J. , Sun, Y. , Zhu, Z. , Liu, N. , & Lu, J. (2017). Near‐infrared reflectance spectroscopy for the prediction of chemical composition in walnut kernel. International Journal of Food Properties, 20(7), 1633–1642. 10.1080/10942912.2016.1217006 [DOI] [Google Scholar]
  59. Yuan, X. , Huang, S. , Ma, H. , Huang, N. , & Ye, N. (2019). Differential responses of walnut cultivars to cold storage and their correlation with postharvest physiological parameters. Horticulture, Environment, and Biotechnology, 60(3), 345–356. 10.1007/s13580-019-00126-8 [DOI] [Google Scholar]
  60. Zhang, C. , Wang, K. , Ren, H. , Chang, J. , & Yao, X. (2024). Variation in pigments in pecan testa during kernel development and storage. Food Chemistry, 438, 137989. 10.1016/j.foodchem.2023.137989 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1. Precursor ion (m/z), product ion (m/z), dwell time (ms), fragmentor voltage (V), collision energy (V), and cell accelerator voltage (V), and polarity for each phenolic compound analyzed.

Figure S1. Maximum temperature (°C) recorded for September of 2003‐2022 in weather stations located in Hanford 1S, Hanford area, Marysville, Red Bluff, and Visalia in California, USA.

Figure S2. Precipitation (mm) recorded during 74‐144 days after walnuts were generally pollinated (20th July – 28th September 2022) in weather stations located in Hanford 1S, Hanford area, Marysville, Red Bluff, and Visalia in California, USA.

Figure S3. Maximum temperature (°C) recorded during 74‐144 days after walnuts were generally pollinated (20th July – 28th September 2022) in weather stations located in Hanford 1S, Hanford area, Marysville, Red Bluff, and Visalia in California, USA.

Figure S4. Moisture content of light and dark walnuts (cv. Chandler, Howard, and Tulare). The values were expressed as average ± standard deviation.

JFDS-89-2730-s001.docx (13.4MB, docx)

Articles from Journal of Food Science are provided here courtesy of Wiley

RESOURCES