Flavor and Acceptance of Roasted California Almonds During

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Flavor and Acceptance of Roasted California Almonds During Accelerated Storage Lillian May Franklin, Ellena S King, Dawn Chapman, Nadia K. Byrnes, and Alyson Elayne Mitchell J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.7b05295 • Publication Date (Web): 09 Jan 2018 Downloaded from http://pubs.acs.org on January 9, 2018

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Journal of Agricultural and Food Chemistry

Flavor and Acceptance of Roasted California Almonds During Accelerated Storage

Lillian M. Franklina, Ellena S. Kingb1, Dawn Chapmanb, Nadia Byrnesc, Guangwei Huangd Alyson E. Mitchella* a: Department of Food Science and Technology, University of California, Davis, One Shields Avenue, Davis, California 95616, United States

b: Covance Food Solutions, 365 North Canyons Parkway, Suite #201, Livermore, California 94551 United States c: International Flavors and Fragrances, 800 Rose Ln, Union Beach, NJ 07735, USA d: Almond Board of California, Suite 1500, 1150 Ninth Street, Modesto, California 95354, United States *Address correspondence to this author at Department of Food Science and Technology, University of California, Davis, CA 95616 [telephone (530) 304-6618; fax (530) 752-4759; email [email protected]]

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Current address: MMR Research Worldwide, 345 Tompkins Ave, Pleasantville, NY 10570

ACS Paragon Plus Environment


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Abstract

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Monitoring oxidative flavor changes in almonds is only possible if the chemical and

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sensory profile during roasting and storage is first established. Herein almonds roasted at two

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different temperatures (115 and 152 °C) were stored at 39 °C for 0 to 12 months and were

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analyzed by Headspace Solid-phase Microextraction Gas Chromatography-Mass Spectrometry

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(HS-SPME-GC/MS), descriptive analysis and consumer hedonic analysis. Volatile profiles,

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descriptive sensory profiles and consumer hedonic scores were analyzed for predictive

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relationships. Descriptive attributes involving Roasted and Nutty, as well as consumer liking

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were highest in fresh almonds, while flavors typically associated with oxidative rancidity such as

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Cardboard, Painty/Solvent, Soapy, and Total Oxidized increased during storage. Compounds

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most important for predicting rancidity-related attributes were lipid oxidation products, including

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pentanal, hexanal, heptanal, and octanal. Consumer liking was best predicted by similar

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compounds to those predicting Clean Nutty flavor, including Maillard reaction products, such as

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2- and 3-methylbutanal, 2-methylpyrazine and 2,5-dimethylpyrazine.

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Key words: Almond, Prunus dulcis, HS-SPME GC/MS, Descriptive analysis, Partial Least-

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Squares Analysis

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Introduction As the most heavily produced and consumed tree nut worldwide,1 sweet almonds (Prunus

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dulcis) represent a valuable agricultural commodity to California, which produces 80% of the

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world almond supply.2 Almonds possess a number of beneficial nutritional and eating qualities,

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including high levels of mono-unsaturated fatty acids, vitamin E, fiber and protein, as well as a

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relatively long shelf life.3 Demand for almonds continues to grow worldwide,4 and California

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almond products are increasingly shipped over longer distances and stored for extended periods

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of time under variable conditions. Making it is more important for producers and processors to

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monitor the flavor of almonds to ensure that the quality and value of exports and products are

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preserved.

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In order to maintain the quality of almonds products, almond flavor must first be

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established and understood from a chemical and sensory standpoint. Flavor is perceived through

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a combined sensation of ortho- and retronasal detection of volatile compounds, and stimulation

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of taste receptors in mouth during mastication.5 Almond volatiles are derived from the raw

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almond and the various conditions that the almonds are exposed to, either directly or indirectly.

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During harvesting and shelling, almond nutmeats may be exposed to sunlight, heat, ambient

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humidity, metal ions, and atmospheric oxygen, all of which can elicit changes in almond

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volatiles by promoting oxidative degradation of almond lipids.6 Further processing (e.g. roasting)

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can involve heating almonds to create flavor or texture changes. During roasting food

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temperature can exceed 100 °C; promoting Maillard reactions that can result in the production

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of volatile heterocycles such as pyrazines, furans, pyrans, pyrroles, and pyridines, as well as

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some small ‘Strecker’ aldehydes, ketones, and sulfides/thiols.7–9 Secondary volatile products



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from oxidation of fatty acids may also be produced during roasting, and interactions and

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reactions of these compounds may further alter volatile profiles.10

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Measuring food volatiles can give insight into flavor and benchmarks for determining

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flavor change. Almond volatile profiles have been previously assessed in raw 8,11–14 and roasted

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almonds.8,9,13–17

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Although measuring almond volatiles can offer understanding into the flavor changes

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occurring due to lipid oxidation and non-enzymatic browning, they may not necessarily correlate

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with flavor as it is perceived by humans. Therefore, descriptive analysis is frequently used to

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identify and measure specific flavor and texture attributes in food products using trained

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panelists. Descriptive analysis has been applied to almonds in order to study the variability in

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almond varieties grown in California18, the effects of irradiation on flavor19,20, the effects of

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almond coating on oxidative rancidity21, and the effects of toasting/roasting on flavor and

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texture.22,23 Though descriptive analysis is useful for directly assessing human perception of food

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products, it is a time- and resource-intensive analysis, and may not be practical for routine

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quality control. Combining descriptive analysis with volatile profile analysis may reveal volatile

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drivers of flavor attributes, which can then be used in place of descriptive analysis to indicate

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flavor changes in almonds. Of the aforementioned applications of descriptive analysis to

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almonds, only Larrauri et al.21 measured volatile compounds in almond samples, though volatile

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measurements were limited to hexanal and nonanal.

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Possibly even more important to almond processors than assessing almond flavor is

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measuring consumer acceptance of almonds and almond products. Though a number of studies

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have included some type of consumer evaluation of stored raw24–28 and roasted29–31 almonds,

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none of the existing studies include consumer liking, descriptive analysis and volatile analysis of


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the same sample set. Measuring consumer acceptance in conjunction with descriptive analysis

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and headspace volatiles may identify the most important flavor attributes and volatile compounds

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driving consumer liking or disliking. However, as volatile compounds correlated with consumer

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liking are not necessarily responsible for certain sensory attributes (e.g. Clean Nutty flavor)

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addition or omission experiments still need to be conducted to prove caustitive relationships.

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Herein, California Nonpareil almonds were dry-roasted at two different temperatures and

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stored under accelerated aging conditions for up to 12 months, in order to assess changes typical

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to the entire shelf life of almonds32. Samples were assessed by descriptive analysis, volatile

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profiling by HS-SPME-GC/MS and consumer acceptance. The goals of this study were to

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identify changes in sensory attributes throughout accelerated storage, identify compounds related

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to sensory attributes, and assess which of these are most related to consumer liking. To our

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knowledge, this is the first study to combine general descriptive analysis, headspace volatile

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profiling, and consumer hedonic assessment in almonds.

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Materials and Methods

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Chemicals and Reagents

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Stable isotope standards of octanal-d16, 2-methylpyrazine-d6, and n-hexyl-d13 alcohol,

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representing three main categories of identified compounds (aldehydes, pyrazines, and alcohols)

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were purchased from C/D/N Isotopes Inc. (Pointe-Claire, QC, Canada). Authentic standards of 2-

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methylpropanal (97+%), butanal (97+%), 3-methylbutanal (97+%), hexanal (99%), heptanal

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(95%), (E)-2-hexanal (98%), octanal (99%), 1-chloro-2-propanol (70%), 2,5-dimethylpyrazine

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(98%), nonanal (95%), furfural (98+%), 2-acetylpyrrole (99%), 2-furanmethanol (99%), methyl

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acetate (99.9%), 2-pentanone (98+%), pentanal (99%), dimethyl disulfide (99+%), 2-methyl-1-



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propanol (99+%), 2-heptanone (98%), methyl hexanoate (99.8+%), 2-nonanone (99.5+%),

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decanal (95+%), 4-hydroxy-2,5-dimethylfuran-3-one (99+%), 1-nonanol (98+%), heptanoic acid

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(99+%), alpha-pinene (98%), and octanoic acid (99+%) were obtained from Aldrich Chemical

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Co., Inc. (Milwaukee, WI, USA). Authentic standards of 2-butylfuran (98%), 2,3-butanediol

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(97+%), 2-methylpyrazine (99%), (E)-2-octenal (94%), acetic acid (99+%), benzaldehyde

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(99+%), 2-methylbutanal (95+%), 3-methyl-1-butanol (98+%), 1-pentanol (99+%), 2-octanone

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(99+%), 1-heptanol (99+%), 1-octen-3-ol (98+%), (E)-2-nonenal (95+%), 1-hexanol (99+%), 1-

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H-pyrrole (98%), 1-octanol (99+%), butanoic acid (99+%), 3-methylbutanoic acid (98+%) and

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nonanoic acid (99+%), were obtained from Sigma-Aldrich (St. Louis, MO, USA). Standards of

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hexanoic acid (98%), 1-butanol (99%), and ethyl 2-(methylthio)-acetate (95%) were obtained

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from Acros Organics (Thermo Fisher Scientific Inc., Waltham, MA, USA). Solvents, including

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HPLC/spectrophotometric ethanol and methanol were purchased from Fisher Scientific

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Company (Fairlawn, NJ, USA) or Sigma-Aldrich Chemical Company (St. Louis, MO, USA).

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Standard of 2-(ethylthio)-ethanol (96%) was purchased from Alfa Aesar, (Ward Hill, MA, USA)

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and 3-hydroxybutan-2-one from Supelco (Bellefonte, PA, USA).

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Accelerated Storage

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A 200 kg sample of de-hulled, raw, 23/25 Nonpareil almond (Prunus dulcis ‘Nonpareil’)

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kernels with skin (from the 2014 harvest year) were obtained from Blue Diamond Growers

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(Sacramento, CA). Almonds were dry roasted in a BCO-E1 electric convection (Bakers Pride,

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Allen, TX) under two different conditions: 115 ± 6°C for 60 min and 152 ± 6°C for 15 min to

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achieve a ‘light’ degree of roast (LR) and ‘dark’ degree of roast (DR). After roasting almonds

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were cooled, and vacuum-flushed with nitrogen in mylar foil laminated-type packages for

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transport to a controlled atmosphere chamber (KMF 240 Constant Climate Chamber by Binder


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Inc. Bohemia, NY). The batches of LR and DR almonds were then divided into 460 g lots and

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stored in open brown paper bags. Samples were stored at 15% ± 1% relative humidity and 39 ±

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1°C for intervals of 1 to 12 months to mimic accelerated storage conditions. Eight brown paper

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bags of LR and DR almonds each (previously designated to the current time point through

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random assignment) were withdrawn from the chamber every month, mixed thoroughly, and

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repackaged into polyethylene vacuum sealed packages, which were then stored at -80°C (Revco

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Inc. Trumbull, CT) until further analyzed. For comparison, the control samples were repacked

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and stored at -80°C immediately after roasting, with no accelerated storage. Headspace solid-

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phase gas chromatography mass spectrometry [HS-SPME-GC/MS] of almond samples was

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performed within 2 weeks of sample storage at -80°C. Descriptive and hedonic sensory analyses

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of almond samples were performed after all almond samples had completed accelerated aging,

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no more than 10 months of storage at 39 ± 1°C.

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Sensory Analysis

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Sample Selection for Descriptive and Hedonic Analysis

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The fatiguing nature and breadth of the entire sample set necessitated the creation of a

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sample subset for sensory testing. In order to select samples for this subset, the entire sample set

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of 13 storage times for each roast level was evaluated in a benchtop tasting by three authors of

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the paper. A subset of six samples of each roast level (12 samples in total) were chosen by verbal

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consensus, consisting of 0 (Control), 2, 4, 6, 8, and 10 month-old samples stored at accelerated

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temperatures. These storage times were chosen to best represent the sensory changes in the

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almond samples during storage while avoiding samples that were offensively oxidized.

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Quantitative Descriptive Analysis



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Ten panelists were recruited from a large group of experienced assessors, employed by

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Covance Laboratories, Inc. (Livermore, California) for sensory analysis. Panelists were trained

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during one 2-hour session, in which they evaluated all 12 of the aforementioned samples and

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discussed product aroma, taste, and texture attributes. During this time, panelists also reviewed

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product attribute references, attribute term definitions, and evaluation methodology (attribute

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definitions given in Table 1S).

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Panelists evaluated nine samples in a 2-hour period including a 15-minute break after the

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first 5 samples. Samples were evaluated three times (replicates) for a total of 30 observations of

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each sample. All samples were served in 5 oz soufflé cups with lids, labeled with randomly

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generated 3-digit numbers. Each 3-digit coded sample was presented as a pair, with a labeled

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Control sample. Panelists used a degree of difference scale to indicate how different each of the

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blinded samples were from the labeled Control sample on an overall basis, using a 15-point

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linescale anchored with 0 ‘no difference’ to 15 ‘extremely different’, respectively.

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Samples were assessed monadically and sequentially in a balanced William’s Latin

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Square design within each roast level. Panelists assessed all light roast samples before dark roast

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samples. Panelists were instructed to expectorate after tasting, and to score the intensity of each

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attribute. Panelists evaluated the samples in individually partitioned booths with controlled

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ventilation and lighting. Ambient purified drinking water, toothpicks and unsalted crackers were

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provided for palate cleansing. Data were collected using Sensory Information Management

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System, Version 6.0, 2016.

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Consumer Hedonic Analysis


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The method applied was identical to that of Franklin et al.33 Briefly, ninety-nine

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untrained consumers between the ages of 14 and 80, who were not pregnant and consumed

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almonds at least once a month, were recruited for hedonic analysis. Consumers were served

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samples of six or seven almonds at room temperature. Consumers were asked to taste two

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almonds at a time and indicate their liking of samples by marking a 9-pt hedonic scale.

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Consumers tasted samples in a random and counter-balanced order to minimize carry-over

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effects.

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Volatile Analysis

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HS-SPME-GC/MS detection of headspace volatiles was performed according to the

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procedure of Franklin et. al.33 Briefly, almonds were ground and sieved with a size 20 Tyler

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sieve (W.S. Tyler, Mentor, OH), a 5 ± 0.02 g sample was weighed into a 20 mL glass headspace

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vials (Restek, Bellefonte, PA). Vials were capped and crimped with caps containing 3 mm thick

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PTFE-lined silicone septa (Supelco, Bellefonte, PA) and allowed to equilibrate for at least 4

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hours. Sample handling and GC were performed using an Agilent 7890A (Santa Clara, CA)

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equipped with a CTC Combi/PAL autosampler (Zwingen, Switzerland). Samples were agitated

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at 500 rpm and pre-equilibrated at 40 °C for 45 min, after which they were extracted with a 1 cm

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30/50um StableFlex divinylbenzene/carboxen/polydimethylsiloxane fiber at a depth of 29 mm

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for 45 min at 250 rpm. After extraction the fiber was desorbed in a splitless injection at 250°C

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for 0.9 min, at which time the split vent was opened at a 50:1 split for a total injection time of 10

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min. The headspace volatiles were separated using a 30 m X 0.25 mm X 0.25 µm Agilent DB-

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Wax column (Santa Clara, CA) with a helium flow rate of 1.2 mL/min at 35 °C for 1 min then a

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ramp of 3 °C/min until 65 °C was attained, followed by a ramp of 6 °C/min to 180 °C, and



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finally 30 °C/min to 250 °C, which was held for 5 min. Quantitative data used for multivariate

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statistics consists of normalized headspace compound peak areas.

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Mass spectrometric detection was performed by an Agilent 5975C inert XL EI/ CI MSD

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(Santa Clara, CA) with a source temperature of 230 °C, quadrupole temperature of 150 °C, and

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electron energy of -70 eV. Volatile profiling was performed using a scan method in the range of

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30-300 m/z. Tentative volatile identification in the resulting total ion chromatogram was

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performed using the NIST Mass Spectral Search Program (v. 2.2). Identifications were

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confirmed using retention index calculation and comparison with reference values (Kovat’s

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Index), and retention time confirmation with standards when available. Integration was

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performed using Agilent MassHunter Quantitative Analysis software (v. B.07.00).

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Data Analysis

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Results from all measurements were analyzed using a two-way Analysis of Variance

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(ANOVA) with interactions, and when appropriate, a two-way Multivariate Analysis of Variance

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(MANOVA) with interactions, testing roast level and sample storage time as main effects.

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Where appropriate, a Bonferroni correction was made to the p-value to adjust for multiple

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iterations of ANOVA, and main effects were re-tested against significant interactions to confirm

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significance. When significant main effects were found, Tukey’s Honestly Significant Difference

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post-hoc testing was performed to identify samples that significantly differed at 95% confidence

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level (p 6 months, while ‘clean’ attributes, were

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associated with the Control and 2-month samples, and negatively correlated with Dimension 1

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(Figure 3). Also negatively correlated with Dimension 1 was liking of all consumer groups, of

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which average liking was almost perfectly correlated with Clean Nutty flavor and oppositely

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correlated with Total Oxidized flavor (Figure 3).


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Dimension 2 somewhat corresponds with variance due to roast level of samples (Figure

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3). Clustering of sample ‘D6’, ‘L8’, and ‘L10’ indicate a degree of similarity in these samples.

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Sensory attributes Total Flavor Intensity and Total Aroma Intensity best correlate with

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Dimension 2 (Figure 3). Clean Roasted aroma, Hardness, Astringent and Bitter flavor are

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similarly correlated with Dimension 2, though to a lesser extent, indicating that these attributes

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along with Total Flavor Intensity and Total Aroma Intensity were important to distinguishing DR

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samples from LR samples, especially in fresh samples and very oxidized samples (Figure 3).

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Headspace volatile compounds separate into two main groups along Dimension 1,

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corresponding to early and late storage times. The majority of compounds negatively correlated

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with Dimension 1, and positively correlated with Clean Nutty and Clean Roasted attributes and

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consumer liking are compounds typically generated from Maillard reactions, including

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alkylpyrazines, Strecker aldehydes, acetoin, furfural, and 2,3-pentanedione. 8,9,14 Compounds

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most positively associated with Dimension 1 and Total Oxidized, Cardboard, and Painty/Solvent

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attributes include volatiles typically generated during lipid oxidation, including 5 to 9-carbon

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aldehydes and organic acids, 5 to 8-carbon alcohols, 7- and 8-carbon ketones and 2-

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pentylfuran.41

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In addition to Total Flavor and Total Aroma Intensity, Dimension 2 correlates most

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negatively with compounds present in highest amounts either at the beginning or end of the

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storage period in DR samples (Figure 3). Pyrazines, Strecker aldehydes, and other Maillard

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products are negatively correlated with both dimensions, similar to sample DR0 and Clean

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Roasted aroma and Flavor. On the opposite side of the plot, secondary oxidation compounds

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(e.g. 7-,8-, and 9-carbon aldehydes, alcohols and ketones) and tertiary oxidation compounds (e.g.

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5- to 9-carbon organic acids) are negatively associated with Dimension 2 and positively



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associated with Dimension 1, as are DR8 and DR10 samples, Total Aroma Intensity, Astringent

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and Bitter flavor (Figure 3). The plotted orientation of the aforementioned compounds with

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respect to Dimension 2 and Total Flavor Intensity indicates that this attribute is related to both

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intensity in roast flavor of fresh DR samples, and intensity of oxidation character in DR samples

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of the latest accelerated storage times.

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PLS1 Regression Coefficients for Individual Descriptors

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PLS1 models involving prediction of each attribute by a selection of the most relevant

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headspace compound predictors were assessed for PLS regression coefficient values at the

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minimum number of dimensions needed to substantially reduce the root mean squared error of

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prediction (RMSEP) of the model using cross validation. The regression coefficient values of the

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X variables in a PLS1 model give an indication of their relative importance in predicting a Y

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variable, and were used in this case to prioritize individual volatile compounds by their

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predictive importance for each descriptive attribute and identify volatiles potentially responsible

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for these attributes.45 The regression coefficients of select headspace volatiles for each

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descriptive attribute are displayed in Figure 4, where regression values have been scaled within

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each descriptive attribute column by the standard deviation (Figure 4). Values were not centered,

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however, so that a coefficient value of “0” (mauve in the heatmap, Figure 4) indicates a

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compound with no predictive importance to an attribute. The dendrogram depicted at the top of

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the heatmap indicates attributes and consumer clusters most similar to each other based on

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complete clustering and a Euclidean distance matrix.

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The dendrogram identifies three main groups of predicted variables based on similarity in

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scaled regression coefficient values. These include a group containing all measures of consumer

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liking and Clean Nutty attributes, a group containing Clean Roasted attributes, Bitter flavor,


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Astringent, Hardness, Total Flavor Intensity and Total Aroma Intensity, and a group containing

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Cardboard flavor, Total Oxidized attributes, and Painty/Solvent flavor (Figure 4). These groups

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are in good agreement with the plotted locations of descriptive attributes and consumer clusters

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with respect to Dimension 1 in the PLS2 correlation loadings plot (Figure 3).

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Clean Nutty flavor and Clean Nutty aroma were best predicted by a similar set of

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compounds to those best predicting consumer liking. Ethyl 2-(methylthio)-acetate and 2,5-

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dimethylpyrazine, 2-methylpyrazine, 1-(acetyloxy)-2-propanone, furfural, acetoin, and 2-

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methylpropanal were especially important to predicting Clean Nutty aroma (with positive

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coefficients) whereas Clean Nutty flavor was best predicted by 2,5-dimethylpyrazine, 2- and 3-

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methylbutanal, and 1-chloro-2-propanol, though the aforementioned Maillard products were also

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important to predicting this attribute. Maillard reaction products 2- and 3-methylbutanal, 2-

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methylpropanal, and furfural, have malty/nutty, fruity/cocoa, fresh/floral, and

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sweet/almond/bread aromas, respectively (Table 3). 45 Civille et al. 18 identified some of the

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same aromas (fruity, sweet, nut) as part of a standard descriptive lexicon of almond flavor and

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therefore these compounds may contribute to the typical flavor of almonds. The close

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correlation of average consumer liking and liking of Cluster 2 with Clean Nutty flavor (Figure 3)

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reveals a positive perception of these compounds and associated aromas within the almond

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flavor profile.

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Consumer clusters were similar to Clean Nutty attributes in that they had positive

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predictive coefficients for a variety of Maillard compounds and negative coefficients for a wide

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variety of lipid oxidation compounds. In the PLS2 model, average consumer liking and liking of

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Cluster 2 were correlated with Clean Nutty flavor (Figure 3), while liking of consumer Cluster 1

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is better correlated with Clean Nutty aroma and Clean Roasted Attributes.



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Liking of Cluster 1 (24% consumers) and 2 (55% of consumers) and average liking was

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generally best predicted by 2,5-dimethylpyrazine, methylpyrazine, 2-methylbutanal, 3-

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methylbutanal, and furfural, as well as compounds associated with fresh roasted almonds such as

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1,2-propanediol, and 1-chloro-2-propanal (Figure 3). However, 1,2-propanediol and 1-chloro-2-

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propanal are unlikely to play an important role in flavor perception due to the high flavor

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threshold for small alcohols.43 Of the above predictors only 2-methylpyrazine, 2,5-

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dimethylpyrazine and 2- and 3-methylbutanal were previously observed in almonds at

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concentrations above sensory threshold (threshold: 0.06 mg/Lin water9, 10 and 5.4 µg/kg45

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respectively).9,13,14,17,43 Common to best positive predictors of liking for all consumer groups

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were 2- and 3-methylbutanal, and 2,5-dimethylpyrazine (Figure 4), indicating that these

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compounds may serve well as analytical correlates or indicators of consumer liking.

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Important predictors for consumer clusters with negative coefficient values include lipid

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oxidation products (i.e. pentanal, hexanal, 1-pentanol, acetic acid, 1-octen-3-ol, 1-heptanol,

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nonanal, heptanal, octanal, hexanoic acid, pentanoic acid, gamma-hexalactone, 2-heptanone, 1-

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hexanol, and 2-pentylfuran). The importance of these predictors to average liking scores was

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relatively uniform, though more negative values are given for 1-pentanol, octanal and heptanal

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than for other volatile predictors (indicated by the value trace line, light blue in Figure 4). These

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results are well in agreement with results of regression analysis between consumer liking and

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headspace volatile concentration of Franklin et al.33 which showed that heptanal, 1-pentanol, and

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octanal were among the best volatile correlates of consumer liking in roasted Nonpareil almonds.

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Clean Roasted aroma and Clean Roasted flavor were set apart from other ‘clean’

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attributes by a very high positive coefficient value for trimethylpyrazine, as well as low but

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positive scaled coefficient values for pentanal, hexanal, 2-octenal and acetic acid (Figure 4).


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Other important predictors of these attributes include ethyl 2-(methylthio)-acetate, and 2,5-

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dimethylpyrazine. Trimethylpyrazine is a Maillard reaction product with cocoa/roasted peanut

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aroma that has been previously identified in heat-treated Butte/Padre14, and Nonpareil43 almonds.

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The predictive importance to ‘Clean Roasted’ attributes of trimethylpyrazine confirms the

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importance of alkylpyrazines to the flavor associated with roasting. Ethylpyrazine was reported

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to convey an ‘intense roasted’ character during GC-olfactometry of peanut samples.8,14,45 This

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group also found that intensity of ‘dark roasted’ flavor decreased in peanut samples over 74 days

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of storage, in conjunction with decreases in the level of methylpyrazine, 2,5-dimethylpyrazine,

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2,3-dimethylpyrazine and 2-ethyl-5-methylpyrazine.41,44,47

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Bitter flavor, Astringent, Hardness, Total Aroma Intensity and Total Flavor Intensity are

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best predicted by a combination of compounds generated by roasting and most associated with

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fresh samples, and lipid oxidation secondary volatiles, associated with samples of longer storage

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periods. These attributes also did not reflect the scaled coefficient patterns of any consumer

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cluster and indicates that flavor intensity was not as important as Clean Nutty attributes in

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consumer liking of almonds.

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The last grouping of attributes includes Cardboard Flavor, Total Oxidized attributes, and

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Painty/Solvent Flavor. These attributes are well predicted (with positive coefficients) by hexanal,

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pentanal, and 1-pentanol, as well as (with negative coefficients) by most of the compounds

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related to Maillard reactions and fresh samples. Painty/Solvent Flavor and Total Oxidized Flavor

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are the most similar based on scaled coefficient values (Figure 4).

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Cardboard flavor is uniquely well-predicted by the unsaturated aldehydes 2-octenal and

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2-heptenal. Previous studies on the source of cardboard off-flavor in peanuts 21 and whey protein

443

isolate 38 have implicated a number of lipid oxidation products. Whitson, Miracle, and Drake38



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reported that pentanal and heptanal can each have a cardboard-like aroma, though a mixture of

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pentanal, heptanal, nonanal, 1-octene-3-one, and dimethyl trisulfide produced the most similar

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aroma to the real cardboard reference.

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Total Oxidized aroma is best predicted by hexanal, pentanal,1-pentanol, 1-hexanol, 2-

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butylfuran, 2-pentylfuran, and heptanal. Total Oxidized flavor was best predicted by a wide

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variety of lipid oxidation compounds, with slightly more importance given for 1-pentanol and

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hexanoic acid. Hexanal is frequently identified in oxidized almonds and other foods, and has a

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grassy, pungent, fatty aroma.41,45 Hexanal was determined to be one of the most intense aroma

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compounds formed during oxidation of linoleic acid.47 The peak area of hexanal exceeded that of

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all other headspace compounds in samples stored for 4 or more months (Table 3 and 4 in

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Supporting Information) and may have therefore been the principle compound responsible for

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Total Oxidized aroma. Pentylfuran is formed by oxidation of linoleic acid and can lend a beany,

456

grassy, or rancid note to oil at concentrations of 5-20 ppm.49 Lipid oxidation products 1-pentanol

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and 1-hexanol can have a fermented and grassy aroma, respectively, while hexanoic acid is often

458

characterized by a sweaty or goat aroma (Table 3).

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Painty/Solvent flavor was best predicted by saturated and unsaturated aldehydes (i.e. 2-

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octenal, nonanal, heptanal, octanal, and 2-heptenal). Saturated aldehydes are implicated in

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creating the ‘painty’ flavor frequently perceived in products undergoing oxidative rancidity. 50

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Lloyd et al.50 reported identifying a good correlation between heptanal (r = 0.95), nonanal (r =

463

0.95), and octanal (r = 0.87) and ‘painty’ flavor in whole milk powder stored for up to a year at

464

room temperature.

465 466

The purpose of this study was to highlight volatile compounds potentially responsible for specific attributes and consumer liking of almonds undergoing accelerated storage. Such


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volatiles could serve as chemical indicators of oxidative flavor changes and consumer acceptance

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in roasted and stored almonds. A number of volatile predictors of consumer liking were

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identified, including 2,5-dimethylpyrazine, 2- and 3-methylbutanal, which were predictors of

470

‘Clean Nutty’ and ‘Clean Roasted’ attributes. Additionally, a number of volatile correlates of

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rancid flavor attributes were identified which may be used to indicate rancidity in roasted

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almonds, including hexanal, pentanal, 1-pentanol, 2-heptenal, 2-octenal, heptanal, octanal,

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nonanal, 2-heptanone and 2-pentylfuran. Though hexanal had the most abundant peak area in

474

almonds stored longer than 4 months and was the most important predictor of Total Oxidized

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aroma, heptanal and octanal were better predictors of average consumer liking and may be more

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reliable indicators of consumer perception of rancidity in roasted almonds. Further study

477

involving addition or omission experiments are needed to form causative relationships between

478

compounds, and perceived flavors and acceptance.

479

480

Acknowledgements

481

The authors would like to thank W. Scott Moore and Brian Dunning of Blue Diamond Almonds

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for providing almonds and the facilities and expertise to roast almonds. The authors would also

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like to thank Susan Ebeler and Anna Hjelmeland, UC Davis Food Safety and Measurement

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Facility and Phil Wylie, Agilent Technologies, for their input on methods development,

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troubleshooting, and data analysis, and Hildegarde Heymann, UC Davis, for her input on

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experimental design and instruction in statistical methods.

487

Funding

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This work was funded by the Almond Board of California, Modesto, CA.



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Figure Captions

494 495

Figure 1.

Descriptive attribute intensity over time in DR (black) and LR (grey) almonds.

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Average liking over storage time for each consumer group, along with percentage of consumers

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in each group. DR samples are depicted in black, and LR samples are depicted in grey.

498 499

Figure 2.

Mean liking for each consumer group. Values in the same row followed by the

500

same letter were not found to be significantly different by Tukey’s HSD post-hoc testing (p

501

Flavor and Acceptance of Roasted California Almonds During

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