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Optimization Analysis of Sunflower Butter

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ABSTRACTA sunflower butter product was formulated and processing conditions were varied to study their effect on the overall sensory and physical characteristics of the final product. Amounts of salt, sugar, and stabilizer as well as roast level were optimized to yield a sunflower butter that most closely resembles peanut butter, in both physical and sensory quality. To produce a wide range of flavor, aroma, color, and texture attributes, 2 roasting levels (high and low), 2 sugar levels (7% and 9%), 2 salt levels (0.9% and 1.1%), and 3 stabilizer (Dritex-C) levels (1.6%, 1.7%, and 1.8%) were selected. Sunflower butter formulations were rated more “earthy” and less “salty” than peanut butter, but differences in the “sweet” attribute were small. Largest differences in the textural sensory attributes were denoted for the initial firmness and spreadability, with panel judging sunflower butter samples less spreadable and having a higher initial firmness. The panel rated sunflower butters more adhesive at the 1st bite; however, once chewed, sunflower butters were rated as less adhesive and higher on the “ease of swallow”. Cluster analysis on sensory data revealed the “best” formulation to have 1.8% stabilizer, 7% sugar, 1.1% salt, and a low roast level. Cluster analysis on the instrumental hardness, adhesion, oil separation, and color profile revealed the formulation closest to the controls to have the same amount of sugar and roast level, but 1.6% of stabilizer and 0.9% salt instead.
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Vol. 70, Nr. 6, 2005JOURNAL OF FOOD SCIENCE S365
Published on Web 7/13/2005
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S: Sensory & Nutritive Qualities of Food
Optimization Analysis of Sunflower Butter
II
II
ISABELSABEL
SABELSABEL
SABEL M. L M. L
M. L M. L
M. LIMAIMA
IMAIMA
IMA
ANDAND
ANDAND
AND H H
H H
HARMEETARMEET
ARMEETARMEET
ARMEET S. G S. G
S. G S. G
S. GURAURA
URAURA
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ABSTRAABSTRA
ABSTRAABSTRA
ABSTRACTCT
CTCT
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: A sunflo: A sunflo
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er butter product was foroduct was for
oduct was foroduct was for
oduct was formulated and prmulated and pr
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mulated and processing conditions wocessing conditions w
ocessing conditions wocessing conditions w
ocessing conditions werer
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ere ve v
e ve v
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aried to study theiried to study their
ied to study theiried to study their
ied to study their
effect on the oeffect on the o
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all sensory and physical chary and physical char
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oduct. Amounts of salt, sugar, and
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stabilizer as well as roast level were optimized to yield a sunflower butter that most closely resembles peanutstabilizer as well as roast level were optimized to yield a sunflower butter that most closely resembles peanut
stabilizer as well as roast level were optimized to yield a sunflower butter that most closely resembles peanutstabilizer as well as roast level were optimized to yield a sunflower butter that most closely resembles peanut
stabilizer as well as roast level were optimized to yield a sunflower butter that most closely resembles peanut
butterbutter
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butter, in both physical and sensor, in both physical and sensor
, in both physical and sensor, in both physical and sensor
, in both physical and sensory qualityy quality
y qualityy quality
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To pro pr
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oduce a wide range of flavange of flav
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oma, color, and textur, and textur
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tributes, 2 roasting levels (high and low), 2 sugar levels (7% and 9%), 2 salt levels (0.9% and 1.1%), and 3tributes, 2 roasting levels (high and low), 2 sugar levels (7% and 9%), 2 salt levels (0.9% and 1.1%), and 3
tributes, 2 roasting levels (high and low), 2 sugar levels (7% and 9%), 2 salt levels (0.9% and 1.1%), and 3tributes, 2 roasting levels (high and low), 2 sugar levels (7% and 9%), 2 salt levels (0.9% and 1.1%), and 3
tributes, 2 roasting levels (high and low), 2 sugar levels (7% and 9%), 2 salt levels (0.9% and 1.1%), and 3
stabilizer (Dritex-C) levels (1.6%, 1.7%, and 1.8%) were selected. Sunflower butter formulations were rated morestabilizer (Dritex-C) levels (1.6%, 1.7%, and 1.8%) were selected. Sunflower butter formulations were rated more
stabilizer (Dritex-C) levels (1.6%, 1.7%, and 1.8%) were selected. Sunflower butter formulations were rated morestabilizer (Dritex-C) levels (1.6%, 1.7%, and 1.8%) were selected. Sunflower butter formulations were rated more
stabilizer (Dritex-C) levels (1.6%, 1.7%, and 1.8%) were selected. Sunflower butter formulations were rated more
earear
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earthy” and less thy” and less
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salty” than peanut buttersalty” than peanut butter
salty” than peanut buttersalty” than peanut butter
salty” than peanut butter, but differ, but differ
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ere denoted for the initial fire denoted for the initial fir
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e denoted for the initial firmness and sprmness and spr
mness and sprmness and spr
mness and spreadabilityeadability
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eadability, with panel, with panel
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judging sunflower butter samples less spreadable and having a higher initial firmness. The panel rated sun-judging sunflower butter samples less spreadable and having a higher initial firmness. The panel rated sun-
judging sunflower butter samples less spreadable and having a higher initial firmness. The panel rated sun-judging sunflower butter samples less spreadable and having a higher initial firmness. The panel rated sun-
judging sunflower butter samples less spreadable and having a higher initial firmness. The panel rated sun-
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.” Cluster analysis on sensor” Cluster analysis on sensor
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, 7% sugar, 1.1% salt, and a lo, 1.1% salt, and a lo
, 1.1% salt, and a lo, 1.1% salt, and a lo
, 1.1% salt, and a low rw r
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oast level. Cluster analysis on the instrel. Cluster analysis on the instr
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umental hardnessdness
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,
adhesion, oil separation, and color profile revealed the formulation closest to the controls to have the sameadhesion, oil separation, and color profile revealed the formulation closest to the controls to have the same
adhesion, oil separation, and color profile revealed the formulation closest to the controls to have the sameadhesion, oil separation, and color profile revealed the formulation closest to the controls to have the same
adhesion, oil separation, and color profile revealed the formulation closest to the controls to have the same
amount of sugar and roast level, but 1.6% of stabilizer and 0.9% salt instead.amount of sugar and roast level, but 1.6% of stabilizer and 0.9% salt instead.
amount of sugar and roast level, but 1.6% of stabilizer and 0.9% salt instead.amount of sugar and roast level, but 1.6% of stabilizer and 0.9% salt instead.
amount of sugar and roast level, but 1.6% of stabilizer and 0.9% salt instead.
KK
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Keyworeywor
eyworeywor
eywords: sunflods: sunflo
ds: sunflods: sunflo
ds: sunfloww
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wer butterer butter
er butterer butter
er butter, sensor, sensor
, sensor, sensor
, sensory evy ev
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y evaluation, optimizationaluation, optimization
aluation, optimizationaluation, optimization
aluation, optimization
Introduction
Sunflower is native to North America where it was used in dyes,
food preparations, and medicines (Skrypetz 2003). Total sun-
flower seed production (oil and non-oil varieties) in 2003 was 1.3
million metric tons (NASS 2004). Europe is a large importer of sun-
flower seeds, with 1.7 million metric tons of sunflower seeds im-
ported in 2001 (Dept. of Commerce, U.S. Census Bureau, Foreign
Trade Statistics). Increasing freight rates have decreased the U.S.
sunflower seed marketability in comparison to nearby countries
such as Ukraine, Russia, Bulgaria, and Romania, which combined
represent more than 1/3 of the world’s supply for sunflower seeds.
Furthermore, more recently, Europe is able to import sunflower
seeds from other countries at prices below the profitability range
for the American farmer (D. Hofland, Vice President of Marketing,
Red River Commodities, Fargo, N.Dak., personal communication
2001). This resulted in a 30% decrease of U.S. sunflower seed ex-
ports in 2003 (National Sunflower Association, world supply and dis-
appearance statistics). One way to expand the U.S. sunflower seed
market and increase the demand for sunflower seeds is to develop
new value-added products, such as a superior-quality sunflower
butter. Commercial versions of sunflower butters were 1st launched
in the early 1980s. In general, attempts to launch sunflower butters
have failed mainly because of the presence of the fibrous outer
layer of the sunflower seed and associated moisture retention
upon improper roasting. This resulted in poor texture and appear-
ance of the product, including a green color and a bitter under-
roast taste leading to poor flavor and contributing to the unaccept-
ability of the product. Mullis and others (1983) conducted a sensory
evaluation of commercial sunflower seed butters and peanut but-
ters, using 5 different age groups (3 to 6 y, 12 to 13 y, 15 to 17 y, 24 to
26 y, and 62 to 85 y). It was found that ratings for all attributes (col-
or, texture, flavor, and overall acceptability) were generally lower
for sunflower butter. All groups, except the youngest, rated the color
of sunflower butter poorly, which may prejudice the other evalua-
tions (Mullis and others 1983). Similar findings were reported by
Dreher and others (1983) in their study of the nutritional, sensory,
and physical qualities of sunflower butters. Dreher and others
(1983) reported that roasting conditions had a significant impact on
both the nutritional and sensory quality, color, and spreadability of
sunflower butter.
Peanut butters and spreads are one of the most desired foods
among the population ages 20 y and below. However, peanut con-
sumption by children under the age of 3 is discouraged to prevent
the development of peanut sensitivity to this product. According to
the American Academy of Allergy Asthma and Immunology, about
1.5 million Americans are believed to be allergic to peanuts, with
symptoms ranging from a mild case of hives to severe anaphylac-
tic shock. It is estimated that in the United States, food allergies can
cause 30000 allergic reactions per year, resulting in approximately
2000 hospitalizations and up to 200 deaths (Sampson 2003). Chil-
dren make up a growing number of those allergic (Burks and Samp-
son 1993). When conducting a random telephone survey to 4855
participant households, Sicherer and others (2003) found that the
rate of peanut and nut allergy among children increased from 0.6%
to 1.2% between 1997 and 2002. Sunflower butter may provide this
segment of the population with an alternative product. The litera-
ture does not provide sufficient data on the allergenicity status of
sunflower seeds. Nutritive properties of sunflower butter are
equivalent to those of peanut butter (Dreher and others 1983) with
sunflower butter having 8 times more vitamin E and 4 times more
iron (D. Hofland, Vice President of Marketing, Red River Commod-
ities, Fargo, N.Dak., personal communication 2001). Sunflower
seeds are a good source of protein, fiber, vitamin E, zinc, and iron.
The objective of our research was to develop a sunflower butter
that resembled the texture, flavor, and nutty appearance of com-
mercially available peanut butter. To accomplish this, several sun-
flower butter formulations were investigated by varying sunflow-
JFS S: Sensory and Nutritive Qualities of Food
MS 20040823 Submitted 12/24/04, Revised 2/18/05, Accepted 3/8/05. The
authors are with USDA ARS Southern Regional Research Center, PO Box
19687, New Orleans, LA 70179. Direct inquiries to author Lima
(E-mail: imlima@srrc.ars.usda.gov).
S: Sensory & Nutritive Qualities of Food
S366 JOURNAL OF FOOD SCIENCE—Vol. 70, Nr. 6, 2005 URLs and E-mail addresses are active links at www.ift.org
Formulation of a sunflower butter . . .
er seed roasting as well as sugar, salt, and stabilizer levels. The op-
timal formulation was determined using cluster analysis for both
instrumental (sunflower butter texture [hardness and adhesive-
ness], color profile and stability measurements [oil separation])
and sensory attributes (12 flavor and 12 textural attributes).
Materials and Methods
Ingredient selectionIngredient selection
Ingredient selectionIngredient selection
Ingredient selection
Roasted U.S. confection grade sunflower kernels were provided
by Red River Commodities (Fargo, N.Dak., U.S.A.). Raw hulled sun-
flower kernels were roasted using soybean oil at 4 different roast lev-
els from light to very dark (low, medium-low, medium-high, and
high). Because the exact roasting conditions are proprietary informa-
tion, they are designated as levels. Additives (sugar, salt, and stabi-
lizer) were selected based on their properties to mimic the sweetness,
saltiness, and smoothness of peanut butter. Sugar and salt were
obtained at local food stores. Stabilizers were obtained from Humko
Foods Inc. (Cordova, Tenn., U.S.A.). Two different stabilizers were
selected: Dritex-C (consisting of hydrogenated cottonseed and rape-
seed oils) and PST consisting of hydrogenated palm oil. Both stabi-
lizers have a low iodine value and a high melting point (ranging from
59 °C to 63 °C). Ingredients (equilibrated at 25 °C) were weighed ac-
cording to the specific formulation and were added to each other in
the same order. The sunflower butter formulations included 90%
roasted sunflower seeds, 7.2% sugar, 1% salt, and 4 levels of stabiliz-
er, 1.5%, 1.6%, 1.7%, and 1.8%. The amount of sunflower seeds added
varied between 90.0% and 90.3%, depending on the amount of sta-
bilizer. Ingredients were processed in a vertical-cutter mixer UMC 5
electronic, Model 50/95 (Stephan Machinery Corp., Columbus, Ohio,
U.S.A.) until homogenous. The mixer regime was as follows: mix at
1500 rpm for 2 min followed by 3000 rpm for 16 min and finally back
to 1500 rpm for 2 min. During sunflower processing, sunflower but-
ter was kept at 70 °C via hot water flowing in an insulated external
jacket. Water temperature was switched to 21 °C during the last 2 min.
When sunflower butter temperature reached 39 °C, it was poured
onto 8-oz aluminum containers and allowed to set. The resulting
sunflower butters were evaluated for texture by using a Precision
Universal Penetrometer (Precision Scientific Co., Chicago, Ill., U.S.A.).
The penetration test involved measuring how deep a 150-g cone
plunger travels into the product 10 s after its release. The probe’s
travel is resisted by the hardness of the product, and results are re-
ported in terms of depth traveled by the probe in millimeters. Sam-
ples were informally evaluated for color and flavor by an informal
panel consisting of 5 people. From these preliminary studies, 1 sta-
bilizer was selected for its versatility and roasting was narrowed down
to 2 levels (medium-low and medium-high).
Formulation preparationFormulation preparation
Formulation preparationFormulation preparation
Formulation preparation
Two roasting levels (high and low), 2 sugar levels (7% and 9%), 2
salt levels (0.9% and 1.1%), and 3 stabilizer (Dritex-C) levels (1.6%,
1.7%, and 1.8%) were selected to produce a wide range of flavor,
aroma, color, and texture attributes, yielding a total of 24 different
sunflower butter samples. The 24 samples and corresponding
treatment combinations are presented in Table 1. Roasted sun-
flower seeds, sugar, salt, and stabilizer (equilibrated at 25 °C) were
again weighed according to the specific formulation, added to each
other in the same order, and mixed. Ingredients were blended and
processed into sunflower butter using a 45.72-cm Bauer Mill (18 in,
1800 rpm, Springfield, Ohio, U.S.A.). Mill blades were set at 0.1 mm
apart and the mixture was loaded onto the mill and allowed to pass
through 2 times. Samples were immediately packaged into 2-oz
containers to preserve aroma and flavor. The 24 sunflower butter
samples as well as 3 commercial peanut butter samples (JIF creamy
[Procter and Gamble, Cincinnati, Ohio], Peter Pan creamy [ConA-
gra Foods, Irvine, Calif.] and Skippy creamy [Unilever Best Foods,
Englewood Cliffs, N.J.]) were analyzed for physicochemical tests
and sensory analysis, as follows.
TT
TT
Texturextur
exturextur
exture analysise analysis
e analysise analysis
e analysis
Texture was evaluated by a Stevens QTS25 texturometer (New-
town, Pa., U.S.A.). For texture analysis, the average of 3 replicates
was obtained. Approximately 150 g of sunflower butter was placed
in a plastic container (65-mm dia 63-mm depth) fastened to a sup-
port frame to measure hardness of product. A 12.7-mm-dia flat
probe traveled downward through the sample at 30 mm/min, pen-
etrating to a 25-mm depth and returning upward at the same speed,
to the initial position. Hardness was measured as the height of the
positive peak (maximum penetration force) generated by the force
deformation curve, in Newtons (N). Low values of hardness mean
greater fluidity or reduced toughness and therefore, improved
sunflower butter spreadability (Lima and others 2000). Cohesive-
ness was measured during the upward movement of the probe and
was calculated as the maximum height of the negative peak in
Newtons (N).
Color analysisColor analysis
Color analysisColor analysis
Color analysis
A color assessment of samples was conducted using a Hunter
color meter (HunterLab, Reston, Va., U.S.A.) on duplicate samples.
The Hunter color meter measures spectra from the surface of the
sample based upon the degree to which the sample absorbs cer-
tain wavelengths from a weak illumination source. From the L, a, b
color index, L gives lightness on a scale from 0 to 100, black to white,
a gives red values for positive and green for negative, and b gives
Table 1—Sample treatments, levels 0, 1, and 2a
Nr Sample StabilizerbSugarcSaltdRoaste
1 A 0 000
2 B 0 100
3 C 0 010
4 D 0 001
5 E 0 110
6 F 0 101
7 G 0 011
8 H 0 111
9 I 1 000
10 J 1 100
11 K 1 010
12 L 1 001
13 M 1 110
14 N 1 101
15 O 1 011
16 P 1 111
17 Q 2 000
18 R 2 100
19 S 2 010
20 T 2 001
21 U 2 110
22 V 2 101
23 W 2 011
24 X 2 111
25 JIF — — —
26 Peter Pan
27 Skippy — — —
aSunflower seed quantity was adjusted based on other ingredient’s quantities
in the formulation.
bStabilizer: 0 = 1.6%; 1 = 1.7%; 2 = 1.8%.
cSugar: 0 = 7%; 1 = 9%.
dSalt: 0 = 0.9%; 1 = 1.1%.
eRoast level: 0 = low; 1 = high.
Vol. 70, Nr. 6, 2005JOURNAL OF FOOD SCIENCE S367
S: Sensory & Nutritive Qualities of Food
URLs and E-mail addresses are active links at www.ift.org
Formulation of a sunflower butter . . .
yellow values for positive and blue for negative. To measure color
attributes, thoroughly mixed sunflower butter was transferred to a
polystyrene clear petri dish. The lid of the dish was pressed onto the
surface of the sunflower butter to remove air bubbles. The covered
dish was then turned upside-down to provide a uniform surface.
Calibration was based on a standard tile with color space coordi-
nates: L, a, and b. Hunterlab Tile standard nr. C2-26739 (L = 79.12;
a = –2.20; b = 24.90) was used for color standardization.
Oil separation analysis (stability)Oil separation analysis (stability)
Oil separation analysis (stability)Oil separation analysis (stability)
Oil separation analysis (stability)
A simple procedure by Lima and others (2000) was adapted to
estimate oil separation. Thirty grams of sunflower butter was
placed into 50-mL polyethylene centrifuge tubes and centrifuged
in a Sorvall RC 5C Plus (Asheville, N.C., U.S.A.) for 10 min at 4000
rpm (2322 g of relative centrifugal force). Preliminary testing deter-
mined that centrifugation speed of 4000 rpm gave good differen-
tiation between samples in terms of the percentage of supernatant
oil. Oil separation was measured as the percentage of supernatant
oil over total amount of sample.
Sensory analysisSensory analysis
Sensory analysisSensory analysis
Sensory analysis
A descriptive sensory panel from North Carolina State Univ. con-
sisting of 8 trained panelists was selected to evaluate the intensi-
ties of sensory qualities of the sunflower butter samples. Panelists
were nonsmoking females between the ages of 30 and 68 y. These
panelists received extensive training in the Spectrum method of
descriptive analysis for generation of qualitative and quantitative
data. Descriptive analysis was conducted using a 15-point univer-
sal intensity scale for flavor and a 15-point product-specific scale for
texture (Meilgaard, and others 1999). The sunflower butter sensory
attributes evaluated were based on those from Johnsen and others
(1988), Crippen and others (1989), Civille and Lyon (1996), and
McNeill and others (2000). Two attributes not found in the previous
references—spreadability, the ease with which a 1/2-tsp sample
can be spread across the smooth side of a cracker using a dinner
knife; and ease of swallow, the ease with which the sample is gath-
ered in the mouth and swallowed—were obtained from Pominski
and others (1991) and Crippen and others (1989), respectively.
Additionally, 2 attributes were added that were not previously
found in the literature as associated with sunflower seeds—raw/
beany, the aromatic associated with light-roasted sunflower seed;
and roast nut/seed, the aromatic associated with medium-roasted
seeds and nut meats. The panel members developed the lexicon
and ballot with the aid of product-specific references and deter-
mined reference intensities during preliminary training sessions.
Products used during lexicon development included various brands
of peanut butter, sunflower seeds, sunflower seed paste, and basic
taste standard solutions of known intensities. Evaluation tech-
niques were standardized. The sensory lexicon included 12 flavor
attributes and 12 texture attributes of which 4 are 1st bite assess-
ments (done before mastication and labeled with “i” as a prefix for
easy distinction from the attributes in mastication that have the
same name). Fresh samples were presented randomly each session
to panelists in booths under red lights. Panelists were trained to
avoid letting their preferences taint their objective evaluations, but
their human quality biases them to possibly make preconceived
judgments about the flavor when they notice color. Panelists rinsed
Table 2—Sunflower butter properties (hardness, oil separation, color) as affected by stabilizer type,a amount, and roast
level
Stabilizer Roast Penetrometer Hardness Oil separation Color profile
(%) levelbdepth (mm) (N)c(%)cLcacbc
1.8 3 17.6 2989 ± 23 2.2 ± 0.6 39.9 ± 0.3 10.8 ± 0.0 34.6 ± 0.1
Dritex-C 1.7 3 17.8 2639 ± 67 1.7 ± 0.3 39.8 ± 0.1 10.6 ± 0.1 34.6 ± 0.3
1.6 3 20.3 1343 ± 112 3.6 ± 0.4 39.9 ± 0.0 10.7 ± 0.0 34.3 ± 0.0
1.5 3 28.3 124 ± 1 6.8 ± 1.5 39.6 ± 0.1 10.9 ± 0.0 35.0 ± 0.1
1.8 3 17.6 2444 ± 29 2.0 ± 0.2 39.9 ± 0.2 10.6 ± 0.0 34.3 ± 0.1
PST 1.7 3 17.6 2123 ± 340 2.9 ± 0.8 39.9 ± 0.1 10.6 ± 0.1 33.8 ± 0.3
1.6 3 18.5 1779 ± 78 3.3 ± 0.5 39.9 ± 0.1 10.5 ± 0.1 34.0 ± 0.1
1.5 3 21.9 1813 ± 71 3.1 ± 0.1 39.2 ± 0.1 10.8 ± 0.0 34.3 ± 0.1
1.8 4 21.0 2007 ± 177 3.1 ± 0.5 38.8 ± 0.1 11.1 ± 0.1 34.6 ± 0.1
Dritex-C 1.8 3 17.6 2989 ± 23 2.2 ± 0.6 39.9 ± 0.3 10.8 ± 0.0 34.6 ± 0.1
1.8 2 21.7 1464 ± 7 3.6 ± 0.4 42.7 ± 0.2 9.1 ± 0.1 33.8 ± 0.1
1.8 1 20.7 1891 ± 68 2.7 ± 0.8 43.1 ± 0.1 8.8 ± 0.0 33.4 ± 0.1
aDritex-C (hydrogenated cottonseed and rapeseed oils); PST (hydrogenated palm oil).
bRoast levels: 1 = low; 2 = medium-low; 3 = medium-high; 4 = high.
cValues represent mean ± standard deviation.
Table 3—Sensory attribute means by cluster
Clustera
12 (n = 3; 3 4 5
Attribute (n = 21) control) (n = 1) (n = 1) (n = 1)
Roast seed 3.81 3.54 4.50 2.87 3.25
Over roast 1.56 1.12 1.25 1.50 1.50
Raw beany 2.09 2.37 2.75 1.87 2.25
Green 1.61 1.58 3.25 1.75 1.87
Hulls skins 2.17 1.83 2.25 2.62 2.50
Earthy 2.95 1.08 4.12 5.75 3.00
Metallic 1.73 1.00 2.75 2.75 2.37
Sweet 3.38 3.83 2.62 3.00 2.62
Salty 4.44 5.96 4.25 3.12 3.62
Sour 2.14 1.62 2.62 2.25 2.62
Bitter 2.40 2.29 2.87 2.37 2.75
Astringent 2.81 2.67 2.87 2.75 3.37
iFirmness 9.20 6.83 7.75 7.87 8.25
iAdhesiveness 5.09 4.87 5.25 4.25 4.87
iOiliness 2.61 3.46 3.50 2.50 3.12
iSmooth 9.48 11.00 10.25 9.62 10.37
Cohesive 6.34 4.58 5.87 7.12 5.00
Adhesive 6.59 7.87 7.37 6.25 7.37
Oiliness 2.99 4.00 3.75 2.37 3.12
Particles 1.03 1.21 1.00 1.00 1.12
Ease of swallow 5.43 4.96 5.00 5.62 4.12
Mouth coating 4.89 5.71 5.25 4.00 5.12
Oil mouth coating 2.55 3.08 3.50 2.25 3.12
Spreadability 7.45 10.17 8.62 8.00 8.37
an = number of samples in each cluster.
S: Sensory & Nutritive Qualities of Food
S368 JOURNAL OF FOOD SCIENCE—Vol. 70, Nr. 6, 2005 URLs and E-mail addresses are active links at www.ift.org
Formulation of a sunflower butter . . .
with deionized water between samples and used unsalted saltine
crackers to cleanse their palates.
Experimental designExperimental design
Experimental designExperimental design
Experimental design
The experimental design was constructed under the constraints
that there could be at most 5 sessions with a maximum of 6 samples
presented to a panelist per session. Each panelist evaluated 5 sam-
ples during sessions 1, 2, and 5, and 6 samples during sessions 3
and 4 for a total of 27 samples per panelist for all sessions. There
were a total of 24 treatment formulations plus 3 commercial controls
(JIF creamy, Peter Pan creamy, and Skippy creamy). To accommo-
date these constraints, the experimental design was constructed as
a 5×5 Latin square design with session and sample order as the
blocking variables. The treatment structure was a 2×2×2×3 factorial
with 3 controls. The treatments consisted of roasting, salt, and sugar
each with 2 levels, and stabilizer with 3 levels. A separate Latin
square was constructed for each of the 8 panelists. The experimen-
tal design was constructed to meet the constraints, accommodate
any number of panelists, and eliminate any bias that could be in-
troduced from a nonrandom assignment of formulations to the pan-
elist (Hastie and others 2001).
Statistical analysisStatistical analysis
Statistical analysisStatistical analysis
Statistical analysis
Sensory and instrumental data were analyzed by SAS Inst.
(1996) using cluster analysis. Cluster analysis is a common explor-
atory data analysis technique used in classification problems. The
goal of cluster analysis is to sort observations, in this case sunflow-
er butter formulations/controls, into groups, or clusters, so that the
degree of association or similarity is strong between members of the
same cluster and weak between members of different clusters
(Johnson 1998; Hastie and others 2001). The cluster analysis was
conducted by representing the sensory attributes for each formu-
lation/control in a similarity matrix of Euclidean distances (the dis-
tance between 2 observations in the P-dimensional space mea-
sured by a ruler) and then using the nearest neighbor hierarchical
clustering algorithm to construct the “clusters.” The nearest neigh-
bor method starts with each observation in its own cluster and com-
bines the 2 closest observations based on their Euclidean distance.
This process continues until all observations are in 1 cluster. A
pseudo Hotelling’s T2 test was used to determine the stopping
point for the number of clusters.
Results and Discussion
Selection of stabilizerSelection of stabilizer
Selection of stabilizerSelection of stabilizer
Selection of stabilizer
Two stabilizers were considered for the formulation of sunflower
butters: hydrogenated palm oil (PST) and a hydrogenated blend of
rapeseed/cottonseed oil (Dritex-C). Stabilizers are typically used to
control oil migration. Excessive stabilizer in the product results in a
hard butter that is difficult to spread and insufficient stabilizer
results in unacceptable oil separation. Descriptive statistics for the
2 stabilizers are presented in Table 2. Because the statistics were
calculated by taking 3 measurements on a single batch of the for-
mulation, inferential statistics are not appropriate. As expected, oil
separation decreased with the amount of stabilizer added for both
stabilizers. Dritex-C added at the 1.5% level resulted in consider-
able oil separation (accelerated oil test). Moreover, the high pene-
trometer readings show that this stabilizer should not be applied at
levels below 1.6%. An inverse relationship was observed between
penetrometer depth and amount of stabilizer. Variation in penetra-
tion was more pronounced for Dritex-C than PST, giving the former
stabilizer more versatility. From Table 2 it can be seen that the
amount of stabilizer used for the sunflower butter samples was
appropriate as penetration readings between 17.0 and 21.0 mm are
typical for peanut butter samples. Penetrometer readings are quick
estimates of spreadability and give a general idea of textural qual-
ity; however, hardness measurements are more accurate and a
better representation of butter texture. Sunflower butter hardness
is given in Table 2. Regression analysis indicated that hardness
increased linearly with the amount of stabilizer for both Dritex-C
(R2 = 0.95) and PST (R2 = 0.86). When comparing peanut butters
stabilized with palm oil with those stabilized with hydrogenated
vegetable oil, Gills and Resurreccion (2000) determined that palm
oil was not effective at stabilizing peanut butter for 1 y. More recent-
ly, Aryana and others (2003) determined palm oil stabilized peanut
butter to be inferior than peanut butters stabilized with a blend of
both hydrogenated rapeseed and cottonseed oils. Typical fatty
acid composition shows that rapeseed oil contains approximately
49% of erucic acid, a 22-carbon monounsaturated acid, which after
hydrogenation to its saturated form, develops a very effective crys-
tal lattice structure (A. Rittenberg, personal communication, 2001).
This network of linked fatty acids entraps oil within the butter
matrix at very low levels of addition. Hydrogenated rapeseed oil is
usually blended with hydrogenated cottonseed oil in specified ra-
tios to extend the melting-point range and facilitate spreadability
at room temperature and mouthmelt at body temperature. In light
of this and an associated consumer aversion to tropical oils, Dritex-
C was selected as the stabilizer of choice for further sunflower but-
ter evaluations.
Color evaluationColor evaluation
Color evaluationColor evaluation
Color evaluation
Sunflower butters roasted at 4 roast levels (from low to high roast)
were manufactured using Dritex-C as stabilizer at the 1.8% level.
Table 2 displays the color profiles for these samples. The L value
(lightness) varies between 0 (black) and 100 (white); therefore,
darker colors are represented by lower L values. As roast level in-
creases from light to dark, sunflower butter color darkens as con-
firmed by a decrease in the L values (Table 2). From determination
of L values of 6 national brands of peanut butter to range from 46 to
51, Falk (1981) observed that acceptable color ranges for sunflow-
er butter to be darker than most commercially prepared peanut
butters. Yellowness is represented by the b values, but there was no
observable change with roast. On the other hand, redness values
represented by positive a values increased with level of roast. A
decrease in a values means that green tones will become more
prevalent. Dreher and others (1983) reported a values of 1.6, 2.9,
3.3, and 2.9 for sunflower butters made respectively with raw ker-
nels, conventionally roasted, microwave roasted, and from a health
food store. Green tones contribute to the unacceptability of the
product. Roasting levels were reduced to 2 (roast levels 4 and 2) for
further testing, based on color profile data and an informal taste
studies done on the samples.
Sensory studySensory study
Sensory studySensory study
Sensory study
A trained panel composed of 8 panelists analyzed sunflower
butter samples having 2 different sugar, salt, and roast levels, and
3 different stabilizer levels, for a total of 24 samples. They were also
given 3 peanut butter samples, as controls. When looking at the
panel scores as a whole, in general, the flavor attributes were closer
to the controls than the texture attributes (Table 3). Sunflower
butter formulations and peanut butter samples differed the most
in their “earthy tones” and “salty” attributes. Sunflower butters
were rated more “earthy” and less “salty” (Table 3). In general,
peanut butters were rated as sweeter, but differences in the “sweet
attribute between sunflower butters and peanut butters were not
as pronounced as the 2 previous attributes. As a whole, sunflower
Vol. 70, Nr. 6, 2005JOURNAL OF FOOD SCIENCE S369
S: Sensory & Nutritive Qualities of Food
URLs and E-mail addresses are active links at www.ift.org
Formulation of a sunflower butter . . .
butter formulations were closest to the controls for the following
flavor attributes: “green,” “hulls/skins,” “bitter,” and “astringent
(Table 3). Shifting to the textural sensory attributes, the largest
overall differences were denoted for initial firmness and spread-
ability, with the panel judging sunflower samples less spreadable
and having a higher initial firmness. Interestingly, the panel rated
sunflower butters as more adhesive at the 1st bite, but once
chewed, sunflower butters were rated as less adhesive. Moreover,
sunflower butters were rated higher on the “ease of swallow” than
their peanut butter counterparts. Sunflower butters were rated less
oily, both initially and after 3 to 5 bites. Perception of oiliness is
strongly affected by the amount of stabilizer used. Furthermore,
stability tests revealed that the percentage of oil released was high-
er for the peanut butter samples, which might indicate shorter
shelf stability. Additionally, the low linoleic acid content of sunflow-
er oil imparts a favorable heat stability. The panel found the sun-
flower butter samples to look very similar to peanut butter and to
have a mild yet distinctive sunflower seed flavor.
Determining an optimal formulationDetermining an optimal formulation
Determining an optimal formulationDetermining an optimal formulation
Determining an optimal formulation
Two separate cluster analysis were performed, 1 for the sensory
data (12 flavor attributes and 12 texture attributes) and 1 for the
instrumental data (texture, oil separation, and color profile). For
the sensory data cluster analysis, the algorithm grouped all 3 con-
trols (JIF, Peter Pan, and Skippy peanut butters) into a single cluster
Table 5—Instrumental attribute means by cluster
Cluster
Attribute 1 (n = 12) 2 (n = 8) 3 (n = 3) 4 (n = 2, control) 5 (n = 1)
Hardness (N) 6671 8809 4710 2499 3681
Cohesiveness (N) 2875 3734 2033 1179 1508
Separation (%) 1.3 0.8 1.3 1.7 1.6
L41.3 42.4 43.5 50.7 43.2
a7.1 7.3 5.9 7.0 5.9
b31.1 31.9 31.4 32.0 28.2
(Table 3). The 24 sunflower butter formulations were placed into 4
clusters based on their Euclidean distances between sensory scores,
that is, on the similarity in their sensory scores (Table 3). Because
the algorithm was able to group the 3 commercial peanut butters in
their own cluster (cluster 2, control), the sum of squared differences
between the control cluster attribute means and the attribute
means of the other 4 clusters was used as the metric to define the
“best” formulation. The cluster with the smallest sum of squares
was defined as the “best.” For the sensory analysis, this cluster was
identified as cluster 5 with only 1 sample, 19 (2010) (Table 4). Sample
19 (Table 1) was formulated with a high level of stabilizer, low lev-
el of sugar, high level of salt, and low level of roasting. When looking
at the individual treatment means for each sensory attribute,
“roast seed” flavor was closest to the control cluster for this sample
(Table 3). A typical roasted seed flavor was characteristic of all sun-
flower butter formulations, and when used at the low level, this at-
tribute was judged more subdued and closer to the control samples.
For the instrumental data cluster analysis, the algorithm generat-
ed 5 different clusters (Table 5). The algorithm also grouped controls
(JIF and Peter Pan) into a cluster because these samples were similar
to each other as far as their instrumental hardness and cohesiveness,
color profiles, and stability. Once again, because the algorithm was
able to group 2 commercial peanut butters in their own cluster (clus-
ter 4, control) (Table 5), the sum of squared differences between the
control cluster “instrumental data” means and the “instrumental
Table 6—Samples grouped based on the cluster analysis on instrumental dataa
Cluster Samples
Cluster 1 (n = 12) 4 (0001), 5 (0110), 7 (0011), 8 (0111), 9 (1000), 10 (1110), 11 (1000), 16 (1111),
20 (2001), 21 (2110), 23 (2011), 24 (2111)
Cluster 2 (n = 8) 6 (0101), 12 (1001), 13 (1110), 14 (1111), 15 (1011), 17 (2000), 18 (2100), 22 (2101)
Cluster 3 (n = 3) 2 (0100), 3 (0010), 19 (2010)
Cluster 4 (n = 2; controls) 25 (JIF), 26 (PP)
Cluster 5 (n = 1) 1 (0000)
aInstrumental data included hardness and cohesiveness, color profile (L, a, and b), and stability (% oil separation).
Table 4—Samples grouped based on the cluster analysis on sensory dataa
Cluster Samples
22 (0100), 7 (0011), 8 (0111), 12 (1001), 13 (1110), 14 (1101),
Cluster 1 (n = 21) 15 (1011), 17 (2000), 20 (2001), 21 (2110), 3 (0010), 4 (0001),
5 (0110), 6 (0101), 9 (1000), 11 (1010), 16 (1111), 18 (2100),
22 (2101), 23 (2011), and 24 (2111)
Cluster 2 (n = 3; controls) 25 (JIF), 26 (PP), 27 (Skippy)
Cluster 3 (n = 1) 1 (0000)
Cluster 4 (n = 1) 10 (1100)
Cluster 5 (n = 1) 19 (2010)
aSensory data included 12 flavor attributes and 12 texture attributes.
S: Sensory & Nutritive Qualities of Food
S370 JOURNAL OF FOOD SCIENCE—Vol. 70, Nr. 6, 2005 URLs and E-mail addresses are active links at www.ift.org
Formulation of a sunflower butter . . .
Although instrumental hardness and adhesion scored closer to the
controls for the low level of stabilizer, the sensory panel through 12
textural attributes rated a formulation having the high level of sta-
bilizer as closer to the control. “Best” formulation as determined by
cluster analysis on the sensory data included 7% sugar, 1.1% salt,
1.8% stabilizer, and a low roast level.
Acknowledgments
The authors would like to thank Michelle Yates for the sensory anal-
ysis, Vicky Lancaster for helping with the experimental design and
statistical analysis, and Joe Kannankeril and Bonnie Dillon who
assisted in sample analysis. The mention of firm names or trade
products does not imply that they are endorsed or recommended
by the U.S. Dept. of Agriculture over other firms or similar products
not mentioned.
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data” means of the other clusters was used as the metric to define the
“best” formulation. The cluster analysis done on the instrumental
analysis identified sample 1 (0000) in cluster 5 as the formulation that
is closest to the controls (now defined within cluster 4) (Table 6). This
sample was formulated at the low level of all treatments, that is, low
stabilizer, sugar, salt, and roast levels. The color profile had little con-
tribution to the “best” sample. This happened for 2 reasons: (1) L, a,
and b values were very consistent throughout all formulations with
the largest difference being lightness, represented by L; (2) to
achieve certain roasting characteristics that can be considered desir-
able in sunflower butter formulations (to provide the desirable aro-
ma and flavor), the level of roast was such to yield darker tones when
compared with the peanut butter controls. Differences in oil separa-
tion between the control cluster and the “best” formulation found
from cluster of instrumental data, were minimal. In contrast to the
color profiles and oil separation, Table 5 illustrates that instrumen-
tal textural properties (both hardness and cohesiveness) were deci-
sive in identifying sample 1 as the “best” formulation. Instrumental
hardness is a good estimate of sample spreadability. Hardness in-
creased consistently with the amount of stabilizer used in the formu-
lation. Because sunflower butter formulations were consistently
harder than peanut butter samples (as judged by both the sensory
panel as well as texture measurements), the “best” formulation was
the one for which the stabilizer was at the low level. Interestingly, the
contrast between “best” formulations obtained by clustering sensory
and clustering instrumental data was in the level of stabilizer. De-
spite “best” by clustering instrumental data being a sample having
a low level of stabilizer, the sensory panel rated “best” a sample hav-
ing high stabilizer. Of interest, sample 1 formed an individual cluster
for both sensory (cluster 3) and instrumental (cluster 5).
Conclusions
The panel found the sunflower butter samples had a similar ap
pearance to that of peanut butter and to have a mild yet dis-
tinctive sunflower seed flavor. Texture and stability testing revealed
that the most suitable stabilizer was found to be a blend of hy-
drogenated cottonseed and rapeseed oils. Level of stabilizer affect-
ed the overall properties of the sunflower butter, as detected by
both sensory and instrumental analysis. The manufacture of sun-
flower butter was optimized, from roasting to ingredient formula-
tion, for sensory and physicochemical parameters, using cluster
analysis, to mimic peanut butter. From cluster analysis, done inde-
pendently on sensory and instrumental data, it was found that the
“best” formulation included 1.8% and 1.6% stabilizer, respectively.
... În Rusia a fost emisă "Concepţia politică privind alimentaţia sănătoasă a populaţiei Federaţiei Ruse pentru perioada până în 2005", din 15 iunie 1996. Astfel, începând cu 1998 şi până în prezent, volumul produselor cu proprietăţi funcţionale a crescut considerabil, practic s-a dublat de la 230,31 mii bucăţi în 1998 până la 400,90 mii bucăţi în 2002 [101,102]. ...
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... Iodul ocupă un loc special în asigurarea funcțiilor vitale ale corpului uman [13,18,50]. Importanța și indispensabilitatea deosebită a iodului constă în formarea hormonilor glandei tiroide, fiind componenta lor structurală [23,69,93,101,146]. ...
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... The first stage of the process involves washing and drying sesame seeds using special machines to remove the sand and other foreign material. Some studies describe the use of different seeds such as those from the sunflower to produce sunflower tahini (Damir, 1984), or sunflower butter (González-Pérez and Vereijken, 2007;Lima and Guraya, 2006). After washing, dried sesame seeds are dehulled and roasted, and these steps represent the basic and most important operations in tahini and halva manufacturing that affect the quality of these products. ...
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Oily, low water activity (OL aw) products including tahini (sesame seed paste), halva (tahini halva), peanut butter, and chocolate, have been recently linked to numerous foodborne illness outbreaks and recalls. This review discusses the ingredients used and processing of OL aw products with a view to provide greater understanding of the routes of their contamination with foodborne pathogens and factors influencing pathogen persistence in these foods. Adequate heat treatment during processing may eliminate bacterial pathogens from OL aw foods; however, post-processing contamination commonly occurs. Once these products are contaminated, their high fat and sugar content can enhance pathogen survival for long periods. The physiological basis and survival mechanisms used by pathogens in these products are comprehensively discussed here. Foodborne outbreaks and recalls linked to OL aw foods are summarized and it was observed that serotypes of Salmonella enterica were the predominant pathogens causing illnesses. Further, intervention strategies available to control foodborne pathogens such as thermal inactivation, use of natural antimicrobials, irradiation and hydrostatic pressure are assessed for their usefulness to achieve pathogen control and enhance the safety of OL aw foods. Sanitation, hygienic design of manufacturing facilities, good hygienic practices, and environmental monitoring of OL aw food industries were also discussed.
... The probe was set to penetrate the samples to a depth of 8 mm and the trigger force was 0.049 N. Hardness was measured as the height of the positive peak (maximum penetration force) generated by the force deformation curve, in Newtons (N). Low values of hardness means greater fluidity or reduced toughness (Lima & Guraya, 2005). ...
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Honey and Nigella sativa (Black Seeds) mixture is well known as a traditional Islamic medicine with its high health benefits to human. This study attempts to determine the physicochemical properties and presence of microorganisms of honey and black seed along 78 days accelerated storage at 55°C that represent two years shelf life. The physical properties of mixtures were obtained using colourimeter, pH meter and texture analyser while antioxidant properties were studied by total phenolic content (TPC) and 1,1-diphenyl-2-picrylhdrazyl (DPPH) scavenging capacity. Microbiological activities were determined using Total Plate Count and Yeast and Mold Count. Results indicated that the colour of sample turns darker, more acidic and harder in texture across 78 days accelerated shelf life. A fluctuation of total phenolic content (353.36–796.09 mg/L) and a gradual increasing in DPPH free radical-scavenging activity (54.2–85.6%) were obtained. Microorganisms were found <1.0 × 10² CFU/g for both Total Plate Count and Yeast and Mold Count on the last day of storage analysis. Overall, honey and black seed mixture can be labelled using “best-before” shelf life dating that can last for at least two years. The mixture still can be consumed after the best before date but might have some losses of quality. © 2018, Malaysian Society of Applied Biology. All rights reserved.
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The study was designed to explore potential of almond skin in improving storage stability of almond paste. A concentration‐dependent increase in phenolic content, and antioxidant potential, was observed for the skin‐fortified samples. Skin fortification at 1.25%, 2.5%, 3.75% & 5% level in pastes resulted in corresponding increase in TPC by 114%, 311%, 445% and 633% compared to control. Lipid oxidation was measured in terms of peroxide value, Thiobarbituric Acid value, and Free Fatty Acids content over a 28 days storage at accelerated storage temperature (60°C). TBA value for the control was higher (0.013 to 0.194 mg malonaldehyde/kg) compared to that of fortified samples with values ranging from 0.011 to 0.183 mg malonaldehyde/kg, indicating role of skin fortification in the prevention of oxidation. Almond skin at 5% effectively inhibited lipid oxidation throughout the entire storage. Therefore, almond skin supplementation could be effective in preventing the oxidation of fat‐rich food products.
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Walnut Butter is a very valuable product in terms of nutrition that comes from walnut. One of the main problems in the production of such products is the separation of the oil phase in the product. In this research, the amount of water activity and physical stability of walnut (in the presence of 0.5, 1, 1.5 and 2% concentrations of monoglycurids, lecithin, spin 80 and tween 20) in 75 days of storage at 25 ° C Reviewed. The results indicated a slight decrease in wold walnut samples containing lecithin, span 80 and monoglyciride until the 15th day, and then showed a roughly constant trend. Lecithin was more effective in reducing the percentage of oil removal during the maintenance period than other emulsifiers, while spin 80 had no significant effect on the stability of walnut emulsion, and tween 20 had a negative effect on the oil removal percentage from walnut during storage.
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ABSTRACTA commercial peanut flour (12% fat) was mixed with water (30% w/w), homogenized and drum-dried in a double drum dryer. The drum clearance was adjusted to result in thin dried sheets which on milling resulted in a very fine, single banded particle size flour. The flour was no longer gritty and was used to dilute fat by mixing with full fat (52.5%) paste to obtain a 30% fat reduction in the peanut butter product. Response surface methodology, RSM, was used to optimize drum temperature (T), speed (S), and clearance (C) in order to minimize stickiness and hardness, maximizing oil separation and particle size. Based on surface responses and contour plots, optimum conditions were: T = 135 °C, S = 1 rpm and C = 0.33 mm. Optimum values predicted by RSM for peanut flour particle size, peanut butter stickiness, hardness, and oil separation were: 49.65 μm, 12347 N, 311.4 N and 6.87% respectively. Close agreement between experimental and predicted values was obtained.
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The nutritional and sensory quality and physical characteristics of commercially and experimentally processed sunflower butters were evaluated. The analyses included: proximate analyses, calories, available lysine, in vitro protein digestibility, C- and DC-PER, phytic acid, a 9-point hedonic test, Gardner color, and spreadability determinations. Sunflower butter was found to have a good overall nutritional value with a protein quality approximately equal to that of peanuts. Roasting conditions had a significant impact on nutritional and sensory quality, color and spreadability of sunflower butter. Taste panelists generally rated sunflower butter lower than peanut butter.
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A lexicon of terms to describe desirable as well as undesirable flavors in peanuts has been developed. The lexicon and an intensity rating scale was developed by a 13 member panel of flavor and peanut specialists representing industry and the USDA-Agricultural Research Service. This system is intended to provide definitive, common terminology for use in communicating differences in peanut flavor variables among all phases of peanut research and industry.
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Studies have shown that palm oil is an effective stabilizer in peanut butter. The objective of our investigation was to better define the role of palm oil as a stabilizer. Peanut butters without and with palm oil added at concentrations of 1.5, 2.0, and 2.5% (w/w of peanuts), and Fix-X™ (hydrogenated rapeseed and cottonseed oils as commercial control) were stored at 0, 21, 30, and 45 °C for 23 wk. Palm oil improved the oil holding capacity (OHC) of peanut butters, but had no effect on their adhesiveness and hardness characteristics. The unstabilized and palm oil-stabilized peanut butters were not as good as the Fix-X™ stabilized peanut butters with regard to their OHC, hardness, and adhesiveness characteristics.
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