Journal of Food Measurement and Characterization (2022) 16:102–113
https://doi.org/10.1007/s11694-021-01138-8
ORIGINAL PAPER
Tender coconut water processing: hurdle approach, quality,
and accelerated shelf‑life measurements
Nikhil Kumar Mahnot1
· Charu Lata Mahanta1
Received: 18 February 2021 / Accepted: 28 August 2021 / Published online: 21 September 2021
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
Abstract
In the current study, we examined the applicability of hurdle technology as a simple cost effective viable approach to
process tender coconut water (TCW). The hurdles included one or more combinations of the following (i) acidification:
ascorbic acid (180 ppm), citric acid (200 ppm), and malic acid (400 ppm); (ii) antimicrobial additives: potassium metabisulphite (≃ 75 ppm ­SO2) and potassium sorbate (700 ppm); (iii) mild heat treatment (50 °C for 20 min), and; (iv) two-stage
microfiltration process (0.8 µm and 0.45 µm filters). Results suggested that two or more hurdles in combination effectively
restricted browning enzymes and microbial growth while maintaining quality during storage. Storage time and temperature
significantly affected quality parameters viz. pH, titratable acidity, and percent transmission/clarity. Accelerated shelf-life
studies revealed that ascorbic acid followed both zero-order and first-order degradation kinetics models amongst different
hurdle combinations. Arrhenius estimations of ascorbic acid levels and rate constant at 27 °C and 5 °C were comparable to
experimental results. Also, shelf-life predictions considering 50% ascorbic acid degradation suggested a shelf-life extension
of 3–12 months under refrigeration, depending on the combination of hurdles used. The results demonstrate the sufficient
utility of combining hurdles to process TCW.
Keywords Tender coconut water · Hurdle technology · Accelerated shelf-life studies · Ascorbic acid · Arrhenius modeling
Introduction
Tender coconut water (TCW) has been receiving considerable attention amongst consumers, food technologists, and
food processors over the years. This is due to its unique
delicate tropical flavor, its nutritional and medicinal value,
and its ever-increasing popularity as a natural rehydration
drink among consumers. The TCW industry is estimated to
grow at the rate of 19.7% during 2019–2025 [1], suggesting
an impressive consumer demand. TCW refers to the sweettasting liquid encased inside 7–10 months old immature
coconut fruit; it is also sometimes referred to as the liquid
endosperm or even green coconut water. TCW is a well-balanced natural ergogenic drink and has a unique composition
of sugars, amino acids, vitamins and indispensable macro
* Charu Lata Mahanta
charu@tezu.ernet.in
Nikhil Kumar Mahnot
nikhil.mahnot@gmail.com
1
Department of Food Engineering and Technology, School
of Engineering, Tezpur University, Tezpur, Assam, India
13
Vol:.(1234567890)
and micro-nutrients, making it a health-benefiting therapeutic drink with excellent rehydration properties [2, 3]. High
perishability, nutritive value, and rehydration properties of
TCW, and high transportation costs of bulky coconuts, and
consumer awareness towards natural juices have fueled the
need for its processing.
Processing of TCW has posed quite a challenge for food
processors owing to its very short shelf-life. Once extracted
and on contact with air, it starts deteriorating, developing
an off taste within a day or two at room or refrigeration
temperature. The major threat for TCW shelf-life is its
nutritive composition which is an excellent medium for
microbial growth; moreover, the presence of enzymes like
polyphenol oxidase (PPO) and peroxidase (PO) contributes
towards fast oxidative deterioration leading to color deterioration [4]. Nonetheless, thermal processing strategies used
commercially are efficient in attaining microbial safety and
increasing the shelf-life of TCW. However, thermal treatment negatively impacts its nutritional value; more importantly causes drastic flavor loss and simultaneous development of undesirable flavors and color [5, 6]. Furthermore,
non-thermal processing techniques have also been utilized
Tender coconut water processing: hurdle approach, quality, and accelerated shelf‑life…
for TCW processing to overcome the ill effects of thermal
strategies. Techniques viz. dense phase carbon dioxide,
microfiltration [7, 8], high pressure processing (HPP) [9],
and more recently cold plasma technology [10, 11] have
been successfully employed to achieve microbial safety for
the preservation of TCW. Certain limitations in their use
exist like longer processing times and membrane fouling for
microfiltration; complex machinery for dense phase carbon
dioxide processing and HPP; and scale-up issues with cold
plasma processing. It is also important to highlight that these
interventions markedly increased the shelf-life of products
under refrigerated storage.
For a processing strategy to be taken up by the juice
industry, processors look for a longer shelf-life of products
to be preferably between 3 and 6 months, to allow for proper
distribution and end-use whilst maintaining process feasibility and costs. The industry also looks for strategies that
minimally impact the fresh-like characteristics of juices as
consumers are more driven towards natural and wholesome
juices and beverages [12] as compared to artificial carbonated drinks. Therefore, a hurdle technology-based approach
was hypothesized to fulfill the industry criteria for shelf-life.
Hurdle technology is widely applied for the production of
safe, nutritious, stable, and economical foods with substantial shelf-life without significantly affecting the fresh-like
characteristics [13, 14]. Also, there is a lot of support for
the use of hurdles in combination with non-thermal processing techniques for the effective processing of heat-sensitive
foods [6, 15, 16].
In the current study, a combination of hurdle approach
and microfiltration as a non-thermal technique was applied
for TCW processing. These combinations were tested and
compared for quality changes on storage. Thereon, preliminary shelf-life predictions of the treatment combinations
were examined by conducting accelerated shelf-life studies (ASLS) employing ascorbic acid degradation kinetics
model.
Materials and methods
Chemicals and glassware
All chemicals used were of analytical grade and obtained
from Merck. The glasswares were obtained from Borosil
Glass Works Limited, India. Media for microbial enumeration were obtained from HiMedia, India. Empty glass bottles
of 200 mL capacity for packaging of TCW were procured
from a local vendor.
103
Procurement and initial preparation
before processing of tender coconut water
The tender coconuts were obtained from a market located
near the Tezpur University campus. On the day of processing, the nuts were dipped in a 300 ppm sodium hypochlorite
solution for 10 min to sanitize and remove dirt, followed by
rinsing in sterile water. The nuts were cracked open with a
sharp stainless steel knife, and the inside liquid (TCW) was
filtered through a Whatman No. 4 filter paper to remove
suspended materials and collected in a sterile glass bottle of
1 L volume. The filtered TCW was further processed immediately. For packaging of TCW, 200 mL glass bottles were
washed with detergent and rinsed with potable water and
made sterile by autoclaving at 15 lb pressure for 15 min.
The bottles came with a lug and a cap (Fig. S1) which were
similarly washed and cleaned and subjected to hot water
washing at 85 ± 2 °C and dried in a hot air oven (JEIO Tech,
Korea) at the same temperature.
Hurdles to process tender coconut water
TCW was processed using various combinations of hurdles along with microfiltration. Briefly, the first hurdle was
the addition of acids viz. malic acid (MA, 400 ppm), citric
acid (CA, 200 ppm), and ascorbic acid (AA, 180 ppm). The
second hurdle was the addition of chemical preservatives
viz. potassium metabisulphite (KMS) and potassium sorbate (KSOR). KMS was added equivalent to 75 ppm sulphur
dioxide and 700 ppm of KSOR was used. The third hurdle
was a mild temperature long time (MTLT) treatment i.e.,
heating at 50 °C for 20 min followed by immediate cooling
to 5 °C using an ice bath. The fourth hurdle was a twostage microfiltration processing; the first microfiltration was
carried out through a 0.8 µm bacteriological filter and the
second through 0.45 µm membrane filter using a vacuum
pump as per previous works [17, 18]. KSOR was added to
microfiltered samples only along with the acids. Thereafter,
100 mL of processed TCW samples were filled in 200 mL
glass bottles and immediately, the lugs were pressed and the
caps were tightened, and the bottles were stored for further
studies. The codes of the variously processed TCW samples
are mentioned in Table 1.
Evaluation of physicochemical properties
The pH of samples was measured using a pH meter (Eutech
pH 700, Singapore). Juice clarity expressed as percent transmission was measured using a spectrophotometer (Cecil
Aquarius 7400, England) at 610 nm following the works of
Jackson et al. [19] with respect to distilled water taken as
13
104
N. K. Mahnot, C. L. Mahanta
Table 1 Processing conditions of tender coconut water studied along with sample codes
Sl. No Combinations
Codes
1
2
3
TCW​
TCW + AA + CA + MA
TCW + AA + CA + MA + KMS
4
5
6
7
8
Tender coconut water
Tender coconut water with added ascorbic acid, citric acid and malic acid
Tender coconut water with ascorbic acid, citric acid, malic acid and potassium metabisulphite
Tender coconut water with ascorbic acid, citric acid, malic acid, potassium metabisulphite and potassium sorbate
Tender coconut water with ascorbic acid, citric acid, malic acid, potassium metabisulphite and mild heating
Tender coconut water with ascorbic acid, citric acid, malic acid, potassium metabisulphite, mild heating and added potassium sorbate
Tender coconut water microfiltered with added ascorbic acid, citric acid, malic acid
and potassium sorbate
Tender coconut water microfiltered with added ascorbic acid, citric acid, malic acid,
mild heating and added potassium sorbate
reference. Titratable acidity (TA) was measured with respect
to malic acid by titrating 5 mL of the samples with 0.1 N
NaOH using phenolphthalein indicator [20]. Ascorbic acid
(AA) estimation was performed following the 2, 6-dichlorophenol-indophenol dye titration method [21].
Browning enzymes activity measurements
Polyphenol oxidase (PPO) and peroxidase (POD) activity
were measured for all the samples as Campos et al. [22].
Briefly, for PPO activity, 5.5 mL of 0.2 M phosphate buffer
(pH 6.0) and 1.5 mL of 0.2 M catechol as the phenolic substrate were added to a test tube and placed in a water bath at
25 °C. After temperature stabilization, 1 mL of the sample
under test was added, and the mixture was vortexed. Absorbance was measured after a one-minute interval in a UV–visible spectrophotometer (Cecil Aquarius 7400, England) at
425 nm. POD activity was measured by adding 7 mL of
0.2 M phosphate buffer (pH 5.5) and 1 mL of the liquid
sample under test in a test tube. The tube was immersed in a
35 °C water bath after which 1.5 mL of 0.05% guaiacol and
0.5 mL of hydrogen peroxide (0.1%) were added, vortexed
and absorbance was read at 470 nm after a one-minute interval. For both PPO and POD, the data were reported as ∆A/
min/mL, where ∆A is the change in absorbance.
Microbial enumeration
Total plate counts, total coliform counts were enumerated
on plate count agar and Hiveg coliform agar media, respectively, and total yeast and mold counts were enumerated on
bromocresol green yeast and mold agar media. For enumeration, 100 µL of a sample was spread-plated directly on each
of the three media Petri plates or after serial dilution (dilution range: ­101–105, using 0.1% peptone water) where direct
13
TCW + AA + CA + MA + KMS + KSOR
TCW + AA + CA + MA + KMS + Heat
TCW + AA + CA + MA + KMS + Heat + KSOR
TCW + MF + AA + CA + MA + KSOR
TCW + MF + AA + CA + MA + Heat + KSOR
spreading was not possible. The plates for total plate count
(TPC) and total coliforms counts (TCC) were incubated at
37 °C for 24 h, while for yeast and mold counts (YMC)
the plates were incubated at 27 °C for 72 h in an incubator (Labtech, India). The visible colonies were counted and
reported as cfu/mL.
Accelerated shelf‑life study (ASLS) using Arrhenius
modelling
Storage and sampling
Accelerated shelf-life studies were carried out for the variously processed samples by storing them at 27 °C, 37 °C,
and 47 °C in incubators (Labtech, India). The high temperatures were selected to accelerate the physicochemical
reactions in the samples. Changes in pH, TA, percent transmission, and AA content were evaluated during the storage
period, along with monitoring of microbial and browning
enzyme activity. For validation of the extrapolation ability of the ASLS model developed, one lot from each of the
processed samples was stored at 5 °C. For ASLS model
development, sampling for analysis was carried out every
10 days till the end of 30 days for all the samples at all
temperatures with some exceptions. The exceptions were
sample TCW and sample TCW + AA + CA + MA. These two
exceptions were made because these samples deteriorated
quickly due to no/minimal use of hurdles. Briefly, TCW and
TCW + AA + CA + MA were sampled on the 0th day, 3rd
day, and 10th day of storage at the three different storage
temperatures used. For validation, another set of the variously processed samples were kept at 5 °C, except for TCW
and TCW + AA + CA + MA. These samples were analyzed
for ascorbic acid content after every 20 days till the end of
60 days.
Tender coconut water processing: hurdle approach, quality, and accelerated shelf‑life…
105
Ascorbic acid (AA) degradation kinetics calculation
Statistical analysis
At first, for kinetic calculation for ascorbic acid loss, the
experimental storage data were plotted against time to obtain
regression equations. Regression equations for both zeroorder and first-order kinetics were obtained using Eq. 1 and
Eq. 2, respectively.
[ ]
[ ]
Ct = −kt + C0
(1)
Single samples were taken at every sampling point for all
the experiments. The experiments were performed in triplicates, and the results were reported as mean ± standard
deviation, and the error bars denoted the standard deviation. For the storage study, a two-way analysis of variance
(ANOVA) was done for individual samples to ascertain the
significance of the effect of temperature and period of storage. Again, the significance of the storage period for each
storage temperature was assessed using Duncan’s multiple
range test (DMRT). For both the statistical analysis, the level
of significance was tested at p ≤ 0.05. The statistical analysis
was carried out using SPSS software (IBM® SPSS® Statistics ver.20).
[ ] [ ] −kt
Ct = C0 e
(2)
­ 0 are AA concentrations (mg AA/100 mL of
where ­Ct and C
the sample) at the time ‘t’ days and zero-days, respectively.
Further, ‘k’ denotes rate losses for AA during storage. The
rates of reaction, whether zero or first order, were selected
based on the regression coefficient (­ R2) that was closer to
1 on fitting Eq. 1 and Eq. 2. Secondly, activation energies
­(Ea) for AA loss for the different samples were calculated
based on the temperature dependence of the rate constants
calculated by the Arrhenius equation as under
( )( )
E
1
+ ln A
ln k = − a
(3)
R
T
where, ­Ea, is the activation energy of each reaction
(­ Jmol−1), R is the universal gas constant (8.314 ­Jmol−1 ­K−1),
T is the absolute temperature (K) and A is the pre-exponential factor ­(day−1).
The third step included the prediction of shelf-life based
on AA degradation. Equation 4 and Eq. 5 were used when
the reaction rates followed zero and first order, respectively.
Tpred =
ΔC
k
(
)/
k
Tpred = ln CF∕C
I
(4)
(5)
where ­Tpred is the time predicted to incur the change, ΔC is
the change in AA concentration, and ­CF/CI is the ratio of
final to initial AA concentration.
Next, based on the Arrhenius equation (Eq. 3), the theoretical rate constants for AA degradation were estimated
for the validation sample groups kept at 5 °C for storage
study and compared with the experimentally derived rate
constant values for the same sample groups. Lastly, shelf-life
predictions were made based on the time required for 50%
degradation of AA to occur. AA is an important nutritional
parameter and being heat-labile it is widely used as a quality
indicator in processing and storage, and thus used commonly
for predicting the shelf-life of juices [23, 24]. Thus, AA
degradation was taken as the criteria to determine product
failure i.e. end of shelf-life.
Results and discussion
Background, safety, and effect of hurdles
TCW samples under study were processed by selecting hurdles following a definite pattern and some important considerations. The first hurdle was adding acids viz. MA, CA, and
AA. Acidulation is a well-recognized processing step and
is recommended as a control step for processing low-acid
foods. These acids were used to achieve a pH value less than
4.6, in order to eliminate the chance of Clostridium botulinum growth. C. botulinum poses serious health hazards on
the consumption of unprocessed and also minimally heated
food products [25]. The pH of TCW was 4.76 ± 0.2, therefore an acidification step was necessary. These particular
acids were selected because MA is a major organic acid
in TCW [3], and CA and AA have been employed widely
as additives for juice processing, particularly for TCW [10,
11, 17, 18, 26] to make pH and flavor adjustments. Moreover, the acids under consideration are naturally present in
foods and are quite safe to use, as the levels used were very
low. KMS and KSOR were used as preservatives to restrict
microbial growth including spoilage-causing bacteria, yeasts
and molds. Antimicrobial effects are well proven for both
KMS and KSOR and have been used for the processing of
juices [27]. Both KMS and KSOR have been given Generally Regarded as Safe (GRAS) status as chemical preservatives when used in accordance with good manufacturing
practices for different beverages by the Food and Drug
Administration (FDA). Further, the levels used in the study
are below the maximal permitted values [28, 29]. Pereria
et al. [30] have also suggested the effective use of both the
preservatives for TCW processing. The next hurdle was
the use of a mild temperature long time treatment (MTLT)
i.e., 50 °C for 20 min. This time–temperature combination
was determined after preliminary lab experiments (data not
13
106
shown). MTLT processing, over the years, has been used
effectively in the processing of various fruit and vegetable
juices. A recent review highlighted the multifarious effects
of MTLT processing like increased juice stability, better
retention of ascorbic acid, preservation of color, inactivation of browning enzymes, and significant microbial reduction up to 6 logs that ensures food safety [31]. Thus, MTLT
offers great promise for TCW processing. The fourth hurdle, a two-stage microfiltration process was utilized to filter
the natural microbiota of TCW. This process has been well
proven to effectively remove microbes reported previously
from our lab [8, 18] and confer a significant shelf-life increment. In microfiltered samples, only potassium sorbate was
added along with the acids. An important point that needs
mention here is that when mild heating was utilized as a
hurdle, KSOR was added immediately after the cooling step
as a precautionary measure as literature reports suggested
that heating AA and salts of sorbate together could form
compounds with mutagenicity and DNA damaging activity
[32, 33].
Changes in pH during storage
The results of pH changes on storage of samples
are given in Table 2. It was observed that except
for samples TCW, TCW + AA + CA + MA, and
TCW + AA + CA + MA + KMS, there was no measurable
pH change even on storage at different temperatures. TCW
and TCW + AA + CA + MA showed variable effects on pH
at different storage temperatures. The variability in the same
sample at different temperature conditions in the current
work could possibly be due to external variable contamination. It is well known that changes in pH of fruits depend
mainly upon microbial action and maturation or ripening.
Although a common notion exists that fermentation causes
a decrease in pH, however, certain proteolytic bacteria like
Bacillus sp. in juices have been shown to cause a subsequent
increase in pH. This is in line with the reports of Rodriguez et al. [34] for stored tomato juice. Statistical analysis
of pH storage data revealed that in samples, namely TCW,
TCW + AA + CA + MA, TCW + AA + CA + MA + KMS and
TCW + MF + AA + CA + MA + Heat + KSOR, both storage
time and temperature significantly (p < 0.05) affected the pH,
while in TCW + AA + CA + MA + KMS + KSOR and TCW
+ AA + CA + MA + KMS + Heat + KSOR only storage time
was a significant factor (p < 0.05).
Changes in percent transmission during storage
Percent transmission refers to the clarity of any juice
sample. A higher clarity or low turbidity corresponds to
higher values of percent transmission or vice versa. The
changes in percent transmission in the samples on storage
13
N. K. Mahnot, C. L. Mahanta
at different temperatures are reported in Table 2. For all
storage temperatures, it was observed that in TCW and
TCW + AA + CA + MA samples, a decrease in percent
transmission was observed, which might be attributed to the
growth of microbes. However, the decrease was less at 47 °C
due to restricted microbial growth owing to non-optimum
growing conditions. Some interesting results were observed
in TCW + AA + CA + MA + KMS. Even though no microbial activity was noted in this sample after processing, yet
a decreasing trend in percent transmission was observed at
all storage temperatures at the end of 30 days: at 27 °C a
slight pinkish turbidity was observed, at 37 °C a brownish
turbidity was observed, while at 47 °C the sample remained
mostly clear. This decrease in transmission can be due to
denaturation of proteins that rendered the proteins insoluble
during storage or might be due to protein-phenol interaction forming insoluble complexes that differentially scattered
light causing turbidity development [35, 36]. The pinking
phenomenon may be due to protein-phenol interaction [37]
and the brown discoloration with time can be attributed to
ascorbic acid degradation [38, 39]. The changes in sample
color concerning different temperatures and time of storage are given in the supplementary file (Fig. S2). Statistical
analysis revealed that both time of storage and temperature of storage were significant factors (p < 0.05) affecting
percent transmission in TCW, TCW + AA + CA + MA,
and TCW + AA + CA + MA + KMS. Only storage time was a significant factor (p < 0.05) for sample
TCW + AA + CA + MA + KMS + KSOR and TCW + AA
+ CA + MA + KMS + Heat. For TCW + AA + CA + MA +
KMS + Heat + KSOR, only storage temperature was found
to be a significant factor (p < 0.05). In samples where microfiltration was an added hurdle, statistical analysis could not
be performed as the samples were 100% clear without any
variation.
Changes in titratable acidity (TA) during storage
The effect of storage time and temperature on the
changes in TA in the samples under study is reported
in Table 2. Statistically, both factors viz. storage time
and storage temperature significantly affected the TA
changes (p < 0.05) in TCW, TCW + AA + CA + MA, and
TCW + AA + CA + MA + KMS. In all other samples, only
time of storage was a significant factor (p < 0.05) affecting
TA, not storage temperature (p > 0.05). The increase in TA
was more in case of TCW and TCW + AA + CA + MA as
compared to the rest of the samples due to microbial growth;
a similar increase in coconut water and other juices has been
reported by different researchers [17, 40]. In samples where
microbial growth was not detected, the increase in TA during storage could be attributed to juice maturation [19] or
a
4.29 ± 0.10 4.13 ± 0.13
a
37 °C
47 °C
47 °C
4.25 ± 0.03a
4.26 ± 0.02a
4.21 ± 0.07a 4.23 ± 0.03a
47 °C
b
4.23 ± 0.01 4.23 ± 0.03
a
37 °C
47 °C
a
a
0.179 ± 0.005
b
0.218 ± 0.015
b
a
0.172 ± 0.010
ab
a
a
0.164 ± 0.002
a
0.160 ± 0.005
a
4.40 ± 0.03c 0.135 ± 0.010a 0.141 ± 0.003a
4.35 ± 0.04b
4.27 ± 0.02a 4.30 ± 0.02ab 4.36 ± 0.05bc
47 °C
4.38 ± 0.06b
4.37 ± 0.01b
4.32 ± 0.02b
4.26 ± 0.03a 4.26 ± 0.03a
4.26 ± 0.03a 4.36 ± 0.02b
4.26 ± 0.03a 4.32 ± 0.03b
27 °C
37 °C
47 °C
4.33 ± 0.02b 0.141 ± 0.008a 0.159 ± 0.001b
4.38 ± 0.03b 0.141 ± 0.008a 0.157 ± 0.005b
4.43 ± 0.04b 0.141 ± 0.008a 0148 ± 0.010ab
4.37 ± 0.02b 0.135 ± 0.010a 0.147 ± 0.005ab
4.30 ± 0.04a
4.27 ± 0.02a 4.27 ± 0.02a
4.38 ± 0.05b 0.135 ± 0.010a 0.147 ± 0.011ab
4.43 ± 0.02 0.160 ± 0.010
b
4.44 ± 0.08 0.160 ± 0.010
b
4.44 ± 0.10b 0.160 ± 0.010a 0.167 ± 0.011a
4.29 ± 0.03a 0.160 ± 0.005a 0.164 ± 0.009ab
4.31 ± 0.07a 0.167 ± 0.005a 0.170 ± 0.007a
4.46 ± 0.11a 0.167 ± 0.005a 0.179 ± 0.001b
4.40 ± 0.01 0.160 ± 0.010
a
4.42 ± 0.09a 0.160 ± 0.010a 0.169 ± 0.010a
4.41 ± 0.07a 0.160 ± 0.010a 0.160 ± 0.010a
4.37 ± 0.08 0.160 ± 0.010
a
4.11 ± 0.09 0.160 ± 0.010
a
–
c
–
–
c
0th
3rd
95.40 ± 2.20a
19.57 ± 4.08
b
39.60 ± 6.50b
80.33 ± 3.02b
12.20 ± 5.00b
24.20 ± 4.89b
98.07 ± 1.50
a
98.07 ± 1.50
a
98.00 ± 1.05
b
58.20 ± 4.02
b
0.200 ± 0.012
c
97.80 ± 1.50
a
95.77 ± 1.55
ab
0.192 ± 0.012b 97.80 ± 1.50a 96.27 ± 1.25a
0.186 ± 0.010c 97.80 ± 1.50a 97.80 ± 1.50a
0.218 ± 0.009
d
0.301 ± 0.025
c
a
0.189 ± 0.009
b
0.179 ± 0.010
b
96.40 ± 1.10
0.161 ± 0.007b
0.160 ± 0.003b
0.181 ± 0.002c 100 ± 0.00
0.179 ± 0.004c 100 ± 0.00
100 ± 0.00
0.186 ± 0.012c 100 ± 0.00
0.186 ± 0.011c 100 ± 0.00
0.167 ± 0.012bc 0.169 ± 0.01c
0.167 ± 0.005b
0.160 ± 0.007b
a
96.40 ± 1.10
a
100 ± 0.00
100 ± 0.00
100 ± 0.00
100 ± 0.00
100 ± 0.00
100 ± 0.00
96.20 ± 1.10
a
96.20 ± 0.90
a
0.179 ± 0.010a 96.40 ± 1.10a 94.20 ± 1.20ab
0.159 ± 0.012bc 0.173 ± 0.009c 100 ± 0.00
0.173 ± 0.007
0.179 ± 0.009
b
0.167 ± 0.012a
99.73 ± 0.10a 96.20 ± 1.10a
0.195 ± 0.006b 99.73 ± 0.10a 98.07 ± 1.50a
0.173 ± 0.009bc 0189 ± 0.01c
0.185 ± 0.004b
96.00 ± 2.94a
94.87 ± 2.43ab
90.93 ± 4.50c
94.00 ± 2.90a
94.27 ± 3.10a
95.53 ± 1.23b
14.07 ± 1.98d
9.07 ± 1.22d
20th
–
–
–
–
–
–
20th
100 ± 0.00
100 ± 0.00
100 ± 0.00
100 ± 0.00
100 ± 0.00
100 ± 0.00
95.80 ± 3.10
a
96.00 ± 2.50a
100 ± 0.00
100 ± 0.00
100 ± 0.00
100 ± 0.00
100 ± 0.00
100 ± 0.00
94.40 ± 1.60a
95.90 ± 2.90a
93.87 ± 1.10ab 91.60 ± 3.60b
94.53 ± 3.10ab 94.40 ± 2.60b
96.50 ± 2.50a
96.20 ± 2.10a
94.40 ± 2.30
ab
95.47 ± 2.10a
94.80 ± 2.40a
97.88 ± 1.14
b
20.60 ± 2.89
c
32.60 ± 2.99c
10th
92.60 ± 2.92a
16.47 ± 4.50b
22.60 ± 3.50c
48.40 ± 3.24c
10.63 ± 3.59c
15.40 ± 2.99c
10th
Percent transmission/clarity (%)
3rd
0.333 ± 0.019c 98.07 ± 1.50a 38.93 ± 4.89b
20th
95.47 ± 2.50a
95.47 ± 2.50
a
95.47 ± 2.50a
95.47 ± 2.50a
95.47 ± 2.50a
95.40 ± 2.50a
0th
0.186 ± 0.009bc 0.192 ± 0.001c 99.73 ± 0.10a 97.60 ± 1.20a
0.186 ± 0.009
bc
0.192 ± 0.014b
0.186 ± 0.008b
0.198 ± 0.011
0.218 ± 0.014
b
0.244 ± 0.014b
10th
0.189 ± 0.005c –
0.231 ± 0.011
0.152 ± 0.01c
0.091 ± 0.015a –
0.205 ± 0.015b –
0.122 ± 0.01b
20th
T Temperature and t time are the significant factors affecting quality parameters based on 2-way ANOVA (p < 0.5), N.A. not applicable. Different letters in superscript (a, b, c…) suggests significant difference based on DMRT (p < 0.05) between columns for a quality parameter
[pH: T & t
TA: only t
Clarity: N.A.]
3rd
4.27 ± 0.02a 4.28 ± 0.02a
b
0th
27 °C
4.42 ± 0.05
4.40 ± 0.15
b
0.192 ± 0.015
b
0.115 ± 0.004a 0.167 ± 0.015b
0.115 ± 0.004
a
0.115 ± 0.004a 0.131 ± 0.009b
0.083 ± 0.005a 0.083 ± 0.004a
0.083 ± 0.005a 0.186 ± 0.006b
10th
TA (%)
3.89 ± 0.15b 0.160 ± 0.010a 0.179 ± 0.012a
20th
–
–
–
3rd
0.083 ± 0.005a 0.102 ± 0.015ab
0th
Quality parameters and storage period (day)
37 °C
TCW + MF + AA + CA + MA + Heat + KSOR
[pH: only t
TA: only t
Clarity: N.A.]
4.23 ± 0.01 4.41 ± 0.04
a
4.23 ± 0.01a 4.25 ± 0.02a
a
27 °C
TCW + MF + AA + CA + MA + KSOR
[pH: only t
TA: only t
Clarity: only T]
4.34 ± 0.07a
4.34 ± 0.09a
4.21 ± 0.07a 4.23 ± 0.03a
4.21 ± 0.07a 4.21 ± 0.04a
27 °C
4.38 ± 0.04
a
37 °C
TCW + AA + CA + MA + KMS + Heat + KSOR
[pH: only t
TA: only t
Clarity: only t]
4.27 ± 0.11 4.35 ± 0.08
a
4.37 ± 0.01a
a
4.38 ± 0.08a
4.27 ± 0.11a 4.35 ± 0.12a
4.27 ± 0.11a 4.36 ± 0.09a
a
27 °C
4.31 ± 0.11
4.13 ± 0.10
a
37 °C
TCW + AA + CA + MA + KMS + Heat
[pH: only t
TA: only t
Clarity: only t]
4.29 ± 0.10 4.30 ± 0.12
a
a
4.29 ± 0.10a 4.28 ± 0.11a
4.10 ± 0.10b
10th
0th
27 °C
4.33 ± 0.11a
4.29 ± 0.11a 4.33 ± 0.09a
47 °C
3rd
6.50 ± 0.12
4.29 ± 0.11
37 °C
4.61 ± 0.10
c
4.92 ± 0.14b
b
a
4.29 ± 0.11a 4.76 ± 0.09b
7.33 ± 0.11b
4.76 ± 0.12a 4.88 ± 0.12a
47 °C
27 °C
–
–
–
6.36 ± 0.13b
20th
4.76 ± 0.12a 4.98 ± 0.11ab 5.01 ± 0.11b
10th
pH
4.76 ± 0.12a 4.83 ± 0.10a
3rd
27 °C
0th
37 °C
Temperature
TCW + AA + CA + MA + KMS + KSOR
[pH: T& t
TA: T & t
Clarity: T & t]
TCW + AA + CA + MA + KMS
[pH: T & t
TA: T & t
Clarity: T& t]
TCW + AA + CA + MA
[pH: T & t
TA: T & t
Clarity: T & t]
TCW​
Samples
Table 2 Changes in quality parameters on storage of the variously combined hurdles with respect to changes in quality parameter
Tender coconut water processing: hurdle approach, quality, and accelerated shelf‑life…
107
13
108
due to degradation of polysaccharides [41]. Thus, an overall
increase in TA content was evident on storage.
Effect of processing and storage
on polyphenoloxidase (PPO) and peroxidase (POD)
PPO and POD are enzymes responsible for brown discoloration in fruits and vegetables. PPO and POD activity
was detected in TCW and TCW + AA + CA + MA only;
therefore, the data for other samples are not shown. From
Fig. 1A–D, it could be observed that the presence of additives like AA and CA lowered PPO and POD enzyme
activity. Both CA and AA were used effectively to restrict
enzymatic browning in sugarcane juice [42], coconut water
[17], and fresh-cut produce [43]. Ali et al. [44] have also
put forward the mechanism of combined inhibitory effects
of AA and CA on PPO and POD enzymes. It was also
observed that the activity of PPO enzyme in TCW and
TCW + AA + CA + MA increased with increasing storage
N. K. Mahnot, C. L. Mahanta
temperature. Sample TCW + AA + CA + MA showed no
POD activity initially but as the time of storage increased
there was an increase in activity, which was lower than TCW
owing to the addition of AA and CA. As for the other samples under study, no PPO and POD activity was observed,
possibly due to combined effects of KMS, mild heat treatment, microfiltration along with organic acids like CA and
AA. Furthermore, acids and mild treatment may have also
caused denaturation of the enzymes [45, 46]. Thus, the hurdle combinations effectively restricted the activity of browning enzymes which is important for a juice product.
Changes in microbial load
FAO has set critical limits for microbes in good quality
TCW [47], specifying that the TPC and TCC should not
exceed 5000 cfu/mL and 10 cfu/mL, respectively. However,
there is no specific limit set for yeast and mold counts to
ascertain quality. Nevertheless, in this study, yeast and mold
Fig. 1 Changes in polyphenol oxidase activity for sample TCW (A) and TCW + AA + CA + MA + KMS (B) and peroxidase activity for sample
TCW (C)and TCW + AA + CA + MA + KMS (D) during storage at 27 °C, 37 °C, and 47 °C
13
109
N.D. not detected or below detection limits. T Temperature and t time are the significant factors affecting microbial parameters based on 2-way ANOVA (p < 0.5). Different letters in superscript
(a, b, c…) for a particular storage temperature suggests significant difference based on DMRT (p < 0.05)
1342 ± ­31b
9910 ± ­31b
N.D
879 ± ­42a
1440 ± ­66a
N.D
N.D
N.D
N.D
509 ± ­23c
111 ± ­8c
N.D
21 ± ­3b
12 ± ­5b
N.D
2 ± ­1a
2 ± ­1a
2 ± ­1a
9765 ± ­45b
10,000 ± ­21b
221 ± ­32b
25 ± ­5a
25 ± ­5a
25 ± ­5a
TCW + AA + CA + MA
[TPC: T & t
YMC:: T & t
TCC: T & t]
27 °C
37 °C
47 °C
15,678 ± ­128c
18,929 ± ­87c
5612 ± ­96c
1210 ± ­22b
1724 ± ­31b
N.D
177 ± ­12a
921 ± ­25a
N.D
N.D
N.D
N.D
17,938 ± ­163c
2500 ± ­190c
N.D
1028 ± ­120b
814 ± ­23b
N.D
28 ± ­3a
28 ± ­3a
28 ± ­3a
151,210 ± ­541c
174,434 ± ­124c
12,098 ± ­76c
6212 ± ­521b
11,432 ± ­1241b
5641 ± ­181b
30 ± ­5a
30 ± ­5a
30 ± ­5a
10th
3rd
27 °C
37 °C
47 °C
TCW​
[TPC: T & t
YMC: T & t
TCC: T & t]
3rd
0th
10th
0th
0th
3rd
Yeast and molds count (cfu/mL)
Microbial loads on storage (day)
Temper-ature
Samples
Table 3 Changes in microbial load on storage
Accelerated shelf‑life study (ASLS) results
for the variously processed samples
Figure 2A–I shows the changes in AA content in all the
samples under study. In all samples, AA levels showed
a decreasing trend with time and temperature of storage.
Higher storage temperature corresponded to greater degradation, as evident from the rate constant values (Table 4).
AA degradation followed first-order reaction kinetics for all
samples except for TCW + AA + CA + MA + KMS + KSOR
and TCW + AA + CA + MA + KMS + Heat + KSOR where
zero-order kinetics was a better fit. It has been well-reviewed
that AA degradation in juices mostly follows first-order
reaction [23, 49]. However, zero-order reaction is not
uncommon as reported for model AA solutions [50] and
orange, grape, and pomegranate juices [51]. This variability
could be due to the degradation mechanism of AA which
is affected by various factors like processing conditions,
soluble oxygen, catalytic metals, and enzymes like ascorbic acid oxidase [52]. Again, applying Arrhenius equation
Total plate counts (cfu/mL)
Total coliform count (cfu/mL)
counts were additionally considered due to acidification
as a processing step as they can grow under acidic conditions. The data for changes in microbial load for only TCW
and TCW + AA + MA + CA are presented in Table 3. No
microbial count could be obtained for rest of the samples
either immediately after processing or during storage study.
Initially, TCW had a TPC of 30 ± 5 cfu/mL and YMC of
28 ± 3 cfu/mL, while the TCC was below the detection limit.
During storage at 27 °C and 37 °C, at the end of 3 days,
the TPC and TCC for TCW and TCW + AA + MA + CA
exceeded the critical limits, rendering them microbiologically unsuitable or they can be said to have deteriorated in quality. However, TCW + AA + MA + CA stored
at 47 °C became microbially unsuitable at the end of the
10th day of storage as TPC exceeded the critical limits.
Also, at 47 °C storage temperature, no YMC and TCC
could be detected till 10 days of storage for both TCW and
TCW + AA + MA + CA, majorly due to the effect of higher
storage temperature. Additionally, on comparing YMC
data, it was observed that the acidification step restricted
the growth of yeast and molds. This is possibly due to the
addition of CA which is known to restrict or control the
growth of yeast and molds. Similar results were observed on
CA addition to sweet potato puree [48]. Statistically, storage period and storage temeperature were significant factors
(p < 0.05) affecting microbial load. Overall, no microbial
colonies including TPC, TCC, and YMC could be detected
during the storage period for the rest of the samples. This
suggested that the various hurdles viz. acidulation, mild heat
treatment, addition of KMS and KSOR, and microfiltration
were effective enough for microbial stability or one can say
to maintain a good quality of the product during storage.
10th
Tender coconut water processing: hurdle approach, quality, and accelerated shelf‑life…
13
110
N. K. Mahnot, C. L. Mahanta
Fig. 2 Changes in ascorbic acid content during storage of samples (A–H) at 27 °C, 37 °C, and 47 °C, Arrhenius plot (ln k vs 1/T) for ascorbic
acid degradation (I), Ascorbic acid degradation of the processed tender coconut water samples stored at 5 °C (J)
13
37.08
59.73
72.58
55.90
55.85
33.33
29.16
45.38
0.999
0.781
0.986
0.798
0.763
0.896
0.802
0.938
0.999
0.954
0.984
0.853
0.956
0.980
0.864
0.887
0.100
0.044
0.021
0.015
0.053
0.018
0.032
0.059
Validation of the Arrhenius model: experimental vs
predicted
‘ko’ & ‘k1’ are rate of reaction for zero order and first order, respectively
TCW​
TCW + AA + CA + MA
TCW + AA + CA + MA + KMS
TCW + AA + CA + MA + KMS + KSOR
TCW + AA + CA + MA + KMS + Heat
TCW + AA + CA + MA + KMS + Heat + KSOR
TCW + MF + AA + CA + MA + KSOR
TCW + MF + AA + CA + MA + Heat + KSOR
0.124
0.220
0.137
0.122
0.231
0.187
0.267
0.325
0.925
0.919
0.962
0.994
0.991
0.965
0.986
0.729
0.162
0.566
0.326
0.250
0.529
0.279
0.403
0.608
0.973
0.885
0.983
0.892
0.881
0.989
0.784
0.887
0.207
0.495
0.504
0.498
0.515
0.433
0.409
0.631
0.966
0.916
0.986
0.905
0.699
0.950
0.729
0.931
0.067
0.012
0.007
0.007
0.014
0.011
0.016
0.022
0.968
0.934
0.962
0.992
0.999
0.963
0.982
0.697
0.170
0.053
0.043
0.045
0.056
0.036
0.033
0.068
R2
k1
R2
k1
R2
ko
ko
ko
R2
37 °C
R2
47 °C
R2
k1
47 °C
37 °C
27 °C
27 °C
111
(Eq. 3) by plotting ln k vs 1/T (Fig. 2J), the ­Ea values of
the variously processed samples were calculated (Table 4).
TCW + MF + AA + CA + MA + KSOR had the least ­Ea value
(29.16 kJ/mol) and TCW + AA + CA + MA + KMS had the
maximum ­Ea value (72.58 kJ/mol). Activation energy could
be an indirect quantitative way to effectively compare the
temperature sensitivity of food products under storage which
has also been shown in the works of Ganje et al. [53] on
accelerated shelf-life testing of tomato paste. Higher ­Ea values for AA imply less temperature sensitivity which points
towards less AA degradation i.e. better stability or longer
retention of AA in samples, in comparison low E
­ a values
tend to point towards a greater sensitivity to temperaturerelated degradation. Thus, from the Arrhenius model, it
could be concluded that the combination of various hurdles
was effective in increasing product stability.
1.87E + 05
3.69E + 08
3.21E + 10
6.63E + 08
9.06E + 07
1.18E + 05
2.12E + 03
2.00E + 06
A ­(day−1)
Ea ­(kJmol−1)
Rate constant for first order kinetics (­ day−1)
Rate constant for zero order kinetics (mg per100 ml
­day−1)
Sample
Table 4 Rate constant values for ascorbic acid degradation of the samples stored at different temperatures and Activation energy (­ Ea) and pre exponential factor (A) calculated based on Arrhenius equation plot (ln k vs 1/T)
Tender coconut water processing: hurdle approach, quality, and accelerated shelf‑life…
The results of ASLS for validation and predictions are given
in Table 5 for both 27 °C and 5 °C storage temperature. The
experimental and predicted values of AA content at the end
of 30 days of storage at 27 °C were compared using Eq. (4)
and (5), respectively for samples following zero and firstorder reaction, as discussed earlier. However, the predictions
were not made for sample TCW and TCW + AA + CA + MA
due to non-compliance with microbial quality (as stated earlier). Further, predictions for time required to attain 50%
AA loss for samples kept at 27 °C and 5 °C were made, as
in the current experiments, > 50% loss resulted in an unacceptable brown color. In our previous work [11], a similar unacceptable brown discoloration was observed above
75% loss and this increase could be due to the variability in
TCW matrix. 50% AA degradation as a mark for the end of
shelf-life has also been applied for orange juice [54]. Using
50% ascorbic acid loss as the criteria, it was observed that
the maximum extended shelf-life of 198 days at 27 °C was
recorded by TCW + AA + CA + MA + KMS. But, the sample
showed turbidity at room temperature, thus making it unacceptable [55]. Considering clarity as a crucial factor, sample
TCW + AA + CA + MA + KMS + Heat + KSOR was the best,
with an estimated shelf-life of 125 days. TCW + MF + AA
+ CA + MA + Heat + KSOR had a shelf-life of 31 days at
27 °C. The rate constants of the sample lots kept at 5 °C
were compared both experimentally using experimental
data (Fig. 2I) and also calculated using Arrhenius equation
(Eq. 3) at 5 °C as reported in Table 5. The rate constants
were more or less comparable thus, validating the model.
Consequently, it could be inferred that Arrhenius equation
could help in the shelf-life evaluation of products. A further
prediction of shelf-life was made at 5 °C and results suggested an extension of shelf-life ranging from 3 to 12 months
13
112
N. K. Mahnot, C. L. Mahanta
Table 5 Comparison of experimental and predicted values for ascorbic acid degradation
Samples
Rate constant
at 27 °C
Experimental vs. predicted ascorbic acid
degradation at the end of 30th day 27 °C
Experimental vs. predicted rate constants
Predicting time
required for 50%
ascorbic acid
degradation to occur
(days)
Experimental value
(mg per 100 mL)
Predicted value (mg
per 100 mL)
Experimental rate
constant at 5 °C
Predicted rate
constant at 5 °C
At 27 °C
At 5 °C
–
TCW​
0.124
–
–
–
–
–
TCW + AA + CA + MA
0.220
–
–
–
–
–
–
TCW + AA + CA + MA + KMS
0.137
16.62
16.36
0.001
7.38 × ­10–4
99.02
939.85
TCW + AA + CA + MA + KMS + KSOR
0.122
16.86
16.59
0.002
0.020
83.34
492.68
TCW + AA + CA + MA + KMS + Heat
0.231
13.01
13.20
0.004
2.9 × ­10–3
49.51
238.86
TCW + AA + CA + MA + KMS + Heat +
KSOR
0.187
14.93
14.54
0.068
0.064
54.36
158.23
TCW + MF + AA + CA + MA + KSOR
0.267
12.76
12.46
0.011
7.03 × ­10–3
38.29
98.59
TCW + MF + AA + CA + MA + Heat + K
SOR
0.325
10.12
10.72
0.007
5.93 × ­10–3
31.48
116.71
for the processed samples under study. Overall, it can be
concluded that low-temperature storage is vital for shelf-life
extension of TCW subjected to various hurdles.
to be done. The current research data suggest that shelf-life
improvement of TCW using different hurdles is very promising and has the scope for commercial processing as against
thermal processing in practice.
Conclusions
Supplementary Information The online version contains supplementary material available at https://d​ oi.o​ rg/1​ 0.1​ 007/s​ 11694-0​ 21-0​ 1138-8.
In this work, we explored the possibility of employing a
combination of hurdles to extend the shelf-life of TCW
along with the use of an accelerated shelf-life testing procedure with AA as a quality marker. In general, TCW subjected to only acidification treatment or acidification with
metabisulphite salt had poor shelf-life on account of microbial growth and turbidity development. In samples, where
other hurdles were applied in addition to acidification and
addition of metabisulphite salt, shelf-life stability could be
effectively extended by 1 to 3 months at room temperature
(27 °C) and 3–12 months at refrigerated storage (5 °C),
depending upon applied hurdle combinations. The hurdles
using organic acids and chemical preservatives with and
without mild heating treatments effectively arrested browning enzymes- PPO and POD, and resisted microbial growth.
Microfiltration was also effective in removing all microbes
and reducing enzyme activity and along with acids and
KSOR increased product stability. The quality parameters
were affected variably with time and temperature of storage as influential factors. The shelf-life prediction model
based on AA degradation kinetics proved to be suitable for
evaluating shelf-life of products at 27 °C and 5 °C. Lowtemperature storage of TCW is necessary for shelf-life
extension; however, hurdles were effective in significantly
extending the shelf-life even at 27 °C i.e., room temperature. This opens up the possibility for small-scale processors
to take up TCW processing. However, further experiments
specifically related to microbial and sensory aspects need
13
Acknowledgements NKM is thankful to the Department of Science
and Technology, Ministry of Science and Technology, New Delhi for
fellowship (DST/INSPIRE Fellowship/2013/482).
Data availability The authors confirm that the relevant data generated
and analyzed during the study are available in the article and its supplementary information files.
Declarations
Conflict of interest The authors declare no conflict of interest.
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