A model for the prediction of the microbial spoilage of sun-dried tropical fish

Journal of Food Engineering 8 (1988) 47-72

A Model for the Prediction of the Microbial Spoilage of Sun-Dried Tropical Fish P. E. Doe University of Tasmania, GPO Box 252C Hobart, Tasmania, Australia 7001

Endang

Sri Heruwati

Research Institute of Fish Technology, PO Box 30, Palmerah, Jakarta Pusat, Indonesia (Received 9 June 1987; revised versions received 1 March 1988 and 1.5 May 1988; accepted 26 May 1988)

ABSTRACT A mathematical model has been developed which simulates sun-drying of tropical lo w-fat jish species, salted or unsalted prior to drying. Measurements of the growth rates of two species of bacteria and 13 species of moulds isolatedffom partially-dried and dried tropical fish are combined into a three-dimensional figure which shows the relative dominance of the micro-organisms under different combinations of temperature and water activity. Measured bacterial growth rates are used to predict bacteria levels during drying. The predictions are compared with measurements from a series of sun-drying trials on two fish species in Indonesia.

INTRODUCTION Drying is a traditional and widely used method of preserving fish in tropical countries (Waterman, 1976). With adverse climatic conditions or poor preparation and processing, losses of fish due to microbial spoilage, insect infestation and other causes can be high (James, 1984). The traditional sun-drying process is affected by the amount of sunlight, by the ambient air temperature, humidity and speed; and by the size, shape, species and condition of the fish and its salt content. 47 Journal of Food Engineering 0260-8774/88/$03.50 Publishers Ltd, England. Printed in Great Britain

-

0

1988

Elsevier

Science

48

P. E. Doe, E. S. Heruwati

Microbial spoilage is known to be affected by temperature and water activity (Troller & Christian, 1978). The temperature and water activity of fish change during sun-drying. This paper describes the formulation and validation of a mathematical model of the traditional sun-drying process which combines the calculated drying rates for the fish with the measured growth characteristics of the bacteria and moulds implicated in the spoilage of tropical fish. The model provides a means of predicting the onset of spoilage of tropical fish during sun-drying. The effects of air temperature, humidity, wind speed, salt content, etc., on the spoilage rate can readily be determined using the model. The model has been tested against the results of fish salting and drying trials conducted by the authors in Indonesia.

DRYING

CHARACTERISTICS

Jason (1958) found that a two-phase, falling-rate drying period model represented the forced-air drying of non-fatty fish. Jason’s model is based on an analytical solution of equations governing unsteady state diffusion of water through fish muscle with convective evaporation from the surface. The model predicts the time at which the constant-rate drying period ends and falling-rate drying begins. Fat in fish muscle was found to affect the diffusion coefficients (Jason, 1965). Doe (1969) applied Jason’s model to describe the operation of the Torry fish drying kiln. Jason & Peters (1973) extended the model to include the effect of brining the fish prior to drying. Recent experiments on drying tropical fish species (Wuttijumnong, 1987) confirm that a two-term diffusion model fits the measured drying behaviour of medium-fat tropical fish under constant, forced draught conditions. The authors are not aware of previous attempts to model natural- or sun-drying of fish. This may be due to the complications of diurnal insolation, air temperature and humidity variations and an uncontrolled and variable air flow over the fish. Many measurements have been made of sun-drying by traditional methods but, in the reports of these, the data tend to be incomplete as regards details of fillet size, shape, etc., and are therefore of little use in formulating a predictive drying model. In any case, natural drying is so intimately dependent on local climatic conditions that drying models based on constant conditions of air temperature, air speed, etc., may predict drying rates very different from the actual rates.

Microbial spoilage of sun-driedjish

49

The approach adopted is to revert to the fundamental heat and mass transfer equations for calculating drying rates on a step-wise continuous basis using known or anticipated climatic data. By using well-established physical relationships, for example the effect of wind speed on the evaporation rate, much experimental work can be avoided. Some aspects of fish drying are well understood and permit accurate predictions - the relationship between salt content, moisture content and water activity, for example (Doe et al., 1983). Other aspects, such as the effects of skin or fat layers, can be estimated less accurately.

DRYING

MODEL

A computer program ‘SUNDRY’ was written to represent the sun-drying of fish. The program is written in PASCAL and runs on an IBM-PC computer. The variables and constants used in the program fall into two groups - those associated with the fish, and those associated with climatic conditions. The group associated with the fish can be further divided into those which vary with time (fish mass, moisture content and temperature; number of bacteria on fish) and those considered constant during the drying process (fish thickness, surface area). Factors such as the diffusivity of water in fish muscle are known to change with temperature and fat content (Jason, 1958; Jason, 1965). The boundary conditions, such as the heat and mass transfer coefficients at the fish surface, will vary considerably over the surface of the fish. To accommodate this variability, ‘lumped’ values are used, which are arrived at by an overall comparison between predicted and measured drying behaviour (Doe, 1975). Likewise, the thickness of the fish is not constant along its length. For this analysis the thickness of the largest cross-section of the fish is chosen; this represents the shortest diffusion path for the part of the fish which dries most slowly, and presumably spoils first. The fish also shrinks as it dries. This reduces the absolute length of the diffusion path for the water within the fish and thus will tend to accelerate the drying. However, there are other factors such as case-hardening and shrinking of the internal capillaries which have the opposite effect. Hence as a compromise, constant thickness is assumed. The second set of variables describes the climatic conditions. This model assumes that the air temperature, humidity and wind speed are constant during the period the fish is set out for drying. Solar radiation is

50

P. E. Doe, E. S. Heruwati

calculated for a standard atmosphere at tropical latitudes, with diurnal variation. Cloud cover is included on a scale of O-8 oktas, and is assumed to be constant during the day. Complete cloud cover (8 oktas) reduces the solar radiation by half (Groundwater, 1957). Written into the computer program is a function for calculating water activity from salt and moisture contents; a function for calculating the equilibrium moisture content for a given air humidity and salt content; and a function for calculating the solar energy input for a particular time of day (see Appendix). The program also contains procedures for calculating the fish mass and temperature during constant- and falling-rate drying and to test for the end of the constant rate period. Initially it is assumed that the fish is uniformly at a particular moisture content and temperature. Fish not dry after a single day is assumed to be stacked under cover overnight. Depending on the ambient temperature and humidity overnight the fish may gain or lose water. Comparisons between calculated and measured drying behaviour use daily averages of actual measured values of variables (such as air temperature, humidity, wind speed and cloud cover).

SPOILAGE

MODEL

The spoilage envelope The use of a ‘spoilage envelope’ based on microbial growth rates in the prediction and control of the spoilage of tropical fish was suggested by Doe ( 1983). Every micro-organism has a range of temperature and water activity within which it can reproduce and grow, given the necessary nutritional environment. At a particular water activity and temperature, one organism will tend to outgrow its competitors. The spoilage envelope is a three-dimensional figure built up from the growth characteristics of all the micro-organisms likely to be implicated in the spoilage of a particular fish product. Growth rates, at different temperatures, of the bacteria Staphylococcus xylosus and Halobacterium salirwum isolated from Indonesian cured fish were measured using a thermal gradient incubator in liquid media adjusted to different water activities by addition of NaCl (McMeekin et al., 1987). These growth rates were used to calculate the time at which the fish was likely to be spoilt. The point at which any foodstuff is considered spoilt can vary from place to place and according to the tastes and socio-economic status of the consumer. Organoleptic or taste panel tests are widely regarded as

Microbial spoilage of sun-driedfish

51

the definitive means of assessment of food spoilage. For the purposes of this model, however, spoilage is assessed on the basis of bacterial growth. If, for example, it is assumed that the fish are spoilt when bacterial numbers have increased by a factor of lo5 then the time to spoilage can be calculated from the measured growth rates by the expression, t, =

(log, lO”)t, = (16*6)t,,

(1)

where t, is the time to the specified spoilage level in hours and tg is the generation time in hours. In order to predict spoilage by moulds, measurements were made of the germination times and radial growth rates on salt and sugar-based media of 13 fungi isolated from Indonesian dried fish (Pitt and Hocking, 1985; Wheeler et al., 1986). If spoilage by mould is assumed to occur when a colony has grown to a diameter of 2 mm, the time to spoilage from mould can be calculated as follows: t, =(24t,,

Fig. 1.

+

1000/r),

Spoilage envelope - times to spoilage based on measured teristics of certain moulds and bacteria.

(4

growth charac-

52

P. E. Doe, E. S. Heruwati

where t, is the time in hours for germination and growth to 2 mm dia. colonies, t,,,g is mould germination time in days and r is the radial growth rate in pm/h. All the mould growth data were combined into a single mould spoilage envelope by selecting the minimum values of t, for the predominant moulds at each temperature and water activity combination. Figure 1 combines the spoilage data for the moulds and the bacteria into a single, three-dimensional figure. The significance of the figure is that if the fish is held at a particular temperature and water activity for longer than the spoilage time then the fish is likely to be spoiled. Clearly the actual time taken for a fish to spoil because of bacterial growth, given constant conditions of temperature and water activity, will depend on the initial bacterial load; other factors, such as pH and available oxygen, will also have an effect. Because of this, the ‘spoilage envelope’ is more of a conceptual device which illustrates the relative combinations of temperature.and water activity at which different bacteria and moulds are likely to predominate. It can be seen from Fig. 1 that moulds appear to predominate over bacteria below a water activity of around O-87 in the range of temperatures for which mould growth data are available. A variant of the computer program (Doe, 1987) includes a mathematical description of the mould growth envelope. Shelf life predictions from the mould growth data have not been verified by storage trials. Prediction of spoilage The ‘spoilage envelope’ relies on measurements of microbial growth at constant temperature and water activity. During drying the temperature and water activity are continually changing. Bacterial growth rate is found from curves fitted to the measured growth rates of the bacteria likely to predominate at that combination of temperature and water activity; the equations of McMeekin et al. ( 1987) are used. The reciprocal of the generation time is the growth rate of the particular bacterium. The ratio of the time interval used in the finite difference solution of the computer model to the generation time is used to calculate the number of bacteria at the end of that time interval according to the expression N(t+At)=iV(t)2’*“”

(3)

where iV(t) is the number of bacteria at time t, N( t + At) is the number of bacteria at time (t + At), and tg is the generation time.

Microbial spoilage of sun-dried

fish

53

As Fig. 1 illustrates, different bacteria predominate at different temperatures and water activities; Staphylococcus xyZosusis the predominant bacterium over a wide range of water activities above about 085. Below this value, Halobacterium salinarum and moulds grow at a faster rate while the numbers of S. xy1osu.sremain constant (McMeekin, personal communication). This behaviour was simulated by calculating the growth of both organisms and choosing the greater number; when the water activity drops below the point of zero growth rate of S. xylosus the number of bacteria is held constant until it is exceeded by the numbers of H. salinurum. Either measured values of the initial bacterial load are used to start the calculation or an estimation is made based on likely bacterial loads for the particular fish species (Anggawati, 1987). The computer program also requires measured values or estimates of the initial numbers of the bacteria such as S. xyZosu.sand H. sulinarum likely to predominate at reduced water activities. Using these measured or predicted initial bacterial loadings, the fish is assumed to be spoilt when the number of any of the bacterial species exceeds 10*/g.

SUN-DRYING

MEASUREMENTS

Four drying trials were conducted at the Research Institute for Fish Technology, Jakarta, Indonesia, lo-26 March 1987. Details of the trials are given in Tables l-5. The first drying trial used 10 kg (87 fish) of mackerel (Rustrelliger negZectus) not split but with gut and gills removed. Coarse salt was rubbed over the fish, which were placed in layers in a plastic container, with additional salt spread over the layers. Saturated brine was added and the fish were covered by a similar container, weighted to ensure that the fish remained submerged in the brine. Ratio of fish:salt : water was 10 kg: 7 kg: 4 litres. Prior to brining, the fish were washed in a 3% salt solution. One batch (33 fish) was brined for 2 1 h, then dried on horizontal plastic fly screens, supported about O-8 m above the ground; the fish were covered with a second plastic fly screen which prevented flies landing on the fish. The remaining 39 fish were brined for a further 24 h and similarly dried. Readings of fish weight, air temperature, air humidity, and fish temperature and a visual estimate of the cloud cover were made at hourly intervals (45rnin intervals on the first day). Fresh and brined fish were assessed organoleptically using a score sheet (Branch & Vail, 1985), adapted for use in Indonesia. The score sheet rates various

P. E. Doe, E. S. Heruwati

54

Details of Sun-Drying Date: Fish species: Trial number

TABLE 1 Trials March 1987, Jakarta, Indonesia

lo-12 March R. neglectus 1

17-18 March R. neglectus 2

mean 114.9 95.8

SD n.a. n.a.

mean 132.1 110.1

SD n.a. n.a.

mean 115.7 92.1

SD n.a. n.a.

504.0 449.0

SD n.a. n.a.

171” 22.6 46.0

6.9 1.3 1.5

173” 26.1 48.0

7.8 1.7 1.7

171” 22.6 46.0

6.9 1.3 1.5

180” 22.8 150.0

4.7 2.0 6.7

19 March R. neglectus 3

23-27 March K. pelamis 4

Fish weight (g) - whole - gutted Fish size (mm) - length - thickness -width Treatment:

Saturated brine

Three brine concentrations

“Length of body not including caudal fin. hLength of body not including head or tail. SD Standard deviation. n.a. Not available (mean weights were calculated weighed individually).

Unbrined

mean

Four salted one unsalted

from total weight - fish were not

characteristics (e.g. slime on a 4-point scale - 0 points for no slime, 3 points for very slimy), and a 3- or 2-point scale for other characteristics considered less important (for example gill colour and skin appearance). Points are accumulated with a maximum demerit score of 39 points. A demerit score of 24 or more correlates with taste panel rejection based on odour, flavour, and texture (Anggawati, 1987). Dried fish were assessed visually on a 0-9-point scale (Gorczyca, 1985) and tasted. The moisture, ash, salt, protein, fat, pH, total volatile base (TVB-N), trimethylamine (TMA-N), and ammonia contents of fresh, brined and dried fish were measured using standard methods (AOAC, 197 5 ). Samples taken for water activity (a,,) and pH determination were mixed muscle cut from areas near the head, abdomen and tail. For a, measurements, the sample size was 3-4 g; for pH, 15 g of sample were blended in 30 ml of distilled water. The water activity of fresh, brined and dried fish was measured at 25°C using a Nova Sina water activity meter (range O-5-1.0) and sensor (Type BS/ePP) standardised at monthly intervals against saturated solutions of potassium nitrate (a, = O-936), sodium chloride (a, = O-753) and magnesium nitrate (a, = O-529).

Microbial spoilage of sun-dried fish

55

TABLE 2 Results of Drying Trial 1, lo-12 March 1987, Jakarta Organoleptic

Bacteria

assessment: Fresh fish demerit Dried fish (brined 2 1 h) score Dried fish (brined 45 h) score

(total viable count/g

Fresh fish Fish brined Fish brined Fish brined Fish brined

for for for for

19 8.9 8.7

wet weight): Nutrient Agar 2.2 x 10”

21 h 2 1 h and dried 45 h 45 h and dried

MHA“ 0.%8 0.850 0.736 0.794 0,737

< 300 2.3 x lo3 < 300 < 300

1.2 x 104 < 300

Chemical assessment:

Before brining Fish brined 21 h dried 24 h Fish brined 45 h dried 24 h Drying conditions: Air temp. (“C) Air rel. hum. (%) Wind speed (m/s) Cloud (oktas)

Moisture content %wet basis 77.9 58.4 50.8 57.2 49.8 Day 1 Mean (min-max) 32.4 (32-33) 6 1.9 (57-66) 1.2 (0.79-1.6) 3(1-6)

Drying behaviour (fish weight in g): Number of fish Weight before brining Weight after brining Weight after day 1 Weight after day 2 Weight after day 3

Salt content %dry basish 1.4 39.7 36.2 39.3 35.2 Day 2 Mean (min-max) 31.0 (29-32) 68.0 (60-83) 1.2 (0.78-2.1) 5 (2-8)

Fat content %wet basis 2.5 1.5

pH 6.2 6.1

0.9

6.0

Day 3 Mean (min-max) 32.4 (31-34) 61.0 (54-70) 1.1 (1.1-2.0) 5 (2-8)

l-1.. brined 21 h rrsh 33.0 95.8 87.9 75.5

Fish brined 45 h 39.0 95.8 86.7 -

68.5 -

76.2 73.1

“Moderate Halophilic Agar. “g salt/g fat-free, dry solids (%).

Bacteria assessments were as follows: 25 g of fish muscle sample was cut from around the belly flap. The muscle was blended in 225 ml of saline solution 035% w/v. Dilutions were made with saline solution of the same concentration. Dilutions were plated on to Nutrient Agar (NA) for fresh fish and Moderate Halophilic Agar (MHA- 8% NaCl) for

P. E. Doe, E. S. Heruwati

56

TABLE 3 Results of Drying Trial 2, 17- 18 March 1987, Jakarta Organoleptic

assessment: Fresh Dried Dried Dried

fish demerit fish (40% brine) score fish (30% brine) score fish (20% brine) score

Bacteria (total viable count/g wet weight): Nutrient 5.2 x Fresh fish 2.0 x After 2 1 h in 20% brine 1.2 x Dried 2 days (20”/0 brine) 65 x After 2 1 h in 30% brine 6.7 x Dried 2 days (30% brine) 4.3 x After 2 1 h in 40% brine 1.3 x Dried 2 days (40% brine)

22.8 7.7 8.0 6.7 MHA

Agar lOY 104 10’ lo3 lo6 103 105

3.3 5.3 5.0 8.0 4.1 2.5

x x x x x x

a, 0.997 0.940 0.862 0.880 0.821 0.834 0.728

103 104 103 lo3 103 lo3

Chemical assessment:

Before brining After 20% brine dried 2 days After 30% brine dried 2 days After 40% brine dried 2 days

Moisture content ‘/owet basis 77.8 70.1 59.0 68.7 56.2 64.6 55.4

Drying conditions: Air temp. (“C) Air rel. hum. (%) Wind speed (m/s) Cloud (oktas) Drying behaviour (fish weight in g): Fish in 20% brine Number of fish 21 Weight before brining 110.1 Weight after brining 111.1 Weight after day 1 90-o Weight after day 2 76.1

Salt content %dry basis 4.8 25.2 33.4 27.8 32.5 31.9 35.6 Day 1 Mean (mm-max) 32.2 (31-33) 62.1(58-69) 0.68 (0.1-3.6) 5 (l-8) Fish in 30% brine 23 110.1 106.7 87-8 76.5

Fat content %wet basis 0.6 1.5

pH

0.1

61

0.7

6.1

6.0 6.1

Day 2 Mean (min-max) 32.2 (29-34) 62.6 (52-79) O-43 (0.1-0.8) 3 (O-6) Fish in 40% brine 23 110.1 1076 93.3 82.9

salted and dried fish, and incubated for 48 h at 37°C. Numbers of viable colonies, reported in Tables 2-5, are on the basis of 1 g of sample. The second drying trial used 11.1 kg of the same species of mackerel (84 fish), which were gutted and washed as before, then divided into

Microbial spoilage of sun-driedfih TABLE 4 Results of Drying Trial 3,19 March 1987, Jakarta Organoleptic

assessment: Fresh fish demerit Spoilt fish demerit

26 35

Bacteria (total viable count/g wet weight): Nutrient 1.7 x 7.7 x 45 x 1.8~ 1.5 x

Fresh fish Washed then kept indoors Not washed then kept indoors Washed then dried Not washed then dried

Agar 10’ 10h 10” 10’ 10’

OG5

Chemical assessment:

Fish before treatment

Moisture content %wet basis 76.3

Salt content %dry basis 2.2 Outdoors Mean (min-max) 32.6 (31-33) 62.5 (60-69) 0.28 (O-1.0) 5 (4-7)

Drying conditions: Air temp. (“C) Air rel. hum. (%) Wind speed (m/s) Cloud (oktas)

Fat content %wet basis 1.2

pH 6.1

Indoors Mean (min-max) 302 (29-32) 67.0 (61-69) -

Drying behaviour (fish weight in g):

Number of fish Weight before drying Weight after 5.5 h Weight after 12.5 h Weight after 22.3 hb “Fish assessed to be spoilt. bFish held at ambient temperature

Outdoors Unwashed Washed 19 18 94.4 103.2 80.6 89.0 82.1”

75.0”

Indoors Unwashed Washed 18 18 95.0 109.0 91.1 95.0 85.0” 88.9” -

(29°C) overnight were spoilt by morning.

three equal lots which were totally submerged for 22 h at ambient temperature (26-29°C) in brines prepared at concentrations of 0.20, 0.30 and 0.40 kg commercial salt/litre water (20, 30, 40% weight/volume brines). The three lots were sun-dried for 2 days as for the first drying trial; the same instrumentation and measurement methodology as for the first trial were employed. The third drying trial used 9.05 kg (83 fish) of unbrined gutted mackerel, split into two lots; one lot (36 fish) was washed in 3% (w/v) salt brine while the other lot (37 fish) was not washed after gutting (the remaining 10 fish were sent for chemical and bacterial analysis). Half of

P. E. Doe, E. S. Heruwati

58

TABLE 5

Results of Drying Trial 4,23-26 Organoleptic

March 1987, Jakarta

assessment: Fresh fish demerit Dried fish scores - dry salted - saturated brine - 30% brine - 20% brine - unsalted

Bacteria (total viable count/g wet weight): Nutrient Fresh before treatment 4.5 x Fish frozen overnight 2.0 x dried (unbrined) 8.0 x Fish after 20% brine 4.3 x dried (20% brine) 75 x Fish after 30% brine 6.2 x 1.7 x dried (30% brine) Fish after sat. brine 3.6 x dried (sat. brine) 6.7 x Fish after dry salting 1.2 x dried (dry salted) 3.5 x

9.5 6.8 7.4 7-4 7.6 5.5

Agar 104 104 10’ 104 10’ lo3 10” lo3 lo4 105 105

a,

MHA

2.2 x 3.1 x 1.2 x 1.2 x 2.7 x 3.8 x 4.2 x 3.2 x

104 10’ 10’ 104 lo3 10’ lo4 lo5

0.994 1.000 0.922 0.864 0.855 0.810 0.866 0.767 0.772 0.770

Chemical :

Fish before treatment dried 4 days After 20% brine dried 4 days After 30% brine dried 4 days After sat. brine dried 4 days After dry salting dried 4 days Drying conditions: Air temp. (“C) Air rel. hum. (%) Wind speed (m/s) Cloud (oktas)

Moisture content %wet basis 74.2 65.2 68.6 55.2 63.9 54.5 62.1 53.2 50.5 Day 1 30.5 70.2 0.2 3

Drying behaviour (fish weight in g): Salting treatment Dry salted Number of fish 2 Weight before brining 468 Weight after brining 383

Sat. brine 2 460 406

Salt content %dry basis 4.7 3.2 291 26.4 27.2 31.5 37.2 42.3 32.0 37.1

Fat content %wet basis 1.2

Day 2 30.7 69.8 0.2 3

Day 3 30.7 71.5 0.7 3

30% brine 2 500 468

0.7 l-1 1.8 0.5

20% brine 2 458 437

PH 6.2 6.8 6.2 6.0 6.3 6.0 6.0 5.9 6.1 6.0 Day 4 32.4 65.0 1.2 2 Unbrined 2 468 450

59

Microbial spoilage of sun-dried fish TABLE 5 - conrd. Weight Weight Weight Weight

after after after after

day day day day

1 2 3 4

344 313 300 294

357 319 303 294

413 366 348 336

382 328 307 287

388 316 280 248

each lot was dried in the sun as for the previous trials and the other half was placed on fly-wire racks inside a well-ventilated building. All fish were screened from flies. Climatic conditions (inside and outside) were monitored. Chemical, bacteriological and organoleptic assessments were made of the fish before drying. All four treatments were monitored until the fish were organoleptically judged to be spoilt when a further bacteriological assessment was made. The fourth trial used 15 skipjack tuna (Kutsuwonw pahmis) with an average fresh weight of O-504 kg each. Fish were gutted, split and washed in 3% salt solution. Five treatments were used - salting with dry salt and saturated brine as for the first trial; saturated brine; 30% weight/ volume brine; 20% weight/volume brine; and unbrined. The unbrined fish were placed in a freezer at - 20°C overnight whilst the other treatments were in brine so as to minimise bacterial growth prior to drying. All treatments were for 24 h. Fish were sun-dried under fly screens as for the previous trials. Results of drying runs The results of the drying trials are summarised in Tables 2-5. Drying conditions were far from ideal as indicated by the extent of cloud cover during the trials. However, this suited the purpose of the experiment as, in two of the trials, fish spoiled during drying. The numbers of bacteria measured in trial 4 (Table 5) can be explained by reference to the spoilage envelope (Fig. 1). Total plate counts on Nutrient Agar for the unbrined and 20% brined fish increased by more than three orders of magnitude (2.0 x 104-8.0 X lo7 colonies/g and 4.3 x 104-7.5 X lo7 colonies/g respectively) whereas bacterial counts from the more heavily salted fish increased by a factor of around 10 only. During the drying of the unbrined and 20% brined fish the water activity of both treatments remained in excess of O-86 where Fig. 1 shows S. xyZosus to predominate and grow rapidly. However, the water activity of the more heavily salted fish was already below 0.86 before the drying started and resulted in the more slowly growing H. sdinarum predomi-

60

P. E. Doe, E. S. Heruwati

nating and in lower bacterial counts in the dried fish. A similar trend was evident in trial 2 (Table 3) which shows that the bacterial count for the 20% brined fish increased from 2-O x lo4 to 1.2 x lo7 colonies/g during drying whereas the bacterial count from the fish salted in 40% brine increased from 4.3 x lo3 to 1.3 X lo5 colonies/g.

MODEL

FITTING

AND VERIFICATION

The results from drying trial 1 were used to determine three quantities in the computer model of sun-drying, ‘SUNDRY’, namely the diffusion coefficient (D,), the surface heat transfer coefficient (h,), and the surface mass transfer coefficient (h,). All other data used in the computer simulation were averages of the recorded climatic conditions, fish dimensions and measured salt content. The ‘lumped values of D,, h, and h, were chosen by trial and error as follows: (1) h, was selected so that the calculated fish temperatures matched as closely as possible the measured fish temperatures for drying in trial 1; (2) h, was selected so that the calculated drying rate matched as closely as possible the drying rate measured during the constant rate period of drying in trial 1, and (3) D, was selected so that the calculated drying rate matched as closely as possible the measured drying rate during the falling rate period of drying in trial 1. Values thus determined

were:

Heat transfer coefficient h, = 10 W/m2 “C, Mass transfer coefficient h, = 0.005 kg/m* s. The diffusivity of water, D,, in fish muscle was calculated Arrhenius-type equation: De = D, exp( - E/RT ),

using an (4)

where D, is independent of temperature, and E and R are respectively the activation energy and the gas constant; T is the temperature in Kelvin. In order to accommodate the effect on drying rate of the range of salt contents in the fish dried during trial 4 it was necessary to allow Do to vary with the salt content. The value of Do used ranged from 8 X 10e5 to 10 x lo-* m2/s for salt contents between 0.66 and 0.05 (fat-free, salt-

Microbial spoilage of surt-driedjish

61

free, dry basis), respectively; this is consistent with Jason & Peters (1973) who found that increasing the salt content reduced the drying rate for fish muscle. The effect of fat on diffusivity could not be evaluated from the drying trials as the fish used were low in fat content. Built in to the computer program is an expression for diffusion coefficient as a function of fat content following Jason ( 1965). The fit of the computer simulation to the measured drying data for trials 1 and 4 is shown in Figs 2 and 5. Figures 3 and 4 show the compmter predictions and measured data for the other two trials. It can be seen that the computer model gives a reasonable fit to trial 3, but predicts a much slower drying rate than measured for trial 2. The reason for this is not known.

10 I-

12

14

DAY1

10

12 DAY2

TIME

Fig. 2.

16

1 IOF DAY (hours)

14

16 Li

Measured (0) and model-predicted drying behaviour of mackerel (R. negfectus) - Trial 1.

62

P. E. Doe, E. S. Heruwati

20% brine

30% br ine

*

L

0

\_...\

O”o 0 40% brine

\ 0

0

o Measured 10

12

fish

14

weight

16

DAY1 I-

Fig. 3.

Measured

10

12

14

16

DAY2 -I IJJME OF DAY

-I (hours)

( 0 ) and model predicted drying behaviour tus) - Trial 2.

of mackerel (R. neglec-

Table 6 gives the predicted and measured water activities and bacterial counts at the end of each drying trial, using measured initial bacterial numbers and measured bacterial growth rates.

DISCUSSION

OF RESULTS

The model of drying and spoilage has been verified for a limited range of climatic conditions, fish sizes and species. The computer program will be made available to fisheries research institutions in the hope that it will be modified to include the results of drying trials of fish sizes and species, and other drying methods.

Microbial spoilage of sun-dried&h

63

unwashed

\ 95 -

5 2 ;

go-

u_ 0

2

85-

2 80 -

75_

o Measured I

10

I

II

fish I

I

I

I

12

13

14

75

TJME OF DAY

Fig. 4.

Measured

0

weight

16

(hours)

( 0) and model-predicted drying behaviour tus) - Trial 3.

of mackerel (R. neglec-

There are too many variables to illustrate all the results of the computer model. In presenting the results, the authors are guided by the practicalities of using the model to effect possible improvements in the traditional sun-drying process. It is not considered practical to control the ambient conditions (air temperature, humidity, wind speed, insolation). However, changes to the salt content are possible and Fig. 6 shows the computed minimum values of salt content needed to avoid spoilage of split and unsplit chub mackerel under different climatic conditions. It can be seen that the salt content has a greater effect on controlling spoilage than air temperature, humidity or cloud cover. When the program is validated for other fish species, fish thicknesses, effects of fat and wind speed, etc., it may be possible to recommend other changes which will have the effect of minimising spoilage, given a particular set of ambient conditions. Table 6 shows that the predicted bacterial counts at the end of drying in most cases were greater than those measured. The exceptions were the 40% brine treatment in trial 2 and the 20% brine treatment in trial 4. In

64

P. E. Doe, E. S. Heruwati

45

35

. . ..__...__.._

11

....._ LJl-l___

11 13 1s

15

k-4

L-2

9

11 13 1s

L._IDAY

3

9

II 13 1s

MDAY

4

JfME OF DAY (hours) Fig. 5.

Measured

( 0)

and

model-predicted drying pelamis) - Trial 4.

behaviour

of skipjack

(K.

the former, the low predicted count on Nutrient Agar (NA) reflects the short time available for the growth of the moderate halophiles (e.g. S. xylosus) before the water activity dropped to below 0.85 when the extreme halophiles (e.g. H. saZinawm) are expected to dominate. In the latter, the water activity had not dropped below O-85 for sufficiently of halophiles The unbrined able midway

in trial 4 became organoleptically the second day of This coincided level of bacteria reached initial bacteria causing may have been lower than the measured initial bacteria drying. is supported levels of trirnethylamine base (TVB) in the unbrined TMA before drying was 12.3 mg-N/100 g in the salted treatments. treatment g

0.86 0.82 0.73

1.00 1.00

1.00 0.86 0.81 0.77 0.77

2 brine brine brine

Trial 20% 30% 40%

Trial 3 Unwashed Washed

Trial 4 Unbrined 20% brine 30% brine Sat. brine Dry salted

NA = Nutrient Agar. MHA= Moderate Halophilic Agar. nc. = not calculated. n.m. = not measured.

0.74 0.74

Trial 1 Brined 21 h Brined 45 h

Measured

0.99 0.82 O-80 0.74 0.74

0.99 0.99

0.89 O-86 O-82

0.74 0.74

Predicted

a, after drying

2.0 4.3 6.3 3.6 1.2

x x x x x

104 104 lo3 lo3 105

4.5 x 10” 7.7 x 10”

2.0 x 104 6.5 x lo3 4.3 x 103

2.2 x 10” 2.2 x 105

NA

1.2 x 10’ 2,7 x lo3 4.2 x lo4

2.2Ynio4

nm. nm.

3.3 x 10” 5.0 x 103 4.1 x 103

< 300 < 300

MHA

Before drying

10’ 10” lo4 104

x 10’ 7.5 x 1.7 x 6.7 x 3.5 x

8.0

1.5 x 10’ 1.8 x 10’

1.2 x 10’ 6.7 x 10” 1.3 x 105

1.2 x 104 < 300

Measured

NA

> > 5.4 l-6 2.1

10” 10’2 x 10’ x lo6 x 10’

3.8 x 10’ 6.5 x 10’

> 10’” > 10’” 3.9 x 104

2.2 x 105 2.2 x 105

Predicted

After drying

Number of viable bacteria/g offish muscle

TABLE 6 Measured and Predicted Water Activities and Bacterial Numbers at the End of Drying

1.2 x 103 2.7 x lo3 4.2 x 10”

2.;;(“;04

n.m. n.m.

5.3 x 104 8.0 x 103 2.5 x lo3

2.3 x lo3 < 300

Measured

MHA

g.3Y;o3 1.3 x 105 1.6 x lo6 2.1 x 105

n.c. n.c.

> 10’0 > 1O’O 7.9 x 10s

5.7 x 103 3.5 x 103

Predicted

66

P. E. Doe, E. S. Heruwati whole

HUMIDITY CLOUD

-x

fish

(X)

55

65

70

80

coktas)

0

3

6

8

55

Fish

-I

65 70

80

036

8

I

I

I

I

I

I

I

I

I

3

4

5

6

7

8

9

10

11

SALT

Fig. 6.

split

I-

CONTENT

(X wet

basIsI

Predicted minimum salt contents required to avoid spoilage of split and unsplit chub mackerel under different drying conditions.

compared with l-4 to 3.14 mg-N/100 g in the salted treatments. The unbrined fish had an initial TVB level of 1852 mg-N/100 g which was not significantly different from the salted treatments; at the end of drying the TVB level had risen to 159.33 mg-N/100 g compared with 39 to 86 mg-N/100 g in the salted treatments. TMA is the metabolic end product from the psychrotrophic bacteria; the mesophiles use a different metabolic pathway. The reason for the predictions of higher bacterial levels than measured can be attributed to a number of factors not included in the mathematical model. Sunlight inhibits the growth rate of bacteria (McCambridge & McMeekin, 1981); the bacteria may generate more rapidly in liquid media than in whole fish, metabolic products can limit the access of bacteria to the fish protein substrate. There is also evidence that bacteria grow less well in a regime of changing temperature (H. H. HUSS,personal communication).

CONCLUSION Developing a computer simulation of the traditional sun-drying of fish has brought together models of the drying process, the sorption isotherms for salted fish and bacterial growth. The computer simulation of sun-drying and spoilage of salted fish is credible considering the limitations of the model and the variability of the experiment.

Microbial spoilage of sun-dried fish

67

The results underline the importance of salt content in the control of bacterial spoilage. There are some gaps in the picture which need further study, for example: the effect of stmlight on bacterial growth in this particular context; the correlation between bacterial growth rates in liquid media and bacterial growth rates on whole fish; the effect on bacterial growth rate of changing temperature and water activity regimes changing with time; the effect of the differences in moisture and salt content in different parts of the fish. The model can be extended to apply to storage where it is expected that the role of moulds will be of prime importance. It may also be possible to extend the model to the effects of insects during storage if their dependence on water activity and their salt tolerances can be established. It is hoped that the model will be used to determine if fish can be dried by the traditional sun-drying method or whether changes are necessary, either in the preparation of the fish or in the method of drying. The model may assist in answering the questions as to when or where mechanical drying should replace sun-drying. Perhaps the most benefit to be obtained from the model is by using the computer program in an interactive ‘WHAT IF?’ mode. By trying out different combinations of fish size, thickness, salt content etc., the user of the model gains a feel for the process and an understanding of the effects of changes in the various factors involved. ACKNOWLEDGEMENTS This work has resulted from a 3-year cooperative research programme supported by the Agency for Agricultural Research and Development, Indonesia and the Australian Centre for International Agricultural Research. The authors would like to record their appreciation of assistance rendered by J. N. Olley, J. I. Pitt and staff at CSIRO, T. A. McMeekin and assistants at the University of Tasmania, and Sumpeno Putro and staff of the Research Institute for Fish Technology, Jakarta. REFERENCES AOAC ( 1975). 0jjkial Metho& of Analysis, 12th edn. Association of Official Analytical Chemists, Washington, DC. Anggawati, A. M. ( 1987). Effect of storage methods on keeping quality of milkfish ( Chanos chanos). ASEAN Food Journal, 3,60-5. Branch, A. C. & Vail, A. M. A. (1985). Bringing fish inspection into the computer age. Food Technol. in Australia, 37,352-5.

P. E. Doe, E. S. Heruwati

68

Doe, P. E. (1969). A mathematical Technol., 4,319-38.

model of the Torry fish drying kiln. J. Food

Doe, P. E. (1975). Mathematical modelling of food operations and its implications for food quality. In Proceedings, 6th European Symposium - Food: Engineering

&Food

Quality European

Federation of Chemical Engineering,

Cambridge, UK, pp. 207-14. Doe, P. E., Curran, C. A. & Poulter, R. G. ( 1983). Determination of the water activity and shelf life of dried fish products. In The Production and Storage of Dried Fish, ed. D. James. FAO Fish. Rep. (279), Suppl. 265, pp. 113-20. Doe, P. E. (1983). Spoilage of dried fish - the need for more data on water activity and temperature effects on spoilage organisms. In The Production and Storage of Dried Fish, ed. D. James. FAO Fish. Rep. (279), Suppl. 265, pp. 209-15. Doe, P. E. ( 1985). An integral approach to fish spoilage. In Proceedings of the ASEAN Food Conference ‘85, Philippines, ed. E. F. Alabastro, T. P. Acevedo & L. L. Chavez, pp. 150-62. Doe, P. E. (1987). Drying and storage of tropical fish - a model for the prediction of microbial spoilage. In Proceedings of the International Symposium on the Preservation of Foods in Tropical Regions by Control of the Internal Aqueous Environment, Malaysian Institute of Food Technology, ISOPOW/

IUFoST, Universiti Sains Malaysia, Penang, Malaysia, 21-24 September 1987. Gorczyca, E. (1985). Report of research sponsored by the Agency for Agricultural Research and Development (AARD) and Australian Centre for International Agricultural Research (ACIAR) Collaborative Project No. 8304. Royal Melbourne Institute of Technology (unpublished). Groundwater, I. S. (1957). Solar Radiation in Air Conditioning. C. Lockwood, London. James, D. (1984). The future for fish in nutrition. Znfofish Market. Digest., 4, 41-4.

Jason, A. C. (1958). A study of evaporation and diffusion processes in the drying of fish muscle. In Fundamental Aspects of the Dehydration of Foodstuffs. Society of Chemical Industry, London, pp. 103-3 5. Jason, A. C. (1965). Effects of fat content on diffusion of water in fish muscle. J. Sci. FoodAgric.,

16,281-8.

Jason, A. C. & Peters, G. R. ( 1973). Analysis of bimodal diffusion of water in fish muscle. J. Phys. D.: Appl. Phys., 6,5 12-21. McCambridge, J. & McMeekin, T. A. (1981). Effect of solar radiation and predacious microorganisms on survival of fecal and other bacteria. Appl. Environ. Microbial., 41, 1083-7.

McMeekin, T. A., Chandler, R. E., Doe, P. E., Garland, C. D., Olley, J. N., Putro, S. & Ratkowsky, D. A. (1987). Model for combined effect of temperature, salt concentration/water activity on the growth rate of Staphylococcus xylosus. J. Appl. Bacterial., 62,543-50.

Pitt, J. I. & Hocking, A. D. (1985). New species of fungi from Indonesian dried fish. Mycotaxon, 22,197-208. Troller, J. A. & Christian, J. H. B. (eds) (1978). Water Activity and Food. Academic Press, New York. Waterman, J. J. (1976). The production of dried fish. FAO Fish. Tech. Pap. (16).

69

Microbial spoilage of sun-driedfish

Wheeler, K. A., Hocking, A. D., Pitt, J. I. & Anggawati, A. M. (1986). Fungi associated with Indonesian dried fish. Food Microbiology, 3,35 l-7. Wuttijumnong, P. (1987). Studies on moisture sorption isotherms, salting kinetics and drying behaviour of fish. PhD thesis, Department of Food Science and Technology, University of New South Wales, Sydney, Australia.

APPENDIX: USED IN THE PROGRAM

ALGORITHMS

FUNCTION

‘SUNDRY’

Wsat(Temp:REAL):REAL;

(Moisture

content

of

air

saturated

at

temperature

Temp)

BEGIN Wsat:=0.001texp(0.063tTemp+1.41); END:

FUNCTION

Aw(s,md:REAL)

(Calculates (both VAR

water

expressed

Awn,Awo

BEGIN

IF

IF

on

from

salt

a salt-free,

and

fat-free

moisture dry

contents)

basis)

:REAL;

(s
OR

(s>=O.O75)

THEN

:REAL;

activity

(md
AND

THEN

Awn:=(1.007-0.684tsl

IF

s>O.36

THEN

IF

s
IF

(l/md
IF

(l/md

ELSE

Awn:=l.OO;

AND

< 2.5)

ERROR',s,md);

ELSE

Awn:=0.75

THEN

WRITELN('Aw

(s<=0.36)

(l/md>=2.5) THEN

THEN

Awe:=

0.99

Awo:=(1.160-O.O66/md)

ELSE

ELSE

Awo:=5tMD;

Aw:=AwotAwn; END;

FUNCTION

md(aw,TT:real)

:reaI;

(Calculates

moisture

content(dry

temperature

and

content)

var

ma,s,mx,a,b,c,mdd

BEGIN

IF aw>l.O

IF

aw

>=0.5

IF au>0 s:= IF

salt

writeln('error1

ma:=0.066/(1.160-aw)

ma:=aw/5.0

ELSE

s<=

0.075 IF

BEGIN

aw

THEN

mdd:=

ma

> 0.5

THEN

a:=

ELSE

1.1681-au;

b:=0.7934*sdt0.0665; c:=O.O451tsd: mx:=(btsqrt(btb_4*a*c) END

ELSE

mx:=

in md')

water

activity,

)/2/a;

(awt3.42*sd)/5.035;

ELSE

ELSE

writeln('Error2

sd/ma;

BEGIN

from

:real;

THEN THEN

THEN

basis)

in md');

70

P. E. Doe, E. S. Heruwati IF s<=O.36 then BEGIN IF mx<=0.4 THEN mdd:=mx ELSE mdd:= 0.684*sd/(1.007-au); END ELSE (if S>O.36) BEGIN IF aw<=0.375 THEN mdd:=aw/3.75 ELSE (if aw > ,375) BEGIN IF aw <= 0.75 THEN mdd:=0.066/(1.160-1.33*aw) ELSE {if aw >0.75) mdd:= END;

0.684tsd/(1.007-aw);

END; END; md:=mdd*exp(0.8*ln(298/(273+TT)jj; END;

FUNCTION qi(t:REAL):REAL; (Calculates heat transfer VAR tsol,qit : REAL; BEGIN

to fish from solar radiation)

tsol:= time-6' {hours) IF tsol<=6 THEN qit:= (l.O-oktas/16)*(222*tsol - 320*exp(1.2*(tsol-6))) ELSE IF tsol < 12 THEN qit:= (l.O-oktas/16)*(-222:(tsol-12) - 320texp(-1.2:(tsol-6))) ELSE qit:= 0; IF night q TRUE THEN qi:= 0 ELSE qi:=0.4tqit; (watts/sq.m.) IF sundry = FALSE THEN qi:=O; END; FUNCTION Bacnum(Tf,Aw,N:real):real; (Calculates bacterial growth rate hence number of bacteria given fish temperature (Tf), water activity (Au) and present number of bacteria (N)) VAR rootr: REAL; BEGIN BEGIN Staph:= TRUE: IF (Aw < 0.98) AND NOT staph THEN N:= Nmhao; Em; (Nmhao is the initial number of moderately halophilic bacteria) BEGIN halo:=TRUE; IF (Aw < 0.85) A&D NOT halo THEN N:=300. END; (Initial number of extreme halophiles arbitrarily'set at 300/g) IF Au >= 0.70 THEN rootr := O.O0884tsqrt(Aw-0.701*tTf-8.34) ELSE rootr := 0.0; IF Aw >= 0.78 THEN rootr := O.O025t(Tf-8.34); IF Aw >= 0.85 THEN rootr := 0.0205*sqrt(Aw-0.838)t(Tf-2.74)t(l exp(0.408*lTf-42.4)));

71

Microbial spoilage of sun-dried fish Au >= rootr := 0.0025*(Tft6.21; IF Tf > 42.4 THEN rootr := 0; Num:= Ntexp(0.6931tdelttrootrtrootr/60); Bacnum:= num; IF Num > 1.0612 then Bacnum := l.OE12; END;

PROCEDURE ENDCRP; (Checks for end of constant rate drying) VAR n zinteger; term,fac,sum,Dl,ecalc,mde,mdo,Ce,Co,c,cc :real; BEGIN term:=lOO; n:=O; c:=d/2; cc:=c*c; Dl:=DO*exp(-3620/(TfOt273)); D1:=D1/(1t1.OE8*Dlt(fc*lOO.O-l.O)); fat:=-9,8696tDltt/cc; sum:=O; WHILE abs(term)>O.OOOOl do BEGIN n:=ntl; term:=exp(n*n*fac)/n/n; sum:=sumtterm; END: mdo:=mco/(l-mco-sco-fc); mde:=,md(rh,Ta); Ce:=1000*mde/(ltmdetsdtfd); (Ce is equilibrium moisture concentration at humidity) (of ambient air) Co:= 1000tmdo/(ltmdotsdtfd); (density of fish before drying assumed to be1 (close to 1000 kg/cu.m.) ecalc:= A*DlS(Co - Ce)/c/(Dl*t/cct0.33333-0.20264tsum); IF ecalc <= e THEN BEGIN FRP:= TRUE; MC =' ,M:4:3); writeln(lst,'end of crp.. tc=',t:6:1,' tc*=t* . 8 END; END;

PROCEDURE Constant-rate-drying; (Calculates mass of fish during constant rate drying) VAR Wa :REAL; BEGIN Wa:= rhtWsat(Ta1; (Absolute humidity of ambient

air )

e:=2thm*(Wsat(TfO) - Wa)*A; (kg/secl Tfl:=(2thttAt(Ta-TfO) + qi(t)*A - e*L*(l-O.O00983*TfO)) tdelt/MO/Cp t TfO; Ml := -e*delt t MO; mdt:=Ml/Mb -1-sd-fd; Mtc:=Ml; END;

I? E. Doe, E. S. Henrwuti

72

PROCEDURE FaIling_rate_drying; (Calculates mass of fish during falling rate drying) :REAL; VAR Dt,Tau,mce,Kd BEGIN mde:=md(rh,Ta); Me:= Mb@ll+mde+sd+fd); Di_ := DOtexp(-3620/(Tf0+273)); Dt := Dt/~l+l.OE8*Dt*~fc*lOO.O-l,O)); Tau:= O.IBtdtd/Dt; Ml := IMtc-Me)*exp(-(t-tc)/Tau) 4 Me; e := IMtc-fie)/Tautewp(-(t-tc)/Teu): Tfl:=12*htrAi(Ta-TfO) + qi(tI*A -eaLltdelt/MQ/Cp mdt:=Ml/Mb -1 -sd-fd; END;

+ TfQ;