IJA-2016v6n19 - page 9

International Journal of Aquaculture, 2016, Vol.6, No.19, 1
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10
4
solution into the outer part. The dish was then covered immediately with a glass plate and left at room temperature
for 24 hrs. Later, the HCl in the inner part of the conical flask was titrated with 0.025N sodium hydroxide (NaOH)
using 2-3 drops of methyl red indicator. Results were reported in mg, TVB-N/100g of fish (Conway, 1968).
Determination of Trimethylamine (TMA-N)
A slight modification of Conway (1968) Micro-diffusion Method (Okoro et al., 2010) was used by removing 25 g
of the fish flesh, chop and mixing thoroughly with 75 mls of distilled water in a 250 ml beaker. A few drops of 2N
HCl were added to obtain a pH of 5.2 then heating at 70
o
C and cooling to room temperature. The sample was after
cooling filtered into a conical flask using Whatman No. 1 filter paper. Later, 2 mls of 0.025N HCl were transferred
into the micro diffusion dish (central area) using a pipette followed by 2 mls of the extract and 0.5 mls of 35%
formaldehyde with 1 ml of saturated K
2
CO
3
solution into the outer compartment. A glass plate was used to cover
the dish and left at room temperature for 24 hrs. HCl in the inside chamber was then titrated with 0.025N NaOH
using 2-3 drops of methyl red/methylene blue indicator. Results were reported in mg, TMA-N/100g of fish
(Conway, 1968).
2.4 Physico-chemical analysis
Determination of pH
To determine pH of the fish, flesh (muscle) was removed and weighed out 10 g which was homogenized in 50 ml
of distilled water and centrifuged using a Yamato Mag-Mixer Model MH 800 (Yamato Scientific Company
Limited, Japan). The mixture was filtered using Whatman filter paper No.1. A pH meter
(Wissenschaftlich-Technische Werkstätten, West Germany) electrode was then inserted into the homogenate to
measure the pH at ambient temperature after calibration using standard buffers of pH 7 and 4 at 25
o
C (Okoro et al.,
2010).
Statistical analysis
Data were analysed using the statistical software SPSS 15.0 for Windows. A linear regression analysis of sensory
changes against storage time of fish in ice was performed in Microsoft Excel 2010 (Sveinsdóttir et al., 2003).
Means were compared using analysis of variance (ANOVA) and significantly different means were checked using
Duncan’s Multiple-Comparison Test. Means ±SD were reported and considered different at 95% level of
significance. Pearson Correlations between sensory, biochemical and microbiological parameters were also
performed. Principal Components Analysis (PCA) was used to reduce multivariate sensory attributes with full
cross validation in The Unscrambler® X Version 10.1 Statistical Software Package ©2009-2011 (CAMO, Norway)
(Mai et al., 2009).
3 Results
3.1 Sensory evaluation
Fresh Chambo samples were rejected by the sensory panel after 16 days (Figure 2) from day of catch when
highest quality index (QI) demerit scores were recorded. QI for day 16 was 15.5 and 15.7 for day 19. A strong
linear correlation (P < 0.01, R
2
= 0.95) between sensory quality index scores and days of storage in ice of the fish
was observed (Figure 2). Results from the sensory evaluation apparently demonstrated that the appearance of the
gills (colour), mucus; and appearance of eye cornea were the major attributes that the sensory panel observed
noticeable changes in the freshness and quality of the fish during storage (Figure 3). Gill colour, mucus and eye
cornea appearance are ironically, the commonest traditional freshness and quality parameters that consumers
immediately look for before buying fresh fish on the market in Malawi. Changes in gill and eye cornea colour
were more evident after 8 and 4 days respectively in ice storage. Gill colour changed from bright red in freshly
caught fish to pale red then reddish brown at spoilage time. Eye cornea changed from glass clear on day 0 to grey
and even opaque when fish was completely spoiled. Gaping in the prepared fillets was observed from day 12 of
storage in ice and became more pronounced at day 16. Findings in Figure 3 were well supported by the principal
components analysis (PCA) (Figure 4).
1,2,3,4,5,6,7,8 10,11,12,13,14,15,16
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