Acknowledgement

1
Construction of a bitterness prediction model
with an electronic tongue and a trained sensory
panel for the assessment of dairy hydrolysates

J. Newman, N. Harbourne, D. ORiordan, J.C.
Jacquier, M. OSullivan. Email
Jessica.Newman_at_ucd.ie Food for Health Ireland,
UCD School of Agriculture, (Institute of Food and
Health) University College Dublin, Belfield, D4
Experiment 1 The PCAs shown in Fig. 2 and 3
demonstrates the e-tongues excellent ability to
discriminate between basic tastes and types of
bitter agents with resulting discrimination
indexes of 93 and 89, respectively. It should
also be noted in Fig. 2 that the compounds were
grouped by basic tastes e.g. citric acid and
tartaric acid, sour. Experiment
2 Experiment 3 Fig. 5A and B show
strong correlation between the bitterness values
assigned to both whey and casein hydrolysates by
a trained sensory panel and those predicted by
the e-tongue, with resultant R2 values of 0.907
0.829 respectively. The robustness of each model
was tested using randomly selected hydrolysate
samples as unknowns, these are denoted on the
graphs as red data points. The predicted values
and actual sensory scores are shown in Table 1.
Fig. 5C. is the combination of all the dairy
protein hydrolysates into one PLS. There is a
strong correlation between bitterness scores as
rated by the sensory panel and by the e-tongue
with a R20.8422. The bitterness of one whey
sample and one casein sample was predicted by the
e-tongue, again shown as red data points on
Fig.5C., the values of which are also shown in
Table 1.
Dairy manufacture is one of the Republic of
Irelands most important industrial sectors in
2011 over 5,400 million litres of milk was
produced and total dairy exports accounted for
2.67 billion (Teagasc 2012). The potential of
dairy protein hydrolysates to become part of that
market as functional ingredients is increasingly
being researched. They have numerous improved
characteristics over un-hydrolysed dairy proteins
e.g. improved gelation and foaming abilities in
food systems and enhanced nutritional properties
in the form of bioactive peptides. However, the
hydrolysis process can produce bitter off-tastes
in dairy proteins, limiting their potential use
as food ingredients. This is a result of the
alteration of the native proteins to short
chained peptides with exposed hydrophobic amino
acids (Ney, 1979). Therefore, it is necessary to
screen dairy protein hydrolysates according to
their sensory character prior to application in
food products To date the main method
for the evaluation of taste has been the trained
sensory panel, but in recent years, a number of
electrochemical devices or electronic tongues
(e-tongues) have been developed as an alternative
method. The benefits of using such a tool over a
trained sensory panel are that it is rapid,
reliable and does not suffer from sensory
fatigue. The advantage of the e-tongue is that it
is less time consuming than a sensory panel and
can screen potentially toxic or unpleasant
samples. However, it is a relatively new
technology and its reliability and accuracy needs
to be established. The e-tongue assessed in this
study was the a-Astree e-tongue (Alpha M.O.S.,
Toulouse, France) (Fig.1). It is composed of
seven lipid/polymer membrane sensors which were
developed for food applications and a Ag/AgCl
reference electrode. Each sensor has a different
membrane and depending on each sensors
selectivity for a taste solution, it will
generate electrical potential of different
magnitudes, which are monitored and subsequently
analysed using multivariate analysis. The
objective of this study was to construct a
bitterness prediction model using the e-tongue
for the assessment of dairy hydrolysates to
reduce the reliance on sensory panel analysis.

Results
Introduction
Figure 2. PCA of tongue response to basic taste
solutions
Figure 3. PCA of tongue response to PROP (PR),
Quinine (QU) Caffeine (CA)
The PLS (Fig.4) displays the predicted caffeine
concentration by the e-tongue Vs. actual
concentration in SMUF. There is a strong linear
correlation R 2 0.99. The result suggests that
the e-tongue may also be used to quantify bitter
compounds in more complex solutions.
Figure 4. PLS regression of correlation between
caffeine concentration as predicted by the
e-tongue and actual caffeine concentration in
SMUF.
Figure 1. The electronic tongue with 7 sensor
array
Figure 5B. PLS regression of correlation between
bitterness intensity in casein protein
hydrolysates as predicted by the e-tongue the
e-tongue vs. assigned by the sensory panel.
Figure 5A. PLS regression of correlation between
bitterness intensity in whey protein hydrolysates
as predicted by the e-tongue the e-tongue vs.
assigned by the sensory panel.
Materials and Methods
Experiment 1 A number of experiments were
conducted with the e-tongue to assess its ability
to discriminate between basic taste compounds
salty (10 mmol/l KCl NaCl), sweet (10 mmol/l
sucrose), sour (10 mmol/l citric acid tartaric
acid) and bitter (1 mmol/l tryptophan
caffeine). 6-n-propylthiouracil (PROP) , quinine
and caffeine (13 mmol/l) were also analysed to
ensure the tongue could distinguish between a
variety of bitter agents. The results are
expressed using principle component analysis
(PCA) constructed in the statistical package R
version 2.11.1 (The R project for statistical
computing, 2012). Experiment 2 The ability of
the e-tongue to quantify bitterness was assessed
using a series of caffeine solutions (4.119-16.47
mmol/l) solubilised in simulated milk
ultra-filtrate (SMUF). The results were
correlated using partial least square regression
(PLS) generated using the Alpha M.O.S.
statistical software. Experiment 3 A variety
of casein and whey hydrolysates at a
concentration of 10 w/w ranging in degree of
hydrolysis (DH) from 4-60, were then analysed by
a sensory panel (n10) with more than 70 hours of
training. The panellists were required to assign
bitterness intensity using the 15 point scale.
Each sample was assessed in triplicate with no
more than four samples analysed per session. The
samples were also analysed by the e-tongue. The
results were correlated using PLS. The bitterness
of one whey sample and two casein samples were
predicted by the e-tongue using the PLS model.
All the results in the analysis of dairy protein
hydrolysates were then combined for a larger PLS
model where the bitterness of one whey and one
casein sample was predicted.
Table 1. Bitterness intensity of dairy protein
hydrolysates as rated by sensory and predicted by
e-tongue
Figure Protein Hydrolysate DH Sensory E-tongue Prediction
5.A Whey 30 6.03 6.32
5.B Casein 60 6.88 6.58
5.B Casein 9.6 11.13 12.33
5.C Whey 7.8 7.33 7.38
5.C Casein 9.6 11.13 12.3
Figure 5C. PLS regression of correlation between
bitterness intensity in Casein Whey protein
hydrolysates as predicted by the e-tongue vs.
assigned by the sensory panel.
Conclusion

• The e-tongue was able to discriminate between
  tastant compounds and group them by basic taste.
• The e-tongue was used to quantify the
  concentration of a bitter tastant in SMUF,
  proving that the e-tongue could be used in more
  complex systems i.e. real food samples.
• The bitterness values predicted by the e-tongue
  showed strong correlation with a trained sensory
  panel in the evaluation of dairy protein
  hydrolysates. The PLS models were robust enough
  to predict the relative bitterness on the 15
  point scale of both whey and casein samples.
• This study shows the potential for the e-tongue
  to be used in bitterness screening to reduce the
  reliance on time consuming sensory panels.

• References
• Teagasc (2012 ) http//www.teagasc.ie/agrifood/
• Ney, K. H. (1979) Bitterness of peptides - amino
  acid- composition and chain- length. Abstracts of
  Papers of the American Chemical Society, 11577.
• Alpha M.O.S., (2010) a-Astree e-tongue technical
  notes.
• Meilgaard. C.G.V., Carr T., Ed.(2000). Sensory
  Evaluation Techniques. Michigan, CRC Press

Acknowledgement The work described herein was
supported by Enterprise Ireland under Grant
Number CC20080001