Acknowledgement The work described herein was supported by Enterprise Ireland under Grant Number CC J. Newman, N. Harbourne, D. O’Riordan, J.C. Jacquier, & M. O’Sullivan. Food for Health Ireland, UCD School of Agriculture, (Institute of Food and Health) University College Dublin, Belfield, D4  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. Dairy manufacture is one of the Republic of Ireland’s 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 α-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 sensor’s 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. Construction of a bitterness prediction model with an electronic tongue and a trained sensory panel for the assessment of dairy hydrolysates Conclusion 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 (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 ( 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 (n=10) 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. Figure 1. The electronic tongue with 7 sensor array Materials and Methods References 1.Teagasc (2012 ) 2.Ney, K. H. (1979) Bitterness of peptides - amino acid- composition and chain- length. Abstracts of Papers of the American Chemical Society, 115:77. 3.Alpha M.O.S., (2010) α-Astree e-tongue technical notes. 4.Meilgaard. C.G.V., Carr T., Ed.(2000). Sensory Evaluation Techniques. Michigan, CRC Press Introduction Experiment 1: The PCA’s 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 R 2 values of & 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 R 2 = 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. Results PC 1 – 78.6% PC 2 – 15.7% DI= 89 Figure 2. PCA of tongue response to basic taste solutions PC % Salty Bitter Sour Sweet DI= 93 PC % Figure 3. PCA of tongue response to PROP (PR), Quinine (QU) & Caffeine (CA) 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 Ca Ci KC Na su Ta Tr Figure 4. PLS regression of correlation between caffeine concentration as predicted by the e-tongue and actual caffeine concentration in SMUF. 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. 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. FigureProtein Hydrolysate DHSensoryE-tongue Prediction 5.AWhey BCasein BCasein CWhey CCasein Table 1. Bitterness intensity of dairy protein hydrolysates as rated by sensory and predicted by e- tongue 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.