Due to its composition of unsaturated and polyunsaturated fatty acids, oils and fats are very susceptible to oxidation, with rancidity being one of the main defects. Among the several existing methodologies to monitor oxidation in foods, sensory analysis stands out because of the sensitivity of responses. Accordingly, this study aimed to select and train a panel of expert assessors to identify the rancid flavor, showing the statistical steps in the process. Assessors were selected according to their individual performance, statistically analyzed by ANOVA and Tukey’s mean comparison, Wald Sequential Analysis and chi-square test. The validation of the trained panel was carried out with the sensory analysis of fish burgers and soybean oil. F Value and box-plot graphic methods were effective for better visualization of results when used along with the mean and standard deviation tables. The final trained panel consisted of seven assessors, who have been able to identify and differentiate rancid taste in both samples used for validation.
The sensory perception of food is placed first on the in-mouth transformation and that is the reason why it is so dynamic (Ares
Therefore, selection and training are required to assess reliable measurements from individual reactions (Latreille
The steps to be followed to achieve a reliable sensory trained panel are basically: assessor selection, basic, and specific training, assessor qualification and method validation (Etaio
Several studies, from the oldest (Banfield and Harries,
To our knowledge, there are no published studies reporting the selection and training of assessors for the rancid defect in oils and fats specifically, including the time and temperature of rancidity with results demonstrated statistically. The closest to it is the sunflower oil shelf-life estimation detailed by Houhg and Fiszman, (
The undesirable compounds known as
To encourage the consumption of fish, due to the high nutritional value of this type of meat, one of the strategies is to turn the fish into a practical product, such as the hamburger (Corbo
Due to proven importance, the aim of this study is to select and train a panel of assessors specialized in recognizing the rancid defect taste in fish hamburgers and oils and to demonstrate the entire process, emphasizing on the statistical treatment of data.
Approved by the Ethics Committee – CAAE number 48687815.0.0000.5547 – UTFPR, Pato Branco/PR, the study was performed with professors (6), undergraduates (12) and graduate students (8) of UTFPR, 26 subjects in total. The ones involved already had prior contact with the Sensory Analysis discipline facilitating the understanding of the analysis and the terms involved, but none had previously participated of a rancid flavor defect training session. Each assessor performed the analysis in a sensory cabin, properly lit and isolated from the others and from the sample preparation area, with access to a sink for sample disposal and water at will.
The procedure for selection included a previous interview before the difference test addressed to the product (Dutcosky,
The selection of assessors was performed through the triangle test, a modality of sensory analysis called discriminative, which differentiates two samples that received different treatments (ASTM,
The test consisted of two samples: rancid and regular sunflower oil. The rancid oil was produced in an oven at 60 °C for 14 days (Houhg and Fiszman,
The main conditions were kept constant; 15 mL of oil (Borràs
The number of correct answers from the assessors so that there was a significant difference between the samples was found in a table based on the chi-square test; if the assessor reached the minimum of correct answers he was selected– 10 replicates requires 7 right answers (p < 0.05). Another statistical analysis applied to the selection was Wald Sequential Analysis, according to the graphical method (ISO,
The decision system was obtained through hypothesis testing (ISO,
An unstructured scale of 10 cm was used for training, presented with the numbers 0 and 10 at the extremes (Houhg and Fiszman,
The training procedure consisted of three different days/stages of analysis, to calculate the accuracy of the answers and consistency of the team. On each day, four dilutions with rancid oil (0%, 10%, 50% and 100%) were provided to the assessors selected in a sufficient amount of 15mL (Borràs
Dilutions of 0% and 100% were presented as the extremes of the scale, where 100% represented the sample at its maximum rancidity (14 days – 60 °C) and 0% represented the regular oil sample with no rancidity. The remaining, 10% and 50% dilutions were placed between 0 and 10 cm by assessors, corresponding to little–none/much rancid flavor. This procedure was repeated three times within the same day in order to have mean and standard deviations for each day. Each session lasted 10-15 minutes.
Assuming that samples were only 10% and 50%, a paired test was applied to check whether there was a difference between them, and those who inverted the order of samples on the scale (placed 50% before 10%) had their responses considered incorrect. To check the difference, the bilateral paired test table was consulted (p < 0.05) (ASTM,
The responses were measured in centimeters along the 10 cm scale. ANOVA statistical analysis evaluated individual results, means and standard variations, giving the three days’ precision using Tukey’s mean comparison test (p < 0.05) performed by Statistica® software 12.7.
Similarly to the individual performance, the mean of each day’s responses was calculated, with respective standard deviations, to evaluate panel homogeneity. Assessors that did not differ statistically (p > 0.05) from each other, by Tukey’s mean comparison test, coinciding in the analysis of both samples, 10% and 50%, were selected for the final trained sensory panel.
Validation is important to test the panel reproducibility, which means that if the test is repeated after some time, or by another sensory panel trained exactly as in the present study, the results would not differ significantly (Lea
The validation was performed eight times with the products under study, fish burgers which had been stored for 30 days, and soybean oil with two distinct antioxidants. The burgers were made with grass carp fish meat (79.00%), where 33% of the total fatty acids were polyunsaturated (Wu and Mao,
The burgers were thawed and grilled to serve to the assessors. The samples were cut into uniform sizes of about 1.5 cm3, and maintained at 75 ºC (internal center) to the time of delivery (Mitterer-Daltoé
Samples of soybean oil with the tertiary butylhydroquinone (TBHQ) antioxidant 200 mg/Kg, 100 and 200 mg/Kg of
An unstructured scale of 10 cm was applied again, for the distribution of burger and oil samples within range (different sheets), anchored in little–none/much rancid flavor. ANOVA was applied to the trained team’s results to check for differences between samples (p < 0.05). The recognition of the difference between samples were compared for equivalence with the training rancid oil to validate the trained panel.
Twenty-six people attended the selection (9 males;17 females; ages ranging from 20–50), all of which were assessors (A), 15 of which, got seven right responses or more of the ten replicates provided, based on the chi-square table for the triangle discriminatory test, and they were considered suitable for training. By means of Wald Sequential Analysis, eight more assessors were between the acceptance lines (ax = 2.0789 + 0.5
Wald Sequential Analysis for selection of assessors; α=β=0.05; p0 = 0.33; p1 = 0.67.
From the 23 assessors selected, 18 agreed to continue the training. According to the unilateral paired test table (p < 0.05) 13 assessors should set the right order of sample concentrations, 10% before 50%, in the unstructured 10 cm scale, so that, according to the paired test, the standard dilutions would present significant difference (
Number of correct responses regarding the order of samples in each triplicate, per day of training
Assessor | Day 1 | Day 2 | Day 3 | Total |
---|---|---|---|---|
0 – Zero. No incorrect answers regarding the order of samples | 9 | |||
3 | 2 | 2 | 7 | |
3 | 2 | 3 | 8 | |
2 | 3 | 3 | 8 |
Eliminated
To be approved, the assessors should have shown a total of nine correct responses, which means no change in the order of sample concentrations inside the triplicate, for every day of training.
According to the results of each assessor, the mean and the standard deviations of the responses were calculated in triplicate for each day of training through ANOVA, with the mean comparison analysis of p-values (Tukey), the mean of the tested three days which assessors presented homogeneity among the days (
Inter-day precision of the rancid flavor (cm) in 10% and 50% oil dilutçions
Assessor | Dilution (%) | Day 1 | Day 2 | Day 3 |
---|---|---|---|---|
10 | 3.47a ± 0.46 | 3.53a ± 0,06 | 1.67b ± 0.06 | |
50 | 8.07a ± 0.21 | 8.37a ± 0.15 | 6.87b ± 0.11 | |
10 | 1.70a ± 0.36 | 1.07a ± 0.74 | 0.37a ± 0.23 | |
50 | 4.33a ± 2.40 | 6.30a ± 1.15 | 5.83a ± 4.37 | |
10 | 1.50a ± 0.30 | 1.53a ± 1.10 | 1.27a ± 0.68 | |
50 | 5.77a ± 1.42 | 5.33a ± 1.15 | 5.90a ± 0.66 | |
10 | 2.50a ± 1.28 | 3.63a ± 1.91 | 4.67a ± 1.53 | |
50 | 5.93a ± 2.50 | 6.83a ± 0.76 | 6.80a ± 0.72 | |
10 | 2.23a ± 0.25 | 2.03a ± 0.84 | 2.13a ± 0.60 | |
50 | 7.30a ± 0.75 | 7.47a ± 0.50 | 6.93a ± 0.93 | |
10 | 3.53a ± 2.15 | 4.43a ± 2.18 | 4.93a ± 7.20 | |
50 | 4.93a ± 2.41 | 7.70a ± 0.62 | 7.24a ± 0.80 | |
10 | 3.43a ± 1.85 | 3.60a ± 0.17 | 1.63a ± 0.23 | |
50 | 7.10a ± 2.13 | 7.33a ± 0.47 | 7.13a ± 0.32 | |
10 | 1.50a ± 0.44 | 0.93a ± 0.06 | 1.03a ± 0.15 | |
50 | 4.73a ± 0.67 | 4.00a ± 0.62 | 4.73a ± 0.46 | |
10 | 1.43a ± 0.31 | 1.77a ± 0.67 | 3.40a ± 1.40 | |
50 | 8.10a ± 0.78 | 5.67a ± 2.75 | 8.50a ± 0.87 | |
10 | 2.47a ± 0.49 | 2.50a ± 1.04 | 2.33a ± 0.29 | |
50 | 5.57a ± 1.20 | 5.80a ± 1.21 | 5.73a ± 0.68 | |
10 | 2.93a ± 0.59 | 1.63a ± 0.38 | 1.77a ± 1.29 | |
50 | 7.10a ± 0.79 | 5.40a ± 2.33 | 5.87a ± 1.27 | |
10 | 1.60a ± 0.53 | 1.50a ± 1.21 | 2.47a ± 1.27 | |
50 | 6.50a ± 1.50 | 6.90a ± 0.10 | 8.37a ± 0.85 | |
10 | 2.37a ± 0,15 | 2.23a ± 0.40 | 2.40a ± 0.53 | |
50 | 6.23a ± 0.64 | 6.00a ± 1.81 | 5.00a ± 0.62 | |
10 | 1.17a ± 0.42 | 1.57a ± 0.35 | 1.43a ± 0.21 | |
50 | 5.57a ± 0.51 | 5.60a ± 0.10 | 6.10a ± 0.30 |
A1: Eliminated; Same letters in the same line: means do not differ (Tukey p ˃ 0.05).
Assessor A1, whose day 3 differed significantly from the others (p ˂ 0.01), was eliminated at this stage of the statistical analysis. The remaining assessors exhibited homogeneity among days, with no significant differences among means.
For better visualization of this outcome, the F value of ANOVA (one-way) was calculated along with the p-value to test the individual performance of each assessor. F values higher than Fcritical (5.1432) demonstrate significant differences among days of training.
The inability of A1 (F10 = 45.87 and F50 = 70.87) was also computed by the F value (
Column chart for F values with logarithmic scale (significance level: 5%). The F values were divided by assessors and samples with concentrations of 10% (F10) and 50% (F50).
The individual performance, in terms of homogeneity among the days and differentiation of samples, removed five assessors thus far. The thirteen remaining were evaluated according to panel homogeneity. Those who did not differ from each other for both samples (rancid oil; 10% and 50% standard solution) were considered part of the final trained panel (Tukey p > 0.05; n = 9).
By analyzing the p-values, only seven (A3, A4, A9, A12, A13, A17 and A18) of the thirteen remaining assessors showed homogeneity in their responses (
Panel consistency for 10% and 50% standard solutions of sunflower rancid oil
10% | 50% | |
---|---|---|
3.60ab ± 1.08 | 6.52ab ± 0.52 | |
2.13bc ± 0.10 | 7.23a ± 0.27 | |
4.30a ± 0.71 | 6.62ab ± 1.48 | |
2.88abc ± 1.09 | 7.19a ± 0.13 | |
2.20bc ± 1.05 | 7.42a ± 1.53 | |
1.85bc ± 0.53 | 7.25a ± 0.98 | |
Eliminated. Same letters in the same column: means do not differ (Tukey p ˃ 0.05).
Although A3 showed a large standard deviation for the 50% dilution (
Box-plot for rancid oil sample diluted to 10%, from three-day training, separated by assessor; N = 9. Means (□); 1–99% Range├—┤; Medians (—–).
Box-plot for rancid oil sample diluted to 50%, from three-day training, separated by assessor; N = 9. Means (□); 1–99% Range├―┤; Medians (—–).
The validation of the trained panel was carried out in the analysis of samples: fish hamburgers and soybean oil with
Values (mean ± standard deviation) for the rancid flavor of samples equivalent to standards
Sample | Rancid flavor (cm) |
---|---|
H1 – initial | 0.00e ± 0.00 |
H2 – 7 days | 0.16e ± 0.30 |
H3 – 14 days | 0.76de ± 0.62 |
H4 – 17 days | 1.54de ± 1.03 |
H5 – 21 days | 2.30cde ± 1.46 |
H6 – 23 days | 3.31bcd ± 2.02 |
H7 – 25 days | 4.77abc ± 2.60 |
H8 – 30 days | 5.97a ± 2.67 |
Soybean Oil + TBHQ | 2.32B ± 1.75 |
Soybean Oil + 100 mg/Kg |
2.92B ± 2.35 |
Soybean Oil + 200 mg/Kg |
2.10B ± 2.19 |
Equal letters in the column show that the means do not differ significantly (Tukey p < 0.05; n = 7). Capital letters indicate oil samples; lower case letters for burger samples.
The assessors’ perception showed an equivalent degree of difference in standards and hamburger samples. At the first phase of training it became clear that 10% and 50% standards are different, proving that hamburguers are too. The trained panel was able to find differences equivalent to the training of samples, which have a more complex matrix, and therefore validates the assessors and the method.
For oil samples, after 96 hours in an oven at 60 °C, there was no significant difference among the samples tested (Soybean oil with TBHQ, soybean oil with 100 mg/Kg of
The sequence of analysis applied was efficient for selection, training and panel validation for the rancid taste in oil and fish hamburgers. Wald Sequential Analysis proved to be more effective than the chi-square method for selection, especially since eight important assessors would be automatically excluded already without the Wald test. Each assessor has a different sensitivity for flavors, and the Wald Analysis shows the individual performance graphically, giving a chance to those with potential.
Another point to be emphasized about the Wald Sequencial Analysis is the evaluation of assessors who had acuity at first, but saturated before the end of repetitions, such as A20 who presented saturation on repetitions 5 to 10, performing right only on the 9thand even then was selected for training.
As well as the selection, the training also proved to be an important part of the process as established by A1 and A2, both with the best results, 9 correct answers, in the triangle test, but didn’t pass through training. This demonstrated the fact that it is possible to have an optimum performance during selection, but not the same happens during the second phase, which confirms the importance of training in order to select a panel.
ANOVA with Tukey mean comparisons represented the means and standard deviations, F value column plot representation, and box-plots completed the statistical analysis and proved effective in the training process. Graphic representations, such as the F value plot and box-plot, provided a better visualization of why only seven were chosen in the end, or why some were eliminated. Tables represented by means and standard deviations seem confusing at times, especially with a larger amount of data.
F values in the table format were used by Braghieri
The box-plot in the present work allowed better understanding and visualization in the variation of assessors’ responses for each standard, also emphasizing the difference between standards. Regarding box-plot graphics, a study by Williamson
By means of the box-plot it is noted that the standard deviations shown by 50% dilution (
Sinesio
For soybean oil, López-Aguilar
The validation of the trained panel with a product is essential, because the products’ matrix is often more complex than the solutions used in the selection and training, and this may confuse the panel. Elortondo
Seven proved to be a good number of trained assessors in the rancid flavor defect case. This number was not explored in depth in the literature, appearing in studies with other flavors and defects in descriptive analyses (Sinesio
Because of this difficulty, researchers hire trained panels or use untrained assessors/consumers. Wang
The International Organization for Standardization usually gives some directions for the selection and training of sensory panels. Companies and universities in some countries follow the methods undergo a lengthy training process to further borrow or rent teams to research, showing that training a panel for a particular research is unusual for the reasons already mentioned. However, according to Resolution CNS/MS 196 (BRASIL,
Therefore, the value and importance of a trained panel previously justified, along with the fact that in some countries such as Brazil there is no possibility of hiring a trained panel without third parties involved, or more financial resources, highlights the relevance of the results presented here which detail and discussed the selection and training of a sensory panel.
It was possible to select and train a panel for the rancid flavor, with seven assessors that were able to distinguish the target products through triangular discriminative analysis, unstructured and hedonic scale, using ANOVA for data statistics, in addition to the Wald Sequential Analysis, box-plot and bar graph for F values. This study is important for other studies that require trained panels, especially if training is necessary because it demonstrated clearly and in a reproducible way, step by step, how to achieve a trained panel.
The authors are grateful to the National Council for Scientific and Technological Development – CNPq – Brazil (Universal Process nᵒ 456102/2014-0).