**Antonio López-López \*, Antonio Higinio Sánchez-Gómez, Alfredo Montaño, Amparo Cortés-Delgado and Antonio Garrido-Fernández**

Food Biotechnology Department, Instituto de la Grasa (CSIC), Campus Universitario Pablo de Olavide, Edificio 46, Ctra. Utrera km 1, 41013 Sevilla, Spain; ahiginio@ig.csic.es (A.H.S.-G.); amontano@cica.es (A.M.); acortes@cica.es (A.C.-D.); garfer@cica.es (A.G.-F.)

**\*** Correspondence: all@cica.es; Tel.: +34-9-5461-1550

Received: 17 October 2019; Accepted: 6 November 2019; Published: 8 November 2019

**Abstract:** There is vast experience in the application of sensory analysis to green Spanish-style olives, but ripe black olives (≈<sup>1</sup> <sup>×</sup> <sup>10</sup><sup>6</sup> kg for 2016/2017) have received scarce attention and panelists have less experience on the evaluation of this presentation. Therefore, the study of their performance during the assessment of this presentation is critical. Using previously developed lexicon, ripe olives from Manzanilla and Hojiblanca cultivars from different origins were sensory analysed according to the Quantitative Descriptive Analysis (QDA). The panel (eight men and six women) was trained, and the QDA tests were performed following similar recommendations than for green olives. The data were examined while using SensoMineR v.1.07, programmed in R, which provides a diversity of easy to interpret graphical outputs. The repeatability and reproducibility of panel and panelists were good for product characterisation. However, the panel performance investigation was essential in detecting details of panel work (detection of panelists with low discriminant power, those that have interpreted the scale in a different way than the whole panel, the identification of panelists who required training in several/specific descriptors, or those with low discriminant power). Besides, the study identified the descriptors of hard evaluation (skin green, vinegar, bitterness, or natural fruity/floral).

**Keywords:** panel performance; panelist; black ripe table olives; sensory descriptors; sensory profile

#### **1. Introduction**

World table olive production was around 2.6 <sup>×</sup> 10<sup>6</sup> tones in season 2016/2017 according to the last consolidated balance of the International Olive Oil Council [1]. Approximately, 40% of them were processed as black ripe table olives (Californian style). This style was first developed in the USA, which is still one of the most relevant contributors with current production of about 80 <sup>×</sup> <sup>10</sup><sup>3</sup> tons [1], but other countries, like Spain, Greece, Turkey, or Egypt, are progressively increasing their productions. Black ripe table olive processing includes a phase of storage, which is usually accomplished by immersing the fruits in brine or acidified solution, followed by a darkening step, which consists of the application of one (or several) lye treatments and subsequent immersion in tap water to remove the excess of alkali. During this oxidation phase, air is also bubbled through the suspension to accelerate browning. The colour is then fixed by a ferrous gluconate solution, after which the olives are packed and the cans sterilised [2]. The products usually offer a rather plain organoleptic profile, which has been a favourable condition for its introduction in new markets, due to their numerous treatments in aqueous solutions. In fact, according to the Trade Standards Applying to Table Olives [3], the only requisites for these olives are sensory characteristics and texture in agreement with their processing system.

Along the last decade, the International Olive Council developed a method for the sensory evaluation of table olives. However, it was mainly focused on green Spanish-style, since most of the descriptors included in the evaluation sheet are exclusively related to this product (e.g., abnormal fermentation, acidity, or bitterness) [4]. However, methods for the evaluation and classification of black ripe olives were developed in California, where this processing has a long tradition [5].

On the other hand, Quantitative Descriptive Analysis (QDA) is widely used for studying the sensory profile of diverse foods ([6–8], among many others). Recently, researchers have applied QDA to a list of 33 descriptors for the sensory comparison of American black ripe table olives with respect to those that are imported from other countries (Spain, Egypt, or Morocco) [9]. Similar descriptors were used to study the sensory profile of black ripe table olives from Spanish Manzanilla and Hojiblanca cultivars and successfully distinguishing among cultivars, farming origins, and storage period [10]. López-López et al. [11] have developed an entirely new lexicon for the application of QDA to Spanish-style green table olives; the results showed relevant differences between cultivars and origins. Therefore, the use of the QDA to black ripe table olives from the most important Spanish cultivar devoted to this elaboration is relevant.

Traditionally, the sensory analysis of table olive, regardless of style, has been mainly devoted to the characterization of products [12–16], but the panelists and panel performances were rarely studied in detail. However, along the last two decades, different authors have developed methodologies for evaluating the reliability of the panel [17–22]. Its application to the panel performance, discrimination power of descriptors of diverse green and black ripe table olives, following the COI/OT/MO No. 1/Rev. 2 methodology, has been recently published [17]. Nevertheless, the performance of a panel and panelists that were devoted to the sensory analysis of black ripe table olives using QDA has never been studied.

This work aims for the application of Quantitative Descriptive Analysis to black ripe table olives from Spanish Manzanilla and Hojiblanca cultivars, focusing interest on the panel and panelist performances as a tool for improving their training and reliability.

#### **2. Materials and Methods**

#### *2.1. Olives and Their Processing*

The olives were of the Manzanilla and Hojiblanca cultivars, harvested at green maturation stage in October 2016. Their origins were: Aljarafe (Sevilla) and Lora de Estepa (Sevilla) for *Manzanilla*, and Lora de Estepa (Sevilla), and Alameda (Málaga) for *Hojiblanca*. The samples were identified as MAL, ML, HL, and HA, according to cultivar (initial letter) and growing area (remaining letter/s).

Just harvested olives from each cultivar and origin were directly brined in 25 L (15 kg olives) PVC (polyvinyl chloride) fermenters in an acidified (2.4% acetic acid) solution. After three months of storage, the fruits were subjected to the darkening process. For this purpose, horizontal stainless steel cylindrical containers (0.4 m diameter, 0.7 m length) were used. The fruits were treated with a 3% lye solution until the alkali reached the pit. After removing the alkali, the olives were washed to low the pH up to 8.0 units. During both operations, an oxygen-saturated ambient was maintained in the suspension by bubbling air through a perforated tube lying along the bottom of the oxidation vessels. Subsequently, the black colour developed was fixed, while using a 0.1% ferrous gluconate solution with pH adjusted to 4.5 to prevent the precipitation of the element as hydroxide. Afterwards, the darkened olives were introduced in glass jars (145 g of olives), together with 170 mL of 3.5% NaCl cover solution, which also contained 0.2 g ferrous gluconate/L and had the pH adjusted to 4.5 with acetic acid. Finally, the jars were closed and sterilised at 130 ◦C for 20 min [23].

The sensory analysis of the above-prepared black ripe olives was achieved after storage at room temperature for 30 (to allow complete olive flesh/brine equilibrium) and 210 days (estimated maximum normal period of the product in the shelves before reposition). The new codes were those previously mentioned, plus 1 (one-month storage) and 2 (seven-month storage), respectively. Therefore, the

symbols of the final samples: were: MAL1, MAL2, ML1, ML2, HL1, HL2, HA1, and HA2, which indicated the successive letters and figures cultivar, growing area, and the storage period, respectively.

A panel composed of eight men and six women, making a total of 14 panelists (40 years' average age) performed the analysis. They all belonged to the Instituto de la Grasa staff and had vast experience on sensory studies due to their participation in the development of the Sensory Analysis Method for Table Olives [4] and the permanent involvement in diverse IG table olive sensory projects (e.g., [10,11]). Before the tests, the panelists were trained for one h twice a week for two months to familiarise them with the QDA techniques and the black ripe olive descriptors, while using industrially processed Spanish cultivars black ripe olives. The presentation of the samples was always made in the standard glasses [24], which were coded with three randomly chosen digits. After each test, the mouth was washed with tap water, freely available in each booth. Therefore, the panelists were progressively familiarised with the product, the sensory descriptors that were included in the evaluation sheet, informal tentative evaluations, and, finally, allowed for practicioning with the unstructured scale (1, complete absence; 11, strongest perception) of the evaluation sheet for another month. After these periods, they were considered ready for the evaluation of the real samples because of the previous expertise of the panelists in sensory testing. The assessed descriptors included appearance (skin red, skin green, skin sheen, flesh red, flesh yellow, and flesh green), aroma (briny, mushroom, earth/soil, oak/barrel, nutty, artificial fruity/floral, natural fruity/floral, vinegary, alcohol, fishy smell/ocean, and cheese smell), taste (sourness, bitterness, and saltiness), flavor (ripeness, buttery, metallic, rancid, soapy smell/medicinal, and gassy smell), and texture/mouthfeel (firmness, fibrousness, moisture release, mouth coating, chewiness, astringency, and residual). Their definitions and references may be found elsewhere [10].

For performing the tests, the black ripe olive samples were presented to panelists at an ambient temperature (20 ± 1 ◦C) and in a panel room that was equipped with individual booths under incandescent white lighting and free from any odors. The panelists were asked to mark the intensity of the different descriptors in the evaluation sheets. The scores of the attributes were measured with the exactitude of one decimal point and the results tabulated.

#### *2.2. Data Analysis*

The data were mainly studied while using the SensoMineR v.1.07 software (Agrocampus Ouest, Rennes, France) [25], a package that was designed and programmed in R language [26]. It is characterized by combining classical sensory statistical methods as well as others directly conceived in the developers' laboratory. In this way, SensoMineR provides a synthesis of the results of the usual analysis of variance (ANOVA) models, as well as a diversity of easy to interpret graphical outputs. Notably, the package includes several options for the panel evaluation, such as multivariate analysis and the generation of virtual panels, by bootstrapping techniques, which allow for the estimation of the corresponding confidence limits. XLSTAT [27] was also applied in specific analysis and tests.

#### **3. Results and Discussion**

The matrix of data was constituted by the following variables: sample-storage period (just sample from now on), panelist, session, and the 33 descriptors making a total of 36 columns. Additionally, sample, panelist, and session had 8, 14, and 3 levels, respectively, making a total of 336 rows. Therefore, the overall number of cells was 12,096. The generated database was already used for product characterization [10], but, in this work, the analysis is focused on the panel and panelists performance as an exercise for improving their evaluation and training.

#### *3.1. Overview of Results*

After checking the dataset for possible outliers and typing errors, they were also subjected to a first overview (frequency histograms and boxplots), which indicates that several descriptors received low scores and they were hardly noticed; however, others were perceived by the panelists, distributed along the scale, and allowed for discrimination among samples (data not shown). Further details can be found elsewhere [10].

#### *3.2. Panel Performance*

The techniques that are available for panel and panelists performance are numerous, with ANOVA and multivariate analysis being the most common. Kermit and Lengard Almli [16] presented univariate and multivariate data analysis methods to assess the individual and group performances in a sensory panel. Notably, Husson et al., [25] developed the SensoMineR, which includes several innovative tools with this objective.

#### 3.2.1. Effect of Sample (Power of Discrimination)

The evaluation of the panel performance is an essential premise not only for obtaining reliable results on sensory analysis, but also for improving the selection of panelists and their training. In this work, the *panelperf* instruction from SensoMineR, with the appropriate models and the corresponding analysis of variance, was used. The ANOVA was fitted to the following full model:

Score = sample + panelist + session + sample panelist + sample session + panelist session

where score stands for the expected evaluation value, while sample, panelist, and session for the predictive variables, with the effect of storage being included as levels of the variable sample. The panelist and the session were both studied as random effects, but the sample was considered to be fixed [28].

The results regarding performance (Table 1) showed that the panel was able to discriminate the samples based on skin green, flesh green, skin sheen, flesh red, firmness, fibrousness, flesh yellow, skin red, vinegary, moisture release, fishy smell/ocean, and saltiness. Good segregation among the samples or products by panelists is systematically reported in numerous publications ([6,17,28–30], among others).

#### 3.2.2. Effect of Panelist

The significant effect of the panelist, with very low *p*-values, regardless of descriptors, indicates a different interpretation of the scales. Such an effect is not desirable, but it is usually observed. However, its presence does not represent any inconvenience for achieving appropriate conclusions, since the panelists' variance can be eliminated thanks to the ANOVA analysis and by centring the data with respect to panelists [31]. The assessors' performance will be studied in detail later.

#### 3.2.3. Effect of Session

The effect of the session was not significant for any descriptor (Table 1), which indicates an overall good panelist performance over time (the samples were assessed in the same way from one session to another), which is an appropriated and desired situation. Subsequently, no further comments regarding this aspect are also required.


