Università Degli Studi Di Pisa
Dipartimento di Scienze Agrarie, Alimentari e
Agro-ambientali
Corso di Laurea in
Biosicurezza e Qualità degli Alimenti
A.A 2013/2014
Relatore: Chiar. mo Prof. Gianpaolo Andrich
Correlatore: Chiar.ma Prof.ssa Ma Teresa Sánchez Pineda de las Infantas
The author is particularly grateful to:
Dr. María Teresa Sánchez Pineda de las Infantas, Chairman of the Department of Bromathology and Food Technology at the University of Cordoba (Spain), and co-supervisor of this Thesis for her patient help during these five months.
Dr. Mª José De la Haba De la Cerda, Assistant Professor of the Department of Bromathology and Food Technology at the University of Cordoba (Spain), and supervisor of this Thesis for be there even when her baby was coming.
Dr. Prof. Gianpaolo Andrich, Italian supervisor of this Thesis for having consecrated me some of his precious time.
All the people of the Department of Bromathology and Food Technology at the University of Cordoba (Spain).
Irina, for teaching me Spanish, or to be more precise, Andalusian, and the Cordoba culinary specialities.
All my friends here in Cordoba, Anita, Maria, Chiara, Anastasia, Mariló, Seren, Meryame, Sanja, Eva, Genesis, Eleonora, Michal, Philipp, Julian, Zeki, Fredy, Lolo, Christian, Mivan, Leo and all the others who made me feel at home.
All my friends in Pisa, because they have always been close to me even when I was far away.
Marta and Pietro, because with all their questions, they remind me that I do not have all the answers.
My family, for the steadfast support in these months. To my father, for teaching me to always give the best, to my mother, for teaching me to always see the best in the others, to Alessandro, because you are now part of the family, to my sister, because you made me a world citizen and to my grandmother, for reminding me where my roots are.
CHAPTER 1. INTRODUCTION
3
CHAPTER 2. OBJECTIVES
8
2.1. General objective 8
2.2. Specific objectives 8
CHAPTER 3. STATE OF THE ART
10
3.1 The Olive Oil 10
3.1.1 History 10
3.1.2 Olive oil production and consumption 13
3.1.3 Olive oil production techniques 16
3.1.4 Olive oil quality: definition and regulations 19
3.1.4.1 Olive oil sensory analysis 20
3.1.4.2 Chemical characterization of olive oil 21
3.1.4.3 Factors affecting olive oil quality 25
3.2 Traditional chemical and sensory methods for quality determination in
virgin olive oil 27
3.2.1 Thin Layer Chromatography (TLC) and Gas Chromatography (GC) 27
3.2.2 High Performance Liquid Chromatography 29
3.2.3. UV spectrophotometric analysis 30
3.2.4 Iodine number determination 31
3.2.5 Panel test 32
3.3. Near-infrared reflectance spectroscopy (NIRS) for quality determination in
the olive oil industry 33
3.3.1 Theoretical basis 34
3.3.2 Equipment 38
3.3.3 Chemometric analysis 39
3.3.4 NIRS application for the virgin olive oil quality assurance and
authentication 44
CHAPTER 4. MATERIAL AND METHODS
48
4.1 Sampling 48
4.3.1 Median of fruitiness 54
4.3.2 Median of bitterness 55
4.3.3 Median of pungency 55
4.3.4 Median of defect 55
4.4 Chemical analysis 56
4.4.1 Free fatty acid content 56
4.4.2 Peroxide index 57
4.4.3 K232 58
4.4.4 K270 58
4.4.5 Alkyl and ethyl esters 59
4.5 Quantitative calibration: set and data processing 60
4.6 Construction and validation of prediction models by MPLS regression 63
4.6.1 Statistics used to select the best equations 64
4.6.2 External validation 67
4.7 Construction of prediction models for major sensory parameters in virgin
olive oil using the LOCAL algorithm 69
CHAPTER 5. RESULTS AND DISCUSSION
74
5.1 Descriptive data for NIR calibration and validation sets 74
5.2 Prediction of quality parameters using MPLS regression and NIR spectra 76
5.2.1 Sensory parameters 76
5.2.1.1 Median of fruitiness 76
5.2.1.2 Median of bitterness 78
5.2.1.3 Median of pungent 80
5.2.1.4 Median of the defect 81
5.2.2 Chemical parameters 83 5.2.2.1 Acidity 83 5.2.2.2 Peroxide index 84 5.2.2.3 K232 86 5.2.2.4 K270 87 5.2.2.5 Alkyl esters 88 5.2.2.6 Ethyl esters 90
algorithm versus MPLS regression 96
CHAPTER 6. CONCLUSIONS
100
INDEX OF TABLES
Table 3.1 Composition of fatty acids in Cornicabra olive oil 26 Table 3.2 Absorption bands of chemical bonds in the NIR region 40 Table 3.3 Recommended values for the RPD and RER statistics for NIRS
control of products and processes
45
Table 3.4 Guidelines for the interpretation of the coefficient of variation in NIRS control of products and processes
46
Table 4.1 Olive oil sample set 53
Table 4.2 Olive oil samples employed for chemical analysis 54 Table 4.3 Calendar of samples receiving in the University of Córdoba 55 Table 4.4 Technical characteristics of FNS SY-I equipment 56 Table 4.5 Range, mean, standard deviation (SD) and coefficient of
variation (CV) for the sensory parameters studied in calibration and validation sets. 1st strategy
67
Table 4.6 Range, mean, standard deviation (SD) and coefficient of variation (CV) for the chemical parameters studied in calibration and validation sets. 1st strategy
67
Table 4.7 Range, mean and standard deviation (SD) for the sensory parameters studied in calibration and validation sets. 2nd strategy
68
Table 4.8 Range, mean and standard deviation (SD) for the chemical parameters studied in calibration and validation sets. 2nd strategy
68
Table 4.9 Optimization design for the LOCAL algorithm 75 Table 5.1 Statistical analysis of calibration and validation sets for sensory
parameters (both strategies)
79
Table 5.2 Statistical analysis of calibration and validation sets for chemical parameters (both strategies)
80
Table 5.3 Calibration statistics for the calibration models obtained for predicting median of fruitiness. 1st calibration strategy
82
Table 5.4 Calibration statistics for the equations obtained for predicting median of fruitiness. 2nd calibration strategy
82 Table 5.5 Calibration statistics for the equations obtained for predicting
median of bitterness. 1st calibration strategy
INDEX OF TABLES
Table 5.6 Calibration statistics for the equations obtained for predicting median of bitterness. 2nd calibration strategy
84 Table 5.7 Calibration statistics for the equations obtained for predicting
median of pungent. 1st calibration strategy
85
Table 5.8 Calibration statistics for the equations obtained for predicting median of pungent. 2nd calibration strategy
85
Table 5.9 Calibration statistics for the equations obtained for predicting median of the defect. 1st calibration strategy
86
Table 5.10 Calibration statistics for the equations obtained for predicting median of the defect. 2nd calibration strategy
87
Table 5.11 Calibration statistics for the equations obtained for predicting acidity (% oleic acid). 1st calibration strategy
88
Table 5.12 Calibration statistics for the equations obtained for predicting acidity (% oleic acid). 2nd calibration strategy
88
Table 5.13 Calibration statistics for the equations obtained for predicting peroxide index (meq/kg). 1st calibration strategy
90
Table 5.14 Calibration statistics for the equations obtained for predicting peroxide index (meq/kg). 2nd calibration strategy
90
Table 5.15 Calibration statistics for the equations obtained for predicting K232 (U.A.). 1
st
calibration strategy
91
Table 5.16 Calibration statistics for the equations obtained for predicting K232 (U.A.). 2
nd
calibration strategy
91
Table 5.17 Calibration statistics for the equations obtained for predicting K270 (U.A.). 1
st
calibration strategy
92
Table 5.18 Calibration statistics for the equations obtained for predicting K270 (U.A.). 2
nd
calibration strategy
93
Table 5.19 Calibration statistics for the equations obtained for predicting alkyl esters (mg/kg). 1st calibration strategy
94
Table 5.20 Calibration statistics for the equations obtained for predicting alkyl esters (mg/kg). 2nd calibration strategy
INDEX OF TABLES
Table 5.21 Calibration statistics for the equations obtained for predicting ethyl esters (mg/kg). 1st calibration strategy
95 Table 5.22 Calibration statistics for the equations obtained for predicting
ethyl esters (mg/kg). 2nd calibration strategy
95
Table 5.23 Validation statistics for best equations obtained for sensory parameters. 1st and 2nd validation strategy
98
Table 5.24 Validation statistics for best equations obtained for chemical parameters. 1st and 2nd strategy
99
Table 5.25 Statistics for the prediction of the sensory parameters tested using Local algorithm. 2nd strategy
102
Table 5.26 Validation statistics for the best models for predicting sensory quality parameters in olive oils using MPLS and LOCAL algorithms
INDEX OF FIGURES
Figure 3.1 Olive tree diffusion through the Mediterranean area 14 Figure 3.2 Archaeological find, in the Phestos Palace, Crete: base plate of a
press
14
Figure 3.3 Main olive oil productive countries 16
Figure 3.4 Distribution of olive oil extraction systems used in some Mediterranean countries
17
Figure 3.5 Olive oil production in Spain and Italy in the period 1990-2014 18 Figure 3.6 World olive oil consumption in 1990 and 2013 19 Figure 3.7 Olive oil production systems: a) traditional process, b)
three-phase centrifugation system, c) two-three-phase centrifugation system
20
Figure 3.8 Chemical structure of a generic triglyceride 25 Figure 3.9 Different types of light interaction with a sample 39 Figure 3.10 Basic components of a NIR spectrophotometer 41
Figure 4.1 FNS 6500 SY-I equipment 57
3
CHAPTER 1. INTRODUCTION
Virgin olive oil (VOO) is a fundamental and indispensable food product in the traditional Mediterranean diet, with its long valued aroma, taste and nutritional properties, directly related to minor components of its composition, chiefly phenolic and volatile compounds (Inarejos-García et al., 2013).
Despite its excellent properties, the olive oil consumption covers only the 3% of the global vegetable oil consumption, due to the fact that important countries such as China, India, Malaysia, Indonesia and USA consume mainly other types of vegetable oils, like palm oil, soya oil, canola oil or sunflower oil (Mercasa, 2012).
However the olive oil history is strictly connected with the Mediterranean basin, where the olive tree has become part of the landscapes and, in some cases, contributes consistently to the economy of the region (Vossen, 2007). In fact in this area is concentrated the 95% of the global olive oil production (FAOSTAT, 2012).
According to the International Olive Oil Council (IOOC), since 1990 the global olive oil production has more than doubled, from the 1,453,000 tons produced in the 1990/1991 to the 3,321,000 tons produced in the 2011/2012. Spain and Italy represent together the 59% of the global production (42 and 17% respectively) and the 85% of the European production (63 and 22% respectively).
Moreover the global olive oil consumption has doubled since 1990, from the 1,666,500 tons consumed in the 1990/1991 to the 3,085,000 tons in 2011/2012. That means that the increase in the production has been followed by an increase in the consumption, or that the increase in the production has been the answer to an increasing consumption over the years.
Nowadays the traditional olive grove (80-120 trees/ha) is still the most diffuse, particularly in dry-farmed areas, even though is highly inefficient due to the long delay before the full production (15-40 years) and the inefficient harvest system. In the last years producers have started to plant new high density (600-640 trees/ha) and super high density (2200-2240 trees/ha) plantations, that come to the full bearing in 5 or 6 years and have equal yields, though requiring soil of better quality, rescue irrigation and high mechanization, meaning higher productivity and lower production costs (Vossen, 2007). This increase in the potential production ensures that actually the virgin olive oil
4 market is highly competitive and globalized so that the producers need to produce high quality olive oil, generally denominated superior or premium extra virgin olive oil, to be successful (Inarejos-García et al., 2013).
This is being possible thanks to a deeper knowledge of the effect of harvesting and processing conditions on the final quality of this highly appreciate food product (Inarejos-García et al., 2013).
Nowadays quality controls are performed using traditional chemical methods that do not preserve the integrity of the sample (Fernández-Gutiérrez and Segura-Carretero, 2009). Those methods are officially recognized by the European Union, and are grouped in the Commission Implementing Regulation (EU) N. 1348/2013 that modifies the Commission Regulation (EEC) N. 2568/91 referred to the characteristic of olive oils and olive pomace oils and their analytical methods.
Numerous efforts have been made to develop and implement non-destructive analytical methods that will neither damage nor undermine the product, which can subsequently be sold or used for other measurements (Peiris et al., 1998). Numerous non-destructive methods are currently being evaluated and used by the industry to measure quality attributes, including VIS/NIRS spectroscopy, fluorescence and X-rays (Hutton, 1998; Peiris et al., 1998).
Near-infrared reflectance spectroscopy has been recognised as a powerful analytical technique for rapid determination of various constituents in food. This technology is non-destructive, low-cost and provides a safe working environment. It is also a technique suitable for on-line work. In the last years, application of NIR to oils and fats has become more popular for quality and composition studies. In olive oils, NIR is used for prediction–identification of adulteration, differentiation–classification of vegetable oils and monitoring on-line carotenoid and chlorophyll pigments (Márquez
et al., 2005; Woodcock et al., 2008).
Applying this technique on-site or at-line could be very useful in order to early detect possible failure in the process, to guarantee a permanent monitoring of the conditions, to permit an assessment of the conditions at any desired time and to test the method under realistic environment (Armenta et al., 2010).
5 Even though the NIRS technology has already been used in the olive oil sector for the on-line olive fruit quality control (Cayuela and Camino, 2010; Salguero-Chaparro et al., 2013) or for the quality control in olive oils (Cayuela-Sánchez et al., 2013; Inarejos-García et al., 2013) or mostly for the detection of adulteration in olive oil (Downey et al., 2002; Christy et al., 2004; Ozdemir and Ozturk, 2007), however there are few studies about the characterization of the olive oils through the NIR spectroscopy, particularly regarding the characterization using chemical and sensory attributes (Cayuela-Sánchez et al., 2013; Sinelli et al., 2010a).
The Department of Bromatology and Food Technology of the University of Cordoba (Spain), based on their wide experience in traditional destructive analysis of agro-food products, has joined R&D project launched by the Spanish Ministry of Agriculture, Food and Environment entitled “Identification of an instrumental technology to complement the authorized analytical method named “Panel Test” in virgin olive oils”, in which this Thesis can be framed. The aim is to develop a quality assurance system to support the olive oil industry based on the application of NIR spectral fingerprint to monitor quality and traceability in products. In this context the objectives are set out in the next chapter.
It is important to highlight that the main purpose of this Thesis has been to generate new scientific knowledge that will be disseminated via the papers published in peer-reviewed international journal, while at the same time, taking into consideration the practical application of this knowledge to the reality of the olive oil industry.
To facilitate the reading, the dissertation has been structured in the following chapters:
Chapter 1 deals with the presentation, context and justification of the research work done in this Thesis.
Chapter 2 presents and specifies the objectives of the research.
Chapter 3 highlights the present day situation in the olive oil industry which has served as the starting point and justification of the current study. The first section is dedicated to the olive oil history, production and regulations. The second section contains a review of the traditional chemical and sensory methods applied for quality determination in virgin olive oils. The third section
6 is mainly directed to the revision of the theoretical, instrumental and chemometric relevant aspects of NIR spectroscopy technology.
Chapter 4 highlights the materials, methods and equipment used in this research.
Chapter 5 presents the main results obtained and their discussion.
Chapter 6 encompasses the main conclusions which can be drawn from the results presented in this Master Thesis.
Chapter 7 compiles the bibliographic references used in the writing of this dissertation.
8
CHAPTER 2. OBJECTIVES
2.1. General objective
The general objective of this Master Thesis is the assessment of the potential of Near Infrared Reflectance Spectroscopy for the at-line quality control in the olive oil industry, focusing on both chemical and sensory parameters and using the whole near-infrared spectrum.
2.2. Specific objectives
The specific objectives of this Master Thesis are:
1. Assessment of the viability of NIRS models developed for the prediction of sensory parameters in olive oil
2. Assessment of the viability of NIRS models developed for the prediction of chemical parameters in olive oil.
3. .Comparison between the robustness of the models obtained for the prediction of quality parameters in olive oil using different strategies for the construction of the calibration sets involved.
4. Comparison between linear and non-linear regression algorithms for the prediction of sensory parameters in olive oil.
10
CHAPTER 3. STATE OF THE ART
3.1 The Olive Oil
3.1.1 History
The origin of the olive tree is still controversial. The botanical ancestor could be Oleaster Olea Silvestris that is a wild tree growing in Italy, Portugal, southern France, Northern Africa and Caspian Sea coast (Fernández-Gutiérrez et al., 2009). According to another theory the actual Olea europaea could be derived from the ancient
Olea chrysophylla, that was typical of Kenia, Uganda and Ethiopia, but both species
could be derived from a more ancient common ancestor that spread from central Africa (Standish, 1960).
The most ancient evidence of the olive tree presence in Europe is dated to one million years ago, due to the finding of olive fossilized pits in Mongardino (Bologna, Italy), but the first human use of the olive berry is ascribable to some Paleolithic settlements near Mentone, France, 35,000 to 8,000 years ago (Schäfer-Schuchardt, 1988). However the origin of the edible olive can be traced to areas along the eastern Mediterranean coast, particularly Syria or Lebanon, based on written tablets, olive pits, and wood fragments found in ancient tombs (Vossen, 2007).
From this area the plant spread to the west of the Mediterranean basin, due to the commercial activity of the Phoenicians, and later of the Greeks and Romans (Vossen, 2007). As it can be seen from the Figure 3.1, the movement towards west took place in three different waves, resulting in a primary centre in Syria, from where it was spread mainly in Greece and northern Africa, a secondary centre in the Aegean sea area from where it was spread in the Black sea area, “Magna Grecia” (southern Italy) and southern France and a tertiary centre in southern Italy from where it was spread in Spain, northern Italy and northern Africa.
11
Figure 3.1: Olive tree diffusion through the Mediterranean area
Source: Fernández-Gutiérrez and Segura-Carretero, 2009.
All the ancient civilizations knew how to obtain oil from the olive berry and we have many proofs that since the third millennium B.C. in Syria, Mesopotamia, Crete and Egypt there were production of olive oil for internal use and for the commerce (Figure 3.2).
Figure 3.2: Archaeological find, in the Phestos Palace, Crete: base plate of a press.
12 Although the Romans, as did the Greeks, used olive oil mainly as an ointment, for pharmaceutical use, and for lighting (lampante oil), they began to use the oil as food. The Romans also contributed to the technological developments in olive processing by expediting the crushing operation with a millstone crusher, the trapetum (Schäfer-Schuchardt, 1988), and improving the separation system with the introduction of presses (Harwood and Aparicio, 2000). Another important use was during holy rituals, like anointment of kings, warriors and prophets and to make offering to the gods (Vossen, 2007). It has to be reminded that in Hebrew “messiah” means “the anointed”.
After the collapse of the Roman Empire, the cultivation of the olive tree decreases in Europe till the middle age, when the religious communities recover it. There was no technical progress in the process of oil extraction till the 18th century when the hydraulic press was invented (Harwood and Aparicio, 2000).
In 1513 Antonio Herrera published the “Agricultura General”, an agricultural treatise in which reports useful advice about the collection and preservation of olives. However there were no real improvements in the olive production till the last decades of the 19th century, when the increasing demand due to the growing of the cities stimulates some improvements like new pruning techniques, renewing of old trees, selecting more rentable varieties (Fernández-Gutiérrez et al., 2009).
During the 1900s, some improvements for mechanically extracting oil presented themselves as a result of the studies on percolation and centrifugation systems. These innovative systems materialized in 1951 with the Buendía patent for a percolation system and, toward the end of the 1960s, with the introduction of the Centriolive plant, the first industrial decanter based on the continuous centrifugation of the olive paste (Harwood and Aparicio, 2000).
The pressing system was being continuously improved with the increasing application of electrically driven hydraulic pumps, the introduction of the cage presses, column presses, and, finally, the arrival of the modem open mono-block super-presses that permit reaching pressures of 350-500 atmospheres (Harwood and Aparicio, 2000).
13
3.1.2 Olive oil production and consumption
The olive oil production is basically concentrated in the Mediterranean basin, particularly in southern Europe, Middle East and northern Africa. Lately some other countries, like United States, New Zealand, Australia, Chile, Argentina and South Africa have started to plant olive orchards, trying to increase their home production to face the increasing internal demand. However nowadays the three mayor producing countries, meaning Spain, Italy and Greece are still producing around the 70% of the world olive oil production that amount at 3,320,000 tons per year (Faostat, 2012), while the first seven producers, meaning Spain, Italy, Greece, Syria, Tunisia, Turkey and Morocco are producing the 91% of the global production, as it can be seen from the Figure 3.3.
Figure 3.3: Main olive oil productive countries
Source: FAOSTAT dataset, 2012.
However, analysing the production of olive fruit, it can be seen that not all the olives produced are transformed straight into olive oil. Effectively Spain shows a huge olive oil production (42%, meaning about 1,400,000 tons per year) but its olive production is only the 22% of the global production, meaning 3,600,000 tons of olive produced per year. To the contrary, Italy shows an olive oil production that is about 2/5 of the Spanish one, meaning 572,000 tons per year, while its olive production is more than 3,000,000 tons per year. It should be noticed that in Italy the most common
10% 17% 4% 42% 6% 6% 6% 9%
Olive oil production (2012)
Greece Italy Morocco Spain
Syrian Arab Republic Tunisia
Turkey Others
14 extraction system is the three-phase centrifugation process, as it can be seen in Figure 3.4, while in Spain the most widespread system is the two-phase centrifugation process.
Figure 3.4: Distribution of olive oil extraction systems used in some Mediterranean countries
Source: Roig et al., 2006.
Figure 3.5 shows historical series of olive oil production in Spain and Italy. As it can be seen Spain has a higher production except for the years 1991-92, 1995-96 and 1999-00, with an incremental trend in the past 15 years, while Italy after having a more or less steady production till the year 2003-04 has then shown a decreasing trend in the last 10 years.
Figure 3.5: Olive oil production in Spain and Italy in the period 1990-2014. 0% 20% 40% 60% 80% 100%
Spain Italy Greece Portugal Cyprus Croatia Malta
Press Three-phase Two-phase 0 0,2 0,4 0,6 0,8 1 1,2 1,4 1,6 1,8 2 1990-91 1991 -92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008 -09 2009-10 2010-11 2011-12 2012-13 2013-14 Pro d u ctio n (m il li o n to n s) Spain Italy
15 Source: IOOC, 2014.
For both countries it could be seen that the production between years is not perfectly steady, but shows an up and down gait, due to the physiological process of “loading and unloading” of olives and the weather variability that affect the susceptibility to some pathogens as Bactrocera oleae (Bonari and Ercoli, 2008).
Regarding the consumption of olive oil there have been great changes in the last few years due to the increasing demand from non EU countries (Mercasa, 2012). As it can be seen from the Figure 3.6 in 1990 the European countries consumed the 73% of the world olive oil production, while in 2013 this percentage has been reduced to the 58%. This reduction is due to a more or less stable consumption for the EU countries while the world production has been increased from 1,666,600 tons in 1990 to 3,041,000 tons in 2013. Among the EU countries, Italy is traditionally the first of world consumption (19% in 2013), followed by Spain (17%), Greece (7%) and France (3%). Among the non-EU countries it can be seen that USA, Turkey and Morocco have doubled their consumption in percentage, while Syria has been stable, meaning that the consumption in this country has increased like the global production.
Figure 3.6: World olive oil consumption in 1990 and 2013 Source: IOOC, 2014.
The first world exporter is traditionally Italy, with 216,000 tons in 2013, followed by Spain (177,500 tons) and Tunisia (175,000) (IOOC, 2014). Togheter this three countries sum the 74% of the global exportation of olive oil. That means that
24% 32% 12% 2% 3% 5% 3% 4% 2% 13% 1990 Spain Italy Greece France Others EU USA Turkey Syria Morocco Others World 17% 19% 7% 3% 10% 10% 5% 4% 4% 21% 2013 Spain Italy Greece France Others EU USA Turkey Syria Morocco Others World
16 about half of the italian olive oil production is consumed abroad, while in Italy foreign olive oils are consumed.
The increasing demand of olive oil is due to its healthy properties, that are scientifically well know and start to be also appreciated in non traditionally producing countries (Civantos et al., 2008). Effectively, the olive oil has an high content of monounsaturated fatty acid, that are necessary for the human nutrition but cannot be synthetized by the human organism. Moreover the presence of antioxidant compounds as tyrosol and hidroxityrosol and other poliphenolic compounds represent an important source for the organism.
3.1.3 Olive oil production techniques
The extraction process of olive oil includes some different phases: washing, grinding/crushing, malaxation and extraction. Nowadays there are three technologies that are still used for the olive oil extraction: traditional batch pressure process, basically unchanged since centuries, except for technical changes linked to the scientifical progress; continuously three-phase centrifugation system, established in the second half of the last century; continuously two-phase centifugation system, developed in the last three decades. The three different production systems are represented in Figure 3.7
17 Figure 3.7: Olive oil production systems: a) traditional process, b) three-phase centrifugation system, c) two-three-phase centrifugation system.
As it can be seen in Figure 3.4, the traditional process is still used in many European countries, even if is quite an antiquated technology. In Italy the press system is used especially in the central regions, while in the south the three-phase centrifugation system prevails.
The three extraction systems have some common phases (Petrakis, 2006):
Washing, consisting in leaf removal and washing with recycled water, mixed in pre-set proportion with clean water
Grinding/crushing, consisting in the crushing of the pulp, due to pit fragments obtained with a rotating stone (grinding) or due to high speed rotating disk (crushing).
Malaxation, consisting in rotating blades that enhances the coalescence of oil droplets and make the paste uniform.
Then they differ for the extraction method used: the press system nowadays employs hydraulic super-presses that gradually increase the pressure on the paste up to
18 400 atm within 45-60 minutes, remaining at that high pressure for an additional 10-20 minutes. Then the liquid phase has to be centrifuged in order to separate the oil from the wastewaters. This technique guarantees top quality oils, due to the short beating time and the low temperatures throughout the entire operation (Petrakis, 2006). Disadvantages are the higher operational costs and the non-continuity of the process.
Both centrifugation systems employ horizontal and vertical centrifugation. The main difference between the two systems is that in the three-phase it is necessary to add warm water to ensure the extraction, while in the two-phase system this is not needed (CAR/PL, 2000). Once the first horizontal centrifugation has been done the oil has to be purified from the little percentage of water left and in order to reach this purpose another vertical centrifugation has to be done.
The two-phase system has been defined as “ecological” mainly due to the absence of the addition of warm water that means lower consumption of water, energy and a lower environmental impact. Furthermore there are other reasons that make the two-phase system more suitable than the three-phase one (CAR/PL, 2000):
Considerably lower amount of wastewaters, with reduction of removal costs.
The two-phase decanter is easier to build, with lower purchase costs. The capacity of the two-phase system is higher due to the absence of
the additional water that means process-time reduction.
Quality of the oil produced with the two-phase decanter is better in terms of oxidation resistance due to the higher quantity of phenolic compounds left in the oil.
Operational costs are lower.
Despite these advantages, in Italy the two-phase decanter is not largely used, mainly due to the impossibility of the extraction plants to sustain the costs of the purchase of a new decanter, when they are still sustaining the amortisation costs of the three-phase decanter.
19
3.1.4 Olive oil quality: definition and regulations
According to the Codex Alimentarius Commission, the olive oil has been defined in the Codex-Stan 33-1981 as follow:
“Virgin olive oils are the oils obtained from the fruit of the olive tree (Olea europaea L.) solely by mechanical or other physical means under conditions, particularly thermal conditions that do not lead to alterations in the oil, and which have not undergone any treatment other than washing, decantation, centrifugation and filtration”.
Defining the quality however has result to be more complex, and various definitions have been elaborated:
Hidalgo-Casado et al., (1993) have defined the quality of a product as “the set of those characteristics that are substantial to determine acceptance level that the consumer would or should appreciate”. This definition is out of doubt very general and subjective, because the product acceptance depends on the destination of the product.
ISO 9000:2005 defines quality as “the degree to which a set of inherent characteristics fulfil needs or expectation that are stated, generally implied or obligatory”. This definition contains both the official regulation and the voluntary regulation, referred to product quality and system quality.
Uceda et al., (2008) referring to the olive oil quality, underline that different concepts of quality exist starting from the definition contained in the regulation to the nutritional quality, commercial quality, sensory quality and so on.
As it can be seen is not easy to define the quality, especially when different actors are involved: for example, the quality concept that has the producer is not the same that has the transformer, or the seller or the consumer, because every actor focusses on those characteristics that are optimal from his point of view and those characteristics not necessarily overlap to those of the other actors.
20 Moreover, in a product as the olive oil, quality is deeply affected by some agronomic parameters (cultivar, soil, weather) that could have great influence on the organoleptic (colour, flavour) and chemical characteristics of this product. Those parameters are named intrinsic quality (Civantos et al., 2008).
3.1.4.1 Olive oil sensory analysis
Sensory analysis is essential to determine a product quality due to the impossibility to define in an unmistakable way the quality only using chemical analysis, in as much in a complex matrix there are so many compounds and so many interactions between them that is unrealistic to try to define quality only based on the chemical analysis.
Sensory analysis has been defined as a scientific discipline employed to measure, analyse and understand human reactions against those characteristics of food perceptible by senses. This evaluation could be done by a sensory panel (Jiménez-Herrera and Carpio-Dueñas, 2008).
Nowadays the panel test is mandatory for the olive oil, being expected from the Commission Implementing Regulation (EU) No. 1348/2013. Due to the subjectivity of a single panellist, the results could not be used scientifically, but if the number of panellist increase, the results became objective and allow to fix thresholds (Civantos et
al., 2008). The parameters that are evaluated by the panellists are “fruitiness” that with
the acidity level is used to distinguish different classes of olive oils, together with “bitterness” and “pungent”. Furthermore the panellists are required to evaluate some defects as “fusty”, “rancid”, “winey” and so on.
Referring to the taste of the olive oil, the sweet taste cannot be perceived, due to the lack of sucrose in the olive oil. Also the salty taste cannot be perceived unless there are problems in the olive oil production process, due to the lack of sodium chloride in the matrix. The acid taste cannot be caused by the free fatty acids due to their low dissociation constant but can be perceived as the bitter taste due to the presence of secoiridoids, a polyphenol class of compounds (Aparicio and Harwood, 2003).
These compounds could be also responsible for the astringent sensation that sometimes could be appreciated in some olive oils. According to several authors (Lee
21 and Lawless, 1991; Naish et al., 1993; Lule and Xia, 2007) this sensation is caused by the precipitation of salivary proteins due to hydrogen bonds with the hydroxityrosol, a secoiridoid compound, that produce the lack of lubricant ability of the saliva.
In the same way the pungent attribute has been related with the content of ligustroside and oleuropein derivatives (other secoiridoids) (Fernández-Gutiérrez and Segura-Carretero, 2009).
Regarding the aroma, the sensation derives from many volatile compounds that bond with neural receptors in the olfactory epithelium (Aparicio and Harwood, 2003). Those volatile compounds derive from the action of some enzymes as lipoxygenase, that originate aldehydes, and alcohol dehydrogenase that originate alcohols (Fernández-Gutiérrez and Segura-Carretero, 2009). The intensity of the perception is more related with some chemical factors like volatility, hydrophobicity and stereochemical structure than the concentration. Several authors (Pelosi, 1994; Rossiter, 1996) have demonstrated that the stereochemical structure is essential to form the correct bond with the olfactory proteins and origin the perception.
The content in volatile compounds in olive oil, that are low molecular weight aldehydes, alcohols and ketones, is affected by the enzymes activity, in turn affected by the genotype, the agronomic and technological parameters (Aparicio and Harwood, 2003).
3.1.4.2 Chemical characterization of olive oil
The olive oil is basically composed by triglycerides and in fewer amounts by free fatty acids and non-glycerides compounds. Those components are divided into two groups (Fernández-Gutiérrez and Segura-Carretero, 2009):
1. Saponifiable fraction: it represent almost the total of the olive oil weight (98-99%) and gather together triglycerides, diglycerides (1.3%) and monoglycerides (0.2%) and some free fatty acids.
Triglycerides are esters of three fatty acids and glycerol, as it can be seen in Figure 3.8.
22 Figure 3.8: Chemical structure of a generic triglyceride
The three fatty acids that form the triglyceride are normally constituted by a carbon chain of 14-24 atoms. If they are saturated there will not be double bonds in the carbon chain, while if they are unsaturated there will one or more double bonds in the chain. The olive oil has a high percentage of monounsaturated fatty acids that allow the oil to be liquid at room temperature due to the lower fusion point. Furthermore a higher number of double bonds, particularly conjugated dienes and trienes, typical of linoleic and linolenic acids make an oil more susceptible to the oxidation.
In Table 3.1 can be seen the percentage of the major fatty acids present in an olive oil obtained from the most diffuse Spanish cultivar, Picual.
23 Table 3.1: Composition of fatty acids in Picual olive oil
Fatty acid Composition (%)
Palmitic acid (C16:0) 11.93 Palmitoleic acid (C16:1) 0.90 Heptadecanoic (C17:0) 0.04 Heptadecenoic acid (C17:1), 0.07 Stearic acid (C18:0) 2.10 Oleic acid (C18:1) 79.1 Linoleic acid (C18:2) 2.95 Linolenic acid (C18:3) 0.64 Arachidic (C20:0) 0.31 Eicosenoic acid (C:20:1) 0.24 Behenic acid (C22:0) 0.10
Saturated fatty acids 14.48
Monounsaturated fatty acids 80.31
Polyunsaturated fatty acids 3.59
Source: Beltran et al., 2004.
Fatty acid composition changes from sample to sample, depending to the production area, cultivar, environmental condition and maturity level of the olives.
A high level of mono and diglycerides in the olive oil is a sign of hydrolysis in the olive oil and consequently of reduction in olive oil quality (Aparicio and Harwood, 2003).
2. Unsaponifiable fraction: these compounds constitute only the 1-1.5% of the olive oil weight, but they are essential for the preservation of the olive oil and the biological value besides the stability, taste, flavour and quality of the olive oil (Boskou, 1999).
Compounds of this fraction, even though they constitute a little part of the global weight of olive oil, can be divided into two groups due to their large variety (Fernández-Gutiérrez and Segura-Carretero, 2009):
24 I. Fatty acids derivatives (phospholipids, waxes and sterols esters). The
phospholipids are set up by a glycerol molecule with two fatty acids and a phosphate group bond in the place of the third fatty acid. The phosphate group can bond with other molecules, normally containing nitrogen, as choline or ethanolamine. The main phospholipids detected in olive oil are phosphatidylcholine, phosphatidylethanolamine, phosphatidylinositol and phosphatidylserine (Hatzakis et al., 2008). Their antioxidant activity has been demonstrated and attributed to their catalytic activity in hydroperoxide decomposition or to form complexes with prooxidant metals (Hatzakis et al., 2008).
Waxes are compounds formed by esterification of high-molecular-mass alcohols with fatty acids. The length and structure of the alcoholic group is variable; thus, if the alcoholic groups are long chain aliphatic alcohols they result in aliphatic waxes of 34-46 carbon atoms. If the alcoholic groups are sterols, triterpenic alcohols or methylsterols, the compounds are generally named terpenic waxes (Pérez-Camino et al., 2003).
II. Non fatty acid related compounds, as terpenic hydrocarbons, carotenoids and chlorophylls responsible for the colour, tocopherols and polyphenols, major responsible of the antioxidant activity and taste and volatile compounds responsible of flavour.
According to the Commission Implementing Regulation (EU) N. 1348/2013, the chemical parameters that have to be measured in order to monitoring the olive oil quality are the following:
Titratable acidity, determining the concentration of free fatty acids expressed in % of oleic acid.
Peroxide index, determining the primary oxidation before the detecting of rancid taste and flavour (Jiménez-Herrera and Carpio-Dueñas, 2008)
Waxes content, determining if there have been adulterations by pomace oil (the maximum of wax content for virgin olive oils is 150 mg/Kg) (Regulation N. 1348/2013)
Saturated fatty acids in position 2 of triglycerids, determining if there have been adulterations by refined olive oils that have this
25 characteristic, while virgin olive oil typically have unsaturated fatty acid in position 2 (98%) (Civantos et al., 2008).
Stigmadienes, determining the adulteration due to its formation at high temperatures and decolouration.
Difference between ECN 42 by HPLC and by calculation, determining the adulteration by seed oils due to the different composition in triglycerides (triolein in olive oil and trilinolein in seed oil) (Jiménez-Herrera and Carpio-Dueñas, 2008).
UV absorbance: K232 (conjugated dienes), K270 (conjugated trienes) and
ΔK (purity criteria).
Fatty acid composition.
Isomeric transoleic, translinoleic and translinolenic acids sum, determining the adulteration by refined oils.
Sterols composition, determining adulteration by seed oils.
Erythrodiol and uvaol, determining the adulteration by pomace oil.
3.1.4.3 Factors affecting olive oil quality
There are many factors that can affect the olive oil quality, involving those which affect the olive fruit, those which affect the olive oil extraction process and those affecting the olive oil conservation.
Pre-harvest factors: divided into intrinsic factors, that cannot be easily modified, like cultivar, soil and climate conditions, affecting polyphenols content, fatty acid composition and peroxide index, and extrinsic factors, like cultivation techniques, irrigation and pest infestation, affecting mainly acidity content, polyphenols and carotenoids content. (Jiménez-Herrera and Carpio-Dueñas, 2008).
Harvest factors: harvest time, affecting polyphenols content and consequently flavour, bitterness and stability of the oil. Delaying the harvest time can cause the fruit fall, and the harvest of fallen fruits can origin defects as winey in the olive oil, also increasing the acidity level. The harvest system can affect both the quality of olives and the health of the plant. There are three harvest systems: hand-picking, the less
26 damaging technique, but with higher costs; beating the three benches that cause damage at the tree and cause losses in the next year production. If the fallen olives are not collected from canvas but from the soil there will be alteration in the acidity level and there could be herbicide residues in the olive oil; mechanical shaking of the tree that is the most used technique due to the lower costs and damage to the tree (Civantos et al., 2008).
Post-harvest factors: transport to the olive mill can affect the olive oil quality due to wounds and temperature increase of the olives. It is necessary to avoid the use of plastic bags, due to the pressure on the olives and the temperature increase that enhances fermentation and drop of quality parameters (Hermoso et al., 1991). It is better to employ drilled plastic trunks or put the olives directly in the trailer, paying attention to divide the ones harvested from soil from the ones harvested from the tree.
Process factors: initial screening, cleaning of equipment, malaxation temperature. The initial screening is fundamental in order to avoid alteration of the organoleptic properties of the olive oils (Barranco et al., 2008). The fruit cleaning is recommended only if fruits are immediately processed, due to the possibility of starting of degradation processes, polyphenol losses and stability loss in a wet fruit (Barranco et al., 2008). For the malaxation, low temperatures (25°C) and short times (35-40 min) are recommended to obtain the best quality, avoid defects and avoid losses in polyphenol content (Angerosa et al., 2001; Di Giovacchino et al., 2002; Inarejos-García et al., 2009). Furthermore, the extraction process can also affect the polyphenol and aromas content. The two-phase centrifugation system, due to the lack of water addition, seems to diminish the intensity of the organoleptic defects and delay the oxidation processes.
27 3.2 Traditional chemical and sensory methods for quality determination in virgin olive oil
3.2.1 Thin Layer Chromatography (TLC) and Gas Chromatography (GC)
Thin Layer Chromatography and Gas Chromatography are two separative techniques in which the components are divided into two phases: the stationary phase and the mobile phase.
The Thin Layer Chromatography is used to separate non-volatile compounds (Lewis and Moody, 1989). The stationary phase is constituted by silica gel or aluminium oxide or cellulose that is mixed with an inert binder as calcium sulphate and water. Then the mixture is spread as a thin layer (0.1 – 2 mm) on an unreactive carrier sheet, usually glass, aluminium or plastic. Next the plate is dried and activated in oven at 110°C for 30 min. Then a little drop of solution with the sample is applied to the plate, about 1.5 – 2 cm above the bottom edge. The solvent is allowed to evaporate completely and if it is non-volatile the plate has to be dried in a vacuum chamber. Next a small amount of a proper solvent (eluent) that is selected depending on the compounds that have to be separated is put in a separation chamber to a depth less than 1 cm. A strip of filter paper is put in the chamber so that the bottom touches the solvent and the top is almost at the top of the container. The container is closed in order to saturate the chamber with solvent vapours. Than the plate is put into the separation chamber, paying attention that the sample does not touch the eluent and the chamber is closed. The solvent moves up the plate by capillary action, meets the sample mixture and carries it up the plate. The filter paper acts like a wick to assure that the chamber is uniformly saturated with the solvent vapours. This gives a straight solvent front, rounder spots and it shortens the developing time by about 1/3. The plate should be removed from the chamber before the solvent front reaches the top of the stationary phase (continuation of the elution will give a misleading result) and dried (Mangold, 1961). This technique is mandatory in the analysis on the olive oils for the determination of sterols content and composition, for the determination of erythrodiol and uvaol content and for the determination of the aliphatic alcohols content; is employed as preparation for the capillary column gas chromatography (Commission Implementing Regulation (EU) N. 1348/2013).
28
The Gas Chromatography is used to separate different compounds through vaporization. The stationary phase could be a liquid phase, and this will be named Gas-Liquid Chromatography, or a solid phase, naming the technique Gas-Solid Chromatography. The mobile phase is an inert carrier gas, like nitrogen or helium. The sample can be either a gas, a liquid or a solid, all that is required is that the sample components be stable, have a vapour pressure of approximately 0.1 Torr at the operating temperature and interact with the column material (Grob and Barry, 2004). When the sample is injected in the column the components interact with the stationary phase and are divided in distinct manner between the two phases due to their different solubility or volatility. Then the mobile phase trails the various components through the column and to the detector, where a measurable signal is produced (Skoog et al., 2008). Separation of the sample components may be achieved by one of three techniques: frontal analysis, displacement development or elution development.
In the frontal analysis the mixture (liquid or gas) is fed into a column containing solid packing, and act as its own mobile phase. The separation depends on the ability of each component to become a sorbate. Once the column has been saturated the mixture flows through with its original composition, so the least-sorbed component is the first to break to the front leaving the column and is the only one to be obtained in a pure form (Grob and Barry, 2004).
In the displacement development the developer is contained in the moving phase that has to be sorbed more than any sample components. It is always obtained a single pure band of the first component in the sample and in addition there is always an overlap zone for each succeeding component. The disadvantage is that the components band is not separated by a region of pure mobile phase (Grob and Barry, 2004).
In the elution development the eluent is equally distributed through the column. The different components travel through the column at rates determined by their retention on the solid packing. If the differences in the sorption are sufficient or the column is long enough a complete separation of the components is possible. The continuous addition of eluent causes the emergence of separated bands or zones from the column. The main disadvantage is the very long time interval
29 required to remove a highly sorbed component, but this could be overcome by increasing the temperature during the separation process.
When the components are eluted they reach the detector; the intensity and duration of the signal will be related to the quantity of the component. Normally the signal is amplified and recorded by an electronic device that produces the chromatogram through which the component can be identified and quantified. There are several types of detectors, but the most used are the Thermal Conductivity Detector, the Flame Ionization Detector, the Electron Capture Detector and the Mass Spectrometer (Skoog et al., 2008).
This technique is mandatory in the analysis on the olive oils for the determination of wax content, of sterols composition and content, for the determination of erythrodiol and uvaol content, for the determination of 2-glycerylpalmitate, for the determination of the aliphatic alcohols content, for the determination of methyl esters, ethyl esters, volatile halogenated solvents, and stigmastadienes content (Commission Implementing Regulation (EU) N. 1348/2013).
3.2.2 High Performance Liquid Chromatography
The High Performance Liquid Chromatography is a separative technique in which the sample components are distributed between a mobile phase (the eluent) and a stationary phase (the material in the column). The stationary phase used today are called micro-particulate column packing and are commonly uniform, porous silica particles, with spherical or irregular shape, and nominal diameters of 10.5 or 3 μm, capable to improve the efficiency of the separation compared with the Gas Chromatography (Lindsay, 1997). The reduction of the particle diameter results in a higher working pressure, reaching the 350 bar. Furthermore this technique can be applied to non-volatile and non-stable samples and there are more eluent that can be used respect to the Gas Chromatography, allowing a major selectivity of the process (Gismera-García and Quintana-Mani, 2009).
According to the polarity of the two phases can be distinguished two type of chromatography:
30 Normal phase chromatography, in which the stationary phase is polar and the
mobile phase is nonpolar. In this case the nonpolar components are eluted firstly while the polar components are hold stronger in the column.
Reverse phase chromatography, in which the stationary phase is nonpolar and the mobile phase is polar. In this case the polar components are eluted firstly. The columns used for this type of HPLC could have some alkyl derivate silica particles and are normally more difficult to damage than the normal phase columns.
The most used detectors are those based on the UV/visible absorption or those, more specifics that depends to specific properties of each solute, like fluorescence, electric conductivity, radioactivity and especially mass spectrometry.
This technique is mandatory in the olive oil for detection of actual content of triacylglycerols (Commission Implementing Regulation (EU) N. 1348/2013).
3.2.3. UV spectrophotometric analysis
Ultraviolet spectroscopy refers to absorption spectroscopy or reflectance spectroscopy in the ultraviolet spectral region. This means it uses light in the UV range (100 - 380 nm). The absorption or reflectance in the visible range directly affects the perceived colour of chemicals involved. This technique is complementary to fluorescence spectroscopy, in that fluorescence deals with transition from the excited state to the ground state, while absorption measures transition from the ground state to the excited state (Skoog et al., 2008). The transition is due to the presence of π-electrons or non-bonding electrons that can absorb the energy of the ultraviolet light exciting themselves to higher anti-bonding molecular orbitals. The functional groups containing those electrons are called chromophores (Korshin et al., 1997)The measurement comply the Beer-Lambert law that states that the absorbance of a solution is directly proportional to the concentration of the absorbing species in the solution and the path length. Thus, for a fixed path length, UV spectroscopy can be used to determine the concentration of the absorber in a solution. It is necessary to know how quickly the absorbance changes with concentration and that can be determined from a calibration curve (Mehta, 2012).
31 The Beer-Lambert law states:
( ⁄ ) where:
A = Measured absorbance, in absorbance units (AU)
I0 = Intensity of the incident light at a given wavelength
I = Transmitted intensity.
L = Path length through the sample
c = Concentration of the absorbing specie
ε = extinction coefficient, typical for each species and wavelength
Actually, a spectrophotometer does not measure the absorbance but the transmittance, meaning the ratio between the intensity of the light passing through the sample (I) and the intensity of the light before it passes through the sample (I0). The
absorbance formula is modified as follows:
( )⁄
This technique is mandatory in the olive oil analysis for the determination of the absorbance at 232 nm and 270 nm. The two coefficients of absorbance, K232 and K270,
underline respectively the absorbance due to the conjugated dienes and trienes that are present in the olive oil and are considered quality parameters (Commission Implementing Regulation (EU) N. 1348/2013).
3.2.4 Iodine number determination
According to the Commission Implementing Regulation (EU) N. 1348/2013 the iodine value is defined as the mass of iodine absorbed by the sample under the operating conditions specified in the International Standard, and is expressed as grams of iodine per 100 g of sample.
For the analysis in the olive oil, a test portion of the sample, varying from 0.10 g to 3 g according to the expected iodine value, is placed in a 500 ml flask and 20 ml of solvent solution of cyclohexane and acetic acid in equal volume has to be added to
32 dissolve the fat. 25 ml of Wijs reagent, containing iodine monochloride in acetic acid, has to be added, then the content has to be swirled and the flask placed in the dark. Another flask has to be prepared without the sample in order to carrying a blank test. If the expected iodine value is under 150 the flask has to be left in the dark for one hour, otherwise for two hours. Then 20 ml of potassium iodide solution and 150 ml of water has to be added and a titration with a standard volumetric sodium thiosulfate solution has to be done.
The iodine value is given by the following expression:
( )
where:
c = Numerical value of the exact concentration (mol/L) of the standard volumetric sodium thiosulfate solution
V1 = Numerical value of the volume (ml) of the standard volumetric sodium thiosulfate
solution used for the blank test
V2 = Numerical value of the volume (ml) of the standard volumetric sodium thiosulfate
solution used for the determination
m = Numerical value of the mass (g) of the test portion used
The arithmetical mean of two determinations should be taken, providing that the requirement for the repeatability is satisfied.
3.2.5 Panel test
The evaluation of the organoleptic characteristics of an olive oil is an essential criterion in order to determine its quality. The principal parameters evaluated are taste, colour and flavour and, in order to avoid misunderstanding, the International Olive Oil Council has regulated the vocabulary that has to be employed (COI/T.20/Doc. nº 4/Rev. 1), the glasses (COI/T.20/Doc. nº 5/Rev. 1), the trial room (COI/T.20/Doc. nº 6/Rev. 1) and the selection and training of the panellists (COI/T.20/Doc. nº 14/Rev. 4). Those methods have been accepted by the European legislation, being published in the Annex V of the Commission Implementing Regulation (EU) N. 1348/2013.
33 The olive oil colour ranges from green to yellow or gold, depending on the content of chlorophylls and carotenoids (Mínguez-Mosquera et al., 1991; Visioli et al., 2002). Colour could be determined analytically, but as taste and flavour it is normally determined by a panel test.
The principal positive flavour notes that are evaluated are fruity and greenly fruity, remembering respectively sound, fresh olives and green fruit. Furthermore there are other flavour notes, typical of very high quality olives and oils, that remember the flavour of some vegetables or fruits, as apple, almond, walnut husk, banana and so on (Angerosa et al., 2000).
The principal taste notes that are evaluated are fruitiness, bitterness, pungent, astringent, and sweet. Moreover there are some negative attributes that are classified as defects and that origin from low quality olives, or bad process conditions or bad conservation conditions (Alba et al., 1997). Those attributes are fusty, muddy, musty, humid, earthy, winey, rancid, greasy, and metallic (Commission Implementing Regulation (EU) N. 1348/2013).
3.3. Near-infrared reflectance spectroscopy (NIRS) for quality determination in the olive oil industry
Since its first appearance in the sixties years of the last century, this techniques has become one of the most employed in various agro-industrials sectors, because it has all the necessary characteristics to perform a modern and computerized quality control (Garrido-Varo et al., 1996). In fact it requires little or no sample preparation, it is both flexible and versatile (applicable to multiproduct and multicomponent analysis), since it can examine a wide range of samples and simultaneously provide results for multiple analytes in each sample (De la Haba et al., 2014); since it is non-destructive, the sample is not harmed by analysis, which is a key advantage in fruits (Sánchez and Pérez-Marín, 2011); the technique is environment-friendly, in that it generate no waste, thus obviating the problem of residue disposal, characteristic of traditional chemical methods (Bendini
et al., 2007); operating costs are lower that with conventional methods, and although the
instrument itself is expensive, its purchase may be considered a short-to medium-term investment; it is easy to operate, requiring no specific training and it can be built into the processing line, enabling large-scale individual analysis and real-time decision making (Sánchez and Pérez-Marín, 2011).
34 However this technology has some drawbacks. The major limitation of NIRS technology is that it is a secondary method of analysis that needs to be calibrated against a conventional reference method (Sánchez and Pérez-Marín, 2011). Furthermore the NIR spectrum provides a large amount of data that have to be managed, but the great majority of this information is redundant due, among other things, to the overlapping of absorbance bands which make harder the attribution of those bands to specific molecular groups (Miller, 2001). Additionally, a number of physical effects related to product particle size, to the form of sample presentation, and to the texture of the product, make difficult to extract the information required. Over recent years, however, advances in data compression methods and in the pre-treatment of the spectral signal have helped to overcome these initial limitations. Finally, although the routine use of NIRS technology is easy and instantaneous, the development of calibrations and qualitative or classificatory models requires a good knowledge of chemometrics, while it may take years to obtain sufficiently robust models, particularly for food products (Sánchez and Pérez-Marín, 2011).
3.3.1 Theoretical basis
The infrared radiation, discovered in 1800 by Herschel, include the spectral range between 780 and 2*106 nm. In this range, the near-infrared range is considered between 780 and 2500 nm, between the visible region and the mid infrared region (Davies, 2006).
However, almost a century passed before its first analytical application in what came to be termed NIRS (Near Infrared Reflectance Spectroscopy) (Sánchez and Pérez-Marín, 2011) and before the invention of modern NIR spectroscopy, which incorporates the use of multivariate analysis in the treatment of data generated by high-performance spectrophotometers, this spectral region was of a little commercial or academic interest (Workman and Shenk, 2004). The ultimate push towards the acceptance of this technology as an analytic method is certainly due to the work of Karl Norris and his research group that started to test the uses of NIR spectroscopy in the agricultural produce analysis, determining the major components of meats, intact apples and cereals (Hart et al., 1962; Ben-Gera and Norris, 1968).
NIRS is defined as a vibrational spectroscopic technique, in that it exploits the absorption taking place when radiation from the near-infrared vibrates at the same
