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Linking Chemical Parameters to Sensory Panel Results through Neural Networks to Distinguish Olive Oil Quality John C Cancilla, Selina C Wang, PABLO DIAZ-RODRIGUEZ, Gemma Matute, John D. Cancilla, Dan Flynn, and José S. Torrecilla J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/jf503482h • Publication Date (Web): 09 Oct 2014 Downloaded from http://pubs.acs.org on October 14, 2014

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Journal of Agricultural and Food Chemistry

Linking Chemical Parameters to Sensory Panel Results through Neural Networks to Distinguish Olive Oil Quality John C. Cancilla1, Selina C. Wang2, Pablo Díaz-Rodríguez1, Gemma Matute1, John D. Cancilla3, Dan Flynn2, José S. Torrecilla1* 1

Departamento de Ingeniería Química, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, 28040-Madrid, Spain. 2

University of California-Davis, Olive Center, Davis, California 95616, United States.

3

CHERRYGATE, S.L., 28224-Madrid, Spain.

*

Corresponding author. Tel.: +34 91 394 42 44; Fax: +34 91 394 42 43. E-mail address: [email protected].

1 ACS Paragon Plus Environment


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Abstract

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A wide variety of olive oil samples from different origins and olive types have

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been chemically analyzed, as well as evaluated by trained sensory panelists. Six

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chemical parameters have been obtained for each sample (free fatty acids, peroxide

5

value, two UV absorption parameters (K232 and K268), 1,2-diacylglycerol content, and

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pyropheophytins), and were linked to their quality using an artificial neural network

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based model. Herein, the nonlinear algorithms were used to distinguish olive oil quality.

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Two different methods were defined to assess the statistical performance of the model

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(a K-fold cross-validation (K = 6) and three different blind tests) and both of them

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showed around a 95-96% correct classification rate. These results support that a

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relationship between the chemical and sensory analyses exists, and that the

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mathematical tool can potentially be implemented into a device that could be employed

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for various useful applications.

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Key Words: Olive Oil; Quality Control; Artificial Neural Networks.

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Introduction

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Olive oil plays a major role in the Mediterranean diet due to being its main

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source of fat.1 Many studies have shown that diets supplemented with olive oil provide

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multiple beneficial biological effects for humans, some of which include a high

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antioxidant activity and favoring lower amounts of low-density lipoproteins (LDL).2

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These traits, among others, can potentially make people that follow a Mediterranean diet

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on a daily basis less prone to developing cardiovascular diseases or even cancer,3-4

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although further clinical trials would be necessary to firmly attest to this.

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The valuable features that olive oil can offer both gastronomy- and health-wise,

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have turned its research into a very important part of the food technology field. This fact

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is reinforced by the numerous scientific articles that exist on the topic, which cover a

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wide variety of themes such as specific compound extraction as well as assessing their

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biological effect,4-5 volatile molecule detection and quantification,6,7 or quality controls

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to locate adulterated olive oils and to authenticate them by their geographical or

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protected denomination of origin.8-11

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A different type of quality control that could be heavily exploited in the olive oil

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sector would be one that is able to accurately distinguish extra virgin olive oils

34

(EVOOs) from lower grades, such as virgin (VOO), ordinary virgin (OVOO), or

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“lampante” (LOO) olive oils. Similar classifications have already been attempted to

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distinguish among edible and non-edible olive oils (according to European Union

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marketing standards) using quartz crystal microbalance arrays,12 metal-oxide sensors,13

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fluorescence and luminescence studies,14 or gas chromatography-mass spectrometry,15

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all combined with linear approaches. Other methodologies that have been examined as

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well are based on the analysis of the headspace in order to evaluate volatile molecule



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profiles. It has been carried out directly with mass spectrometry to design a tool that can

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potentially prevent the commercialization of non-edible olive oils or through multi-

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capillary column-ion mobility spectrometry to differentiate three types of olive oils

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(EVOO, VOO, and LOO).16,17 The results attained during these last two studies were

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also coupled with lineal algorithms.

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Lineal mathematical modeling is commonly used due to the many available

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options and relative simplicity that characterizes it. Nevertheless, in many cases there

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are relations among parameters that these models are unable to interpret adequately.

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This is when alternative algorithms, such as non-linear artificial neural networks

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(ANNs), should come into play. ANNs are a broadly applied set of mathematical tools

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that are successfully able to discover non-linear trends that exist between different

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variables thereby allowing the design of accurate estimative models that may be out of

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reach for classic lineal approaches.11 ANNs can and have been utilized for numerous

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applications in a variety of fields, including food technology and,18 specifically, olive

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oil analysis.9,11 Additionally, ANNs have also been employed to address similar tasks as

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the olive oil quality control proposed here in terms of classifying through pattern

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recognition in, for instance, chemical and food-related applications.19,20

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In the present article, a reliable mathematical model based on ANNs that is able

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to classify olive oil samples from a wide variety of origins and grades (EVOO, VOO, or

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OVOO) is proposed. To do so, six different chemical parameters (free fatty acids

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(FFAs), peroxide value (PV), two UV absorption parameters (K232 and K268), 1,2-

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diacylglycerol content (DAGs), and pyropheophytins (PPPs)), some or all of which are

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considered by various national and international standards as quality or grade indicators

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of olive oil,21-23 have been linked to results provided by trained sensory panelists to, in

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the end, be able to provide a quality assessment of blind olive oil samples. 4 ACS Paragon Plus Environment

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Materials and Methods

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Olive Oil Samples

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Two hundred and twenty samples of extra virgin olive oil were collected before

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their best-before date during 2010-2012, when this information was included on labels;

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some samples did not have labels indicating such information. Samples were stored in

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an air-conditioned laboratory (20-25 °C) away from light. Nitrogen gas was used to

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displace air in the space above the oil.

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The sampled olive oils are a blend of different olive varieties, and came from

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various olive oil producing countries, such as Spain, Italy, Greece, Tunisia, and the

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United States.

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Chemical and Sensory Analyses of the Olive Oil Samples

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Six different chemical parameters were obtained from the 220 olive oil samples

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studied. They are FFA, PV, two UV absorption parameters (K232 and K268), DAGs, and

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PPP. These were selected as they have been linked to in the past to olive oil quality

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evaluation by multiple national and international sources. Specifically, FFA, PV, and

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both UV absorption parameters are associated with olive oil grades by numerous

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relevant organizations like the International Olive Council (IOC)22 and United State

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Department of Agriculture (USDA),23 while DAGs and PPP are related to olive oil

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quality, for instance, by Australian Standards®21 or Guillaume et al. (2014).24



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In addition, sensory analyses were carried out by a trained taste panel (IOC-

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certified Australian Oils Research Laboratory (AORL))25 to attain a reliable olive oil

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grading of the samples. The reasons for the parameter selection, protocols followed, and

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equipment employed are described in the next subsections.

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Free Fatty Acids

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The FFA is linked to olive oil quality as it manifests the carefulness taken during

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the production process as well as the intrinsic quality of the employed fruit.23 FFAs are

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formed when triacylglycerols in olive oils suffer hydrolysis during extraction,

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processing, distribution, and storage. For this reason, high levels of FFA can be linked

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to poor-quality olive oils.

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FFA values were determined by the method of the American Oil Chemists Society (AOCS), Ca 5a-40 and were expressed as a percentage of oleic acid.26

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Peroxide Values

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The PV of an olive oil is a crude measurement of its primary oxidation due to

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oxygen exposure. This oxidation leads to secondary oxidation products which give

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undesirable flavors and odors. In general, high PVs imply an oxidized oil and, therefore,

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a lower quality product.23

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PVs were determined according to International Standard ISO 3960:2007 and were expressed as meq O2/kg.27

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Ultraviolet Absorption Measurements

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The information gathered from these tests offer information concerning quality,

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state of preservation, and modifications originated by the processing method.23

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Conjugated double bonds are formed from natural non-conjugated molecules in olive oil

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due to oxidation. This means that an elevated UV absorbance (high K232 and K268) is

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related to oxidized and low grade oils.

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Ultraviolet absorbance was determined using IOC method COI/T.20/Doc. 19.28

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1,2-Diacylglycerol Content

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DAGs have shown to be reliable markers of olive oil freshness and quality in the

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past and are employed by Australian Standards® as a quality assessing parameter.21

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They are excellent indicators of initial poor quality or low grade olive oils, and high

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temperatures affect their values. High quality and fresh olive oils contain a high level of

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1,2-Diacylglycerols.24,29

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1,2-Diacylglycerols were determined using the method ISO 29822:2009.30,31 A

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miniaturized chromatography column of silica gel 60 at 5% moisture was used to

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separate the isomeric diacylglycerols. The diacylglycerol profiles were determined by

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gas chromatography using a SGE BP5 capillary column (30 m, 0.25 mm, 0.25 µm film)

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and a flame ionization detector (FID).

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Pyropheophytin Quantification



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The determination of PPPs can also be used as an indicator of general quality

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and freshness of olive oil samples.24 Comparable to DAGs, PPPs are utilized by

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Australian Standards® for quality evaluation processes,21 but, in contrast, these are not

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influenced by the initial quality of the olive oil. As a matter of fact, cultivar or growing

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conditions do not affect PPPs either. High temperatures and light exposure are

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responsible of PPP amount alteration.24 Their values increase upon thermal degradation

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of olive oil thus being related to lower quality products.

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Pyropheophytin A was measured using method ISO 29841:2009.32,33 A

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miniaturized chromatography column of silica gel 60 at 5% moisture was used to

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separate the pheophytins. Separated sample was dissolved in 200 µL of acetone and

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analyzed by HPLC with a mobile phase of water:methanol:acetone/4:36:60, using a

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Phenomonex Luna 5 µ silica column (250 x 4.60 mm). The separated components were

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measured at 410 nm using a photodiode array detector and data was analyzed with

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Waters Empower Pro version 5.00.

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Sensory Analyses

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The sensory profiles of the oils collected in the study were evaluated following

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official IOC procedures by the Taste Panel of the Australian Oils Research Laboratory

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(AORL).25

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The sensory attributes which were rated under official IOC procedures were

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defects – fusty/muddy sediment, musty/humid/earthy, winey/vinegary/acid/sour, rancid,

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and other, and desirable attributes - fruity (green and ripe), pungent, and bitter. The

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olive oils were graded to either ‘EVOO’ or other grades by the sensory taste panel.


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Artificial Neural Networks

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The mathematical model designed to classify olive oil samples according to their

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quality is based on non-linear ANNs. These intelligent algorithms have been used to

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link chemical information from 220 olive oil samples, which came from different

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geographical origins and olive types, with the sensory results provided by trained

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panelists to achieve a potential tool for accurate quality control. Of the 220 olive oil

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samples, the panelists considered 94 of them to be EVOOs.

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The specific type of ANN that has been selected to fulfill the objective is a

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supervised multilayer perceptron (MLP), which relies on target data to be adequately

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optimized.34 These particular ANNs have offered successful results in terms of accurate

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estimations and classifications in multiple fields such as chemistry or food

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technology.11,19,35,36 It is important to note that these tools are designed to carry out

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accurate interpolations, or, in other words, they originate large errors when tests are

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done with data points outside the operational window of the ANN.19 For this reason, the

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final optimized model will perform adequately with samples that possess the nature of

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some of those included into the training dataset. This confers a great additional value to

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the vast analytical work carried out during sampling and to the wide variety of olive oil

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types found in the database assembled in terms of olive variety, geographic origin, and

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quality grade.

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MLPs possess a layer topology. The first one is the input layer, which is formed

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by nodes that are used to introduce all the desired independent variables into the non-

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linear model.37 In this case, the inputs correspond to six chemical parameters (FFAs,

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PV, K232, K268, DAGs, and PPPs) from the olive oil samples. The second layer of the 9 ACS Paragon Plus Environment


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MLP contains neurons, which are the calculation centers of the ANN, and it is known as

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the hidden layer. The hidden neuron number (HNN) should be optimized appropriately

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to get the most out of the MLP (vide infra).38 Finally, the third and last layer is the

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output layer, which is also formed by neurons. These define the dependent variables of

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the mathematical tool, which in the end allow the evaluation of the statistical

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performance of the ANN.35 In the designed model, the single output resembles the

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quality of the olive oil samples.

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Every node from the input layer is connected to all the neurons from the hidden

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layer, while these last ones are also connected to each neuron from the output layer.

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Each individual connection is controlled by a specific weighted coefficient or weight,

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which initially is a random value, but is optimized during the training phase of the MLP.

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This optimization is crucial because the final performance of the model (in this case,

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correct olive oil classification percentage) will strictly depend on it.39 Additionally, so it

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can take place, a suitable training function should be selected according to the database

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variables and size (vide infra).

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Prior to training the model, and so its optimization can be successfully carried

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out, the initial database (220 data points) was randomly divided into two. This led to the

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appearance of training and verification datasets, which approximately possessed 85%

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and 15% of the data points respectively. The training dataset is comprised of the data

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that the MLP employs to modify the values of the weights in order to attain lower

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estimation or, in this case, classification errors. On the other hand, the role of the

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verification dataset is to ensure that the system is not only able to correctly classify data

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points from the training dataset, but that it is capable of correctly classifying data

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outside it, which allows assessing the generalization and applicability range of the

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designed, and now optimized, ANN. This second dataset is vital in order to avoid 10 ACS Paragon Plus Environment

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obtaining over-fit models that are not accurate for samples outside the data used to train

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it because the mentioned second dataset is never involved in the weight modification

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process.19

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There are additional parameters, different from the weights, which should be

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optimized to get the best possible results. For instance, the HNN has been optimized

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following a heuristic approach,35 testing possibilities from 10 to 25 neurons due to the

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size of the database. The HNN selected is the one that provides the most accurate

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model. This step is very important because ANNs with a low HNN will offer a poor

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learning capability, while an excessive one may lead towards over-fit systems.38

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Moreover, other parameters that should be optimized are the Marquardt adjustment

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parameter (Lc), the decrease factor for Lc (Lcd), and the increase factor for Lc (Lci).40

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The Lc parameter acts like the learning coefficient in usual back-propagation

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algorithms. Its value is modified by Lcd and Lci up until the Lc changes originate a less

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accurate MLP.38 These three parameters have been optimized following a “Box-Wilson

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Central Composite Design 23 + star points” experimental design,38 and, once again, the

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combination selected has been the one that provided the best statistical performance.

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The range analyzed for Lc and Lcd was between 0.001 and 1, while for Lci it was

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between 2 and 100.35

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All of the ANN-related calculations have been completed using Matlab version

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7.0.1.24704 (R14),40 while the experimental design has been carried out with

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Statgraphics Centurion 16.1.18.

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Results and Discussion



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Selection and Optimization of Multilayer Perceptron Parameters

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The design of an ANN requires a series of steps that must be followed to attain

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the most accurate non-linear model possible. First of all, the architecture or topology of

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the MLP must be defined by selecting the independent and dependent variables, as well

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as optimizing the HNN. Furthermore, a well-suited training function must be chosen

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depending on the database, and a set of ANN parameters (Lc, Lcd, and Lci) should be

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optimized.

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Topology of the Model

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The independent variables, or MLP inputs, selected have been the six chemical

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parameters obtained from different measurements of all the olive oil samples. They are

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FFA, PV, K232 K268, DAG, and PPP. On the other hand, the single dependent variable,

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or output, of the ANN is the quality evaluation of the olive oil provided by trained

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panelists. The output was transformed into a qualitative response based on the results

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from the panel. Ones (1s) were assigned to the samples that were identified as EVOOs

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by the panelists, while zeroes (0s) were given to all other lower quality samples (VOO

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and OVOO) found in the database.

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After testing all HNN possibilities between 10 and 25, it was observed that the

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accuracy of the model was stable throughout the whole range. For example, when

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physicochemical property estimation is the goal of the MLP, every different HNN

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provides different prediction errors.35,38 However, when single output classifications are

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attempted, as is the case, and estimations are converted into 1s or 0s, the different

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HNNs provide very similar results. As a matter of fact, all HNN tests offered the same


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exact accuracy in terms of percentage classification of olive oil samples according to

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their quality. HNN 10 was finally chosen in order to lower the required calculations and

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obtain a more manageable model. It is worth mentioning that the optimal threshold

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found was 0.64 and, therefore, estimations >0.64 were turned into 1s and ≤0.64 into 0s.

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In Figure 1, a schematic representation of the architecture of the designed and optimized MLP can be seen.

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Training Function Selection

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The correct selection of a training function that is well-suited for accomplishing

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the desired task is essential. Due to the database size and attempted goal, training

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function trainBR (Bayesian regularization function) has been designated. It is a

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modified version of the Levenberg-Marquardt training algorithm, and it is employed to

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create MLPs that are able to generalize well, thus avoiding over-fit systems. It reduces

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the difﬁculty of determining the optimum network topology and regularly updates the

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values of the weights turning trainBR into a compelling and adaptable option.41

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Neural Network Parameter Optimization

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An experimental design based on a “Box-Wilson Central Composite Design 23 +

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star points” was carried out in order to define the combination of Lc, Lcd, and Lci that

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offers the model with the highest accuracy (greatest correct classification rate).

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Nonetheless, similar to the HNN, the correct classification percentage did not

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practically vary with different values of these parameters due to the defined objective.



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Therefore, the following optimized values were selected: Lc = 0.005, Lcd = 0.1, and Lci

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= 10.

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Statistical Performance of the Model

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In order to evaluate the statistical performance of the designed and optimized

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MLP, and to confirm that it is applicable for the entire database, a K-fold cross-

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validation (K = 6) was implemented so every data point is used to test the model (except

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for data points with the highest or the lowest values of any independent variable to limit

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the MLP to interpolations (vide supra); for this reason, only 203 out of 220 data points

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have been used to test the model).35 The main and only difference between all six tests

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carried out is that the verification dataset is completely different for each one of the

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ANNs (randomly divided into six). The role of these verification datasets is to permit

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the design of widely applicable models that can generalize well. They have been used to

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test each individual system as well, due to the fact that they are not involved in the

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weight optimization process (vide supra) thus avoiding potential over-fitting issues.42

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The results for all 6 tests can be found in Table 1.

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As shown, the MLP offers accurate results for every different dataset employed

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to test the tool proving its wide applicability range and generalization capability. It

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shows a correct classification rate of about 96% on average, verifying that the chemical

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information provided has a clear relationship with the results from the trained panelists.

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Nevertheless, to further validate this statement, three blind tests were carried out.

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In this case, the database has been randomly divided into three: training, verification,

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and simulation (or internal validation) datasets, containing 80%, 10%, and 10% of the


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data points respectively.43 The MLP was optimized following the same procedure

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previously explained, using the training and verification datasets. The only difference is

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that now the model was tested with the simulation dataset, which has never been seen

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by the ANN for any purpose, instead of with the verification one. The results of these

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three internal validations are shown in Table 2.

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These results imply that the tool designed could be employed as an accurate

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quality control for diverse olive oil samples outside of the database employed in the

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present study. The tool could be set as a yes/no (EVOO/not EVOO) test for olive oil

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samples prior to being evaluated by trained panelists. This could allow lowering the

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amount of necessary tastings when the panelists are looking for only specific types of

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olive oil, classified by their quality, for sensory analysis.

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The successful performance and accuracy of the MLP model corroborates that

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there is at least an indirect relation between the selected independent variables (six

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chemical parameters) and the results from the sensory analyses that can be adequately

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interpreted through non-linear modeling. The manifestation of negatively affecting

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factors such as excessive amounts of fatty acids, peroxides, or PPPs, elevated absorption

309

indices, and low quantities of 1,2-DAGs,21,23,24,29 can be related to low quality grade

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olive oils which present one or more of the main sensory defects (vinegary, humid,

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fusty, and/or rancid). In other words, the appearance and emission of volatile organic

312

compounds that characterize determined olive oil defects is non-linearly correlated with

313

the variables selected to train the MLP model presented.

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It must be noted that ANNs are dynamic tools that can improve their

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performance and robustness with new data. In other words, tests carried out with new

316

data points from more olive oil samples can be incorporated into the model leading to 15 ACS Paragon Plus Environment


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an enhanced version of the non-linear model, with a greater applicability span. Ideally,

318

in the end, there will be enough data from such a wide variety of olive oils and panel

319

results that the tool will be able to convert a subjective interpretation into an objective

320

classification.

321

To sum up, in the present article, a nonlinear model based on artificial neural

322

networks has been designed and optimized to discriminate between extra virgin olive oil

323

samples and other lower quality ones. The mathematical tool employs six chemical

324

parameters (percentage of free acidity, peroxide value, two UV absorption parameters

325

(K232 and K268), 1,2-diacylglycerol content, and pyropheophytins) as independent

326

variables to accurately classify the olive oil samples. Two tests were carried out to

327

determine the statistical performance of the model: (a) a K-fold cross-validation (K = 6)

328

that showed a 96.0% of correct classifications and (b) three blind tests which provided a

329

95.4% on average. These successful results prove the existence of a relationship

330

between the six parameters utilized and the quality of the olive oil leading to the

331

potential design of an objective evaluating method of the quality of diverse olive oil

332

samples.

333 334

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466 FIGURES CAPTIONS 467 Figure 1. Schematic representation of the topology of the designed MLP. It is formed by six input nodes that correspond with chemical parameters measured from different olive oil samples, 10 hidden neurons, and a single output neuron, which classifies samples according to their quality.

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TABLES

Table 1: Statistical Performance of the MLP Represented by the Results of the K-Fold Cross-Validation.

Tests (K = 6) 1 2 3 4 5 6 Total & Average

Classification Accuracy Correctly Percentage of classified corrects (%) 32/33 97.0 30/34 88.2 33/34 97.0 34/34 100 34/34 100 32/34 94.1 195/203 96.0


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Table 2: Statistical Performance of the MLP for Three Independent Blind Tests.

Blind tests 1 2 3 Total & Average

Classification Accuracy Correctly Percentage of classified corrects (%) 20/22 90.9 21/22 95.4 22/22 100 63/66 95.4



FIGURES

Figure 1


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TOC GRAPHIC


Linking Chemical Parameters to Sensory Panel Results through

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