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Correlation between electronic nose signals and fruit quality indicators on shelf-life measurements with pinklady apples
Sensors and Actuators B 80 (2001) 41±50
Correlation between electronic nose signals and fruit quality indicators on shelf-life measurements with pinklady apples J. Brezmes*, E. Llobet, X. Vilanova, J. Orts, G. Saiz, X. Correig Departament d'Enginyeria Electronica, EleÁctrica i Automatica, Universitat Rovira i Virgili, DEEEA-ETSE-URV, Avinguda dels PaõÈsos Catalans 26, Campus Sescelades, 43007 Tarragona, Spain Received 20 March 2001; received in revised form 28 May 2001; accepted 9 June 2001
Abstract In this work, an electronic nose is used to assess the ripeness state of pinklady apples through their shelf-life. In order to evaluate the electronic nose performance, fruit quality indicators are also obtained to compare results from both techniques. Pinklady apples were harvested at their optimal date so that electronic nose measurements and fruit quality measurements could be performed on the fruit samples during their ripening process. A PCA analysis, a well-known linear classi®cation technique, does not show any clustering behaviour that might be attributed to ripening. On the other hand, Fuzzy art, an unsupervised neural network classi®cation algorithm, shows a tendency to classify measurements regarding to their shelf-life period. Finally, electronic nose signals are correlated with classical fruit quality parameters such as ®rmness, starch index and acidity. Good correlation coef®cients are obtained, a clear indication that electronic nose signals are related to the ripening process of apples. # 2001 Elsevier Science B.V. All rights reserved. Keywords: Electronic nose; Fruit ripeness; Pinklady apples; Pattern recognition; Metal oxide gas sensors; Fuzzy art
1. Introduction Monitoring and controlling ripeness is becoming a very important issue in fruit management since ripeness is perceived by customers as the main quality indicator [1]. Many methods to monitor fruit ripeness have already been proposed. The main disadvantage of the majority of these techniques is that they are not useful for packinghouses [2,3]. Moreover, most of them require the destruction of the samples used for analysis. This is why, nowadays, predictions of shelf-life ripeness state is mainly based on practical experience. Leaving these critical decisions to subjective interpretation implies that large quantities of fruit reach consumer markets in poor condition. Since the most important changes in fruit ripeness are experienced during the shelf-life period, it is important to monitor the ripening process from harvest until consumption. In this paper, an electronic nose is used to assess the ripeness state of pinklady apples after being harvested at their optimal date. Although the physique-chemical properties of mature apples have been extensively studied, maturity indicators that correlate well with consumer acceptance are still being investigated. For example, aroma production is being studied * Corresponding author. Tel.: 34-977-559619; fax: 34-977-559605. E-mail address: [email protected] (J. Brezmes).
as an important index when evaluating fruit [4,5]. The formation of aroma compounds in fruits is a dynamic process, because volatile substances are continuously synthesised and developed during fruit growth and maturation [6]. The purpose of this study is to evaluate the electronic nose performance as an additional instrument to describe fruit ripeness. Although other works have envisaged the feasibility of using an electronic nose to asses fruit ripeness [7±12], recently, only fruit maturity indicators are starting to be correlated with electronic nose signals. To perform an objective evaluation of the olfactory system it is absolutely necessary to compare electronic nose predictions with fruit quality indicators since, nowadays, although far from perfect, these indicators are the only means to describe fruit ripeness in an objective manner. Moreover, since ripening is a monotonically increasing process, sensor drift has to be monitored to make sure it does not affect the measurement process. The research in this area has to be applied to different types of fruits, task that we are doing in our labs. This paper describes an experiment where electronic nose signals are correlated with traditional apple quality parameters such as ®rmness, starch index and acidity. Hence, fruit quality parameters used to assess apple maturity are predicted using sensor signals from the electronic nose. This paper is arranged in four sections. Section 2 deals with the experimental set-up, including the experiment
0925-4005/01/$ ± see front matter # 2001 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 5 - 4 0 0 5 ( 0 1 ) 0 0 8 6 7 - X
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layout and goals, the fruit quality assessment techniques applied and the description of the electronic nose that has been designed for this particular application. Section 3 discusses results based on different signal processing algorithms, with special emphasis on shelf-life period classi®cation and correlation between electronic nose signals and quality indicators. Finally, Section 4 draws the conclusions derived from our study. Sensor drift is also discussed in a special appendix (Appendix A) appended at the end of the paper. 2. Experimental 2.1. Experimental planing Pinklady apples were used for the study. These apples are known for they high quality and are marketed as a very expensive apple cultivar. For this reason, a high amount of money is poured into quality control to assure good consumer acceptance. Since they are a rather new variety, shelflife behaviour is still being investigated. The orchard was located in Lleida, in the north-eastern part of Spain. The total of 200 apples were collected in a single harvest day, the one that was considered optimal by experts on the ®eld. Then, four groups of six apples each were formed randomly selecting samples from the 200 pieces collected (the remaining 176 pieces were used to substitute samples destroyed to obtain quality indicators). At every measurement session, the electronic olfactory system measured separately each group. At the end of each session, nine samples were selected (one from group 2, two from group 3 and six from group 4) for quality analysis. Each piece destroyed was substituted by a new one with similar colour, weight and size from the 176 remaining samples not chosen initially. Samples from the ®rst group were kept the same until the end of the experiment. Measurements spanned from day 1 to 29 after harvest, keeping the fruit under standard shelf-life conditions (208C and 50±60% RH). Due to limited resources, measurements sessions were not carried everyday and, except for group 1, all the groups were measured one time on each session. Group one was measured twice on each session. In all, a total of 88 electronic nose measurements were carried out. At the end of each session, a calibration measurement was performed. An amount of 1 ml of ethanol was injected into the electronic nose chamber and the sensor response was stored. Of course, each calibration measurement was done exactly the same way during the entire experiment, so that sensor drift could be monitored in the time period comprised between harvest and the last measurement with pinklady apples. 2.2. Extraction of fruit quality indicators To asses fruit ripeness, traditional techniques were applied. In all, from each fruit quality measurement, three
quality indicators were obtained. Standard physique-chemical methods were applied to determine the ripeness of apples included ®rmness, acidity and starch index. These methods implied the destruction of the sample. Firmness is one of the easiest, fastest and cheapest methods to assess ripeness. It is also one of the parameters that correlates best with ripeness. That is why it is a very popular technique among professionals. In this study, firmness was measured with an 8 mm tip penetrometer (Effegi, Milan, Italy) on two opposite sides of each fruit. The firmness of the samples determines the force that has to be applied to penetrate completely the tip of the instrument into the fruit. The values used were obtained averaging the results from the nine pieces selected at the end of each measurement session. Acidity is a chemical indicator that is used to asses fruit quality and ripeness. It was measured in juice pressed from the whole fruit using a professional pH meter. Again, data was obtained averaging the measurements performed on nine samples selected at the end of each measurement session. Starch index was rated visually using a 1±6 scale (1: full starch; 6: no starch) after staining an equatorial section of the fruit measured with a 0.5% I2±KI solution. 2.3. Design of the electronic nose Electronic noses are based on a chemical sensor array and suitable pattern recognition techniques [13]. This general approach is implemented in different modules for each application. Fig. 1 shows a schematic diagram of our olfactory system which comprises three basic modules. 2.4. Headspace module This module controls the air ¯ow circuit during the measurement process. Inside this module, two chambers, an air pump, tubes and several electrovalves are included. The concentration chamber is where fruit is placed. It has a volume of 5 l and its main purpose is to accumulate all the aromatic compounds released by fruit during the concentration phase. Its size allowed six apples to be measured simultaneously. The measurement chamber houses the sensor array. Its volume is 1 l. A pump creates an air ¯ow of 2 l/min. During the measurement process, three different phases can be distinguished: concentration, measurement and stand-by. The electrovalves, controlled by a computer program, guide the air through different circuits depending on the measurement phase the system is. No matter the phase, air ¯ow is always kept constant through the measurement chamber. Fig. 2 shows a schematic view of the different air ¯ow paths. When the system is at the concentration phase, air does not cross the concentration chamber since electrovalves close that path to seal the fruit vessel. The pump gets air
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Fig. 1. Schematic diagram of the olfactory system.
from the lab and the electrovalves guide it through the measurement chamber. Finally, air exits the system. This phase lasted 90 min and was designed to strengthen the aromatic concentration to obtain higher sensor responses. During the measurement phase, the pump pushes the volatiles through a closed loop that includes the measurement and concentration chambers. No air enters nor exits the loop. The measurement phase lasts 10 min, time enough for sensors to reach a stable value. Finally, when a measurement is completed, a stand-by phase is activated. Its main purpose is to clean the circuit and return sensors to their baseline. Clean air enters the circuit, crosses the measurement chamber ®rst, the empty concentration chamber afterwards, and pushes the remaining volatiles out of the circuit. Between measurements, a rest time of 15 min was considered appropriate. 2.5. Sensor array module In this module, we included the gas sensor array, the humidity and temperature sensors and all the associated electronics necessary to power sensors. The sensor array of the electronic nose included 21 gas sensors. As mentioned before, the sensor array along with humidity and temperature sensors were housed in the measurement chamber. Table 1 lists all the sensors used and their main applications. All of the gas sensors that formed the array were semiconductor metal oxide devices made by Figaro Inc. (8 series) and FIS Inc. (SB and SP series). Electronics were necessary to heat sensor elements and to translate conductivity changes
into voltage signals the computer could acquire, store and process. Power supply and signal conditioning were necessary for temperature and humidity probes. 2.6. Computer module The PC compatible computer included in our olfactory system controls the measurement process and afterwards processes raw data into useful information for the user using pattern recognition algorithms. With the help of a commercial acquisition board with analogue and digital input/output channels, a computer program controls the measuring process. Electrovalves are controlled by binary output signals generated by the program to redirect air ¯ow during the Table 1 Sensors used by the electronic nose Sensors
Application
FIS (SB series) S1.-SB-AQ1A-00 S2.-SB-19-00 S3.-SB-AQ4-00 S4.-SB-11A-00 S5.-SB-31-00 S6.-SB-30-00 S7.-SB-15-00
General purpose: interior air quality Hydrogen Cigarette smoke General purpose: flammable vapours General purpose: organic solvents Alcohol: organic solvents Propane/butane
Taguchi (8 series) S1.-TGS-800-1 S2.-TGS-800-2 S3.-TGS-880-2 S4.-TGS-880-1 S5.-TGS-825-1 S6.-TGS-822 S7.-TGS-882-5
Fig. 2. Air flow paths at different measurement phases.
FIS (SP series) S1.-SP-MW0 S2.-SP-12A-00 S3.-SP-11-00 S4.-SP-MW1-00 S5.-SP-AQ1-00 S6.-SP-19-00 S7.-SP-AQ3-00
Air quality: cigarette smoke, gasoline vapours Air quality: cigarette smoke, gasoline vapours Kitchen control: food odours, fumes, vapours, humidity Kitchen control: food odours, fumes, vapours, humidity Hydrogen sulphide Organic solvents Kitchen control: alcohol vapours from food General purpose: kitchen control Methane General purpose: flammable vapours Humidity: kitchen control General purpose: interior air quality Hydrogen Cigarette smoke
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different phases of each measurement. When sensors are exposed to volatiles, during the measurement phase, the computer records the resistance changes that sensors experience. When a measurement is completed, the acquired data is stored in a hard disk as a text ®le for later use. A mathematical package (Matlab, The Mathworks Inc., USA) is then used to extract relevant features of each measurement ®le and to test different pattern recognition methods for each particular goal of the study. 3. Results and discussion 3.1. Electronic nose response to fruit aroma Fig. 3 shows a typical response by twelve sensors when measuring fruit. Each curve represents a different sensor transient. The curves plotted represent sensor conductivity against time when the volatiles from the fruit reach the measurement chamber due to electrovalve activation. In that transition, the clean air ¯ow that reaches the measurement chamber is substituted by air ¯ow that comes from the concentration chamber, closing a loop circuit between both chambers. It can be seen that, after an initial period of low and stable conductivity (when only clean air is crossing the measurement chamber), conductivity increases sharply and then stabilises before ending the measurement phase. From the response of each gas sensor many parameters can be extracted. Usual parameters (sometimes called static parameters) include initial conductance (Gi), ®nal conductance (Gf), conductance increment (dG Gf Gi ) and normalised conductance increment ((Gf Gi )/Gi). As the formula suggest this four parameters are correlated between them for each sensor. Different combinations were tried, including humidity and sample weight values that helped to re®ne the correlation results. 3.2. Classification of samples by their shelf-life period using PCA analysis In order to see whether the electronic nose was able to distinguish between different ripeness states, a PCA analysis was applied to the 88 measurements performed with the olfactory system. Only projections made with FIS-SB sensor signals showed meaningful clusters regarding to the shelf-life
Fig. 3. Twelve different sensor responses to fruit aroma. Each curve represents the conductance transient of a single sensor under a step change in the concentration of fruit aroma.
period, although these clusters were later attributed to sensor drift, as it can be seen in Appendix A included at the end of this paper. As mentioned before, similar analysis with Taguchi and FIS-SP sensor signals showed no clear clustering, an indication that their drift was very small (if there was any). 3.3. Classification of samples by unsupervised neural networks Fuzzy art [14] is an unsupervised classi®cation neural network that works very well when there are a few samples to classify. Other advantages include its plasticity to adapt to drift situations and the automatic determination of the number of clusters given a classi®cation criteria (speci®ed with the vigilance parameter). In fact, from a practical point of view, Fuzzy art can be considered an automatic and objective clustering algorithm version of a PCA analysis, where clusters have to be identi®ed manually and drawn subjectively. First, the best classi®cation in terms of shelf-life period was obtained for each type of sensors using fruit measurements alone. The vigilance parameter was always tuned to obtain ®ve output classes. Table 2 shows the best results obtained when using FIS-SB sensors. Each measurement is de®ned by a tag which speci®es the group (the number after
Table 2 Fuzzy art unsupervised classification using FIS-SB sensors Class
Measurements
1 2
g1d01, g1d02, g1d02, g2d02, g3d02, g4d02, g1d03, g1d03, g2d03, g3d03, g4d03, g1d04, g1d04, g2d04, g3d04, g4d04, g1d07, g1d07, g2d07 g1d08, g1d08, g3d08, g4d08, g1d09, g1d09, g2d09, g4d09, g1d10, g1d10, g2d10, g3d10, g4d10, g1d13, g1d13, g2d13, g3d13, g4d13, g1d14, g1d14, g2d14, g3d14, g1d15, g1d15, g2d15, g3d15, g4d15, g1d16, g2d16, g3d16, g4d16 g1d16, g1d17, g1d17, g3d17, g4d17, g1d19, g1d19, g2d19, g3d19, g4d19, g1d21, g1d21, g2d21, g3d21, g4d21, g1d23, g1d23, g4d23, g1d24, g1d24, g2d24, g3d24, g4d24, g1d27, g4d27 g2d23, g1d27, g2d27, g3d27, g1d28, g1d28, g2d28, g3d28, g1d29, g1d29, g2d29, g3d29 g4d29
3 4 5
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Table 3 Fuzzy art unsupervised classification using Taguchi sensors Class
Measurements
1 2 3 4
g1d01, g1d02, g1d02, g2d02, g3d02, g4d02, g1d03, g2d03 g1d03, g3d03, g1d04, g1d04, g2d04, g4d04 g4d03, g3d04 g1d07, g1d07, g2d07, g1d08, g1d08, g3d08, g4d08, g1d09, g1d09, g2d09, g4d09, g1d10, g1d10, g2d10, g3d10, g4d10, g1d13, g1d13, g2d13, g3d13, g4d13, g1d14, g1d14, g2d14, g3d14, g1d15, g1d15, g2d15, g3d15, g4d15, g1d16, g1d16, g2d16, g4d16, g1d17, g1d17, g3d17, g4d17, g1d19, g2d19, g3d19, g4d19, g2d21, g3d21, g4d21, g1d23, g1d23, g2d23, g4d23, g2d24, g3d24, g2d27, g3d27, g4d27 g3d16, g1d19, g1d21, g1d21, g1d24, g1d24, g4d24, g1d27, g1d27, g1d28, g1d28, g2d28, g3d28, g1d29, g1d29, g2d29, g3d29, g4d29
5
Table 4 Fuzzy art unsupervised classification using FIS-SP sensors Class
Measurements
1
g1d01, g1d02, g1d02, g2d02, g3d02, g4d02, g1d03, g1d03, g2d03, g4d03, g1d04, g1d04, g4d04, g1d07, g1d07, g2d07, g1d08, g3d08, g4d08, g1d09, g1d09, g2d09, g4d09, g1d10, g1d10, g2d10, g3d10, g4d10, g2d13, g4d13, g4d16, g3d17, g3d19 g3d03, g2d04, g3d04, g1d08, g1d13, g1d13, g1d14, g1d14, g2d14, g3d14, g1d15, g1d15, g2d15, g1d16, g1d16, g2d16, g1d17, g2d21, g3d27, g4d29 g3d13, g3d15, g4d15, g3d16, g4d17, g4d19, g4d21, g4d24, g4d27 g1d17, g1d19, g1d19, g2d19, g1d21, g1d21, g3d21, g1d23, g1d24, g1d24, g2d24, g3d24, g1d27, g1d27, g2d27, g1d28, g1d28, g2d28, g3d28, g1d29, g2d29, g3d29 g1d23, g2d23, g4d23, g1d29
2 3 4 5
`g') and the number of days since harvest (the number after `d'). As expected, very good results can be observed. The classi®cation follows a similar evolution as the one found on the PCA analysis, and probably drift is responsible for such behaviour. In Table 3, best results for Taguchi sensors are presented. As it can be seen, there are no major misclassi®cations, although the resolution is not very good, since most of the measurements are classi®ed under class 4. Finally, Table 4 shows the classi®cation results obtained with FIS-SP sensors with some clear outliers which shows the poor resolution of these sensors. A closer look at drift (described in Appendix A) showed that results obtained for FIS-SB sensors could be attributed again to sensor drift, rather than to fruit maturity. On the other hand, results obtained for Taguchi and FIS-SP sensors could not be attributed to drift. For this reason, FIS-SB sensors where not used for correlation purposes. 3.4. Correlation between electronic nose signals and quality indicators As mentioned in Section 2, some fruit samples from the four groups measured with the electronic nose were used the same day to extract quality parameters. In order to compare the electronic nose performance with fruit quality techniques, measures done with the olfactory system were coupled with the values obtained from quality indicators at the same measurement session. In this way, a total of 88 pairs of measurements were coupled. Although back-propagation neural networks were initially considered it seemed inappropriate to use them with such a small number of measurements. Partial least squares (PLS), a well known linear algorithm [15], was considered
the best option due to the constrains of the application. During a training phase, the PLS algorithm builds a model that describes the relationship between sensor signals and the fruit quality parameter to be predicted. In the evaluation phase, the model predicts a fruit quality indicator using new electronic nose measurements not used for training. To see whether the electronic nose was able to predict fruit quality parameters a leave-one-out approach was performed for each quality indicator. Under this approach, 87 measures were used to build the PLS model while the remaining one was predicted using the model and the corresponding olfactory signals. This process, which was repeated 88 times (so that each measure was used once for evaluation and 87 times for training) optimises the use of a small set of measurements (the leave-one-out method sometimes is referred as a cross-validation of order one). For each PLS model built, the data used for training were mean-centred and scaled by their variance; data used for testing was centred and scaled using the mean and variance of the training set. Table 5 shows the average square error (Ssq), correlation coef®cient, optimal number of latent variables (LV), slope and intercept for each destructive parameter predicted for Table 5 Prediction results on quality indicatorsa Quality indicator
Ssq
Correlation coefficient
m
b
LV
Firmness Starch index pH
0.1262 0.6 0.29
0.93 0.68 0.84
0.91 0.63 0.78
0 0.02 0
18 27 12
a
Ideal predictions would give a zero average square error (Ssq) and intercept (b), while the correlation coefficient and the slope would be 1. The optimal number of latent variables (LV) is also specified.
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Fig. 4. Physique-chemical parameter predictions using electronic nose fruit measures.
pink lady apples using Taguchi and FIS-SP gas sensors. It can be seen that ®rmness is the best predicted parameter. Fig. 4 shows the prediction ability of the electronic nose, where each square represents the predicted against the real value of each measure. Ideal predictions would line all points along the diagonal of the plot, where predicted and real values are the same. It should be kept in mind that real/ predicted parameters were also scaled, so that numerical values in the ®gures do not represent the original values obtained applying the standard physique-chemical methods. Since some of the quality indicators (for example, ®rmness) are monotonically increasing or decreasing with time, it is important to check whether sensor drift is used by the PLS model to make accurate predictions of the quality parameters. These checks (as explained in Appendix A) con®rmed that drift was not interfering in the correlation process under shelf-life conditions. Although it may seem surprising to see that physical measurements such as ®rmness can be predicted with sensor responses to organic volatiles generated by fruit, such results are meaningful since the physiological characteristics of fruit are closely related to chemical processes that take place during the ripening process of fruit. In other words, the electronic nose does not measure ®rmness directly, it actually measures volatiles that are well correlated with the ®rmness of the fruits. This is also true for the remaining well predicted indicators.
4. Conclusions Electronic nose measurements have been performed on pinklady apples to see whether an arti®cial olfactory system can be used as a novel instrument to follow their ripening process. Ripening state classi®cation has been attempted using linear PCA algorithms and Fuzzy art neural classi®ers. Fuzzy art techniques clearly outperform their linear counterparts since they are the only ones that show a tendency to classify samples regarding to their shelf-life period (which is related to ripeness). In order to compare the electronic nose performance against established techniques, electronic nose signals have been correlated with fruit quality indicators. Results show that three classical quality parameters (®rmness, starch index and pH) correlate reasonably well with electronic nose signals obtained without destroying the apples, although this study cannot be generalised to other fruit varieties. Further work needs to be done with other cultivars to fully understand the relationship between maturity, fruit volatiles and gas sensor signals. This is why our research work is expanding to other fruit varieties, such as peaches, nectarines and pears. Anyway, the results obtained in this study indicates that electronic noses could become, in the near future, a useful instrument to control fruit quality and ripeness.
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Acknowledgements This 2-year project has been possible through the funding by the CICYT-FEDER Project no. 2FD-97-0436 (TIC), with additional funding from ``Catalonia Qualitat''. The authors wish to thank the helpful collaboration of ``Cooperativa Agricola de Cambrils''. Appendix A In fruit maturity measurements, sensor drift is an important factor that has to be closely monitored, since the ripening process is strongly related to time. In this paper, calibration measurements have been systematically performed to check whether the results obtained were achieved from the ripening process of fruit alone or drift helped to reach meaningful results. In this experiment, at the end of each day, a 1 ml of liquid ethanol was injected into the concentration chamber of the electronic nose through a septum. Since a chromatographic syringe was used to inject ethanol, the volatilisation of the liquid was almost instant. The concentration time was reduced to 1 min, while measurement and stand-by periods were kept the same. Of course, all the calibration measurements were done exactly the same way during the entire experiment, so that sensor drift could be monitored in the time period comprised between harvest and the last measurement with pinklady apples.
47
In the PCA analysis, only PCA plots from FIS-SB sensors showed meaningful clusters. To check for drift in¯uence, calibration measures were included in the PCA plots of fruit measurements in two different ways. Figs. 5 and 6 show the plots obtained when using both approaches. In these ®gures, each measure is represented by a string tag which speci®es the days since harvest when the measurements where carried out. Fruit measurement tags start with `d' while calibration measurements have a `cal' initial string. With a few exceptions, each session comprised a total of ®ve fruit measurements (two for group 1 and one for groups 2±4), and at the end of the day a calibration measurement was always performed. Fig. 5 shows the PCA plot where fruit and calibration measurements are used together to ®nd the principal components of the analysis. Then, all the measurements are projected onto the ®rst two principal components. As it can be seen, fruit and calibration measurements are clearly separated, but a closer look reveals that the distribution of clusters inside the calibration area is very similar to the fruit measurement distribution, as indicated in the plot. That is why we decided to use the PCA analysis in a different way. Fig. 6 shows the projections using the second approach. In this situation, the PCA analysis is ®rst carried with the fruit measurements alone. After the principal components are computed and ®xed, both fruit and calibration measurements are projected onto the two ®rst principal components. As it can be observed, calibration measurements performed
Fig. 5. PCA scores using electronic nose signals. Principal components are computed using fruit and calibration measurements together.
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Fig. 6. PCA scores using electronic nose signals. Principal components are computed using fruit measurements alone. Then, calibration and fruit measurements are projected.
the same day as fruit measurements lay inside the same clusters. This is a clear indication that sensor drift is the main reason for the clustering of the measurements. Therefore, we can conclude that FIS-SB sensors experience important drift that in¯uence PCA results. To see whether Fuzzy art results were in¯uenced by drift, the neural classi®cation algorithm was applied to fruit and calibration measurements together. Tables 6±8 show the new results for FIS-SB, Taguchi and FIS-SP sensors respectively. Calibration measurements are denoted by a tag with a `cal' string followed by the day of the calibration measurement since harvest, while fruit measurements follow the same format detailed in Section 3.3. From the results, it can be derived that the algorithm separates ¯awlessly the calibration from the fruit measurements
for all three types of sensors. In the case of FIS-SB sensors, the calibration measures are perfectly grouped (regarding their measurement day) in three classes. On the other hand, for Taguchi and FIS sensors they are not grouped by a time basis, an indication that drift is not as important in this type of sensors. That is why, for correlation purposes, FIS-SB sensors were not used. To check for drift in¯uence on correlation between electronic nose signals and quality indicators, PLS predictions were made using the calibration measurements alone. Accurate predictions would mean that prediction was entirely done using sensor drift information, whereas inaccurate predictions would mean that fruit measurements carry the information actually used by the PLS model to make accurate predictions.
Table 6 Fuzzy art unsupervised classification using FIS-SB sensors including calibration measurements Class
Measurements
1
g1d01, g1d02, g1d02, g2d02, g3d02, g4d02, g1d03, g1d03, g2d03, g3d03, g4d03, g1d04, g1d04, g2d04, g3d04, g4d04, g1d07, g1d07, g2d07, g1d08, g1d13 cal2, cal3, cal4, cal6, cal7 g1d08, g3d08, g4d08, g1d09, g1d09, g2d09, g4d09, g1d10, g1d10, g2d10, g3d10, g4d10, g1d13, g2d13, g3d13, g4d13, g1d14, g1d14, g2d14, g3d14, g1d15, g1d15, g2d15, g3d15, g4d15, g1d16, g1d16, g2d16, g3d16, g4d16, g1d17, g1d17, g4d17, g1d19, g1d19, g3d19, g4d19, g2d21, g1d23 cal8, cal9, cal10, cal13, cal14, cal15, cal16 g3d17, g2d19, g1d21, g1d21, g3d21, g4d21, g1d23, g2d23, g4d23, g1d24, g1d24, g2d24, g3d24, g4d24, g1d27, g1d27, g2d27, g3d27, g4d27, g1d28, g1d28, g2d28, g3d28, g1d29, g2d29 cal17, cal19, cal21, cal23, cal24, cal27, cal28, cal29 g1d29, g3d29, g4d29
2 3 4 5 6 7
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Table 7 Fuzzy art unsupervised classification using Taguchi sensors including calibration measurements Class
Measurements
1
g1d01, g1d02, g1d02, g2d02, g3d02, g4d02, g1d03, g1d03, g2d03, g1d04, g1d04, g2d07, g4d09, g1d10, g2d10, g4d10, g1d13, g2d13, g4d13, g4d15, g1d16, g1d19, g2d19, g3d19 cal2, cal3, cal4, cal6, cal9, cal21, cal23 g3d03, g4d03, g2d04, g3d04, g4d04, g1d27, g1d29, g1d29 g1d07, g1d07, g1d08, g1d08, g3d08, g4d08, g1d09, g1d09, g2d09, g1d10, g3d10, g1d13, g3d13, g1d14, g1d14, g2d14, g3d14, g1d15, g1d15, g2d15, g3d15, g1d16, g2d16, g3d16, g4d16, g1d17, g1d17, g3d17, g4d17, g1d19, g4d19, g1d21, g1d21, g2d21, g3d21, g4d21, g1d23, g1d23, g2d23, g4d23, g1d24, g1d24, g2d24, g3d24, g4d24, g1d27, g2d27, g3d27, g4d27, g1d28, g1d28, g2d28, g3d28, g3d29 cal7, cal8, cal10, cal13, cal14, cal15, cal16, cal17, cal19, cal27, cal28 cal24 g2d29, g4d29 cal29
2 3 4 5 6 7 8
Table 8 Fuzzy art unsupervised classification using FIS-SP sensors including calibration measurements Class
Measurements
1
g1d01, g1d02, g1d02, g2d02, g3d02, g4d02, g1d03, g1d03, g2d03, g3d03, g4d03, g1d04, g1d04, g4d04, g1d07, g1d07, g2d07, g1d08, g1d08, g3d08, g4d08, g2d09, g1d13, g1d13, g4d13, g1d14, g3d14, g1d19, g1d24, g1d28 cal2, cal3, cal4, cal6, cal7, cal8, cal9, cal10, cal13, cal14, cal15, cal16, cal17, cal21, cal23, cal24, cal27, cal29 g2d04, g3d04, g1d09, g1d09, g4d09, g1d10, g1d10, g2d10, g3d10, g4d10, g2d13, g1d14, g2d14, g1d15, g1d15, g2d15, g1d16, g1d16, g2d16, g4d16, g1d17, g1d17, g3d17, g1d19, g1d21, g2d21, g3d21, g1d23, g4d23, g1d24, g2d24, g3d24, g4d24, g1d27, g2d27, g3d27, g1d28, g1d29, g1d29, g4d29 g3d13, g3d15, g4d15, g4d17, g2d19, g3d19, g4d19, g4d21, g1d23, g2d23, g1d27, g4d27, g3d28 g3d16, g1d21, g2d28, g2d29, g3d29 cal19, cal28
2 3 4 5 6
Fig. 7. Physique-chemical parameter predictions using electronic nose calibration measurements.
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Fig. 7 shows the prediction results obtained using the calibration measurements as the input information for the PLS algorithm. As it can be seen, the PLS model is making random predictions, a clear indication that accurate predictions are due to the evolution of sensor signals produced by fruit aroma. References [1] R.E.K. Ogers, Instrumentation and Sensors for the Food Industry, Butterworths, Oxford, UK, 1993. [2] C.M. Christensen, Effects of color on aroma, flavor and texture judgements of foods, J. Food Sci. 48 (1983) 787±790. [3] M.J.W. Povey, Ultrasonics in food engineering Part II: Applications, J. Food Eng. 9 (1989) 1±20. [4] H. Shiota, Changes in the volatile composition of La France pear during maduration, J. Sci. Food Agric. 52 (3) (1990) 421. [5] M.L. LoÂpez, M.T. Lavilla, I. Recasens, J. Graell, M. Vendrell, Changes in aroma quality of Golden Delicious apples after storage at different oxygen and carbon dioxide concentrations, J. Sci. Food Agric. 80 (2000) 311±324. [6] P. Eccher, R. Balzarotti, A. Rrizzolo, G.L. Spada, Effect of picking on quality and sensory characteristics of pears after storage and ripening, Acta Horti. 326 (1993) 291±298. [7] R.J. Horvat, S.D. Senter, Identification of additional volatile compounds from cantaloupe, J. Food Sci. 52 (4) (1987) 1097±1098. [8] M. Benady, Fruit ripeness determination by electronic sensing of aromatic volatiles, Trans. ASAE 38 (1995) 251±257. [9] J.E. Simon, Electronic sensing of aromatic volatiles for quality sorting of blueberries, J. Food Sci. 61 (1996) 967±970. [10] E. Llobet, E.L. Hines, J.W. Gardner, S. Franco, Non-destructive banana ripeness determination using a neural network-based electronic nose, Meas. Sci. Technol. 10 (1999) 538±548. [11] J. Brezmes, E. Llobet, X. Vilanova, G. Saiz, X. Correig, Fruit Ripeness Monitoring using an electronic nose, Sens. Actuators B 69 (2000) 223±229. [12] E.L. Hines, E. Llobet, J.W. Gardner, Neural network based electronic nose for apple ripeness determination, Electron. Lett. 35 (1999) 821. [13] W. Gardner, J.W. Gardner, P.N. Barlett, A brief history of electronic noses, Sens. Actuators B 18/19 (1994) 211±220. [14] G.A. Carpenter, S. Grossberg, D.B. Rosen, Fuzzy art: Fast stable learning and categorization of analog patterns by an adaptive resonance algorithm for rapid, stable classification of analog patterns, Neural Networks 4 (1991) 759±771. [15] B.M. Wise, N.B. Gallaher, PLS_Toolbox 2.0, Eigenvector Research Inc., 1998.
Biographies J. Brezmes graduated in telecommunication engineering from the Universitat PoliteÁcnica de Catalunya (UPC) (Barcelona, Spain) in 1993. Since 1993, he has been a PhD student in the Signal Processing and Communications Department at the same university. He has been a Lecturer in the Electronic Engineering Department at the Universitat Rovira i Virgili (Tarragona, Spain) since 1993. His main area of interest is in the application of signal processing techniques such as neural networks to chemical sensor arrays for complex aroma analysis. E. Llobet graduated in telecommunication engineering from the Universitat PoliteÁcnica de Catalunya (UPC) (Barcelona, Spain) in 1991, and received his PhD in 1997 from the same university. His thesis focussed on selectivity enhancement of metal oxide chemical sensors via the study of sensor dynamics. He has been a Lecturer in the Electronic Engineering Department at the Universitat Rovira i Virgili (Tarragona, Spain) since 1992. During 1998, he was a Visiting Fellow at the School of Engineering, University of Warwick (UK). His main areas of interest are in the fabrication, and modelling, of semiconductor chemical sensors and in the application of intelligent systems to complex odour analysis. He is a member of the Institute of Electrical and Electronic Engineers. X. Vilanova graduated in telecommunication engineering from the Universitat PoliteÁcnica de Catalunya (UPC) (Barcelona, Spain) in 1991, and received his PhD in 1998 from the same university. In his thesis, he discussed the effect of electrode geometry and position, both in the static and dynamic responses of metal oxide chemical sensors. He has been a Lecturer in the Electronic Engineering Department at the Universitat Rovira i Virgili (Tarragona, Spain) since 1991. His main areas of interest are in semiconductor chemical sensors modelling and simulation. G. Saiz holds a Bachelor degree in industrial engineering from the Universitat Rovira i Virgili (Tarragona, Spain) since 1999. He is currently getting his Master degree in industrial automation. His main areas of interest are in electronic noses and measurement automation. J. Orts holds a Bachelor degree in Industrial Engineering from the Universitat Rovira i Virgili (Tarragona, Spain) since 1997 and a Master degree in industrial automation (2001) from the same university. His main areas of interest are in electronic noses and measurement automation. X. Correig graduated in telecommunication engineering from the Universitat PoliteÁcnica de Catalunya (UPC) (Barcelona, Spain) in 1984, and received his PhD in 1988 from the same university. He is a full Professor of electronic technology in the Electronic Engineering Department at the Universitat Rovira i Virgili (Tarragona, Spain). His research interests include heterojunction semiconductor devices and solid-state gas sensors. Correig is a member of the Institute of Electrical and Electronic Engineers.
Correlation between electronic nose signals and fruit quality indicators on shelf-life measurements with pinklady apples J. Brezmes*, E. Llobet, X. Vilanova, J. Orts, G. Saiz, X. Correig Departament d'Enginyeria Electronica, EleÁctrica i Automatica, Universitat Rovira i Virgili, DEEEA-ETSE-URV, Avinguda dels PaõÈsos Catalans 26, Campus Sescelades, 43007 Tarragona, Spain Received 20 March 2001; received in revised form 28 May 2001; accepted 9 June 2001
Abstract In this work, an electronic nose is used to assess the ripeness state of pinklady apples through their shelf-life. In order to evaluate the electronic nose performance, fruit quality indicators are also obtained to compare results from both techniques. Pinklady apples were harvested at their optimal date so that electronic nose measurements and fruit quality measurements could be performed on the fruit samples during their ripening process. A PCA analysis, a well-known linear classi®cation technique, does not show any clustering behaviour that might be attributed to ripening. On the other hand, Fuzzy art, an unsupervised neural network classi®cation algorithm, shows a tendency to classify measurements regarding to their shelf-life period. Finally, electronic nose signals are correlated with classical fruit quality parameters such as ®rmness, starch index and acidity. Good correlation coef®cients are obtained, a clear indication that electronic nose signals are related to the ripening process of apples. # 2001 Elsevier Science B.V. All rights reserved. Keywords: Electronic nose; Fruit ripeness; Pinklady apples; Pattern recognition; Metal oxide gas sensors; Fuzzy art
1. Introduction Monitoring and controlling ripeness is becoming a very important issue in fruit management since ripeness is perceived by customers as the main quality indicator [1]. Many methods to monitor fruit ripeness have already been proposed. The main disadvantage of the majority of these techniques is that they are not useful for packinghouses [2,3]. Moreover, most of them require the destruction of the samples used for analysis. This is why, nowadays, predictions of shelf-life ripeness state is mainly based on practical experience. Leaving these critical decisions to subjective interpretation implies that large quantities of fruit reach consumer markets in poor condition. Since the most important changes in fruit ripeness are experienced during the shelf-life period, it is important to monitor the ripening process from harvest until consumption. In this paper, an electronic nose is used to assess the ripeness state of pinklady apples after being harvested at their optimal date. Although the physique-chemical properties of mature apples have been extensively studied, maturity indicators that correlate well with consumer acceptance are still being investigated. For example, aroma production is being studied * Corresponding author. Tel.: 34-977-559619; fax: 34-977-559605. E-mail address: [email protected] (J. Brezmes).
as an important index when evaluating fruit [4,5]. The formation of aroma compounds in fruits is a dynamic process, because volatile substances are continuously synthesised and developed during fruit growth and maturation [6]. The purpose of this study is to evaluate the electronic nose performance as an additional instrument to describe fruit ripeness. Although other works have envisaged the feasibility of using an electronic nose to asses fruit ripeness [7±12], recently, only fruit maturity indicators are starting to be correlated with electronic nose signals. To perform an objective evaluation of the olfactory system it is absolutely necessary to compare electronic nose predictions with fruit quality indicators since, nowadays, although far from perfect, these indicators are the only means to describe fruit ripeness in an objective manner. Moreover, since ripening is a monotonically increasing process, sensor drift has to be monitored to make sure it does not affect the measurement process. The research in this area has to be applied to different types of fruits, task that we are doing in our labs. This paper describes an experiment where electronic nose signals are correlated with traditional apple quality parameters such as ®rmness, starch index and acidity. Hence, fruit quality parameters used to assess apple maturity are predicted using sensor signals from the electronic nose. This paper is arranged in four sections. Section 2 deals with the experimental set-up, including the experiment
0925-4005/01/$ ± see front matter # 2001 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 5 - 4 0 0 5 ( 0 1 ) 0 0 8 6 7 - X
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layout and goals, the fruit quality assessment techniques applied and the description of the electronic nose that has been designed for this particular application. Section 3 discusses results based on different signal processing algorithms, with special emphasis on shelf-life period classi®cation and correlation between electronic nose signals and quality indicators. Finally, Section 4 draws the conclusions derived from our study. Sensor drift is also discussed in a special appendix (Appendix A) appended at the end of the paper. 2. Experimental 2.1. Experimental planing Pinklady apples were used for the study. These apples are known for they high quality and are marketed as a very expensive apple cultivar. For this reason, a high amount of money is poured into quality control to assure good consumer acceptance. Since they are a rather new variety, shelflife behaviour is still being investigated. The orchard was located in Lleida, in the north-eastern part of Spain. The total of 200 apples were collected in a single harvest day, the one that was considered optimal by experts on the ®eld. Then, four groups of six apples each were formed randomly selecting samples from the 200 pieces collected (the remaining 176 pieces were used to substitute samples destroyed to obtain quality indicators). At every measurement session, the electronic olfactory system measured separately each group. At the end of each session, nine samples were selected (one from group 2, two from group 3 and six from group 4) for quality analysis. Each piece destroyed was substituted by a new one with similar colour, weight and size from the 176 remaining samples not chosen initially. Samples from the ®rst group were kept the same until the end of the experiment. Measurements spanned from day 1 to 29 after harvest, keeping the fruit under standard shelf-life conditions (208C and 50±60% RH). Due to limited resources, measurements sessions were not carried everyday and, except for group 1, all the groups were measured one time on each session. Group one was measured twice on each session. In all, a total of 88 electronic nose measurements were carried out. At the end of each session, a calibration measurement was performed. An amount of 1 ml of ethanol was injected into the electronic nose chamber and the sensor response was stored. Of course, each calibration measurement was done exactly the same way during the entire experiment, so that sensor drift could be monitored in the time period comprised between harvest and the last measurement with pinklady apples. 2.2. Extraction of fruit quality indicators To asses fruit ripeness, traditional techniques were applied. In all, from each fruit quality measurement, three
quality indicators were obtained. Standard physique-chemical methods were applied to determine the ripeness of apples included ®rmness, acidity and starch index. These methods implied the destruction of the sample. Firmness is one of the easiest, fastest and cheapest methods to assess ripeness. It is also one of the parameters that correlates best with ripeness. That is why it is a very popular technique among professionals. In this study, firmness was measured with an 8 mm tip penetrometer (Effegi, Milan, Italy) on two opposite sides of each fruit. The firmness of the samples determines the force that has to be applied to penetrate completely the tip of the instrument into the fruit. The values used were obtained averaging the results from the nine pieces selected at the end of each measurement session. Acidity is a chemical indicator that is used to asses fruit quality and ripeness. It was measured in juice pressed from the whole fruit using a professional pH meter. Again, data was obtained averaging the measurements performed on nine samples selected at the end of each measurement session. Starch index was rated visually using a 1±6 scale (1: full starch; 6: no starch) after staining an equatorial section of the fruit measured with a 0.5% I2±KI solution. 2.3. Design of the electronic nose Electronic noses are based on a chemical sensor array and suitable pattern recognition techniques [13]. This general approach is implemented in different modules for each application. Fig. 1 shows a schematic diagram of our olfactory system which comprises three basic modules. 2.4. Headspace module This module controls the air ¯ow circuit during the measurement process. Inside this module, two chambers, an air pump, tubes and several electrovalves are included. The concentration chamber is where fruit is placed. It has a volume of 5 l and its main purpose is to accumulate all the aromatic compounds released by fruit during the concentration phase. Its size allowed six apples to be measured simultaneously. The measurement chamber houses the sensor array. Its volume is 1 l. A pump creates an air ¯ow of 2 l/min. During the measurement process, three different phases can be distinguished: concentration, measurement and stand-by. The electrovalves, controlled by a computer program, guide the air through different circuits depending on the measurement phase the system is. No matter the phase, air ¯ow is always kept constant through the measurement chamber. Fig. 2 shows a schematic view of the different air ¯ow paths. When the system is at the concentration phase, air does not cross the concentration chamber since electrovalves close that path to seal the fruit vessel. The pump gets air
J. Brezmes et al. / Sensors and Actuators B 80 (2001) 41±50
43
Fig. 1. Schematic diagram of the olfactory system.
from the lab and the electrovalves guide it through the measurement chamber. Finally, air exits the system. This phase lasted 90 min and was designed to strengthen the aromatic concentration to obtain higher sensor responses. During the measurement phase, the pump pushes the volatiles through a closed loop that includes the measurement and concentration chambers. No air enters nor exits the loop. The measurement phase lasts 10 min, time enough for sensors to reach a stable value. Finally, when a measurement is completed, a stand-by phase is activated. Its main purpose is to clean the circuit and return sensors to their baseline. Clean air enters the circuit, crosses the measurement chamber ®rst, the empty concentration chamber afterwards, and pushes the remaining volatiles out of the circuit. Between measurements, a rest time of 15 min was considered appropriate. 2.5. Sensor array module In this module, we included the gas sensor array, the humidity and temperature sensors and all the associated electronics necessary to power sensors. The sensor array of the electronic nose included 21 gas sensors. As mentioned before, the sensor array along with humidity and temperature sensors were housed in the measurement chamber. Table 1 lists all the sensors used and their main applications. All of the gas sensors that formed the array were semiconductor metal oxide devices made by Figaro Inc. (8 series) and FIS Inc. (SB and SP series). Electronics were necessary to heat sensor elements and to translate conductivity changes
into voltage signals the computer could acquire, store and process. Power supply and signal conditioning were necessary for temperature and humidity probes. 2.6. Computer module The PC compatible computer included in our olfactory system controls the measurement process and afterwards processes raw data into useful information for the user using pattern recognition algorithms. With the help of a commercial acquisition board with analogue and digital input/output channels, a computer program controls the measuring process. Electrovalves are controlled by binary output signals generated by the program to redirect air ¯ow during the Table 1 Sensors used by the electronic nose Sensors
Application
FIS (SB series) S1.-SB-AQ1A-00 S2.-SB-19-00 S3.-SB-AQ4-00 S4.-SB-11A-00 S5.-SB-31-00 S6.-SB-30-00 S7.-SB-15-00
General purpose: interior air quality Hydrogen Cigarette smoke General purpose: flammable vapours General purpose: organic solvents Alcohol: organic solvents Propane/butane
Taguchi (8 series) S1.-TGS-800-1 S2.-TGS-800-2 S3.-TGS-880-2 S4.-TGS-880-1 S5.-TGS-825-1 S6.-TGS-822 S7.-TGS-882-5
Fig. 2. Air flow paths at different measurement phases.
FIS (SP series) S1.-SP-MW0 S2.-SP-12A-00 S3.-SP-11-00 S4.-SP-MW1-00 S5.-SP-AQ1-00 S6.-SP-19-00 S7.-SP-AQ3-00
Air quality: cigarette smoke, gasoline vapours Air quality: cigarette smoke, gasoline vapours Kitchen control: food odours, fumes, vapours, humidity Kitchen control: food odours, fumes, vapours, humidity Hydrogen sulphide Organic solvents Kitchen control: alcohol vapours from food General purpose: kitchen control Methane General purpose: flammable vapours Humidity: kitchen control General purpose: interior air quality Hydrogen Cigarette smoke
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different phases of each measurement. When sensors are exposed to volatiles, during the measurement phase, the computer records the resistance changes that sensors experience. When a measurement is completed, the acquired data is stored in a hard disk as a text ®le for later use. A mathematical package (Matlab, The Mathworks Inc., USA) is then used to extract relevant features of each measurement ®le and to test different pattern recognition methods for each particular goal of the study. 3. Results and discussion 3.1. Electronic nose response to fruit aroma Fig. 3 shows a typical response by twelve sensors when measuring fruit. Each curve represents a different sensor transient. The curves plotted represent sensor conductivity against time when the volatiles from the fruit reach the measurement chamber due to electrovalve activation. In that transition, the clean air ¯ow that reaches the measurement chamber is substituted by air ¯ow that comes from the concentration chamber, closing a loop circuit between both chambers. It can be seen that, after an initial period of low and stable conductivity (when only clean air is crossing the measurement chamber), conductivity increases sharply and then stabilises before ending the measurement phase. From the response of each gas sensor many parameters can be extracted. Usual parameters (sometimes called static parameters) include initial conductance (Gi), ®nal conductance (Gf), conductance increment (dG Gf Gi ) and normalised conductance increment ((Gf Gi )/Gi). As the formula suggest this four parameters are correlated between them for each sensor. Different combinations were tried, including humidity and sample weight values that helped to re®ne the correlation results. 3.2. Classification of samples by their shelf-life period using PCA analysis In order to see whether the electronic nose was able to distinguish between different ripeness states, a PCA analysis was applied to the 88 measurements performed with the olfactory system. Only projections made with FIS-SB sensor signals showed meaningful clusters regarding to the shelf-life
Fig. 3. Twelve different sensor responses to fruit aroma. Each curve represents the conductance transient of a single sensor under a step change in the concentration of fruit aroma.
period, although these clusters were later attributed to sensor drift, as it can be seen in Appendix A included at the end of this paper. As mentioned before, similar analysis with Taguchi and FIS-SP sensor signals showed no clear clustering, an indication that their drift was very small (if there was any). 3.3. Classification of samples by unsupervised neural networks Fuzzy art [14] is an unsupervised classi®cation neural network that works very well when there are a few samples to classify. Other advantages include its plasticity to adapt to drift situations and the automatic determination of the number of clusters given a classi®cation criteria (speci®ed with the vigilance parameter). In fact, from a practical point of view, Fuzzy art can be considered an automatic and objective clustering algorithm version of a PCA analysis, where clusters have to be identi®ed manually and drawn subjectively. First, the best classi®cation in terms of shelf-life period was obtained for each type of sensors using fruit measurements alone. The vigilance parameter was always tuned to obtain ®ve output classes. Table 2 shows the best results obtained when using FIS-SB sensors. Each measurement is de®ned by a tag which speci®es the group (the number after
Table 2 Fuzzy art unsupervised classification using FIS-SB sensors Class
Measurements
1 2
g1d01, g1d02, g1d02, g2d02, g3d02, g4d02, g1d03, g1d03, g2d03, g3d03, g4d03, g1d04, g1d04, g2d04, g3d04, g4d04, g1d07, g1d07, g2d07 g1d08, g1d08, g3d08, g4d08, g1d09, g1d09, g2d09, g4d09, g1d10, g1d10, g2d10, g3d10, g4d10, g1d13, g1d13, g2d13, g3d13, g4d13, g1d14, g1d14, g2d14, g3d14, g1d15, g1d15, g2d15, g3d15, g4d15, g1d16, g2d16, g3d16, g4d16 g1d16, g1d17, g1d17, g3d17, g4d17, g1d19, g1d19, g2d19, g3d19, g4d19, g1d21, g1d21, g2d21, g3d21, g4d21, g1d23, g1d23, g4d23, g1d24, g1d24, g2d24, g3d24, g4d24, g1d27, g4d27 g2d23, g1d27, g2d27, g3d27, g1d28, g1d28, g2d28, g3d28, g1d29, g1d29, g2d29, g3d29 g4d29
3 4 5
J. Brezmes et al. / Sensors and Actuators B 80 (2001) 41±50
45
Table 3 Fuzzy art unsupervised classification using Taguchi sensors Class
Measurements
1 2 3 4
g1d01, g1d02, g1d02, g2d02, g3d02, g4d02, g1d03, g2d03 g1d03, g3d03, g1d04, g1d04, g2d04, g4d04 g4d03, g3d04 g1d07, g1d07, g2d07, g1d08, g1d08, g3d08, g4d08, g1d09, g1d09, g2d09, g4d09, g1d10, g1d10, g2d10, g3d10, g4d10, g1d13, g1d13, g2d13, g3d13, g4d13, g1d14, g1d14, g2d14, g3d14, g1d15, g1d15, g2d15, g3d15, g4d15, g1d16, g1d16, g2d16, g4d16, g1d17, g1d17, g3d17, g4d17, g1d19, g2d19, g3d19, g4d19, g2d21, g3d21, g4d21, g1d23, g1d23, g2d23, g4d23, g2d24, g3d24, g2d27, g3d27, g4d27 g3d16, g1d19, g1d21, g1d21, g1d24, g1d24, g4d24, g1d27, g1d27, g1d28, g1d28, g2d28, g3d28, g1d29, g1d29, g2d29, g3d29, g4d29
5
Table 4 Fuzzy art unsupervised classification using FIS-SP sensors Class
Measurements
1
g1d01, g1d02, g1d02, g2d02, g3d02, g4d02, g1d03, g1d03, g2d03, g4d03, g1d04, g1d04, g4d04, g1d07, g1d07, g2d07, g1d08, g3d08, g4d08, g1d09, g1d09, g2d09, g4d09, g1d10, g1d10, g2d10, g3d10, g4d10, g2d13, g4d13, g4d16, g3d17, g3d19 g3d03, g2d04, g3d04, g1d08, g1d13, g1d13, g1d14, g1d14, g2d14, g3d14, g1d15, g1d15, g2d15, g1d16, g1d16, g2d16, g1d17, g2d21, g3d27, g4d29 g3d13, g3d15, g4d15, g3d16, g4d17, g4d19, g4d21, g4d24, g4d27 g1d17, g1d19, g1d19, g2d19, g1d21, g1d21, g3d21, g1d23, g1d24, g1d24, g2d24, g3d24, g1d27, g1d27, g2d27, g1d28, g1d28, g2d28, g3d28, g1d29, g2d29, g3d29 g1d23, g2d23, g4d23, g1d29
2 3 4 5
`g') and the number of days since harvest (the number after `d'). As expected, very good results can be observed. The classi®cation follows a similar evolution as the one found on the PCA analysis, and probably drift is responsible for such behaviour. In Table 3, best results for Taguchi sensors are presented. As it can be seen, there are no major misclassi®cations, although the resolution is not very good, since most of the measurements are classi®ed under class 4. Finally, Table 4 shows the classi®cation results obtained with FIS-SP sensors with some clear outliers which shows the poor resolution of these sensors. A closer look at drift (described in Appendix A) showed that results obtained for FIS-SB sensors could be attributed again to sensor drift, rather than to fruit maturity. On the other hand, results obtained for Taguchi and FIS-SP sensors could not be attributed to drift. For this reason, FIS-SB sensors where not used for correlation purposes. 3.4. Correlation between electronic nose signals and quality indicators As mentioned in Section 2, some fruit samples from the four groups measured with the electronic nose were used the same day to extract quality parameters. In order to compare the electronic nose performance with fruit quality techniques, measures done with the olfactory system were coupled with the values obtained from quality indicators at the same measurement session. In this way, a total of 88 pairs of measurements were coupled. Although back-propagation neural networks were initially considered it seemed inappropriate to use them with such a small number of measurements. Partial least squares (PLS), a well known linear algorithm [15], was considered
the best option due to the constrains of the application. During a training phase, the PLS algorithm builds a model that describes the relationship between sensor signals and the fruit quality parameter to be predicted. In the evaluation phase, the model predicts a fruit quality indicator using new electronic nose measurements not used for training. To see whether the electronic nose was able to predict fruit quality parameters a leave-one-out approach was performed for each quality indicator. Under this approach, 87 measures were used to build the PLS model while the remaining one was predicted using the model and the corresponding olfactory signals. This process, which was repeated 88 times (so that each measure was used once for evaluation and 87 times for training) optimises the use of a small set of measurements (the leave-one-out method sometimes is referred as a cross-validation of order one). For each PLS model built, the data used for training were mean-centred and scaled by their variance; data used for testing was centred and scaled using the mean and variance of the training set. Table 5 shows the average square error (Ssq), correlation coef®cient, optimal number of latent variables (LV), slope and intercept for each destructive parameter predicted for Table 5 Prediction results on quality indicatorsa Quality indicator
Ssq
Correlation coefficient
m
b
LV
Firmness Starch index pH
0.1262 0.6 0.29
0.93 0.68 0.84
0.91 0.63 0.78
0 0.02 0
18 27 12
a
Ideal predictions would give a zero average square error (Ssq) and intercept (b), while the correlation coefficient and the slope would be 1. The optimal number of latent variables (LV) is also specified.
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Fig. 4. Physique-chemical parameter predictions using electronic nose fruit measures.
pink lady apples using Taguchi and FIS-SP gas sensors. It can be seen that ®rmness is the best predicted parameter. Fig. 4 shows the prediction ability of the electronic nose, where each square represents the predicted against the real value of each measure. Ideal predictions would line all points along the diagonal of the plot, where predicted and real values are the same. It should be kept in mind that real/ predicted parameters were also scaled, so that numerical values in the ®gures do not represent the original values obtained applying the standard physique-chemical methods. Since some of the quality indicators (for example, ®rmness) are monotonically increasing or decreasing with time, it is important to check whether sensor drift is used by the PLS model to make accurate predictions of the quality parameters. These checks (as explained in Appendix A) con®rmed that drift was not interfering in the correlation process under shelf-life conditions. Although it may seem surprising to see that physical measurements such as ®rmness can be predicted with sensor responses to organic volatiles generated by fruit, such results are meaningful since the physiological characteristics of fruit are closely related to chemical processes that take place during the ripening process of fruit. In other words, the electronic nose does not measure ®rmness directly, it actually measures volatiles that are well correlated with the ®rmness of the fruits. This is also true for the remaining well predicted indicators.
4. Conclusions Electronic nose measurements have been performed on pinklady apples to see whether an arti®cial olfactory system can be used as a novel instrument to follow their ripening process. Ripening state classi®cation has been attempted using linear PCA algorithms and Fuzzy art neural classi®ers. Fuzzy art techniques clearly outperform their linear counterparts since they are the only ones that show a tendency to classify samples regarding to their shelf-life period (which is related to ripeness). In order to compare the electronic nose performance against established techniques, electronic nose signals have been correlated with fruit quality indicators. Results show that three classical quality parameters (®rmness, starch index and pH) correlate reasonably well with electronic nose signals obtained without destroying the apples, although this study cannot be generalised to other fruit varieties. Further work needs to be done with other cultivars to fully understand the relationship between maturity, fruit volatiles and gas sensor signals. This is why our research work is expanding to other fruit varieties, such as peaches, nectarines and pears. Anyway, the results obtained in this study indicates that electronic noses could become, in the near future, a useful instrument to control fruit quality and ripeness.
J. Brezmes et al. / Sensors and Actuators B 80 (2001) 41±50
Acknowledgements This 2-year project has been possible through the funding by the CICYT-FEDER Project no. 2FD-97-0436 (TIC), with additional funding from ``Catalonia Qualitat''. The authors wish to thank the helpful collaboration of ``Cooperativa Agricola de Cambrils''. Appendix A In fruit maturity measurements, sensor drift is an important factor that has to be closely monitored, since the ripening process is strongly related to time. In this paper, calibration measurements have been systematically performed to check whether the results obtained were achieved from the ripening process of fruit alone or drift helped to reach meaningful results. In this experiment, at the end of each day, a 1 ml of liquid ethanol was injected into the concentration chamber of the electronic nose through a septum. Since a chromatographic syringe was used to inject ethanol, the volatilisation of the liquid was almost instant. The concentration time was reduced to 1 min, while measurement and stand-by periods were kept the same. Of course, all the calibration measurements were done exactly the same way during the entire experiment, so that sensor drift could be monitored in the time period comprised between harvest and the last measurement with pinklady apples.
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In the PCA analysis, only PCA plots from FIS-SB sensors showed meaningful clusters. To check for drift in¯uence, calibration measures were included in the PCA plots of fruit measurements in two different ways. Figs. 5 and 6 show the plots obtained when using both approaches. In these ®gures, each measure is represented by a string tag which speci®es the days since harvest when the measurements where carried out. Fruit measurement tags start with `d' while calibration measurements have a `cal' initial string. With a few exceptions, each session comprised a total of ®ve fruit measurements (two for group 1 and one for groups 2±4), and at the end of the day a calibration measurement was always performed. Fig. 5 shows the PCA plot where fruit and calibration measurements are used together to ®nd the principal components of the analysis. Then, all the measurements are projected onto the ®rst two principal components. As it can be seen, fruit and calibration measurements are clearly separated, but a closer look reveals that the distribution of clusters inside the calibration area is very similar to the fruit measurement distribution, as indicated in the plot. That is why we decided to use the PCA analysis in a different way. Fig. 6 shows the projections using the second approach. In this situation, the PCA analysis is ®rst carried with the fruit measurements alone. After the principal components are computed and ®xed, both fruit and calibration measurements are projected onto the two ®rst principal components. As it can be observed, calibration measurements performed
Fig. 5. PCA scores using electronic nose signals. Principal components are computed using fruit and calibration measurements together.
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Fig. 6. PCA scores using electronic nose signals. Principal components are computed using fruit measurements alone. Then, calibration and fruit measurements are projected.
the same day as fruit measurements lay inside the same clusters. This is a clear indication that sensor drift is the main reason for the clustering of the measurements. Therefore, we can conclude that FIS-SB sensors experience important drift that in¯uence PCA results. To see whether Fuzzy art results were in¯uenced by drift, the neural classi®cation algorithm was applied to fruit and calibration measurements together. Tables 6±8 show the new results for FIS-SB, Taguchi and FIS-SP sensors respectively. Calibration measurements are denoted by a tag with a `cal' string followed by the day of the calibration measurement since harvest, while fruit measurements follow the same format detailed in Section 3.3. From the results, it can be derived that the algorithm separates ¯awlessly the calibration from the fruit measurements
for all three types of sensors. In the case of FIS-SB sensors, the calibration measures are perfectly grouped (regarding their measurement day) in three classes. On the other hand, for Taguchi and FIS sensors they are not grouped by a time basis, an indication that drift is not as important in this type of sensors. That is why, for correlation purposes, FIS-SB sensors were not used. To check for drift in¯uence on correlation between electronic nose signals and quality indicators, PLS predictions were made using the calibration measurements alone. Accurate predictions would mean that prediction was entirely done using sensor drift information, whereas inaccurate predictions would mean that fruit measurements carry the information actually used by the PLS model to make accurate predictions.
Table 6 Fuzzy art unsupervised classification using FIS-SB sensors including calibration measurements Class
Measurements
1
g1d01, g1d02, g1d02, g2d02, g3d02, g4d02, g1d03, g1d03, g2d03, g3d03, g4d03, g1d04, g1d04, g2d04, g3d04, g4d04, g1d07, g1d07, g2d07, g1d08, g1d13 cal2, cal3, cal4, cal6, cal7 g1d08, g3d08, g4d08, g1d09, g1d09, g2d09, g4d09, g1d10, g1d10, g2d10, g3d10, g4d10, g1d13, g2d13, g3d13, g4d13, g1d14, g1d14, g2d14, g3d14, g1d15, g1d15, g2d15, g3d15, g4d15, g1d16, g1d16, g2d16, g3d16, g4d16, g1d17, g1d17, g4d17, g1d19, g1d19, g3d19, g4d19, g2d21, g1d23 cal8, cal9, cal10, cal13, cal14, cal15, cal16 g3d17, g2d19, g1d21, g1d21, g3d21, g4d21, g1d23, g2d23, g4d23, g1d24, g1d24, g2d24, g3d24, g4d24, g1d27, g1d27, g2d27, g3d27, g4d27, g1d28, g1d28, g2d28, g3d28, g1d29, g2d29 cal17, cal19, cal21, cal23, cal24, cal27, cal28, cal29 g1d29, g3d29, g4d29
2 3 4 5 6 7
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Table 7 Fuzzy art unsupervised classification using Taguchi sensors including calibration measurements Class
Measurements
1
g1d01, g1d02, g1d02, g2d02, g3d02, g4d02, g1d03, g1d03, g2d03, g1d04, g1d04, g2d07, g4d09, g1d10, g2d10, g4d10, g1d13, g2d13, g4d13, g4d15, g1d16, g1d19, g2d19, g3d19 cal2, cal3, cal4, cal6, cal9, cal21, cal23 g3d03, g4d03, g2d04, g3d04, g4d04, g1d27, g1d29, g1d29 g1d07, g1d07, g1d08, g1d08, g3d08, g4d08, g1d09, g1d09, g2d09, g1d10, g3d10, g1d13, g3d13, g1d14, g1d14, g2d14, g3d14, g1d15, g1d15, g2d15, g3d15, g1d16, g2d16, g3d16, g4d16, g1d17, g1d17, g3d17, g4d17, g1d19, g4d19, g1d21, g1d21, g2d21, g3d21, g4d21, g1d23, g1d23, g2d23, g4d23, g1d24, g1d24, g2d24, g3d24, g4d24, g1d27, g2d27, g3d27, g4d27, g1d28, g1d28, g2d28, g3d28, g3d29 cal7, cal8, cal10, cal13, cal14, cal15, cal16, cal17, cal19, cal27, cal28 cal24 g2d29, g4d29 cal29
2 3 4 5 6 7 8
Table 8 Fuzzy art unsupervised classification using FIS-SP sensors including calibration measurements Class
Measurements
1
g1d01, g1d02, g1d02, g2d02, g3d02, g4d02, g1d03, g1d03, g2d03, g3d03, g4d03, g1d04, g1d04, g4d04, g1d07, g1d07, g2d07, g1d08, g1d08, g3d08, g4d08, g2d09, g1d13, g1d13, g4d13, g1d14, g3d14, g1d19, g1d24, g1d28 cal2, cal3, cal4, cal6, cal7, cal8, cal9, cal10, cal13, cal14, cal15, cal16, cal17, cal21, cal23, cal24, cal27, cal29 g2d04, g3d04, g1d09, g1d09, g4d09, g1d10, g1d10, g2d10, g3d10, g4d10, g2d13, g1d14, g2d14, g1d15, g1d15, g2d15, g1d16, g1d16, g2d16, g4d16, g1d17, g1d17, g3d17, g1d19, g1d21, g2d21, g3d21, g1d23, g4d23, g1d24, g2d24, g3d24, g4d24, g1d27, g2d27, g3d27, g1d28, g1d29, g1d29, g4d29 g3d13, g3d15, g4d15, g4d17, g2d19, g3d19, g4d19, g4d21, g1d23, g2d23, g1d27, g4d27, g3d28 g3d16, g1d21, g2d28, g2d29, g3d29 cal19, cal28
2 3 4 5 6
Fig. 7. Physique-chemical parameter predictions using electronic nose calibration measurements.
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Fig. 7 shows the prediction results obtained using the calibration measurements as the input information for the PLS algorithm. As it can be seen, the PLS model is making random predictions, a clear indication that accurate predictions are due to the evolution of sensor signals produced by fruit aroma. References [1] R.E.K. Ogers, Instrumentation and Sensors for the Food Industry, Butterworths, Oxford, UK, 1993. [2] C.M. Christensen, Effects of color on aroma, flavor and texture judgements of foods, J. Food Sci. 48 (1983) 787±790. [3] M.J.W. Povey, Ultrasonics in food engineering Part II: Applications, J. Food Eng. 9 (1989) 1±20. [4] H. Shiota, Changes in the volatile composition of La France pear during maduration, J. Sci. Food Agric. 52 (3) (1990) 421. [5] M.L. LoÂpez, M.T. Lavilla, I. Recasens, J. Graell, M. Vendrell, Changes in aroma quality of Golden Delicious apples after storage at different oxygen and carbon dioxide concentrations, J. Sci. Food Agric. 80 (2000) 311±324. [6] P. Eccher, R. Balzarotti, A. Rrizzolo, G.L. Spada, Effect of picking on quality and sensory characteristics of pears after storage and ripening, Acta Horti. 326 (1993) 291±298. [7] R.J. Horvat, S.D. Senter, Identification of additional volatile compounds from cantaloupe, J. Food Sci. 52 (4) (1987) 1097±1098. [8] M. Benady, Fruit ripeness determination by electronic sensing of aromatic volatiles, Trans. ASAE 38 (1995) 251±257. [9] J.E. Simon, Electronic sensing of aromatic volatiles for quality sorting of blueberries, J. Food Sci. 61 (1996) 967±970. [10] E. Llobet, E.L. Hines, J.W. Gardner, S. Franco, Non-destructive banana ripeness determination using a neural network-based electronic nose, Meas. Sci. Technol. 10 (1999) 538±548. [11] J. Brezmes, E. Llobet, X. Vilanova, G. Saiz, X. Correig, Fruit Ripeness Monitoring using an electronic nose, Sens. Actuators B 69 (2000) 223±229. [12] E.L. Hines, E. Llobet, J.W. Gardner, Neural network based electronic nose for apple ripeness determination, Electron. Lett. 35 (1999) 821. [13] W. Gardner, J.W. Gardner, P.N. Barlett, A brief history of electronic noses, Sens. Actuators B 18/19 (1994) 211±220. [14] G.A. Carpenter, S. Grossberg, D.B. Rosen, Fuzzy art: Fast stable learning and categorization of analog patterns by an adaptive resonance algorithm for rapid, stable classification of analog patterns, Neural Networks 4 (1991) 759±771. [15] B.M. Wise, N.B. Gallaher, PLS_Toolbox 2.0, Eigenvector Research Inc., 1998.
Biographies J. Brezmes graduated in telecommunication engineering from the Universitat PoliteÁcnica de Catalunya (UPC) (Barcelona, Spain) in 1993. Since 1993, he has been a PhD student in the Signal Processing and Communications Department at the same university. He has been a Lecturer in the Electronic Engineering Department at the Universitat Rovira i Virgili (Tarragona, Spain) since 1993. His main area of interest is in the application of signal processing techniques such as neural networks to chemical sensor arrays for complex aroma analysis. E. Llobet graduated in telecommunication engineering from the Universitat PoliteÁcnica de Catalunya (UPC) (Barcelona, Spain) in 1991, and received his PhD in 1997 from the same university. His thesis focussed on selectivity enhancement of metal oxide chemical sensors via the study of sensor dynamics. He has been a Lecturer in the Electronic Engineering Department at the Universitat Rovira i Virgili (Tarragona, Spain) since 1992. During 1998, he was a Visiting Fellow at the School of Engineering, University of Warwick (UK). His main areas of interest are in the fabrication, and modelling, of semiconductor chemical sensors and in the application of intelligent systems to complex odour analysis. He is a member of the Institute of Electrical and Electronic Engineers. X. Vilanova graduated in telecommunication engineering from the Universitat PoliteÁcnica de Catalunya (UPC) (Barcelona, Spain) in 1991, and received his PhD in 1998 from the same university. In his thesis, he discussed the effect of electrode geometry and position, both in the static and dynamic responses of metal oxide chemical sensors. He has been a Lecturer in the Electronic Engineering Department at the Universitat Rovira i Virgili (Tarragona, Spain) since 1991. His main areas of interest are in semiconductor chemical sensors modelling and simulation. G. Saiz holds a Bachelor degree in industrial engineering from the Universitat Rovira i Virgili (Tarragona, Spain) since 1999. He is currently getting his Master degree in industrial automation. His main areas of interest are in electronic noses and measurement automation. J. Orts holds a Bachelor degree in Industrial Engineering from the Universitat Rovira i Virgili (Tarragona, Spain) since 1997 and a Master degree in industrial automation (2001) from the same university. His main areas of interest are in electronic noses and measurement automation. X. Correig graduated in telecommunication engineering from the Universitat PoliteÁcnica de Catalunya (UPC) (Barcelona, Spain) in 1984, and received his PhD in 1988 from the same university. He is a full Professor of electronic technology in the Electronic Engineering Department at the Universitat Rovira i Virgili (Tarragona, Spain). His research interests include heterojunction semiconductor devices and solid-state gas sensors. Correig is a member of the Institute of Electrical and Electronic Engineers.