Comparison of sensory and consumer results with electronic nose and tongue sensors for apple juices

Food Quality and Preference 13 (2002) 409–422 www.elsevier.com/locate/foodqual

Comparison of sensory and consumer results with electronic nose and tongue sensors for apple juices Rebecca N. Bleibauma,*, Herbert Stonea, Tsung Tanb, Said Labrecheb, Emmanuelle Saint-Martinb, Sandrine Iszb a

Tragon Corporation, 365 Convention Way, Redwood City, CA 94063, USA b Alpha M.O.S. S.A., 20 Avenue Didier Daurat, 31400 Toulouse, France Accepted 8 February 2002

Abstract Research was conducted to compare apple juice quality measured by consumers, a trained sensory panel [using the quantitative descriptive analysis (QDA) method], and instrument analysis using ‘‘a-ASTREE’’ Liquid Taste Analyzer (electronic tongue) and the Prometheus (electronic nose—sensor array and mass spectrometry). Results from these analyses demonstrate that the electronic tongue and electronic nose, in combination, can be used to predict the sensory characteristics and their relationship to the quality of apple juices measured by consumers. Applications of these findings are important for Quality Control and Quality Assurance (QA/ QC) as they demonstrate that these instruments may be used to track ‘‘consumer-defined quality’’ of apple juice, as long as key measures have been identified a priori. That is, sensory, consumer, physical/chemical, and electronic tongue and nose research must be conducted initially to identify key measures used to define quality for the target consumer. # 2002 Elsevier Science Ltd. All rights reserved. Keywords: Consumer acceptance; Sensory; QDA; Electronic nose; Electronic tongue; Sensor array; Quality control; Quality assurance; Apple juice

1. Introduction Quality expectations of typical consumers have increased as a result of more choices in the marketplace along with targeted advertising, which emphasizes product quality. Competition for market share and the added emphasis on quality has increased pressure on product development and QA/QC to better match consumer expectations. Consumers repeat purchase behavior will depend on whether the product consistently meets those expectations, especially with regard to the product itself. There are, of course, other important factors that influence purchase behavior. These include availability of competitive products, pricing, advertising, and promotion. This paper specifically covers the

* Corresponding author. Tel.: +1-650-365-1833; fax: +1-650-3653737. E-mail address: [email protected] (R.N. Bleibaum).

role of sensory QC/QA as it relates to consumer liking, and its influence on repeat purchase behavior. Studies conducted in the mid-to-late 1980s demonstrated that American consumers perceived differences among products of different qualities. They preferred the most reliable product options, similar to Japanese consumer responses (Mun˜oz, Civille, & Carr, 1991). Recognizing the challenge in a competitive market place, American companies began to consider quality programs and in particular quality programs in sensory QC/QA. These programs allowed manufactures to focus on producing consistent products that conformed to specifications for appearance, aroma, flavor, texture, and aftertaste. Over the years, various sensory programs have been developed specifically for QA/QC purposes (Lawless, 1995; Mun˜oz et al., 1991; Stone & Sidel, 1983). Sensory evaluation is the only method that provides integrated, direct measurements of perceived intensities of target attributes. For the product(s) of interest, these key or target attributes should relate to consumer acceptance

0950-3293/02/$ - see front matter # 2002 Elsevier Science Ltd. All rights reserved. PII: S0950-3293(02)00017-4

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and purchase behavior. Historically, sensory methods when used in production facilities have been time consuming and expensive to operate. This has been attributed to reliance on plant personnel who are subject to typical economic and personal issues such as attrition, lay offs, absenteeism, scheduling conflicts, etc. Given these challenges with in-plant sensory panels, there are many advantages to developing instrumental methods. To be successful, instrumental methods must provide rapid, reproducible results, with continuous operation, and be cost effective. To date, the instrumental methods have lacked the ability to consistently perceive all of the key sensory attributes of interest, and predictive relationships between sensory and instrumental measures have been relatively weak and unreliable. Clearly, the advantages of instrumental methods in QA/QC imply that where they are shown to have sufficient correlation with sensory and consumer research, they should be implemented. This manuscript outlines an approach to the sensory, consumer, and instrumental research required to utilize instrumental measures within a QC operation. Implementation following the research program is usual straightforward, taking into account the requirements of the specific instruments. In addition, the disadvantages of instrumental analysis are considered within this research in assessing the usefulness of the electronic nose and electronic tongue for use in the apple juice industry as a case study.

2. Background To consistently meet consumer-defined quality, it is ideal to determine key sensory drivers for acceptance using optimization modeling techniques (Schutz, 1983; Stone & Sidel, 1983; Sidel, Stone, & Thomas, 1994). These techniques integrate sensory and consumer data, which are used to establish quality specifications for key attributes. Product quality is monitored using the specification and an appropriately trained sensory panel. The interest in using electronic tongue and nose instruments to supplement human judgement has been a topic of research and discussion for a number of years. It was not until the early 1960s that Schutz, Vely, and Iden (1961) and Wilkens and Hartman (1964) demonstrated the feasibility of an electronically based odor sensing system, the early versions of electronic noses. Almost two decades passed before contemporary electronic noses were described in the literature (Bartlett, Elliott, & Gardner, 1997; Freund & Lewis, 1995; Lewis, 1996; Persaud & Dodd, 1982). Advances in the development of sensor array technologies, use of pattern recognition techniques, and neural networks, have had a significant impact on the sensitivity and the utility of these instruments (Lonergan et al., 1996; Taubes, 1996). Today they are capable of identifying and classifying

the composition of a variety of products (Wilhelmsen et al., 1998). Not surprisingly, there has been a growing interest in their applications in the food and beverage industry especially for quality control purposes, in part because it represents a means of reducing reliance on the human judgment, thus reducing time and cost (Griese, J., 1993; Sawyer, 1997). The majority of instruments such as electronic tongue and electronic nose that have been used for quality control purposes were developed using the following methodology. Typically, R&D, plant QA/QC personnel, and sensory staff have been responsible for determining sensory specifications based on bench-top screening and/or experience of technical staff, often with little input from consumers (Alpha MOS, 2000). Using the expertise of R&D, QA/QC, and sensory personnel; samples representing acceptable, borderline, and unacceptable sensory quality specifications were selected. These selected samples were then used to develop models on the electronic nose and electronic tongue. (Alpha MOS, 1999; Arnold, 2000; Bazzo, Loubet, Tan, HewittJones, Engelen-Cornax, & Quadt, 1998; Braggins, Frost, Agnew, & Farouk, 1999; Grypta, 2001; Hansen, Wiedemann, Van der Bol, & Wortel, 1999; Madsen & Grypta, 2000; Pichat, Olivier, Koch, & Gygax, 2000; Strassburger, 1998). Once the electronic noses and electronic tongues were trained based on the selected product array, unknown samples were used to validate the models developed and verified against the product specifications. The selected product array is critical for appropriate use of electronic tongues and noses for reliable QA/QC application. It also is critical for the selected product array to be evaluated using sensory evaluation and consumer research. In this way, consumer defined quality can be determined and specific relationships with the instrumental measures can be established. Alpha MOS has been marketing and developing electronic tongues and noses since 1994 for measuring aroma and taste. This study used an electronic tongue called ‘‘a-ASTREE’’ Liquid Taste Analyzer and an electronic nose called ‘‘a-Prometheus’’ (combination of a sensor array and mass spectrometry). The results obtained from both instruments can be combined to obtain a better product characterization, enabling data collection from 25 different chemical sensors (18 gas sensors+7 liquid sensors). By using the mass spectrometry system, up to 299 atomic mass units (amu) can be obtained. However, in headspace (HS) the fragments or amu detector rarely exceed 150. The objective of this research was to determine if results from electronic tongue and electronic nose (sensors and mass spectrometry) have high correlation with those obtained from a qualified sensory panel using the QDA method (Stone & Sidel, 1993; Stone & Sidel, 1998) and consumers (acceptance). The results will indicate if

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these instruments can be used to predict/monitor sensory characteristics, which lead to consumer acceptance. This research is a preliminary study to correlate the electronic instruments and human perception.

3. Materials and methods 3.1. Materials Apple juice was selected for this study, because it is readily available and easy to be measured by both instruments and humans. A series of nine apple-based juices (Table 1) were tested including three-apple blend, vitamin C fortified, apple pear juice, and an apple cider. All but one product base was Tree Top brand apple juice. Three of the nine products were modified to individually measure the effects of added sucrose at two levels (10 and 20 g/l) and added citric acid (3 g/l). In addition, one product from a competitive manufacturer (Motts) was included to determine how this product was perceived relative to the single manufacturer’s products. This product array was selected to stretch the range of differences typically found among products produced within one production facility, and to determine how these products were perceived as different by the sensory panel, consumers, and the electronic nose and electronic tongue instruments. Apple juices, sucrose, and citric acid samples were purchased by Tragon Corporation, in the local area markets. Tragon shipped the products to Alpha MOS for instrument measurement. To maintain freshness, sucrose and citric acid were added to the juice on the day of testing.

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analysis (QDA) method (Stone & Sidel, 1993; Stone & Sidel, 1998). Thirty panelists were recruited to participate in sensitivity screening tests based on consumption of apple juice, interest in testing, and verbal/social skills. From this group, twelve were selected based on their performance in discrimination tests. Ten apple juice pairs were selected to represent the product set and to provide an array of easy to difficult discrimination. Panelists who demonstrated the highest levels of discrimination among the group were selected to continue with the QDA training. Under direction of a trained QDA moderator, panelists developed a sensory language to describe the array of apple juices. These sessions were conducted over a 4-day period, and each session lasted approximately 90 min. The panelists developed 34 sensory terms (attributes) to describe the products. The scorecard included all 34 attributes, with each attribute placed adjacent to a 6-inch (15 cm) horizontal line, anchored one-half inch (13 mm) from each end with directional terms (e.g. weak/strong, slightly/very). To evaluate apple juices, 90 ml of product were served at room temperature in a clear 225-ml plastic cup coded with a three-digit number, along with a scorecard and definition of terms. Panelists marked each line at the point they perceived the intensity of that attribute for each test product. Over the course of five data collection sessions, each product was evaluated three times (replications) by each panelist. There were 3-min timed rest intervals between products, and a 5-min interval after the 4th product (to further minimize sensory fatigue). A balanced-block serving order across products and panelists was used. 3.3. Consumer acceptance test

5. 120% Vitamin C

Tree Top 100% Apple Juice with 120% Vitamin C

Approximately 200 consumers in the San Francisco Bay Area, California and Chicago, Illinois participated in the test. Participants were selected based on the following criteria; ages of 21–65, males and females, liked and drank bottled apple juice at least once per month, no food allergies, standard security screening, and interested in participation. Products were served under the same conditions as in the QDA. Consumers evaluated all nine products on 1 day following a sequential monadic, balanced bock design, with 2-min timed rest intervals between products, and 5-min rest intervals after the third and sixth product. Consumers were asked to rate their overall opinion of each product using a nine-point hedonic scale.

6. Three 7. blend

Tree Top 100% Apple Juice Three Apple Blend (Not from concentrate)

3.4. Instrumental measurements—electric tongue and electronic nose

7. Apple 8. Apple 9. Motts

Tree Top 100% Apple Pear Juice Tree Top 100% Juice Apple Cider Motts 100% Apple Juice

3.2. Sensory test Complete sensory attribute descriptions of the products were obtained using the quantitative descriptive

Table 1 Apple juices tested Product name Base juice 1. 2. 3. 4.

Control Sweet 1 Sweet 2 Sour 1

Tree Top 100% Apple Tree Top 100% Apple Tree Top 100% Apple Tree Top 100% Apple citric acid

Juice Juice—with 10 g/l added sucrose Juice—with 20 g/l added sucrose Juice—with 3 g/l added

The same set of products tested for the sensory and consumer acceptance research was sent by Tragon to

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Alpha MOS and evaluated by ‘‘a-ASTREE’’ Liquid and Taste Analyzer (electronic tongue) and ‘‘a-Prometheus’’ (electronic nose). These instruments were developed by Alpha MOS. The electronic tongue, a-ASTREE, is composed a 16position auto-sampler, an array of liquid sensors, and an advanced chemometric software package. The array consists of seven different liquid cross-selective sensors (ZZ36, BB06, CA07, BA07, AB07, HA06, CB07). These chemical sensors are potentiometric sensors with a membrane that gives each sensor a specific sensitivity and selectivity characteristic. (Alpha MOS, 2001a, 2001b) They measure dissolved organic compounds (DOC) in liquids including taste and flavor compounds. They provide measurements on a test liquid without sample preparation. The entire process of measurement (auto-sampling, data collection, and data treatment) is automatically conducted and controlled by a PC with a complete software package. Each apple juice was evaluated using three replications by each of the seven sensors under the conditions listed in Table 2. The electronic nose, a-Prometheus, consists of a sensor array system (a-Fox) and a mass spectrometry system (a-Kronos), both controlled by a single PC. a-Fox, is composed a 96-position auto-sampler, an array of gas sensors, and an advanced chemometric software package. The sensor array consists of three metal oxide sensor chambers and 18 sensors (SY/AA, SY/G, SY/gCT, SY/gCTl, SY/Gh, SY/LG, P10/1, P10/ 2, P30/1,P30/2, P40/1, P40/2, PA2,T30/1, T40/2, T70/2, T40/1, and TA2). These chemical sensors are resistive sensors where each sensor has a specific sensitivity and selectivity characteristic. (Alpha MOS, 1998, 2000b) They measure volatile organic compounds (VOC) in headspace. Their characteristics are dependent on the materials used in the manufacture of the sensors. a-Fox automatically collects data (auto-sampling, data acquisition, and data treatment) through a complete software package. A synthetic air (P=5 psi) was used as a carrier gas and humidity was controlled by an ACU500 (RH= 20%, T=36  C) using pure water. The samples were injected to a-Fox by an autosampler and measured under the conditions listed in Table 3. The mass spectrometry system, a-Kronos, was for a mass range of

1–300 amu, with an electronic impact ionization (E.I.) of 70 eV and an electron multiplier detector. The samples were injected to the a-Kronos by an autosampler and measured using three replications under the conditions listed in Table 4. The mass spectrometry (MS) system, however, did not provide informative results on this array of products; thus the results are not discussed in this research. The sensitivity of the MS system was not high enough to detect direct headspace injection. Additional sample preparation would be necessary for a-Kronos to discriminate the products. Two methods used routinely include solid state microextraction (SPME) and Purge and Trap (P&T) techniques to increase concentration of VOCs and decrease the level of nitrogen and oxygen that is typically injected into the MS system using direct syringe injection methods. As the MS system operates under a high vacuum to work correctly, injection of samples into the system is limited. A high amount of headspace containing air would reduce the vacuum of the MS system and thus cause the system to stop operating correctly. In typical gas chromotography (GC), the typical flow-rate is a few ml/min. Similarly, a direct headspace technique must not exceed the flow requirements which in turn limits the amount of sample that can introduced into the system. The P&T and the SPME techniques allow the concentration of the VOC responsible for apple juice aroma without the air and therefore a more concentrated amount of sample can be introduced at the same flow-rate. A Themoquest GCQ (without GC) did not discriminate the products either. This result was unsurprising given that the vacuum issue

Table 3 E-nose sensory array system- a-Fox test conditions Characteristic

Test parameter

Headspace generation time Headspace generation temperature Injected volume Injected speed Acquisition time

300 s 50  C 1 ml 1 ml/s 120 s

Table 4 E-nose mass spectrometry a-Kronos test conditions Table 2 Sensor test conditions Characteristic

Test parameter

Sample volume Time per analysis Acquisition time Sample temperature

100 ml 3 min 2 min Room temperature

Characteristic

Test parameter

Incubation time Incubation temperature Syringe temperature Injected volume Injected speed Acquisition time Scan range

600 s 70  C 75  C 5000 ml 45 ml/s 120 s 45–150 amu

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ensured that the sample introduced to this system was also limited. This gave the same problem as the MS system as previously described.

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rated Sweet 2 highest, and at parity with Apple Pear, Three-Apple Blend, and Sweet 1. Control (6.6) was rated significantly lower than Sweet 2, whereas Sour 1 was rated significantly lower than all other products (Table 5).

3.5. Statistical analysis 4.2. Sensory descriptive results For the sensory data, responses were converted to numeric values ranging from 0 to 60, and statistically analyzed by one- and two-way analyses of variance using Tragon’s QDA Analysis Program (platformed using S-Plus 2000), and Duncan multiple range tests to establish product differences at P=0.05. For consumer data, analyses were conducted using SAS (Statistical Analysis System, Ver. 8.0). Means were calculated and statistically tested using analysis of variance to determine if a statistical difference existed at P < 0.05, and Duncan’s multiple range tests were used to identify statistical separation among the means. Cluster analysis using SAS identified clusters based on overall acceptance. To determine relationships among sensory, acceptance, and instrumental data, analyses included Pearson correlation coefficients and principal component analysis. In addition, Alpha MOS. conducted a variant of correspondence factorial analysis for the instrumental and QDA data using Matlab Version 5 (Benzecri, Bellier, L., Benier, B., Blaisen, S., Bourgarit, Ch., Briane, J. P., Cazes, P. et al., 1973; Greenacre, 1989).

4. Results and discussion 4.1. Consumer acceptance results Overall acceptance ratings for this product array ranged from 7.2 for Sweet 2 to 5.0 for Sour 1, indicating consumers were clearly able to distinguish among these products and sort them based on liking. Consumers

Table 5 Consumer acceptance results—nine-point hedonic scale Product

Overall acceptance ratinga,b

Sweet 2 Apple pear Three apple blend Sweet 1 120% Vitamin C Control Apple cider Motts Sour 1

7.2 7.0 6.9 6.7 6.7 6.6 5.9 5.9 5.0

a

a a,b a,b a,b b b c c d

Based on responses from 97 consumers in San Francisco Bay and Chicago areas on 14–15 June 2000. b Means with the same letter(s) are not significantly different at the 95% confidence level.

For the QDA results, there were statistically significant differences for 32 of the 34 attributes (P < 0.05) used to describe the juices (Table 6); pear aroma and sour aroma were non-significant. Control and Sweet 1 were most similar to each other, and were significantly different on only three attributes. Sweet 1 had a sweeter flavor and sweeter aftertaste, and had more mouth coating than Control. Apple Pear Juice and Sweet 2 were similar, with significant differences on seven attributes. Apple Pear Juice had more sweet and apple, and fresh apple aromas, less sweet flavor, less mouth coating, and less sweet and pear aftertastes than Sweet 2. Juice Apple Cider was the darkest in color, had the most viscous appearance, and the most overripe aroma and flavor. Three Apple Blend had the lightest color, the lowest viscosity, and was among the highest for sweet, apple, and fresh apple aromas and flavors. It also had among the lowest overripe apple aroma and flavor, with significantly more apple aftertaste than all other products. Mott’s was relatively high on musty aroma but within the mid-range for most other attributes. The largest outlier was Sour 1, which was rated as the highest or lowest on 19 of the 34 attributes, clearly separating it from the other products. Sour 1 had low overall aroma, low sweet and apple aromas, low sweet, apple, and pear flavors and aftertastes, and high sour, bitter, and tinny flavors and aftertastes. The mouthfeel of Sour 1 was the least smooth, most puckery, and most drying. 4.3. Correlation of sensory (QDA) and instrumental results Relationships between the QDA means (34 attributes) and instrumental measurement means (25 sensors) were identified using principal component analysis (PCA; Fig. 1). As Sour 1 was quite different from the other products and influential on the analysis, another PCA was conducted without Sour 1 to avoid converging small differences (Fig. 2). Based on Fig. 2, a wheel plot (Fig. 3) was created to understand the relationships more easily. In Fig. 2, a sensory attribute/instrumental sensor name close to a product indicates that the product was rated high for that attribute/sensor. An attribute/sensor that is located at the opposite side of a product indicates a low rating for that product. For example, Juice Apple Cider was strong for musty and overripe aromas, coating mouthfeel, burnt sugar flavor, and weak for apple flavor and aftertaste, pear aftertaste, and was rated low by a sensor SY/LG.

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Table 6 QDA, mean values for apple juicea Color appearance Juice apple cider Motts Sweet 1 Apple pear juice Control Sweet 2 Sour 1 Sour 2 120% Vitamin C Three apple blend

44.44 40.06 35.97 35.83 34.42 34.31 32.75 29.78 23.50 15.53

a b c c c c cd d e f

Viscosity appearance Juice apple cider Control Sour 2 Sweet 2 Sour 1 Apple pear juice Motts Sweet 1 120% Vitamin C Three apple blend

26.64 23.56 23.25 23.03 22.42 22.25 22.06 21.97 18.97 16.11

a b b b b b b b c d

Overall aroma Juice apple cider Three apple blend Apple pear juice Motts 120% Vitamin C Control Sour 2 Sweet 2 Sweet 1 Sour 1

31.92 31.47 29.97 29.56 28.25 28.00 27.19 25.81 24.86 24.78

a a ab ab abc abc bc bc c c

Sweet aroma Three apple blend Apple pear juice Juice apple cider Motts Control Sour 2 120% Vitamin C Sweet 1 Sour 1 Sweet 2

24.39 23.47 22.39 22.31 21.42 21.11 20.78 19.28 19.28 19.08

a a ab ab ab ab ab b b b

Apple aroma Three apple blend Apple pear juice Juice apple cider 120% Vitamin C Control Sour 2 Sweet 2 Motts Sweet 1 Sour 1

26.67 23.86 20.89 20.86 19.75 18.56 18.17 17.86 16.72 16.36

a ab bc bc cd cd cd cd d d

Pear aroma Three apple blend Apple pear juice Motts Control 120% Vitamin C Sweet 2 Juice apple cider Sour 2 Sour 1 Sweet 1

11.00 10.47 10.36 9.58 9.56 9.33 9.14 9.11 9.03 8.64

Sour aroma Juice apple cider Sour 1 Sour 2 Motts 120% Vitamin C Sweet 2 Three apple blend Control Apple pear juice Sweet 1

11.31 9.67 9.61 9.33 9.14 8.92 8.53 8.39 8.33 6.72

Musty aroma Motts Juice apple cider Sweet 2 Control Sour 1 120% Vitamin C Apple pear juice Sweet 1 Sour 2 Three apple blend

12.00 11.61 9.50 8.69 8.50 8.28 7.58 7.50 6.86 5.89

a a ab bc bc bc bc bc bc c

Fresh apple aroma Three apple blend Apple pear juice 120% Vitamin C Sour 2 Juice apple cider Control Motts Sweet 2 Sour 1 Sweet 1

26.64 17.50 16.08 14.94 14.50 13.31 12.11 12.06 11.50 11.25

a b bc bcd bcd cd cd cd d d

Overripe aroma Juice apple cider Motts Sweet 2 Control Sour 1 Apple pear juice Sweet 1 120% Vitamin C Sour 2 Three apple blend

17.44 13.17 13.11 13.00 12.03 10.81 10.75 10.22 9.25 8.11

a b b b bc bc bc bc bc c

Overall flavor Sour 2 Sour 1 Three apple blend Juice apple cider

40.36 39.78 37.64 37.47

a a ab ab

Sweet flavor Sweet 2 Sweet 1 Three apple blend Motts

35.94 32.17 31.61 29.89

a ab bc bcd

(continued on next page)

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R.N. Bleibaum et al. / Food Quality and Preference 13 (2002) 409–422 Table 6 (continued) Motts Sweet 2 Apple pear juice Sweet 1 120% Vitamin C Control

37.00 36.22 35.42 33.58 32.83 31.64

abc bcd bcd cde de e

apple pear juice 120% Vitamin C Control Juice apple cider Sour 2 Sour 1

28.28 28.03 27.19 25.67 24.08 20.25

bcde bcde cde de ef f

Apple flavor Three apple blend Apple pear juice 120% Vitamin C Sweet 1 Sour 2 Sweet 2 Control Juice apple cider Motts Sour 1

30.69 26.28 24.83 24.50 24.28 23.53 23.17 22.86 22.31 19.83

a b b b bc bc bc bc bc c

Fresh apple flavor Three apple blend Sour 2 Apple pear juice 120% Vitamin C Sweet 1 Control Juice apple cider Motts Sour 1 Sweet 2

29.64 22.31 20.00 19.14 17.61 16.08 15.97 15.69 15.00 14.83

a b bc bc bc c c c c c

Pear Flavor Sweet 2 120% Vitamin C Sweet 1 Motts Control Three apple blend Apple pear juice Juice apple cider Sour 2 Sour 1

12.50 12.47 11.83 10.97 10.92 10.39 9.81 9.44 9.00 8.28

a a ab abc abc abc abc bc bc c

Burnt sugar flavor Juice apple cider Sour 2 Motts Sweet 1 Sour 1 Control Sweet 2 Three apple blend Apple pear juice 120% Vitamin C

14.58 11.64 11.36 10.83 10.58 10.28 9.92 7.94 7.86 6.58

a ab abc abc abc abc abc bc bc c

Sour flavor Sour 1 Sour 2 Motts Juice apple cider 120% Vitamin C Three apple blend Control Apple pear juice Sweet 1 Sweet 2

34.81 32.92 20.75 18.64 13.75 12.97 12.81 12.36 10.11 9.81

a a b b c c c c c c

Overripe flavor Juice apple cider Motts Sweet 1 Sour 1 Control Sweet 2 Apple pear juice Sour 2 120% Vitamin C Three apple blend

21.72 a 16.69 b 15.97 bc 15.78 bc 15.39 bc 15.00 bc 13.44 bcd 12.78 bcd 10.50 cd 9.22 d

Bitter flavor Sour 1 Sour 2 Motts Juice apple cider 120% Vitamin C Apple pear juice Control Sweet 1 Three apple blend Sweet 2

24.19 21.08 15.97 15.64 11.61 9.92 9.28 9.11 9.11 7.50

a a b b bc c c c c c

Tinny flavor Sour 1 Sour 2 Motts Juice apple cider 120% Vitamin C Apple pear juice Control Three apple blend Sweet 1 Sweet 2

18.00 15.22 14.53 13.75 11.14 10.06 10.00 9.14 8.97 8.83

a ab abc bcd cde de de e e e

Smooth mouthfeel Sweet 2 Three apple blend Control Sweet 1 Apple pear juice 120% Vitamin C Juice apple cider Motts Sour 2 Sour 1

36.83 35.36 35.00 34.83 34.44 32.19 30.17 29.50 24.92 20.97

a a ab ab ab abc bc c d d

Body mouthfeel Three apple blend Sweet 2 Motts Sweet 1 Juice apple cider Control 120% Vitamin C Apple pear juice Sour 2 Sour 1

27.97 27.64 27.53 26.56 26.53 24.39 23.67 23.64 22.83 21.72

a a a ab ab ab ab ab ab b

(continued on next page)

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Table 6 (continued) Coating mouthfeel Sweet 1 Juice apple cider Sour 1 Motts Sweet 2 120% Vitamin C Sour 2 Three apple blend Control Apple pear juice

21.22 21.22 21.19 20.86 20.72 20.58 20.14 18.17 17.56 17.17

a a a ab ab ab abc abc bc c

Puckery mouthfeel Sour 1 Sour 2 Motts Juice apple cider Three apple blend 120% Vitamin C Apple pear juice Sweet 1 Sweet 2 Control

28.81 25.67 16.67 16.50 13.75 12.72 11.50 10.97 10.75 10.64

a a b b bc bc c c c c

Drying mouthfeel Sour 1 Sour 2 Juice apple cider Motts Sweet 1 120% Vitamin C Three apple blend Control Apple pear juice Sweet 2

27.17 26.39 21.08 20.67 17.36 15.97 15.92 15.39 14.50 13.86

a a b b bc c c c c c

Overall aftertaste Sour 1 Sour 2 Juice apple cider Sweet 2 Three apple blend Motts 120% Vitamin C Sweet 1 Apple pear juice Control

31.31 30.17 29.19 28.44 27.81 27.64 27.28 26.19 25.75 24.33

a ab abc abcd bcd bcd bcd cde de e

Sweet aftertaste Sweet 2 Sweet 1 Three apple blend Apple pear juice 120% Vitamin C Motts Juice apple cider Control Sour 2 Sour 1

28.25 26.00 23.94 22.31 22.28 21.22 21.19 20.67 17.92 15.86

a ab bc bc bc cd cd cd de e

Apple aftertaste Three apple blend Sweet 2 Apple pear juice 120% Vitamin C Juice apple cider Sweet 1 Sour 2 Motts Control Sour 1

22.36 19.47 19.17 18.53 17.56 17.33 17.08 17.06 16.58 14.89

a b b b bc bc bc bc bc c

Pear aftertaste Sweet 2 Three apple blend 120% Vitamin C Motts Sweet 1 Control Apple pear juice Juice apple cider Sour 1 Sour 2

12.97 11.36 10.67 10.44 10.17 9.92 9.92 9.75 9.28 8.83

a ab b b b b b b b b

Sour aftertaste Sour 1 Sour 2 Juice apple cider Motts 120% Vitamin C Control Three apple blend Sweet 1 Apple pear juice Sweet 2

23.72 20.22 12.06 11.83 8.67 7.61 7.31 6.81 6.69 6.39

a a b b bc c c c c c

Bitter aftertaste Sour 1 Sour 2 Juice apple cider Motts 120% Vitamin C Control Three apple blend Apple pear juice Sweet 1 Sweet 2

20.72 16.94 12.17 11.50 8.25 6.97 6.83 6.53 6.19 5.56

a a b b bc c c c c c

Tinny aftertaste Sour 1 Sour 2 Juice apple cider Motts 120% Vitamin C Apple pear juice Control Sweet 1 Sweet 2 Three apple blend

14.92 12.44 11.36 10.39 8.33 7.79 7.31 6.75 6.67 6.08

a ab bc bcd cde cde de de de e

Coating aftertaste Sour 1 Motts

18.36 a 17.33 ab

Drying aftertaste Sour 1 Sour 2

26.17 a 23.61 ab (continued on next page)

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R.N. Bleibaum et al. / Food Quality and Preference 13 (2002) 409–422 Table 6 (continued) Juice apple cider Sour 2 Sweet 2 Sweet 1 120% Vitamin C Apple pear juice Three apple blend Control

16.89 16.83 16.22 15.28 14.53 13.56 13.39 13.11

abc abc abcd abcd bcd cd d d

Motts Juice apple cider Sweet 1 Sweet 2 120% Vitamin C Three apple blend Control Apple pear juice

21.53 19.89 17.69 17.19 16.22 15.94 15.72 15.28

bc cd de de e e e e

QDA, quantitative descriptive analysis.

Fig. 1. Principal component analysis of sensory and instrument measurement on all products.

It is also possible to analyze the relationship between QDA and sensor array measures by the wheel chart (Fig. 3). The closer a sensor name to a QDA attribute, the more positively correlated they are with each other. For example, sensor SY/LG is highly positively correlated with apple flavor and aftertaste and negatively corre-

lated with burnt sugar flavor and overripe aroma and flavor. To further analyze the relationship between the 25 sensors and QDA attributes, a pair-wise single correlation was calculated and analyzed in conjunction with the principal component analysis results (Table 7).

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Fig. 2. Principal component analysis of sensory and instrument measurement—without sour 1.

Electronic sensors have high correlations with specific sensory attributes, thus have potential to predict these attributes. Those that are labeled as ‘similar’ have a positive correlation, and those that are labeled as ‘opposite’ have a negative correlation. However, there also were attributes that were not highly correlated with any of the electronic sensors, such as pear flavor and aftertaste, coating mouthfeel and aftertaste, sweet aftertaste, and drying aftertaste. A separate analysis was conducted also by Alpha MOS to compare instrument results based on the 25 sensors (Fig. 4) and QDA results (Fig. 5) using factor analysis. Figs. 4 and 5 show that the data structure between instrumental analyses and QDA are similar. For example, similar discrimination was observed between the instrument and QDA results for products with large differences. Both results indicated that Sour 1 was quite different from the other products, especially from Control, Sweet 1, and Sweet 2, and Apple Pear Juice. The relative positions between instrumental and QDA results were similar.

Fig. 3. Relationships among sensory and instrumental measurements.

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R.N. Bleibaum et al. / Food Quality and Preference 13 (2002) 409–422 Table 7 Correlation between sensory and instrument measurements Electronic sensor measurements

Sensory measurements Similar (r0.50)

Opposite (r 0.50)

SY/AA, SY/G, SY/gCT SY/gCTl, SY/Gh (e-Nose)

Burnt sugar flavor Color appearances Musty aroma Overripe aroma/flavor Viscosity appearances

Apple aroma/flavor/aftertaste Fresh apple aroma/flavor Pear aroma Sweet aroma

SY/LG (e-Nose)

Apple aroma/flavor/aftertaste Fresh apple aroma/flavor Pear aroma Sweet aroma

Burnt sugar flavor Color appearances Musty aroma Overripe aroma/flavor Viscosity appearances

P10/1, P10/2, P30/1 P30/2, P40/1, P40/2, PA2 T30/1, T40/2, T70/2 (e-Nose)

Apple aroma/flavor/aftertaste Fresh apple aroma/flavor Pear aroma Sweet aroma

Burnt sugar flavor Color appearances Overripe aroma/flavor Viscosity appearances

T40/1, TA2 (e-Nose)

Apple aroma/flavor/aftertaste Fresh apple Aroma/flavor Overall aroma/flavor Sweet aroma Pear aroma

Color appearances Overripe aroma/flavor Viscosity appearances

ZZ36, BB06, CA07 (e-Tongue)

Smooth mouthfeel

Bitter flavor/aftertaste Drying mouthfeel Overall aroma/flavor/aftertaste Puckery mouthfeel Sour aroma/flavor/aftertaste Tinny flavor/aftertaste

BA07, AB07 (e-Tongue)

Overall flavor/aftertaste Overripe aroma/flavor Sour aroma

HA06 (e-Tongue)

Overall aftertaste

CB07 (e-Tongue)

Apple aroma Body mouthfeel Color appearances Fresh apple aroma

However, relationships measured by the instruments were slightly different from the QDA results for products that were relatively similar to each other, such as 120% Vitamin C, Three Apple Blend, Apple Cider, and Motts. These slight differences mean that a 100% correlation with QDA results has not been obtained with the instruments. Further investigation using different sensors or multivariate statistics to obtain better correlation to the QDA results would be required if these specific attributes were important for QC/QA evaluations. 4.4. Correlation of sensory, consumer acceptance, and instrumental results There were numerous high correlations between QDA, instrumental, and consumer liking judgments.

Sensory and instrumental attributes listed in Table 8 under ‘likes’ represents attributes that have a relatively high (r> 0.60) positive correlation with overall acceptance, and those listed under ‘dislikes’ represents attributes which have a relatively negative (r> 0.60) correlation with overall acceptance. This research introduces opportunities to use sensory and instrumental research to better understand consumer likes and dislikes. Target values for these instrumental attributes can be developed for QC purposes. QC can monitor these instrumental attributes, and when they fall outside the desired range, plant personnel can then take the appropriate next steps. In this way, the potential for electronic sensors (nose and tongue) could be used to track production on a continual basis to ensure that key characteristics are identified and tracked.

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Fig. 4. Electronic tongue and nose measurements, analyzed by Alpha MOS.

Fig. 5. Quantitative descriptive analysis measurements analyzed by Alpha MOS.

R.N. Bleibaum et al. / Food Quality and Preference 13 (2002) 409–422 Table 8 Correlation of sensory and instrumental data with consumer acceptance Unique characteristics

Sensory QDA data

Instrumental data

Likes

Dislikes

Sweet Flavor/ Aftertaste Apple Flavor/ Aftertaste Pear Aftertaste Smooth Mouthfeel

Dark Color

E-Nose SY/LG T30/1 P10/1 P10/2 P40/1 T70/2 PA2 P30/1 P40/2 P30/2 T40/2 T40/1 TA2 E-Tongue CNMB-7–36 9 CNM6–7-4 CNM7–35

Sour Aroma/Flavor/ Aftertaste Musty Aroma Overripe Aroma/Flavor Burnt Sugar Flavor Bitter Flavor/Aftertaste Tinny Flavor/Aftertaste Puckery Mouthfeel Drying Mouthfeel/Aftertaste Coating Aftertaste/Effect SY/G SY/AA SY/Gh SY/gCTl SY/gCT

5. Conclusion To summarize, electronic tongues and electronic noses may be used to track ‘‘consumer-defined quality’’ of apple juice by using results from correlation analysis and predictive equations between QDA and consumer data. Electronic sensors can be used to predict acceptance in a more precise manner allowing greater tolerances on less important attributes while maintaining stricter control over key QDA sensory attributes. This additional and powerful option using QDA compliments the successful use of these instruments already at the QC/QA level as reported within the introduction section with the reference publications. The references used in the introduction describe the most recent results on the electronic nose in the QC/QA environment. (Alpha MOS, 1999; Arnold, 2000; Bazzo et al., 1998; Braggins et al., 1999; Grypta et al., 2001; Hansen et al., 1999; Madsen and Grypta, 2000; Pichat et al., 2000; Strassburger, 1998.) The training or model building in these publications was based on samples defined only by

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QC/QA and R&D personal; no consumer correlation was attempted. The sample array selected for training of the electronic sensors is very important and must be done with the consumers in mind. The selected samples must be relevant; i.e. representing specific defects and/or products that are within and outside of the sensory specification. Once the electronic sensors are ‘trained’, they can be integrated into the production quality monitoring system.

6. Future work These research results demonstrated that practical applications are possible. Each company must determine the extent to which this approach can be applied to their products.

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