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Monitoring of black tea fermentation process using electronic nose
Journal of Food Engineering 80 (2007) 1146–1156 www.elsevier.com/locate/jfoodeng
Monitoring of black tea fermentation process using electronic nose Nabarun Bhattacharyya a,*, Sohan Seth b, Bipan Tudu b, Pradip Tamuly c, Arun Jana a, Devdulal Ghosh a, Rajib Bandyopadhyay b, Manabendra Bhuyan d b
a Centre for Development of Advanced Computing, Salt Lake, Kolkata 700 091, India Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata 700 032, India c Department of Bio-Chemistry, Tea Research Association, Jorhat, Assam, India d Department of Electronics, Tezpur University, Tezpur, Assam, India
Received 29 May 2006; received in revised form 5 September 2006; accepted 6 September 2006 Available online 7 November 2006
Abstract Black tea fermentation is essentially an oxidation process. After the plucked tea leaves are treated by series of processes called withering (removal of moisture by air flow), pre-conditioning and CTC (essentially maceration and cutting of leaves), the leaves are subjected to the process of fermentation by exposing them to air by laying the cut tea leaves on floor, trough or moving conveyor under controlled temperature, humidity and air-flow conditions. During this process, the leaves change colour from green to coppery brown and the grassy smell gets transformed to floral smell. It is critical that the leaves be allowed to ferment only up to the desired limit and both under and over fermentation result in deteriorated quality of black tea. Out of the two detectable parameters (colour and smell), smell is very important since a strong, very specific fragrance emanates from the leaves once leaves are optimally fermented. A new electronic nose-based approach for monitoring of tea aroma during fermentation is proposed. Two methods namely the 2-Norm method (2NM) and the Mahalanobis distance method (MDM) were tested and the results were correlated with the results of colorimetric tests and human expert evaluation. 2006 Elsevier Ltd. All rights reserved. Keywords: Electronic nose; Fermentation, Sensors; Singular value decomposition (SVD); 2-Norm; Mahalanobis distance; CTC
1. Introduction Tea plantation is highly season-specific and climate dependent all over the world. Tea leaves are plucked from the field and brought to processing plants where finished tea is produced. Tea is manufactured by a variety of processes producing a range of variants from green, nonfermented tea to black fermented tea. India is famous for production and export of black tea while green tea is produced in countries like China and Japan. Black tea has got two major varieties, viz., (1) orthodox and (2) CTC (Cut– Tear–Curl operations are performed during production of this type of tea). While CTC tea gives strong liquor when brewed, orthodox tea is characterized as flavoury and fetches *
Corresponding author. Tel.: +91 33 23575989; fax: +91 33 23575141. E-mail address: [email protected] (N. Bhattacharyya). 0260-8774/$ - see front matter 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.jfoodeng.2006.09.006
higher commercial price. After tea leaves are plucked, a number of processing stages: (a) withering, (b) pre-conditioning, (c) CTC, (d) fermentation and (e) drying are involved in producing finished black tea (Robertson, 1992). Out of these processing stages, fermentation is one critical operation where residence time of tea leaves on fermentation floor, trough or conveyor plays the pivotal role in deciding final quality of the finished black tea (Gonzalez, Coggon, & Sanderson, 1972). In this process, tea leaves change colour from green to coppery brown or black. Also, at this stage of processing, grassy smell of leaves is converted into floral smell through a complex chain of biochemical reactions resulting in generation of a number of volatile flavoury compounds (Sanderson, 1972). A number of studies have been reported on application of electronic nose for black tea production. Pioneering work had been done by Dutta, Hines, Gardner, Kashwan, and Bhuyan (2003); where efficacy of electronic nose systems in classify-
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ing black tea aroma and flavour was established. Bhattacharyya, Bandyopadhyay, Bhuyan, Ghosh, and Mudi (2005) demonstrated co-relation of multi-sensor data with ‘‘Tea Taster’’ marks with high accuracy. This paper suggests a new approach in deriving an aroma profile for teas in fermentation process so that optimum time for fermentation completion may be decided unambiguously in real time using electronic nose.
development of black/brown appearance of tea, conversion of chlorophyll to pheophytin, degradation of lipids and formation of some flavoured components, loss of some highly volatile components are few of numerous bio-chemical changes that occur during the drying process (Wickremasinghe, Ekanayake, Rajasingham, & De Silva, 1979).
2. Black tea processing – A brief overview
The most important constituents of tea are catechins. These are colorless, odorless, soluble substances that have low molecular weight and constitute about 25% of total dry matter. These substances are oxidized by polyphenol oxydase or plant ferment during the fermentation in tea manufacture. Also known as the oxidisable matter of tea, catechins absorb oxygen with the help of enzymes. Once the oxygen is absorbed, catechins begin to form larger molecules through condensation and some non-volatile compound such as theaflavins (TF) and thearubigins (TR) are produced (Obanda, Owour, & Mang’oka, 2001). These compounds together are responsible for colour and taste of tea liquor. The smell of tea depends upon certain volatile aromatic substances developed during the fermentation process. Fermentation is the most crucial of all the processes, since aroma and flavour are developed in this process through multi-dimensional bio-chemical pathways. These volatile substances are made up of essential oils and a few others like amino acids (Co & Sanderson, 1970). During the process of fermentation, amino acids combine with orthoquinon, which is an oxidized form of catechin, and play the most important role for the black tea aroma. As a result of extensive research, Tea Research Association, Tocklai, Assam, India has identified major flavour determining chemical compounds available in black tea (www.tocklai.net) as given in Table 1. The smell of teas in fermentation process changes progressively as the process progresses. Age-old empirical knowledge in black tea processing in India has established the fact that odour emanation in the fermentation process travels through two defined peaks of intense emission of volatiles with much reduced intensity of emission during intermediate spans over the fermentation time for black tea. Such smell peaks are popularly termed as ‘‘First Nose’’ and ‘‘Second Nose’’ in Indian tea industry parlance. Experienced floor supervisors can detect such distinct peaks of intense volatile emission by manually smelling the teas. As soon as the ‘‘Second Nose’’ is detected, the supervisors
Black tea processing is performed through a few sequential operations: (a) plucking, (b) withering, (c) pre-conditioning, (d) cut–tear–curl (CTC), (e) fermentation and (f) drying as shown in Fig. 1. Quality of leaf depends upon the delivery and skill with which plucking is performed. Conventionally, only the bud with first and second leaves is plucked. The larger and coarser leaves are left in the bush. Plucked leaves are brought to withering process where leaf moisture content is reduced by blow of air. The plucked tea leaves are spread over the withering troughs of bed size approximately 20 · 100 ft. at a thickness of about 20–25 cm and air is blown through the tea leaves (Mahanta & Baruah, 1989). The cell-structures of withered leaves are disrupted by rotating vanes of specially designed machines in the pre-conditioning process. Thin membranes around the vacuole of the leaf-cells are ruptured during the process separating polyphenols and enzymes within the leaves. The CTC process comprises of CUT, TEAR and CURL operations. In CTC machines, withered and pre-conditioned leaves are fed into a gap between two rollers having circumferential cutting edges and running at differential speeds. Physical parameters of finished black tea, like dimension of tea particles and granular mix of finished bulk may be modulated by varying the pitch of the rollers and gap between the rollers. After the leaf-cells have been ruptured by previous processes, the fermentation process starts. The leaves are exposed to air and oxidation process starts. The green leaves attain coppery brown colour and a fragrant aroma starts emanating. Thereafter, the leaves enter the drying process where they are subjected to a blast of hot air provided by means of furnace. The factors which influence the process of drying are: (a) temperature of the air (b) rate of feed (c) run-through time and (d) volume of air within the drying chamber. De-activation of enzymes, reduction of moisture, moderate
Plucked Leaf
Sorting and Packing
Withering
Drying
Preconditioning and Rolling / CTC
Fermentation
Fig. 1. Critical unit operations in black tea processing.
3. Smell of black tea
Table 1 Bio-chemical compounds in tea responsible for flavour Compounds
Flavour
Linalool, Linalool oxide Geraniol, Phenylacetaldehyde Nerolidol, Benzaldehyde, Phenyl ethanol trans-2-Hexenal, n-hexanal, cis-3-hexenol, grassy, b-ionone
Sweet Floral Fruity Fresh flavour
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call the end to the fermentation process. These so called ‘‘First Nose’’ and ‘‘Second Nose’’ peaks are not only very sharp and prominent but also very much short-lived (Motoda, 1979b). It is quite possible that the supervisors may not always be able to detect such short-lived bursts of odour peaks by their olfactory senses. In the event of such inadvertent mistakes on the part of floor operators/ supervisors; the teas produced will be either under-fermented or over-fermented. Such age-old process, though empirical, is being practised by the Indian Tea Industries from time immemorial. Such practices definitely are highly subjective, non-reliable and prone to human mistakes. Such practices often lead to production of inferior quality tea due to over or under fermentation. In the present study, an attempt has been made to capture this change of smell of teas in fermentation by electronic nose and derive a time domain fermentation profile for tea fermentation process based on the electronic nose outputs. 4. Experimental 4.1. Details on customized electronic nose A customized electronic nose set-up has been developed such that the same can be used in production floor of tea processing units for monitoring of volatile emission pattern
from fermentation process. The electronic nose consists of (a) sensor array, (b) micro-pump with programmable sequence control, (c) PC-based data acquisition and (d) olfaction software as illustrated in Fig. 2. Specially designed sample holders made of glass have been used for the experimental runs. The glass sample holders may be fixed to the instrument by simple threaded fitting. For black manufactured tea, an array of metal oxide semiconductor (MOS) sensors has been used for assessment of volatiles in the set-up. In order to select appropriate sensors adequately sensitive to black tea aroma, the major flavour compounds in black tea (as given in Table 1) were collected from Tea Research Association, Jorhat. A series of experiments were carried out using a number of commercially available MOS sensors. From the response sensitivity of individual sensors, a set of eight sensors (TGS-832, TGS-823, TGS-831, TGS-816, TGS2600, TGS-2610, TGS-2611 and TGS-2620) has been selected for odour capture in fermentation process of black tea. The outputs of the sensors are acquired in the PC through PCI Data Acquisition cards. The MOS sensors are conductometric in nature, and their resistance decreases when subjected to the odour vapour molecules. The change in resistance with respect to their original values (DR/R) is converted into voltage and then taken to the PC through analog to digital converter cards for subsequent consideration in the computational models.
PATTERN RECOGNITION IN COMPUTER
SUCTION BLOWER
SENSOR ARRAY SOLINOID VALVE-III (V3) PURGING
AMBIENT AIR
AMBIENT AIR SOLINOID VALVE-I (V1)
ODOUR MOLECULES AIR
AIR PUMP
SAMPLE VESSEL
Fig. 2. Customized electronic nose set-up.
SOLINOID VALVE-II (V2)
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The experimental sniffing cycle consists of automated sequence of internal operations: (i) headspace generation, (ii) sampling, (iii) purging and (iv) dormancy before the start of the next sniffing cycle. Headspace generation ensures adequate concentration of volatiles released by tea within the sample holder by blowing regulated flow of air on the sample. During sampling, the sensor array is exposed to a constant flow of volatiles through pipelines inside the electronic nose system. During purging operation, sensor heads are cleared with blow of fresh air so that the sensors go back to their baseline values. The programmable time dormancy cycle is the suspended mode of the electronic nose between two consecutive sniffing cycles. The PC-based data acquisition and automated operation of all these cycles are controlled by the specially designed software, called olfaction software. The software has got features like programmable sequence control, dynamic fermentation profile display, data logging, alarm annunciation, data archival, etc. The software has been developed in LabVIEW of National Instruments. 4.2. Black tea fermentation process monitoring using electronic nose Experiments were carried out in three locations of north-east India: (a) Glenburn Tea Estate, Darjeeling, West Bengal (b) Tocklai Experimental Station, Jorhat, Assam, and (c) Dalmia Tea Plantations, West Bengal. We have tried to cover both orthodox and CTC types of black tea while studying fermentation aroma profile by electronic nose. The experiments at the Glenburn Tea Estate, Darjeeling, West Bengal were based on fermenting cycles of orthodox and flavoury tea. The experiments at Tocklai Experimental Station, Jorhat, Assam was on a miniature manufacturing facility with controlled environment using fast fermenting clones. Here the output was CTC type of Black Tea. The Dalmia Tea Plantations, West Bengal, is basically a ‘‘bought leaf factory’’ producing huge quantities of CTC type black tea daily. Longest trials were done at this factory. Altogether, 81 fermentation cycles
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have been considered for data collection, out of which 10 cycles are for orthodox flavoury teas and the remaining 71 cycles for CTC tea. 4.3. Data analysis The sensor array consisting of eight MOS type sensors were found to be adequately sensitive to the volatile emission during the fermentation process. The sensor responses are related to the amount of bio-chemical volatiles that came in touch with the corresponding sensor surface. In fact, the output of each sensor is a direct indication of the intensity of the chemical(s) it has absorbed. By setting the electronic nose in auto-mode, readings had been taken during the fermentation process at regular intervals. Therefore, the variation of intensity of smell during the entire fermentation process was actually recorded. During the fermentation process, the variation of the intensity of smell is a key feature in predicting and/or deciding the suitable fermentation time. In this study, our goal was to capture a pattern of the variation of the intensity of the sensor outputs that are recorded by the electronic nose. Output variation of the sensor array over time has been shown in Fig. 3. The data analysis strategy followed two sequential steps: (a) data exploration and (b) data quantification. In the data exploration step, we tried to observe an overview of basic clusters embedded in the sensor measurements in an unsupervised manner using principal component analysis (PCA). In the data quantification step, the objective was to derive some scalar index (which have been termed as Aroma Scores) proportional to sensor output pattern and intensity of smell. These scalar values may be plotted against time to evolve the time-domain aroma profile against progress of fermentation process. Approach towards aroma scores has been worked out by (a) singular value decomposition (SVD) based 2-Norm method and (b) Mahalanobis distance method. The results obtained from this analysis of data have been presented in Section 5.
Fig. 3. Output variation of sensor array over time.
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4.4. Colorimetric tests and human expert evaluation
5. Results
Colorimetric test is an indirect method of detection of optimum of fermentation time. Colorimetric method is the most popular technique employed by Indian tea industries to detect optimum time of fermentation process (Chakravorty, 1976). In the present study, colour change of in-process tea leaves had been tracked by means of Digital Photo Colorimeter (Model 312E/ 313E). A sample of 3.0 g of tea from fermentation process had been collected in regular intervals and put into 150 ml of boiled water to produce tea extract. Measured quantity of this tea extract had been mixed with sodium carbonate (NaCO3) solution and phenol reagent in a test tube. This test tube had been inserted in the optical path (400–700 nm) of the colorimeter and the optical absorbance was measured. When the fermentation process reached optimum point, colour became coppery brown and the optical absorbance attained maximum value (Ullah, 1972). Time of this optimum colorimetric point had been noted for each fermentation experiments and the result had been correlated with electronic nose results. Human expert evaluation was an essential component of the experiments under the present study. It is a popular practice that shop floor supervisors periodically sniff the teas in fermentation process to find out manually the smell pattern and smell peaks to detect end point of fermentation process. For all the experimental runs under the present study, human experts on the shop floor have evaluated time of occurrence of floral smell peaks during fermentation process. The experiments were carried out in three tea manufacturing factories in North Bengal, India, namely, (a) Dalmia Tea Plantation and Industries Limited, (b) Glenburn Tea Estate and (c) Washabarie Tea Company Limited. Two shop floor supervisors in each of these factories were involved in human evaluation tests for the fermentation runs under experimentation. The experts sniffed a handful of teas in fermentation every 5 min interval and declared the optimum fermentation time using their experienced olfactory senses.
5.1. Results of the principal component analysis Principal component analysis (PCA) is a popular technique for reduction of dimension in a large dimensional data set (Gardner, Schurmer, & Tan, 1992). Time required for fermentation of one batch of tea is generally 1–2 h depending on climatic condition, type of leaves and location of tea plantation (i.e., Northern or Southern part of India). In our experiments, every five minutes, tea odour has been sensed by the sensor array in an automated mode (e.g. for a fermentation run of one-hour duration, the computer would acquire 12 data matrices corresponding to each of the sniffing cycle). We have applied PCA to the entire eight-dimensional data set (comprising of data from all the sniffing cycles) to investigate whether the change in smell for the teas in fermentation is rightly reflected in terms of clustering of data points in reduced two-dimensional space. Distinct clusters have been formed as a result of such PCA analysis clearly classifying individual sniffing cycle data from the others. Sample PCA plots shown in Fig. 4 illustrate smell classification for teas in fermentation with process time. In the sample plots, we have shown clusters of smell-prints at 48th, 72nd and 104th minutes in two different fermentation runs for clarity and ease of visualization. The result conclusively establishes the fact that the electronic nose developed under the present study is capable of differentiating smell of tea in fermentation process. The results of the PCA show that the sensors are strongly correlated, since almost 95% of the variance of the data is contained within the first principal component, PC1. The first two components, PC1 and PC2, can be used to represent almost 98% of the data variance. In another variant of PCA presentation (O’Connell, Valdora, Peltzer, & Negri, 2001), the data matrix consisting of vectors with maximum sensor outputs at each sniffing cycle have been considered. So, each sniffing cycle will be represented by single point in PC1–PC2 plane. A sample of such representation of smell-points in the PC1–PC2
Fig. 4. PCA clusters of electronic nose data.
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plane for a single fermentation run has been shown in Fig. 5. It is interesting to note that the points corresponding to successive sniffing cycles show a shift rightward indicating increase in intensity of smell for teas in fermentation process with time. In the sample plot shown, the smellpoint at 104th minute is represented by the rightmost point in the plot clearly establishing the maximum odour intensity phase of the process and probably the optimum fermentation time. Further, the point corresponding to 112th minute in the above plot has moved left clearly indicating decrease in smell intensity after 104th minute which can be related to the over-fermentation phase of the process. In the sample plot, for sniffing cycles within 40 min of process time, the values on PC1 is less than (1) and for sniffing cycles beyond 40 min, the values of PC1 is more than (1). For all the fermentation runs studied by us, similar behaviors of the odour pattern have been observed. The result conclusively establishes the fact that the electronic nose developed under the present study is capable of tracking change in smell intensity of teas in fermentation process unambiguously. 5.2. Detection of black tea aroma profile with 2-Norm method (2NM) Subsequent to the well-defined clusters obtained by the PCA analysis, approaches towards quantification of smell intensity during the process have been explored. Norm of a matrix is a scalar that gives some measure of the magnitude of the elements in the matrix (www.mathworks.com). For our case, since the matrix comprises of normalized MOS sensor outputs when exposed to teas in fermentation, such norm calculation may be used to derive aroma intensity for the tea samples.
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If A represents a matrix of m · n dimension, the 2-Norm of A is represented by kAkp, where p = 2. For computation of 2-Norm, the largest singular value of the input matrix is computed by singular value decomposition (SVD) technique (Wall, Rechtsteiner, & Rocha, 2003). The SVD technique deals with the factorization of a rectangular matrix and the factorized version of the input matrix A is represented as A ¼ USV T ; where the matrix consisting of eigenvectors of AAT is represented by a new matrix U and the matrix consisting of eigenvectors of ATA is represented by a new matrix V. The diagonal values of S constitute the singular value spectrum of the input matrix. The singular values may be plotted in a one-dimensional bar chart as shown in Fig. 6 for easy visualization. The height of any one singular value is indicative of its importance in explaining the data. Since, the square of each singular value is proportional to the variance explained by each singular vector, such values promise an important indication to the aroma score for an individual sniffing cycle. From the above plot, it may be noticed that the first singular value is quite large compared to the following ones. In fact, a leveling off of the relative variance after the second component may be observed. Percentage information content of in the first SVD component is very high (almost 90%) as has been observed in the experimental data. Hence, the first singular value represents the data trend significantly whereas the following singular values are of negligible significance. This highest singular value is the 2-Norm of the data matrix. Since the 2-Norm value derived out of a data matrix is directly proportional to the square root of the variance of
Fig. 5. Smell-points in PC1–PC2 Plane indication variation of smell intensity with time.
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Fig. 6. Sample SVD plot for one sniffing cycle.
each singular vector, we may relate the 2-Norm value to the overall intensity of smell for a sniffing cycle. More intense is the smell; more will be the voltage output from the sensors thereby increasing the respective 2-Norm value. In the experiments, smell data from teas in fermentation have been acquired at regular intervals in automated fashion and 2-Norm calculations have been done at each sniffing cycle. These 2-Norm values have been plotted against time as shown in the sample curves given in Fig. 7. The curves, thus drawn, clearly show variation of smell intensity of black tea during fermentation process. The peak smell intensity can be detected online from such aroma profile and completion time of fermentation may be decided accordingly. The 2-Norm values calculated as above have been scaled down by the following formula: S N ¼ S C =S MAX where, SN SC SMAX
scaled down 2-Norm value at each sniffing cycle calculated 2-Norm value maximum 2-Norm value in the entire fermentation cycle
The 2-Norm technique has been employed here to extract a scalar index from the smell matrix, which signifies effective smell intensity for the tea sample for an individual
sniffing cycle. These smell intensity values have been considered as Aroma Scores and have been named as ‘‘NORMAROMA Score’’ and these ‘‘NORMAROMA scores’’ have been plotted against time to generate the fermentation aroma profile named as ‘‘NORMAROMA Profile’’ for black tea. The software and hardware of the electronic nose set-up has been designed such that the ‘‘NORMAROMA Profile’’ is drawn on-screen in real time such that the floor operators directly monitor the smell peaks with progress of process in a very handy and convenient manner. 5.3. Detection of black tea aroma profile with Mahalanobis distance method (MDM) In the context of multivariate data, distance computation between a vector and a matrix may provide crucial clues to the measure of dissimilarity among the two. If the distance is less, the data in consideration are more similar than the case where the distance is more (Mitra & Acharya, 2003). The Mahalanobis distance is a multivariate measure of the separation of a data set from a point in space (Anderson, 1958). A common usage of Mahalanobis distance is in computer vision systems for comparing feature vectors whose elements are quantities having different ranges and amounts of variation. Mahalanobis distance,d, is a general-
Fig. 7. Fermentation aroma profile in 2-Norm method.
N. Bhattacharyya et al. / Journal of Food Engineering 80 (2007) 1146–1156 b11 b 21 . .
b12 b 22 . .
. . bh1 bh 2 A= S 11 S 12 S 21 S 22 . . . Sm1
. . . .
. . . .
. . . .
b18 b 28 . .
Sensor Responses During Headspace Generation
. . . . . .
. bh 8 . . . S 18 . . . S 28
. . . . . . . .
. .
. . . . . Sm 2 . . . Sm8
Sensor Responses When Exposed to Tea Odour During Sampling
Fig. 8. Multivariate data matrix formed out of sensor array output.
ized measure of the distance between two groups and is represented as T
d 2 ¼ ðx mÞ V 1 ðx mÞ where xT = {x1, x2, . . . , xn} vector for a single multivariate observation mt = {l1, l2, . . . , ln} vector representing the population mean V co-variance matrix In the domain of electronic nose-based study on black tea fermentation process, each sniffing cycle produces a huge amount of eight-dimensional data. In the system developed by us, each sniffing cycle consists of headspace generation (30 s), sampling (50 s) and purging (100 s) operations. Computer acquires sensor data during headspace generation and sampling cycles only in an automated sequence of operation. Clearly, the sensor outputs will be at their baseline during the headspace generation and significant variation in sensor outputs will be observed during the sampling cycle. Therefore, the data matrix stored in the computer in each sniffing cycle will consist of a mixture of both baseline as well as actual sensor responses as shown in Fig. 8. In the above data matrix, the segment b11 to bh8 represents baseline responses of the sensors during headspace
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generation and S11 to Sm8 represents the sensor responses when exposed to tea odour during sampling. In our study, maximum value of Sij for each column has been found out and a vector M is formed with these maximum values. So, M ¼ ½ S i1max S i8max . Another vector N is formed such that 3 2 b11 b12 b18 7 6b 6 21 b22 b28 7 7 6 6 7 7 6 N ¼6 7 7 6 7 6 4 5 bh1
bh2
bh8
Mahalanobis distance has been calculated between the vector M and matrix N. Obviously, this distance will be more if smell intensity is strong and the distance will be less if smell intensity is weak. The Mahalanobis distance calculated as above promises a very good approach towards quantification of smell intensity and volatile concentration at the headspace during black tea fermentation process. We have defined this scalar index as the Aroma Score calculated by Mahalanobis distance method and named as ‘‘MAHAROMA Score’’. These aroma scores have been derived for each sniffing cycle and plotted against time as shown in Fig. 9, which gives the fermentation aroma profile and has been named as ‘‘MAHAROMA Profile’’ of black tea fermentation. The aroma scores have been scaled (division by 106) for easy visualization. 5.4. Effect of fermentation on colour of black tea During black tea fermentation process, green colour of cut tea leaves changes to coppery brown colour and this change in colour is an important parameter to monitor status of the fermentation process. For all the experimental runs carried out under the present study, colorimetric monitoring was done by the digital photo colorimeter (Model 312E/313E). The absorbance values were plotted against time to draw the colorimetric profile of the fermentation process. A sample colorimetric profile has been illustrated
Fig. 9. Sample aroma profile for black tea fermentation (Mahalanobis distance method).
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Fig. 10. Sample colorimeter curve for black tea fermentation.
in Fig. 10. A close look at the colorimetric graph reveals that there are two distinct peaks over the fermentation process time and as per the popular practices followed by the Indian Tea Industries, the time associated with the second peak of the colorimetric graph is considered as the optimum fermentation time (Chakravorty, 1976; Ullah, 1972). The results obtained from the colorimetric tests for the fermentation experiments are given in Table 2 alongwith the optimum fermentation times as detected by electronic nose. It is observed that the accuracy of detection of fermentation completion time by electronic nose vis-a`vis colorimetric evaluation is to the tune of 95%. 5.5. Sensory properties of fermented black tea The electronic nose data have been found to be accurately matching with the human experts’ assessments. It has been observed that the sensory evaluation of teas in fermentation by periodic sniffing yields very closely correlated results with a variance of less than three from two experts engaged in each of the experiments. The results of sensory evaluation and the optimum fermentation times as detected
by electronic nose are given in Table 3. In this case also, the accuracy of detection of fermentation completion time by electronic nose vis-a`-vis human evaluation is to the tune of 96%. 5.6. Correlation between the results of 2NM, MDM and colorimetric and sensory tests Close scrutiny and examination of the fermentation aroma profile in both 2-Norm as well as Mahalanobis distance methods indicate that there exist distinct smell peaks and troughs with fermentation time signifying change in volatile emission pattern and change of smell as detected by electronic nose. Closer look at NORMAROMA and MAHAROMA Profiles reveal some very interesting facts: • Both the profiles are similar in shape. • Appearances of NORMAROMA and MAHAROMA peaks are coincident, i.e., they occur exactly on at the similar time instants as observed in both the profiles for a specific fermentation run.
Table 2 Results of colorimetric tests Name of the tea garden
Number of experiments
Time of experiments
Average fermentation time estimated by colorimetric test
Average fermentation time predicted by electronic nose
Glenburn Tea Estate, Darjeeling, West Bengal, India Tocklai Experimental Station, Jorhat, Assam, India Dalmia Tea Plantations, West Bengal, India
10 10 22 15 24
August 2005 September 2005 October 2005 November 2005 December, 2005
58–63 40–45 90–100 105–120 110–125
60–65 40–45 95–100 105–115 110–120
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Table 3 Results of sensory evaluation Name of the tea garden
Number of experiments
Time of experiments
Number of experts engaged
Average fermentation time estimated by sensory evaluation
Variance of fermentation time estimated by the human experts
Average fermentation time predicted by electronic nose
Glenburn Tea Estate, Darjeeling, West Bengal, India Tocklai Experimental Station, Jorhat, Assam, India Dalmia Tea Plantations, West Bengal, India
10
August 2005
2
60–65
<3
60–65
10
September 2005
2
40–50
<2
40–45
22 15 24
October 2005 November 2005 December, 2005
2
85–105 100–120 110–125
<3 <4 <5
95–100 105–115 110–120
Table 4 Summary of results Number of fermentation runs Accuracy of detection of fermentation completion time by electronic nose vis-a`-vis colorimetric test Accuracy of detection of fermentation completion time by electronic nose vis-a`-vis human evaluation
81 95% 96%
• Both the profiles can be calculated and displayed online during fermentation process. Hence, it is felt that both NORMAROMA and MAHAROMA methods might be very useful for monitoring of smell in black tea fermentation process. It can be further observed that the aroma score value changes widely from one sample to the other. This behavior may be attributed to variation of clones, location of garden as well as climatic condition during processing. The results obtained by electronic nose have been compared with those obtained from popular practices in Indian Tea Industries, namely, sensory evaluation and colorimetric tests. It has been observed that accuracy of detection of optimum fermentation time by electronic nose vis-s-vis sensory as well as colorimetric tests is to the tune of 95%. The results of cross-validation analysis have been given in Table 4. 6. Discussion A number of research and development initiatives have been reported on fermentation process of black tea (Opie, Robertson, & Clifford, 1990; Owuor & McDowell, 1994). These studies were based on chemical analysis, and on-line instrumental monitoring of the fermentation aroma profile of black tea has not been attempted. At the same time, the new technology based on electronic nose has been attempted by scientists and it has shown excellent promise in many applications like food and beverages (Aishima et al., 1991; Borjesson, Eklov, Jonsson, Sundgren, & Schnurer, 1996; Pearce, Garner, Friel, Bartlett, & Blair, 1993; Pisanelli, Qutob, Traverse, Szyszko, & Persaud, 1994; Nanto, Shokoshi, & Kawai, 1991; Olafsson, Martinsdottir, Olafsdottir, Sigfusson, & Gardner, 1992; Stetter, Findlay, Shroeder, Yue, & Penrose, 1993; Winquist, Hornsten,
Sundgren, & Lundstrom, 1993). But reports of applications of electronic nose for black tea are very few in number and the earlier works were mainly focused on characterization of smell of finished tea. The novelty of the proposed methodology is that the electronic nose technology can be employed for detection of aroma during the manufacturing of tea, thereby maintaining the quality standard of the finished product. The present study opens up numerous applications of electronic nose for black tea processing. Further, the present study proposes a new working methodology of electronic nose in black tea processing floor with requisite operational automation. Such new method of detection of optimum fermentation time has the potential of ushering new paradigm of non-invasive, real-time and fast method of monitoring of black tea fermentation process. 7. Conclusion Under the present study, total 81 fermentation cycles have been experimented with at various locations of India at various seasons. Most of the Electronic Nose readings accurately matched with colorimetric as well as human panel data. It is felt that electronic nose can definitely be used for monitoring of volatile emission pattern for black tea fermentation process with a very high degree of accuracy, reliability and repeatability. Electronic nose-based new method as discussed above may be highly beneficial for black tea processing in terms of dispensing with too much human expert dependence and cumbersome chemical or indirect tests which are invasive and offline. Such objective methodology might be instrumental in manufacturing consistent quality of black tea. References Aishima, T. (1991). Discrimination of varieties and roasting levels in coffee beans by pattern recognition analysis of responses from a semiconductor gas sensor array. In 14th Colloque Scientifique Internationale Sur La cafe´, San Fransisco, pp. 137–145. Anderson, T. W. (1958). Introduction to multivariate statistical analysis (3rd ed.). New York: John Wiley and Sons. Bhattacharyya, N., Bandyopadhyay, R., Bhuyan, M., Ghosh, A. & Mudi, R. K. (2005). Correlation of multi-sensor array data with ‘‘Tasters’’ panel evaluation for objective assessment of black tea flavour. In International symposium on olfaction and electronic noses (ISOEN 2005), 13–15 April 2005, Barcelona, Spain.
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Borjesson, T., Eklov, T., Jonsson, A., Sundgren, H., & Schnurer, J. (1996). Electronic nose for odour classification of grains. Cereal Chem., 73, 457–461. Chakravorty, S. (1976). Tocklai fermentation test. Two and a Bud, 23(2), 50–51. Co, H., & Sanderson, G. W. (1970). Biochemistry of tea fermentation: Conversion of amino acids to black tea aroma constituents. Journal of Food Science, 35, 160–164. Dutta, R., Hines, E. L., Gardner, J. W., Kashwan, K. R., & Bhuyan, M. (2003). Tea quality prediction using a tin oxide-based electronic nose: An artificial intelligence approach. Sensors and Actuators, B, 94(2), 228–237. Gardner, J. W., Schurmer, H. V., & Tan, T. T. (1992). Application of an electronic nose to the discrimination of coffee. Sensors and Actuators, B, 6, 71–75. Gonzalez, J. L., Coggon, P., & Sanderson, G. W. (1972). Biochemistry of tea fermentation: Formation of t-2 hexenal from linolenic acid. Journal of Food Science, 37, 797–798. Mahanta, P. K., & Baruah, S. (1989). Relationship between process of withering and the aroma characteristics of black tea. Journal of the Science of Food and Agriculture, 46, 461–468. Mitra, S., & Acharya, T. (2003). Data mining: John multimedia, soft computing and bioinformatics. New Jersy, Hoboken: Wiley & Sons, Inc.. Motoda, S. (1979b). Formation of aldehydes from amino acids by polyphenol oxidase. Journal of Fermentation Technology, 57, 395–399. Nanto, H., Shokoshi, H., & Kawai, T. (1991). Freshness detection of sea foods using ZnO thin film gas sensor. In 10th sensor symposium, Japan, pp. 195–198. Obanda, M., Owour, P. O., & Mang’oka, R. (2001). Changes in the chemical and sensory quality parameters of black tea due to variations of fermentation time and temperature. Elsevier, Food Chemistry, 75, 395–404. O’Connell, M., Valdora, G., Peltzer, G., & Negri, R. M. (2001). A practical approach for fish freshness determinations using a portable electronic nose. Sensors and Actuators, B, 80, 149–154. Olafsson, R., Martinsdottir, E., Olafsdottir, G., Sigfusson, S. L., & Gardner, J. W. (1992). Monitoring of fish freshness using tin oxide
sensors. In J. W. Gardner & P. N. Bartlett (Eds.), Sensors and sensory systems for electronic nose (pp. 257–272). Dordrecht: Kluwer. Opie, S. C., Robertson, A., & Clifford, M. N. (1990). Black tea thearubijins: Their HPLC separation and preparation during in-vitro oxidation. Journal of the Science of Food and Agriculture, 50, 547–561. Owuor, P. O., & McDowell, I. (1994). Changes in theaflavin composition and astringency during black tea fermentation. Food Chemistry, 51, 251–254. Pearce, T. C., Garner, J. W., Friel, S., Bartlett, P. N., & Blair, N. (1993). An electronic nose for the monitoring of the flavour of beers. Analyst, 118, 371–377. Pisanelli, A., Qutob, A. A., Traverse, P., Szyszko, S., & Persaud, K. C. (1994). Applications of multi array sensors to food industries. Life Chemistry, 11, 303–308. Robertson, A. (1992). In K. C. Willson & M. N. Clifford (Eds.), The chemistry and biochemistry of black tea production – the non volatiles, tea cultivation to consumption (pp. 555–601). London: Chapman and Hall. Sanderson, G. W. (1972). The chemistry of tea and tea manufacturing. In V. C. Reneckles & T. C. Tso (Eds.). Structural and functional aspects of phytochemistry: Recent advance in phytochemistry (vol. 5, pp. 247–317). New York: Academic Press. Stetter, J. R., Findlay, M. W., Shroeder, K. M., Yue, C., & Penrose, W. R. (1993). Quality classification of grain using a sensor array and pattern recognition. Analytica Chimica Acta, 284, 1–11. Ullah, M. R. (1972). A simplified spectrophotometric method for measuring theaflavins and thearubigins of black tea liquor. Current Science, 41, 422–423. Wall, M. E., Rechtsteiner, A., & Rocha, L. M. (2003). Singular value decomposition and principal component analysis. In D. P. Berrar, W. Dubitzky, & M. Granzow (Eds.), In a practical approach to microarray data analysis (pp. 91–109). Norwell, MA: Kluwer. Wickremasinghe, R., Ekanayake, L. A., Rajasingham, C. C., & De Silva, M. J. (1979). Changes in polyphenols, amino acids and volatile compounds during fermentation and firing in orthodox processing of tea. Journal of the National Science Council of SriLanka, 7, 5–9. Winquist, F., Hornsten, E. G., Sundgren, H., & Lundstrom, I. (1993). Performance of an electronic nose for quality estimation of ground meat. Measurement Science and Technology, 4, 1493–1500.
Monitoring of black tea fermentation process using electronic nose Nabarun Bhattacharyya a,*, Sohan Seth b, Bipan Tudu b, Pradip Tamuly c, Arun Jana a, Devdulal Ghosh a, Rajib Bandyopadhyay b, Manabendra Bhuyan d b
a Centre for Development of Advanced Computing, Salt Lake, Kolkata 700 091, India Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata 700 032, India c Department of Bio-Chemistry, Tea Research Association, Jorhat, Assam, India d Department of Electronics, Tezpur University, Tezpur, Assam, India
Received 29 May 2006; received in revised form 5 September 2006; accepted 6 September 2006 Available online 7 November 2006
Abstract Black tea fermentation is essentially an oxidation process. After the plucked tea leaves are treated by series of processes called withering (removal of moisture by air flow), pre-conditioning and CTC (essentially maceration and cutting of leaves), the leaves are subjected to the process of fermentation by exposing them to air by laying the cut tea leaves on floor, trough or moving conveyor under controlled temperature, humidity and air-flow conditions. During this process, the leaves change colour from green to coppery brown and the grassy smell gets transformed to floral smell. It is critical that the leaves be allowed to ferment only up to the desired limit and both under and over fermentation result in deteriorated quality of black tea. Out of the two detectable parameters (colour and smell), smell is very important since a strong, very specific fragrance emanates from the leaves once leaves are optimally fermented. A new electronic nose-based approach for monitoring of tea aroma during fermentation is proposed. Two methods namely the 2-Norm method (2NM) and the Mahalanobis distance method (MDM) were tested and the results were correlated with the results of colorimetric tests and human expert evaluation. 2006 Elsevier Ltd. All rights reserved. Keywords: Electronic nose; Fermentation, Sensors; Singular value decomposition (SVD); 2-Norm; Mahalanobis distance; CTC
1. Introduction Tea plantation is highly season-specific and climate dependent all over the world. Tea leaves are plucked from the field and brought to processing plants where finished tea is produced. Tea is manufactured by a variety of processes producing a range of variants from green, nonfermented tea to black fermented tea. India is famous for production and export of black tea while green tea is produced in countries like China and Japan. Black tea has got two major varieties, viz., (1) orthodox and (2) CTC (Cut– Tear–Curl operations are performed during production of this type of tea). While CTC tea gives strong liquor when brewed, orthodox tea is characterized as flavoury and fetches *
Corresponding author. Tel.: +91 33 23575989; fax: +91 33 23575141. E-mail address: [email protected] (N. Bhattacharyya). 0260-8774/$ - see front matter 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.jfoodeng.2006.09.006
higher commercial price. After tea leaves are plucked, a number of processing stages: (a) withering, (b) pre-conditioning, (c) CTC, (d) fermentation and (e) drying are involved in producing finished black tea (Robertson, 1992). Out of these processing stages, fermentation is one critical operation where residence time of tea leaves on fermentation floor, trough or conveyor plays the pivotal role in deciding final quality of the finished black tea (Gonzalez, Coggon, & Sanderson, 1972). In this process, tea leaves change colour from green to coppery brown or black. Also, at this stage of processing, grassy smell of leaves is converted into floral smell through a complex chain of biochemical reactions resulting in generation of a number of volatile flavoury compounds (Sanderson, 1972). A number of studies have been reported on application of electronic nose for black tea production. Pioneering work had been done by Dutta, Hines, Gardner, Kashwan, and Bhuyan (2003); where efficacy of electronic nose systems in classify-
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ing black tea aroma and flavour was established. Bhattacharyya, Bandyopadhyay, Bhuyan, Ghosh, and Mudi (2005) demonstrated co-relation of multi-sensor data with ‘‘Tea Taster’’ marks with high accuracy. This paper suggests a new approach in deriving an aroma profile for teas in fermentation process so that optimum time for fermentation completion may be decided unambiguously in real time using electronic nose.
development of black/brown appearance of tea, conversion of chlorophyll to pheophytin, degradation of lipids and formation of some flavoured components, loss of some highly volatile components are few of numerous bio-chemical changes that occur during the drying process (Wickremasinghe, Ekanayake, Rajasingham, & De Silva, 1979).
2. Black tea processing – A brief overview
The most important constituents of tea are catechins. These are colorless, odorless, soluble substances that have low molecular weight and constitute about 25% of total dry matter. These substances are oxidized by polyphenol oxydase or plant ferment during the fermentation in tea manufacture. Also known as the oxidisable matter of tea, catechins absorb oxygen with the help of enzymes. Once the oxygen is absorbed, catechins begin to form larger molecules through condensation and some non-volatile compound such as theaflavins (TF) and thearubigins (TR) are produced (Obanda, Owour, & Mang’oka, 2001). These compounds together are responsible for colour and taste of tea liquor. The smell of tea depends upon certain volatile aromatic substances developed during the fermentation process. Fermentation is the most crucial of all the processes, since aroma and flavour are developed in this process through multi-dimensional bio-chemical pathways. These volatile substances are made up of essential oils and a few others like amino acids (Co & Sanderson, 1970). During the process of fermentation, amino acids combine with orthoquinon, which is an oxidized form of catechin, and play the most important role for the black tea aroma. As a result of extensive research, Tea Research Association, Tocklai, Assam, India has identified major flavour determining chemical compounds available in black tea (www.tocklai.net) as given in Table 1. The smell of teas in fermentation process changes progressively as the process progresses. Age-old empirical knowledge in black tea processing in India has established the fact that odour emanation in the fermentation process travels through two defined peaks of intense emission of volatiles with much reduced intensity of emission during intermediate spans over the fermentation time for black tea. Such smell peaks are popularly termed as ‘‘First Nose’’ and ‘‘Second Nose’’ in Indian tea industry parlance. Experienced floor supervisors can detect such distinct peaks of intense volatile emission by manually smelling the teas. As soon as the ‘‘Second Nose’’ is detected, the supervisors
Black tea processing is performed through a few sequential operations: (a) plucking, (b) withering, (c) pre-conditioning, (d) cut–tear–curl (CTC), (e) fermentation and (f) drying as shown in Fig. 1. Quality of leaf depends upon the delivery and skill with which plucking is performed. Conventionally, only the bud with first and second leaves is plucked. The larger and coarser leaves are left in the bush. Plucked leaves are brought to withering process where leaf moisture content is reduced by blow of air. The plucked tea leaves are spread over the withering troughs of bed size approximately 20 · 100 ft. at a thickness of about 20–25 cm and air is blown through the tea leaves (Mahanta & Baruah, 1989). The cell-structures of withered leaves are disrupted by rotating vanes of specially designed machines in the pre-conditioning process. Thin membranes around the vacuole of the leaf-cells are ruptured during the process separating polyphenols and enzymes within the leaves. The CTC process comprises of CUT, TEAR and CURL operations. In CTC machines, withered and pre-conditioned leaves are fed into a gap between two rollers having circumferential cutting edges and running at differential speeds. Physical parameters of finished black tea, like dimension of tea particles and granular mix of finished bulk may be modulated by varying the pitch of the rollers and gap between the rollers. After the leaf-cells have been ruptured by previous processes, the fermentation process starts. The leaves are exposed to air and oxidation process starts. The green leaves attain coppery brown colour and a fragrant aroma starts emanating. Thereafter, the leaves enter the drying process where they are subjected to a blast of hot air provided by means of furnace. The factors which influence the process of drying are: (a) temperature of the air (b) rate of feed (c) run-through time and (d) volume of air within the drying chamber. De-activation of enzymes, reduction of moisture, moderate
Plucked Leaf
Sorting and Packing
Withering
Drying
Preconditioning and Rolling / CTC
Fermentation
Fig. 1. Critical unit operations in black tea processing.
3. Smell of black tea
Table 1 Bio-chemical compounds in tea responsible for flavour Compounds
Flavour
Linalool, Linalool oxide Geraniol, Phenylacetaldehyde Nerolidol, Benzaldehyde, Phenyl ethanol trans-2-Hexenal, n-hexanal, cis-3-hexenol, grassy, b-ionone
Sweet Floral Fruity Fresh flavour
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call the end to the fermentation process. These so called ‘‘First Nose’’ and ‘‘Second Nose’’ peaks are not only very sharp and prominent but also very much short-lived (Motoda, 1979b). It is quite possible that the supervisors may not always be able to detect such short-lived bursts of odour peaks by their olfactory senses. In the event of such inadvertent mistakes on the part of floor operators/ supervisors; the teas produced will be either under-fermented or over-fermented. Such age-old process, though empirical, is being practised by the Indian Tea Industries from time immemorial. Such practices definitely are highly subjective, non-reliable and prone to human mistakes. Such practices often lead to production of inferior quality tea due to over or under fermentation. In the present study, an attempt has been made to capture this change of smell of teas in fermentation by electronic nose and derive a time domain fermentation profile for tea fermentation process based on the electronic nose outputs. 4. Experimental 4.1. Details on customized electronic nose A customized electronic nose set-up has been developed such that the same can be used in production floor of tea processing units for monitoring of volatile emission pattern
from fermentation process. The electronic nose consists of (a) sensor array, (b) micro-pump with programmable sequence control, (c) PC-based data acquisition and (d) olfaction software as illustrated in Fig. 2. Specially designed sample holders made of glass have been used for the experimental runs. The glass sample holders may be fixed to the instrument by simple threaded fitting. For black manufactured tea, an array of metal oxide semiconductor (MOS) sensors has been used for assessment of volatiles in the set-up. In order to select appropriate sensors adequately sensitive to black tea aroma, the major flavour compounds in black tea (as given in Table 1) were collected from Tea Research Association, Jorhat. A series of experiments were carried out using a number of commercially available MOS sensors. From the response sensitivity of individual sensors, a set of eight sensors (TGS-832, TGS-823, TGS-831, TGS-816, TGS2600, TGS-2610, TGS-2611 and TGS-2620) has been selected for odour capture in fermentation process of black tea. The outputs of the sensors are acquired in the PC through PCI Data Acquisition cards. The MOS sensors are conductometric in nature, and their resistance decreases when subjected to the odour vapour molecules. The change in resistance with respect to their original values (DR/R) is converted into voltage and then taken to the PC through analog to digital converter cards for subsequent consideration in the computational models.
PATTERN RECOGNITION IN COMPUTER
SUCTION BLOWER
SENSOR ARRAY SOLINOID VALVE-III (V3) PURGING
AMBIENT AIR
AMBIENT AIR SOLINOID VALVE-I (V1)
ODOUR MOLECULES AIR
AIR PUMP
SAMPLE VESSEL
Fig. 2. Customized electronic nose set-up.
SOLINOID VALVE-II (V2)
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The experimental sniffing cycle consists of automated sequence of internal operations: (i) headspace generation, (ii) sampling, (iii) purging and (iv) dormancy before the start of the next sniffing cycle. Headspace generation ensures adequate concentration of volatiles released by tea within the sample holder by blowing regulated flow of air on the sample. During sampling, the sensor array is exposed to a constant flow of volatiles through pipelines inside the electronic nose system. During purging operation, sensor heads are cleared with blow of fresh air so that the sensors go back to their baseline values. The programmable time dormancy cycle is the suspended mode of the electronic nose between two consecutive sniffing cycles. The PC-based data acquisition and automated operation of all these cycles are controlled by the specially designed software, called olfaction software. The software has got features like programmable sequence control, dynamic fermentation profile display, data logging, alarm annunciation, data archival, etc. The software has been developed in LabVIEW of National Instruments. 4.2. Black tea fermentation process monitoring using electronic nose Experiments were carried out in three locations of north-east India: (a) Glenburn Tea Estate, Darjeeling, West Bengal (b) Tocklai Experimental Station, Jorhat, Assam, and (c) Dalmia Tea Plantations, West Bengal. We have tried to cover both orthodox and CTC types of black tea while studying fermentation aroma profile by electronic nose. The experiments at the Glenburn Tea Estate, Darjeeling, West Bengal were based on fermenting cycles of orthodox and flavoury tea. The experiments at Tocklai Experimental Station, Jorhat, Assam was on a miniature manufacturing facility with controlled environment using fast fermenting clones. Here the output was CTC type of Black Tea. The Dalmia Tea Plantations, West Bengal, is basically a ‘‘bought leaf factory’’ producing huge quantities of CTC type black tea daily. Longest trials were done at this factory. Altogether, 81 fermentation cycles
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have been considered for data collection, out of which 10 cycles are for orthodox flavoury teas and the remaining 71 cycles for CTC tea. 4.3. Data analysis The sensor array consisting of eight MOS type sensors were found to be adequately sensitive to the volatile emission during the fermentation process. The sensor responses are related to the amount of bio-chemical volatiles that came in touch with the corresponding sensor surface. In fact, the output of each sensor is a direct indication of the intensity of the chemical(s) it has absorbed. By setting the electronic nose in auto-mode, readings had been taken during the fermentation process at regular intervals. Therefore, the variation of intensity of smell during the entire fermentation process was actually recorded. During the fermentation process, the variation of the intensity of smell is a key feature in predicting and/or deciding the suitable fermentation time. In this study, our goal was to capture a pattern of the variation of the intensity of the sensor outputs that are recorded by the electronic nose. Output variation of the sensor array over time has been shown in Fig. 3. The data analysis strategy followed two sequential steps: (a) data exploration and (b) data quantification. In the data exploration step, we tried to observe an overview of basic clusters embedded in the sensor measurements in an unsupervised manner using principal component analysis (PCA). In the data quantification step, the objective was to derive some scalar index (which have been termed as Aroma Scores) proportional to sensor output pattern and intensity of smell. These scalar values may be plotted against time to evolve the time-domain aroma profile against progress of fermentation process. Approach towards aroma scores has been worked out by (a) singular value decomposition (SVD) based 2-Norm method and (b) Mahalanobis distance method. The results obtained from this analysis of data have been presented in Section 5.
Fig. 3. Output variation of sensor array over time.
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4.4. Colorimetric tests and human expert evaluation
5. Results
Colorimetric test is an indirect method of detection of optimum of fermentation time. Colorimetric method is the most popular technique employed by Indian tea industries to detect optimum time of fermentation process (Chakravorty, 1976). In the present study, colour change of in-process tea leaves had been tracked by means of Digital Photo Colorimeter (Model 312E/ 313E). A sample of 3.0 g of tea from fermentation process had been collected in regular intervals and put into 150 ml of boiled water to produce tea extract. Measured quantity of this tea extract had been mixed with sodium carbonate (NaCO3) solution and phenol reagent in a test tube. This test tube had been inserted in the optical path (400–700 nm) of the colorimeter and the optical absorbance was measured. When the fermentation process reached optimum point, colour became coppery brown and the optical absorbance attained maximum value (Ullah, 1972). Time of this optimum colorimetric point had been noted for each fermentation experiments and the result had been correlated with electronic nose results. Human expert evaluation was an essential component of the experiments under the present study. It is a popular practice that shop floor supervisors periodically sniff the teas in fermentation process to find out manually the smell pattern and smell peaks to detect end point of fermentation process. For all the experimental runs under the present study, human experts on the shop floor have evaluated time of occurrence of floral smell peaks during fermentation process. The experiments were carried out in three tea manufacturing factories in North Bengal, India, namely, (a) Dalmia Tea Plantation and Industries Limited, (b) Glenburn Tea Estate and (c) Washabarie Tea Company Limited. Two shop floor supervisors in each of these factories were involved in human evaluation tests for the fermentation runs under experimentation. The experts sniffed a handful of teas in fermentation every 5 min interval and declared the optimum fermentation time using their experienced olfactory senses.
5.1. Results of the principal component analysis Principal component analysis (PCA) is a popular technique for reduction of dimension in a large dimensional data set (Gardner, Schurmer, & Tan, 1992). Time required for fermentation of one batch of tea is generally 1–2 h depending on climatic condition, type of leaves and location of tea plantation (i.e., Northern or Southern part of India). In our experiments, every five minutes, tea odour has been sensed by the sensor array in an automated mode (e.g. for a fermentation run of one-hour duration, the computer would acquire 12 data matrices corresponding to each of the sniffing cycle). We have applied PCA to the entire eight-dimensional data set (comprising of data from all the sniffing cycles) to investigate whether the change in smell for the teas in fermentation is rightly reflected in terms of clustering of data points in reduced two-dimensional space. Distinct clusters have been formed as a result of such PCA analysis clearly classifying individual sniffing cycle data from the others. Sample PCA plots shown in Fig. 4 illustrate smell classification for teas in fermentation with process time. In the sample plots, we have shown clusters of smell-prints at 48th, 72nd and 104th minutes in two different fermentation runs for clarity and ease of visualization. The result conclusively establishes the fact that the electronic nose developed under the present study is capable of differentiating smell of tea in fermentation process. The results of the PCA show that the sensors are strongly correlated, since almost 95% of the variance of the data is contained within the first principal component, PC1. The first two components, PC1 and PC2, can be used to represent almost 98% of the data variance. In another variant of PCA presentation (O’Connell, Valdora, Peltzer, & Negri, 2001), the data matrix consisting of vectors with maximum sensor outputs at each sniffing cycle have been considered. So, each sniffing cycle will be represented by single point in PC1–PC2 plane. A sample of such representation of smell-points in the PC1–PC2
Fig. 4. PCA clusters of electronic nose data.
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plane for a single fermentation run has been shown in Fig. 5. It is interesting to note that the points corresponding to successive sniffing cycles show a shift rightward indicating increase in intensity of smell for teas in fermentation process with time. In the sample plot shown, the smellpoint at 104th minute is represented by the rightmost point in the plot clearly establishing the maximum odour intensity phase of the process and probably the optimum fermentation time. Further, the point corresponding to 112th minute in the above plot has moved left clearly indicating decrease in smell intensity after 104th minute which can be related to the over-fermentation phase of the process. In the sample plot, for sniffing cycles within 40 min of process time, the values on PC1 is less than (1) and for sniffing cycles beyond 40 min, the values of PC1 is more than (1). For all the fermentation runs studied by us, similar behaviors of the odour pattern have been observed. The result conclusively establishes the fact that the electronic nose developed under the present study is capable of tracking change in smell intensity of teas in fermentation process unambiguously. 5.2. Detection of black tea aroma profile with 2-Norm method (2NM) Subsequent to the well-defined clusters obtained by the PCA analysis, approaches towards quantification of smell intensity during the process have been explored. Norm of a matrix is a scalar that gives some measure of the magnitude of the elements in the matrix (www.mathworks.com). For our case, since the matrix comprises of normalized MOS sensor outputs when exposed to teas in fermentation, such norm calculation may be used to derive aroma intensity for the tea samples.
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If A represents a matrix of m · n dimension, the 2-Norm of A is represented by kAkp, where p = 2. For computation of 2-Norm, the largest singular value of the input matrix is computed by singular value decomposition (SVD) technique (Wall, Rechtsteiner, & Rocha, 2003). The SVD technique deals with the factorization of a rectangular matrix and the factorized version of the input matrix A is represented as A ¼ USV T ; where the matrix consisting of eigenvectors of AAT is represented by a new matrix U and the matrix consisting of eigenvectors of ATA is represented by a new matrix V. The diagonal values of S constitute the singular value spectrum of the input matrix. The singular values may be plotted in a one-dimensional bar chart as shown in Fig. 6 for easy visualization. The height of any one singular value is indicative of its importance in explaining the data. Since, the square of each singular value is proportional to the variance explained by each singular vector, such values promise an important indication to the aroma score for an individual sniffing cycle. From the above plot, it may be noticed that the first singular value is quite large compared to the following ones. In fact, a leveling off of the relative variance after the second component may be observed. Percentage information content of in the first SVD component is very high (almost 90%) as has been observed in the experimental data. Hence, the first singular value represents the data trend significantly whereas the following singular values are of negligible significance. This highest singular value is the 2-Norm of the data matrix. Since the 2-Norm value derived out of a data matrix is directly proportional to the square root of the variance of
Fig. 5. Smell-points in PC1–PC2 Plane indication variation of smell intensity with time.
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Fig. 6. Sample SVD plot for one sniffing cycle.
each singular vector, we may relate the 2-Norm value to the overall intensity of smell for a sniffing cycle. More intense is the smell; more will be the voltage output from the sensors thereby increasing the respective 2-Norm value. In the experiments, smell data from teas in fermentation have been acquired at regular intervals in automated fashion and 2-Norm calculations have been done at each sniffing cycle. These 2-Norm values have been plotted against time as shown in the sample curves given in Fig. 7. The curves, thus drawn, clearly show variation of smell intensity of black tea during fermentation process. The peak smell intensity can be detected online from such aroma profile and completion time of fermentation may be decided accordingly. The 2-Norm values calculated as above have been scaled down by the following formula: S N ¼ S C =S MAX where, SN SC SMAX
scaled down 2-Norm value at each sniffing cycle calculated 2-Norm value maximum 2-Norm value in the entire fermentation cycle
The 2-Norm technique has been employed here to extract a scalar index from the smell matrix, which signifies effective smell intensity for the tea sample for an individual
sniffing cycle. These smell intensity values have been considered as Aroma Scores and have been named as ‘‘NORMAROMA Score’’ and these ‘‘NORMAROMA scores’’ have been plotted against time to generate the fermentation aroma profile named as ‘‘NORMAROMA Profile’’ for black tea. The software and hardware of the electronic nose set-up has been designed such that the ‘‘NORMAROMA Profile’’ is drawn on-screen in real time such that the floor operators directly monitor the smell peaks with progress of process in a very handy and convenient manner. 5.3. Detection of black tea aroma profile with Mahalanobis distance method (MDM) In the context of multivariate data, distance computation between a vector and a matrix may provide crucial clues to the measure of dissimilarity among the two. If the distance is less, the data in consideration are more similar than the case where the distance is more (Mitra & Acharya, 2003). The Mahalanobis distance is a multivariate measure of the separation of a data set from a point in space (Anderson, 1958). A common usage of Mahalanobis distance is in computer vision systems for comparing feature vectors whose elements are quantities having different ranges and amounts of variation. Mahalanobis distance,d, is a general-
Fig. 7. Fermentation aroma profile in 2-Norm method.
N. Bhattacharyya et al. / Journal of Food Engineering 80 (2007) 1146–1156 b11 b 21 . .
b12 b 22 . .
. . bh1 bh 2 A= S 11 S 12 S 21 S 22 . . . Sm1
. . . .
. . . .
. . . .
b18 b 28 . .
Sensor Responses During Headspace Generation
. . . . . .
. bh 8 . . . S 18 . . . S 28
. . . . . . . .
. .
. . . . . Sm 2 . . . Sm8
Sensor Responses When Exposed to Tea Odour During Sampling
Fig. 8. Multivariate data matrix formed out of sensor array output.
ized measure of the distance between two groups and is represented as T
d 2 ¼ ðx mÞ V 1 ðx mÞ where xT = {x1, x2, . . . , xn} vector for a single multivariate observation mt = {l1, l2, . . . , ln} vector representing the population mean V co-variance matrix In the domain of electronic nose-based study on black tea fermentation process, each sniffing cycle produces a huge amount of eight-dimensional data. In the system developed by us, each sniffing cycle consists of headspace generation (30 s), sampling (50 s) and purging (100 s) operations. Computer acquires sensor data during headspace generation and sampling cycles only in an automated sequence of operation. Clearly, the sensor outputs will be at their baseline during the headspace generation and significant variation in sensor outputs will be observed during the sampling cycle. Therefore, the data matrix stored in the computer in each sniffing cycle will consist of a mixture of both baseline as well as actual sensor responses as shown in Fig. 8. In the above data matrix, the segment b11 to bh8 represents baseline responses of the sensors during headspace
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generation and S11 to Sm8 represents the sensor responses when exposed to tea odour during sampling. In our study, maximum value of Sij for each column has been found out and a vector M is formed with these maximum values. So, M ¼ ½ S i1max S i8max . Another vector N is formed such that 3 2 b11 b12 b18 7 6b 6 21 b22 b28 7 7 6 6 7 7 6 N ¼6 7 7 6 7 6 4 5 bh1
bh2
bh8
Mahalanobis distance has been calculated between the vector M and matrix N. Obviously, this distance will be more if smell intensity is strong and the distance will be less if smell intensity is weak. The Mahalanobis distance calculated as above promises a very good approach towards quantification of smell intensity and volatile concentration at the headspace during black tea fermentation process. We have defined this scalar index as the Aroma Score calculated by Mahalanobis distance method and named as ‘‘MAHAROMA Score’’. These aroma scores have been derived for each sniffing cycle and plotted against time as shown in Fig. 9, which gives the fermentation aroma profile and has been named as ‘‘MAHAROMA Profile’’ of black tea fermentation. The aroma scores have been scaled (division by 106) for easy visualization. 5.4. Effect of fermentation on colour of black tea During black tea fermentation process, green colour of cut tea leaves changes to coppery brown colour and this change in colour is an important parameter to monitor status of the fermentation process. For all the experimental runs carried out under the present study, colorimetric monitoring was done by the digital photo colorimeter (Model 312E/313E). The absorbance values were plotted against time to draw the colorimetric profile of the fermentation process. A sample colorimetric profile has been illustrated
Fig. 9. Sample aroma profile for black tea fermentation (Mahalanobis distance method).
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Fig. 10. Sample colorimeter curve for black tea fermentation.
in Fig. 10. A close look at the colorimetric graph reveals that there are two distinct peaks over the fermentation process time and as per the popular practices followed by the Indian Tea Industries, the time associated with the second peak of the colorimetric graph is considered as the optimum fermentation time (Chakravorty, 1976; Ullah, 1972). The results obtained from the colorimetric tests for the fermentation experiments are given in Table 2 alongwith the optimum fermentation times as detected by electronic nose. It is observed that the accuracy of detection of fermentation completion time by electronic nose vis-a`vis colorimetric evaluation is to the tune of 95%. 5.5. Sensory properties of fermented black tea The electronic nose data have been found to be accurately matching with the human experts’ assessments. It has been observed that the sensory evaluation of teas in fermentation by periodic sniffing yields very closely correlated results with a variance of less than three from two experts engaged in each of the experiments. The results of sensory evaluation and the optimum fermentation times as detected
by electronic nose are given in Table 3. In this case also, the accuracy of detection of fermentation completion time by electronic nose vis-a`-vis human evaluation is to the tune of 96%. 5.6. Correlation between the results of 2NM, MDM and colorimetric and sensory tests Close scrutiny and examination of the fermentation aroma profile in both 2-Norm as well as Mahalanobis distance methods indicate that there exist distinct smell peaks and troughs with fermentation time signifying change in volatile emission pattern and change of smell as detected by electronic nose. Closer look at NORMAROMA and MAHAROMA Profiles reveal some very interesting facts: • Both the profiles are similar in shape. • Appearances of NORMAROMA and MAHAROMA peaks are coincident, i.e., they occur exactly on at the similar time instants as observed in both the profiles for a specific fermentation run.
Table 2 Results of colorimetric tests Name of the tea garden
Number of experiments
Time of experiments
Average fermentation time estimated by colorimetric test
Average fermentation time predicted by electronic nose
Glenburn Tea Estate, Darjeeling, West Bengal, India Tocklai Experimental Station, Jorhat, Assam, India Dalmia Tea Plantations, West Bengal, India
10 10 22 15 24
August 2005 September 2005 October 2005 November 2005 December, 2005
58–63 40–45 90–100 105–120 110–125
60–65 40–45 95–100 105–115 110–120
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Table 3 Results of sensory evaluation Name of the tea garden
Number of experiments
Time of experiments
Number of experts engaged
Average fermentation time estimated by sensory evaluation
Variance of fermentation time estimated by the human experts
Average fermentation time predicted by electronic nose
Glenburn Tea Estate, Darjeeling, West Bengal, India Tocklai Experimental Station, Jorhat, Assam, India Dalmia Tea Plantations, West Bengal, India
10
August 2005
2
60–65
<3
60–65
10
September 2005
2
40–50
<2
40–45
22 15 24
October 2005 November 2005 December, 2005
2
85–105 100–120 110–125
<3 <4 <5
95–100 105–115 110–120
Table 4 Summary of results Number of fermentation runs Accuracy of detection of fermentation completion time by electronic nose vis-a`-vis colorimetric test Accuracy of detection of fermentation completion time by electronic nose vis-a`-vis human evaluation
81 95% 96%
• Both the profiles can be calculated and displayed online during fermentation process. Hence, it is felt that both NORMAROMA and MAHAROMA methods might be very useful for monitoring of smell in black tea fermentation process. It can be further observed that the aroma score value changes widely from one sample to the other. This behavior may be attributed to variation of clones, location of garden as well as climatic condition during processing. The results obtained by electronic nose have been compared with those obtained from popular practices in Indian Tea Industries, namely, sensory evaluation and colorimetric tests. It has been observed that accuracy of detection of optimum fermentation time by electronic nose vis-s-vis sensory as well as colorimetric tests is to the tune of 95%. The results of cross-validation analysis have been given in Table 4. 6. Discussion A number of research and development initiatives have been reported on fermentation process of black tea (Opie, Robertson, & Clifford, 1990; Owuor & McDowell, 1994). These studies were based on chemical analysis, and on-line instrumental monitoring of the fermentation aroma profile of black tea has not been attempted. At the same time, the new technology based on electronic nose has been attempted by scientists and it has shown excellent promise in many applications like food and beverages (Aishima et al., 1991; Borjesson, Eklov, Jonsson, Sundgren, & Schnurer, 1996; Pearce, Garner, Friel, Bartlett, & Blair, 1993; Pisanelli, Qutob, Traverse, Szyszko, & Persaud, 1994; Nanto, Shokoshi, & Kawai, 1991; Olafsson, Martinsdottir, Olafsdottir, Sigfusson, & Gardner, 1992; Stetter, Findlay, Shroeder, Yue, & Penrose, 1993; Winquist, Hornsten,
Sundgren, & Lundstrom, 1993). But reports of applications of electronic nose for black tea are very few in number and the earlier works were mainly focused on characterization of smell of finished tea. The novelty of the proposed methodology is that the electronic nose technology can be employed for detection of aroma during the manufacturing of tea, thereby maintaining the quality standard of the finished product. The present study opens up numerous applications of electronic nose for black tea processing. Further, the present study proposes a new working methodology of electronic nose in black tea processing floor with requisite operational automation. Such new method of detection of optimum fermentation time has the potential of ushering new paradigm of non-invasive, real-time and fast method of monitoring of black tea fermentation process. 7. Conclusion Under the present study, total 81 fermentation cycles have been experimented with at various locations of India at various seasons. Most of the Electronic Nose readings accurately matched with colorimetric as well as human panel data. It is felt that electronic nose can definitely be used for monitoring of volatile emission pattern for black tea fermentation process with a very high degree of accuracy, reliability and repeatability. Electronic nose-based new method as discussed above may be highly beneficial for black tea processing in terms of dispensing with too much human expert dependence and cumbersome chemical or indirect tests which are invasive and offline. Such objective methodology might be instrumental in manufacturing consistent quality of black tea. References Aishima, T. (1991). Discrimination of varieties and roasting levels in coffee beans by pattern recognition analysis of responses from a semiconductor gas sensor array. In 14th Colloque Scientifique Internationale Sur La cafe´, San Fransisco, pp. 137–145. Anderson, T. W. (1958). Introduction to multivariate statistical analysis (3rd ed.). New York: John Wiley and Sons. Bhattacharyya, N., Bandyopadhyay, R., Bhuyan, M., Ghosh, A. & Mudi, R. K. (2005). Correlation of multi-sensor array data with ‘‘Tasters’’ panel evaluation for objective assessment of black tea flavour. In International symposium on olfaction and electronic noses (ISOEN 2005), 13–15 April 2005, Barcelona, Spain.
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