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Fuzzy controller based E-nose classification of Sitophilus oryzae infestation in stored rice grain
Food Chemistry 283 (2019) 604–610
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Food Chemistry journal homepage: www.elsevier.com/locate/foodchem
Research Article
Fuzzy controller based E-nose classification of Sitophilus oryzae infestation in stored rice grain
T
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Shubhangi Srivastava , Gayatri Mishra, Hari Niwas Mishra Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
A R T I C LE I N FO
A B S T R A C T
Keywords: Electronic nose Fuzzy analysis Infested rice Principal component analysis Sensors
Fuzzy controller artmap based algorithms via E-nose selective metal oxides sensor (MOS) data was applied for classification of S. oryzae infestation in rice grains. The screened defuzzified data of selective sensors was further applied to detect S. oryzae infested rice with PCA and MLR techniques. Reliability of data was cross validated with reference methods of protein and uric acid content. Out of 18 MOS, 6 sensors namely P30/2, P30/1, T30/1, P40/2, T70/2 and PA/2 showed maximum resistivity change. Defuzzified score of 62.17 for P30/2 and 59.33 for P30/1 MOS further confirmed validity studies of E-nose sensor response with reference methods. The PCA plots were able to classify up to 84.75% of rice with variable degree of S. oryzae infestation. The MLR values of predicted versus reference values of protein and uric acid content were found to be fitting with R2 of 0.972, 0.997 and RMSE values of 2.08, 1.05.
1. Introduction Rice is considered as one of the primary staple source of food for more than 2.5 billion population in the world. Nevertheless, when the same rice grain is stored for a longer period the chances of insect infestation, especially by Sitophilus oryzae are elevated (Gandhi, Pillai, & Patel, 2010; Zhou & Wang, 2011). Consequently, it is a prime concern to track down constructive methods to minimize the Sitophilus oryzae infestation in stored rice. Timely detection of insects or pets could be alternative which would help in eliminating infestation. Classical methods of such detection comprised of sieving, manual traps and probes, cracking-flotation (Neethirajan, Karunakaran, Jayas, & White, 2007; Xi & Wang, 2012). These classical methods for detection of insect plea repeated work and trained workforce, at the same time some observations are imprecise as they are subjected to naked eyes. In recent times several techniques such as near-infrared spectroscopy, X ray method, uric acid, and acoustic measurement, etc. are being studied for insect infestation in rice grains but these are not so much cost effective in nature (Neethirajan et al., 2007). Chiefly, E-nose may be defined as the machine used for the detection and identification of compounds based on electrical or gas sensors specific to different type of volatiles (Peris & Escuder-Gilabert, 2009; Tian, Wang, & Cui, 2013; Song et al., 2013). Appropriate pattern recognition algorithms from E-nose are used for classification purposes. These specific patterns are then analyzed using statistical tools such as
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principal component analysis (PCA), multiple linear regressions (MLR) etc. (Pavon et al., 2006; Zhang, Wang, Tian, Yu, & Yu, 2007). The convenience of faster assay and their specificity have made them quite popular in the food industry notably in fruits (Marrazzo, Heinemann, Crassweller, & LeBlanc, 2005), milk (Labreche, Bazzo, Cade, & Chanie, 2006), oil (Hai & Wang, 2006), wheat (Evans et al., 2000; Zhang & Wang, 2008), barley (Balasubramanian, Panigrahi, Kottapalli, & WolfHall, 2007) etc. Moreover, several other studies on cereal grains fungal infection have also been reported (Evans et al., 2000; Needham, Williams, Beales, Voysey, & Magan, 2005). Therefore, E-nose has a great potential to foresee food quality changes and with the help of this technology in-line systems in the grain industry could be established. Another application of it would be in the detection of mycotoxins in grains or any other food products as humans would not smell it from the safety point of view. The analyses with respect to fuzzy sets are influential as they permit researchers for calibration of sectional membership function in the set with values between 1 (full membership) and 0 (non-membership) without dropping out the core set of theoretic principles. This framework of fuzzy membership comprise of various tools which grant the planner to catch sight of relevant concepts which match up the real time situation. Some of the surveyed literature described implementation of fuzzy logic approach in fried potatoes shelf life (Chatterjee, Bhattacharjee, & Bhattacharyya, 2014), mango drinks (Jaya & Das, 2003), drinks formulated from yoghurt (Routray & Mishra, 2012),
Corresponding author. E-mail address: [email protected] (S. Srivastava).
https://doi.org/10.1016/j.foodchem.2019.01.076 Received 5 June 2018; Received in revised form 6 January 2019; Accepted 6 January 2019 Available online 19 January 2019 0308-8146/ © 2019 Elsevier Ltd. All rights reserved.
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was used for the data acquisition; IBM SPSS 20 and MATLAB 2016 version were used for the data analysis.
sunflower oil frying time (Upadhyay, Sehwag, & Mishra, 2017a, 2017b), and peanuts quality (Raigar, Upadhyay, & Mishra, 2017). Currently, electronic nose have been used for the identification of infestation in rice grains with the presence of volatiles composition (Pang, Wang, Lu, & Yu, 2008; Zhang & Wang, 2008; Zhou & Wang, 2011; Huichun, Zuozhou, & Yong, 2012). However, limited research is available on infestation in stored rice grains with E-nose. Thus, eventually it becomes crucial to structure a steady classification model for detection of S. oryzae infestation in rice grains. The objective of the work was to investigate the capability of hybrid fuzzy logic assisted E-nose approach to screen out the major metal oxide sensors which can be used to monitor the S. oryzae infestation in stored rice grains. The filtered metal oxides sensory data obtained from E-nose was then fed to a fuzzy logic artmap controller tool box and defuzzified scores were calculated. The fuzzy screened data thus obtained was subjected to principal cluster analysis (PCA), and multiple linear regression (MLR) to predict the correlation with the conventional methods.
2.3. Modeling of fuzzy controller in MATLAB The modeling of fuzzy controller fundamentally comprise of distilling knowledge of human about how a set of rules could supervise a system. The rule based detection of S. oryzae infestation in stored rice grains was formulated in a symmetric fashion generally with linguistic values of sensor sensitivity as non, fair, medium, good, and excellent with number of days as 0, 45, 90, 135, 180, 225 and S. oryzae number as 0, 5, 10, 15. The membership functions for the storage days, S. oryzae number, and storage days rules were normal, symmetric, and evenly distributed triangular membership function. Singleton fuzzification was used with implication kept at minimal and aggregation at maximal level. Defuzzification was implemented using the centroid method. The membership functions ‘trimf’ was used for storage days, ‘trapmf’ for S. oryzae number, and ‘gaussmf’ for the sensor responses respectively. The analogy drawn with the conventional sensory evaluation equivalent to hedonic scores was used in ranking of fuzzy logic assisted E-nose data (ΔR/R) (Upadhyay et al., 2017b). Weightage or importance to peculiar attributes is the basic criterion on which fuzzy logic is applied (Jaya & Das, 2003; Raigar et al., 2017). Therefore, an assumption was made to accredit an equal statistical weight to fresh and S. oryzae infested rice grains. Each sensor was designated with different response scale factor (non, fairly, medium, good, and extremely sensitive) for which signal response (ΔR/R) ranges: 0.002–0.098 (non-sensitive, A1), 0.098–0.181 (fairly sensitive, A2), 0.181–0.543 (medium sensitive, A3), 0.543–0.791 (good sensitive, A4), and more than 0.791 (excellent sensitive, A5) (Table 1). For each sensor, triangular membership function, a fuzzy triplet set (x–z) was calculated by Eqs. (1)–(3).
2. Material and methods 2.1. Preparation of infested rice samples Freshly milled rice cv. Badshah bhog was obtained from the Rice Mill located at Agricultural and Food Engineering Department, IIT Kharagpur, West Bengal, India. Insect, Sitophilus oryzae was obtained from the Entomology Department of G.B. Pant University, Pantnagar, India. Rearing of S. oryzae was performed in jars with screen lids at RH 65–70% and temperature 25 °C. A total of 80 kg rice grains, 200 g each were stored for periods namely: 45, 90, 135, 180, and 225 days respectively at 27 °C temperature, and 65% RH, by varying the S. oryzae adult insects number as per the experimental plan i.e. 0, 5, 10, and 15 (Srivastava, Mishra, & Mishra, 2018a, 2018b). Samples at zero days with nil live adult insects were taken as control. Infested rice samples were stored in small containers with lids having hole of about 2 cm for ventilation maintenance (Mishra, Srivastava, Panda, & Mishra, 2018a; Mishra, Srivastava, Panda, & Mishra, 2018b). The samples were taken out of storage environment analyzed at regular interval of 45 days and analyzed for the insect infestation.
x = (0 × A1 + 25 × A2 + 50 × A3 + 75 × A 4 + 100 × A5)/100
(1)
y = (0 × A1 + 25 × A2 + 25 × A3 + 25 × A 4 + 25 × A5)/100
(2)
z = (25 × A1 + 25 × A2 + 25 × A3 + 25 × A 4 + 0 × A5)/100
(3)
where A1, A2, A3, A4, and A5 are the response scale factors. Defuzzification of overall sensor response scores (0–100) was obtained by calculation of centroid value with analogous triplets by using Eq. (4). The ranking of sensors was done with these defuzzified values. The data extracted from the fuzzy screened sensors generated odor map by principal component analysis (PCA). Multiple linear regression (MLR) was also generated with the fuzzy data of screened sensors for the comparative analysis with the reference methods.
2.2. Robotic E-nose setup A robotic E-nose system (Alpha Soft Fox 2.0, Alpha M.O.S, France) was used for sampling of different days infested rice with S. oryzae. The E-nose had a fully automated HS100 robotee air auto sampler, FID gas station and 18 metal-oxide sensors. These sensors are classified as Ltype (LY2/ LG, LY2/AA, LY2/G, LY2/GH, LY2/gCT, LY2/gCTI), P-type (P10/1, PA2, P10/2, P30/1, P40/1, P30/2, P40/2), and T-type (T30/1, T40/1, T40/2, T70/ 2, TA/2 (Oliveros et al., 2002; Mishra et al., 2018a, 2018b; Raigar et al., 2017). Each sensor is responsive to different volatiles. Grounded rice samples (2 g) in a 20 mL glass vials, fitted with magnetic polytetrafluoroethylene (PTFE) crimp septum was thermoincubated at 60 °C with an agitation speed of 500 rpm for 240 s to generate the headspace volatiles. The volatiles accumulated were injected into E-nose with the help of robotee auto-sampler, using purified air stream pressure of 3.5 × 104 Pa with a flow rate of 150 mL min−1 and an injection volume of 500 µl/s. The flush time was set to 120 s. The responses of sensors were computed with respect to change in resistance (ΔR/R) in reference to base values for 120 s followed by a recovery period (1080 s) so as to permit the sensors to return to their respective baseline resistance. An absolute value of maximum ΔR/Ro was extracted for each sensor. It is noteworthy to remark here that the training of the instrument was done previously so as the E-nose was well acquainted with the volatile odor fingerprints of rice samples of different storage days (Raigar et al., 2017). The software Alpha Soft v14
A= (3x − y + z )/3
(4)
where A denoted the defuzzified sensor signal response score. 2.4. Conventional method for protein and uric acid determination Infested S. oryzae rice samples were analyzed for the major quality changes in protein content and amount of uric acid generated due to S. oryzae infestation in rice using the standard AOAC methods. The protein content on wet basis was determined by Kjeldahl method which is based on the determination of the amount of reduced nitrogen present in the rice sample (AOAC, 2000). A conversion factor of 5.95 was used to obtain the refined expression of protein percentage in rice samples. The concerned conversion factor was derived by taking into account the amino acid composition of the rice grains (FAO & Nutritional studies No. 24, 1970). The determination of uric acid (wet basis) in rice grains due to S. oryzae infestation was done following the standard AOAC Method No. 970.24 (AOAC, 2000). The method is based on the proteins precipitation and treatment of protein free filtrate with sodium cyanide and uric acid, and resultant optical density of the blue color is measured colorimetrically at 520 nm. 605
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Table 1 Triplet and defuzzified scores of MOS sensors obtained by fuzzy logic analysis of S. oryzae infested rice. Sensors
Non sensitive (−0.002 to 0.098) A1#
Fairly sensitive (0.098–0.181) A2#
Medium sensitive (0.181–0.543) A3#
Good sensitive (0.543–0.791) A4 #
Extremely sensitive (0.791–0.986) A5#
x*
y*
z*
Defuzzified score$ A$
LY2/LG LY2/G LY2/AA LY2/GH LY2/gCTl LY2/gCT T30/1 P10/1 P10/2 P40/1 T70/2 PA/2 P30/1 P40/2 P30/2 T40/2 T40/1 TA/2
88 10 20 86 92 56 30 65 79 78 30 8 25 32 16 67 34 56
12 80 80 14 8 44 6 16 6 10 5 16 11 5 15 7 46 42
0 10 0 0 0 0 10 19 15 12 16 46 6 10 10 19 20 2
0 0 0 0 0 0 10 0 0 0 9 24 10 10 12 7 0 0
0 0 0 0 0 0 44 0 0 0 40 6 48 43 47 0 0 0
3 25 20 3.5 2 11 58 13.5 9 8.5 56 51 61.25 56.75 64.75 16.5 21.5 11.5
3 22.5 20 3.5 2 11 17.5 8.75 5.25 5.5 17.5 23 18.75 17 21 8.25 16.5 11
25 25 25 25 25 25 14 25 25 25 15 23.5 13 14.25 13.25 25 25 25
10.33 25.83 21.67 10.67 9.67 15.67 56.83 18.92 15.58 15.00 55.17 51.17 59.33 55.83 62.17 22.08 24.33 16.17
All the values represented are mean of 10 determinations having coefficient of variation < 5%. * x, y, z are triplet scores of sensors (refer to Eqs. (1)–(3)). # A1, A2, A3, A4, A5 are the sensor signal response scale (ΔR/R). $ Refer to Eq. (4).
acids such as uric (P30/2, P40/2) (Mason et al., 2014). The ammonia, uric acid and other polar by products were recorded by the metal oxide sensors due to the excreta produced by the insect. The presence of other carbon compounds, aldehydes, ketones and acids was due to the aromatic nature of rice.
2.5. Multivariate statistical techniques The data interpretation of more than 20 samples with several sensors array data is much more complex, if not impossible. Therefore, to study the data sets as a whole, it is necessary to reduce the number of variable (data reduction). The ability to process and display in two or three dimensions the responses of 18 sensors to a large set of samples could be done easily with multivariate statistical algorithms. They allow determining which differences between samples are significant and to what extent. The fuzzy logic calculations, principal cluster analysis (PCA), and multiple linear regression (MLR) analyses were performed using Origin Pro v9.0 (Origin Lab Corporation, Northampton, USA). PCA and MLR were executed on auto-scaled data matrix of screened MOS sensors. The authenticity of prediction models was signified by fitting coefficient of regression (R2), and root mean square error (RMSE) of prediction.
3.2. Fuzzy logic art map analysis The fuzzy logic usefulness could be attributed in the cases where sensory panel cannot test the samples due to toxicity safety concerns. The membership function plots of input functions (storage days, S. oryzae number, sensor response, and output function (rice classification) is shown in Fig. 1. These input membership function were the basic layout on which the output membership was based. When the inputs function with respect to storage days (225), S. oryzae number (15 nos.), sensor response (0.986) were higher, the output function recommended the rice to be totally infested and should be discarded. At lower storage (0–45 days), S. oryzae number (0–5 Nos.), sensor response (−0.02 to 0.098), the output function recommended good quality of rice. Supplementary Fig. S2 shows three D surface plots obtained by fuzzy logic sensor response and storage days with respect to rice classification based on its infestation level. With the surface plots it can be easily correlated that with increase in storage days, number of S. oryzae, and sensor response the degree of infestation increases and chances of treatment for the rice grains decreases and vice versa. Thus, a set of rules for fuzzy function easily classified the rice samples as non-infested, low-infested, medium infested, and highly infested. Hence, based on such recommendations the utilization of the stored rice can be suitably planned; it can be discarded if the degree of infestation is higher than the level when it is difficult to disinfest. Table 1 shows triplet and defuzzified scores of MOS obtained by fuzzy logic analysis of S. oryzae infested rice grains. The sensors displayed diversified responses to volatiles produced at different storage days of S. oryzae infestation. Extensive divergent responses often lead to misleading data interpretation and hence necessitate the importance of suitable screening and ranking approach to identify the best responsive ones. The sensors were treated equivalent to sensory descriptors, and the responses were considered as sensory panel scores for dispensing to fuzzy logic calculations (Raigar et al., 2017). Each sensor was designated a peculiar set of signal response scale factors (A1–A5) which
3. Results and discussion 3.1. Different metal oxide sensors response by E-nose The volatiles generation by E-nose was recorded for different samples of rice of different storage days. The time versus intensity graph of 18 metal oxide sensors (MOS) response to rice samples is represented in supplementary Fig. 1. The volatiles generation in sensors P30/2, P30/1, T30/1, P40/2, T70/2, and PA/2 was more pronounced than other 12 metal oxide sensors. The sensors LY2/LG, LY2/AA, LY2/G, LY2/GH gave negative responses to the volatiles resistivity graphs indicating that the sensors had not significant role in volatile characterization of infested rice samples. Increase in duration of infestation with S. oryzae majorly affected P30/2, P30/1 and T 30/1 sensors more than P40/2, T70/2 and PA/2. The change in the resistance (ΔR/R) was more prominent in samples with more number of S. oryzae insects and more days of storage. Zheng et al. (2009) studied four long grain varieties using polymer sensors in E-nose but good recognition rate was not achieved mainly due to small sample size and imprecise resistivity of the polymer sensors. Hence, the large sample size and MOS would give far better results than polymer sensors. The major volatiles produced during S. oryzae infested stored rice were polar compounds, aldehydes, ketones (T30/1, P30/2); ammonia (P30/1, PA/2); carbon compounds (T70/2); 606
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identify the S. oryzae infested fingerprints of rice grains (p < 0.05) (Table 1). Higher the number of screened sensors in the zone of extremely sensitive (0.791–0.986), higher was the triplet scores of the sensors and so was the defuzzfied score and vice versa. In extremely sensitive range, 47 sensors data were screened for the P30/2, 48 for the P30/1, 44 for the T30/1, 43 for P40/2, and 40 for T70/2 respectively; while in good sensitive range (0.543–0.791) 24 sensors data was screened for the PA/2. Although there was one unit more in the P30/1 screened sensors data for extremely sensitive range, but still the overall screened sensors in the good, medium and fair sensitive range gave a higher defuzzified score to P30/2 (62.17) than the P30/1 (59.33) MOS. The other MOS which gave appreciable results for the response scale factors had a defuzzified score of 56.83 (T30/1), 55.83 (P40/2), 55.17 (T70/2) and 51.17 (PA/2). Major lower sensitive sensors were LY2/G, T40/1, T40/2, LY2/AA, P10/1, LY2/gCT, P10/2, P40/1, with a defuzzified score of 25.83, 24.33, 22.08, 21.67, 18.92, 15.67, 15.58, and 15.00, respectively. Limited scientific researches related to the current study could not allow many comparisons to be made with literature report. It is inferred that the trends recognised in present research could propose advantages of allowing analogies to be made with combined fuzzy logic–E-nose research in future. 3.3. Comparative analysis of the defuzzified E-nose data with those of the standard analytical methods The fuzzy screened data obtained from the E-nose was subjected to PCA. The 3-D PCA plot of S. oryzae infested rice with different storage (0–225) days is shown in Fig. 2. The three dimensional plot confirmed the validity of the results by grouping the rice grains infested with S. oryzae in different groups based on number of days and insect infestation. The PC scores for the matrix were 84.757 (PC1) and 4.131% (PC3). The PCA analysis clearly showed that the fresh rice samples of 0 days (red triangle) made a different group than 45 days (blue square), 90 days (dark pink circle), 135 days (light pink inverted triangle), 180 days (hollow black square), and 225 days (green square) infested rice samples respectively and the same was validated by the variance of 84.757%. The variance in the PC 2 score was quite less (0.251%) hence the 3D plot was made between the PC1 and the PC 3 scores. Thus, the samples stored for 45 days were far separated from those of 225 days due to difference in the degree of infestation. The validity of E-nose results was periodically examined with standard analytical method (AOAC, 2000) for protein and uric acid content in rice grains infested with S. oryzae and was further cross validated with the defuzzified scores obtained by the fuzzy logic (p < 0.05). The data of all the S. oryzae infested rice grains samples along with the screened sensors would be too exhaustive to depict in terms of their characteristics with respect to infested/non infested or requiring adequate treatment. Table 2 shows metal oxide sensors response along with uric acid and protein content of selected S. oryzae infested/non infested rice samples. The MOS namely P30/2, P30/1, T30/1, P40/2, T70/2 and PA/2 showed maximum resistivity changes due to infestation with significant changes in the protein and uric acid content. The multiple linear regression plots actual versus predicted protein and uric content due to S. oryzae infested rice is shown in Figs. 3 and 4 respectively. The predicted protein values with standard analysis methods (4.89–0.56%) closely correlated (R2, 0.997) with the MLR predicted values (4.77–0.61%) with a RMSE value of 1.05 obtained by screened MOS obtained by fuzzy art map (Fig. 3). Moreover, the predicted values followed a similar trend with those of the standard methods of protein indicating that with increase in storage days from 0 to 225, the prevalence of S. oryzae infestation was more; thus the degradation in protein content was observed due to such infestation. Another important attribute that was considered for S. oryzae infestation was the percentage of uric acid found in the rice grains. The predicted values of uric acid content (1.80–15.72%) with the fuzzy screened MOS data were found to be in accordance with those of the reference values (1.86–16.90%) of the uric
Fig. 1. Membership function plots of input functions (a) storage days, (b) S. oryzae number, (c) sensor response, and output function (d) rice classification.
varied as a function of their responses over the days of storage (0–225). The variations in intensity of sensor could be related to the S. oryzae infestation developed in rice grains during 225 days storage period. Each sensor was computed with a peculiar set of triplet values (x–z), and defuzzified scores which were treated as a measure of their eventual performance (Eqs. (1)–(4)) (Table 1). The sensors with higher loadings of response scale factors (A3, A4, and A5) were rated with medium to excellent sensitivity than those with higher (A1 and A2) which were rated with no to fair sensitivity. Consequently, six sensors (P30/1, P30/2, T30/1, P40/2, T70/2, and PA/2) were screened as the finest among others to efficiently detect and 607
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Fig. 2. 3-D PCA Plots of S. oryzae infested rice with different storage (0–225) days.
acid found in the S. oryzae infested grains (R2, 0.972) with a lower RMSE value of 2.08 (Fig. 4). The MLR data extracted from fuzzy screened sensors with respect to uric acid content further indicated that with increase in S. oryzae infestation along with storage days, the uric acid found in the infested grains increased significantly (p < 0.05). The residual values of 0.06–1.17 (uric acid) and 0.05–0.11 (protein) further justified the correlation between the screened sensors with fuzzy logic and standard analytical protocols. There was a decrease in the protein content while an increase in the uric acid content due to insect biological activities in the infested rice grains. The resistivity of the MOS response for screened sensors (P30/2, P30/1, T30/1, P40/2, T70/2) when increased (0.004–0.825), there were significant changes in protein and uric acid content. Thus, a vivid analysis could be drawn with the defuzzified scores with respect to sensors in terms of whether rice is infested with S. oryzae or is not infested one; also if it is infested whether is it at lower stage of infestation or higher stage, and whether any sort of treatment for rice grains can be adopted to disinfest and use or it has to be discarded.
Fig. 3. Multiple linear regression plots of actual vs predicted protein content in S. oryzae infested rice.
4. Conclusion The utility of the electronic nose could be a befitting one when the Table 2 Metal oxide sensors response along with uric acid and protein contents of selective S. oryzae infested/non infested rice grains. S. No.
MOS sensor responses (ΔR/R)
Uric acid (%)
Protein (%)
P30/2
P30/1
T30/1
P40/2
T70/2
PA/2
Reference analysis
Predicted value
Residuals
Reference analysis
Predicted value
Residuals
Remarks
1 2 3 4 5 6
0.058 0.049 0.143 0.11 0.232 0.36
0.066 0.054 0.128 0.108 0.228 0.257
0.008 0.008 0.19 0.156 0.369 0.48
0.007 0.008 0.113 0.132 0.157 0.283
0.004 0.005 0.009 0.118 0.142 0.189
0.045 0.054 0.037 0.076 0.072 0.092
1.867 2.234 5.708 5.018 9.067 10.587
1.803 2.560 5.618 4.514 8.750 11.264
0.064 0.326 0.090 0.504 0.317 0.677
4.891 4.610 3.890 3.670 3.330 3.090
4.773 4.589 3.887 3.668 3.280 3.111
0.118 0.021 0.003 0.002 0.050 0.021
7
0.53
0.39
0.673
0.378
0.383
0.21
12.690
13.534
0.844
2.980
3.001
0.021
8
0.454
0.348
0.588
0.467
0.431
0.165
15.971
15.635
0.336
1.990
2.003
0.013
9
0.739
0.684
0.781
0.592
0.521
0.49
16.860
15.696
1.164
0.670
0.711
0.041
10
0.825
0.758
0.741
0.691
0.665
0.482
16.904
15.729
1.175
0.560
0.612
0.052
Non-infested Non-infested Low Low Low Treatment/ discard Treatment/ discard Treatment/ discard Treatment/ discard Treatment/ discard
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Shelf life determination by electronic nose: application to milk. Sensors and Actuators B: Chemical, 106, 199–206. Marrazzo, W. N., Heinemann, P. H., Crassweller, R. E., & LeBlanc, E. (2005). Electronic nose chemical sensor feasibility study for the differentiation of apple cultivars. Transactions of the ASAE, 48, 1995–2002. Mason, A., Mukhopadhyay, S. C., & Jayasundera, K. P. (Eds.). (2014). Sensing technology: Current status and future trends III. Springer. Mishra, G., Srivastava, S., Panda, K. B., & Mishra, H. N. (2018a). Prediction of Sitophilus granarius infestation in stored wheat grain using fuzzy logic based electronic nose analysis. Computer and Electronics in Agriculture, 152, 324–332. https://doi.org/10. 1016/j.compag.2018.07.022. Mishra, G., Srivastava, S., Panda, K. B., & Mishra, H. N. (2018b). Sensor array optimization and determination of Rhyzopertha dominica infestation in wheat using hybrid neuro-fuzzy-assisted electronic nose analysis. Analytical Methods, 10, 5687–5695. Needham, R., Williams, J., Beales, N., Voysey, P., & Magan, N. (2005). Early detection and differentiation of spoilage of bakery products. Sensors and Actuators B: Chemical, 106, 20–23. Neethirajan, S., Karunakaran, C., Jayas, & White, N. D. G. (2007). Detection techniques for stored product insects in grain. Food Control, 18, 157–162. Oliveros, M. C. C., Pavón, J. L. P., Pinto, C. G., Laespada, M. E. F., Cordero, B. M., & Forina, M. (2002). Electronic nose based on metal oxide semiconductor sensors as a fast alternative for the detection of adulteration of virgin olive oils. Analytica Chimica Acta, 459, 219–228. Pang, L., Wang, J., Lu, X., & Yu, H. (2008). Discrimination of storage age for wheat by Enose. Transactions of the ASAE, 51, 1707–1712. Pavon, J. L. P., Sanchez, M. D., Pinto, C. G., Laespada, M. E. F., Cordero, B. M., & Pena, A. G. (2006). Strategies for qualitative and quantitative analyses with mass spectrometry-based electronic noses. TrAC Trends in Analytical Chemistry, 25, 257–266. Peris, M., & Escuder-Gilabert, L. (2009). A 21st century technique for food control: Electronic noses. Analytica Chimica Acta, 638, 1–15. Raigar, R. K., Upadhyay, R., & Mishra, H. N. (2017). Storage quality assessment of shelled peanuts using non-destructive electronic nose combined with fuzzy logic approach. Postharvest Biology and Technology, 132, 43–50. Routray, W., & Mishra, H. N. (2012). Sensory evaluation of different drinks formulated from dahi (Indian yogurt) powder using fuzzy logic. Journal of Food Processing and Preservation, 36, 1–10. Song, S., Yuan, L., Zhang, X., Hayat, K., Chen, H., Liu, F., ... Niu, Y. (2013). Rapid measuring and modeling flavor quality changes of oxidized chicken fat by electronic nose profiles through the partial least squares regression analysis. Food Chemistry, 141, 4278–4288. Srivastava, S., Mishra, G., & Mishra, H. N. (2018a). FTNIR-a robust diagnostic tool for the rapid detection of Rhyzopertha dominica and Sitophilus oryzae infestation and quality changes in stored rice grains. Food and Bioprocess Technology, 11, 785–796. https:// doi.org/10.1007/s11947-017-2048-3. Srivastava, S., Mishra, G., & Mishra, H. N. (2018b). Identification and differentiation of insect infested rice grains varieties with FTNIR spectroscopy and hierarchical cluster analysis. Food Chemistry, 268, 402–410. Tian, X., Wang, J., & Cui, S. (2013). Analysis of pork adulteration in minced mutton using electronic nose of metal oxide sensors. Journal of Food Engineering, 11(9), 744–749. Upadhyay, R., Sehwag, S., & Mishra, H. N. (2017a). Electronic nose guided determination of frying disposal time of sunflower oil using fuzzy logic analysis. Food Chemistry, 221, 379–385. Upadhyay, R., Sehwag, S., & Mishra, H. N. (2017b). Frying disposal time of sunflower oil
Fig. 4. Multiple linear regression plots of actual vs predicted uric acid content in S. oryzae infested rice.
compounds to be detected are obnoxious or toxic in nature. The applicability of fuzzy art map assisted approach with E-nose is beneficial when the data is too exhaustive and genuine deductions about analysis are to be done. The study focused on fuzzy controller based E-nose tandem approach on Sitophilus oryzae infestation classification in rice grains has the ability to identify the grains which are infested, noninfested, need treatment to disinfest etc. The MOS namely: P30/2, P30/ 1, T30/1, P40/2, T70/2 and PA/2 showed maximum resistivity changes due to infestation with significant changes in the protein and uric acid contents. The defuzzified score of 62.17 for the P30/2 and 59.33 for the P30/1 MOS further confirmed the validity studies of the E-nose sensor response with the reference analysis methods. The PCA plots gave 84.757% of classification of rice with variable degree of S. oryzae infestation. The MLR values of predicted versus reference values of protein and uric acid content were found to be fitting with a R2 of 0.972, sa0.997 and with a lower RMSE values of 2.08 and 1.05 respectively. The hybrid E-nose fuzzy logic controller technology can be used in food and grain industry as to detect infestations at early stages so that the post-harvest losses are minimized and better quality of grains is obtained.
5. Compliance with ethical standards This article does not contain any studies with human participants or animals performed by any of the authors.
Funding This work is financially supported by Ministry of Human Resource Development (MHRD), Govt. of India under DAG project, supported by Food Security Research (FSR) scheme at Agricultural & Food Engineering Department, IIT Kharagpur.
Conflict of interest The authors of the manuscript declare that there is no conflict of interest.
Ethical approval Not applicable.
Informed consent Not applicable. 609
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S. Srivastava et al. using hybrid electronic nose-fuzzy logic approach. LWT – Food Science and Technology, 78, 332–339. Xi, Z., & Wang, F. (2012). Research progress of grain quality nondestructive testing methods. Science and Technology of Food Industry, 15, 394–396. Zhang, H., & Wang, J. (2008). Identification of stored-grain age using electronic nose by ANN. Applied Engineering in Agriculture, 24, 227–231. Zhang, H. M., Wang, J., Tian, X. J., Yu, H. C., & Yu, Y. (2007). Optimization of sensor
array and detection of stored duration of wheat by electronic nose. Journal of Food Engineering, 82, 403–408. Zheng, X. Z., Lan, Y. B., Zhu, J. M., Westbrook, J., Hoffmann, W. C., & Lacey, R. E. (2009). Rapid identification of rice samples using an electronic nose. Journal of Bionic Engineering, 6, 290–297. Zhou, B., & Wang, J. (2011). Use of electronic nose technology for identifying rice infestation by Nilaparvata lugens. Sensors and Actuators B: Chemical, 160(1), 15–21.
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Research Article
Fuzzy controller based E-nose classification of Sitophilus oryzae infestation in stored rice grain
T
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Shubhangi Srivastava , Gayatri Mishra, Hari Niwas Mishra Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
A R T I C LE I N FO
A B S T R A C T
Keywords: Electronic nose Fuzzy analysis Infested rice Principal component analysis Sensors
Fuzzy controller artmap based algorithms via E-nose selective metal oxides sensor (MOS) data was applied for classification of S. oryzae infestation in rice grains. The screened defuzzified data of selective sensors was further applied to detect S. oryzae infested rice with PCA and MLR techniques. Reliability of data was cross validated with reference methods of protein and uric acid content. Out of 18 MOS, 6 sensors namely P30/2, P30/1, T30/1, P40/2, T70/2 and PA/2 showed maximum resistivity change. Defuzzified score of 62.17 for P30/2 and 59.33 for P30/1 MOS further confirmed validity studies of E-nose sensor response with reference methods. The PCA plots were able to classify up to 84.75% of rice with variable degree of S. oryzae infestation. The MLR values of predicted versus reference values of protein and uric acid content were found to be fitting with R2 of 0.972, 0.997 and RMSE values of 2.08, 1.05.
1. Introduction Rice is considered as one of the primary staple source of food for more than 2.5 billion population in the world. Nevertheless, when the same rice grain is stored for a longer period the chances of insect infestation, especially by Sitophilus oryzae are elevated (Gandhi, Pillai, & Patel, 2010; Zhou & Wang, 2011). Consequently, it is a prime concern to track down constructive methods to minimize the Sitophilus oryzae infestation in stored rice. Timely detection of insects or pets could be alternative which would help in eliminating infestation. Classical methods of such detection comprised of sieving, manual traps and probes, cracking-flotation (Neethirajan, Karunakaran, Jayas, & White, 2007; Xi & Wang, 2012). These classical methods for detection of insect plea repeated work and trained workforce, at the same time some observations are imprecise as they are subjected to naked eyes. In recent times several techniques such as near-infrared spectroscopy, X ray method, uric acid, and acoustic measurement, etc. are being studied for insect infestation in rice grains but these are not so much cost effective in nature (Neethirajan et al., 2007). Chiefly, E-nose may be defined as the machine used for the detection and identification of compounds based on electrical or gas sensors specific to different type of volatiles (Peris & Escuder-Gilabert, 2009; Tian, Wang, & Cui, 2013; Song et al., 2013). Appropriate pattern recognition algorithms from E-nose are used for classification purposes. These specific patterns are then analyzed using statistical tools such as
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principal component analysis (PCA), multiple linear regressions (MLR) etc. (Pavon et al., 2006; Zhang, Wang, Tian, Yu, & Yu, 2007). The convenience of faster assay and their specificity have made them quite popular in the food industry notably in fruits (Marrazzo, Heinemann, Crassweller, & LeBlanc, 2005), milk (Labreche, Bazzo, Cade, & Chanie, 2006), oil (Hai & Wang, 2006), wheat (Evans et al., 2000; Zhang & Wang, 2008), barley (Balasubramanian, Panigrahi, Kottapalli, & WolfHall, 2007) etc. Moreover, several other studies on cereal grains fungal infection have also been reported (Evans et al., 2000; Needham, Williams, Beales, Voysey, & Magan, 2005). Therefore, E-nose has a great potential to foresee food quality changes and with the help of this technology in-line systems in the grain industry could be established. Another application of it would be in the detection of mycotoxins in grains or any other food products as humans would not smell it from the safety point of view. The analyses with respect to fuzzy sets are influential as they permit researchers for calibration of sectional membership function in the set with values between 1 (full membership) and 0 (non-membership) without dropping out the core set of theoretic principles. This framework of fuzzy membership comprise of various tools which grant the planner to catch sight of relevant concepts which match up the real time situation. Some of the surveyed literature described implementation of fuzzy logic approach in fried potatoes shelf life (Chatterjee, Bhattacharjee, & Bhattacharyya, 2014), mango drinks (Jaya & Das, 2003), drinks formulated from yoghurt (Routray & Mishra, 2012),
Corresponding author. E-mail address: [email protected] (S. Srivastava).
https://doi.org/10.1016/j.foodchem.2019.01.076 Received 5 June 2018; Received in revised form 6 January 2019; Accepted 6 January 2019 Available online 19 January 2019 0308-8146/ © 2019 Elsevier Ltd. All rights reserved.
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was used for the data acquisition; IBM SPSS 20 and MATLAB 2016 version were used for the data analysis.
sunflower oil frying time (Upadhyay, Sehwag, & Mishra, 2017a, 2017b), and peanuts quality (Raigar, Upadhyay, & Mishra, 2017). Currently, electronic nose have been used for the identification of infestation in rice grains with the presence of volatiles composition (Pang, Wang, Lu, & Yu, 2008; Zhang & Wang, 2008; Zhou & Wang, 2011; Huichun, Zuozhou, & Yong, 2012). However, limited research is available on infestation in stored rice grains with E-nose. Thus, eventually it becomes crucial to structure a steady classification model for detection of S. oryzae infestation in rice grains. The objective of the work was to investigate the capability of hybrid fuzzy logic assisted E-nose approach to screen out the major metal oxide sensors which can be used to monitor the S. oryzae infestation in stored rice grains. The filtered metal oxides sensory data obtained from E-nose was then fed to a fuzzy logic artmap controller tool box and defuzzified scores were calculated. The fuzzy screened data thus obtained was subjected to principal cluster analysis (PCA), and multiple linear regression (MLR) to predict the correlation with the conventional methods.
2.3. Modeling of fuzzy controller in MATLAB The modeling of fuzzy controller fundamentally comprise of distilling knowledge of human about how a set of rules could supervise a system. The rule based detection of S. oryzae infestation in stored rice grains was formulated in a symmetric fashion generally with linguistic values of sensor sensitivity as non, fair, medium, good, and excellent with number of days as 0, 45, 90, 135, 180, 225 and S. oryzae number as 0, 5, 10, 15. The membership functions for the storage days, S. oryzae number, and storage days rules were normal, symmetric, and evenly distributed triangular membership function. Singleton fuzzification was used with implication kept at minimal and aggregation at maximal level. Defuzzification was implemented using the centroid method. The membership functions ‘trimf’ was used for storage days, ‘trapmf’ for S. oryzae number, and ‘gaussmf’ for the sensor responses respectively. The analogy drawn with the conventional sensory evaluation equivalent to hedonic scores was used in ranking of fuzzy logic assisted E-nose data (ΔR/R) (Upadhyay et al., 2017b). Weightage or importance to peculiar attributes is the basic criterion on which fuzzy logic is applied (Jaya & Das, 2003; Raigar et al., 2017). Therefore, an assumption was made to accredit an equal statistical weight to fresh and S. oryzae infested rice grains. Each sensor was designated with different response scale factor (non, fairly, medium, good, and extremely sensitive) for which signal response (ΔR/R) ranges: 0.002–0.098 (non-sensitive, A1), 0.098–0.181 (fairly sensitive, A2), 0.181–0.543 (medium sensitive, A3), 0.543–0.791 (good sensitive, A4), and more than 0.791 (excellent sensitive, A5) (Table 1). For each sensor, triangular membership function, a fuzzy triplet set (x–z) was calculated by Eqs. (1)–(3).
2. Material and methods 2.1. Preparation of infested rice samples Freshly milled rice cv. Badshah bhog was obtained from the Rice Mill located at Agricultural and Food Engineering Department, IIT Kharagpur, West Bengal, India. Insect, Sitophilus oryzae was obtained from the Entomology Department of G.B. Pant University, Pantnagar, India. Rearing of S. oryzae was performed in jars with screen lids at RH 65–70% and temperature 25 °C. A total of 80 kg rice grains, 200 g each were stored for periods namely: 45, 90, 135, 180, and 225 days respectively at 27 °C temperature, and 65% RH, by varying the S. oryzae adult insects number as per the experimental plan i.e. 0, 5, 10, and 15 (Srivastava, Mishra, & Mishra, 2018a, 2018b). Samples at zero days with nil live adult insects were taken as control. Infested rice samples were stored in small containers with lids having hole of about 2 cm for ventilation maintenance (Mishra, Srivastava, Panda, & Mishra, 2018a; Mishra, Srivastava, Panda, & Mishra, 2018b). The samples were taken out of storage environment analyzed at regular interval of 45 days and analyzed for the insect infestation.
x = (0 × A1 + 25 × A2 + 50 × A3 + 75 × A 4 + 100 × A5)/100
(1)
y = (0 × A1 + 25 × A2 + 25 × A3 + 25 × A 4 + 25 × A5)/100
(2)
z = (25 × A1 + 25 × A2 + 25 × A3 + 25 × A 4 + 0 × A5)/100
(3)
where A1, A2, A3, A4, and A5 are the response scale factors. Defuzzification of overall sensor response scores (0–100) was obtained by calculation of centroid value with analogous triplets by using Eq. (4). The ranking of sensors was done with these defuzzified values. The data extracted from the fuzzy screened sensors generated odor map by principal component analysis (PCA). Multiple linear regression (MLR) was also generated with the fuzzy data of screened sensors for the comparative analysis with the reference methods.
2.2. Robotic E-nose setup A robotic E-nose system (Alpha Soft Fox 2.0, Alpha M.O.S, France) was used for sampling of different days infested rice with S. oryzae. The E-nose had a fully automated HS100 robotee air auto sampler, FID gas station and 18 metal-oxide sensors. These sensors are classified as Ltype (LY2/ LG, LY2/AA, LY2/G, LY2/GH, LY2/gCT, LY2/gCTI), P-type (P10/1, PA2, P10/2, P30/1, P40/1, P30/2, P40/2), and T-type (T30/1, T40/1, T40/2, T70/ 2, TA/2 (Oliveros et al., 2002; Mishra et al., 2018a, 2018b; Raigar et al., 2017). Each sensor is responsive to different volatiles. Grounded rice samples (2 g) in a 20 mL glass vials, fitted with magnetic polytetrafluoroethylene (PTFE) crimp septum was thermoincubated at 60 °C with an agitation speed of 500 rpm for 240 s to generate the headspace volatiles. The volatiles accumulated were injected into E-nose with the help of robotee auto-sampler, using purified air stream pressure of 3.5 × 104 Pa with a flow rate of 150 mL min−1 and an injection volume of 500 µl/s. The flush time was set to 120 s. The responses of sensors were computed with respect to change in resistance (ΔR/R) in reference to base values for 120 s followed by a recovery period (1080 s) so as to permit the sensors to return to their respective baseline resistance. An absolute value of maximum ΔR/Ro was extracted for each sensor. It is noteworthy to remark here that the training of the instrument was done previously so as the E-nose was well acquainted with the volatile odor fingerprints of rice samples of different storage days (Raigar et al., 2017). The software Alpha Soft v14
A= (3x − y + z )/3
(4)
where A denoted the defuzzified sensor signal response score. 2.4. Conventional method for protein and uric acid determination Infested S. oryzae rice samples were analyzed for the major quality changes in protein content and amount of uric acid generated due to S. oryzae infestation in rice using the standard AOAC methods. The protein content on wet basis was determined by Kjeldahl method which is based on the determination of the amount of reduced nitrogen present in the rice sample (AOAC, 2000). A conversion factor of 5.95 was used to obtain the refined expression of protein percentage in rice samples. The concerned conversion factor was derived by taking into account the amino acid composition of the rice grains (FAO & Nutritional studies No. 24, 1970). The determination of uric acid (wet basis) in rice grains due to S. oryzae infestation was done following the standard AOAC Method No. 970.24 (AOAC, 2000). The method is based on the proteins precipitation and treatment of protein free filtrate with sodium cyanide and uric acid, and resultant optical density of the blue color is measured colorimetrically at 520 nm. 605
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Table 1 Triplet and defuzzified scores of MOS sensors obtained by fuzzy logic analysis of S. oryzae infested rice. Sensors
Non sensitive (−0.002 to 0.098) A1#
Fairly sensitive (0.098–0.181) A2#
Medium sensitive (0.181–0.543) A3#
Good sensitive (0.543–0.791) A4 #
Extremely sensitive (0.791–0.986) A5#
x*
y*
z*
Defuzzified score$ A$
LY2/LG LY2/G LY2/AA LY2/GH LY2/gCTl LY2/gCT T30/1 P10/1 P10/2 P40/1 T70/2 PA/2 P30/1 P40/2 P30/2 T40/2 T40/1 TA/2
88 10 20 86 92 56 30 65 79 78 30 8 25 32 16 67 34 56
12 80 80 14 8 44 6 16 6 10 5 16 11 5 15 7 46 42
0 10 0 0 0 0 10 19 15 12 16 46 6 10 10 19 20 2
0 0 0 0 0 0 10 0 0 0 9 24 10 10 12 7 0 0
0 0 0 0 0 0 44 0 0 0 40 6 48 43 47 0 0 0
3 25 20 3.5 2 11 58 13.5 9 8.5 56 51 61.25 56.75 64.75 16.5 21.5 11.5
3 22.5 20 3.5 2 11 17.5 8.75 5.25 5.5 17.5 23 18.75 17 21 8.25 16.5 11
25 25 25 25 25 25 14 25 25 25 15 23.5 13 14.25 13.25 25 25 25
10.33 25.83 21.67 10.67 9.67 15.67 56.83 18.92 15.58 15.00 55.17 51.17 59.33 55.83 62.17 22.08 24.33 16.17
All the values represented are mean of 10 determinations having coefficient of variation < 5%. * x, y, z are triplet scores of sensors (refer to Eqs. (1)–(3)). # A1, A2, A3, A4, A5 are the sensor signal response scale (ΔR/R). $ Refer to Eq. (4).
acids such as uric (P30/2, P40/2) (Mason et al., 2014). The ammonia, uric acid and other polar by products were recorded by the metal oxide sensors due to the excreta produced by the insect. The presence of other carbon compounds, aldehydes, ketones and acids was due to the aromatic nature of rice.
2.5. Multivariate statistical techniques The data interpretation of more than 20 samples with several sensors array data is much more complex, if not impossible. Therefore, to study the data sets as a whole, it is necessary to reduce the number of variable (data reduction). The ability to process and display in two or three dimensions the responses of 18 sensors to a large set of samples could be done easily with multivariate statistical algorithms. They allow determining which differences between samples are significant and to what extent. The fuzzy logic calculations, principal cluster analysis (PCA), and multiple linear regression (MLR) analyses were performed using Origin Pro v9.0 (Origin Lab Corporation, Northampton, USA). PCA and MLR were executed on auto-scaled data matrix of screened MOS sensors. The authenticity of prediction models was signified by fitting coefficient of regression (R2), and root mean square error (RMSE) of prediction.
3.2. Fuzzy logic art map analysis The fuzzy logic usefulness could be attributed in the cases where sensory panel cannot test the samples due to toxicity safety concerns. The membership function plots of input functions (storage days, S. oryzae number, sensor response, and output function (rice classification) is shown in Fig. 1. These input membership function were the basic layout on which the output membership was based. When the inputs function with respect to storage days (225), S. oryzae number (15 nos.), sensor response (0.986) were higher, the output function recommended the rice to be totally infested and should be discarded. At lower storage (0–45 days), S. oryzae number (0–5 Nos.), sensor response (−0.02 to 0.098), the output function recommended good quality of rice. Supplementary Fig. S2 shows three D surface plots obtained by fuzzy logic sensor response and storage days with respect to rice classification based on its infestation level. With the surface plots it can be easily correlated that with increase in storage days, number of S. oryzae, and sensor response the degree of infestation increases and chances of treatment for the rice grains decreases and vice versa. Thus, a set of rules for fuzzy function easily classified the rice samples as non-infested, low-infested, medium infested, and highly infested. Hence, based on such recommendations the utilization of the stored rice can be suitably planned; it can be discarded if the degree of infestation is higher than the level when it is difficult to disinfest. Table 1 shows triplet and defuzzified scores of MOS obtained by fuzzy logic analysis of S. oryzae infested rice grains. The sensors displayed diversified responses to volatiles produced at different storage days of S. oryzae infestation. Extensive divergent responses often lead to misleading data interpretation and hence necessitate the importance of suitable screening and ranking approach to identify the best responsive ones. The sensors were treated equivalent to sensory descriptors, and the responses were considered as sensory panel scores for dispensing to fuzzy logic calculations (Raigar et al., 2017). Each sensor was designated a peculiar set of signal response scale factors (A1–A5) which
3. Results and discussion 3.1. Different metal oxide sensors response by E-nose The volatiles generation by E-nose was recorded for different samples of rice of different storage days. The time versus intensity graph of 18 metal oxide sensors (MOS) response to rice samples is represented in supplementary Fig. 1. The volatiles generation in sensors P30/2, P30/1, T30/1, P40/2, T70/2, and PA/2 was more pronounced than other 12 metal oxide sensors. The sensors LY2/LG, LY2/AA, LY2/G, LY2/GH gave negative responses to the volatiles resistivity graphs indicating that the sensors had not significant role in volatile characterization of infested rice samples. Increase in duration of infestation with S. oryzae majorly affected P30/2, P30/1 and T 30/1 sensors more than P40/2, T70/2 and PA/2. The change in the resistance (ΔR/R) was more prominent in samples with more number of S. oryzae insects and more days of storage. Zheng et al. (2009) studied four long grain varieties using polymer sensors in E-nose but good recognition rate was not achieved mainly due to small sample size and imprecise resistivity of the polymer sensors. Hence, the large sample size and MOS would give far better results than polymer sensors. The major volatiles produced during S. oryzae infested stored rice were polar compounds, aldehydes, ketones (T30/1, P30/2); ammonia (P30/1, PA/2); carbon compounds (T70/2); 606
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identify the S. oryzae infested fingerprints of rice grains (p < 0.05) (Table 1). Higher the number of screened sensors in the zone of extremely sensitive (0.791–0.986), higher was the triplet scores of the sensors and so was the defuzzfied score and vice versa. In extremely sensitive range, 47 sensors data were screened for the P30/2, 48 for the P30/1, 44 for the T30/1, 43 for P40/2, and 40 for T70/2 respectively; while in good sensitive range (0.543–0.791) 24 sensors data was screened for the PA/2. Although there was one unit more in the P30/1 screened sensors data for extremely sensitive range, but still the overall screened sensors in the good, medium and fair sensitive range gave a higher defuzzified score to P30/2 (62.17) than the P30/1 (59.33) MOS. The other MOS which gave appreciable results for the response scale factors had a defuzzified score of 56.83 (T30/1), 55.83 (P40/2), 55.17 (T70/2) and 51.17 (PA/2). Major lower sensitive sensors were LY2/G, T40/1, T40/2, LY2/AA, P10/1, LY2/gCT, P10/2, P40/1, with a defuzzified score of 25.83, 24.33, 22.08, 21.67, 18.92, 15.67, 15.58, and 15.00, respectively. Limited scientific researches related to the current study could not allow many comparisons to be made with literature report. It is inferred that the trends recognised in present research could propose advantages of allowing analogies to be made with combined fuzzy logic–E-nose research in future. 3.3. Comparative analysis of the defuzzified E-nose data with those of the standard analytical methods The fuzzy screened data obtained from the E-nose was subjected to PCA. The 3-D PCA plot of S. oryzae infested rice with different storage (0–225) days is shown in Fig. 2. The three dimensional plot confirmed the validity of the results by grouping the rice grains infested with S. oryzae in different groups based on number of days and insect infestation. The PC scores for the matrix were 84.757 (PC1) and 4.131% (PC3). The PCA analysis clearly showed that the fresh rice samples of 0 days (red triangle) made a different group than 45 days (blue square), 90 days (dark pink circle), 135 days (light pink inverted triangle), 180 days (hollow black square), and 225 days (green square) infested rice samples respectively and the same was validated by the variance of 84.757%. The variance in the PC 2 score was quite less (0.251%) hence the 3D plot was made between the PC1 and the PC 3 scores. Thus, the samples stored for 45 days were far separated from those of 225 days due to difference in the degree of infestation. The validity of E-nose results was periodically examined with standard analytical method (AOAC, 2000) for protein and uric acid content in rice grains infested with S. oryzae and was further cross validated with the defuzzified scores obtained by the fuzzy logic (p < 0.05). The data of all the S. oryzae infested rice grains samples along with the screened sensors would be too exhaustive to depict in terms of their characteristics with respect to infested/non infested or requiring adequate treatment. Table 2 shows metal oxide sensors response along with uric acid and protein content of selected S. oryzae infested/non infested rice samples. The MOS namely P30/2, P30/1, T30/1, P40/2, T70/2 and PA/2 showed maximum resistivity changes due to infestation with significant changes in the protein and uric acid content. The multiple linear regression plots actual versus predicted protein and uric content due to S. oryzae infested rice is shown in Figs. 3 and 4 respectively. The predicted protein values with standard analysis methods (4.89–0.56%) closely correlated (R2, 0.997) with the MLR predicted values (4.77–0.61%) with a RMSE value of 1.05 obtained by screened MOS obtained by fuzzy art map (Fig. 3). Moreover, the predicted values followed a similar trend with those of the standard methods of protein indicating that with increase in storage days from 0 to 225, the prevalence of S. oryzae infestation was more; thus the degradation in protein content was observed due to such infestation. Another important attribute that was considered for S. oryzae infestation was the percentage of uric acid found in the rice grains. The predicted values of uric acid content (1.80–15.72%) with the fuzzy screened MOS data were found to be in accordance with those of the reference values (1.86–16.90%) of the uric
Fig. 1. Membership function plots of input functions (a) storage days, (b) S. oryzae number, (c) sensor response, and output function (d) rice classification.
varied as a function of their responses over the days of storage (0–225). The variations in intensity of sensor could be related to the S. oryzae infestation developed in rice grains during 225 days storage period. Each sensor was computed with a peculiar set of triplet values (x–z), and defuzzified scores which were treated as a measure of their eventual performance (Eqs. (1)–(4)) (Table 1). The sensors with higher loadings of response scale factors (A3, A4, and A5) were rated with medium to excellent sensitivity than those with higher (A1 and A2) which were rated with no to fair sensitivity. Consequently, six sensors (P30/1, P30/2, T30/1, P40/2, T70/2, and PA/2) were screened as the finest among others to efficiently detect and 607
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Fig. 2. 3-D PCA Plots of S. oryzae infested rice with different storage (0–225) days.
acid found in the S. oryzae infested grains (R2, 0.972) with a lower RMSE value of 2.08 (Fig. 4). The MLR data extracted from fuzzy screened sensors with respect to uric acid content further indicated that with increase in S. oryzae infestation along with storage days, the uric acid found in the infested grains increased significantly (p < 0.05). The residual values of 0.06–1.17 (uric acid) and 0.05–0.11 (protein) further justified the correlation between the screened sensors with fuzzy logic and standard analytical protocols. There was a decrease in the protein content while an increase in the uric acid content due to insect biological activities in the infested rice grains. The resistivity of the MOS response for screened sensors (P30/2, P30/1, T30/1, P40/2, T70/2) when increased (0.004–0.825), there were significant changes in protein and uric acid content. Thus, a vivid analysis could be drawn with the defuzzified scores with respect to sensors in terms of whether rice is infested with S. oryzae or is not infested one; also if it is infested whether is it at lower stage of infestation or higher stage, and whether any sort of treatment for rice grains can be adopted to disinfest and use or it has to be discarded.
Fig. 3. Multiple linear regression plots of actual vs predicted protein content in S. oryzae infested rice.
4. Conclusion The utility of the electronic nose could be a befitting one when the Table 2 Metal oxide sensors response along with uric acid and protein contents of selective S. oryzae infested/non infested rice grains. S. No.
MOS sensor responses (ΔR/R)
Uric acid (%)
Protein (%)
P30/2
P30/1
T30/1
P40/2
T70/2
PA/2
Reference analysis
Predicted value
Residuals
Reference analysis
Predicted value
Residuals
Remarks
1 2 3 4 5 6
0.058 0.049 0.143 0.11 0.232 0.36
0.066 0.054 0.128 0.108 0.228 0.257
0.008 0.008 0.19 0.156 0.369 0.48
0.007 0.008 0.113 0.132 0.157 0.283
0.004 0.005 0.009 0.118 0.142 0.189
0.045 0.054 0.037 0.076 0.072 0.092
1.867 2.234 5.708 5.018 9.067 10.587
1.803 2.560 5.618 4.514 8.750 11.264
0.064 0.326 0.090 0.504 0.317 0.677
4.891 4.610 3.890 3.670 3.330 3.090
4.773 4.589 3.887 3.668 3.280 3.111
0.118 0.021 0.003 0.002 0.050 0.021
7
0.53
0.39
0.673
0.378
0.383
0.21
12.690
13.534
0.844
2.980
3.001
0.021
8
0.454
0.348
0.588
0.467
0.431
0.165
15.971
15.635
0.336
1.990
2.003
0.013
9
0.739
0.684
0.781
0.592
0.521
0.49
16.860
15.696
1.164
0.670
0.711
0.041
10
0.825
0.758
0.741
0.691
0.665
0.482
16.904
15.729
1.175
0.560
0.612
0.052
Non-infested Non-infested Low Low Low Treatment/ discard Treatment/ discard Treatment/ discard Treatment/ discard Treatment/ discard
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Fig. 4. Multiple linear regression plots of actual vs predicted uric acid content in S. oryzae infested rice.
compounds to be detected are obnoxious or toxic in nature. The applicability of fuzzy art map assisted approach with E-nose is beneficial when the data is too exhaustive and genuine deductions about analysis are to be done. The study focused on fuzzy controller based E-nose tandem approach on Sitophilus oryzae infestation classification in rice grains has the ability to identify the grains which are infested, noninfested, need treatment to disinfest etc. The MOS namely: P30/2, P30/ 1, T30/1, P40/2, T70/2 and PA/2 showed maximum resistivity changes due to infestation with significant changes in the protein and uric acid contents. The defuzzified score of 62.17 for the P30/2 and 59.33 for the P30/1 MOS further confirmed the validity studies of the E-nose sensor response with the reference analysis methods. The PCA plots gave 84.757% of classification of rice with variable degree of S. oryzae infestation. The MLR values of predicted versus reference values of protein and uric acid content were found to be fitting with a R2 of 0.972, sa0.997 and with a lower RMSE values of 2.08 and 1.05 respectively. The hybrid E-nose fuzzy logic controller technology can be used in food and grain industry as to detect infestations at early stages so that the post-harvest losses are minimized and better quality of grains is obtained.
5. Compliance with ethical standards This article does not contain any studies with human participants or animals performed by any of the authors.
Funding This work is financially supported by Ministry of Human Resource Development (MHRD), Govt. of India under DAG project, supported by Food Security Research (FSR) scheme at Agricultural & Food Engineering Department, IIT Kharagpur.
Conflict of interest The authors of the manuscript declare that there is no conflict of interest.
Ethical approval Not applicable.
Informed consent Not applicable. 609
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