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Identification of rice storage time based on colorimetric sensor array combined hyperspectral imaging technology
Journal of Stored Products Research 85 (2020) 101523
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Journal of Stored Products Research journal homepage: www.elsevier.com/locate/jspr
Identification of rice storage time based on colorimetric sensor array combined hyperspectral imaging technology Hao Lin*, Zhuo Wang, Waqas Ahmad, Zhongxiu Man, Yaxian Duan School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, PR China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 19 June 2019 Accepted 30 September 2019 Available online 21 November 2019
The study reports a novel colorimetric sensor array (CSA) based hyperspectral imaging (HSI) system and chemometrics algorithms for the identification of rice storage time. CSA fabricated by borondipyrromethene (BODIPY) dyes was used to capture the volatile organic compounds (VOCs) of rice samples. CSA hypercube before and after the reaction were obtained with HSI. Genetic synergy interval partial least square algorithm (GA-Si-PLS) was used to filter spectral information. Fifty-four spectral data variables and five dominant wavelength images was selected from CSA hypercube. Then three grayscale difference values were extracted from each dominant wavelength image, thus totaling to 15 variables as imaging data variables. Linear discriminant analysis (LDA) and k-Nearest Neighbor (KNN) model were established to comparing the performance of spectral variables, imaging variables and combined datasets. The result showed the optimal model was linear discriminant analysis (LDA) model built by using spectral variables and the correct rate of calibration set for rice storage time discrimination was 92.73% and the obtained rate of prediction set was 90.91%. It is indicated the applicability of the proposed CSA combined with HSI technology towards rice storage time identification. © 2019 Elsevier Ltd. All rights reserved.
Keywords: Rice aging Colorimetric sensor array BODIPYs Hyperspectral imaging GA-Si-PLS
1. Introduction Rice is among the most widely consumed grains in the world and greatly valued by the people due to its high nutritional value (Khush, 1997; Dong-Hwa and Seung-Taik, 2016; Zhai et al., 2001). Rice is one of the main sources of dietary fiber, fat, protein, trace elements and other nutrients and it has become the staple food of more than half of the world’s population. Many countries need to stock up the rice in order to ensure consumer demands throughout the whole year in case of any emergency (Choi et al., 2015). During the storage process, the nutrients in rice undergo physical and chemical changes to some extent over time due to changes in environmental factors and the catalysis of its own enzymes. For example, the hydrolysis or oxidation of unstable lipids result in the conversion of fresh rice to aging rice with the development of unpleasant flavors (Zheng et al., 2017). Besides, the change of the rice VOCs and the storage time have a certain positive correlation. Eating severely aging rice causes damage to human health, therefore research on the freshness of rice becomes particularly
* Corresponding author. Fax: þ86 0511 88780201. E-mail address: [email protected] (H. Lin). https://doi.org/10.1016/j.jspr.2019.101523 0022-474X/© 2019 Elsevier Ltd. All rights reserved.
important. As the major criterion of rice aging, flavor is related to the changes in culinary quality and nutrient content (Kovach et al., 2009; Griglione et al., 2015). Traditional analysis methods including sensory evaluation and gas chromatography mass spectrometry (GC-MS) are mostly used in analyzing the food flavor (Wijit et al., 2017). Sensory analysis provides people with intuitive information. However, subjectivity of human sense and some harmful gases produced by aging rice make sensory analysis unsuitable for testing. Objectivity and accuracy of GC-MS is superior to that of sensory evaluation (Huang et al., 2016; Khulal et al., 2016a). However, reagent demand, elongated time, and cost are some shortcomings associated (Cosio et al., 2007). Hence, the rapid and non-destructive characterization of volatile gas changes is the key to solving the problem. CSA technology was proposed as a new approach for odor analysis (Rakow and Suslick, 2000). To recognize complex odors, this novel system was consisted of CSA composed of printed dyes with pattern recognition system and partial specificity (Suslick et al., 2004). Colorimetric dyes were the core part in CSA to determine the sensitivity and specificity. Generally, they are made by porphyrins dyes and pH indicators (Zou et al., 2016). The array was used to discriminate among VOCs by probing a wide range of
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intermolecular interactions, including Lewis acid/base, metal ion coordination, hydrogen bonding, and dipolar interactions (Janzen et al., 2006). CSA have been applied to organic small-molecular compounds such as nitroaromatic molecules (Lu et al., 2016), aliphatic amines (Bang et al., 2008), TVB-N (Huang et al., 2014). Thus, CSA technology has the potential to analyze VOCs in rice at different storage times. A variety of VOCs including certain specific organics such as heterocyclic compounds and aldehydes volatilize and change dynamically, due to prolonged storage time. Traditional colorimetric sensors made by porphyrins dyes and pH indicators hardly discriminates early aging stage of rice (Guan et al., 2016). In CSA, the information was extracted by a tri-CCD camera and data analysis were performed on the obtained RGB values (Guan et al., 2014). At the beginning of storage, the change in rice odor is not sufficient to initiate a significant color reaction of conventional colorimetric materials. Besides, poor information is also a factor limiting the correct identification of rice aging time. Presumably, the awkward situation would have some change due to the new colorimetric materials that are sensitive to specific samples and the expansion of information volume. HSI technique integrated spectroscopy and computer imaging together form an emerging technology platform to provide spectral-spatial information (Gowen et al., 2007). Compared with conventional spectroscopic techniques, HSI technology not only acquires spectral data from a single point, but also each pixel of the image. In imaging data, conventional color cameras were poor identifiers for surface features that were sensitive to bands other than RGB. However, there were hundreds of HSIs in each contiguous waveband (Gowen et al., 2007). Both imaging data and spectral data are suitable for characterizing CSA color change information. The increased dimensionality of the data acquired would make it possible to obtain more useful information to discriminate rice storage time. This study provides a new idea for data processing of colorimetric sensors. A novel method was developed for rice storage time discrimination using CSA based on BODIPY dyes and HSI technology. Spectral data, imaging data and combination of them was used to build prediction model respectively, and these models was employed to select the appropriate data type for the establishment of a prediction model of rice storage time. GA-Si-PLS algorithm was applied to select optimal spectral data variables and domain wavelength from HSIs. Imaging data variables were extracted from Gray-level images at domain wavelengths. Multivariate analysis (KNN and LDA) based on spectral and imaging data variables were employed to fabricate the rice storage time prediction models.
According to previous studies, three types materials that are positively correlated with the characteristic gas content that characterizes the degree of rice aging are screened out. BODIPY shows more specificity and excellent color rendering performance than general porphyrins and pH indicators. Three kinds of wellperforming BODIPY dyes were used in this experiment namely: (1) 8-(4-bromophenyl) 4, 4-difluoro- BODIPY (BrBDP) (2) 8-(4-nitrophenyl)-6-bromo-4, 4-difluoro-BODIPY (NO2BrBDP) (3) 8-(4-nitrophenyl)-6, 6-dibromo-4, 4-difluoro-BODIPY (NO2Br2BDP) Three dyes were synthesized by classic Lindsey methodology (Ulrich et al., 2008; Loudet and Burgess, 2008). BODIPY was dissolved in methylene chloride at a concentration of 2.0 mg ml1 and poured manually on a reversed-phase silica gel plate (Merck Millipore, Germany) with a capillary to construct 3 1 sensor array. A total of 165 CSA samples were used to characterize the odor substances of rice. The fabricated CSA samples were then tested to prevent any possible influence of the sensor during storage. 2.3. Systems of hyperspectral imaging combined with colorimetric sensor array As shown in Fig. 1, the images of samples were acquired by the ViseNIR HSI system in a wavelength range of 430e960 nm with a spectral interval of 0.858 nm. HSI system was preheated for 30 min prior to image acquisition. During image acquisition, a motion controller drives a motorized positioning table at the speed of 0.9 mm s1 to avoid image distortion. Each CSA was transported by the motorized positioning table to the camera’s field of view (FOV), where a raw image was captured and stored in the computer (Khulal et al., 2016b). After completing scanning for an entire CSA, one CSA hypercube can be obtained. Before the test, valves were opened, and the vacuum was turned on for 10.0 min to remove residual gas or other unrelated gases. CSA hypercube before exposure was first acquired on a conveyor platform and then immediately placed in a reaction chamber. Meanwhile, rice samples were put into a gas-collecting chamber with valves closed, and water temperature was raised to 40 C to promote the volatilization of the gas in the rice to equilibrate. Afterward, valves were opened and the vacuum was turned on again to extract the VOCs of rice sample into the reaction chamber for 20.0 min. Finally, CSA after exposure to VOCs was taken out and
2. Material and methods 2.1. Material Japonica rice samples (Fulingmen, Cofco, China) with vacuum package were purchased from the local tourism supermarket, and kept in incubator at 40 C with a relative humidity (RH) of 85%. The samples were divided into six groups based on their respective storage time. A total of 165 rice samples were further classified as follows: 15 samples for the fresh rice; 30 samples for 1-month age; 30 samples for 2-months age; 30 samples for 4-months age; 30 samples for 6-months age and 30 samples for 10-months age. The respective calibration and prediction sets were arranged at ratio of 2:1 to build and examine the performance of the algorithmic models. 2.2. Dyes of colorimetric sensor array BODIPY dyes were selected to develop a CSA platform.
Fig. 1. Diagram of CSA hyperspectral imaging acquisition system.
H. Lin et al. / Journal of Stored Products Research 85 (2020) 101523
placed on the conveyor platform again to get the CSA hypercube. To reduce effects of external environmental factors, all acquired HSIs were calibrated by the following formula (Yan et al., 2017).
R ¼ ðI DÞ=ðW DÞ
(1)
where I was the raw HSI, D was the dark current image acquired by cover the camera lens from light with a black cap. W means standard white reference image captured using a white Teflon board (Edmund Optics Inc., Barrington, NJ, USA). The image acquisition and collection process were controlled by Spectral-Cube software.
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algorithm were applied in this study. Variables selection of spectra data was divided into two steps: firstly, partial least square synergy interval (Si-PLS) algorithm was applied to select optimal combinations of subintervals for dye y and fused the filtered intervals together to form a dataset, detailed description of its basic principles can be found in (Chen et al., 2008). Secondly, the optimal spectral data variables were selected by genetic algorithm (GA) from combined dataset. Only the optimal wavelength chosen were used to build rice storage time prediction model instead of using the entire spectral range of three dyes. Select 5 wavelengths as the domain wavelength of the image data according to the frequency selected by the GA algorithm.
2.4. Extraction of characteristic variable HSI data analysis was started by examining spectral information and imaging information to discriminate the storage time of rice. Based on this, the extracted characteristic variable from spectral and image information were fused for further identification. As shown in Fig. 2, the extraction of characteristic variable can be summarized as spectra and imaging data analysis from the hyperspectral system and data fusion from the spectral and imaging information. 2.4.1. Spectra data analysis The spectrum data of dye y (y represent the yth dye in the array) were extracted from the CSA hypercube after exposure. To eliminate or minimize factitious non-uniformity, a circle region of about 1500 pixels within the area where BODIPY dyes printed were selected as the region of interests (ROI). The center of ROI was coinciding with the center of BODIPY dyes to acquire a mean spectrum in the range of 430e960 nm. Then, standard normal variate (SNV) was used to preprocess the average spectrum for correcting light scatter from these spectra (Rinnan et al., 2009). For each sample, three average spectral curves were obtained resulting in 495 spectral curves for 165 samples. Sufficient information data and variables in CSA hypercube make online applications inefficient. Hence, some variable optimization algorithms are needed to overcome this problem. To choose the lowest number of wavelengths which maximize correlation between spectral data and rice storage time, GA-Si-PLS
2.4.2. Imaging data analysis In this section, imaging data characteristic variable of CSA hypercubes had been explored to discriminate the storage time of rice. Variations in the gray levels of hyperspectral scattering profiles (at the range of 430e960 nm) were applied to quantify the signal change of the dye spot. Five gray-level images were selected according to the frequency of each wavelength selected by GA and saved in BMP format (Khulal et al., 2016b). Three difference values could be gotten by digitally subtracting the image before exposure to VOCs of rice from the image after exposure, using a 1500-pixel gray average from the center of each dye spot (the interference point caused by the excessive concentration of dye were deleted by threshold segmentation as introduced in Section 3.3) as follows.
Gyq1 d ¼ Gyq1 a Gyq1 b
(2)
Gyql d ¼ Gyql a Gyql b
(3)
Gyq5 d ¼ Gyq5 a Gyq5 b
(4)
where a is after exposure, b is before exposure, d is difference, Ɵ1, Ɵl, Ɵ5 represent the number of characteristic wavelengths filtered out. Gyq1 d , Gyql d and Gyq5 d represent the gray-level difference of dye y with corresponding characteristic wavelength. five gray-level difference, Gyq1 d Gyql d , … Gyq5 d , were used to characteristic dye y signal change based on their characteristic
Fig. 2. Flow chart of the experimental procedure to determine rice storage time by HIS system.
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wavelength. Therefore, 15-dimensional vector (3 dyes * 5 differences) as imaging data variables to build rice storage time prediction model. 2.4.3. Data fusion from spectral and imaging information In addition, combining different data may result in better classification results. Considering the comprehensiveness of data analysis, spectral data and image data are combined into one data set for further classification analysis. 2.4.4. Model establishment Once the characteristic data (spectral data variables, imaging data variables and fusion dataset) was extracted from CSA hypercubes, rice storage time detection becomes a classification problem. The final step of this study was to employ LDA and KNN to relate characteristic data of CSA hypercubes to rice storage time. KNN is a classification method based on the basic assumption of pattern recognition, that is, similar samples were close to each other in the model space. LDA is a dimensionality reduction technique for supervised learning. The idea of LDA can be summarized in one sentence, that is, the smallest variance within the category after
projection and the largest variance between classes. In this work, LDA and KNN were employed to discriminate rice storage time (Yang and Yang, 2003). To quantify the predictive ability of the models, correct identification rate of samples was determined. Hence, the most suitable data type among the three characteristic data was decided for CSA data expression. 2.5. Software Spectra extraction of HSI was executed using ENVI 4.5 (Research Systems Inc., Boulder, CO, USA). Image processing was finished using programs developed in Halcon 13(the MVTec Inc, Germany). All the above data analysis procedures were performed using software MATLAB 2014(The MathWorks Inc, MA, USA). 3. Results and discussions 3.1. Feasibility analysis The RGB image of CSA obtained before and after exposure are shown in Fig. 3a. From the visual point of view, only NO2BrBDP
Fig. 3. Spectral and imaging data of CSA. (a) RGB image and gray-level image. (b)spectral data. (c) spectral data and gray-level image at 546.55 nm.
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Fig. 4. Result of spectral data variables. (a), (b) and (c) represent Si-PLS result. (d) combine dataset. (e) GA selection result. (f) selected wavelength of each dye.
could see the obvious color change. The same situation also appears in the gray-level image of RGB channel. However, in contrast, some gray-level images at different wavelengths extracted from CSA hypercube shows better differentiation. Except for 452.51 nm, the
gray-level variation of each dye can show significant changes at the corresponding wavelengths. Therefore, it is feasible to use graylevel images at different wavelengths instead of RGB channel to characterize the color change of CSA. As Fig. 3b shows, the average spectra of each dye for a storage period of 0, 4 and 10 months was extracted from CSA hypercubes after exposure. It can be found that there is a difference in the obtained spectrum for each dye after exposure to rice VOCs with different storage time. It indicates that the color change of the CSA will be differentiated as the change of sample storage time. In addition, as Fig. 3c shows, according to the spectral data of each dye spot on a single CSA, it is found that the spectral data has a large difference at 546.55 nm, so the gray-level image at this wavelength is extracted and the 3 1 matrix is used to quantify the gray-level value of each dye. Combined with the spectra data, it is easy to find that the gray-level value of the BODIPY material is positively correlated with the spectra intensity. Therefore, each dye has different gray-level value at the same wavelength after CSA exposing with different storage period rice. In summary, it is feasible to predict the rice storage period by extracting the spectral and imaging data from CSA hypercubes.
3.2. Extraction of spectral data variables
Fig. 5. (a)Gray-level images at domain wavelength. (b) ROI selection.
HSI contain a lot of extraneous information due to its huge data volume. In fact, one of the major problems in multivariate data analysis is to select appropriate frequency variables to simplify the process of calibrating models to achieve better performance. In recent years, both theoretical and experimental evidence suggested that variables selection significantly improves the performance of these calibration techniques (Mehl, 2002). Therefore, many algorithms models are increasingly used in hyperspectral data analysis to remove redundant variables. Herein, the best spectral intervals
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Table 1 The prediction accuracy of KNN and LDA with spectral data. Variable selection algorithm
Number of variables
Discrimination algorithm
Discrimination rates of calibration set
Discrimination rates of prediction set
Spectral data variables
54
Imaging data variables
15
Fusion dataset
69
KNN LDA KNN LDA KNN LDA
81.82% 92.73% 88.18% 90.91% 90.91% 89.09%
76.36% 90.91% 87.27% 80.00% 87.27% 87.27%
KNN, k-Nearest Neighbor; LDA, linear discriminant analysis.
were selected was first extracted by Si-PLS algorithm from three dyes spectral data that had been preprocessed by SNV. Dividing the full-spectrum system into 15 sub-intervals, three sub-intervals were combined from each dye spectra to establish a PLS with storage time. The result of data processing is shown in from Fig. 4a to Fig. 4c. The optimal spectral intervals of BrBDP are the 1st, 2nd, and 5th sub-ranges. The optimal spectral intervals of NO2BrBDP are the 3rd, 4th, and 9th sub-ranges and the optimal spectral intervals of NO2Br2BDP are the 1st, 4th, and 13th sub-ranges. Therefore, a total of 373 variables (9 sub-intervals) were filtered out. A new dataset is formed by combining each sub-range as Fig. 4d showed. However, the number of variables is still very large and the reproducibility of the screening results is low. The combined dataset is further filtered by GA algorithm. 54 variables are finally selected as spectral data variables. Fig. 4e shows a histogram of frequency selected for the variables in combined dataset, and here the variables with corresponding frequency 7 could be selected. The obtained 54 variables then were used to filter domain wavelengths. Fig. 4f shows the final selected wavelength in each dye at the range of 430e780 nm. Due to the high correlation between the wavelengths, the chosen wavelength interval must be greater than 10.0 nm. The domain wavelengths were selected by the GA algorithm for the analysis of the remaining 54 variables result in five wavelengths, i.e. 452.51 nm, 485.09 nm, 538.09 nm, 558.42 nm and 723.47 nm. 3.3. Extraction of imaging data variables The gray-level images at 5 domain wavelengths were shown in Fig. 5a, Intuitively, the characteristic wavelength at 495.98 nm clearly show the external outline of each dye, and the characteristic image at 607.87 nm clearly shows the interference point generated
due to the high density of the central region of the dye. As Fig. 5b shows, by setting artificially the threshold value for the gray-level value, the overall region of each dye spot and the region of the interference point could be obtained. Finally, a ’Subtraction’ operation was applied to the above two regions using morphological methods. Therefore, the gray-level value extraction region as ROI at each wavelength is determined by these two wavelengths. By calculating the difference of the average gray-level value on the ROI before and after exposure, 15dimensional vector (3 dyes * 5 differences) of each sample was obtained as imaging data variables. 3.4. Modeling for rice storage time classification Three types of characteristic datasets including spectral data variables, imaging data variables and fusion dataset, respectively establish a model for predicting rice storage time. Among them, the fusion data set is formed by combining spectral and image feature data. KNN and LDA are used in the qualitative classification of this study. Two third of the samples from each class have been used for training, and the remaining one third of samples for testing of the trained model. Therefore, the two kinds of algorithms are used respectively for the classification of rice storage time and the method was carried out by means of taking the filtered variables as inputs. The recognition rate of the model established by these three datasets is shown in Table .1. From the results, the dataset of spectral data variables has the highest discrimination rate after LDA qualitative analysis. As shown in Fig. 6, the LDA model has good discrimination in rice storage when PCs ¼ 10. The discrimination rates of calibration and prediction set are 92.73 and 90.91% with the ideal number of PCs. There are five samples of fresh rice and storage time of one month, were incorrectly identified in prediction set. 4. Conclusion The study explores several prediction models based on the spectral data and image data for identifying the rice storage time. Herein, a novel CSA, synthesized by BODIPY material was applied to classify rice with different storage time. This is an innovation in information processing of olfactory visualization technology obtained with great progress and sensitivity. Compared with our previous work, the sensitivity of the current strategy has been improved significantly (Guan et al., 2016). The 54 wavelength variables selected by GA- Si-PLS screening was applied to identify rice storage time with good characterization skills by LDA analysis. This work shows that the rice storage time prediction model using spectral data has the best performance by analyzing spectral and image information of CSA by HSI technique to identify rice storage time. Acknowledgements
Fig. 6. Discrimination rates of LDA model according to different PCs.
We are also grateful to many of our colleagues for stimulating
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discussion in this field. This work was supported by the National Key Technology R&D Program of China (Grant No. 2016YFD0401205-3), China Postdoctoral Natural Science Foundation (2016M601746), and Priority Academic Program Development of Jiangsu Higher Institutions (PAPD). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jspr.2019.101523. References Bang, J.H., Lim, S.H., Park, E., Suslick, K.S., 2008. Chemically responsive nanoporous pigments: colorimetric sensor arrays and the identification of aliphatic amines. Langmuir the Acs Journal of Surfaces & Colloids 24, 13168e13172. Chen, Q., Zhao, J., Liu, M., Cai, J., Liu, J., 2008. Determination of total polyphenols content in green tea using FT-NIR spectroscopy and different PLS algorithms. J. Pharm. Biomed. Anal. 46, 568e573. Choi, S., Jun, H., Bang, J., Chung, S.H., Kim, Y., Kim, B.S., Kim, H., Beuchat, L.R., Ryu, J.H., 2015. Behaviour of Aspergillus flavus and Fusarium graminearum on rice as affected by degree of milling, temperature, and relative humidity during storage. Food Microbiol. 46, 307e313. Cosio, M.S., Ballabio, D., Benedetti, S., Gigliotti, C., 2007. Evaluation of different storage conditions of extra virgin olive oils with an innovative recognition tool built by means of electronic nose and electronic tongue. Food Chem. 101, 485e491. Dong-Hwa, C., Seung-Taik, L., 2016. Germinated brown rice and its bio-functional compounds. Food Chem. 196, 259e271. Gowen, A.A., O’Donnell, C.P., Cullen, P.J., Downey, G., Frias, J.M., 2007. Hyperspectral imaging - an emerging process analytical tool for food quality and safety control. Trends Food Sci. Technol. 18, 590e598. Griglione, A., Liberto, E., Cordero, C., Bressanello, D., Cagliero, C., Rubiolo, P., Bicchi, C., Sgorbini, B., 2015. High-quality Italian rice cultivars: chemical indices of ageing and aroma quality. Food Chem. 172, 305e313. Guan, B., Zhao, J., Cai, M., Lin, H., Yao, L., Sun, L., 2014. Analysis of volatile organic compounds from Chinese vinegar substrate during solid-state fermentation using a colorimetric sensor array. Anal. Methods 6, 9383e9391. Guan, B., Zhao, J., Jin, H., Lin, H., 2016. Determination of rice storage time with colorimetric sensor array. Food Anal. Methods 10, 1054e1062. Huang, X., Zou, X., Zhao, J., Shi, J., Zhang, X., Li, Z., Shen, L., 2014. Sensing the quality parameters of Chinese traditional Yao-meat by using a colorimetric sensor combined with genetic algorithm partial least squares regression. Meat Sci. 98, 203e210. Huang, X., Xu, H., Wu, L., Dai, H., Yao, L., Han, F., 2016. A data fusion detection method for fish freshness based on computer vision and near-infrared spectroscopy. Anal. Methods 8, 2929e2935.
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Journal of Stored Products Research journal homepage: www.elsevier.com/locate/jspr
Identification of rice storage time based on colorimetric sensor array combined hyperspectral imaging technology Hao Lin*, Zhuo Wang, Waqas Ahmad, Zhongxiu Man, Yaxian Duan School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, PR China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 19 June 2019 Accepted 30 September 2019 Available online 21 November 2019
The study reports a novel colorimetric sensor array (CSA) based hyperspectral imaging (HSI) system and chemometrics algorithms for the identification of rice storage time. CSA fabricated by borondipyrromethene (BODIPY) dyes was used to capture the volatile organic compounds (VOCs) of rice samples. CSA hypercube before and after the reaction were obtained with HSI. Genetic synergy interval partial least square algorithm (GA-Si-PLS) was used to filter spectral information. Fifty-four spectral data variables and five dominant wavelength images was selected from CSA hypercube. Then three grayscale difference values were extracted from each dominant wavelength image, thus totaling to 15 variables as imaging data variables. Linear discriminant analysis (LDA) and k-Nearest Neighbor (KNN) model were established to comparing the performance of spectral variables, imaging variables and combined datasets. The result showed the optimal model was linear discriminant analysis (LDA) model built by using spectral variables and the correct rate of calibration set for rice storage time discrimination was 92.73% and the obtained rate of prediction set was 90.91%. It is indicated the applicability of the proposed CSA combined with HSI technology towards rice storage time identification. © 2019 Elsevier Ltd. All rights reserved.
Keywords: Rice aging Colorimetric sensor array BODIPYs Hyperspectral imaging GA-Si-PLS
1. Introduction Rice is among the most widely consumed grains in the world and greatly valued by the people due to its high nutritional value (Khush, 1997; Dong-Hwa and Seung-Taik, 2016; Zhai et al., 2001). Rice is one of the main sources of dietary fiber, fat, protein, trace elements and other nutrients and it has become the staple food of more than half of the world’s population. Many countries need to stock up the rice in order to ensure consumer demands throughout the whole year in case of any emergency (Choi et al., 2015). During the storage process, the nutrients in rice undergo physical and chemical changes to some extent over time due to changes in environmental factors and the catalysis of its own enzymes. For example, the hydrolysis or oxidation of unstable lipids result in the conversion of fresh rice to aging rice with the development of unpleasant flavors (Zheng et al., 2017). Besides, the change of the rice VOCs and the storage time have a certain positive correlation. Eating severely aging rice causes damage to human health, therefore research on the freshness of rice becomes particularly
* Corresponding author. Fax: þ86 0511 88780201. E-mail address: [email protected] (H. Lin). https://doi.org/10.1016/j.jspr.2019.101523 0022-474X/© 2019 Elsevier Ltd. All rights reserved.
important. As the major criterion of rice aging, flavor is related to the changes in culinary quality and nutrient content (Kovach et al., 2009; Griglione et al., 2015). Traditional analysis methods including sensory evaluation and gas chromatography mass spectrometry (GC-MS) are mostly used in analyzing the food flavor (Wijit et al., 2017). Sensory analysis provides people with intuitive information. However, subjectivity of human sense and some harmful gases produced by aging rice make sensory analysis unsuitable for testing. Objectivity and accuracy of GC-MS is superior to that of sensory evaluation (Huang et al., 2016; Khulal et al., 2016a). However, reagent demand, elongated time, and cost are some shortcomings associated (Cosio et al., 2007). Hence, the rapid and non-destructive characterization of volatile gas changes is the key to solving the problem. CSA technology was proposed as a new approach for odor analysis (Rakow and Suslick, 2000). To recognize complex odors, this novel system was consisted of CSA composed of printed dyes with pattern recognition system and partial specificity (Suslick et al., 2004). Colorimetric dyes were the core part in CSA to determine the sensitivity and specificity. Generally, they are made by porphyrins dyes and pH indicators (Zou et al., 2016). The array was used to discriminate among VOCs by probing a wide range of
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intermolecular interactions, including Lewis acid/base, metal ion coordination, hydrogen bonding, and dipolar interactions (Janzen et al., 2006). CSA have been applied to organic small-molecular compounds such as nitroaromatic molecules (Lu et al., 2016), aliphatic amines (Bang et al., 2008), TVB-N (Huang et al., 2014). Thus, CSA technology has the potential to analyze VOCs in rice at different storage times. A variety of VOCs including certain specific organics such as heterocyclic compounds and aldehydes volatilize and change dynamically, due to prolonged storage time. Traditional colorimetric sensors made by porphyrins dyes and pH indicators hardly discriminates early aging stage of rice (Guan et al., 2016). In CSA, the information was extracted by a tri-CCD camera and data analysis were performed on the obtained RGB values (Guan et al., 2014). At the beginning of storage, the change in rice odor is not sufficient to initiate a significant color reaction of conventional colorimetric materials. Besides, poor information is also a factor limiting the correct identification of rice aging time. Presumably, the awkward situation would have some change due to the new colorimetric materials that are sensitive to specific samples and the expansion of information volume. HSI technique integrated spectroscopy and computer imaging together form an emerging technology platform to provide spectral-spatial information (Gowen et al., 2007). Compared with conventional spectroscopic techniques, HSI technology not only acquires spectral data from a single point, but also each pixel of the image. In imaging data, conventional color cameras were poor identifiers for surface features that were sensitive to bands other than RGB. However, there were hundreds of HSIs in each contiguous waveband (Gowen et al., 2007). Both imaging data and spectral data are suitable for characterizing CSA color change information. The increased dimensionality of the data acquired would make it possible to obtain more useful information to discriminate rice storage time. This study provides a new idea for data processing of colorimetric sensors. A novel method was developed for rice storage time discrimination using CSA based on BODIPY dyes and HSI technology. Spectral data, imaging data and combination of them was used to build prediction model respectively, and these models was employed to select the appropriate data type for the establishment of a prediction model of rice storage time. GA-Si-PLS algorithm was applied to select optimal spectral data variables and domain wavelength from HSIs. Imaging data variables were extracted from Gray-level images at domain wavelengths. Multivariate analysis (KNN and LDA) based on spectral and imaging data variables were employed to fabricate the rice storage time prediction models.
According to previous studies, three types materials that are positively correlated with the characteristic gas content that characterizes the degree of rice aging are screened out. BODIPY shows more specificity and excellent color rendering performance than general porphyrins and pH indicators. Three kinds of wellperforming BODIPY dyes were used in this experiment namely: (1) 8-(4-bromophenyl) 4, 4-difluoro- BODIPY (BrBDP) (2) 8-(4-nitrophenyl)-6-bromo-4, 4-difluoro-BODIPY (NO2BrBDP) (3) 8-(4-nitrophenyl)-6, 6-dibromo-4, 4-difluoro-BODIPY (NO2Br2BDP) Three dyes were synthesized by classic Lindsey methodology (Ulrich et al., 2008; Loudet and Burgess, 2008). BODIPY was dissolved in methylene chloride at a concentration of 2.0 mg ml1 and poured manually on a reversed-phase silica gel plate (Merck Millipore, Germany) with a capillary to construct 3 1 sensor array. A total of 165 CSA samples were used to characterize the odor substances of rice. The fabricated CSA samples were then tested to prevent any possible influence of the sensor during storage. 2.3. Systems of hyperspectral imaging combined with colorimetric sensor array As shown in Fig. 1, the images of samples were acquired by the ViseNIR HSI system in a wavelength range of 430e960 nm with a spectral interval of 0.858 nm. HSI system was preheated for 30 min prior to image acquisition. During image acquisition, a motion controller drives a motorized positioning table at the speed of 0.9 mm s1 to avoid image distortion. Each CSA was transported by the motorized positioning table to the camera’s field of view (FOV), where a raw image was captured and stored in the computer (Khulal et al., 2016b). After completing scanning for an entire CSA, one CSA hypercube can be obtained. Before the test, valves were opened, and the vacuum was turned on for 10.0 min to remove residual gas or other unrelated gases. CSA hypercube before exposure was first acquired on a conveyor platform and then immediately placed in a reaction chamber. Meanwhile, rice samples were put into a gas-collecting chamber with valves closed, and water temperature was raised to 40 C to promote the volatilization of the gas in the rice to equilibrate. Afterward, valves were opened and the vacuum was turned on again to extract the VOCs of rice sample into the reaction chamber for 20.0 min. Finally, CSA after exposure to VOCs was taken out and
2. Material and methods 2.1. Material Japonica rice samples (Fulingmen, Cofco, China) with vacuum package were purchased from the local tourism supermarket, and kept in incubator at 40 C with a relative humidity (RH) of 85%. The samples were divided into six groups based on their respective storage time. A total of 165 rice samples were further classified as follows: 15 samples for the fresh rice; 30 samples for 1-month age; 30 samples for 2-months age; 30 samples for 4-months age; 30 samples for 6-months age and 30 samples for 10-months age. The respective calibration and prediction sets were arranged at ratio of 2:1 to build and examine the performance of the algorithmic models. 2.2. Dyes of colorimetric sensor array BODIPY dyes were selected to develop a CSA platform.
Fig. 1. Diagram of CSA hyperspectral imaging acquisition system.
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placed on the conveyor platform again to get the CSA hypercube. To reduce effects of external environmental factors, all acquired HSIs were calibrated by the following formula (Yan et al., 2017).
R ¼ ðI DÞ=ðW DÞ
(1)
where I was the raw HSI, D was the dark current image acquired by cover the camera lens from light with a black cap. W means standard white reference image captured using a white Teflon board (Edmund Optics Inc., Barrington, NJ, USA). The image acquisition and collection process were controlled by Spectral-Cube software.
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algorithm were applied in this study. Variables selection of spectra data was divided into two steps: firstly, partial least square synergy interval (Si-PLS) algorithm was applied to select optimal combinations of subintervals for dye y and fused the filtered intervals together to form a dataset, detailed description of its basic principles can be found in (Chen et al., 2008). Secondly, the optimal spectral data variables were selected by genetic algorithm (GA) from combined dataset. Only the optimal wavelength chosen were used to build rice storage time prediction model instead of using the entire spectral range of three dyes. Select 5 wavelengths as the domain wavelength of the image data according to the frequency selected by the GA algorithm.
2.4. Extraction of characteristic variable HSI data analysis was started by examining spectral information and imaging information to discriminate the storage time of rice. Based on this, the extracted characteristic variable from spectral and image information were fused for further identification. As shown in Fig. 2, the extraction of characteristic variable can be summarized as spectra and imaging data analysis from the hyperspectral system and data fusion from the spectral and imaging information. 2.4.1. Spectra data analysis The spectrum data of dye y (y represent the yth dye in the array) were extracted from the CSA hypercube after exposure. To eliminate or minimize factitious non-uniformity, a circle region of about 1500 pixels within the area where BODIPY dyes printed were selected as the region of interests (ROI). The center of ROI was coinciding with the center of BODIPY dyes to acquire a mean spectrum in the range of 430e960 nm. Then, standard normal variate (SNV) was used to preprocess the average spectrum for correcting light scatter from these spectra (Rinnan et al., 2009). For each sample, three average spectral curves were obtained resulting in 495 spectral curves for 165 samples. Sufficient information data and variables in CSA hypercube make online applications inefficient. Hence, some variable optimization algorithms are needed to overcome this problem. To choose the lowest number of wavelengths which maximize correlation between spectral data and rice storage time, GA-Si-PLS
2.4.2. Imaging data analysis In this section, imaging data characteristic variable of CSA hypercubes had been explored to discriminate the storage time of rice. Variations in the gray levels of hyperspectral scattering profiles (at the range of 430e960 nm) were applied to quantify the signal change of the dye spot. Five gray-level images were selected according to the frequency of each wavelength selected by GA and saved in BMP format (Khulal et al., 2016b). Three difference values could be gotten by digitally subtracting the image before exposure to VOCs of rice from the image after exposure, using a 1500-pixel gray average from the center of each dye spot (the interference point caused by the excessive concentration of dye were deleted by threshold segmentation as introduced in Section 3.3) as follows.
Gyq1 d ¼ Gyq1 a Gyq1 b
(2)
Gyql d ¼ Gyql a Gyql b
(3)
Gyq5 d ¼ Gyq5 a Gyq5 b
(4)
where a is after exposure, b is before exposure, d is difference, Ɵ1, Ɵl, Ɵ5 represent the number of characteristic wavelengths filtered out. Gyq1 d , Gyql d and Gyq5 d represent the gray-level difference of dye y with corresponding characteristic wavelength. five gray-level difference, Gyq1 d Gyql d , … Gyq5 d , were used to characteristic dye y signal change based on their characteristic
Fig. 2. Flow chart of the experimental procedure to determine rice storage time by HIS system.
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wavelength. Therefore, 15-dimensional vector (3 dyes * 5 differences) as imaging data variables to build rice storage time prediction model. 2.4.3. Data fusion from spectral and imaging information In addition, combining different data may result in better classification results. Considering the comprehensiveness of data analysis, spectral data and image data are combined into one data set for further classification analysis. 2.4.4. Model establishment Once the characteristic data (spectral data variables, imaging data variables and fusion dataset) was extracted from CSA hypercubes, rice storage time detection becomes a classification problem. The final step of this study was to employ LDA and KNN to relate characteristic data of CSA hypercubes to rice storage time. KNN is a classification method based on the basic assumption of pattern recognition, that is, similar samples were close to each other in the model space. LDA is a dimensionality reduction technique for supervised learning. The idea of LDA can be summarized in one sentence, that is, the smallest variance within the category after
projection and the largest variance between classes. In this work, LDA and KNN were employed to discriminate rice storage time (Yang and Yang, 2003). To quantify the predictive ability of the models, correct identification rate of samples was determined. Hence, the most suitable data type among the three characteristic data was decided for CSA data expression. 2.5. Software Spectra extraction of HSI was executed using ENVI 4.5 (Research Systems Inc., Boulder, CO, USA). Image processing was finished using programs developed in Halcon 13(the MVTec Inc, Germany). All the above data analysis procedures were performed using software MATLAB 2014(The MathWorks Inc, MA, USA). 3. Results and discussions 3.1. Feasibility analysis The RGB image of CSA obtained before and after exposure are shown in Fig. 3a. From the visual point of view, only NO2BrBDP
Fig. 3. Spectral and imaging data of CSA. (a) RGB image and gray-level image. (b)spectral data. (c) spectral data and gray-level image at 546.55 nm.
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Fig. 4. Result of spectral data variables. (a), (b) and (c) represent Si-PLS result. (d) combine dataset. (e) GA selection result. (f) selected wavelength of each dye.
could see the obvious color change. The same situation also appears in the gray-level image of RGB channel. However, in contrast, some gray-level images at different wavelengths extracted from CSA hypercube shows better differentiation. Except for 452.51 nm, the
gray-level variation of each dye can show significant changes at the corresponding wavelengths. Therefore, it is feasible to use graylevel images at different wavelengths instead of RGB channel to characterize the color change of CSA. As Fig. 3b shows, the average spectra of each dye for a storage period of 0, 4 and 10 months was extracted from CSA hypercubes after exposure. It can be found that there is a difference in the obtained spectrum for each dye after exposure to rice VOCs with different storage time. It indicates that the color change of the CSA will be differentiated as the change of sample storage time. In addition, as Fig. 3c shows, according to the spectral data of each dye spot on a single CSA, it is found that the spectral data has a large difference at 546.55 nm, so the gray-level image at this wavelength is extracted and the 3 1 matrix is used to quantify the gray-level value of each dye. Combined with the spectra data, it is easy to find that the gray-level value of the BODIPY material is positively correlated with the spectra intensity. Therefore, each dye has different gray-level value at the same wavelength after CSA exposing with different storage period rice. In summary, it is feasible to predict the rice storage period by extracting the spectral and imaging data from CSA hypercubes.
3.2. Extraction of spectral data variables
Fig. 5. (a)Gray-level images at domain wavelength. (b) ROI selection.
HSI contain a lot of extraneous information due to its huge data volume. In fact, one of the major problems in multivariate data analysis is to select appropriate frequency variables to simplify the process of calibrating models to achieve better performance. In recent years, both theoretical and experimental evidence suggested that variables selection significantly improves the performance of these calibration techniques (Mehl, 2002). Therefore, many algorithms models are increasingly used in hyperspectral data analysis to remove redundant variables. Herein, the best spectral intervals
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Table 1 The prediction accuracy of KNN and LDA with spectral data. Variable selection algorithm
Number of variables
Discrimination algorithm
Discrimination rates of calibration set
Discrimination rates of prediction set
Spectral data variables
54
Imaging data variables
15
Fusion dataset
69
KNN LDA KNN LDA KNN LDA
81.82% 92.73% 88.18% 90.91% 90.91% 89.09%
76.36% 90.91% 87.27% 80.00% 87.27% 87.27%
KNN, k-Nearest Neighbor; LDA, linear discriminant analysis.
were selected was first extracted by Si-PLS algorithm from three dyes spectral data that had been preprocessed by SNV. Dividing the full-spectrum system into 15 sub-intervals, three sub-intervals were combined from each dye spectra to establish a PLS with storage time. The result of data processing is shown in from Fig. 4a to Fig. 4c. The optimal spectral intervals of BrBDP are the 1st, 2nd, and 5th sub-ranges. The optimal spectral intervals of NO2BrBDP are the 3rd, 4th, and 9th sub-ranges and the optimal spectral intervals of NO2Br2BDP are the 1st, 4th, and 13th sub-ranges. Therefore, a total of 373 variables (9 sub-intervals) were filtered out. A new dataset is formed by combining each sub-range as Fig. 4d showed. However, the number of variables is still very large and the reproducibility of the screening results is low. The combined dataset is further filtered by GA algorithm. 54 variables are finally selected as spectral data variables. Fig. 4e shows a histogram of frequency selected for the variables in combined dataset, and here the variables with corresponding frequency 7 could be selected. The obtained 54 variables then were used to filter domain wavelengths. Fig. 4f shows the final selected wavelength in each dye at the range of 430e780 nm. Due to the high correlation between the wavelengths, the chosen wavelength interval must be greater than 10.0 nm. The domain wavelengths were selected by the GA algorithm for the analysis of the remaining 54 variables result in five wavelengths, i.e. 452.51 nm, 485.09 nm, 538.09 nm, 558.42 nm and 723.47 nm. 3.3. Extraction of imaging data variables The gray-level images at 5 domain wavelengths were shown in Fig. 5a, Intuitively, the characteristic wavelength at 495.98 nm clearly show the external outline of each dye, and the characteristic image at 607.87 nm clearly shows the interference point generated
due to the high density of the central region of the dye. As Fig. 5b shows, by setting artificially the threshold value for the gray-level value, the overall region of each dye spot and the region of the interference point could be obtained. Finally, a ’Subtraction’ operation was applied to the above two regions using morphological methods. Therefore, the gray-level value extraction region as ROI at each wavelength is determined by these two wavelengths. By calculating the difference of the average gray-level value on the ROI before and after exposure, 15dimensional vector (3 dyes * 5 differences) of each sample was obtained as imaging data variables. 3.4. Modeling for rice storage time classification Three types of characteristic datasets including spectral data variables, imaging data variables and fusion dataset, respectively establish a model for predicting rice storage time. Among them, the fusion data set is formed by combining spectral and image feature data. KNN and LDA are used in the qualitative classification of this study. Two third of the samples from each class have been used for training, and the remaining one third of samples for testing of the trained model. Therefore, the two kinds of algorithms are used respectively for the classification of rice storage time and the method was carried out by means of taking the filtered variables as inputs. The recognition rate of the model established by these three datasets is shown in Table .1. From the results, the dataset of spectral data variables has the highest discrimination rate after LDA qualitative analysis. As shown in Fig. 6, the LDA model has good discrimination in rice storage when PCs ¼ 10. The discrimination rates of calibration and prediction set are 92.73 and 90.91% with the ideal number of PCs. There are five samples of fresh rice and storage time of one month, were incorrectly identified in prediction set. 4. Conclusion The study explores several prediction models based on the spectral data and image data for identifying the rice storage time. Herein, a novel CSA, synthesized by BODIPY material was applied to classify rice with different storage time. This is an innovation in information processing of olfactory visualization technology obtained with great progress and sensitivity. Compared with our previous work, the sensitivity of the current strategy has been improved significantly (Guan et al., 2016). The 54 wavelength variables selected by GA- Si-PLS screening was applied to identify rice storage time with good characterization skills by LDA analysis. This work shows that the rice storage time prediction model using spectral data has the best performance by analyzing spectral and image information of CSA by HSI technique to identify rice storage time. Acknowledgements
Fig. 6. Discrimination rates of LDA model according to different PCs.
We are also grateful to many of our colleagues for stimulating
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discussion in this field. This work was supported by the National Key Technology R&D Program of China (Grant No. 2016YFD0401205-3), China Postdoctoral Natural Science Foundation (2016M601746), and Priority Academic Program Development of Jiangsu Higher Institutions (PAPD). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jspr.2019.101523. References Bang, J.H., Lim, S.H., Park, E., Suslick, K.S., 2008. Chemically responsive nanoporous pigments: colorimetric sensor arrays and the identification of aliphatic amines. Langmuir the Acs Journal of Surfaces & Colloids 24, 13168e13172. Chen, Q., Zhao, J., Liu, M., Cai, J., Liu, J., 2008. Determination of total polyphenols content in green tea using FT-NIR spectroscopy and different PLS algorithms. J. Pharm. Biomed. Anal. 46, 568e573. Choi, S., Jun, H., Bang, J., Chung, S.H., Kim, Y., Kim, B.S., Kim, H., Beuchat, L.R., Ryu, J.H., 2015. Behaviour of Aspergillus flavus and Fusarium graminearum on rice as affected by degree of milling, temperature, and relative humidity during storage. Food Microbiol. 46, 307e313. Cosio, M.S., Ballabio, D., Benedetti, S., Gigliotti, C., 2007. Evaluation of different storage conditions of extra virgin olive oils with an innovative recognition tool built by means of electronic nose and electronic tongue. Food Chem. 101, 485e491. Dong-Hwa, C., Seung-Taik, L., 2016. Germinated brown rice and its bio-functional compounds. Food Chem. 196, 259e271. Gowen, A.A., O’Donnell, C.P., Cullen, P.J., Downey, G., Frias, J.M., 2007. Hyperspectral imaging - an emerging process analytical tool for food quality and safety control. Trends Food Sci. Technol. 18, 590e598. Griglione, A., Liberto, E., Cordero, C., Bressanello, D., Cagliero, C., Rubiolo, P., Bicchi, C., Sgorbini, B., 2015. High-quality Italian rice cultivars: chemical indices of ageing and aroma quality. Food Chem. 172, 305e313. Guan, B., Zhao, J., Cai, M., Lin, H., Yao, L., Sun, L., 2014. Analysis of volatile organic compounds from Chinese vinegar substrate during solid-state fermentation using a colorimetric sensor array. Anal. Methods 6, 9383e9391. Guan, B., Zhao, J., Jin, H., Lin, H., 2016. Determination of rice storage time with colorimetric sensor array. Food Anal. Methods 10, 1054e1062. Huang, X., Zou, X., Zhao, J., Shi, J., Zhang, X., Li, Z., Shen, L., 2014. Sensing the quality parameters of Chinese traditional Yao-meat by using a colorimetric sensor combined with genetic algorithm partial least squares regression. Meat Sci. 98, 203e210. Huang, X., Xu, H., Wu, L., Dai, H., Yao, L., Han, F., 2016. A data fusion detection method for fish freshness based on computer vision and near-infrared spectroscopy. Anal. Methods 8, 2929e2935.
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