Article pubs.acs.org/EF
Cite This: Energy Fuels XXXX, XXX, XXX-XXX
Rapid Diagnosis of Normal and Abnormal Conditions in Solid-State Fermentation of Bioethanol Using Fourier Transform Near-Infrared Spectroscopy Hui Jiang,*,†,‡ Wei Wang,‡ Congli Mei,‡ Yonghong Huang,‡ and Quansheng Chen*,§ †
School of Agricultural Equipment Engineering, ‡School of Electrical and Information Engineering, and §School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, People’s Republic of China ABSTRACT: Rapid diagnosis of normal and abnormal fermentation conditions in solid-state fermentation (SSF) of bioethanol is crucial to process and quality controls. The Fourier transform near-infrared (FT-NIR) spectroscopy analysis technique combined with pattern recognition methods was employed to monitor fermentation conditions (i.e., normal and abnormal) in this study. To achieve optimum performance in identification of fermentation conditions, linear discriminant analysis (LDA), Knearest neighbors (KNN), and support vector machine (SVM) classifications were employed to construct the recognition models. The optimization of arithmetical parameters and number of principal components (PCs) were implemented simultaneously using leave-one-out cross-validation (LOOCV) during the recognition model training phase. The results of this study revealed that the SVM model presented better performance compared to the other two models, and the best SVM recognition model was finally established by use of four PCs. The SVM model successfully identified 96.30% of independent samples in the validation process. This study demonstrates that the FT-NIR spectroscopy analysis technique with the help of an appropriate chemometrics approach could be successfully applied for rapid diagnosis of fermentation conditions in SSF of bioethanol. different fermentation processes.10−16 Although the technique has been successfully applied in many different bioprocesses, it has not yet been fully investigated for monitoring and diagnosing the process of SSF of bioethanol production, which uses sweet sorghum straw sources of biomass as feedstock. In general, the FT-NIR spectroscopy technique requires a calibrating step to develop a chemometrics model, which is employed for qualitative and quantitative analyses.17 Principal component analysis (PCA), linear discriminant analysis (LDA), K-nearest neighbors (KNN), and support vector machine (SVM) classifications are examples of chemometric methods commonly used in classification of spectrometric calculations. These methods are widely applied for spectral data analysis because the models established can be easily interpreted and the required latent information can be efficiently extracted. Thus, in this study, the aim of this work was to establish a FT-NIR spectroscopy technique to monitor and diagnose the process conditions of normal and abnormal during SSF of bioethanol. For this purpose, the detailed investigation contents of this work are as follows: (i) FT-NIR spectra collections, (ii) MSC spectral pretreatment, (iii) PCA, and (iv) establishing identification models. In our study, three widely used pattern recognition methods, including LDA, KNN, and SVM, were employed for establishing the identification models.
1. INTRODUCTION In recent years, the gradual exhaustion of fossil fuels and the increasing prominence of environmental issues have attracted the extensive attention of the state and society. Thus, countries around the world attach great importance to the development of non-fossil energy, especially clean and renewable biofuel resources.1 Bioethanol is currently the most widely used liquid biofuel, which is both a clean fuel and a gasoline booster.2 The traditional method of bioethanol production is high-temperature cooking and liquid fermentation. However, this method has low efficiency and high energy consumption in production, and the wastewater treatment of liquid fermentation is a big problem, with the pollution being more serious. Solid-state fermentation (SSF) has become a generic and popular biotechnology that converts lignocellulosic biomass into bioethanol because of its relatively simple technique, low energy consumption, and low wastewater discharge.3 However, the SSF process is a complex dynamic process, and it is easy to cause growth of contaminated microorganisms because of the wet semi-solid substrate environment. In SSF of bioethanol, it is difficult to quickly discover yeast contamination as a result of its particular operational conditions. Therefore, to enhance the efficiency of the SSF process and guarantee the quality of final products, a novel method for rapid and accurate monitoring of sequential change information on SSF of bioethanol should be established, especially rapid diagnosis of normal and abnormal fermentation conditions in SSF of bioethanol. Fourier transform near-infrared (FT-NIR) spectroscopy has been developed into a powerful tool for general process monitoring in industrial applications.4−9 Recently, the FT-NIR spectroscopy technique has been successfully applied for monitoring of physical and chemical composition changes in © XXXX American Chemical Society
Received: July 25, 2017 Revised: October 3, 2017 Published: October 11, 2017 A
DOI: 10.1021/acs.energyfuels.7b02170 Energy Fuels XXXX, XXX, XXX−XXX
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Energy & Fuels
LDA recognition model using leave-one-out cross-validation (LOOCV), and the best number of PCs was obtained in terms of the highest recognition rate in the calibration process. KNN is a non-parametric statistical approach widely used in classification problems.22 A target object is categorized primarily based on the attributes of its neighbor, and the target object is assigned to the class that has the greatest number among its K-nearest neighbors. In general, the value of K is a positive integer.23 However, in binary classification issues, it is helpful to set the value of K to be an odd number, which can avoid the fact that the votes from the neighbors are equal. In KNN recognition model development, the value of K has a serious influence on the performance of the KNN recognition model. Meanwhile, the number of PCs has also some influence on the performance of the recognition model. Consequently, in this study, 8 K values (i.e., K = 1, 3, ..., 15) and 10 PCs (i.e., PCs = 1, 2, ..., 10) would be optimized synchronously using of LOOCV during developing the KNN recognition model, and the optimal K value and PCs were determined finally in terms of the maximal recognition rate in the calibration process. SVM is a supervised learning model related to the relevant learning algorithms, which can be used for classification and regression analyses.24 In SVM recognition model development, the original data are first mapped to the high-dimensional characteristic space using a kernel function, and then a linear pattern recognition issue is carried out in the feature space, which can transform a nonlinear problem into a linear problem.25 The dimension of the eigenspace is determined by the selection of the kernel function and its parameter (σ), and the complexity of the SVM recognition model is decided by another additional penalty coefficient (C). The penalty coefficient C is used to control balance between maximizing the boundary and minimizing the training error. If the penalty coefficient C is too large, the algorithm will make the training data overfit. On the contrary, if the penalty coefficient C is too small, there will be insufficient training (that is to say, underfitting).26,27 In this work, the kernel function of the SVM algorithm adopts the Gaussian kernel-type function. In SVM recognition model development, the penalty coefficient C and Gaussian kernel function σ are necessary to be optimized to improve the prediction performance. However, there is no unified theoretical standard for the optimization of these two parameters. Hence, in this work, these two parameters will be optimized via two steps to perform. First, a larger step length was selected to search the optimal values of these two parameters in a wide range of parameter choices. The search results showed that the SVM recognition model had a better identification performance when the value of parameter C was between 1 and 3 and the value of parameter σ was between 2 and 4. Then, 11 values of parameter C with an interval of 0.2 (i.e., C = 1, 1.2, ..., 3) and 11 values of parameter σ with an interval of 0.2 (i.e., σ = 2, 2.2, ..., 4) were optimized simultaneously by LOOCV during developing the SVM recognition model. The optimal parameter C and parameter σ were finally determined in terms of the highest recognition rate in the calibration process. 2.4. Software. All algorithms were implemented in MATLAB R2010a (MathWorks, Natick, MA, U.S.A.) under Windows 7. Result software (Antaris II system, Thermo Fisher Scientific, Waltham, MA, U.S.A.) was used in FT-NIR spectral data acquisition.
2. MATERIALS AND METHODS 2.1. Sample Preparation. 2.1.1. Normal Fermentation. In this study, the sweet sorghum straws were purchased from a local farm in Jiangsu province, China, which were used for the SSF experiments of bioethanol. The sweet sorghum straws collected were first dried to a constant weight by use of a constant temperature air-dry oven at 60 °C. Second, the dried sweet sorghum straws were smashed to 1−2 mm by a crusher, and then the sweet sorghum straw fragments were sterilized for 30 min in an autoclave. The temperature and pressure of the autoclave are set to 112 °C and 0.1 MPa, respectively. After sterilization, 0.5% saccharifying enzyme (100 000 μg/g), 0.2% liquefying enzyme (20 000 μg/g), 0.1% cellulose (50 000 μg/g), 5 mL of yeast suspension, and 40 g of cooled sweet sorghum straw fragments were placed into the volumetric flasks. Finally, the continuous culture of the mixture medium will take place in 64 h using a thermostat incubator at 28 °C. In SSF, sampling was carried out with 8 h intervals from loading to the end of fermentation. In the process, nine samples could be obtained. A total of 12 batches of fermentation were carried out using the same raw materials and methods. Thus, in the whole process, a total of 108 process samples were obtained. 2.1.2. Abnormal Fermentation. To carry out the abnormal fermentation experiments, the materials and conditions were consistent with the normal fermentation experiments. The only difference was that the contaminating microorganism was introduced at the same time as the yeast inoculation. In this study, Acetobacter and Lactobacillus were selected as the contaminating microorganisms to conduct abnormal SSF of bioethanol. There were three types of contaminating microorganisms, i.e., single Acetobacter bacteria culture, single Lactobacillus bacteria culture, and mixed bacteria culture of Acetobacter and Lactobacillus. For each type of contaminating microorganism, four batches of abnormal fermentation were carried out. Thus, a total of 12 batches of abnormal fermentation were conducted, and a total of 108 samples were also obtained throughout the process. 2.2. FT-NIR Spectra Acquisition. An Antaris II FT-NIR spectrophotometer, equipped with a fiber-optic probe, was used to collect the FT-NIR spectral data by reflectance mode (log 1/R). The spectral data were acquired in the range of 10 000−4000 cm−1 using an ensemble average of 32 scans, and the sampling resolution of each spectrum is 3.856 cm−1. Thus, each spectrum contains 1557 variables. To make the collected spectral data more accurate, each sample was collected 3 times at different locations of spectral acquisition. Then, the mean average of the three spectra obtained is calculated as the original spectrum of the sample. In addition, the room temperature was kept at 25 °C during spectral collection. 2.3. Data Analyses. PCA is a classic data feature mining and dimensionality reduction method. The rule of PCA is to convert raw data set to a new coordinate framework by a linear orthogonal transformation, so that the maximum variance by the data projection appears on the first coordinate (which is called the first principal component), the second largest variance presents the second coordinate (which is called the second principal component), etc.18 An original data matrix X is given as (N × P), where N represents the number of data samples and P represents the characteristics of the data samples. By implemention of PCA, the number of principal components (PCs) is less than or equal to the number of raw variables.19 In our study, the obtained FT-NIR spectral data are a multivariate matrix containing a lot of redundant information. Therefore, PCA was first employed to extract latent variables (i.e., PCs) before recognition model construction. LDA is a derivative of the linear discriminant of Fisher. It can well search a linear combination of feature data for classifying two or more categories of target objects. The LDA has been widely used in pattern recognition and machine learning.20 In this study, the resulting combination can be used as a linear classifier using LDA.21 In LDA recognition model development, the number of PCs directly affects the recognition results of the LDA identification model. Thus, the number of PCs is necessary to be optimized during developing the
3. RESULTS AND DISCUSSION 3.1. Spectra Preprocessing. Figure 1a shows the original FT-NIR spectra of all collected process samples. In this study, because the sample is in a semi-solid state, it is easy for light scattering and migration with the original spectral acquisition. Thus, the original spectra obtained by use of a FT-NIR spectrometer contained noises besides sample information. To obtain an accurate, reasonable, and stable recognition model, the spectral pretreatment is an indispensable step before modeling. B
DOI: 10.1021/acs.energyfuels.7b02170 Energy Fuels XXXX, XXX, XXX−XXX
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Energy & Fuels
Multiplicative scatter correction (MSC) is commonly used as one of the important spectral preprocessing methods to correct signals for noise. The principle of MSC is shifted and rotated, so that it fits as closely as possible to the mean spectrum of the data. The fit is achieved by least squares (LS, first-degree polynomial), and the correction depends upon the mean spectrum of samples. Therefore, MSC can effectively remove the effect of physical light scatter from the spectrum, and the MSC spectral pretreatment method was employed in this study. The preprocessed FT-NIR spectra using the MSC method are presented in Figure 1b. 3.2. Spectral Dimensionality Reduction with PCA. In this study, PCA was first applied for exploratory spectral analysis, and subsequently, LDA, KNN, and SVM were performed. By performance of PCA, the first PC (i.e., PC1) can interpret 55.11% variances; that is to say, PC1 can explain 55.11% information on the preprocessed spectra. The second PC (i.e., PC2) can interpret 29.51% variances; similarly, that is to say, PC2 can explain 29.51% information on the preprocessed spectra. The cumulative covariance contribution rate of PC1 and PC2 achieves 84.62%; that is to say, the first two PCs can interpret 84.62% information on the preprocessed spectral data. To directly observe the clustering trends of all samples, a twodimension (2D) scatter plot was constructed using the first two PCs (that is, PC1 and PC2), as shown in Figure 2. As investigated from Figure 2, all samples showed two different clustering trends along the PC1 and PC2 axes. 3.3. Results of Different Identification Models. The PCA score plot, which was constructed using the first and second PCs, can provide the clustering trends of all process samples in the two-dimensional (2D) space; however, it cannot be used directly as a recognition tool for the distinction between normal and abnormal fermentation conditions. As a result, some pattern recognition techniques were needed to build the recognition models for identifying the process samples. Before the recognition model construction, the input vectors of the models (i.e., PCs) were first extracted using PCA,
Figure 1. (a) Original FT-NIR spectra and (b) MSC preprocessed FTNIR spectra of all normal and abnormal fermentation samples.
Figure 2. First two PC score plots of the 216 FT-NIR spectra. C
DOI: 10.1021/acs.energyfuels.7b02170 Energy Fuels XXXX, XXX, XXX−XXX
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Energy & Fuels and the number of PCs needed to be optimized during recognition model construction. Three pattern recognition tools, which were LDA, KNN, and SVM, were adopted to establish the recognition models, respectively. The constructive process of each model would be introduced in detail in the following study. Usually, a recognition model is constructed requiring a calibration set of samples with known categories. The predictive performance of the recognition model developed is evaluated using a validation set containing some external independent samples, and then the recognition rates will be calculated by comparing the predictive labels of the identification model to their own real category labels. According to the above criteria, all 216 process samples were divided into two subsets in this study. A subset that was called the calibration set containing all of the samples would be used for constructing identification models, and another subset that was called the validation set containing all of the samples would be used to validate the predictive performance of the created recognition model. In this study, The calibration set included 162 spectra [i.e., samples from nine batches of normal fermentation and nine batches of abnormal fermentation, 9 samples × (9 + 9) batches], and the validation set included 54 spectra [i.e., samples from the remaining three batches of normal fermentation and the remaining three batches of abnormal fermentation, 9 samples × (3 + 3) batches]. 3.3.1. LDA Identification Model. Figure 3 shows the identification rates of the LDA recognition model in the
Figure 4. Identification rates of the KNN model in the calibration set by LOOCV in terms of the different number of K and PCs.
3. The maximal recognition rate was 97.53% in the calibration set, and the predicted recognition rate was 92.59% in the validation process. 3.3.3. SVM Identification Model. In this study, the first four PCs can interpret 99.11% variances; that is to say, the first four PCs can explain 99.11% information on the preprocessed spectral data. In addition, the optimal number of PCs is both four in the best LDA and KNN recognition models. Therefore, the first four PCs were extracted as the input vectors of SVM recognition models. Figure 5 shows identification results of the
Figure 3. Identification rates of the LDA model in the calibration set by LOOCV in terms of the different number of PCs. Figure 5. Identification rates of the SVM model in the calibration by LOOCV in terms of different parameter values of σ and C.
calibration process by LOOCV in terms of the different number of PCs. As investigated from Figure 3, the highest recognition rate of the LDA recognition model by LOOCV was found when the corresponding number of PCs is four. Therefore, the best LDA recognition model was constructed with the first four PCs as the input vectors of the LDA recognition model. The identification rate was 96.91% in the calibration set, and in the validation set, the recognition rate was 92.59%. 3.3.2. KNN Identification Model. Figure 4 shows the identification rates of the KNN recognition model in the calibration process by LOOCV in terms of the number of K values and PCs. The optimal KNN recognition model was developed with the first four PCs as the input vectors of the KNN recognition model and, at the same time, the K value of
SVM model in the calibration set by LOOCV in terms of different values of σ and C. The optimal SVM model was obtained when σ was 2.2 and C was 2.8. The maximal recognition rate was 99.38% in the calibration set, and the predicted recognition rate was 96.30% in the validation process. 3.4. Comparisons of the Results from the Three Recognition Models. To gain good prediction performance for rapid diagnosis of normal and abnormal fermentation conditions in SSF of bioethanol, three popular recognition approaches, which were LDA, KNN, and SVM, were applied comparatively in our study. Table 1 shows the recognition D
DOI: 10.1021/acs.energyfuels.7b02170 Energy Fuels XXXX, XXX, XXX−XXX
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Energy & Fuels
Table 1. Optimal Identification Results of LDA, KNN, and SVM Recognition Models in the Calibration and Validation Sets discrimination results optimum parameters
a
model
PCs
LDA KNN SVM
4 4 4
training set
testing
other parameters
ratio
percentage (%)
ratio
percentage (%)
K = 3a C = 2.8,b σ = 2.2c
157:162 158:162 161:162
96.91 97.53 99.38
50:54 50:54 52:54
92.59 92.59 96.30
K = parameter K of the KNN algorithm. bC = penalty parameter of the SVM algorithm. cσ = width parameter of the Gauss kernel function.
rapidly diagnose fermentation conditions in SSF of bioethanol. Three popular pattern recognition approaches, which were LDA, KNN, and SVM, were employed to construct the recognition models in this study. The SVM recognition model achieves the best predictive performance in calibration and validation processes. The recognition accuracy of the best SVM identification model is 96.30% in the validation process. It can be concluded that the FT-NIR spectroscopy analysis technique by virtue of an appropriate pattern recognition algorithm has strong engineering application potential to monitor other SSF processes in a non-destructive way. In comparison to traditional time-consuming analysis tools, there has been considerable improvement by introducing the FTNIR spectroscopy analysis technique to realize rapid monitoring of SSF processes.
results of the LDA, KNN, and SVM classification models in the calibration and validation sets. As seen from Table 1, a comparison of the results of the LDA, KNN, and SVM identification models, the SVM recognition model achieved the best identification performance. The FT-NIR spectroscopy technology combined with the SVM pattern recognition algorithm provides the best discrimination results, and the specific reasons can be elaborated from the following two aspects. First of all, on the basis of the analysis theory of FT-NIR spectroscopy, the FT-NIR spectroscopy technique has a distinctive preponderance to monitor fermentation conditions in SSF of bioethanol. During normal SSF of bioethanol, yeast converts sugar into alcohol and carbon dioxide through enzymatic actions. The path of SSF of bioethanol will change when the normal process is dyed bacteria contamination. In other words, the organic components in the substrate will change as the situations occur for bacterial contamination. However, the fermentation substrate is composed of numerous small organic molecules containing hydrogen groups, such as N−H, C−H, O−H, and S−H, which can produce the corresponding characteristic peak in the special band of FTNIR spectroscopy.28 Thus, the small differences of process samples from different fermentation conditions (i.e., normal and abnormal) can be recorded in the obtained FT-NIR spectroscopy. These delicate differences in the obtained spectral data are difficult to visualize only by visual inspection, but they can easily be recognized with the aid of appropriate pattern recognition methods. In the second place, on the basis of the principles of the machine learning theory, the nonlinear pattern recognition approach of SVM possesses its own potential advantages in comparison to the two other linear pattern recognition methods (i.e., LDA and KNN) in this study. Microbial SSF of bioethanol is a complex process involving microbial growth and metabolism during the fermentation process, which gives rise to the differences of the sample spectra from different fermentation conditions with the change of the fermentation time, which are also quite complicated. Therefore, in comparison to nonlinear pattern recognition methods, the linear pattern recognition methods might not be competent to handle such a complex discriminant problem. Generally, the nonlinear pattern recognition algorithm is better than the linear pattern recognition method in terms of self-learning and selfadjusting. Therefore, in our study, the nonlinear SVM recognition model has a higher identification rate for the diagnosis of fermentation conditions in SSF of bioethanol.
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AUTHOR INFORMATION
Corresponding Authors
*Telephone: +86-511-88791960. Fax: +86-511-88780088. Email:
[email protected]. *Telephone: +86-511-88791960. Fax: +86-511-88780088. Email:
[email protected]. ORCID
Hui Jiang: 0000-0001-6245-9958 Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The authors gratefully acknowledge the financial support provided by the National Natural Science Foundation of China (Grant 61705093), the Natural Science Foundation of Jiangsu Province (Grants BK20140538, BK20130531, and BK20151345), the China Postdoctoral Science Foundation (Grant 2016M600381), the Postdoctoral Science Foundation of Jiangsu Province (Grant 1601038C), the College Science Foundation of Jiangsu Province (Grant 16KJB210003), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
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4. CONCLUSION The results of this study convincingly demonstrate that the FTNIR spectroscopy analysis technique coupled with the SVM pattern recognition approach possesses a potential advantage to E
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DOI: 10.1021/acs.energyfuels.7b02170 Energy Fuels XXXX, XXX, XXX−XXX
