Practicability investigation of using near-infrared hyperspectral imaging to detect rice kernels infected with rice false smut in different conditions

Journal Pre-proof Practicability investigation of using near-infrared hyperspectral imaging to detect rice kernels infected with rice false smut in different conditions Na Wu, Hubiao Jiang, Yidan Bao, Chu Zhang, Jingze Zhang, Wenjian Song, Yiying Zhao, Chunxiao Mi, Yong He, Fei Liu

PII:

S0925-4005(20)30043-5

DOI:

https://doi.org/10.1016/j.snb.2020.127696

Reference:

SNB 127696

To appear in:

Sensors and Actuators: B. Chemical

Received Date:

14 June 2019

Revised Date:

12 December 2019

Accepted Date:

8 January 2020

Please cite this article as: Wu N, Jiang H, Bao Y, Zhang C, Zhang J, Song W, Zhao Y, Mi C, He Y, Liu F, Practicability investigation of using near-infrared hyperspectral imaging to detect rice kernels infected with rice false smut in different conditions, Sensors and Actuators: B. Chemical (2020), doi: https://doi.org/10.1016/j.snb.2020.127696

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Practicability investigation of using near-infrared hyperspectral imaging to detect rice kernels infected with rice false smut in different conditions Na Wua,b#, Hubiao Jiangc#, Yidan Baoa,b, Chu Zhanga,b, Jingze Zhangc, Wenjian Songd, Yiying Zhaoa,b, Chunxiao Mia,b, Yong Hea,b, Fei Liua,b* a

College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China

b

Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Areas, Hangzhou 310058,

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China

College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China

d

Agricultural Technology Extension Center, Zhejiang University, Hangzhou 310058, China

#

These authors contributed equally to this work.

*

Corresponding Author at: College of Biosystems Engineering and Food Science, Zhejiang University,

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E-mail: [email protected] (F. Liu).

Graphical abstract

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Hangzhou 310058, China.

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Research Highlights

Detecting rice kernels with different varieties, different infection conditions and different infection status;

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Combining microscopic molecular detection technology with macroscopic spectral im-

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aging technology;

Investigating the practicality and generalization of the detection model;

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Boosting large-scale seeds detection in modern seed industry.

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Abstract: Rice false smut (RFS) is a devastating seed-brone rice disease in many rice-growing countries, endangering the health of rice germplasm resources and reducing the yield and quality of rice. This study aimed to

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propose an effective method for RFS detection in actual production based on near-infrared hyperspectral imaging (NIR-HIS) paired with pathological analysis. The true infection status of rice kernels collected in different conditions was labeled by PCR. The separability between healthy and infected rice kernels was explored using principal component analysis (PCA). Multivariate quantitative analysis models were constructed based on full wavelengths of laboratory-inoculated kernels. Characteristic wavelengths extracted to improve detection performance contained fingerprint information related to RFS infection. The best classification accuracies for healthy and infected mixed kernels with different infection degrees achieved 99.33%

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on calibration set and 99.20% on prediction set, respectively, using RF-ELM model. The practicality of detection model was further verified through obtaining detection accuracies of 91.07% and 89.38% for two

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varieties of field-infected rice kernels and visualizing the category attribute of single rice kernel in hyperspectral images. The overall results indicated the excellent potential of NIR-HSI for on-line large-scale seeds

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detection in modern seed industry.

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Key words: Hyperspectral imaging; Rice kernels; Rice false smut (RFS); Pathological analysis; Prac-

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ticability analysis.

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1. Introduction Rice (Oryza sativa L.) is a staple food for nearly half of the world's population. The safe production of rice is of great significance for ensuring our food security. However, rice is exposed to various fungus during growth and development, which will lead to a serious decline in quality and yield. Rice false smut (RFS) is a disease caused by the ascomycete fungal pathogen Villosiclava virens (anamorph: Ustilaginoidea virens Takahashi) with rice as the primary host [1]. In recent years, RFS has been reported to be a devastating

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disease in many major producing countries of rice, like China, Japan, Indian and USA [2-3]. Villosiclava viren converts single grain into a dark green RFS ball by infecting developing spikelet. Such RFS balls have

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been proved to contain mycotoxins like ustiloxins and ustilaginoidins, which are harmful to plants, humans and animals [4-5]. Moreover, rice grain is not only the initial product of rice-related food, but also the seed

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for propagation in the next year. The vigor and germination rate of rice seeds infected with RFS are lower than that of healthy seeds [6]. And the RFS fungus can be easily spread from the diseased regions to the

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diseased-free regions through the seeds circulation. Thus, strengthening the detection of rice kernels infected with RFS is necessary so that timely measures such as fungicide application can be performed.

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Rice kernels which are heavily infected with RFS can be distinguished by human eyes since they are generally wrapped in a dark green RFS ball. However, this visual inspection method is time consuming and laborious. Moreover, the rice kernels not coated by RFS balls may also be infected with RFS due to contacting

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with kernels infected heavily during growth and postharvest. Some accurate methods such as direct detection of pathogenic fungi by real-time PCR [7] or indirect measurement of toxins in aqueous extracts using enzyme-linked immunosorbent assay (EILSA) and high performance liquid chromatography (HPLC) [8-9] are usually sample destructive and can only detect limited samples. Therefore, it is urgent to develop a rapid and

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accurate method for detecting large-scale rice samples in modern food and seed industry. Near-infrared spectroscopy (NIR), especially the emerging of NIR hyperspectral imaging (NIR-HSI)

technology, provides a reliable alternative to detect the infected grain kernels from the healthy kernels [1011]. The spectral signal from 750 nm to 2500 nm produced by NIR can reflect the fundamental vibration and rotational stretching information of molecular bonds like O-H, N-H, and C-H in the sample [12]. The basis of using these technologies to detect infected grain kernels is that some metabolites have been produced by

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the pathogenic fungi and have changed the internal physiological and biochemical characteristics of grain kernels. Integrating spectroscopy and imaging technologies in one system, NIR-HSI can provide greater amount information than NIR. Visualizing some samples’ properties like category attribute and chemical components distribution become possible when combining the spectral information and corresponding spatial information of each pixel on the sample surface. In addition, the superiority of batch detection makes this technology more suitable in practical applications.

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Previous studies have demonstrated the feasibility of NIR-HSI technology to detect infected grain kernels [13-15]. For example, H. Lee et al. [16] utilized NIR-HSI to detect watermelon seeds infected with

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cucumber green mottle mosaic virus (CGMMV). W. Wang et al. [17] demonstrated that NIR-HSI was a powerful tool for detecting aflatoxin in maize kernels inoculated with Aspergillus flavus conidia in the field.

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S. Tekle et al. [18] investigate the feasibility of NIR-HSI to detect Fusarium damage in single oat kernels. For rice, U. Siripatrawan et al. [19] develop a method based on HSI and chemometrics methods for monitor-

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ing the stored brown rice infected with spoilage fungal. C. D. Sirisomboon et al. [20] applied NIR spectra to determine the degree of fungal infection in rice samples. However, the utility of this technology for detecting

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rice kernels infected with RFS has not been described in existing studies. Additionally, the grain kernels used in above studies were inoculated either in field or in laboratory. Little research explored the results’ difference and mutually verified the models derived by these two infected methods. Few detection models based on

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hyperspectral images considered different rice varieties and different infection degrees. Moreover, more accurate pathological analysis needs to be performed as a reference for the true infection status of rice kernels. There is still a wide gap between the feasibility of this technology and its practical application. Therefore, this study aimed to investigate the practicability and generalizability of utilizing NIR-HSI to

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detect rice kernels infected with RFS and focused on achieving three objectives: (1) to label the true infection status of rice kernels in different conditions and analyze the detection effects of rice kernels with different infection degrees; (2) to explore the possibility of boosting detection accuracy through extracting fingerprint wavelengths; (3) to assess the practicality of detection model based on laboratory-inoculated rice kernels using naturally-infected kernels of different varieties collected in the field. 2. Materials and methods

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2.1. Rice kernel material and inoculation conditions Two types of rice kernel materials were prepared in this study: kernels inoculated artificially in the laboratory and kernels naturally infected in the field. The variety Xiushui 134 was utilized for inoculation in the laboratory. Spore suspension was configured by soaking the RFS balls in sterile water. The initial spore concentration was 2.6 × 105 conidial ml-1. Other spore suspension with concentrations of 2.6 × 104, 2.6 × 103, and 2.6 × 102 conidial ml-1 were configured through continuous dilution. The kernels to be inoculated were

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placed in petri dishes with sterile filter papers, and 50 μl spore suspension was evenly dropped on each rice kernels using a sterile pipetting gun. The inoculated rice kernels were placed in a fume cupboard for 6 hours,

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and placed in indoor environment with temperature of 28 °C for 7 days. Another control group was set up, and the rice kernels was inoculated with sterile water instead of the spore suspension, and the other treatments

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were the same as above. In total, 5 groups of kernels (coded by CK, 1, 10, 100,1000 (the dilution multiples)) with different infection degrees were obtained in the laboratory.

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In order to explore the influence of rice varieties on the detection model, the naturally-infected rice kernels of Xiushui 134 and Zhejing 70 were collected in the field. During the rice harvest in 2018, the rice

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ears infected with RFS were collected in paddy fields in Hangzhou and Ningbo, Zhejiang Province, China by plant protection experts. The healthy rice ears in the same field were also collected for comparison. Each kind of rice ears was independently placed in a plastic bag and transported to the laboratory for analysis in

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24 hours. The rice ears in each group were dried separately at room temperature, and the rice kernels were obtained by removing other impurities such as stems manually. 2.2. Hyperspectral image acquisition

The hyperspectral images of rice kernels were collected by a NIR-HSI system with a spectral range of

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874.41~1734.91 nm. An ImSpector N17E imaging spectrograph (Spectral Imaging Ltd., Oulu, Finland), a Xeva 922 CCD camera (Xenics Infrared Solutions, Leuven, Belgium) and an OLES22 lens (Spectral Imaging Ltd., Oulu, Finland) were the key components of this system. Additionally, two 150 W tungsten halogen lamps (3900e Lightsource; Illumination Technologies Inc.; West Elbridge, NY, USA) equipped symmetrically under the camera were employed to provide the illumination. The whole system worked in a line-scanning manner and each scan yielded 256 wavelengths of 326 pixels. The HSI data of the entire sample can be

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obtained by moving the sample using a miniature conveyer belt controlled by a stepped motor (Isuzu Optics Corp., Taiwan, China). Multiple rice kernel samples were placed on a black plate so that the system can perform batch detection. Prior to formal image acquisition, the instrument parameters were iteratively adjusted so as to produce clear and undistorted hyperspectral images. In this study, the exposure time of the camera, the distance between the camera lens and the rice kernels, and the velocity of conveyer belt movement were finally regulated

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to 3 ms, 15 cm and 13 mm/s, respectively. The original hyperspectral images were further corrected using a white (hyperspectral image of a white Teflon tile with a reflectance close to 100%) and a dark (hyperspectral

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image of a black cloth with a reflectance close to 0) reference image to eliminate interference from dark current and other factors. This process could be automatically carried out using a HSI analysis software

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(Xenics N17E, Isuzu Optics Corp., Taiwan, China). 2.3. Pathological analysis

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The pathological state of rice kernels was identified using PCR method by professionals of College of Agricultural and Biotechnology, Zhejiang University. Genomic DNA was extracted from ground rice kernels

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using Ezup Column Fungus Genomic DNA Kit (Sangon Biotech Co., Ltd., Shanghai, China) according to the manufacturer’s protocol. DNA concentration was measured using a NanoDrop 2000 spectrophotometer

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(Gene Co. Ltd., Hong Kong, China). One pair of primers (F3 and B3) were selected for detection and identification of the RFS fungus [21], as shown in Table 1. PCR was performed in a 50 μl reaction system by blending 2 μl forward primer, 2 μl reverse primer, 1 μl template, 20 μl ddH2O and 25 μl 2X Taq Master Mix contained 0.1 U/μl Taq DNA Polymerase, 0.4 mM dNTP, 3 mM MgCl2, 2X PCR buffer, and bromophenol blue solution. PCR amplification process was implemented using a modified condition: 3 min pre-denatura-

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tion at 94 °C, 30 s denaturation at 94 °C, 30 s annealing at 57 °C, and 30 s extension at 72 °C and storage at 4 °C [21]. The amplicons were then separated using 1% agarose gel electrophoresis. Finally, the gel electropherogram was obtained under UV light. 2.4. Spectral analysis 2.4.1. Spectra extraction and preprocessing

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A hyperspectral image in this study contained multiple rice kernels and black background information. Due to the large difference in reflectance spectra between rice kernels and background, a threshold segmentation algorithm was firstly used to isolate the rice kernels from the black background. Each rice kernel region was defined as a region of interest (ROI), and then the pixel spectra in each ROI were extracted. The head and tail of the spectral curves were observed to contain plenty of noise, which might be due to the system instability. Therefore, only the middle 200 bands from 975 nm to 1646 nm were retained for further analysis. To further remove the random noise and perfect the detection capability of subsequent models, wavelet trans-

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forms (WT) employing Daubechies 6 as basis function and 3 as the decomposition level was introduced to preprocess these pixel spectra. The average spectrum of all pixels in each ROI was treated as the sample

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spectrum. In this study, a total of 1720 average spectra were collected for 1000 laboratory-inoculated Xiushui 134 kernels (200 × 5 = 1000), 560 field-infected Xiushui 134 kernels (280 × 2 = 560) and 160 field-infected

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Zhejing 70 kernels (80 × 2 =160), respectively. Since we aimed at establishing detection model based on the

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laboratory-inoculated rice kernels and applying the model to detect field-infected kernels for evaluating its practicality, a Kennard-Stone algorithm was used in advance to divide the spectra samples of each group of

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laboratory-inoculated rice kernels into a sub calibration set (150 samples) and a sub prediction set (50 samples) at a ratio of 3:1. Thus, a total of 750 spectra were used to construct the detection models and 250 spectra were used to verify the performance of the models. The spectra of the field-infected kernels were all em-

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ployed to construct an auxiliary prediction set to investigate the practicality of the detection model. 2.4.2. Chemometrics analysis

Qualitative analysis of hyperspectral samples by principal component analysis (PCA) were implemented to explore the discriminability between healthy kernels and infected kernels. More accurate quantitative anal-

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ysis was carried out by partial least-squares discriminant analysis (PLS-DA), support vector machines (SVM), and extreme learning machine (ELM). The full wavelengths were firstly utilized to construct PLADA, SVM, and ELM detection models. Due to high correlation between high dimensionality variables in hyperspectral image, wavelengths with fingerprint information were suggested to accelerate the modeling process and improve the model performance [22]. Thus, selecting the proper wavelengths from the full bands was very important for optimizing the detection models. In the subsequent process, different fingerprint

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wavelengths were extracted and compared using three representative variable selection methods including competitive adaptive reweighted sampling (CARS), random frog algorithm (RF), and successive projections algorithm (SPA). They were then used for PLA-DA, SVM, and ELM modeling. All of the above models were based on laboratory-inoculated rice kernels, and were compared to derive an optimal model. This optimal model was further validated by detecting field-infected kernels. The idea of the whole chemometrics analysis was shown in Fig. 1.

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PCA is believed to be a powerful tool for getting a glimpse of patterns hidden in spectral data [23]. It projects the spectral variables into so-called principal components (PCs) by maximizing the sample variance.

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According to the cumulative interpreted variance, the first few PCs can be retained to form a new representation of the original high dimensional matrix. The score images of these PCs can reflect the differences or

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similarities between different samples. In this study, the projection matrix was firstly calculated using the calibration set. Then, it was applied to analyze the hyperspectral images in the prediction set and the auxiliary

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prediction set to explore the relationship between the laboratory-inoculated rice kernels and the field-infected rice kernels.

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PLS-DA is reported to be very effective for exploring the linear relationship between the spectral variables and category variable. Unlike PCA, PLS-DA converts the raw spectral variables into latent variables (LVs) which not only carry the variation information in the raw spectral data as much as possible but also

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maximize the correlation with the categorical variable [24]. The number of LVs n was set to 2~20, and the optimal n was determined through minimizing predicted residual error sum of squares under a leave-one-out cross-validation operation in this study. SVM is another discriminant model widely used in spectral analysis due to its ability to handle both linear and nonlinear problems. The shining point of SVM is the introduction

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of kernel trick, which enables an implicit mapping to transform linear unseparable data into a linear separable space [25]. Radial basis function (RBF) is the most commonly used kernel. The penalty factor c and the kernel parameter g need to be determined prior to modeling. In this study, these two parameters were all set to 2-8~28 and optimized through a five-fold cross validation and a grid-search procedure in this study. ELM is a feedforward neural network with a single hidden layer, where the connecting weights between the input layer and the hidden layer are randomly assigned and never updated. Such settings have been proved to boost

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the generalization performance and learning efficiency of the model [26]. The number of neurons n’ in the hidden layer, the only parameter need to be set, was determined by setting the value to 10~400 with an interval of 5 to build the ELM model and selecting the value corresponding to the best performance in this study. CARS is a novel variable selection method that combines Monte Carlo sampling with PLS model regression. All variables making a high weight of the absolute value of the PLS regression coefficient in each

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sampling are preserved to constitute a subset [27]. The wavelengths in the subset that minimizes the root mean square error (RMSEV) of the PLS model are determined as the final characteristic wavelengths. In this

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study, the sampling ran 1000 times, and a full cross verification was utilized to evaluate the effectiveness of each subset. RF is a promising variable selection method based on reversible jump Markov Chain Monte

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Carlo approach. It works in an iterative manner to select the candidate variables. At the end of the iteration, the selection probability of each variable is calculated as a measure of variable importance [28]. In the present

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study, RF was performed to extract the wavelengths with fingerprint information from the full wavelengths for detecting the response of rice kernels to the infection of RFS. SPA is a forward variable selection algo-

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rithm designed for selecting combinations of variables with minimal redundancy and collinearity [29]. It also works in an iterative manner. During each iteration, each variable is projected into other variables, and the variable with the largest projection vector is employed to establish a candidate subset of variables. Here, the

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optimal wavelengths were finally determined according to minimum RMSEV in the calibration set of a multiple linear regression (MLR) model. 2.4.3. Detection visualization

Visualizing whether a rice kernel is infected provides a quick and intuitive inspection manner for rice-

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related food or seed producers to take effective measures timely. Regions in hyperspectral image with the same spectral features should belong to the same category. Due to the correspondence between spectral features and spatial locations, the calibration model based on the average spectra of the ROIs could be utilized to predict the category of each ROI in the hyperspectral image and present in the form of a "chemical image". In this study, the optimal model based on the laboratory-inoculated rice kernels was selected to predict and

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visualize whether the field-infected rice kernels were infected. The flow for constructing this kind of chemical image was illustrated in Fig. 1. 2.5. Statistical analysis ENVI version 4.6 (ITT Visual Information Solutions, Boulder, CO, USA) was employed to crop hyperspectral images to remove the cluttered background. Unscrambler version 10.1 (CAMO AS, Oslo, Norway) was introduced to conduct PLS-DA. MATLAB version R2018a (The MathWorks, Natick, MA, USA) was

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utilized to perform spectra extraction, chemometrics analysis and image visualization. In addition, Origin version Pro 8.5 SR0 (Origin Lab Corporation, Northampton, MA, USA) was used to prepare the graphs.

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3. Results

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3.1. PCR analysis results

Fig. 2 shows the gel electropherogram, where 1~5 represented the DNA amplification status of CK,

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1000, 100, 10, and 1 group of laboratory-inoculated rice kernels, respectively. 6~9 represented the DNA amplification status of infected Xiushui 134, healthy Xiushui 134, infected Zhejing 70 and healthy Zhejing

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70 (we labeled them according to the initial screening) of rice kernels collected in the field. It could be clearly seen that no amplification of DNA was observed at 1, 7, and 9 which corresponded to three groups rice kernels we labeled as healthy, while other positions corresponding to the rice kernels we believed to be in-

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fected showed strips of different brightness. In addition, the brightness of the strips from 1 to 5 was gradually increased. As they were attributed to the laboratory-inoculated rice kernels with increasing infection degree, this was agreeing with our expectation. Therefore, the results of PCR confirmed the correctness of our labeling of rice kernels from a molecular point of view. This was owing to the strictly screen of rice kernels by

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plant protection experts prior to collection. 3.2. Overview of spectral profiles

The NIR spectral range used for analysis was 975~1646 nm. Fig. 3 shows the average spectra with

standard deviation (SD) of different rice kernels. This figure illustrated that the spectral curves of rice kernels with different varieties, different infection conditions and different infection status shared consistent fluctuation patterns. Similar peak and valley positions appeared at 1123nm, 1207 nm, 1308 nm and 1470 nm in

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these spectral curves. Previous studies have reported that the peak at 1123 nm and the valley around 1207 nm are due to the second overtone of C–H stretching vibrations of carbohydrates [16, 30]. The peak around 1308 nm is mainly related to the first overtone of Amide B, and the valley around 1470 nm is caused by the first overtone of the N–H stretching vibrations of protein [31, 32]. As complex changes have occurred in the infected kernels, some differences could be observed from the average spectra of healthy and infected rice kernels. For both Xiushui 134 and Zhejing 70, the infected rice kernels showed a higher reflectance than the healthy rice kernels. In addition, it could be seen in Fig. 3 (a), the spectral reflectivity of the kernels presented

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a slight rising trend with the infection degree increased, especially at the positions of the two valleys. This might result from the infection of Villosiclava virens which produced toxins in the rice kernels. In addition,

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the reflectivity difference between the two laboratory-inoculated rice kernels was smaller than that between the two field-infected rice kernels. This was owing to that the relatively low concentration of spore suspen-

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sion configured in the laboratory could not make a significant difference between the healthy and infected

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groups. Thus, whether the detection method based on laboratory-inoculated rice kernels could be used to detect field-infected kernels was a problem worth studying.

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3.3. PCA qualitative analysis

PCA was firstly performed to explore the spectra difference between healthy and infected rice kernels in PC space. The projection matrix and the contribution rate of each PC was calculated using 750 laboratory-

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inoculated rice kernels in calibration set. Then, using this projection matrix, the randomly selected spectra of 100 laboratory-inoculated Xiushui 134 rice kernels, 36 field-infected Xiushui 134 rice kernels and 40 fieldinfected Zhejing 70 rice kernels were employed to perform PCA. As the first three PCs were able to represent spectral variations close to 99.76% (98.09% for PC1, 1.40% for PC2, and 0.27% for PC3), the score images

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of these PCs were introduced to identify and visualize the patterns inherent in spectra data. In Fig. 4 ~5, the pixels in seed regions were given different score values, result in different color distributions of seeds with different infection conditions. However, it could be seen that the pixels in the middle part of most seeds had larger score values than those in the edges, thus showing warmer color. This consistency might be due to the slight smaller distance between the lens and the middle part of the seeds than that between the lens and the edges.

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For laboratory-inoculated kernels (shown in Fig. 4), the infection degree was also investigated. It could be seen that the score images of PC1 and PC2 showed similar patterns, that is, healthy kernels had higher scores (shown in red color), while the scores became lower (tends to be blue color) as the infection degree deepened. Nevertheless, an opposite rule seemed to be found in the score images of PC3. Healthy kernels were dominated by blue, while those infected were mostly red. For the field-infected kernels (shown in Fig. 5), consistent representation could be observed in the score images of Xiushui 134 and Zhejing 70. However, unlike PC1 of laboratory-inoculated kernels, the score images here did not show significant difference be-

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tween healthy and infected kernels. The first visualization of this difference was observed in the score images of PC2, and the scoring performance was same as the PC2 of laboratory-inoculated kernels. Differences were

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also existed in the score images of PC3, but healthy kernels showed higher scores than infected kernels. The trend difference between the PC score images of the laboratory-inoculated kernels and those of the field-

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infected kernels might be due to the influence of other pathogens and ash on the rice kernels in the field.

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Even so, PCA has demonstrated the separability between healthy and infected rice kernels for both laboratory-inoculated and field-infected conditions. As this discrimination was only based on trend, more accurate

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quantitative analysis was required.

3.4. Quantitative analysis based on full wavelengths

In this study, PLS-DA, SVM, and ELM were introduced to establish discriminant models based on the

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full wavelengths. Due to the limitation of microscope counting, the number of spores contained in the laboratory-inoculated rice kernels and field-infected kernels could not comparable. Therefore, this study aimed to distinguish healthy and infected rice kernels. The discrimination models were firstly used to distinguish healthy rice kernels and mixed kernels with different infection degrees. As shown in Fig. 6, with the kernels

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having a lower infection degree were added to the negative class, the prediction performance of all three models was gradually decreasing as expected. Yet for all that, the minimum accuracy was above 94%. According to the concentration of spore suspension, the rice kernels with the lowest infection degree had a maximum of 13 spores. This meant that discriminant models could detect the rice kernels with very low infection degree. The CKVS1+10+100+1000 group with most infection degrees in the negative class was used for the subsequent analysis. The detailed accuracy and parameters of the calibration set and prediction

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set of this group were summarized in Table 2. All three models performed well. As the accuracy of the prediction set was lower than that of the correction set, these three discriminant models based on full wavelengths had a slight over-fitting phenomenon. Performance might be improved by picking out characteristic wavelengths with fingerprint information from the redundant full wavelengths. 3.5. Characteristic wavelengths selection Table A1 summarized the characteristic wavelengths identified by CARS, RF, and SPA. After pro-

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cessing by these three approaches, the number of wavelengths was reduced to 28.5%, 23.5% and 7.5%, respectively. Since SPA was designed for selecting variables with minimal redundancy and collinearity, it

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picked the least wavelengths, which might affect the performance of the discriminant models. Fig. 7 showed the specific locations of the wavelengths selected by these three approaches (the spectral curves shifted up

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and down for clarity). It could be clearly seen that many of the wavelengths were coincident. Among them, the spectral region between 990 nm and 1120 nm was related to the second overtone of N-H stretching [33].

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The most bands in 1153~1375 nm were assigned to the combination of the first overtone of amide B with the fundamental amide vibrations [31]. The wavelengths around 1473~1497 nm were the important bands for

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protein characterization resulting from the first overtone of the N–H stretching vibration [31, 34]. And the bands region between 1540 nm and 1600 nm belonged to the N–H stretching vibrations [35].

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Consistent with the description of L. Cséfalvayováa et al. [35], protein could be identified in these bands of NIR region by considering the correlative functional groups. While X. Wang et al. [36] reported that ustiloxins isolated from RFS balls was a group of cyclic peptides with an ether linkage. W. Sun et al. [37] isolated another toxin, ustilaginoidins, from the ethyl acetate extract of RFS balls, which was proved to contain two naphtho-pyrones. The tissue structure and biochemical components in the rice kernels might also be

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damaged by these toxins, making the reflectivity spectra of rice kernels changed. That is to say, some fingerprint information related to RFS infection contained in NIR spectra. The physiological status and the RFS infection situation of rice kernels could be monitored by NIR-HI. This was also the theoretical basis to establish the statistical correlation between the infective state and spectral data. 3.6. Quantitative analysis based on characteristic wavelengths

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The actual roles of the characteristic wavelengths selected in above section were evaluated by using them to develop detection models. The distinguishing effects of healthy kernels and mixed kernels with different infection degrees by different models were also compared. As shown in Fig. 6, for all models, the accuracy of CKVS1+10+100+1000 group was relatively low but still satisfactory, agreeing with the results based on full wavelengths. The performance of the detection models on this group was summarized in Table 2. In general, the detection models based on the characteristic wavelengths presented higher accuracy than those based on the full wavelengths. As previously envisioned, the models constructed based on the charac-

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teristic wavelengths selected by SPA obtained lower accuracy than that based on the wavelengths selected by CARS and RF, since SPA selected least wavelengths and lost some useful information. RF performed

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best among these three variable selection methods. When combined with ELM, it achieved the highest accuracy of 99.33% on the calibration set and 99.20% on the prediction set. The overall results showed that

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selection of suitable characteristic wavelengths with fingerprint information could reduce redundant infor-

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mation and improve model performance. This could also lay the foundation for developing more efficient and reliable multispectral instrument to detect the infected rice kernels in the future.

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3.7. Practicality verification of detection model and visualization of detection results The discriminant models established above could detect the laboratory-inoculated rice kernels effectively, but whether it can be applied in the practical detection of field-infected rice kernels was still in doubt.

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In this study, the optimal model built based on the laboratory-inoculated kernels, RF-ELM, was applied to detect the field-infected kernels to verify its practicability. All rice kernels collected in the field were used as samples to be detect. As shown in Table 3, the detection model performed slightly better on Xiushui 134, the same variety as the laboratory-inoculated kernels. This was understandable because there are some differ-

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ences between the spectral reflectivity of different varieties of rice kernels [38]. Neverthless, this model still achieved an accuracy of 89.38% on Zhejing 70. It was noted that the infected kernels of these two varieties were more likely to be misclassified into healthy kernels. This might be due to the fact that the rice kernels infected in the field has more different infection degrees, which were more easily to be confused with healthy kernels.

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Since NIR-HSI technology can obtain spectral and spatial information simultaneously, it was possible to draw a chemical image to inspect the detection results intuitively and clearly. The optimal model based on laboratory-inoculated kernels, RF-ELM, was further utilized to estimate the class attribute of single kernel collected in the field in the examined hyperspectral images. Different categories were marked with different colors (blue for healthy Xiushui 134, pink for infected Xiushui 134, green for healthy Zhejing 70, and yellow for infected Zhejing 70) in Fig. 8. It could be clearly seen that, in this chemical image, only 5.63% (9/160 × 100 = 5.63%) kernels were misclassified for Xiushui 134, and 10.63% (17/160 × 100 = 10.63%) for Zhejing

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70. That is to say, RF-ELM also produced satisfactory performance on the rice kernels collected in the field, which was agreed with the results of above quantitative analysis. The overall results confirmed the feasibility

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of applying the discriminate model based on the laboratory-inoculated rice kernels to detect the kernels collected in the field and such model generally obtained satisfactory accuracy. NIR-HSI technology paired with

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chemometrics analysis is a promising method for detecting and locating single infected rice kernel quickly

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and accurately, which is expected to be a powerful tool for large-scale samples detection in modern grainrelated food and seed industries.

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4. Discussion

One of the goals of this study was to investigate the utility of the detection model based on laboratory-

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inoculated rice kernels through applying it on identifying field-infected kernels. The construction and prediction of models in many existing researches related to cereal disease detection were mostly based on the kernels under same conditions [15, 39-40]. Since the ultimate goal of our study was to popularize research findings to actual application, it was not enough even if models based on the laboratory-inoculated kernels achieved satisfactory results on discriminating kernels under the same condition. It is relatively convincing

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to use the model based on the field-infected kernels to detect kernels collected in the field. However, in complex and variable field environment, the rice kernels are exposed to multiple pathogens and other substances. They can affect the optical properties of the rice kernels, thus further influence the accurate construction of the detection model. Moreover, as we cannot ensure the samples collected in the field is really infected with the disease we want to detect, it is necessary to measure the pathogen infection using some biochemical methods [41-42] which were almost time consuming and reagent dependent. In this study, we

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employed PCR method to determine the pathological state of rice kernels as described in section 2.3. Fortunately, owing to rigorously screen by plant protection experts before collection, we could guarantee that the samples corresponded to the real labels. Take H. Lee et al. [16] as another example, in order to obtain 96 watermelon seeds infected with CGMMV, they inoculated leaves of watermelon plants raised in an isolated location in Korea, and waited for the fruit to ripen, and then used PCR to determine whether the seed was infected with CGMMV. Therefore, it is very costly to directly construct a model based on the field-infected rice kernels. In contrast, laboratory-inoculated rice kernels are more readily available and experimental con-

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ditions in laboratory are easier to control. Therefore, this study put forward the idea of utilizing the model established based on the laboratory-inoculated rice kernels to detect the field-infected kernels. The experi-

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also be used in the diseases detection of other seeds or crops.

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mental results proved that this idea was feasible and had achieved satisfactory performance. This idea can

In our experiment, some black powder which we think might be the metabolite of pathogens was at-

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tached to the surface of field-infected rice kernels occasionally, while the laboratory-inoculated kernels did not show obvious symptoms even on the 14th day after inoculation (shown in Fig. 9). Because the field-

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infected kernels separated were near the RFS balls in the rice ears while the surface of the laboratory-inoculated kernels with highest infection degree had a maximum of 13 000 spores (higher concentration of spore suspension caused squeezing between the spores, resulting in incalculable within the scope of microscope),

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we believed that the highest infection degree of laboratory-inoculated kernels was still lower than the infection degree of the field-infected kernels. This difference could also be understood from the average spectra in Fig. 3. Thus, we didn’t know how well the idea, establishing the model based on the laboratory-inoculated rice kernels and applying it to detect the field-infected kernels, works at first. Combined the promising prop-

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erty of hyperspectral imaging to highlight the difference between healthy and infected rice kernels with the strong ability of chemometrics methods to construct statistical relationships, we finally achieved satisfactory results. Although two varieties of field-infected rice kernels, Xiushui 134 and Zhejing 70, were collected for exploring the influence of varieties on the detection model in this study, these two varieties all belonged to japonica rice. In the future, the detection effect of the model on glutinous rice and indica rice need to be investigated, since some differences still existed between the spectra of different types of rice kernels [43].

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Nevertheless, NIR-HSI has been proved to be a powerful tool, and the practicability of the detection model has also been validated through this study. 5. Conclusion Rapid and non-destructive detection of rice kernels infected with RFS was investigated using macroscopic NIR-HSI together with microscopic PCR in the present study. The results of pathological analysis labeled the true infection status of rice kernels collected in different conditions. The laboratory-inoculated

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rice kernels and the field-infected kernels were used to construct the detection model and verify the model’s practicability, respectively. Qualitative analysis by PCA showed the spectral difference and separability be-

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tween healthy and infected rice kernels. The fingerprint spectral wavelengths selected by CARS, RF, and SPA implied chemical composition information related to RFS pathogen infection. Further quantitative anal-

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ysis was performed through establishing PLS-DA, SVM, ELM discriminate models based on the full wavelengths and the characteristic wavelengths. All models performed well on distinguishing healthy rice kernels

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and mixed kernels with different infection degrees. RF-ELM model outstood from all the other models. The satisfactory results on detecting the field-infected rice kernels achieved by RF-ELM model proved the pos-

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sibility of popularizing the detection model to practical application. Constructing a detection model based on reduced fingerprint wavelengths would also be conducive for developing a low-cost and reliable multispec-

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tral imaging instrument for online application. The classification visualization of rice kernels also provided a convenient method for rapid detection of large-scale seeds in modern seed industry. The overall results demonstrated that the superiority for rapid and non-destructive image monitoring of infection status of rice kernels makes NIR-HSI an excellent candidate for on-line cereal evaluation and process monitoring.

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Funding

This work was supported by National Key Research and Development Program supported by Ministry of Science and Technology of the P.R. China (2018YFD0101002) Author Statement Na Wu, Hubiao Jiang, and Fei Liu designed the experiment. Na Wu, Hubiao Jiang, Yiying Zhao and Chunxiao Mi performed the experiment. Na Wu and Chu Zhang contributed to the data analysis. Hubiao Jiang performed the pathological analysis. Na Wu wrote the manuscript. Fei Liu, Yidan Bao, Jingze Zhang,

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Chu Zhang, Wenjian Song and Yong He provided suggestions on the experiment design and discussion sections. Yidan Bao and Yong He provided financial support. Wenjian Song provided the rice kernel materials. The authors declare no competing financial interest.

Declaration of interests

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Author Biographies:

Na Wu was born in 1991 in China. She received her Ph.D. degree from University of Science and Technology of China in 2018. She is currently a postdoc in College of Biosystems Engineering and Food Science at Zhejiang University. Her research focuses on non-destructive detection of seeds and plant disease using spectroscopy and imaging techniques.

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Hubiao Jiang is a M.S. student in College of Agriculture and Biotechnology at Zhejiang University. His research direction is studying the pathogenic mechanism and detection method of seed-brone disease.

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Yidan Bao is an associate professor in in College of Biosystems Engineering and Food Science at Zhejiang University. She has engaged in the research of crop and seed diseases detection based on spectroscopy and imaging technology for many years. She is undertaking a National Key Research and Development Program for non-destructive detection of seed diseases. Chu Zhang graduated from Northwest Agriculture & Forestry University with B.S degree in 2010. He received his Ph.D. degree from Zhejiang University in 2016. He is now a

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postdoc in College of Biosystems Engineering and Food Science at Zhejiang University. His research focuses on nondestructive measuring various quality attributes of agricultural products using spectroscopy and imaging techniques.

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Jingze Zhang is an associate professor in College of Agriculture and Biotechnology at Zhejiang University. He has long been engaged in the research of isolation, identification and pathogenic mechanism of plant pathogenic fungus.

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Wenjian Song received his Ph.D. from Zhejiang University in 2006. He is currently an associate professor in Agricultural Technology Promotion Center at Zhejiang University. He is mainly engaged in the research of seed science and has long been committed to the promotion of agricultural technology. Yiying Zhao is currently a Ph.D. student in College of Biosystems Engineering and Food Science at Zhejiang University. Her research focuses on nondestructive detection of quality attributes of agricultural seeds using spectroscopy techniques. Chunxiao Mi is a M.S. student in College of Agriculture and Biotechnology at Zhejiang University. Her involves in the research projects of seed disease detection. 25

Yong He is a Qiushi Distinguish Professor in College of Biosystems Engineering and Food Science at Zhejiang University. His research is related to the precision agriculture, agricultural mechanization and automation, and internet-of-things in agriculture. He has published more than 400 papers, and is listed as 1% of global most cited scientists in agricultural science based on 2018 ESI report.

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Fei Liu received his B.S degree from China Agricultural University in 2006, and Ph.D. degree from Zhejiang University in 2011. He went to Hokkaido University in Japan as a senior visiting scholar in 2012. He is now a professor in in College of Biosystems Engineering and Food Science at Zhejiang University. He ever won a silver award in the Fourth National Young Science Star. His research focuses on multi-source spectroscopy detection technology for agricultural information. He has published over 100 journal papers and conference proceedings.

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Figure captions Fig. 1. Framework of chemometrics analysis and classification visualization for detecting rice kernels in-

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fected with RFS.

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Fig. 2. Gel electropherogram of PCR for pathological analysis

Fig. 3. NIR average spectra with SD: (a) for laboratory-inoculated rice kernels with different infection degrees; (b) for laboratory-inoculated Xiushui 134 rice kernels; (c) for field-infected Xiushui 134 rice kernels; (d)for field-infected Zhejing 70 rice kernels.

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Fig. 4. Score images of the first three PCs of laboratory-inoculated Xiushui 134 rice kernels.

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Fig. 5. Score images of the first three PCs of field-infected Xiushui 134 and Zhejing 70 rice kernels.

Fig. 6. Accuracy of different discriminate models based on full wavelengths and characteristics wavelengths for distinguishing healthy rice kernels and mixed kernels with different infection degrees.

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Fig. 7. The specific locations of the characteristic wavelengths selected by CARS, RF, and SPA.

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Fig. 8. Classification visualization of field-infected rice kernels.

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Fig. 9. Symptom difference between laboratory-inoculated and field-infected rice kernels.

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Table 1 Sequences of the primers used for PCR[21]. Primer name

Primer type

Sequence

Length

F3

Forward primer

GCTCCTGGGATTCTTTGGTG

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B3

Backward primer

GCTCGATCGGGACAACCA

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Table 2 group by different methodsa. Discriminate

Wavelengths selection

methods

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The detailed accuracy and parameters of the calibration set and prediction set of CKVS1+10+100+1000

Calibration set

Raw

10

98.00

95.60

CARS

8

98.67

97.60

RF

6

98.53

98.40

SPA

9

97.20

96.40

(256, 0.0039)

97.33

94.80

(256, 0.017)

97.87

95.20

RF

(256, 0.0082)

96.80

98.00

SPA

(256, 0.017)

96.53

96.00

Raw

330

100

98.40

CARS

150

98.93

99.20

RF

110

99.33

99.20

SPA

70

98.80

97.60

Raw

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CARS

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PLS-DA

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SVM

Prediction set

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ELM

P of different models: (n) for PLS-DA, (c, g) for SVM, (n’) for ELM.

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Table 3

The performance of detection model on field-infected rice kernels. Model

Sample Number

Xiushui 134 Healthy

Infected

Accuracy

Sample

(%)

Number

Zhejing 70 Healthy

Infected

Accuracy (%)

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RFELM

280

275

5

98.21

80

78

2

97.5

280

45

235

83.93

80

15

65

81.25

91.07

160(Total)

89.38

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560(Total)

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