Wheat is one of the earliest cultivated crops in the world, with a large yield and high nutritional value. Approximately 40% of the global population depends on wheat as a dietary staple. Thus, wheat yield and security are crucial for food security. However, with global warming, Fusarium ear blight (scab), one of the three most notable wheat diseases, seriously affects wheat yield and quality. Wheat scab is considered a complex of cereal fungal diseases mostly caused by three types of fungi: Fusarium culmorum, Fusarium graminearum and Fusarium avenaceum (Parry, Jenkinson, & McLEOD, 1995). The infection of wheat ears was shown to mainly occur in the flowering period, which can shrink the wheat grains, decrease the weight of single wheat grains, and lead to a pale or pink grain color at maturity (Barbedo, Tibola, & Fernandes, 2015), resulting in a decrease in the wheat yield of approximately 10%. What is more important than the decreased yield is that the infected grains contain accumulated mycotoxins, such as deoxynivalenol (DON), which poses a serious threat to the health of people and livestock.
DON is highly toxic to animals and plants. The fungal toxin DON produced by the Fusarium pathogens of wheat scab can cause poisoning and lung and reproductive system infections, resulting in infertility, miscarriage and other conditions (Maloney et al., 2014). Developed countries worldwide have set very strict standards for the quarantine of Fusarium scab. If the toxin DON is detected in food, it cannot be consumed by humans. If the DON content exceeds 2 mg/kg, wheat grains cannot be purchased as feed. To protect humans from the risks related to these mycotoxins, it is urgent to control and detect wheat scab accurately and efficiently. Conventionally, several methods have been developed to measure and estimate the levels of DON and its derivatives, and the most common methods are chromatography and immunochemical techniques, such as high-performance liquid chromatography (HPLC), thin-layer chromatography (TLC), enzyme-linked immunosorbent assay (ELISA) and gas chromatography (GC) (Barbedo, Tibola, & Lima, 2017). However, some of the traditional detection methods normally not only require a precise experimental environment but also consume too much time and can be affected by human error and are therefore not suitable for large-scale wheat scab detection (Miedaner, Heinrich, Schneider, Oettler, Rohde, & Rabenstein, 2004). For instance, TLC is cumbersome, with poor accuracy and specificity, and ELISA requires several reagents, produces a relatively large amount of waste, and is known to yield biased detection results.
To predict the disease level of wheat scab more accurately, infrared (IR) spectroscopy has been applied. In recent years, IR spectroscopy has become one of the most promising analytical tools available to researchers. This technique is associated with chemometrics and has been widely used in material identification and quantitative analysis because of its ability to achieve rapid and nondestructive detection with low energy consumption, simplicity and low costs (Garrigues & Guardia, 2010).
Near infrared (NIR) spectra contain information about overtone and combination bands of fundamental vibrations, and they are sensitive to many chemical groups and various molecular interactions (Manley, 2014; Blanco & Villarroya, 2002). NIR spectroscopy techniques have been widely applied in food composition determination and classification (Cortés, Barat, Talens, Blasco, &Lerma-García, 2018; Oliveira-Folador, Bicudo, Andrade, Bureau, Renard & Castilhos, 2018). In recent decades, NIR spectroscopy has been widely used in the wheat industry for the determination of mycotoxins in wheat (Pettersson & Aberg, 2003; Levasseur-Garcia, Pinson-Gadais, Kleiber, & Surel, 2010; De Girolamo, Cervellieri, Visconti, & Pascale, 2014; De Girolamo, Lippolis, Nordkvist, & Visconti, 2009). Using partial least-squares (PLS) regression and linear discriminant analysis (LDA), the authors showed that the overall classification and false compliant rates for the two models were 75—90% and 3—7%, respectively, and these studies proved that NIR techniques were suitable for detecting DON contamination in wheat grain (De Girolamo, Cervellieri, Visconti, & Pascale, 2014). In another article, modeling was performed using a soft independent modeling of class analogy (SIMCA) approach. The model was used to classify sound, scab-damaged, and mold-damaged wheat kernels (Delwiche, 2003). Levasseur-Garcia deduced that artificial neural networks achieved 98.8% successful identification of Fusarium by NIR spectroscopy (Levasseur-Garcia, Pinson-Gadais, Kleiber, & Surel, 2010). The above studies all proved the feasibility of extrapolating wheat scab damage by NIR with high accuracy through different models.
Spectra in the mid-infrared MIR region (4000 − 400\({cm}^{-1}\)), obtained using Fourier transform mid-infrared (FT-MIR) spectroscopy, provide information on the frequencies of fundamental molecular vibrations (Shi & Yu, 2017). Molecules such as proteins and fatty acids can then be identified based on the FT-MIR spectral signals produced by these vibrations, which typically result in multiple and convoluted bands representing one molecule (Pedersen, Wegner, Phansak, Sarath, Gaussoin, & Schlegel, 2017). Currently, FT-MIR spectroscopy has been successfully applied to agricultural products (Vermeulen, Fernández Pierna, Abbas, Dardenne, & Baeten, 2015; Grelet et al., 2016; Zaalberg, Shetty, Janss, & Buitenhuis, 2019). Peiris et al. (Peiris, Bockus, & Dowell, 2012) used mid-infrared attenuated total reflection (Mid-IR-ATR) spectra (4000 − 380\({cm}^{-1}\)) to analyze Fusarium damage of wheat kernels caused by F. graminearum and DON contamination. They discovered a shift in the absorption peak and absorption of some bands that increased due to Fusarium damage of wheat kernels in pericarp and germ. These phenomena indicate that MIR spectroscopy could detect the high concentration of DON that exists in Fusarium-damaged wheat kernels. Gregor Kos et al. (Kos, Lohninger, & Krska, 2002) proposed a model to quickly detect corn infected with Fusarium fungi by attenuated total reflection Fourier transform mid-infrared (FT-MIR-ATR) spectroscopy. When ergosterol and DON levels were more than 8.23 mg/kg and 0.13 mg/kg, respectively, the recognition accuracy exceeded 75%. This result proved the potential of the FT-MIR-ATR spectroscopic technique to identify corn that is infected with Fusarium fungi.
In addition, in the United States, Canada and some European countries, wheat quality requirements are relatively strict, and the wheat grain DON toxin content must be less than 0.5 ~ 2 ppm (Bai & Shaner, 2004). However, the concentration of DON in infected wheat is very low, even if it is substandard. Through experiments, it was found that it was difficult for IR spectroscopy to reflect and distinguish the change in the DON content in a specific band. However, with the aggravation of wheat scab, the luster and quality of wheat seeds change considerably. The contents of crude protein, cellulose and hemicellulose in wheat grains decrease, but the contents of reducing sugars and starch fat increase. There are corresponding bands in the IR spectrum related to the content information of these substances, so the degree of scab impact on wheat can be reflected by extracting and analyzing the characteristic bands of IR spectra.
The approach of combining spectroscopic data and data fusion plays a key role in geographical traceability, safety monitoring and quality control of agricultural products. This method could obtain more comprehensive information than spectral analysis alone and generally achieve better classification and prediction results because of the synergistic effect between different data (Yang, Li, Wang, Li, Guo, Huang, & Zhao, 2019). Data fusion is divided into three levels according to the data fusion at which level occurs: low-level, mid-level and high-level fusion (Ríos-Reina, Azcarate, Camiña, & Goicoechea, 2020). Stefano Schiavone et al. (Schiavone, Marchionni, Bucci, Marini, & Biancolillo, 2020) used the low-level fusion method to distinguish pure and adulterated Grappa spirit, and the classification accuracy reached 100%. Sen Yao et al. (Yao, Li, Li, Liu, & Wang, 2018) used the mid-level data fusion method of FT-IR and ultraviolet (UV) spectroscopies to establish the origin identification model of seven Boletus mushrooms. Yang Li et al. (Li, Huang, Xia, Xiong, & Min, 2020) combined spectral analysis with a high-level fusion strategy for the quantitative analysis of syrup added to honey products.
The aim of this study was to compare the selected NIR and FT-MIR spectral characteristic wavelengths with fingerprint information, and mid-level data fusion with the fingerprint information method was proposed to establish a classification model of Fusarium head blight (FHB) wheat. The effectiveness was proven by the comparison of model results between the mid-level data fusion with fingerprint information method, the single spectrum method and the mid-level data fusion method. The specific objectives were to (1) obtain raw data of different DON levels in FHB wheat by using NIR and the FT-MIR spectroscopic technique; (2) select the characteristic bands by using the CARS, MGS and XLW algorithms after the preprocessing of MSC and optimize the selection of characteristic bands combined with fingerprint information; and (3) build classification models based on the PLS-DA and LS-SVM algorithms by mid-level data fusion with the fingerprint information method, the single spectrum method and the mid-level data fusion method and compare these model results to confirm the influence of the characteristic wavelength with fingerprint information fusion on the model accuracy.