Prediction of milled maize fumonisin contamination by multispectral image analysis

Firrao, Giuseppe; Torelli, Emanuela; Gobbi, Emanuela; Raranciuc, Steluta; Bianchi, Gianluca; Locci, R.

doi:10.1016/j.jcs.2010.06.017

Cited by 38 publications

(20 citation statements)

References 12 publications

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“…Yao [14] studied hyperspectral Bright Green-Yellow Fluorescence (BGYF) imaging to detect single corn kernels contaminated with aflatoxin. Firrao et al [15] used multispectral imaging (720-940 nm) to predict fumonisin content of milled maize. Wang et al [16][17][18] studied the use of hyperspectral imaging, with the range of 400-1000 nm and 1000-2500 nm, to detect aflatoxin B1 on maize kernels, which were artificially titrated on the kernel surface in the laboratory and inoculated with Aspergillus flavus conidia in the field, respectively.…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Aspergillus parasiticus Infection in Maize Kernels Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis

et al. 2017

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Fungi infection in maize kernels is a major concern worldwide due to its toxic metabolites such as mycotoxins, thus it is necessary to develop appropriate techniques for early detection of fungi infection in maize kernels. Thirty-six sterilised maize kernels were inoculated each day with Aspergillus parasiticus from one to seven days, and then seven groups (were determined based on the incubated time. Another 36 sterilised kernels without inoculation with fungi were taken as control (DC). Hyperspectral images of all kernels were acquired within spectral range of 921-2529 nm. Background, labels and bad pixels were removed using principal component analysis (PCA) and masking. Separability computation for discrimination of fungal contamination levels indicated that the model based on the data of the germ region of individual kernels performed more effectively than on that of the whole kernels. Moreover, samples with a two-day interval were separable. Thus, four groups, DC, D 1-2 (the group consisted of D 1 and D 2 ), D 3-4 (D 3 and D 4 ), and D 5-7 (D 5 , D 6 , and D 7 ), were defined for subsequent classification. Two separate sample sets were prepared to verify the influence on a classification model caused by germ orientation, that is, germ up and the mixture of germ up and down with 1:1. Two smooth preprocessing methods (Savitzky-Golay smoothing, moving average smoothing) and three scatter-correction methods (normalization, standard normal variate, and multiple scatter correction) were compared, according to the performance of the classification model built by support vector machines (SVM). The best model for kernels with germ up showed the promising results with accuracies of 97.92% and 91.67% for calibration and validation data set, respectively, while accuracies of the best model for samples of the mixed kernels were 95.83% and 84.38%. Moreover, five wavelengths (1145, 1408, 1935, 2103, and 2383 nm) were selected as the key wavelengths in the discrimination of fungal contamination levels. In general, near-infrared hyperspectral imaging can be used for early detection of fungal contamination in maize kernels.

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Section: Introductionmentioning

confidence: 99%

Early Detection of Aspergillus parasiticus Infection in Maize Kernels Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis

et al. 2017

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“…Application of HSI was done for food quality evaluation soybean seeds, wheat, barley grains and Portobello mushroom (Agaricus bisporu) (Taghizadeh et al, 2011, Tumuluru et al, 2010, Arngren et al, 2011, Huang et al, 2014a, 2014b and detection of defects in apple fruits and lettuce leaves (Baranowski et al, 2012, Simko et al, 2015. In parallel, experiments using HSI techniques for detection of fungal infections in fruits of citrus, leaves of sugar beet, wheat and maize have proven the applicability of the technique (Lorente et al, 2013, Mahlein et al, 2010Hillnhütter et al, 2011, Firrao et al, 2010Yao et al, 2010, Bauriegel and Herppich, 2014, Williams et al, 2012. Nowadays, HSI methodologies based on various indices have been mostly used for detection of photosynthetic pigments like chlorophylls, carotenoids as well as of the other major compounds in leaves and fruits, such as anthocyanins and flavonoids (Deepak et al, 2015;Matros and Mock, 2013, Hölscher et al, 2009, Zhao et al, 2005, Coops et al, 2003, Ferri et al, 2004.…”

Section: Hyperspectral Imaging and Its Utilization In Crop Phenotypinmentioning

confidence: 99%

Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance

Sytar

Brestič

Živčák

et al. 2017

Science of The Total Environment

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• Salinity represents an abiotic stress constraint affecting growth and productivity of plants • Better solutions is to improve the level of salt resistance using natural genetic variability within crop species • Phenomic methodology employing different non-invasive imaging systems for detecting quantitative and qualitative changes caused by salt stress at the whole plant and canopy level. Hyperspectral imaging techniques provide unique opportunities for fast and reliable evaluation of numerous characteristics associated both with various structural, biochemical and physiological traits • Salt-soil-plant interaction and sustainable coastal agriculture need powerful phenotyping tools Salinity represents an abiotic stress constraint affecting growth and productivity of plants in many regions of the world. One of the possible solutions is to improve the level of salt resistance using natural genetic variability within crop species. In the context of recent knowledge on salt stress effects and mechanisms of salt tolerance, this review present useful phenomic approach employing different non-invasive imaging systems for detection of quantitative and qualitative changes caused by salt stress at the plant and canopy level. The focus is put on hyperspectral imaging technique, which provides unique opportunities for fast and reliable estimate of numerous characteristics associated both with various structural, biochemical Science of the Total Environment 578 (2017) 90-99 ⁎ Corresponding authors at:

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“…The common method used to inspect large-scale water pollution level is multispectral analysis using tens of waves under different wavelengths [14]. Firrao et al proposed a method based on ANN to predict the mycotoxin level related to Chla in water [31], which is highly associated with the number of algae and causes the excessive nutrients in water [7,32,33], using 10 different LED lights centered on emission at wavelengths ranging from 720-940 nm to classify the pollution level for different samples rather than using a quantitative prediction for water quality concerned parameters. Their method obtained poor accuracy on the content level of fumonisins because it only classified three levels with R 2 value of approximately 0.6 and prediction accuracy less than 0.8.…”

Section: Introductionmentioning

confidence: 99%

Mapping Water Quality Parameters in Urban Rivers from Hyperspectral Images Using a New Self-Adapting Selection of Multiple Artificial Neural Networks

Zhang

Ren

et al. 2020

Remote Sensing

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Protection of water environments is an important part of overall environmental protection; hence, many people devote their efforts to monitoring and improving water quality. In this study, a self-adapting selection method of multiple artificial neural networks (ANNs) using hyperspectral remote sensing and ground-measured water quality data is proposed to quantitatively predict water quality parameters, including phosphorus, nitrogen, biochemical oxygen demand (BOD), chemical oxygen demand (COD), and chlorophyll a. Seventy-nine ground measured data samples are used as training data in the establishment of the proposed model, and 30 samples are used as testing data. The proposed method based on traditional ANNs of numerical prediction involves feature selection of bands, self-adapting selection based on multiple selection criteria, stepwise backtracking, and combined weighted correlation. Water quality parameters are estimated with coefficient of determination R 2 ranging from 0.93 (phosphorus) to 0.98 (nitrogen), which is higher than the value (0.7 to 0.8) obtained by traditional ANNs. MPAE (mean percent of absolute error) values ranging from 5% to 11% are used rather than root mean square error to evaluate the predicting precision of the proposed model because the magnitude of each water quality parameter considerably differs, thereby providing reasonable and interpretable results. Compared with other ANNs with backpropagation, this study proposes an auto-adapting method assisted by the above-mentioned methods to select the best model with all settings, such as the number of hidden layers, number of neurons in each hidden layer, choice of optimizer, and activation function. Different settings for ANNS with backpropagation are important to improve precision and compatibility for different data. Furthermore, the proposed method is applied to hyperspectral remote sensing images collected using an unmanned aerial vehicle for monitoring the water quality in the Shiqi River, Zhongshan City, Guangdong Province, China. Obtained results indicate the locations of pollution sources.

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Prediction of milled maize fumonisin contamination by multispectral image analysis

Cited by 38 publications

References 12 publications

Early Detection of Aspergillus parasiticus Infection in Maize Kernels Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis

Early Detection of Aspergillus parasiticus Infection in Maize Kernels Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis

Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance

Mapping Water Quality Parameters in Urban Rivers from Hyperspectral Images Using a New Self-Adapting Selection of Multiple Artificial Neural Networks

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