Application of deep learning and near infrared spectroscopy in cereal analysis

Le, Ba Tuan

doi:10.1016/j.vibspec.2019.103009

Cited by 60 publications

(29 citation statements)

References 24 publications

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“…The performance evaluation of the NIRS prediction model of GABA content in GBR was determined by the correlation coefficient for calibration (r c ), the correlation coefficient for prediction (r p ), root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP). The higher r c and r p showed that the linear relationship between spectral information and chemical content was closer [31] . The lower RMSEC and RMSEP showed that the prediction performance of the model was better [31] .…”

Section: Evaluation Methods Of Spectral Prediction Modelmentioning

confidence: 96%

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Nondestructive determination of GABA in germinated brown rice with near infrared spectroscopy based on wavelet transform denoising

Zhang¹,

Liu²,

Wang³

et al. 2021

International Journal of Agricultural and Biological Engineering

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The objective of this study was to analyze the content of γ-aminobutyric acid (GABA) in germinated brown rice (GBR) by using near-infrared spectroscopy (NIRS) and the pretreatment method of wavelet de-noising (WD). The prediction accuracy of the NIRS model established by the Daubechies5 wavelet basis function at 3 level denoising treatment is the highest, the correlation coefficient for calibration (r c ) is 0.931, the root mean square error of calibration (RMSEC) is 0.4038 mg/100 g, the Bias of calibration is 0.006, the correlation coefficient for prediction (r p ) is 0.916, the root mean square error of prediction (RMSEP) is 0.4329 mg/100 g, the Bias of prediction is 0.010, and the ratio of performance to deviation (RPD) is 4.911. Results showed that the predicted and actual values had high correlation. Therefore, these results indicate that the NIRS model treated by WD is feasible to detect GABA content in GBR rapidly and nondestructively.

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Section: Evaluation Methods Of Spectral Prediction Modelmentioning

confidence: 96%

“…The higher r c and r p showed that the linear relationship between spectral information and chemical content was closer [31] . The lower RMSEC and RMSEP showed that the prediction performance of the model was better [31] . The Bias value evaluates the size of the system error of the model.…”

Section: Evaluation Methods Of Spectral Prediction Modelmentioning

confidence: 96%

Nondestructive determination of GABA in germinated brown rice with near infrared spectroscopy based on wavelet transform denoising

Zhang¹,

Liu²,

Wang³

et al. 2021

International Journal of Agricultural and Biological Engineering

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“…T et al, 2019). Also, the kernels of the convolutional layers themselves perform a kind of spectral preprocessing, that is, smoothing, derivative filtering, and detrending (Acquarelli et al, 2017;Cui & Fearn, 2018;Le, 2020). Therefore, this study proposed an end-to-end CNN model that accepts the raw spectral data without spectral preprocessing and feature extraction.…”

Section: Software Toolsmentioning

confidence: 99%

Nondestructive identification of barley seeds variety using near‐infrared hyperspectral imaging coupled with convolutional neural network

Singh

Garg

Iyengar

2021

J Food Process Engineering

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Nondestructive inspection of varietal purity of seeds plays an important role in crop improvement, agricultural production, and plant breeding. In the present study, a rapid and nondestructive technique, that is, near-infrared hyperspectral imaging (NIR-HSI) was applied to discriminate the barley seeds variety. A large dataset of 35,280 seeds was collected from different locations and years incorporating 35 Indian barley varieties (29 hulled and 6 naked barley varieties). The hyperspectral reflectance images of the ventral side and dorsal side of seeds were acquired in the nearinfrared range of 900-1700 nm. Mean spectra were extracted and pretreated by six preprocessing techniques (standard normal variate (SNV), multiplicative scatter correction (MSC), Savitzky-Golay (SG) smoothing, SG first derivative, SG second derivative, and detrending). Subsequently, raw and preprocessed spectral data were fed as input to the convolutional neural network (CNN) including traditional machine learning models (partial least squares discriminant analysis (PLS-DA), K-nearest neighbors (KNN), and support vector machines (SVM)). It was observed that the end-to-end CNN model built on raw spectra overperformed the model using the preprocessing strategies. In addition, the CNN model outperformed the three traditional models with a testing set accuracy of greater than 98%. The results demonstrated that NIR-HSI coupled with end-to-end CNN could be a robust way to quickly, accurately, and nondestructively identify the variety of barley seeds. Practical ApplicationsThe commercial price and quality of barley mainly depend upon its varietal purity. Identification of barley seeds variety is an important step to select the seeds for different purposes such as food, malt, and fodder. Traditional methods for the identification of barley seeds variety are time-consuming, expensive, and destructive. Near-infrared hyperspectral imaging, as an emerging fast and nondestructive technique, looks promising for seed quality and safety evaluation. Moreover, the convolutional neural network has a better capability to accurately discriminate spectra extracted from seeds of different varieties of barley. The results in this study can provide a reference and theoretical basis to develop a real-time inspection system for fast, accurate, and nondestructive barley seeds purity testing.

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“…This method of data convergence allowed deeper understanding of rice germ shelf life and could lead to enhanced NIR modeling of the shelf life of other food products. Le proposed a study that combines deep learning with NIR to provide a much faster method of cereal analysis than traditional NIR models [115]. The deep learning algorithm eliminates interference of NIR signal making modeling much more efficient.…”

Section: Grains (Rice Cereal) and Potatoesmentioning

confidence: 99%

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Bwambok

Siraj

Macchi

et al. 2020

Sensors

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Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.

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Application of deep learning and near infrared spectroscopy in cereal analysis

Cited by 60 publications

References 24 publications

Nondestructive determination of GABA in germinated brown rice with near infrared spectroscopy based on wavelet transform denoising

Nondestructive determination of GABA in germinated brown rice with near infrared spectroscopy based on wavelet transform denoising

Nondestructive identification of barley seeds variety using near‐infrared hyperspectral imaging coupled with convolutional neural network

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

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