Non-destructive classification of apple bruising time based on visible and near-infrared hyperspectral imaging.

BACKGROUND: Bruising time of apple is one of the most important factors for internal quality assessment. The present study aimed to establish a non-destructive method for the classification of apple bruising time using visible and near-infrared (VNIR) hyperspectral imaging. In this study, VNIR hyperspectral images were obtained and analyzed at seven bruising periods. Moreover, regions of interest (ROIs) were chosen to construct the bruised region classification model, and spectra of bruised regions were collected and resampled based on four different methods. Subsequently, machine learning algorithms were employed and used for dealing with the time classification model of apples. In order to reduce data redundancy and improve the accuracy of the classification model, a tree-based assembling learning model was used to select feature wavelengths, and linear discriminant analysis (LDA) was used to improve the discernibility of data. RESULTS: The results revealed that the random forest (RF) model can precisely locate bruised regions, while the gradient boosting decision tree (GBDT) model can validly classify apple bruising times with 70.59% accuracy. Data of 128 wavebands were compressed to 13 wavebands, providing a high accuracy of 92.86%. CONCLUSION: The results prove that the hyperspectral technique can be used for predicting apple bruising time, which will help to assess the internal quality and safety of apples. © 2018 Society of Chemical Industry.

[Research on the Pre-Processing Methods of Wheat Hardness Prediction Model Based on Visible-Near Infrared Spectroscopy].

Grain hardness is an important quality parameter of wheat which has great influence on the classification, usage and composition research of wheat. To achieve rapid and accurate detection of wheat hardness, radial basis function (RBF) neural network model was built to predict the hardness of unknown samples on the basis of analyzing the absorptive characteristics of the composition of wheat grain in infrared, besides, the effects of different spectral pretreatment methods on the predictive accuracy of models were emphatically analyzed. 111 wheat samples were collected from major wheat-producing areas in China; then, spectral data were obtained by scanning samples. Mahalanobis distance method was used to identify and eliminated abnormal spectra. The optimized method of sample set partitioning based on joint X-Y distance (SPXY) was used to divide sample set with the number of calibration set samples being 84 and prediction set samples being 24. Successive projections algorithm (SPA) was employed to extract 47 spectral features from 262. SPA, first derivatives, second derivatives, standard normal variety (SNV) and their combinations were applied to preprocess spectral data, and the interplay of different prediction methods was analyzed to find the optimal prediction combination. Radial basis function (RBF) was built with preprocessed spectral data of calibration set being as inputs and the corresponding hardness data determined via hardness index (HI) method being as outputs. Results showed that the model got the best prediction accuracy when using the combination of SNV and SPA to preprocess spectral data, with the discriminant coefficient (R2), standard error of prediction (SEP) and ratio of performance to standard deviate (RPD) being 0.844, 3.983 and 2.529, respectively, which indicated that the RBF neural network model built based on visible-near infrared spectroscopy (Vis-NIR) could accurately predict wheat hardness, having the advantages of easy, fast and nondestructive compared with the traditional method. It provides a more convenient and practical method for estimating wheat hardness.

Research on variety identification of common bean seeds based on hyperspectral and deep learning.

Accurate, fast and non-destructive identification of varieties of common bean seeds is important for the cultivation and efficient utilization of common beans. This study is based on hyperspectral and deep learning to identify the varieties of common bean seeds non-destructively. In this study, the average spectrum of 3078 hyperspectral images from 500 varieties was obtained after image segmentation and sensitive region extraction, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to achieve the equilibrium of the samples of various varieties. A one-dimensional convolutional neural network model (IResCNN) incorporating Inception module and residual structure was proposed to identify seed varieties, and Support Vector Machine (SVM), K-Nearest Neighbor (KNN), VGG19, AlexNet, ResNet50 were established to compare the identification effect. After analyzing the effects of multiple spectral preprocessing methods on the model, the study selected Savitzky-Golay smoothing correction (SG) for spectral preprocessing and extracted 66 characteristic wavelengths using Successive Projections Algorithm (SPA) as inputs to the discriminative model. Ultimately, the IResCNN model achieved the highest accuracy of 93.06 % on the test set, indicating that hyperspectral technology can accurately identify bean varieties, and the study provides a correct method of thinking for the non-destructive classification of multi-species small-sample bean varieties.

Sugarbeet Seed Germination Prediction Using Hyperspectral Imaging Information Fusion.

Germination rate is important for seed selection and planting and quality. In this study, hyperspectral image technology integrated with germination tests was applied for feature association analysis and germination performance prediction of sugarbeet seeds. In this study, we proposed a nondestructive prediction method for sugarbeet seed germination. Sugarbeet seed was studied, and hyperspectral imaging (HIS) performed by binarization, morphology, and contour extraction was applied as a nondestructive and accurate technique to achieve single seed image segmentation. Comparative analysis of nine spectral pretreatment methods, SNV + 1D was used to process the average spectrum of sugarbeet seeds. Fourteen characteristic wavelengths were obtained by the Kullback-Leibler (KL) divergence, as the spectral characteristics of sugarbeet seeds. Principal component analysis (PCA) and material properties verified the validity of the extracted characteristic wavelengths. It was extracted of six image features of the hyperspectral image of a single seed obtained based on the gray-level co-occurrence matrix (GLCM). The spectral features, image features, and fusion features were used to establish partial least squares discriminant analysis (PLS-DA), CatBoost, and support vector machine radial-basis function (SVM-RBF) models respectively to predict the germination. The results showed that the prediction effect of fusion features was better than spectral features and image features. By comparing other models, the prediction results of the CatBoost model accuracy were up to 93.52%. The results indicated that, based on HSI and fusion features, the prediction of germinating sugarbeet seeds was more accurate and nondestructive.

Convolutional neural network for apple bruise detection based on hyperspectral.

The timely detection of apple bruises caused by collision and squeeze is of great significance to reduce the economic losses of the apple industry. This study proposed a spectral analysis model (SpectralCNN) based on a one-dimensional convolutional neural network to detect apple bruises. The influences of six spectral preprocessing methods on the SpectralCNN model were firstly analyzed in this paper. Compared with traditional chemometric models, the SpectralCNN model had a better accuracy, which was demonstrated not depend on the spectral preprocessing method by experiment results. Then, 20 characteristic wavelengths could be extracted by successive projection algorithm. The SpectralCNN model could achieve an accuracy of 95.79% on the test set of characteristic wavelengths, indicating that the extracted characteristic wavelengths contain most of the features of bruised and healthy pixels.

Intelligent detection of hard seeds of snap bean based on hyperspectral imaging.

As a common problem in snap beans, hard seed has seriously affected the large-scale industrial planting and yield of snap bean. To realize accurate, quick and non-destructive identifying the hard seeds of snap bean is of great significance to avoiding the effects of hard seeds on germination and growth. This research was based on hyperspectral imaging (HSI) to achieve accurate detection of hard seeds of snap bean. This study obtained the characteristic spectra from the hyperspectral image of a single seed, and then combined the synthetic minority over-sampling technique (SMOTE) and Tomek links to balance the numbers of hard and non-hard seed samples. The characteristic wavelengths were extracted from the average spectrum. Then the average spectrum was processed by first derivative (1D). After that, the characteristic wavelengths could be extracted using successive projections algorithm (SPA). Finally, a radial basis function-support vector machine (RBF-SVM) model was established to realize the intelligent detection of hard seeds, and the detection accuracy rate reached 89.32%. The research results showed that HSI technology could achieved accurate, fast and non-destructive testing of the hard seeds of snap bean, which is of great significance to the large-scale and standardized planting of snap bean and increase the yield per unit area.

Hyperspectral prediction of sugarbeet seed germination based on gauss kernel SVM.

How to quickly and accurately select sugarbeet seeds with reliable germination is very important to sugarbeet planting. In this study, the hyperspectral images of 3072 sugarbeet seeds of the same variety were collected, and were successively processed by binarization, morphology, contour extraction and so on. The average spectrum of the single seed image was obtained by image segmentation. Comprehensive analysis of the evaluation parameters of the five spectral preprocessing methods revealed that the second derivative (2D) processing was optimal. Successive projections algorithm (SPA) was used to extract 16 characteristic wavelengths. Support vector machine radial basis function (SVM-RBF), k-nearest neighbor (KNN) and random forest (RF) models were established at the full wavelength and characteristic wavelength respectively to predict the germination of sugarbeet seeds. By analyzing the prediction accuracy of the three models, it was found that the SVM-RBF model provided the highest prediction accuracy in the test set (the prediction accuracy of the full wavelength was 95.5%, and the prediction accuracy of the characteristic wavelength was 92.32%). The research results showed that the hyperspectral image processing technology could accurately predict the germination rate of sugarbeet seeds, and realize the rapid and non-destructive prediction of the germination status of sugarbeet seeds.

Prediction of Talc Content in Wheat Flour Based on a Near-Infrared Spectroscopy Technique.

A procedure for the prediction of talc content in wheat flour based on radial basis function (RBF) neural network and near-infrared spectroscopy (NIRS) data is described. In this study, 41 wheat flour samples adulterated with different concentrations of talc were used. The diffuse reflectance spectra of all samples were collected by NIRS analyzer in the spectral range of 400 to 2,500 nm. A sample of outliers was eliminated by Mahalanobis distance based on near-infrared spectral scanning, and the remaining 40 wheat flour samples were used for spectral characteristic analysis. A calibration set of 26 samples and a prediction set of 14 samples of wheat flour were built as a result of sample set partitioning based on joint x-y distances division. A comparison of Savitzky-Golay smoothing, multiplicative scatter correction (MSC), first derivation, second derivation, and standard normal variation in the modeling showed that MSC has the best preprocessing effect. To develop a simpler, more efficient prediction model, the correlation coefficient method (CCM) was used to reduce spectral redundancy and determine the maximum correlation informative wavelength (MIW). From the full 1,050 wavelengths, 59 individual MIWs were finally selected. The optimal combined detection model was CCM-MSC-RBF based on the selected MIWs, with a determination of prediction coefficients of prediction (Rp) of 0.9999, root-mean-square error of prediction of 0.0765, and residual predictive deviation of 65.0909. The study serves as a proof of concept that NIRS technology combined with multivariate analysis has the potential to provide a fast, nondestructive and reliable assay for the prediction of talc content in wheat flour.

Non-destructive prediction of protein content in wheat using NIRS.

A steady and accurate model used for quality detection depends on precise data and appropriate analytical methods. In this study, the authors applied partial least square regression (PLSR) to construct a model based on the spectral data measured to predict the protein content in wheat, and proposed a new method, global search method, to select PLSR components. In order to select representative and universal samples for modeling, Monte Carlo cross validation (MCCV) was proposed as a tool to detect outliers, and identified 4 outlier samples. Additionally, improved simulated annealing (ISA) combined with PLSR was employed to select most effective variables from spectral data, the data's dimensionality reduced from 100 to 57, and the standard error of prediction (SEP) decreased from 0.0716 to 0.0565 for prediction set, as well as the correlation coefficients (R2) between the predicted and actual protein content of wheat increased from 0.9989 to 0.9994. In order to reduce the dimensionality of the data further, successive projections algorithm (SPA) was then used, the combination of these two methods was called ISA-SPA. The results indicated that calibration model built using ISA-SPA on 14 effective variables achieved the optimal performance for prediction of protein content in wheat comparing with other developed PLSR models (ISA or SPA) by comprehensively considering the accuracy, robustness, and complexity of models. The coefficient of determination increased to 0.9986 and the SEP decreased to 0.0528, respectively.

Application of Visible/Near-Infrared Spectroscopy in the Prediction of Azodicarbonamide in Wheat Flour.

Azodicarbonamide is wildly used in flour industry as a flour gluten fortifier in many countries, but it was proved by some researches to be dangerous or unhealthy for people and not suitable to be added in flour. Applying a rapid, convenient, and noninvasive technique in food analytical procedure for the safety inspection has become an urgent need. This paper used Vis/NIR reflectance spectroscopy analysis technology, which is based on the physical property analysis to predict the concentration of azodicarbonamide in flour. Spectral data in range from 400 to 2498 nm were obtained by scanning 101 samples which were prepared using the stepwise dilution method. Furthermore, the combination of leave-one-out cross-validation and Mahalanobis distance method was used to eliminate abnormal spectral data, and correlation coefficient method was used to choose characteristic wavebands. Partial least squares, back propagation neural network, and radial basis function were used to establish prediction model separately. By comparing the prediction results between 3 models, the radial basis function model has the best prediction results whose correlation coefficients (R), root mean square error of prediction (RMSEP), and ratio of performance to deviation (RPD) reached 0.99996, 0.5467, and 116.5858, respectively. PRACTICAL APPLICATION: Azodicarbonamide has been banned or limited in many countries. This paper proposes a method to predict azodicarbonamide concentrate in wheat flour, which will be used for a rapid, convenient, and noninvasive detection device.