Identification of rice storage time based on colorimetric sensor array combined hyperspectral imaging technology

This study aims to develop a novel hydrophobic film based on a nylon film substrate, SiO2 sol (tetraethyl orthosilicate and trimethylethoxysilane), eight porphyrins, and eight pH indicators for monitoring salmon freshness. The hydrophobic properties of the film were verified by using scanning electron microscope (SEM), atomic force microscope (AFM), Fourier transform infrared spectrometer (FTIR), and contact angle (WCA). The total volatile base nitrogen (TVB-N) and gas chromatography–mass spectrometer (GC-MS) results for silver salmon were collected throughout the storage period. The accuracy and sensitivity of the colorimetric sensor array (CSA) were subsequently validated. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to analyze the color difference maps. The results were generated before and after the sensor array’s response, allowing differentiation between various freshness levels of silver salmon. The K-nearest neighbor (KNN) algorithm achieved a recognition rate of 93% for the prediction set, and the linear discriminant analysis (LDA) model demonstrated a recognition rate of 95 and 93.3% accuracy for calibration and prediction sets, respectively. This development of the CSA with a hydrophobic substrate effectively identified changes in the freshness of silver salmon during storage. Thus, this novel hydrophobic substrate is suitable for detecting food freshness, which could be applied to other types of meat freshness.

Inkjet-printed colorimetric sensor array for the rapid identification of adulterated Fritillariae cirrhosae bulbus

In order to quickly distinguish infertile eggs from fertile eggs, the hyperspectral imaging technology consisting of imaging and spectral information was used for detecting the fertile information of eggs. Before hatching eggs were incubated, a hyperspectral imaging system (wavelength between 400 to 1 000 nm) was used to acquire the images one-by-one manually. The characteristic information of ratios of length to short axis, elongation, roundness and the ratios of the yolk area to the whole area was extracted based on the images. The normalization method was used as the spectral data preprocessing, and then 155 spectral characteristic variables were extracted from 520 nm waveband through the correlation coefficient method. Principal component analysis (PCA) method was adopted to reduce the dimensions of image-spectrum fusion information; the top six principal components were extracted. Support vector machine (SVM) method was used to establish classification of fertile and infertile eggs models, which are based on image, spectrum and image-spectrum fusion information respectively. The accuracy rates of the SVM models were 84.00%, 90.00% and 93.00% respectively. The experimental results show that the model based on image-spectrum fusion information technology is superior to the single information model. Hyperspectral transmission imaging technology is effective and feasible to detect the fertile hatching eggs before incubation. Keywords: hyperspectral image, hatching eggs, information fusion, nondestructive detection DOI: 10.3965/j.ijabe.20150804.1672 Citation: Zhu Z H, Liu T, Xie D J, Wang Q H, Ma M H. Nondestructive detection of infertile hatching eggs based on spectral and imaging information. Int J Agric & Biol Eng, 2015; 8(4): 69－76.

Functional pituitary adenomas (FPAs) are associated with hormonal hypersecretion resulting in systemic endocrinopathies and increased mortality. The heterogenous composition of the FPA population has made modeling predictive factors of postoperative disease remission a challenge. Here, we aim to define a novel scoring system predictive of disease remission following transsphenoidal surgery (TSS) for FPAs and validate our process using supervised machine learning (SML). 392 patients with FPAs treated at one of the three Mayo Clinic campuses were retrospectively reviewed. Variables found significant on multivariate analysis were incorporated into our novel Pit-SCHEME score. The Pit-SCHEME score with a cut-off value ≥ 6 achieved a sensitivity of 86% and positive likelihood ratio of 2.88. In SML models, without the Pit-SCHEME score, the k-nearest neighbor (KNN) model achieved the highest accuracy at 75.6%. An increase in model sensitivity was achieved with inclusion of the Pit-SCHEME score with the linear discriminant analysis (LDA) model achieving an accuracy of 86.9%, which suggests the Pit-SCHEME score is the variable of most importance for prediction of postoperative disease remission. Ultimately, these results support the potential clinical utility of the Pit-SCHEME score and its prospective future for aiding in the perioperative decision making in patients with FPAs.

To retrospectively develop and evaluate computer-aided diagnosis (CAD) models that include both mammographic and sonographic descriptors. Institutional review board approval was obtained for this HIPAA-compliant study. A waiver of informed consent was obtained. Mammographic and sonographic examinations were performed in 737 patients (age range, 17-87 years), which yielded 803 breast mass lesions (296 malignant, 507 benign). Radiologist-interpreted features from mammograms and sonograms were used as input features for linear discriminant analysis (LDA) and artificial neural network (ANN) models to differentiate benign from malignant lesions. An LDA with all the features was compared with an LDA with only stepwise-selected features. Classification performances were quantified by using receiver operating characteristic (ROC) analysis and were evaluated in a train, validate, and retest scheme. On the retest set, both LDAs were compared with radiologist assessment score of malignancy. Both the LDA and ANN achieved high classification performance with cross validation (area under the ROC curve [A(z)] = 0.92 +/- 0.01 [standard deviation] and (0.90)A(z) = 0.54 +/- 0.08 for LDA, A(z) = 0.92 +/- 0.01 and (0.90)A(z) = 0.55 +/- 0.08 for ANN). Results of both models generalized well to the retest set, with no significant performance differences between the validate and retest sets (P > .1). On the retest set, there were no significant performance differences between LDA with all features and LDA with only the stepwise-selected features (P > .3) and between either LDA and radiologist assessment score (P > .2). Results showed that combining mammographic and sonographic descriptors in a CAD model can result in high classification and generalization performance. On the retest set, LDA performance matched radiologist classification performance.

Near infrared reflectance spectroscopy was used to discriminate different species of freshwater fish samples. Samples from seven freshwater fish species of the family Cyprinidae (black carp ( Mylopharyngodon piceus), grass carp ( Ctenopharyngodon idellus), silver carp ( Hypophthalmichthys molitrix), bighead carp ( Aristichthys nobilis), common carp ( Cyprinus carpio), crucian ( Carassius auratus), and bream ( Parabramis pekinensis)) were scanned by near infrared reflectance spectroscopy from 1000 nm to 1799 nm. Linear discriminant analysis models were built for the classification of species. We inspected the effect of partial least square, principal component analysis, competitive adaptive reweighted sampling, and fast Fourier transform on linear discriminant analysis. The results showed that the dimension reduction methods worked very well for this example. Linear discriminant analysis models which were combined with principal component analysis and fast Fourier transform could classify accurately all the samples under multiplicative scatter correction pre-processing. According to the loadings in principal component analysis, spectra wavelengths 1000, 1001, 1154, 1208, 1284, 1288, 1497, 1665, and 1770 nm were selected as effective wavelengths in linear discriminant analysis. The discriminant analysis was simplified by using effective wavelengths as independent variables in a linear discriminant analysis model. This study indicated that linear discriminant analysis combined with near infrared reflectance spectroscopy could be an effective strategy for the classification of freshwater fish species.

Vis-NIR hyperspectral imaging for the classification of bacterial foodborne pathogens based on pixel-wise analysis and a novel CARS-PSO-SVM model

Development of colorimetric sensor array for diagnosis of tuberculosis through detection of urinary volatile organic compounds

The long-lasting outbreak of the pine shoot beetle (PSB, Tomicus spp.) threatens forest ecological security. Effective monitoring is urgently needed for the Integrated Pest Management (IPM) of this pest. UAV-based hyperspectral remote sensing (HRS) offers opportunities for the early and accurate detection of PSB attacks. However, the insufficient exploration of spectral and structural information from early-attacked crowns and the lack of suitable detection models limit UAV applications. This study developed a UAV-based framework for detecting early-stage PSB attacks by integrating hyperspectral images (HSIs), LiDAR point clouds, and structure from motion (SfM) photogrammetry data. Individual tree segmentation algorithms were utilized to extract both spectral and structural variables of damaged tree crowns. Random forest (RF) was employed to determine the optimal detection model as well as to clarify the contributions of the candidate variables. The results are as follows: (1) Point cloud segmentation using the Canopy Height Model (CHM) yielded the highest crown segmentation accuracy (F-score: 87.80%). (2) Near-infrared reflectance exhibited the greatest decrease for early-attacked crowns, while the structural variable intensity percentile (int_P50-int_P95) showed significant differences (p < 0.05). (3) In the RF model, spectral variables were predominant, with LiDAR structural variables serving as a supplement. The anthocyanin reflectance index and int_kurtosis were identified as the best indicators for early detection. (4) Combining HSI with LiDAR data obtained the best RF model accuracy (classification accuracy: 87.31%; Kappa: 0.8275; SDR estimation accuracy: R2 = 0.8485; RMSEcv = 3.728%). RF integrating HSI and SfM data exhibited similar performance. In conclusion, this study identified optimal spectral and structural variables for UAV monitoring and improved HRS model accuracy and thereby provided technical support for the IPM of PSB outbreaks.

Mechanical fault detection has an important influence on production schedule and efficiency. With the development of intelligent technology, more and more intelligent detection technologies are applied to mechanical fault detection. In order to detect mechanical faults more efficiently and accurately, this experiment proposes a production knowledge base model based on genetic algorithm (GA algorithm). The model uses the unique biological genetics principle of genetic algorithm to evolve the interested population, and can conduct spatial search to find the global optimal solution. By comparing the performance of GA algorithm model with other similar detection models, it is found that the model proposed in the experiment has obvious advantages in mechanical fault detection performance. The experimental results show that the maximum accuracy of the GA algorithm is 0.935, 0.074 higher than the support vector machine (SVM) model, 0.118 higher than the linear discriminant analysis (LDA) model, 0.032 higher than the random forest (RF) model, and 0.166 higher than the K nearest neighbor (KNN) model. In addition, the error value of GA algorithm is the lowest among these models, which is 0.028. This proves that the genetic algorithm model has higher diagnostic accuracy and can play an important role in mechanical fault detection.

Rapid differentiation of Ghana cocoa beans by FT-NIR spectroscopy coupled with multivariate classification

Classification of rice wine according to different marked ages using a novel artificial olfactory technique based on colorimetric sensor array

(1) Background: The general use of food-processing facilities in the agro-food industry has increased the risk of unexpected material contamination. For instance, grain flours have similar colors and shapes, making their detection and isolation from each other difficult. Therefore, this study is aimed at verifying the feasibility of detecting and isolating grain flours by using hyperspectral imaging technology and developing a classification model of grain flours. (2) Methods: Multiple hyperspectral images were acquired through line scanning methods from reflectance of visible and near-infrared wavelength (400–1000 nm), reflectance of shortwave infrared wavelength (900–1700 nm), and fluorescence (400–700 nm) by 365 nm ultraviolet (UV) excitation. Eight varieties of grain flours were prepared (rice: 4, starch: 4), and the particle size and starch damage content were measured. To develop the classification model, four multivariate analysis methods (linear discriminant analysis (LDA), partial least-square discriminant analysis, support vector machine, and classification and regression tree) were implemented with several pre-processing methods, and their classification results were compared with respect to accuracy and Cohen’s kappa coefficient obtained from confusion matrices. (3) Results: The highest accuracy was achieved as 97.43% through short-wavelength infrared with normalization in the spectral domain. The submission of the developed classification model to the hyperspectral images showed that the fluorescence method achieves the highest accuracy of 81% using LDA. (4) Conclusions: In this study, the potential of non-destructive classification of rice and starch flours using multiple hyperspectral modalities and chemometric methods were demonstrated.

PurposeMany studies have proposed predictive models for type 2 diabetes mellitus (T2DM). However, these predictive models have several limitations, such as user convenience and reproducibility. The purpose of this study was to develop a T2DM predictive model using electronic medical records (EMRs) and machine learning and to compare the performance of this model with traditional statistical methods.Materials and MethodsIn this study, a total of available 8454 patients who had no history of diabetes and were treated at the cardiovascular center of Korea University Guro Hospital were enrolled. All subjects completed 5 years of follow up. The prevalence of T2DM during follow up was 4.78% (404/8454). A total of 28 variables were extracted from the EMRs. In order to verify the cross-validation test according to the prediction model, logistic regression (LR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and K-nearest neighbor (KNN) algorithm models were generated. The LR model was considered as the existing statistical analysis method.ResultsAll predictive models maintained a change within the standard deviation of area under the curve (AUC) <0.01 in the analysis after a 10-fold cross-validation test. Among all predictive models, the LR learning model showed the highest prediction performance, with an AUC of 0.78. However, compared to the LR model, the LDA, QDA, and KNN models did not show a statistically significant difference.ConclusionWe successfully developed and verified a T2DM prediction system using machine learning and an EMR database, and it predicted the 5-year occurrence of T2DM similarly to with a traditional prediction model. In further study, it is necessary to apply and verify the prediction model through clinical research.

A paper-based colorimetric sensor array for discrimination of monofloral European honeys based on gold nanoparticles and chemometrics data analysis.