Fungal fermentation of Fuzhuan brick tea: A comprehensive evaluation of sensory properties using chemometrics, visible near-infrared spectroscopy, and electronic nose.

Fuzhuan brick tea (FBT) fungal fermentation is a key factor in achieving its unique dark color, aroma, and taste. Therefore, it is essential to develop a rapid and reliable method that could assess its quality during FBT fermentation process. This study focused on using electronic nose (e-nose) and spectroscopy combination with sensory evaluations and physicochemical measurements for building machine learning (ML) models of FBT. The results showed that the fused data achieved 100 % accuracy in classifying the FBT fermentation process. The SPA-MLR method was the best prediction model for FBT quality (R2 = 0.95, RMSEP = 0.07, RPD = 4.23), and the fermentation process was visualized. Where, it was effectively detecting the degree of fermentation relationship with the quality characteristics. In conclusion, the current study's novelty comes from the established real-time method that could sensitively detect the unique post-fermentation quality components based on the integration of spectral, and e-nose and ML approaches.

Design and Optimization of Lower Limb Rehabilitation Exoskeleton with a Multiaxial Knee Joint.

To facilitate rehabilitation training for patients, we proposed the implementation of an anthropomorphic exoskeleton structure that incorporates a variable instantaneous center of rotation (ICR). This design considers the variability in knee ICR among individuals, resulting from the irregular form of the human knee joint, and leverages a double-degrees-of-freedom (2DOF) five-bar mechanism to adapt to these differences. The walking gait of the human lower limb and the corresponding knee ICR were measured and calculated using an optical 3D motion capture system. The optimal dimension parameters of the five-bar mechanism were then obtained through the optimization of human movement position inputs and rod length constraints to minimize the error in knee ICR, gait angle, and ankle trajectory between the human and the exoskeleton. Finally, we established an exoskeleton prototype to conduct relevant experimental tests. The experiment results showed that the average errors of knee ICR trajectory, hip angle, knee angle, and ankle trajectory were 5.52 × 10-4 m, 0.010 rad, 0.014 rad, and 1.57 × 10-3 m, respectively. The experimental results demonstrated that the exoskeleton's movement trajectory was close to the human's, reducing the human-mechanism interaction force and improving patient comfort during rehabilitation training.

A Low-Cost, Portable Device for Detecting and Sorting Aflatoxin-Contaminated Maize Kernels.

Aflatoxin contamination of maize is a major food safety issue worldwide. The problem is of special significance in African countries because maize is a staple food. This manuscript describes a low-cost, portable, non-invasive device for detecting and sorting aflatoxin-contaminated maize kernels. We developed a prototype employing a modified, normalized difference fluorescence index (NDFI) detection method to identify potentially aflatoxin-contaminated maize kernels. Once identified, these contaminated kernels can be manually removed by the user. The device consists of a fluorescence excitation light source, a tablet for image acquisition, and detection/visualization software. Two experiments using maize kernels artificially infected with toxigenic Aspergillus flavus were implemented to evaluate the performance and efficiency of the device. The first experiment utilized highly contaminated kernels (71.18 ppb), while mildly contaminated kernels (1.22 ppb) were used for the second experiment. Evidently, the combined approach of detection and sorting was effective in reducing aflatoxin levels in maize kernels. With a maize rejection rate of 1.02% and 1.34% in the two experiments, aflatoxin reduction was achieved at 99.3% and 40.7%, respectively. This study demonstrated the potential of using this low-cost and non-invasive fluorescence detection technology, followed by manual sorting, to significantly reduce aflatoxin levels in maize samples. This technology would be beneficial to village farmers and consumers in developing countries by enabling safer foods that are free of potentially lethal levels of aflatoxins.

Rapid qualitative detection of titanium dioxide adulteration in persimmon icing using portable Raman spectrometer and Machine learning.

Persimmon icing is the white crystalline powder that adheres to the surface of persimmon cakes when the sugar in the persimmon spills over during processing, which is considered the essence of persimmon. Titanium dioxide is a food additive that is commonly added to the surface of persimmon cakes to impersonate high-quality persimmon cakes. However, excessive titanium dioxide can be harmful to humans, so a quick method is needed to identify persimmon cakes as adulterated. Raman spectroscopy with distinctive advantages of water-insensitivity, real-time, field-deployable, label-free, and fingerprinting-identification has been rapidly developed and used in food quality assurance and safety monitoring. In this study, we investigated Raman spectroscopy integrated with machine learning to assess titanium dioxide adulteration in dried persimmon icing. The adaptive iterative reweighting partial least squares (air-PLS) algorithm as an effective algorithm was used to remove fluorescent background signals in raw Raman spectroscopy. Principal components analysis (PCA) was employed to analyze the spectral data and determine the class memberships, and results showed that 99.9% of information could be explained by PC-1 and PC-2. Compared with extreme learning machine (ELM), support vector machine (SVM), back propagation artificial neural network (BP-ANN), and random forest (RF) models, one-dimensional stack auto encoder convolutional neural network (1D-SAE-CNN) could provide the highest detection accuracy of 0.9825, precision of 0.9824, recall of 0.9825, and f1-score of 0.9824. This study shows that Raman spectroscopy coupled with 1D-SAE-CNN is a promising method to detect titanium dioxide adulteration in persimmon icing.

Effective band selection of hyperspectral image by an attention mechanism-based convolutional network.

The selection of effective and representative spectral bands is extremely important in eliminating redundant information and reducing the computational burden for the potential real-time applications of hyperspectral imaging. However, current band selection methods act as a separate procedure before model training and are implemented merely based on extracted average spectra without incorporating spatial information. In this paper, an end-to-end trainable network framework that combines band selection, feature extraction, and model training was proposed based on a 3D CNN (convolutional neural network, CNN) with the attention mechanism embedded in its first layer. The learned band attention vector was adopted as the basis of a band importance indicator to select effective bands. The proposed network was evaluated by two datasets, a regression dataset for predicting the relative chlorophyll content (soil and plant analyzer development, SPAD) of basil leaves and a classification dataset for detecting the drought stress of pepper leaves. A number of calibration models, including SVM, 1D-CNN, 2B-CNN (two-branch CNN), 3D ResNet and the developed network were established for performance comparison. Results showed that the effective bands selected by the proposed attention-based model achieved higher regression R 2 values and classification accuracies not only than the full-spectrum data, but also than the comparative band selection methods, including traditional SPA (successive projections algorithm) and GA (genetic algorithm) methods and the latest 2B-CNN algorithm. In addition, different from the traditional methods, the proposed band selection algorithm can effectively select bands while carrying out model training and can simultaneously take advantage of the original spectral-spatial information. The results confirmed the usefulness of the proposed attention mechanism-based convolutional network for selecting the most effective band combination of hyperspectral images.

Surface-Enhanced Raman Scattering Spectroscopy Combined With Chemical Imaging Analysis for Detecting Apple Valsa Canker at an Early Stage.

Apple Valsa canker (AVC) with early incubation characteristics is a severe apple tree disease, resulting in significant orchards yield loss. Early detection of the infected trees is critical to prevent the disease from rapidly developing. Surface-enhanced Raman Scattering (SERS) spectroscopy with simplifies detection procedures and improves detection efficiency is a potential method for AVC detection. In this study, AVC early infected detection was proposed by combining SERS spectroscopy with the chemometrics methods and machine learning algorithms, and chemical distribution imaging was successfully applied to the analysis of disease dynamics. Results showed that the samples of healthy, early disease, and late disease sample datasets demonstrated significant clustering effects. The adaptive iterative reweighted penalized least squares (air-PLS) algorithm was used as the best baseline correction method to eliminate the interference of baseline shifts. The BP-ANN, ELM, Random Forest, and LS-SVM machine learning algorithms incorporating optimal spectral variables were utilized to establish discriminative models to detect of the AVC disease stage. The accuracy of these models was above 90%. SERS chemical imaging results showed that cellulose and lignin were significantly reduced at the phloem disease-health junction under AVC stress. These results suggested that SERS spectroscopy combined with chemical imaging analysis for early detection of the AVC disease was feasible and promising. This study provided a practical method for the rapidly diagnosing of apple orchard diseases.

Rapid quantitative characterization of tea seedlings under lead-containing aerosol particles stress using Vis-NIR spectra.

The problem of excessive lead content in tea has become more and more serious with the development of society and industry. This paper investigated the ability of visible and near-infrared (Vis-NIR) spectroscopy to evaluate foliar lead uptake by tea plants through simulating real air pollution. Lead content of tea leaves in different treatment groups during stress time was measured by inductively coupled plasma mass spectrometry (ICP-MS). It was determined that stomata can be a channel for lead particles in the air and most of the lead entering through the stomata accumulates in the leaves. The spectral variation of treated samples was measured, and it was found that a combination of partial least squares-discriminant analysis (PLS-DA) and spectral responses can perfectly classify the tea samples under different lead concentrations stress with an overall accuracy of 0.979. Then the Vis-NIR spectra were used for fast monitoring physiological and biochemical indicators in tea leaves under atmospheric deposition. Relevant spectra pretreatment methods and characteristic wavelength selection approaches were evaluated for quantitative analysis and then optimal prediction models to instantly detect quality indicators in tea samples were built. Among predictive models, PLS had the best results (RMSE = 0.139 mg/g, 0.663 mmol/g, and 1.494 µmol/g) for the prediction of chlorophyll a (Chl-a), ascorbic acid (ASA), and glutathione (GSH), respectively. Also, principal component regression (PCR) gave the best results (RMSE = 0.053 mg/g, 0.024 mg/g, and 0.011%) for prediction of chlorophyll b (Chl-b), carotenoid (Car) and moisture content (MC), respectively. Results of this study can be applied for developing an effective and reliable approach for monitoring atmospheric deposition in plants.

The Identification of Spherical Engineered Microplastics and Microalgae by Micro-hyperspectral Imaging.

Based on the micro-hyperspectral imaging technique, spherical engineered microplastic (polyethylene, 10-45 µm) and microalgae (Isochrysis galbana) (4-7 µm) were identified. In transmittance mode of MHSI, micro image cubes from 400 to 1000 nm were obtained from slides containing MP and MA in thin seawater. Classifiers like Support Vector Machine (SVM(Radial Basis Function (RBF))), Least Squares Support Vector Machine (LSSVM(RBF)), k-Nearest Neighbors, etc. were adopted and compared to classify MP and MA. In order to expand the imaging range of micro imaging, image stitching technology was adopted. In allusion to the stitched image cube, SVM(RBF) is suggested for the identification of MA and MP, with recall and precision > 0.86. The above results demonstrate that the MHSI is a promising technique, which can detect MPs with particle size Limit of Detection of 10-45 µm, and it is potential to further expand this LOD.

A Rapid and Nondestructive Method for Simultaneous Determination of Aflatoxigenic Fungus and Aflatoxin Contamination on Corn Kernels.

Conventional methods for detecting aflatoxigenic fungus and aflatoxin contamination are generally time-consuming, sample-destructive, and require skilled personnel to perform, making them impossible for large-scale nondestructive screening detection, real-time, and on-site analysis. Therefore, the potential of visible-near-infrared (Vis-NIR) spectroscopy over the 400-2500 nm spectral range was examined for determination of aflatoxigenic fungus infection and the corresponding aflatoxin contamination on corn kernels in a rapid and nondestructive manner. The two A. flavus strains, AF13 and AF38, were used to represent the aflatoxigenic fungus and nonaflatoxigenic fungus, respectively, for artificial inoculation on corn kernels. The partial least-squares discriminant analysis (PLS-DA) models based on different combinations of spectral range (I: 410-1070 nm; II: 1120-2470 nm), corn side (endosperm or germ side), spectral variable number (full spectra or selected variables), modeling approach (two-step or one-step), and classification threshold (20 or 100 ppb) were developed and their performances were compared. The first study focusing on detection of aflatoxigenic fungus-infected corn kernels showed that, in classifying the "control+AF38-inoculated" and AF13-inoculated corn kernels, the full spectral PLS-DA models using the preprocessed spectra over range II and one-step approach yielded more accurate prediction results than using the spectra over range I and the two-step approach. The advantage of the full spectral PLS-DA models established using one corn side than the other side were not consistent in the explored combination cases. The best full spectral PLS-DA model obtained was obtained using the germ-side spectra over range II with the one-step approach, which achieved an overall accuracy of 91.11%. The established CARS-PLSDA models performed better than the corresponding full-spectral PLS-DA models, with the better model achieved an overall accuracy of 97.78% in separating the AF13-inoculated corn kernels and the uninfected control and AF38-inoculated corn kernels. The second study focusing on the detection of aflatoxin-contaminated corn kernels showed that, based on the aflatoxin threshold of 20 and 100 ppb, the best overall accuracy in classifying the aflatoxin-contaminated and healthy corn kernels attained 86.67% and 84.44%, respectively, using the CARS-PLSDA models. The quantitative modeling results using partial least-squares regression (PLSR) obtained the correlation coefficient of prediction set ( RP) of 0.91, which indicated the possibility of using Vis-NIR spectroscopy to quantify aflatoxin concentration in aflatoxigenic fungus-infected corn kernels.

[Prediction the Soluble Solid Content in Sugarcanes by Using Near Infrared Hyperspectral Imaging System].

In order to explore the feasibility of prediction soluble solid contents (SSC) in sugarcane stalks by using near infrared hyperspectral imaging techniques, two hundred and forty sugarcane stalks which come from three different varieties were studied. After obtaining the raw hyperspectral images of sugarcane stalks, the spectral information and textural features were discussed respectively. The prediction models were established by using partial least squares regression (PLSR), principal components regression (PCR) and least squares support vector machines (LS-SVM) algorithms. Besides, three different selected wavelengths algorithms such as successive projection (SPA) algorithms, intervals partial least squares (iPLS) algorithms and uninformation variables elimination (UVE) algorithm were analyzed after building partial least squares regression model. The results indicate that partial least squares regression model based on spectral features can be an steady model to predict SSC and the correlation coefficient (R2) of calibration sets and prediction sets are 0.879, 0.843. The root mean square errors of calibration sets and prediction sets are 0.644, 0.742 respectively. The obtained 105 wavelengths which were selected by UVE algorithm are effective spectral features. The R2 results of calibration sets and prediction sets of its PLSR model are 0.860, 0.813. The root mean square errors of calibration sets and prediction sets are 0.693, 0.810 respectively

[Application of near-infrared hyperspectral imaging to predicting water content in salmon flesh].

Near-infrared hyperspectral imaging technique was employed in the present study to determine water contents in salmon flesh rapidly and nondestructively. Altogether 90 samples from different positions of salmon fish were collected for hyperspectral image scanning, and mean spectra were extracted from the region of interest (ROI) inside each image. Sixty samples were randomly selected as calibration set, and the remaining 30 samples formed prediction set. The full-spectrum and water contents were correlated using partial least squares regression (PLSR) and least-squares support vector machines (LS-SVM), which were then applied to predict water contents for prediction samples. A novel variable extraction method called random frog was applied to select effective wavelengths (EWs) from the full-spectrum. PLSR and LS-SVM calibration models were established respectively to detect water contents in salmon based on the EWs. Though the performances of EWs-based models were worse than models using full-spectrum, only 12 wavelengths were used to substitute for the original 151 wavelengths, thus models were greatly simplified and more suitable for practical application. For EWs-based PLSR and LS-SVM models, satisfactory results were achieved with correlation coefficient of prediction (Rp) of 0. 92 and 0. 93 respectively, and root mean square error of prediction (RMSEP) of 1. 31% and 1. 18% respectively. The results indicated that near-infrared hyperspectral imaging combined with chemometrics allows accurate prediction of water contents in salmon flesh, providing important reference for the rapid inspection of fish quality.

[Review of Crop Canopy Spectral Information Detection Technology and Methods].

Compared with the traditional chemical methods and the subjective visual ways for measuring plant physiology information indicators, the assessments of crop canopy information through spectral radiometer are more simple, rapid and accurate. The applications of different types of spectral radiometer, especially for international general used Cropscan multispectral radiometer, for predicting crop canopy leaf area index under different growth stage, biomass, nitrogen, chlorophyll and yield, and monitoring plant diseases and insect pests were summarized based on crop group information acquisition methods in recent years. The varity of vegetation indices (VIs) were concluded after comparing regression coefficients of related models among different crops. In general, the correlation coefficients of mathematical models were high and it can realize the crop detection of various kinds of physiological information. Besides, the combination of multispectral radiometer and other sensors can provide useful information to evaluate the status of crops growth, which is very important in practice.

[Visualization of the chilling storage time for turbot flesh based on hyperspectral imaging technique].

This study proposed a new method using visible and near infrared (Vis/NIR) hyperspectral imaging for the detection and visualization of the chilling storage time for turbot flesh rapid and nondestructively. A total of 160 fish samples with 8 different storage days were collected for hyperspectral image scanning, and mean spectra were extracted from the region of interest (ROD inside each image. Partial least squares regression (PLSR) was applied as calibration method to correlate the spectral data and storage time for the 120 samples in calibration set. Then the PLSR model was used to predict the storage time for the 40 prediction samples, which achieved accurate results with determination coefficient (R2) of 0.966 2 and root mean square error of prediction (RMSEP) of 0.679 9 d. Finally, the storage time of each pixel in the hyperspectral images for all prediction samples was predicted and displayed in different colors for visualization based on pseudo-color images with the aid of an IDL program. The results indicated that hyperspectral imaging technique combined with chemometrics and image processing allows the determination and visualization of the chilling storage time for fish, displaying fish freshness status and distribution vividly and laying a foundation for the automatic processing of aquatic products.

[Identification of green tea brand based on hyperspectra imaging technology].

Hyperspectral imaging technology was developed to identify different brand famous green tea based on PCA information and image information fusion. First 512 spectral images of six brands of famous green tea in the 380 approximately 1 023 nm wavelength range were collected and principal component analysis (PCA) was performed with the goal of selecting two characteristic bands (545 and 611 nm) that could potentially be used for classification system. Then, 12 gray level co-occurrence matrix (GLCM) features (i. e., mean, covariance, homogeneity, energy, contrast, correlation, entropy, inverse gap, contrast, difference from the second-order and autocorrelation) based on the statistical moment were extracted from each characteristic band image. Finally, integration of the 12 texture features and three PCA spectral characteristics for each green tea sample were extracted as the input of LS-SVM. Experimental results showed that discriminating rate was 100% in the prediction set. The receiver operating characteristic curve (ROC) assessment methods were used to evaluate the LS-SVM classification algorithm. Overall results sufficiently demonstrate that hyperspectral imaging technology can be used to perform classification of green tea.