Development and comparison of classification models on VIS-NIR hyperspectral imaging spectra for qualitative detection of the Staphylococcus aureus in fresh chicken breast

CVAE-DF: A hybrid deep learning framework for fertilization status detection of pre-incubation duck eggs based on VIS/NIR spectroscopy

Unfertilized duck eggs not removed prior to incubation will deteriorate quickly, posing a risk of contaminating the normally fertilized duck eggs. Thus, detecting the fertilization status of breeding duck eggs as early as possible is a meaningful and challenging task. Most existing work usually focus on the characteristics of chicken eggs during mid-term hatching. However, little attention has been paid to the detection for duck eggs prior to incubation. In this paper, we present a novel hybrid deep learning detection framework for the fertilization status of pre-incubation duck eggs, termed CVAE-DF, based on visible/near-infrared (VIS/NIR) transmittance spectroscopy. The framework comprises the encoder of a convolutional variational autoencoder (CVAE) and an improved deep forest (DF) model. More specifically, we first collected transmittance spectral data (400-1000 nm) of 255 duck eggs before hatching. The multiplicative scatter correction (MSC) method was then used to eliminate noise and extraneous information of the raw spectral data. Two efficient data augmentation methods were adopted to provide sufficient data. After that, CVAE was applied to extract representative features and reduce the feature dimension for the detection task. Finally, an improved DF model was employed to build the classification model on the enhanced feature set. The CVAE-DF model achieved an overall accuracy of 95.94 % on the test dataset. These experimental results in terms of four metrics demonstrate that our CVAE-DF method outperforms the traditional methods by a significant margin. Furthermore, the results also indicate that CVAE holds great promise as a novel feature extraction method for the VIS/NIR spectral analysis of other agricultural products. It is extremely beneficial to practical engineering.

Rapid Detection of Single Bacteria Using Filter-Array-Based Hyperspectral Imaging Technology

Rapid and accurate detection of bacterial pathogens is crucial for preventing widespread public health crises, particularly in the food industry. Traditional methods are often slow and require extensive labeling, which hampers timely responses to potential threats. In response, we introduce a groundbreaking approach using filter-array-based hyperspectral imaging technology, enhanced by a super-resolution demosaicking technique. This innovative technology streamlines the detection process and significantly enhances the resolution of mosaic hyperspectral imaging. By utilizing a snapshot hyperspectral camera with a 15 ms integration time, it facilitates the identification of bacteria at the single-cell level without requiring chemical labels. The integration of a 3D convolutional neural network optimizes the recognition of pathogenic bacteria, achieving an impressive accuracy of 91.7%. Our approach dramatically improves the efficiency and effectiveness of bacterial detection, providing a promising solution for critical applications in public health and the food industry.

Resilient sustainable current and emerging technologies for foodborne pathogen detection

Foodborne pathogens such as Salmonella, Escherichia coli and Listeria pose significant risks to human health. The World Health Organization estimates that 2.2 million deaths per year are directly caused by foodborne and waterborne bacterial diseases worldwide. Accordingly, detecting pathogens in food is essential to ensure that our food is safe. This review explores the critical role of novel technologies in enhancing food safety practices whilst delving into adopting and integrating innovative, resilient and sustainable approaches in the food supply chain. Further, applying novel, emerging advanced analytical techniques such as Raman spectroscopy and nanotechnology based biosensors in food contamination detection is discussed. These advanced technologies show the promise of real-time monitoring, traceability, and predictive analytics to identify and mitigate potential hazards before they reach consumers. They can provide rapid and accurate results and ensure the integrity of food products. Furthermore, the herein-highlighted synergistic integration of these technologies offers a promising path toward a safer and more transparent food system, thereby addressing the challenges of today's globalised food market and laying the platform for developing multimodal technologies for affordable, sensitive and rapid pathogen detection along the different stages of the food chain, from "farm to fork".

DMAF-NET: Deep Multi-Scale Attention Fusion Network for Hyperspectral Image Classification with Limited Samples

In recent years, deep learning methods have achieved remarkable success in hyperspectral image classification (HSIC), and the utilization of convolutional neural networks (CNNs) has proven to be highly effective. However, there are still several critical issues that need to be addressed in the HSIC task, such as the lack of labeled training samples, which constrains the classification accuracy and generalization ability of CNNs. To address this problem, a deep multi-scale attention fusion network (DMAF-NET) is proposed in this paper. This network is based on multi-scale features and fully exploits the deep features of samples from multiple levels and different perspectives with an aim to enhance HSIC results using limited samples. The innovation of this article is mainly reflected in three aspects: Firstly, a novel baseline network for multi-scale feature extraction is designed with a pyramid structure and densely connected 3D octave convolutional network enabling the extraction of deep-level information from features at different granularities. Secondly, a multi-scale spatial-spectral attention module and a pyramidal multi-scale channel attention module are designed, respectively. This allows modeling of the comprehensive dependencies of coordinates and directions, local and global, in four dimensions. Finally, a multi-attention fusion module is designed to effectively combine feature mappings extracted from multiple branches. Extensive experiments on four popular datasets demonstrate that the proposed method can achieve high classification accuracy even with fewer labeled samples.

Establishment and comparison of in situ detection models for foodborne pathogen contamination on mutton based on SWIR-HSI

Introduction

Rapid and accurate detection of food-borne pathogens on mutton is of great significance to ensure the safety of mutton and its products and the health of consumers.

Objectives

The feasibility of short-wave infrared hyperspectral imaging (SWIR-HSI) in detecting the contamination status and species of Escherichia coli (EC), Staphylococcus aureus (SA) and Salmonella typhimurium (ST) contaminated on mutton was explored.

Materials and methods

The hyperspectral images of uncontaminated and contaminated mutton samples with different concentrations (108, 107, 106, 105, 104, 103 and 102 CFU/mL) of EC, SA and ST were acquired. The one dimensional convolutional neural network (1D-CNN) model was constructed and the influence of structure hyperparameters on the model was explored. The effects of different spectral preprocessing methods on partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM) and 1D-CNN models were discussed. In addition, the feasibility of using the characteristic wavelength to establish simplified models was explored.

Results and discussion

The best full band model was the 1D-CNN model with the convolution kernels number of (64, 16) and the activation function of tanh established by the original spectra, and its accuracy of training set, test set and external validation set were 100.00, 92.86 and 97.62%, respectively. The optimal simplified model was genetic algorithm optimization support vector machine (GA-SVM). For discriminating the pathogen species, the accuracies of SVM models established by full band spectra preprocessed by 2D and all 1D-CNN models with the convolution kernel number of (32, 16) and the activation function of tanh were 100.00%. In addition, the accuracies of all simplified models were 100.00% except for the 1D-CNN models. Considering the complexity of features and model calculation, the 1D-CNN models established by original spectra were the optimal models for pathogenic bacteria contamination status and species. The simplified models provide basis for developing multispectral detection instruments.

Conclusion

The results proved that SWIR-HSI combined with machine learning and deep learning could accurately detect the foodborne pathogen contamination on mutton, and the performance of deep learning models were better than that of machine learning. This study can promote the application of HSI technology in the detection of foodborne pathogens on meat.

Using staining as reference for spectral imaging: Its application for the development of an analytical method to predict the presence of bacterial biofilms

At present, although spectral imaging is known to have a great potential to provide a massive amount of valuable information, the lack of reference methods remains as one of the bottlenecks to access the full capacity of this technique. This work aims to present a staining-based reference method with digital image treatment for spectral imaging, in order to propose a fast, efficient, contactless and non-invasive analytical method to predict the presence of biofilms. Spectral images of Pseudomonasaeruginosa biofilms formed on high density polyethylene coupons were acquired in visible and near infrared (vis-NIR) range between 400 and 1000 nm. Crystal violet staining served as a biofilm indicator, allowing the bacterial cells and the extracellular matrix to be marked on the coupon. Treated digital images of the stained biofilms were used as a reference. The size and pixels of the hyperspectral and digital images were scaled and matched to each other. Intensity color thresholds were used to differentiate the pixels associate to areas containing biofilms from those ones placed in biofilm-free areas. The model facultative Gram-negative bacterium, P. aeruginosa, which can form highly irregularly shaped and heterogeneous biofilm structures, was used to enhance the strength of the method, due to its inherent difficulties. The results showed that the areas with high and low intensities were modeled with good performance, but the moderate intensity areas (with potentially weak or nascent biofilms) were quite challenging. Image processing and artificial neural networks (ANN) methods were performed to overcome the issues resulted from biofilm heterogeneity, as well as to train the spectral data for biofilm predictions.

Hyperspectral Imaging Combined with Chemometrics Analysis for Monitoring the Textural Properties of Modified Casing Sausages with Differentiated Additions of Orange Extracts

The textural properties (hardness, springiness, gumminess, and adhesion) of 16-day stored sausages with different additions of orange extracts to the modified casing solution were estimated by response surface methodology (RSM) and a hyperspectral imaging system in the spectral range of 390-1100 nm. To improve the model performance, normalization, 1st derivative, 2nd derivative, standard normal variate (SNV), and multiplicative scatter correction (MSC) were applied for spectral pre-treatments. The raw, pretreated spectral data and textural attributes were fit to the partial least squares regression model. The RSM results show that the highest R² value achieved at adhesion (77.57%) derived from a second-order polynomial model, and the interactive effects of soy lecithin and orange extracts on adhesion were significant (p < 0.05). The adhesion of the PLSR model developed from reflectance after SNV pretreatment possessed a higher calibration coefficient of determination (0.8744) than raw data (0.8591). The selected ten important wavelengths for gumminess and adhesion can simplify the model and can be used for convenient industrial applications.

Deep residual network enabled smart hyperspectral image analysis and its application to monitoring moisture, size distribution and contents of four bioactive compounds of granules in the fluid-bed granulation process of Guanxinning tablets

Bed collapse is a serious problem in a fluid-bed granulation process of traditional Chinese medicine. Moisture content and size distribution are regarded as two pivotal influencing factors. Herein, a smart hyperspectral image analysis methodology was established via deep residual network (ResNet) algorithm, which was then applied to monitoring moisture content, size distribution and contents of four bioactive compounds of granules in the fluid-bed granulation process of Guanxinning tablets. First, a hyperspectral imaging camera was utilized to acquire hyperspectral images of 132 real granule samples in the spectral region of 389-1020 nm. Second, the moisture content and size distribution of the granules were measured with a laser particle sizer and a fast moisture analyzer, respectively. Moreover, the contents of danshensu, ferulic acid, rosmarinic acid and salvianolic acid B of the granules were determined by using high-performance liquid chromatography-diode array detection. Third, ResNet quantitative calibration models were built, which consisted of convolutional layer, maxpooling layer, four convolutional blocks with residual learning function and two fully connected layers. As a result, the R_c² values for the moisture content, granule sizes and contents of four bioactive compounds are determined to be 0.957, 0.986, 0.936, 0.959, 0.937, 0.938, 0.956, 0.889, 0.914 and 0.928, whereas the R_p² values are calculated as 0.940, 0.969, 0.904, 0.930, 0.925, 0.928, 0.896, 0.849, 0.844, and 0.905, respectively. The predicted values matched well with the measured values. These findings indicated that ResNet algorithm driven hyperspectral image analysis is feasible for monitoring both the physical and chemical properties of Guanxinning tablets at the same time.