Non-destructive Detection and Screening of Non-uniformity in Microwave Sterilization Using Hyperspectral Imaging Analysis

Unveiling microwave and Roasting-Steam heating mechanisms in regulating fat changes in pork using cell membrane simulation

Fat reduction due to heating or cooking is an important issue in a healthy diet. In the current study, pork subcutaneous back fat was treated via microwave heating (MH) within 10-90 s and roasting - steam heating (RSH) within 2-30 min and their dynamic changes of individual adipocytes were explored by using vesicles as a bio-membrane model. The result showed that MH and RSH significantly increased fat loss (P < 0.05), with the maximum losses being 74.1 % and 65.6 %, respectively. The mechanical strength of connective tissue decreased and then increased slightly. The microstructure demonstrated that MH and RSH treatments facilitated a large outflow of fat, showing that the particle size of the vesicle and individual adipocytes increased and then decreased. It is thus feasible to study the dynamic changes of individual adipocytes in regulating fat reduction using cell membrane simulation.

Predicting wheat gluten concentrations in potato starch using GPR and SVM models built by terahertz time-domain spectroscopy

The purpose of this study was for the first time to explore the feasibility of terahertz (THz) spectral imaging for the detection of gluten contents in food samples. Based on the obtained 80 THz spectrum data, Gaussian process regression (GPR) and support vector machine (SVM) models were established to predict wheat gluten concentrations in 40 potato starch mixture samples. The prediction performances of GPR and SVM obtained were R2 = 0.859 and RMSE = 0.070, and R2 = 0.715 and RMSE = 0.101 in the gluten concentration range of 1.3%-100%, respectively, showing that the linear SVM algorithm had better prediction performance. The results indicated that THz spectral imaging combined with GPR could be used to predict the gluten content in food samples. It is thus hoped that this research should provide a novel technique for gluten content detection to ensure gluten-free food samples.

Analyzing the effects of nonthermal pretreatments on the quality of microwave vacuum dehydrated beef using terahertz time-domain spectroscopy and near-infrared hyperspectral imaging

Both nonthermal pretreatment and nondestructive analysis are effective technologies in improving drying processes. This study evaluated the effects of different pretreatment methods on the quality of beef dehydrated by microwave vacuum drying (MVD) and compared the MVD process performance comprising real-time moisture content (MC), MC loss, colour content, and shrinkage rate using different optical sensing methods including terahertz time-domain spectroscopy (THz-TDS) and near-infrared hyperspectral imaging (NIR-HSI). Results indicated that osmotic pretreatment improved the drying rate of MVD beef with lower changes in colour and shrinkage rate. Both THz-TDS-based and NIR-HSI-based on-site direct scanning and in-situ in-direct sensing showed accurate prediction results, with best R²_p of 0.9646 and 0.9463 for MC and R²_p of 0.9817 and 0.9563 for MC loss prediction, respectively. NIR-HSI visualisation of MC results showed that ultrasound pretreatment curbed but osmotic pretreatment promoted nonuniform distribution during MVD. This research should guide improving the industrial MVD drying process.

A Review of Recent Advances for the Detection of Biological, Chemical, and Physical Hazards in Foodstuffs Using Spectral Imaging Techniques

Traditional methods for detecting foodstuff hazards are time-consuming, inefficient, and destructive. Spectral imaging techniques have been proven to overcome these disadvantages in detecting foodstuff hazards. Compared with traditional methods, spectral imaging could also increase the throughput and frequency of detection. This study reviewed the techniques used to detect biological, chemical, and physical hazards in foodstuffs including ultraviolet, visible and near-infrared (UV-Vis-NIR) spectroscopy, terahertz (THz) spectroscopy, hyperspectral imaging, and Raman spectroscopy. The advantages and disadvantages of these techniques were discussed and compared. The latest studies regarding machine learning algorithms for detecting foodstuff hazards were also summarized. It can be found that spectral imaging techniques are useful in the detection of foodstuff hazards. Thus, this review provides updated information regarding the spectral imaging techniques that can be used by food industries and as a foundation for further studies.

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

Chicken is at risk of contaminated foodborne pathogens in the production process. Timely and nondestructive detection of foodborne pathogens in chicken is essential for food security. The study aims to explore the feasibility of developing efficient classification models for qualitative detection of Staphylococcus aureus in chicken breast using the hyperspectral imaging technique. Principal component analysis was used to process the full spectral information and three wavelength selection methods (competitive adaptive reweighted sampling, genetic algorithm, and successive projections algorithm) were applied to extract effective wavelengths. These methods were combined with the support vector machine algorithm to develop conventional classification models, respectively. In addition, a convolutional neural network model based on deep learning was designed and trained for comparison. The performance of the convolutional neural network model was significantly better than that of conventional classification models. The overall accuracy for chicken sample classifications was improved from 83.88% to 91.38%. The results demonstrated that deep learning can effectively extract spectral features and promote the application of hyperspectral imaging in foodborne pathogens detection of chicken products.

Adaptive Design of Fluorescence Imaging Systems for Custom Resolution, Fields of View, and Geometries

Objective and Impact Statement: We developed a generalized computational approach to design uniform, high-intensity excitation light for low-cost, quantitative fluorescence imaging of in vitro, ex vivo, and in vivo samples with a single device. Introduction: Fluorescence imaging is a ubiquitous tool for biomedical applications. Researchers extensively modify existing systems for tissue imaging, increasing the time and effort needed for translational research and thick tissue imaging. These modifications are application-specific, requiring new designs to scale across sample types. Methods: We implemented a computational model to simulate light propagation from multiple sources. Using a global optimization algorithm and a custom cost function, we determined the spatial positioning of optical fibers to generate 2 illumination profiles. These results were implemented to image core needle biopsies, preclinical mammary tumors, or tumor-derived organoids. Samples were stained with molecular probes and imaged with uniform and nonuniform illumination. Results: Simulation results were faithfully translated to benchtop systems. We demonstrated that uniform illumination increased the reliability of intraimage analysis compared to nonuniform illumination and was concordant with traditional histological findings. The computational approach was used to optimize the illumination geometry for the purposes of imaging 3 different fluorophores through a mammary window chamber model. Illumination specifically designed for intravital tumor imaging generated higher image contrast compared to the case in which illumination originally optimized for biopsy images was used. Conclusion: We demonstrate the significance of using a computationally designed illumination for in vitro, ex vivo, and in vivo fluorescence imaging. Application-specific illumination increased the reliability of intraimage analysis and enhanced the local contrast of biological features. This approach is generalizable across light sources, biological applications, and detectors.

Nondestructive evaluation of Zn content in rape leaves using MSSAE and hyperspectral imaging

Zinc (Zn) content plays a decisive role in plant growth. Accurate management of Zn fertilizer application can promote high-quality development of the oilseed rape industry. This study adopted a deep learning (DL) method to predict the Zn content of oilseed rape leaves using hyperspectral imaging (HSI). The dropout mechanism was introduced to improve the stacked sparse autoencoder (SSAE) and named modified SSAE (MSSAE). MSSAE extracted deep spectral features of samples based on pixel-level spectral information (the wavelength range of the spectrum is 431-962 nm). Subsequently, the deep spectral features were applied as the inputs for support vector regression (SVR) and least squares support vector regression (LSSVR) to predict the Zn content in oilseed rape leaves. In addition, the successive projections algorithm (SPA) and the variable iterative space shrinkage approach (VISSA) were investigated as wavelength selection algorithms for comparison. The results showed that the MSSAE-LSSVR model had the best prediction performance (the coefficient of determination (R²) and root mean square error (RMSE) of the prediction set were 0.9566 and 1.0240 mg/kg, respectively). The overall results showed that the MSSAE was able to extract the deep features of HSI data and validated the possibility of HSI combined with a DL method for nondestructive testing of Zn content in oilseed rape leaves.

Applications of machine learning techniques for enhancing nondestructive food quality and safety detection

In considering the need of people all over the world for high-quality food, there has been a recent increase in interest in the role of nondestructive and rapid detection technologies in the food industry. Moreover, the analysis of data acquired by most nondestructive technologies is complex, time-consuming, and requires highly skilled operators. Meanwhile, the general applicability of various chemometric or statistical methods is affected by noise, sample, variability, and data complexity that vary under various testing conditions. Nowadays, machine learning (ML) techniques have a wide range of applications in the food industry, especially in nondestructive technology and equipment intelligence, due to their powerful ability in handling irrelevant information, extracting feature variables, and building calibration models. The review provides an introduction and comparison of machine learning techniques, and summarizes these algorithms as traditional machine learning (TML), and deep learning (DL). Moreover, several novel nondestructive technologies, namely acoustic analysis, machine vision (MV), electronic nose (E-nose), and spectral imaging, combined with different advanced ML techniques and their applications in food quality assessment such as variety identification and classification, safety inspection and processing control, are presented. In addition to this, the existing challenges and prospects are discussed. The result of this review indicates that nondestructive testing technologies combined with state-of-the-art machine learning techniques show great potential for monitoring the quality and safety of food products and different machine learning algorithms have their characteristics and applicability scenarios. Due to the nature of feature learning, DL is one of the most promising and powerful techniques for real-time applications, which needs further research for full and wide applications in the food industry.

Recent advances in muscle food safety evaluation: Hyperspectral imaging analyses and applications

As there is growing interest in process control for quality and safety in the meat industry, by integrating spectroscopy and imaging technologies into one system, hyperspectral imaging, or chemical or spectroscopic imaging has become an alternative analytical technique that can provide the spatial distribution of spectrum for fast and nondestructive detection of meat safety. This review addresses the configuration of the hyperspectral imaging system and safety indicators of muscle foods involving biological, chemical, and physical attributes and other associated hazards or poisons, which could cause safety problems. The emphasis focuses on applications of hyperspectral imaging techniques in the safety evaluation of muscle foods, including pork, beef, lamb, chicken, fish and other meat products. Although HSI can provide the spatial distribution of spectrum, characterized by overtones and combinations of the C-H, N-H, and O-H groups using different combinations of a light source, imaging spectrograph and camera, there still needs improvement to overcome the disadvantages of HSI technology for further applications at the industrial level.

A terahertz time-domain super-resolution imaging method using a local-pixel graph neural network for biological products

The low image acquisition speed of terahertz (THz) time-domain imaging systems limits their application in biological products analysis. In the current study, a local pixel graph neural network was built for THz time-domain imaging super-resolution. The method could be applied to the analysis of any heterogeneous biological products as it only required a small number of sample images for training and particularly it focused on THz feature frequencies. The graph network applied the Fourier transform to graphs extracted from low-resolution (LR) images bringing an invariance of rotation and flip for local pixels, and the network then learnt the relationship between the state of graphs and the corresponding pixels to be reconstructed. With wood cores and seeds as examples, the images of these samples were captured by a THz time-domain imaging system for training and analysed by the method, achieving the root mean square error (RMSE) of pixels of 0.0957 and 0.1061 for the wood core and seed images, respectively. In addition, the reconstructed high-resolution (HR) images, LR images and true HR images at several feature frequencies were also compared in the current study. Results indicated that the method could not only reconstruct the spatial details and the useful signals from high noise signals at high feature frequencies but could also operate super-resolution in both spatial and spectral aspects.

Effects of dielectric properties and microstructures on microwave-vacuum drying of mushroom (Agaricus bisporus) caps and stipes evaluated by non-destructive techniques

This research work aimed to investigate the effects of microstructures, dielectric property and temperature distributions on drying feature difference between the mushroom cap and stipe during the microwave-vacuum drying (MVD) process. Near-infrared hyperspectral imaging (NIR HSI) was employed to visualize distribution maps for moisture content (MC), dielectric constant ε' and dielectric loss factor ε'' of mushroom slices during the MVD process. Infrared (IR) thermal imaging was used to evaluate the temperature distribution of the mushroom slices. Results demonstrated higher MC, ε' and ε'' values in MVD mushroom stipes. Nevertheless, the centre area of the mushroom slice showed the highest temperature, while the MVD mushroom cap displayed a more porous structure. The effect of microstructure could encounter effects of dielectric properties and temperature to cause higher water evaporation in the MVD cap. This work highlights the novelty to combine different detection techniques to investigate the effects of microstructures, dielectric property and temperature distributions on drying patterns of mushroom slices.

Recent developments in vibrational spectral analyses for dynamically assessing and monitoring food dehydration processes

Dehydration is one of the most widely used food processing techniques, which is sophisticated in nature. Rapid and accurate prediction of dehydration performance and its effects on product quality is still a difficult task. Traditional analytical methods for evaluating food dehydration processes are laborious, time-consuming and destructive, and they are not suitable for online applications. On the other hand, vibrational spectral techniques coupled with chemometrics have emerged as a rapid and noninvasive tool with excellent potential for online evaluation and control of the dehydration process to improve final dried food quality. In the current review, the fundamental of food dehydration and five types of vibrational spectral techniques, and spectral data processing methods are introduced. Critical overtones bands related to dehydration attributes in the near-infrared (NIR) region and the state-of-the-art applications of vibrational spectral analyses in evaluating food quality attributes as affected by dehydration processes are summarized. Research investigations since 2010 on using vibrational spectral technologies combined with chemometrics to continuously monitor food quality attributes during dehydration processes are also covered in this review.

Evaluating drying feature differences between ginger slices and splits during microwave-vacuum drying by hyperspectral imaging technique

This study aimed to investigate the difference between ginger slices (vertically cut) and splits (horizontally cut) during microwave-vacuum drying (MVD) procedures. MVD ginger slices showed a higher shrinkage rate and a higher hardness value, with a more porous structure of the surface layer. MVD ginger splits had higher rehydration rates at the first 15 min of the rehydration. Nine optimal wavelengths were selected by regression coefficients (RC) from the partial least squares regression (PLSR) model based on the raw data. A simplified PLSR model based on optimal wavelengths showed a good performance with a coefficient of determination in prediction (Rp2) of 0.973 and a root mean square error in prediction (RMSEP) of 4.63%. Texture features of grey level co-occurrence matrix (GLCM) of moisture prediction maps demonstrated a more uniform moisture distribution in MVD ginger slices than that in splits in the original geometry.

Visualization of the in situ distribution of contents and hydrogen bonding states of cellular level water in apple tissues by confocal Raman microscopy

Raman spectroscopy has been employed for studying the hydrogen bonding states of water molecules for decades, however, Raman imaging data contain thousands of spectra, making it challenging to obtain information on water with different hydrogen bonds. In the current study, a novel method combining confocal Raman microscopy (CRM) imaging with the iterative curve fitting algorithms was developed to determine the distribution of water contents at the cellular level and water states with different hydrogen bonds in apple tissues. Raman imaging data ranging from 2700 to 3800 cm-1 were acquired from whole cells in the apple tissue, which were then decomposed into seven sub-peaks using the fixed-position Gaussian iterative curve fitting (FPGICF) algorithm. The content and hydrogen bonding states of cellular water were calculated as the area sum of the OH stretching vibration and the area ratio of DA-OH over DDAA-OH stretching vibration or the number of hydrogen bonds of each water molecule, respectively. Finally, the area of each sub-peak, the area sum of the OH stretching vibration, and the area ratio of DA-OH over DDAA-OH stretching vibration were used to visualize the distribution of each sub-peak, water contents and water states with different hydrogen bonds, respectively. In addition, it was found that the number of hydrogen bonds of each water molecule could also be considered as a criterion to describe the hydrogen bond states of water in apple tissues. The availability of such information should provide new insights for future study of cellular water in other food materials.

Development of Bacterial Spore Pouches as a Tool to Evaluate the Sterilization Efficiency-A Case Study with Microwave Sterilization Using Clostridium sporogenes and Geobacillus stearothermophilus

In this study, novel spore pouches were developed using mashed potato as a food model inoculated with either Geobacillus stearothermophilus or Clostridium sporogenes spores. These spore pouches were used to evaluate the sterilization efficiency of Coaxially induced microwave pasteurization and sterilization (CiMPAS) as a case study. CiMPAS technology combines microwave energy (915 MHz) along with hot water immersion to sterilize food in polymeric packages. The spore pouches were placed at pre-determined specific locations, especially cold spots in each food tray before being processed using two regimes (R-121 and R-65), which consisted of 121 °C and 65 °C at 12 and 22 kW, respectively, followed by recovery and enumeration of the surviving spores. To identify cold spots or the location for inoculation, mashed potato was spiked with Maillard precursors and processed through CiMPAS, followed by measurement of lightness values (*L-values). Inactivation equivalent to of 1-2 Log CFU/g and >6 Log CFU/g for Geobacillus stearothermophilus and Clostridium sporogenes spores, respectively was obtained on the cold spots using R-121, which comprised of a total processing time of 64.2 min. Whereas, inactivation of <1 and 2-3 Log CFU/g for G. stearothermophilus and C. sporogenes spores, respectively on the cold spots was obtained using R-65 (total processing time of 68.3 min), whereas inactivation of 1-3 Log CFU/g of C. sporogenes spores was obtained on the sides of the tray. The results were reproducible across three processing replicates for each regime and inactivation at the specific locations were clearly distinguishable. The study indicated a strong potential to use spore pouches as a tool for validation studies of microwave-induced sterilization.

Effects of constant power microwave on the adsorption behaviour of myofibril protein to aldehyde flavour compounds

This study explored the influence of constant power microwave on the adsorption ability of myofibril protein from beef to typical aldehyde flavour compounds. Results showed that there was a significant increasing trend in surface hydrophobicity and reactive sulfhydryls content of myofibril protein with an increase in microwave power and treatment time. The adsorption ability of myofibril protein to aldehyde flavour compounds increased with increasing microwave power and time. The percentage of free aldehyde flavour compounds was related to the content of surface hydrophobicity, and reactive and total sulfhydryls of myofibril protein under microwave conditions, which could be fitted according to the multilevel relational (MLR) model. Furthermore, the reduced interface energy was probably the driving force for myofibril protein-flavour compounds adsorption behaviour at the gas-liquid interface.

Identification of Cold Spots Using Non-Destructive Hyperspectral Imaging Technology in Model Food Processed by Coaxially Induced Microwave Pasteurization and Sterilization

The model food in this study known as mashed potato consisted of ribose (1.0%) and lysine (0.5%) to induce browning via Maillard reaction products. Mashed potato was processed by Coaxially Induced Microwave Pasteurization and Sterilization (CiMPAS) regime to generate an F0 of 6-8 min and analysis of the post-processed food was done in two ways, which included by measuring the color changes and using hyperspectral data acquisition. For visualizing the spectra of each tray in comparison with the control sample (raw mashed-potato), the mean spectrum (i.e., mean of region of interest) of each tray, as well as the control sample, was extracted and then fed to the fitted principal component analysis model and the results coincided with those post hoc analysis of the average reflectance values. Despite the presence of a visual difference in browning, the Lightness (L) values were not significantly (p < 0.05) different to detect a cold spot among a range of 12 processed samples. At the same time, hyperspectral imaging could identify the colder trays among the 12 samples from one batch of microwave sterilization.

Ultrasensitive analysis of kanamycin residue in milk by SERS-based aptasensor

An ultrasensitive method for the kanamycin (KANA) detection in milk sample using surface-enhanced Raman spectroscopy-based aptasensor was employed in the current study. Double strand DNA binding bimetallic gold@silver nanoparticles were developed as a sensing platform. Probe DNAs were first embedded on the surface of gold nanoparticles by the end-modified thiol, and after silver shell encapsulating, KANA aptamer DNAs with the Raman reporter Cy3 were then hybridized with probe DNAs by complementary base pairing. Results showed that with increase in the KANA concentration, the Raman intensity of Cy3 decreased. Besides achieving selectivity, an ultralow detection limit of 0.90 pg/mL, a broad linear relationship ranging from 10 μg/mL to 100 ng/mL in aqueous reagent and satisfactory recoveries of 90.4-112% in liquid whole milk were obtained. The result of actual sample proved that this aptasensor was promising in trace determination of KANA residue.