A Review Towards Hyperspectral Imaging for Real-Time Quality Control of Food Products with an Illustrative Case Study of Milk Powder Production

Blood cancer diagnosis using hyperspectral imaging combined with the forward searching method and machine learning

Hyperspectral imaging (HSI), a widely used biosensing technique, has been applied to tumor detection. Rapid, accurate, and low-cost detection of blood cancer using hyperspectral technology remains a challenge. We developed a new method to discriminate blood cancer using hyperspectral imaging (HSI) and the forward searching method (FSM). Four commonly used classification models are applied for four types of blood cancer spectra recognition. The support vector machine (SVM) model with the highest recognition accuracy (94.5%) combined with HSI achieves high-precision tumor identification. For higher recognition accuracy and lower hardware barriers, based on the selection probabilities of spectral lines calculated by a multi-objective atomic orbital search method, the FSM is proposed for HSI feature selection. With the proposed method, the wavelength band range of the spectrum is reduced by at least 50%. Compared with the traditional dimensionality reduction methods, the FSM can obtain a higher accuracy rate with lower hardware requirements. These results show that our proposed method can achieve non-invasive rapid screening of blood cancers with lower hardware requirements. Therefore, HSI assisted with FSM and SVM hybrid models can be a powerful and promising tool for blood cancer detection.

Identification of geographical origins of Radix Paeoniae Alba using hyperspectral imaging with deep learning-based fusion approaches

The Radix Paeoniae Alba (Baishao) is a traditional Chinese medicine (TCM) with numerous clinical and nutritional benefits. Rapid and accurate identification of the geographical origins of Baishao is crucial for planters, traders and consumers. Hyperspectral imaging (HSI) was used in this study to acquire spectral images of Baishao samples from its two sides. Convolutional neural network (CNN) and attention mechanism was used to distinguish the origins of Baishao using spectra extracted from one side. The data-level and feature-level deep fusion models were proposed using information from both sides of the samples. CNN models outperformed the conventional machine learning methods in classifying Baishao origins. The generalized Gradient-weighted Class Activation Mapping (Grad-CAM++) was utilized to visualize and identify important wavelengths that significantly contribute to model performance. The overall results illustrated that HSI combined with deep learning strategies was effective in identifying the geographical origins of Baishao, having good prospects of real-world applications.

The Use of Infrared Spectroscopy for the Quantification of Bioactive Compounds in Food: A Review

Infrared spectroscopy (wavelengths ranging from 750-25,000 nm) offers a rapid means of assessing the chemical composition of a wide range of sample types, both for qualitative and quantitative analyses. Its use in the food industry has increased significantly over the past five decades and it is now an accepted analytical technique for the routine analysis of certain analytes. Furthermore, it is commonly used for routine screening and quality control purposes in numerous industry settings, albeit not typically for the analysis of bioactive compounds. Using the Scopus database, a systematic search of literature of the five years between 2016 and 2020 identified 45 studies using near-infrared and 17 studies using mid-infrared spectroscopy for the quantification of bioactive compounds in food products. The most common bioactive compounds assessed were polyphenols, anthocyanins, carotenoids and ascorbic acid. Numerous factors affect the accuracy of the developed model, including the analyte class and concentration, matrix type, instrument geometry, wavelength selection and spectral processing/pre-processing methods. Additionally, only a few studies were validated on independently sourced samples. Nevertheless, the results demonstrate some promise of infrared spectroscopy for the rapid estimation of a wide range of bioactive compounds in food matrices.

Integrated continuous flow method with dual feedback-based controls for online analysis and process control

The concept of an integrated automated continuous flow method with dual feedback controls is presented for diluting a stock solution to provide a solution of a given concentration. The one control is used for the online process monitoring by a feedback-based flow ratiometry, where the product (the diluted liquid) is titrated through the rapid bidirectional scan of the product/reagent flow ratio. The feedback control limits the scanning to the necessary range to increase the analytical throughput. The other control is used for the process control to output the product with a preset concentration. The merging ratio of the stock solution and a solvent (diluent) is changed based on the information from the online analysis. The concept was verified by applying it to producing 0.1 mol dm^-3 CH₃COOH. When the stock concentration was changed from 0.1 (reference concentration) to 0.3 and then 0.2 mol dm^-3, the system searched for the suitable merging ratio and converged the output concentration to the reference value within 7.43 min with a relative error below 1.05%. The mean throughput rate of the process analysis was 11.2 titrations min^-1. Successful results were also obtained for the 0.1 mol dm^-3 HCl production. The present concept could be the basis for process control with reduced wasteful output and effluent treatment with eco-friendly treated water discharge, resulting in the contribution to SDGs' goals of 6 (Clean water and sanitation), 9 (Industry, innovation and infrastructure), and 14 (Life below water).

Non-destructive detection and classification of textile fibres based on hyperspectral imaging and 1D-CNN

Textile fibre is very common in daily life, and its classification and identification play an important role in textile recycling, archaeology, public security, and other industries. However, traditional identification methods are time-consuming, laborious, and often destructive to the samples. In order to quickly, accurately, and nondestructively classify and recognize textile fibres, this study established a textile fibre classification and recognition method based on hyperspectral imaging (HSI) and a one-dimensional convolutional neural network (1D-CNN) model. Hyperspectral images of 25 kinds of commercial textile fibres were collected and denoised by pixel fusion. Four traditional machine learning classification models, k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), and partial least squares-discriminant analysis (PLS-DA), were used to identify the data. The results show that RF has the highest classification accuracy, reaching 91.4%. Then a back propagation neural network (BPNN) model and a one-dimensional convolutional neural network (1D-CNN) model were constructed and compared with the traditional machine learning methods. The results show that the 1D-CNN models have 97.9% and 98.6% accuracy on the training and test sets, respectively. The precision (Pr), sensitivity (Se), specificity (Sp), and F1 score (F1 score) of the models reached 98.7%, 98.6%, 99.9%, and 98.6%, respectively, which were significantly better than the four traditional machine learning models. It seems that 1D-CNN combined with the HSI technique may be a potential method in the detection and classification of textile fibres.

Non-destructive assessment of quality parameters of white button mushrooms (Agaricus bisporus) using image processing techniques

Considering that appearance of white button mushroom (WBM) as the trigger for registering its quality, this study was aimed at analyzing the visual cues by the application of image processing tools. While L-a-b colour space and skewness was used for estimating chromatic and morphological characteristics; onset of discolouration of WBM was predicted by hyperspectral image analysis. Undamaged (UD) and damaged (D) mushrooms were stored under refrigerated conditions (3-5 °C and 90% R_h). RGB and hyperspectral images were acquired on alternate storage days 1, 3, 5, 7 and 9. Weight loss, texture and moisture content of stored mushrooms were also recorded during the storage period. Colour changes in stored UD and D were found to be in b (21.55) and a (2399) value, respectively. Browning index in D was 83-212% higher than UD mushrooms across the storage period. Weight and firmness losses in D were higher by 65.9 and 31.4%, respectively than UD. Morphological characteristic in terms of aspect ratio and roundness were not found to vary significantly over the storage period for both UD and D mushrooms. Chemometrics revealed that multiplicative scatter correction was the best pre-processing tool and that onset on discolouration is conspicuous in the spectral region of 520-800 nm. k-NN fared better than PLS-DA for correct classification (100%) of UD and D mushrooms.

Near-infrared techniques for fraud detection in dairy products: A review

The dairy products sector is an important part of the food industry, and their consumption is expected to grow in the next 10 years. Therefore, the authentication of these products in a faster and precise way is required for the sake of public health. This review proposes the use of near-infrared techniques for the detection of food fraud in dairy products as they are faster, nondestructive, environmentally friendly, do not require sample preparation, and allow multiconstituent analysis. First, we have described frequent forms of food fraud in dairy products and the application of traditional techniques for their detection, highlighting gaps and counterproductive characteristics for the actual global food chain, as longer sample preparation time and use of reagents. Then, the application of near-infrared spectroscopy and hyperspectral imaging for the detection of food fraud mainly in cheese, butter, and yogurt are described. As these techniques depend on model development, the coverage of different dairy products by the literature will promote the identification of food fraud in a faster and reliable way.

A New Perspective to Tribocharging: Could Tribocharging Lead to the Development of a Non-Destructive Approach for Process Monitoring and Quality Control of Powders?

This study explores a new perspective on triboelectrification that could potentially lead to the development of a non-destructive approach for the rapid characterization of powders. Sieved yellow pea powders at various particle sizes and protein contents were used as a model system for the experimental charge measurements of the triboelectrified powders. A tribocharging model based on the prominent condenser model was combined with a Eulerian-Lagrangian computational fluid dynamics (CFD) model to simulate particle tribocharging in particle-laden flows. Further, an artificial neural network model was developed to predict particle-wall collision numbers based on a database obtained through CFD simulations. The tribocharging and CFD models were coupled with the experimental tribocharging data to estimate the contact potential difference of powders, which is a function of contact surfaces' work functions and depends on the chemical composition of powders. The experimentally measured charge-to-mass ratios were linearly related to the calculated contact potential differences for samples with different protein contents, indicating a potential approach for the chemical characterization of powders.

A Multidisciplinary Approach to Coronavirus Disease (COVID-19)

Since December 2019, humanity has faced an important global threat. Many studies have been published on the origin, structure, and mechanism of action of the SARS-CoV-2 virus and the treatment of its disease. The priority of scientists all over the world has been to direct their time to research this subject. In this review, we highlight chemical studies and therapeutic approaches to overcome COVID-19 with seven different sections. These sections are the structure and mechanism of action of SARS-CoV-2, immunotherapy and vaccine, computer-aided drug design, repurposing therapeutics for COVID-19, synthesis of new molecular structures against COVID-19, food safety/security and functional food components, and potential natural products against COVID-19. In this work, we aimed to screen all the newly synthesized compounds, repurposing chemicals covering antiviral, anti-inflammatory, antibacterial, antiparasitic, anticancer, antipsychotic, and antihistamine compounds against COVID-19. We also highlight computer-aided approaches to develop an anti-COVID-19 molecule. We explain that some phytochemicals and dietary supplements have been identified as antiviral bioproducts, which have almost been successfully tested against COVID-19. In addition, we present immunotherapy types, targets, immunotherapy and inflammation/mutations of the virus, immune response, and vaccine issues.

The effect of process variables on the physical properties and microstructure of HOPO nanoemulsion flakes obtained by refractance window

Refractance window (RW) drying is considered an emerging technique in the food field due to its scalability, energy efficiency, cost and end-product quality. It can be used for obtaining flakes from high-oleic palm oil (HOPO) nanoemulsions containing a high concentration of temperature-sensitive active compounds. This work was thus aimed at studying the effect of temperature, thickness of the film drying, nanoemulsion process conditions, and emulsion formulation on the flakes’ physical properties and microstructure. The results showed that HOPO flakes had good physical characteristics: 1.4% to 5.6% moisture content and 0.26 to 0.58 a_w. Regarding microstructure, lower fractal dimension (FDt) was obtained when RW drying temperature increased, which is related to more regular surfaces. The results indicated that flakes with optimal physical properties can be obtained by RW drying of HOPO nanoemulsions.

Improvement strategies of food supply chain through novel food processing technologies during COVID-19 pandemic

Coronavirus disease-19 (COVID-19) is a contagious disease caused by a novel corona virus (SARS-CoV-2). No medical intervention has yet succeeded, though vaccine success is expected soon. However, it may take months or years to reach the vaccine to the whole population of the world. Therefore, the technological preparedness is worth to discuss for the smooth running of food processing activities. We have explained the impact of the COVID-19 pandemic on the food supply chain (FSC) and then discussed the technological interventions to overcome these impacts. The novel and smart technologies during food processing to minimize human-to-human and human-to-food contact were compiled. The potential virus-decontamination technologies were also discussed. Finally, we concluded that these technologies would make food processing activities smarter, which would ultimately help to run the FSC smoothly during COVID-19 pandemic.

Wavelength Selection FOR Rapid Identification of Different Particle Size Fractions of Milk Powder Using Hyperspectral Imaging

Hyperspectral imaging (HSI) in the spectral range of 400-1000 nm was tested to differentiate three different particle size fractions of milk powder. Partial least squares discriminant analysis (PLS-DA) was performed to observe the relationship of spectral data and particle size information for various samples of instant milk powder. The PLS-DA model on full wavelengths successfully classified the three fractions of milk powder with a coefficient of prediction 0.943. Principal component analysis (PCA) identified each of the milk powder fractions as separate clusters across the first two principal components (PC₁ and PC₂) and five characteristic wavelengths were recognised by the loading plot of the first three principal components. Weighted regression coefficient (WRC) analysis of the partial least squares model identified 11 important wavelengths. Simplified PLS-DA models were developed from two sets of reduced wavelengths selected by PCA and WRC and showed better performance with predictive correlation coefficients (R_p²) of 0.962 and 0.979, respectively, while PLS-DA with complete spectrum had R_p² of 0.943. Similarly, classification accuracy of PLS-DA was improved to 92.2% for WRC based predictive model. Calculation time was also reduced to 2.1 and 2.8 s for PCA and WRC based simplified PLS-DA models in comparison to the complete spectrum model that was taking 32.2 s on average to predict the classification of milk powder samples. These results demonstrated that HSI with appropriate data analysis methods could become a potential analyser for non-invasive testing of milk powder in the future.