Classification, identification, and growth stage estimation of microalgae based on transmission hyperspectral microscopic imaging and machine learning

Nondestructive detection of saponin content in Panax notoginseng powder based on hyperspectral imaging

This study investigated the feasibility of using hyperspectral imaging (HSI) technique to detect the saponin content in Panax notoginseng (PN) powder. The reflectance hyperspectral images of PN powder samples were collected in the spectral range of 400.6-999.9 nm. Savitzky-golay (SG) smoothing combined with detrending correction was utilized to preprocess the original spectral data. Two model population analysis (MPA) based methods, namely bootstrapping soft shrinkage (BOSS) and iteratively retains informative variables (IRIV) were employed to extract feature wavelengths from the full spectra. A generalized normal distribution optimization based extreme learning machine (GNDO-ELM) model was proposed to establish calibration model between spectra and saponin content, and compared with existing methods (GA-ELM, PSO-ELM and SSA-ELM). The result showed that the IRIV-GNDO-ELM model gave the best performance, with coefficient of determination for prediction (R2P) of 0.953 and root mean square error for prediction (RMSEP) of 0.115%. Therefore, it is possible to determine the saponin content of PN powder by using HSI technique.

An aberration-free line scan confocal Raman imager and type classification and distribution detection of microplastics

An aberration-free line scanning confocal Raman imager (named AFLSCRI) is developed to achieve rapid Raman imaging. As an application example, various types and sizes of MPs are identified through Raman imaging combined with a machine learning algorithm. The system has excellent performance with a spatial resolution of 2 µm and spectral resolution of 4 cm-1. Compared to traditional point-scanning Raman imaging systems, the detection speed is improved by 2 orders of magnitude. The pervasive nature of MPs results in their infiltration into the food chain, raising concerns for human health due to the potential for chemical leaching and the introduction of persistent organic pollutants. We conducted a series of experiments on various types and sizes of MPs. The system can give a classification accuracy of 98% for seven different types of plastics, and Raman imaging and species identification for MPs as small as 1 µm in diameter were achieved. We also identified toxic and harmful substances remaining in plastics, such as Dioctyl Phthalate (DOP) residues. This demonstrates a strong performance in microplastic species identification, size recognition and identification of hazardous substance contamination in microplastics.

A high-precision and wide-range pH monitoring system based on broadband cavity-enhanced absorption spectrum

A high-precision pH monitoring system over a wide pH range is introduced. The system comprises a cavity-enhancement module constructed by two high-reflectivity mirrors, a microfluidic pH sensing chip based on a binary-indicator membrane of Congo red and m-cresol purple, and a hyperspectral transmission module. This structure extends the effective absorption optical path of the sensing chip, significantly amplifying the spectral differences at various pH values. The spectrum of the transmitted light is recorded by a self-developed hyperspectral module and then converted to broadband cavity-enhanced absorption spectrum (BBCEAS) via the Beer-Lambert law. An artificial neural network (ANN) is employed to predict pH values of the solution. With such a design, this system exhibits a wide detecting range of 2 M [H+] - 2 M [OH-] (corresponding to pH -0.3-14.3) with a response time of about 120 s. The system can achieve a higher detection accuracy with root mean square error (RMSE) of 0.073, as compared to 0.137 without the cavity enhancement. The system also possesses good properties of repeatability, long-term stability, ion resistance, and organic corrosion resistance. These excellent properties make the proposed system a promising candidate technology for harsh environments, such as seawater acidification warning, chemical plant sewage monitoring, and biological sample detection.

Study on the aging status of insulators based on hyperspectral imaging technology

The acidic environment is one of the main factors leading to the aging of silicone rubber (SiR) insulators. Aging can reduce the surface hydrophobicity and pollution flashover resistance of insulators, threatening the safe and stable operation of the power grid. Therefore, evaluating the aging state of insulators is essential to prevent flashover accidents on the transmission line. This paper is based on an optical hyperspectral imaging (HSI) technology for pixel-level assessment of insulator aging status. Firstly, the SiR samples were artificially aged in three typical acidic solutions with different concentrations of HNO3, H2SO4, and HCl, and six aging grades of SiR samples were prepared. The HSI of SiR at each aging grade was extracted using a hyperspectral imager. To reduce the calculation complexity and eliminate the interference of useless information in the band, this paper proposes a joint random forest- principal component analysis (RF-PCA) dimensionality reduction method to reduce the original 256-dimensional hyperspectral data to 7 dimensions. Finally, to capture local features in hyperspectral images more effectively and retain the most significant information of the spectral lines, a convolutional neural network (CNN) was used to build a classification model for pixel-level assessment of the SiR's aging state of and visual prediction of insulators' defects. The research method in this paper provides an important guarantee for the timely detection of safety hazards in the power grid.

Recent advances in microalgal production, harvesting, prediction, optimization, and control strategies

The market value of microalgae has grown exponentially over the past two decades, due to their use in the pharmaceutical, nutraceutical, cosmetic, and aquatic/animal feed industries. In particular, high-value products such as omega-3 fatty acids, proteins, and pigments derived from microalgae have high demand. However, the supply of these high-value microalgal bioproducts is hampered by several critical factors, including low biomass and bioproduct yields, inefficiencies in monitoring microalgal growth, and costly harvesting methods. To overcome these constraints, strategies such as synthetic biology, bubble generation, photobioreactor designs, electro-/magnetic-/bioflocculation, and artificial intelligence integration in microalgal production are being explored. These strategies have significant promise in improving the production of microalgae, which will further boost market availability of algal-derived bioproducts. This review focuses on the recent advances in these technologies. Furthermore, this review aims to provide a critical analysis of the challenges in existing algae bioprocessing methods, and highlights future research directions.

Nondestructive detection of Pleurotus geesteranus strain degradation based on micro-hyperspectral imaging and machine learning

In the production of edible fungi, the use of degraded strains in cultivation incurs significant economic losses. Based on micro-hyperspectral imaging and machine learning, this study proposes an early, nondestructive method for detecting different degradation degrees of Pleurotus geesteranus strains. In this study, an undegraded strain and three different degradation-level strains were used. During the mycelium growth, 600 micro-hyperspectral images were obtained. Based on the average transmittance spectra of the region of interest (ROI) in the range of 400-1000 nm and images at feature bands, feature spectra and images were extracted using the successive projections algorithm (SPA) and the deep residual network (ResNet50), respectively. Different feature input combinations were utilized to establish support vector machine (SVM) classification models. Based on the results, the spectra-input-based model performed better than the image-input-based model, and feature extraction improved the classification results for both models. The feature-fusion-based SPA+ResNet50-SVM model was the best; the accuracy rate of the test set was up to 90.8%, which was better than the accuracy rates of SPA-SVM (83.3%) and ResNet50-SVM (80.8%). This study proposes a nondestructive method to detect the degradation of Pleurotus geesteranus strains, which could further inspire new methods for the phenotypic identification of edible fungi.

An artificial intelligence approach for identification of microalgae cultures

In this work, a model for the characterization of microalgae cultures based on artificial neural networks has been developed. The characterization of microalgae cultures is essential to guarantee the quality of the biomass, and the objective of this work is to achieve a simple and fast method to address this issue. Data acquisition was performed using FlowCam, a device capable of capturing images of the cells detected in a culture sample, which are used as inputs by the model. The model can distinguish between 6 different genera of microalgae, having been trained with several species of each genus. It was further complemented with a classification threshold to discard unwanted objects while improving the overall accuracy of the model. The model achieved an accuracy of up to 97.27% when classifying a culture. The results demonstrate the effectiveness of the Deep Learning models for the characterization of microalgae cultures, it being a useful tool for the monitoring of microalgae cultures in large-scale production facilities while providing accurate characterization over a wide range of genera.

A critical review of machine-learning for "multi-omics" marine metabolite datasets

During the last decade, genomic, transcriptomic, proteomic, metabolomic, and other omics datasets have been generated for a wide range of marine organisms, and even more are still on the way. Marine organisms possess unique and diverse biosynthetic pathways contributing to the synthesis of novel secondary metabolites with significant bioactivities. As marine organisms have a greater tendency to adapt to stressed environmental conditions, the chance to identify novel bioactive metabolites with potential biotechnological application is very high. This review presents a comprehensive overview of the available "-omics" and "multi-omics" approaches employed for characterizing marine metabolites along with novel data integration tools. The need for the development of machine-learning algorithms for "multi-omics" approaches is briefly discussed. In addition, the challenges involved in the analysis of "multi-omics" data and recommendations for conducting "multi-omics" study were discussed.

Trends in digital image processing of isolated microalgae by incorporating classification algorithm

Identification of microalgae species is of importance due to the uprising of harmful algae blooms affecting both the aquatic habitat and human health. Despite this occurence, microalgae have been identified as a green biomass and alternative source due to its promising bioactive compounds accumulation that play a significant role in many industrial applications. Recently, microalgae species identification has been conducted through DNA analysis and various microscopy techniques such as light, scanning electron, transmission electron, and atomic force -microscopy. The aforementioned procedures have encouraged researchers to consider alternate ways due to limitations such as costly validation, requiring skilled taxonomists, prolonged analysis, and low accuracy. This review highlights the potential innovations in digital microscopy with the incorporation of both hardware and software that can produce a reliable recognition, detection, enumeration, and real-time acquisition of microalgae species. Several steps such as image acquisition, processing, feature extraction, and selection are discussed, for the purpose of generating high image quality by removing unwanted artifacts and noise from the background. These steps of identification of microalgae species is performed by reliable image classification through machine learning as well as deep learning algorithms such as artificial neural networks, support vector machines, and convolutional neural networks. Overall, this review provides comprehensive insights into numerous possibilities of microalgae image identification, image pre-processing, and machine learning techniques to address the challenges in developing a robust digital classification tool for the future.

Identification of the interference spectra of edible oil samples based on neighborhood rough set attribute reduction

Due to numerous edible oil safety problems in China, an automatic oil quality detection technique is urgently needed. In this study, rough set theory and Fourier transform spectrum are combined for proposing a digital identification method for edible oil. First, the Fourier transform spectra of three different types of edible oil samples, including colza oil, waste oil, and peanut oil, are measured. After the input spectra are differentially and smoothly processed, the characteristic wavelength bands are selected with neighborhood rough set attribution reduction (NRSAR). Moreover, the classification models are established based on random forest (RF) and extreme learning machine (ELM) algorithms. Finally, confusion matrix, classification accuracy, sensitivity, specificity, and the distribution of judgment are calculated for evaluating the classification performances of different models and determining the optimal oil identification model. The results show that by using the third-order difference pre-processing method, 193 wavelength bands in the visible range can be reduced to 10 characteristic wavelengths, with a compression ratio of over 88.61%. Using the established NRS-RF and NRS-ELM models, the total identification accuracies are 91.67% and 93.33%, respectively. In particular, the identification accuracy of peanut oil using the NRS-ELM model reaches up to 100%, whereas the identification accuracies obtained using the principal component analysis (PCA)-based models that are commonly used in information processing (PCA-RF and PCA-ELM) are 81.67% and 90.00%, respectively. As compared with feature extraction methods, the proposed NRSAR shows directive advantages in terms of precision, sensitivity, specificity, and the distribution of judgment. In addition, the execution time is also reduced by approximately 1/3. Conclusively, the NRSAR method and NRS-ELM the model in the spectral identification of edible oil show favorable performance. They are expected to bring forth insightful oil identification techniques.

Perspective: Multiomics and Machine Learning Help Unleash the Alternative Food Potential of Microalgae

Food security has become a pressing issue in the modern world. The ever-increasing world population, ongoing COVID-19 pandemic, and political conflicts together with climate change issues make the problem very challenging. Therefore, fundamental changes to the current food system and new sources of alternative food are required. Recently, the exploration of alternative food sources has been supported by numerous governmental and research organizations, as well as by small and large commercial ventures. Microalgae are gaining momentum as an effective source of alternative laboratory-based nutritional proteins as they are easy to grow under variable environmental conditions, with the added advantage of absorbing carbon dioxide. Despite their attractiveness, the utilization of microalgae faces several practical limitations. Here, we discuss both the potential and challenges of microalgae in food sustainability and their possible long-term contribution to the circular economy of converting food waste into feed via modern methods. We also argue that systems biology and artificial intelligence can play a role in overcoming some of the challenges and limitations; through data-guided metabolic flux optimization, and by systematically increasing the growth of the microalgae strains without negative outcomes, such as toxicity. This requires microalgae databases rich in omics data and further developments on its mining and analytics methods.

Determination of geographic origins and types of Lindera aggregata samples using a portable short-wave infrared hyperspectral imager

A portable short-wavelength infrared microscope hyperspectral imager (SMHI) combined with machine learning algorithms for the purpose of classifying geographical origins as well as root types of Lindera aggregata is developed. The spectral range of the SMHI system is 1090-1820 nm (5500-9100 cm-1) with spectral and spatial resolutions of 4 nm and 27.3 μm, respectively. Utilizing PCA-RF algorithms, the geographic origin of tuberous roots and leaves from five different origins were classified with accuracies of 97.5% and 97.8%, respectively. In addition, spatial identification of tuberous root and taproot tubers in a mixed sample was done with an accuracy of 98.98%. The accuracy of origin classification and spatial identification are high enough which indicate the significant potential of applying SMHI system into the non-invasive spatial mapping and rapid quality assessment of medicinal herbs.

Machine learning techniques to characterize functional traits of plankton from image data

Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.

Wavelength selection of multispectral imaging for oil palm fresh fruit ripeness classification

Multispectral imaging has been recently proposed for high-speed sorting and grading machine vision of fruits. It is a prospective method applied in yet traditional sorting and grading of oil palm fresh fruit bunches (FFB). The ripeness of oil palm FFBs determines the quality of crude palm oil (CPO). Implementation of multispectral imaging for the task needs wavelength selection from hyperspectral datasets. This study aimed to obtain the optimum wavelengths and use them for oil palm FFB classification based on three ripeness levels. We have selected eight optimum wavelengths using principal component analysis (PCA) regression which represented the ripeness levels.

Incoherent broadband cavity-enhanced absorption spectroscopy for sensitive measurement of nutrients and microalgae

Incoherent broadband cavity-enhanced absorption spectroscopy (IBBCEAS) can achieve sensitive measurements at trace concentrations for liquid phase marine samples. The IBBCEAS system consists of a cavity-enhancement module (CEM) and a transmission hyperspectral module (THM). The CEM has cavity-enhancement factors up to 78 at 550 nm. Measurements were obtained over a wide wavelength range (420-640 nm) with a halogen lamp, and the optical cavity was formed by two concave highly reflective mirrors (R=0.99). The minimum detectable absorption coefficient α_min of 7.3×10^-7cm^-1 at 550 nm corresponds to a limit of detection for nutrients of 780 pM. The spectral resolution of the THM is 3 nm in the wavelength range of 400 to 750 nm. We performed the IBBCEAS measurements for biological and chemical substances, including nutrients, microalgae, and Cy5 dye. The concentrations of nutrients in a deionized water environment and artificial seawater environment were measured at nanomolar levels; the concentration of microalgae phaeocystis was detected with 3.46×10⁴/mL, and fluorescence substances such as Cy5 dye could be measured at 0.03 mg/L. Experimental results show that the IBBCEAS system has the capability for sensitive measurements of biological and chemical substances and has strong potential forin situ ecological marine environmental monitoring function.

Smart microalgae farming with internet-of-things for sustainable agriculture

Agriculture farms such as crop, aquaculture and livestock have begun the implementation of Internet of Things (IoT) and artificial intelligence (AI) technology in improving their productivity and product quality. However, microalgae farming which requires precise monitoring, controlling and predicting the growth of microalgae biomass has yet to incorporate with IoT and AI technology, as it is still in its infancy phase. Particularly, the cultivation stage of microalgae involves many essential parameters (i.e. biomass concentration, pH, light intensity, temperature and tank level) which require precise monitoring as these parameters are important to ensure an effective biomass productivity in the microalgae farming. Besides, the conventional practices in the current process equipment are still powered by electricity, thus further development by integrating IoT into these processes can ease the production process. Further to that, many researchers has studied the machine learning approach for the identification and classification of microalgae. However, there are still limited studies reported on applying machine learning for the application of microalgae industry such as optimising microalgae cultivation for higher biomass productivity. Therefore, the implementation of IoT and AI in microalgae farming can contribute to the development of the global microalgae industry. The purpose of this current review paper focuses on the overview microalgae biomass production process along with the implementation of IoT toward the future of smart farming. To bridge the gap between the conventional and microalgae smart farming, this paper also highlights the insights on the implementation phases of microalgae smart farming starting from the infant stage that involves the installation and programming of IoT hardware. Then, it is followed by the application of machine learning to predict and auto-optimise the microalgae smart farming process. Furthermore, the process setup and detailed overview of microalgae farming with the integration of IoT have been discussed critically. This review paper would provide a new vision of microalgae farming for microalgae researchers and bio-processing industries into the digitalisation industrial era.

The Extraction of β-Carotene from Microalgae for Testing Their Health Benefits

β-carotene, a member of the carotenoid family, is a provitamin A, and can be converted into vitamin A (retinol), which plays essential roles in the regulation of physiological functions in animal bodies. Microalgae synthesize a variety of carotenoids including β-carotene and are a rich source of natural β-carotene. This has attracted the attention of researchers in academia and the biotech industry. Methods to enrich or purify β-carotene from microalgae have been investigated, and experiments to understand the biological functions of microalgae products containing β-carotene have been conducted. To better understand the use of microalgae to produce β-carotene and other carotenoids, we have searched PubMed in August 2021 for the recent studies that are focused on microalgae carotenoid content, the extraction methods to produce β-carotene from microalgae, and the bioactivities of β-carotene from microalgae. Articles published in peer-reviewed scientific journals were identified, screened, and summarized here. So far, various types and amounts of carotenoids have been identified and extracted in different types of microalgae. Diverse methods have been developed overtime to extract β-carotene efficiently and practically from microalgae for mass production. It appears that methods have been developed to simplify the steps and extract β-carotene directly and efficiently. Multiple studies have shown that extracts or whole organism of microalgae containing β-carotene have activities to promote lifespan in lab animals and reduce oxidative stress in culture cells, etc. Nevertheless, more studies are warranted to study the health benefits and functional mechanisms of β-carotene in these microalgae extracts, which may benefit human and animal health in the future.

On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods

Microalgae are promising sources of fuels and other chemicals. To operate microalgal cultivations efficiently, process control based on monitoring of process variables is needed. On-line sensing has important advantages over off-line and other analytical and sensing methods in minimizing the measurement delay. Consequently, on-line, in-situ sensors are preferred. In this respect, optical sensors occupy a central position since they are versatile and readily implemented in an on-line format. In biotechnological processes, measurements are performed in three phases (gaseous, liquid and solid (biomass)), and monitored process variables can be classified as physical, chemical and biological. On-line sensing technologies that rely on standard industrial sensors employed in chemical processes are already well-established for monitoring the physical and chemical environment of an algal cultivation. In contrast, on-line sensors for the process variables of the biological phase, whether biomass, intracellular or extracellular products, or the physiological state of living cells, are at an earlier developmental stage and are the focus of this review. On-line monitoring of biological process variables is much more difficult and sometimes impossible and must rely on indirect measurement and extensive data processing. In contrast to other recent reviews, this review concentrates on current methods and technologies for monitoring of biological parameters in microalgal cultivations that are suitable for the on-line and in-situ implementation. These parameters include cell concentration, chlorophyll content, irradiance, and lipid and pigment concentration and are measured using NMR, IR spectrophotometry, dielectric scattering, and multispectral methods. An important part of the review is the computer-aided monitoring of microalgal cultivations in the form of software sensors, the use of multi-parameter measurements in mathematical process models, fuzzy logic and artificial neural networks. In the future, software sensors will play an increasing role in the real-time estimation of biological variables because of their flexibility and extendibility.

Machine learning classification of origins and varieties of Tetrastigma hemsleyanum using a dual-mode microscopic hyperspectral imager

A dual-mode microscopic hyperspectral imager (DMHI) combined with a machine learning algorithm for the purpose of classifying origins and varieties of Tetrastigma hemsleyanum (T. hemsleyanum) was developed. By switching the illumination source, the DMHI can operate in reflection imaging and fluorescence detection modes. The DMHI system has excellent performance with spatial and spectral resolutions of 27.8 μm and 3 nm, respectively. To verify the capability of the DMHI system, a series of classification experiments of T. hemsleyanum were conducted. Captured hyperspectral datasets were analyzed using principal component analysis (PCA) for dimensional reduction, and a support vector machine (SVM) model was used for classification. In reflection microscopic hyperspectral imaging (RMHI) mode, the classification accuracies of T. hemsleyanum origins and varieties were 96.3% and 97.3%, respectively, while in fluorescence microscopic hyperspectral imaging (FMHI) mode, the classification accuracies were 97.3% and 100%, respectively. Combining datasets in dual mode, excellent predictions of origin and variety were realized by the trained model, both with a 97.5% accuracy on a newly measured test set. The results show that the DMHI system is capable of T. hemsleyanum origin and variety classification, and has the potential for non-invasive detection and rapid quality assessment of various kinds of medicinal herbs.

Automated defect detection and classification for fiber-optic coil based on wavelet transform and self-adaptive GA-SVM

The quality monitoring of fiber-optic coil (FOC) in winding systems is usually done manually. Aiming at the problem of inefficient and low accuracy of manual detection, this article is dedicated to researching a defect detection framework based on machine vision, which provides a reliable method for automatic defect detection of FOC. For this purpose, a defect detection scheme that integrates wavelet transform and nonlocal means filtering is proposed to accurately locate the defect region. Then, based on the features constructed by wavelet coefficients, a support vector machine (SVM) is used as the classifier. Additionally, a self-adaptive genetic algorithm is proposed to optimize the parameters of the SVM to form the final classifier. Through experiments on the data set obtained by our designed imaging system, the results show that our method has good defect detection performance and high classification accuracy, which provides an optimal solution for the automatic detection of FOC.

Confocal hyperspectral microscopic imager for the detection and classification of individual microalgae

We propose a confocal hyperspectral microscopic imager (CHMI) that can measure both transmission and fluorescent spectra of individual microalgae, as well as obtain classical transmission images and corresponding fluorescent hyperspectral images with a high signal-to-noise ratio. Thus, the system can realize precise identification, classification, and location of microalgae in a free or symbiosis state. The CHMI works in a staring state, with two imaging modes, a confocal fluorescence hyperspectral imaging (CFHI) mode and a transmission hyperspectral imaging (THI) mode. The imaging modes share the main light path, and thus obtained fluorescence and transmission hyperspectral images have point-to-point correspondence. In the CFHI mode, a confocal technology to eliminate image blurring caused by interference of axial points is included. The CHMI has excellent performance with spectral and spatial resolutions of 3 nm and 2 µm, respectively (using a 10× microscope objective magnification). To demonstrate the capacity and versatility of the CHMI, we report on demonstration experiments on four species of microalgae in free form as well as three species of jellyfish with symbiotic microalgae. In the microalgae species classification experiments, transmission and fluorescence spectra collected by the CHMI were preprocessed using principal component analysis (PCA), and a support vector machine (SVM) model or deep learning was then used for classification. The accuracy of the SVM model and deep learning method to distinguish one species of individual microalgae from another was found to be 96.25% and 98.34%, respectively. Also, the ability of the CHMI to analyze the concentration, species, and distribution differences of symbiotic microalgae in symbionts is furthermore demonstrated.

Water monitoring by means of digital microscopy identification and classification of microalgae

Marine and freshwater microalgae belong to taxonomically and morphologically diverse groups of organisms spanning many phyla with thousands of species. These organisms play an important role as indicators of water ecosystem conditions since they react quickly and predictably to a broad range of environmental stressors, thus providing early signals of dangerous changes. Traditionally, microscopic analysis has been used to identify and enumerate different types of organisms present within a given environment at a given point in time. However, this approach is both time-consuming and labor intensive, as it relies on manual processing and classification of planktonic organisms present within collected water samples. Furthermore, it requires highly skilled specialists trained to recognize and distinguish one taxa from another on the basis of often subtle morphological differences. Given these restrictions, a considerable amount of effort has been recently funneled into automating different steps of both the sampling and classification processes, making it possible to generate previously unprecedented volumes of plankton image data and obtain an essential database to analyze the composition of plankton assemblages. In this review we report state-of-the-art methods used for automated plankton classification by means of digital microscopy. The computer-microscope system hardware and the image processing techniques used for recognition and classification of planktonic organisms (segmentation, shape feature extraction, pigment signature determination and neural network grouping) will be described. An introduction and overview of the topic, its current state and indications of future directions the field is expected to take will be provided, organizing the review for both experts and researchers new to the field.

Fluorescence hyperspectral imaging system for analysis and visualization of oil sample composition and thickness

In this paper, a compact fluorescence hyperspectral imaging system based on a prism-grating-prism (PGP) structure is designed. Its spectrometer spectral range is 400-1000 nm with a spectral resolution of 2.5 nm, and its weight is less than 1.7 kg. The PGP imaging spectrometer combines the technical advantages of prism and grating, by not only using six lenses for imaging and collimation to realize the dual telecentres of object and image but also having a "straight cylinder" structure, which makes the installation and adjustment simple, compact, and stable. By the push-broom method, we obtained the three-dimensional cubic data of different oil products. By normalization processing, minimum noise separation transformation processing, visualization processing, and support vector machine classification processing of different oil fluorescence hyperspectral data, we demonstrate that the fluorescence hyperspectral imaging system can identify different kinds of oil and recognize the oil film thickness. The fluorescence hyperspectral imaging system can be used in oil spill detection, resource exploration, natural disaster monitoring, environmental pollution assessment, and many other fields.

WisdomModel: convert data into wisdom

Purpose

Traditional classification algorithms always have an incorrect prediction. As the misclassification rate increases, the usefulness of the learning model decreases. This paper presents the development of a wisdom framework that reduces the error rate to less than 3% without human intervention.

Design/methodology/approach

The proposed WisdomModel consists of four stages: build a classifier, isolate the misclassified instances, construct an automated knowledge base for the misclassified instances and rectify incorrect prediction. This approach will identify misclassified instances by comparing them against the knowledge base. If an instance is close to a rule in the knowledge base by a certain threshold, then this instance is considered misclassified.

Findings

The authors have evaluated the WisdomModel using different measures such as accuracy, recall, precision, f-measure, receiver operating characteristics (ROC) curve, area under the curve (AUC) and error rate with various data sets to prove its ability to generalize without human involvement. The results of the proposed model minimize the number of misclassified instances by at least 70% and increase the accuracy of the model minimally by 7%.

Originality/value

This research focuses on defining wisdom in practical applications. Despite of the development in information system, there is still no framework or algorithm that can be used to extract wisdom from data. This research will build a general wisdom framework that can be used in any domain to reach wisdom.

Spectral Classification Based on Deep Learning Algorithms

Convolutional neural networks (CNN) can achieve accurate image classification, indicating the current best performance of deep learning algorithms. However, the complexity of spectral data limits the performance of many CNN models. Due to the potential redundancy and noise of the spectral data, the standard CNN model is usually unable to perform correct spectral classification. Furthermore, deeper CNN architectures also face some difficulties when other network layers are added, which hinders the network convergence and produces low classification accuracy. To alleviate these problems, we proposed a new CNN architecture specially designed for 2D spectral data. Firstly, we collected the reflectance spectra of five samples using a portable optical fiber spectrometer and converted them into 2D matrix data to adapt to the deep learning algorithms’ feature extraction. Secondly, the number of convolutional layers and pooling layers were adjusted according to the characteristics of the spectral data to enhance the feature extraction ability. Finally, the discard rate selection principle of the dropout layer was determined by visual analysis to improve the classification accuracy. Experimental results demonstrate our CNN system, which has advantages over the traditional AlexNet, Unet, and support vector machine (SVM)-based approaches in many aspects, such as easy implementation, short time, higher accuracy, and strong robustness.

Line-scan Raman scattering image and multivariate analysis for rapid and noninvasive detection of restructured beef

The mean spectral (MS) features were extracted from Raman scattering images (RSI) of beef samples over the region of interest covering the spectral range of 789-1710cm^-1 and the spatial offset range of 0-5 mm (for two sides of the incident laser). The RSI monitored the main change in the protein, amide bands, lipids, and amino acid residues. The classification model performance based on MS features compared the conventional Raman spectral features and confirmed the usefulness of RSI. Finally, the results showed that RSI technology is a reliable tool for rapid and noninvasive detection of restructured beef.

Inelastic hyperspectral Scheimpflug lidar for microalgae classification and quantification

An inelastic hyperspectral Scheimpflug lidar system was developed for microalgae classification and quantification. The correction for the refraction at the air-glass-water interface was established, making our system suitable for aquatic environments. The fluorescence spectrum of microalgae was extracted by principal component analysis, and seven species of microalgae from different phyla have been classified. It was verified that when the cell density of Phaeocystis globosa was in the range of ${{1}}{{{0}}^4}\sim{{1}}{{{0}}^6}\;{\rm{cell}}\;{\rm{m}}{{\rm{L}}^{- 1}}$, the cell density had a linear relationship with the fluorescence intensity. The experimental results show our system can identify and quantify microalgae, with application prospects for microalgae monitoring in the field environment and early warning of red tides or algal blooms.

Identification of spectral signature for in situ real-time monitoring of smoltification

We describe the use of an optical hyperspectral sensing technique to identify the smoltification status of Atlantic salmon (Salmo salar) based on spectral signatures, thus potentially providing smolt producers with an additional tool to verify the osmoregulatory state of salmon. By identifying whether a juvenile salmon is in the biological freshwater stage (parr) or has adapted to the seawater stage (smolt) before transfer to sea, negative welfare impacts and subsequent mortality associated with failed or incorrect identification may be reduced. A hyperspectral imager has been used to collect data in two water flow-through and one recirculating production site in parallel with the standard smoltification evaluations applied at these sites. The results from the latter have been used as baseline for a machine-learning algorithm trained to identify whether a fish was parr or smolt based on its spectral signature. The developed method correctly classified fish in 86% to 100% of the cases for individual sites, and had an overall average classification accuracy of 90%, thus indicating that analysis of spectral signatures may constitute a useful tool for smoltification monitoring.

4D surface shape measurement system with high spectral resolution and great depth accuracy

A 4D surface shape measurement system that combines spectral detection and 3D surface morphology measurements is proposed, which can realize high spectral resolution and great depth accuracy (HSDA system). A starring hyperspectral imager system based on a grating generates precise spectral data, while a structured light stereovision system reconstructs target morphology as a 3D point cloud. The systems are coupled using a double light path module, which realize point-to-point correspondence of the systems' image planes. The spectral and 3D coordinate data are fused and transformed into a 4D data set. The HSDA system has excellent performance with a spectral resolution of 3 nm and depth accuracy of 27.5 μm. A range of 4D imaging experiments are presented to demonstrate the capabilities and versatility of the HSDA system, which show that it can be used in broad range of application areas, such as fluorescence detection, face anti-spoofing, physical health state assessment and green plant growth condition monitoring.