scholarly journals Visual detection of microbial community during three bacteria mixed fermentation through hyperspectral imaging technology

eFood ◽  
2021 ◽  
Author(s):  
Yanxiao Li ◽  
Xuetao Hu ◽  
Jiyong Shi ◽  
Baijing Qiu ◽  
Jianbo Xiao
Keyword(s):  
Microbial Community ◽  
Hyperspectral Imaging ◽  
Support Vector ◽  
Counting Method ◽  
Mixed Fermentation ◽  
Imaging Technology ◽  
Linear Discriminant ◽  
Identification Rate ◽  
Distribution Maps ◽  
Specific Strain

Hyperspectral imaging technology with chemometrics was used for identifying and counting each species in microbial community during mixed fermentation. Hyperspectral images of microbial community of <i>Enterobacter</i> sp, <i>Acetobacter pasteurianus</i>, and <i>Lactobacillus paracasei</i> colonies were obtained and the spectra of strain colonies were extracted. Identification models were developed using linear discriminant analysis (LDA) and least-squares support vector machine (LS-SVM) by using 23 variables selected by genetic algorithm. The optimal LS-SVM model with identification rate of 96.67 % was used to identify colonies and prepare colony distribution maps in color for strains counting. The counting results by hyperspectral imaging technology agree with that of the manual counting method with average relative error of 3.70 %. The developed counting method has been successfully used to identify and count the specific strain from the mixed strains simultaneously. The hyperspectral imaging technology has a great potential to monitor changes in the microbial community structure.

Adaptive and automatic red blood cell counting method based on microscopic hyperspectral imaging technology

2017 ◽  
Vol 19 (12) ◽  
pp. 124014 ◽  
Author(s):  
Xi Liu ◽  
Mei Zhou ◽  
Song Qiu ◽  
Li Sun ◽  
Hongying Liu ◽  
...  
Keyword(s):  
Blood Cell ◽  
Hyperspectral Imaging ◽  
Red Blood Cell ◽  
Cell Counting ◽  
Counting Method ◽  
Imaging Technology ◽  
Blood Cell Counting

scholarly journals Classification of Rice and Starch Flours by Using Multiple Hyperspectral Imaging Systems and Chemometric Methods

2020 ◽  
Vol 10 (19) ◽  
pp. 6724
Author(s):  
Youngwook Seo ◽  
Ahyeong Lee ◽  
Balgeum Kim ◽  
Jongguk Lim
Keyword(s):  
Discriminant Analysis ◽  
Hyperspectral Imaging ◽  
Partial Least Square ◽  
Hyperspectral Images ◽  
Classification Model ◽  
Support Vector ◽  
Chemometric Methods ◽  
Infrared Wavelength ◽  
Linear Discriminant

(1) Background: The general use of food-processing facilities in the agro-food industry has increased the risk of unexpected material contamination. For instance, grain flours have similar colors and shapes, making their detection and isolation from each other difficult. Therefore, this study is aimed at verifying the feasibility of detecting and isolating grain flours by using hyperspectral imaging technology and developing a classification model of grain flours. (2) Methods: Multiple hyperspectral images were acquired through line scanning methods from reflectance of visible and near-infrared wavelength (400–1000 nm), reflectance of shortwave infrared wavelength (900–1700 nm), and fluorescence (400–700 nm) by 365 nm ultraviolet (UV) excitation. Eight varieties of grain flours were prepared (rice: 4, starch: 4), and the particle size and starch damage content were measured. To develop the classification model, four multivariate analysis methods (linear discriminant analysis (LDA), partial least-square discriminant analysis, support vector machine, and classification and regression tree) were implemented with several pre-processing methods, and their classification results were compared with respect to accuracy and Cohen’s kappa coefficient obtained from confusion matrices. (3) Results: The highest accuracy was achieved as 97.43% through short-wavelength infrared with normalization in the spectral domain. The submission of the developed classification model to the hyperspectral images showed that the fluorescence method achieves the highest accuracy of 81% using LDA. (4) Conclusions: In this study, the potential of non-destructive classification of rice and starch flours using multiple hyperspectral modalities and chemometric methods were demonstrated.

scholarly journals Quantitative Evaluation of Food-Waste Components in Organic Fertilizer Using Visible–Near-Infrared Hyperspectral Imaging

2021 ◽  
Vol 11 (17) ◽  
pp. 8201
Author(s):  
Geonwoo Kim ◽  
Hoonsoo Lee ◽  
Byoung-Kwan Cho ◽  
Insuck Baek ◽  
Moon S. Kim
Keyword(s):  
Least Squares ◽  
Hyperspectral Imaging ◽  
Quantitative Evaluation ◽  
Food Waste ◽  
Near Infrared ◽  
Organic Fertilizer ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
High Sodium ◽  
Distribution Maps

Excessive addition of food waste fertilizer to organic fertilizer (OF) is forbidden in the Republic of Korea because of high sodium chloride and capsaicin concentrations in Korean food. Thus, rapid and nondestructive evaluation techniques are required. The objective of this study is to quantitatively evaluate food-waste components (FWCs) using hyperspectral imaging (HSI) in the visible–near-infrared (Vis/NIR) region. A HSI system for evaluating fertilizer components and prediction algorithms based on partial least squares (PLS) analysis and least squares support vector machines (LS-SVM) are developed. PLS and LS-SVM preprocessing methods are employed and compared to select the optimal of two chemometrics methods. Finally, distribution maps visualized using the LS-SVM model are created to interpret the dynamic changes in the OF FWCs with increasing FWC concentration. The developed model quantitively evaluates the OF FWCs with a coefficient of determination of 0.83 between the predicted and actual values. The developed Vis/NIR HIS system and optimized model exhibit high potential for OF FWC discrimination and quantitative evaluation.

scholarly journals Bio-fertilizer Affects Structural Dynamics, Function, and Network Patterns of the Sugarcane Rhizospheric Microbiota

2021 ◽  
Author(s):  
Qiang Liu ◽  
Ziqin Pang ◽  
Zuli Yang ◽  
Fallah Nyumah ◽  
Chaohua Hu ◽  
...  
Keyword(s):  
Microbial Community ◽  
Community Composition ◽  
Pathogenic Bacteria ◽  
Bacterial Community Composition ◽  
Microbial Community Composition ◽  
Fertilizer Efficiency ◽  
Size Analysis ◽  
Support Vector ◽  
Linear Discriminant ◽  
Network Patterns

AbstractFertilizers and microbial communities that determine fertilizer efficiency are key to sustainable agricultural development. Sugarcane is an important sugar cash crop in China, and using bio-fertilizers is important for the sustainable development of China’s sugar industry. However, information on the effects of bio-fertilizers on sugarcane soil microbiota has rarely been studied. In this study, the effects of bio-fertilizer application on rhizosphere soil physicochemical indicators, microbial community composition, function, and network patterns of sugarcane were discussed using a high-throughput sequencing approach. The experimental design is as follows: CK: urea application (57 kg/ha), CF: compound fertilizer (450 kg/ha), BF1: bio-fertilizer (1500 kg/ha of bio-fertilizer + 57 kg/ha of urea), and BF2: bio-fertilizer (2250 kg/ha of bio-fertilizer + 57 kg/ha of urea). The results showed that the bio-fertilizer was effective in increasing sugarcane yield by 3–12% compared to the CF treatment group, while reducing soil acidification, changing the diversity of fungi and bacteria, and greatly altering the composition and structure of the inter-root microbial community. Variance partitioning canonical correspondence (VPA) analysis showed that soil physicochemical variables explained 80.09% and 73.31% of the variation in bacteria and fungi, respectively. Redundancy analysis and correlation heatmap showed that soil pH, total nitrogen, and available potassium were the main factors influencing bacterial community composition, while total soil phosphorus, available phosphorus, pH, and available nitrogen were the main drivers of fungal communities. Volcano plots showed that using bio-fertilizers contributed to the accumulation of more beneficial bacteria in the sugarcane rhizosphere level and the decline of pathogenic bacteria (e.g., Leifsonia), which may slow down or suppress the occurrence of diseases. Linear discriminant analysis (LDA) and effect size analysis (LEfSe) searched for biomarkers under different fertilizer treatments. Meanwhile, support vector machine (SVM) assessed the importance of the microbial genera contributing to the variability between fertilizers, of interest were the bacteria Anaerolineace, Vulgatibacter, and Paenibacillus and the fungi Cochliobolus, Sordariales, and Dothideomycetes between CF and BF2, compared to the other genera contributing to the variability. Network analysis (co-occurrence network) showed that the network structure of bio-fertilizers was closer to the network characteristics of healthy soils, indicating that bio-fertilizers can improve soil health to some extent, and therefore if bio-fertilizers can be used as an alternative to chemical fertilizers in the future alternative, it is important to achieve green soil development and improve the climate.

scholarly journals Identification of Hybrid Okra Seeds Based on Near-Infrared Hyperspectral Imaging Technology

2018 ◽  
Vol 8 (10) ◽  
pp. 1793 ◽  
Author(s):  
Jinnuo Zhang ◽  
Xuping Feng ◽  
Xiaodan Liu ◽  
Yong He
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Projection Algorithm ◽  
Rapid Screening ◽  
Support Vector ◽  
Calibration Model ◽  
Imaging Technology ◽  
Characteristic Wavelength ◽  
Full Band

Near-infrared (874–1734 nm) hyperspectral imaging technology combined with chemometrics was used to identify parental and hybrid okra seeds. A total of 1740 okra seeds of three different varieties, which contained the male parent xiaolusi, the female parent xianzhi, and the hybrid seed penzai, were collected, and all of the samples were randomly divided into the calibration set and the prediction set in a ratio of 2:1. Principal component analysis (PCA) was applied to explore the separability of different seeds based on the spectral characteristics of okra seeds. Fourteen and 86 characteristic wavelengths were extracted by using the successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS), respectively. Another 14 characteristic wavelengths were extracted by using CARS combined with SPA. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were developed based on the characteristic wavelength and full-band spectroscopy. The experimental results showed that the SVM discriminant model worked well and that the correct recognition rate was over 93.62% based on full-band spectroscopy. As for the discriminative model that was based on characteristic wavelength, the SVM model based on the CARS algorithm was better than the other two models. Combining the CARS+SVM calibration model and image processing technology, a pseudo-color map of sample prediction was generated, which could intuitively identify the species of okra seeds. The whole process provided a new idea for agricultural breeding in the rapid screening and identification of hybrid okra seeds.

scholarly journals Research on Camouflage Recognition in Simulated Operational Environment Based on Hyperspectral Imaging Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Donge Zhao ◽  
Shuyan Liu ◽  
Xuefeng Yang ◽  
Yayun Ma ◽  
Bin Zhang ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Spatial Information ◽  
Imaging System ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Practical Significance ◽  
Support Vector ◽  
Operational Environment ◽  
Imaging Technology ◽  
Training Samples

Hyperspectral imaging technology can obtain the spatial information and spectral information of the simulated operational background and its camouflage materials at the same time and identify and classify them according to their differences. In this paper, we collected the hyperspectral images (400–1000 nm) of the desert background, jungle background, desert camouflage netting, jungle camouflage netting, and jungle camouflage clothing through the hyperspectral imaging system, and the samples were preprocessed by denoising and black-and-white correction. Then, we analysed the region of interest (ROI) of the training samples by principal component analysis (PCA). After the pixels in the region of interest and their surrounding areas were averaged, 60% of the data was used as the training samples, and the remaining 40% was used as the test samples. According to their similarities and differences between them and referenced spectrum, the models of classification were established by combining the Naive Bayes (NB) algorithm, K-nearest neighbour (KNN) algorithm, random forest (RF) algorithm, and support vector machine (SVM) algorithm. The results show that among the four models, SVM model has the highest accuracy of classification and the recognition rate of jungle camouflage clothing is the highest. This study verifies the scientific and feasibility of hyperspectral imaging technology for camouflage identification and classification in a simulated operational environment, which has some practical significance.

scholarly journals Identifying Medicinal Plant Leaves using Textures and Optimal Colour Spaces Channel

2017 ◽  
Vol 10 (1) ◽  
pp. 19 ◽  
Author(s):  
C H Arun ◽  
D Christopher Durairaj
Keyword(s):  
Discriminant Analysis ◽  
Medicinal Plant ◽  
Classification Performance ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Identification System ◽  
Dependency Matrix ◽  
Linear Discriminant ◽  
Identification Rate ◽  
Grey Tone

This paper presents an automated medicinal plant leaf identification system. The Colour Texture analysis of the leaves is done using the statistical, the Grey Tone Spatial Dependency Matrix(GTSDM) and the Local Binary Pattern(LBP) based features with 20 different  colour spaces(RGB, XYZ, CMY, YIQ, YUV, $YC_{b}C_{r}$, YES, $U^{*}V^{*}W^{*}$, $L^{*}a^{*}b^{*}$, $L^{*}u^{*}v^{*}$, lms, $l\alpha\beta$, $I_{1} I_{2} I_{3}$, HSV, HSI, IHLS, IHS, TSL, LSLM and KLT).  Classification of the medicinal plant is carried out with 70\% of the dataset in training set and 30\% in the test set. The classification performance is analysed with Stochastic Gradient Descent(SGD), kNearest Neighbour(kNN), Support Vector Machines based on Radial basis function kernel(SVM-RBF), Linear Discriminant Analysis(LDA) and Quadratic Discriminant Analysis(QDA) classifiers. Results of classification on a dataset of 250 leaf images belonging to five different species of plants show the identification rate of 98.7 \%. The results certainly show better identification due to the use of YUV, $L^{*}a^{*}b^{*}$ and HSV colour spaces.

scholarly journals Comparison of Selected Dimensionality Reduction Methods for Detection of Root-Knot Nematode Infestations in Potato Tubers Using Hyperspectral Imaging

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 367
Author(s):  
Janez Lapajne ◽  
Matej Knapič ◽  
Uroš Žibrat
Keyword(s):  
Discriminant Analysis ◽  
Dimensionality Reduction ◽  
Linear Discriminant Analysis ◽  
Hyperspectral Imaging ◽  
Support Vector ◽  
Potato Tubers ◽  
Data Set ◽  
Linear Discriminant ◽  
Extreme Gradient Boosting ◽  
Reduction Methods

Hyperspectral imaging is a popular tool used for non-invasive plant disease detection. Data acquired with it usually consist of many correlated features; hence most of the acquired information is redundant. Dimensionality reduction methods are used to transform the data sets from high-dimensional, to low-dimensional (in this study to one or a few features). We have chosen six dimensionality reduction methods (partial least squares, linear discriminant analysis, principal component analysis, RandomForest, ReliefF, and Extreme gradient boosting) and tested their efficacy on a hyperspectral data set of potato tubers. The extracted or selected features were pipelined to support vector machine classifier and evaluated. Tubers were divided into two groups, healthy and infested with Meloidogyne luci. The results show that all dimensionality reduction methods enabled successful identification of inoculated tubers. The best and most consistent results were obtained using linear discriminant analysis, with 100% accuracy in both potato tuber inside and outside images. Classification success was generally higher in the outside data set, than in the inside. Nevertheless, accuracy was in all cases above 0.6.

Sign in / Sign up

Export Citation Format

Share Document