Quantification of the adulteration concentration of palm kernel oil in virgin coconut oil using near-infrared hyperspectral imaging

The adulteration concentration of palm kernel oil(PKO)in virgin coconut oil(VCO)was quantified using near-infrared(NIR)hyperspectral imaging.Nowadays,some VCO is adulterated with lower-priced PKO to reduce production costs,which diminishes the quality of the VCO.This study used NIR hyperspectral imaging in the wavelength region 900-1,650 nm to create a quantitative model for the detection of PKO contaminants(0-100%)in VCO and to develop predictive mapping.The prediction equation for the adulteration of VCO with PKO was constructed using the partial least squares regression method.The best predictive model was pre-processed using the standard normal variate method,and the coefficient of determination of prediction was 0.991,the root mean square error of prediction was 2.93%,and the residual prediction deviation was 10.37.The results showed that this model could be applied for quantifying the adulteration concentration of PKO in VCO.The prediction adulteration concentration mapping of VCO with PKO was created from a calibration model that showed the color level according to the adulteration concentration in the range of 0-100%.NIR hyperspectral imaging could be clearly used to quantify the adulteration of VCO with a color level map that provides a quick,accurate,and non-destructive detection method.

Research on the detection of early caries based on hyperspectral imaging

Objective:We applied hyperspectral imaging(HSI)system to distinguish early caries from soundand pigmented areas.It will provide a theoretical basis and technical support,for research anddevelopment of an instrument that could be used for screening and detection of early dentalcaries.Methods:Eighteen extracted human teeth(molars and premolars),with varying degrees ofnatural pathology and no degree of decay involving dentin were obtained.HSI system with awavelength range from 400 to 1000nm was used to obtain images of all 18 teeth containingsound,carious and pigmented areas.We compared the spectra of the wavebands at both 500 nmand 780 nm from the different tooth states,and the reflectance diference bet ween sound versuscarious lesions and sound versus pigmented areas,respectively.Results:There was a slight diference in refectance bet ween carious areas and pigmented areas at500 nm.A substantial difference was additionally noted in refectance bet ween carious areas andpigmented areas at 780 nm.Conclusion:The results have shown that the interference of tooth surface pigment can be elim-inated in the near-infrared(NIR)waveband,and the caries can be effectively identifed from the pigmented areas.Thus,it could be used to detect carious areas of teeth in place of the traditionalvisual inspection method or white light endoscopy.Clinical significance:The NIR difused light signal enables the identification of early caries frompigment and other interference,providing a reasonable detection tool for early detection andearly treatment of teeth diseases.

Feasibility study of assessing cotton fiber maturity from near infrared hyperspectral imaging technique

Background Fiber maturity is a key cotton quality property,and its variability in a sample impacts fiber processing and dyeing performance.Currently,the maturity is determined by using established protocols in laboratories under a controlled environment.There is an increasing need to measure fiber maturity using low-cost(in general less than $20000)and small portable systems.In this study,a laboratory feasibility was performed to assess the ability of the shortwave infrared hyperspectral imaging(SWIR HSI)technique for determining the conditioned fiber maturity,and as a comparison,a bench-top commercial and expensive(in general greater than $60000)near infrared(NIR)instrument was used.Results Although SWIR HSI and NIR represent different measurement technologies,consistent spectral characteristics were observed between the two instruments when they were used to measure the maturity of the locule fiber samples in seed cotton and of the well-defined fiber samples,respectively.Partial least squares(PLS)models were established using different spectral preprocessing parameters to predict fiber maturity.The high prediction precision was observed by a lower root mean square error of prediction(RMSEP)(<0.046),higher R_(p)^(2)(>0.518),and greater percentage(97.0%)of samples within the 95% agreement range in the entire NIR region(1000-2500 nm)without the moisture band at 1940 nm.Conclusion SWIR HSI has a good potential for assessing cotton fiber maturity in a laboratory environment.

Differentiation of Wheat Diseases and Pests Based on Hyperspectral Imaging Technology with a Few Specific Bands

Hyperspectral imaging technique is known as a promising non-destructive way for detecting plants diseases and pests.In most previous studies,the utilization of the whole spectrum or a large number of bands as well as the complexity of model structure severely hampers the application of the technique in practice.If a detection system can be established with a few bands and a relatively simple logic,it would be of great significance for application.This study established a method for identifying and discriminating three commonly occurring diseases and pests of wheat,i.e.,powdery mildew,yellow rust and aphid with a few specific bands.Through a comprehensive spectral analysis,only three bands at 570,680 and 750 nm were selected.A novel vegetation index namely Ratio Triangular Vegetation Index(RTVI)was developed for detecting anomalous areas on leaves.Then,the Support Vector Machine(SVM)method was applied to construct the discrimination model based on the spectral ratio analysis.The validating results suggested that the proposed method with only three spectral bands achieved a promising accuracy with the Overall Accuracy(OA)of 83%.With three bands from the hyperspectral imaging data,the three wheat diseases and pests were successfully detected and discriminated.A stepwise strategy including background removal,damage lesions recognition and stresses discrimination was proposed.The present work can provide a basis for the design of low cost and smart instruments for disease and pest detection.

Identification of Early Peach Aphid Infestation Based on Hyperspectral Imaging Technology

Peach aphid is a common pest and hard to detect.This study employs hyperspectral imaging technology to identify early damage in green cabbage caused by peach aphid.Through principal component transformation and multiple linear regression analysis,the correlation relation between spectral characteristics and infestation stage is analyzed.Then,four characteristic wavelength selection methods are compared and optimal characteristic wavelengths subset is determined to be input for modelling.One linear algorithm and two nonlinear modelling algorithms are compared.Finally,support vector machine(SVM)model based on the characteristic wavelengths selected by multi-cluster feature selection(MCFS)acquires the highest identification accuracy,which is 98.97%.These results indicate that hyperspectral imaging technology have the ability to identify early peach aphid infestation stages on green cabbages.

Spatial-spectral identication of abnormal leukocytes based on microscopic hyperspectral imaging technology

Screening and diagnosing of abnormal Leukocytes are crucial for the diagnosis of immune diseases and Acute Lymphoblastic Leukemia(ALL).As the deterioration of abnormal leukocytes is mainly due to the changes in the chromatin distribution,which signicantly affects the absorption and reflection of light,the spectral feature is proved to be important for leukocytes classication and identication.This paper proposes an accurate identication method for healthy and abnormal leukocytes based on microscopic hyperspectral imaging(HSI)technology which combines the spectral information.The segmentation of nucleus and cytoplasm is obtained by the morphological watershed algorithm.Then,the spectral features are extracted and combined with the spatial features.Based on this,the support vector machine(SVM)is applied for classication ofve types of leukocytes and abnormal leukocytes.Compared with different classication methods,the proposed method utilizes spectral features which highlight the differences between healthy leukocytes and abnormal leukocytes,improving the accuracy in the classication and identication of leukocytes.This paper only selects one subtype of ALL for test,and the proposed method can be applied for detection of other leukemia in the future.

A Study of Estimation Model for the Chlorophyll Content of Wheat Leaf Based on Hyperspectral Imaging

In order to explore the spectral features and sensitive wave band of wheat leaf,we establish a quantitative relationship model between wheat chlorophyll content and spectral features to promote the application of hyperspectral technology in precise wheat fertilization and fast,non-destructive growth monitoring.Using the relational analysis,we analyze the relationship between chlorophyll content and spectral reflectance or the first derivative,and establish the chlorophyll content monitoring model.By selection and verification,the best estimation models for wheat chlorophyll content are as follows:SPAD = 36.75 + 188.168R387,SPAD =2094.242R'7153+ 112646.744 R'7152-1.561E7 R'715+42.991.The two models can well estimate the SPAD value of wheat leaf,and comparatively speaking,the SPAD estimation model based on wave band R387 has greater accuracy.

Detection and Discrimination of Tea Plant Stresses Based on Hyperspectral Imaging Technique at a Canopy Level

Tea plant stresses threaten the quality of tea seriously.The technology corresponding to the fast detection and differentiation of stresses is of great significance for plant protection in tea plantation.In recent years,hyperspectral imaging technology has shown great potential in detecting and differentiating plant diseases,pests and some other stresses at the leaf level.However,the lack of studies at canopy level hampers the detection of tea plant stresses at a larger scale.In this study,based on the canopy-level hyperspectral imaging data,the methods for identifying and differentiating the three commonly occurred tea stresses(i.e.,the tea leafhopper,anthrax and sun burn)were studied.To account for the complexity of the canopy scenario,a stepwise detecting strategy was proposed that includes the process of background removal,identification of damaged areas and discrimination of stresses.Firstly,combining the successive projection algorithm(SPA)spectral analysis and K-means cluster analysis,the background and overexposed non-plant regions were removed from the image.Then,a rigorous sensitivity analysis and optimization were performed on various forms of spectral features,which yielded optimal features for detecting damaged areas(i.e.,YSV,Area,GI,CARI and NBNDVI)and optimal features for stresses discrimination(i.e.,MCARI,CI,LCI,RARS,TCI and VOG).Based on this information,the models for identifying damaged areas and those models for discriminating different stresses were established using K-nearest neighbor(KNN),Random Forest(RF)and Fisher discriminant analysis.The identification model achieved an accuracy over 95%,and the discrimination model achieved an accuracy over 93%for all stresses.The results suggested the feasibility of stress detection and differentiation using canopy-level hyperspectral imaging techniques,and indicated the potential for its extension over large areas.

Rapid and Non-destructive Prediction of Protein Content in Peanut Varieties Using Near-infrared Hyperspectral Imaging Method

This study was undertaken to investigate the feasibility of near-infrared(NIR) hyperspectral imaging(1 000–2 500 nm) for non-destructive and quantitative prediction of protein content in peanut kernels. Partial least squares regression(PLSR) calibration model was established between the spectral data extracted from the hyperspectral images and the reference measured protein content values, with the coefficient of determination of prediction(R_P^2) of 0.885 and root mean square error of prediction(RMSEP) of 0.465%.Regression coefficients(RC) from PLSR analysis were used to identify the most essential wavelengths that had the greatest influence on changes in the protein content. Eight optimal wavelengths were selected by RC and its corresponding simplified RC-PLSR prediction model was also obtained, showing better performance with a higher R_P^2 of 0.870 and a lower RMSEP of 0.494%. The results indicate that hyperspectral imaging with PLSR analysis can be used as a rapid and non-destructive method for predicting protein content in peanut.

Study of the best decocting time of sun dried ginseng by using the hyperspectral imaging technology

In this research,a new method based on the hyperspectral imaging for searching the best decocting time of sun dried ginseng is reported.The spectral images at diferent decocting time of test sample have been taken by the st aring hyperspectral fAuorescence imaging systen and the solubility of active ingredients have been discussed by analyzing the changes on the spectral.curves.The spectr al range of the system is 400-720nm and the spectral resolution is 5nm.In the decocting process,the active ingredients of nonsoaked ginseng was dissolved in the tissue fluid at first,and reached equilibrium condition at last after the precipitation-dissolution reciprocating process of boiling.At last,the experiment al results show that the best decoction time of sun dried ginseng is about 60 min after boiling.

Hyperspectral imaging and remote trace detection of cis-1,3,4,6-tetranitrooctahydroimidazo-[4,5 d]imidazole(BCHMX)compared with traditional explosives using laser induced fluorescence

cis-1,3,4,6-Tetranitrooctahydroimidazo-[4,5 d]imidazole(BCHMX)is an advanced energetic compound that expected to spread worldwide in the near future.Since,no approved remote detection methods were reported in current literature for this material,we performed hyper-spectral imaging and laser induced fluorescence(LIF)to a BCHMX sample under low laser fluence for determining the optimum laser wavelength used in any future BCHMX-LIF based remote detection systems.For this purpose,an experimental setup consisted of a sun spectrum lamp and hyper-spectral camera was built to illuminate and image white powder samples of BCHMX in comparison with the traditional explosives,HMX(1,3,5,7-tetranitro-1,3,5,7-tetraazacyclooctane),RDX(1,3,5-trinitro-1,3,5-triazacyclohexane),PETN(2,2-Bis[(nitroxy)methyl]propane-1,3-diyldinitrate).The imaging reveals strong BCHMX sample absorption contrast among other samples at wavelength ranging from 400 to 410 nm.When light source was replaced by a 405 nm laser diode illuminator,a strong BCHMX sample LIF at the spectral range from 425 to 700 nm was observed under low laser fluence condition of 0.1 mJ/cm^(2).Finally,we demonstrated successfully the ability of the 405 nm LIF and the hyperspectral imaging technique to detect finger print traces of BCHMX on white cellulose fabric from a distance of 15 m and a detection limit of 1 mg/cm^(2).

Evaluation of growth characteristics of Aspergillus parasiticus inoculated in different culture media by shortwave infrared(SWIR) hyperspectral imaging

The growth characteristics of Aspergillus parasitic us incubated on two culture media were ex-amined using shortwave infrared(SWIR,1000-2500 nm)hyperspectral imaging(HSI)in this work.HSI images of the A.parasiticus colonies growing on rose bengal medium(RBM)and maize agar medium(MAM)were recorded daily for 6 days.The growth phases of A.parasiticus were indicated through the pixel number and average spectra of colonies.On score plot of the first principal component(PC1)and PC2,four growth zones with varying mycelium densities were identified.Eight characteristic wavelengths(1095,1145,1195,1279,1442,1655,1834 and 1929 nm)were selected from PC1 loading,average spectra of each colony as well as each growth zone.F urthermore,support vector machine(S VM)classifier based on the eight wavelengths was built,and the classification accuracies for the four zones(from outer to inner zones)on the colonies on RBM were 99.77%,9935%,99.75%and 99.60%and 99.77%,9939%,99.31%and 98.22%for colonies on MAM.In addition,a new score plot of PC2 and PC3 was used to differ-entiate the colonies incubated on RBM and MAM for 6 days.Then characteristic wavelengths of 1067,1195,1279,1369,1459,1694,1834 and 1929 nm were selected from the loading of PC2 and PCg.Based on them,a new SVM model was developed to diferentiate colonies on RBM and MAM with accuracy of 100.00%and 9999%,respectively.In conclusion,SWIR hyperspectral image is a powerful tool for evaluation of growth characteristics of A.parasiticus incubated in diferent culture media.

Application of Hyperspectral Imaging Technology in Rapid Detection of Preservative in Milk

To ensure the quality and safety of pure milk,detection method of typical preservative-potassium sorbate in milk was researched in this paper.Hyperspectral imaging technology was applied to realize rapid detection.Influence factors for hyperspectral data collection for milk samples were firstly researched,including height of sample,bottom color and sample filled up container or not.Pretreatment methods and variable selection algorithms were applied into original spectral data.Rapid detection models were built based on support vector machine method(SVM).Finally,standard normalized variable(SNV)-competitive adaptive reweighted sampling(CARS)and SVM model was chosen in this paper.The accuracies of calibration set and testing set were 0.97 and 0.97,respectively.Kappa coefficient of the model was 0.93.It could be seen that hyperspectral imaging technology could be used to detect for potassium sorbate in milk.Meanwhile,it also provided methodological supports for the rapid detection of other preservatives in milk.

NIR Hyperspectral Imaging Measurement of Sugar Content in Peach Using PLS Regression

Near infrared （NIR） hyperspectral imaging measurement of sugar content in peach was introauced. NIR spectral images （650～1 000 nm, resolution： 2 nm） of peach samples were captured with developed hyperspectral imaging setup. Partial least square （PLS） regression prediction model was developed to estimate the sugar content in peach; step-wise backward method was utilized to determine optimal wavelength subsets. Experimental results show that the calibration model with optimal wavelength subsets has a correlation coefficient of prediction of 0.97 and a standard error of prediction of 0.19, the prediction accuracy is higher than the calibration model applied over the whole wavelength, which proves that variable selection plays an important role in improving the prediction accuracy of PLS regression model.

Adaptive deep learning for head and neck cancer detection using hyperspectral imaging

It can be challenging to detect tumor margins during surgery for complete resection.The purpose of this work is to develop a novel learning method that learns the difference between the tumor and benign tissue adaptively for cancer detection on hyperspectral images in an animal model.Specifically,an auto-encoder network is trained based on the wavelength bands on hyperspectral images to extract the deep information to create a pixel-wise prediction of cancerous and benign pixel.According to the output hypothesis of each pixel,the misclassified pixels would be reclassified in the right prediction direction based on their adaptive weights.The auto-encoder network is again trained based on these updated pixels.The learner can adaptively improve the ability to identify the cancer and benign tissue by focusing on the misclassified pixels,and thus can improve the detection performance.The adaptive deep learning method highlighting the tumor region proved to be accurate in detecting the tumor boundary on hyperspectral images and achieved a sensitivity of 92.32%and a specificity of 91.31%in our animal experiments.This adaptive learning method on hyperspectral imaging has the potential to provide a noninvasive tool for tumor detection,especially,for the tumor whose margin is indistinct and irregular.

Hyperspectral Imaging Based on Compressive Sensing: Determining Cancer Margins in Human Pancreatic Tissue <i>ex Vivo</i>, a Pilot Study

Cancer is the second-leading cause of death in the United State and surgery remains the primary treatment for most solid mass tumors. However, accurately identifying tumor margins in real-time remains a challenge. In this study, the design and testing of hyperspectral imaging (HSI) system based on a single-pixel camera engine is discussed. The primary advantage of a single pixel architecture over traditional scanning HSI techniques is its high sensitivity and potential to function at low light levels. The objective for the imaging system described here is to detect changes in the reflectance spectra of tissue and to use these differences to delineate tumor margins. This paper presents the results of a 19-patient pilot study that assesses the ability of the HSI system to use reflectance imaging to delineate adenocarcinoma tumor margins in human pancreatic tissue imaged ex vivo. Pancreatic tissue excised during pancreatectomy was imaged immediately after being sent to the pathology lab. A pathologist sectioned the tissue and placed samples into standard tissue embedding cassettes. These tissue samples were then imaged using the HSI system. After imaging, the samples were returned to the pathologist for processing and analysis. The HSI was later compared to the histological analysis. The spectral angle mapping (SAM) and support vector machine (SVM) algorithms were used to classify pixels in the HSI images as healthy or unhealthy in order to delineate margins. Good agreement between margins determined via HSI (using both SAM and SVM) and histology/white light imaging was found.

Detection of Thrips Defect on Green-Peel Citrus Using Hyperspectral Imaging Technology Combining PCA and B-Spline Lighting Correction Method

In order to find an effective method of detecting thrips defect on green-peel citrus, a defect segmentation method was developed using a single threshold value based on combination of characteristic wavelengths principal component analysis （PCA） and B-spline lighting correction method in this study. At first, four characteristic wavelengths （523, 587, 700 and 768 nm） were obtained using PCA of Vis-NIR （visible and near-infrared） bands and analysis of weighting coefficients; secondarily, PCA was performed using characteristic wavelengths and the second principal component （PC2） was selected to classify images; then, B-spline lighting correction method was proposed to overcome the influence of lighting non-uniform on citrus when thrips defect was segmented; finally, thrips defect on citrus was extracted by global threshold segmentation and morphological image processing. The experimental results show that thrips defect in citrus can be detected with an accuracy of 96.5% by characteristic wavelengths PCA and B-spline lighting correction method. This study shows that thrips defect on green-peel citrus can be effectively identified using hyperspectral imaging technology.

Early surveillance of rice bakanae disease using deep learning and hyperspectral imaging

Bakanae disease,caused by Fusarium fujikuroi,poses a significant threat to rice production and has been observed in most rice-growing regions.The disease symptoms caused by different pathogens may vary,including elongated and weak stems,slender and yellow leaves,and dwarfism,as example.Bakanae disease is likely to cause necrosis of diseased seedlings,and it may cause a large area of infection in the field through the transmission of conidia.Therefore,early disease surveillance plays a crucial role in securing rice production.Traditional monitoring methods are both time-consuming and labor-intensive and cannot be broadly applied.In this study,a combination of hyperspectral imaging technology and deep learning algorithms were used to achieve in situ detection of rice seedlings infected with bakanae disease.Phenotypic data were obtained on the 9th,15th,and 21st day after rice infection to explore the physiological and biochemical performance,which helps to deepen the research on the disease mechanism.Hyperspectral data were obtained over these same periods of infection,and a deep learning model,named Rice Bakanae Disease-Visual Geometry Group(RBD-VGG),was established by leveraging hyperspectral imaging technology and deep learning algorithms.Based on this model,an average accuracy of 92.2%was achieved on the 21st day of infection.It also achieved an accuracy of 79.4%as early as the 9th day.Universal characteristic wavelengths were extracted to increase the feasibility of using portable spectral equipment for field surveillance.Collectively,the model offers an efficient and non-destructive surveillance methodology for monitoring bakanae disease,thereby providing an efficient avenue for disease prevention and control.

Detection of skin defects on loquat using hyperspectral imaging combining both band radio and improved three-phase level set segmentation method

Background and objectives Skin defects are one of the primary problems that occur in post-harvest grading and processing of loquats.Skin defects lead to the loquat being easily destroyed during transportation and storage,which causes the risk of other loquats being infected,affecting the selling price.Materials and Methods In this paper,a method combining band radio image with an improved three-phase level set segmentation algorithm(ITPLSSM)is proposed to achieve high accuracy,rapid,and non-destructive detection of skin defects of loquats.Principal component analysis(PCA)was used to find the characteristic wavelength and PC images to distinguish four types of skin defects.The best band ratio image based on characteristic wavelength was determined.Results The band ratio image(Q782/944)based on PC2 image is the best segmented image.Based on pseudo-color image enhancement,morphological processing,and local clustering criteria,the band ratio image(Q782/944)has better contrast between defective and normal areas in loquat.Finally,the ITPLSSM was used to segment the processing band ratio image(Q782/944),with an accuracy of 95.28%.Conclusions The proposed ITPLSSM method is effective in distinguishing four types of skin defects.Meanwhile,it also effectively segments images with intensity inhomogeneities.

Deep learning reconstruction enables full-Stokes single compression in polarized hyperspectral imaging

Polarized hyperspectral imaging,which has been widely studied worldwide,can obtain four-dimensional data including polarization,spectral,and spatial domains.To simplify data acquisition,compressive sensing theory is utilized in each domain.The polarization information represented by the four Stokes parameters currently requires at least two compressions.This work achieves full-Stokes single compression by introducing deep learning reconstruction.The four Stokes parameters are modulated by a quarter-wave plate(QWP)and a liquid crystal tunable filter(LCTF)and then compressed into a single light intensity detected by a complementary metal oxide semiconductor(CMOS).Data processing involves model training and polarization reconstruction.The reconstruction model is trained by feeding the known Stokes parameters and their single compressions into a deep learning framework.Unknown Stokes parameters can be reconstructed from a single compression using the trained model.Benefiting from the acquisition simplicity and reconstruction efficiency,this work well facilitates the development and application of polarized hyperspectral imaging.