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.

Grading method of soybean mosaic disease based on hyperspectral imaging technology

Soybean is a crop with a long cultivation history that occupies an important position in agricultural production.Soybean mosaic virus disease(SMV)has caused a rapid decline in soybean yields,causing huge losses to the soybean industry,wherefrom its early detec-tion is particularly important.This study proposes a new classification method for the early SMV,dividing its severity into grades 0,1 and 2.In the case of a small number of experi-mental samples of soybeans,this study proposes a combined convolutional neural network and support vector machine(CNN-SVM)method for the early detection of SMV.Experimen-tal results showed that the accuracy of the training set of the CNN-SVM model reached 96.67%,and the accuracy rate of the test set reached 94.17%.The experiment proved the feasibility of using the proposed CNN-SVM model to classify early SMV under the new clas-sification method,and provided a new direction for early SMV detection based on hyper-spectral images.

Hyperspectral Imaging Technology for Revealing the Original Handwritings Covered by the Same Inks

This manuscript presents a preliminary investigation on the applicability of hyperspectral imaging technology for nondestructive and rapid analysis to reveal covered original handwritings.The hyperspectral imager Nuance‑Macro was used to collect the reflected light signature of inks from the overlapping parts.The software Nuance1p46 was used to analyze the reflected light signature of inks which shows the covered original handwritings.Different types of black/blue ballpoint pen inks and black/blue gel pen inks were chosen for sample preparation.From the hyperspectral images examined,the covered original handwritings of application were revealed in 90.5%,69.1%,49.5%,and 78.6%of the cases.Further,the correlation between the revealing effect and spectral characteristics of the reflected light of inks at the overlapping parts was interpreted through theoretical analysis and experimental verification.The results indicated that when the spectral characteristics of the reflected light of inks at the overlapping parts were the same or very similar to that of the ink that was used to cover the original handwriting,the original handwriting could not be shown.On the contrary,when the spectral characteristics of the reflected light of inks at the overlapping parts were different to that of the ink that was used to cover the original handwriting,the original handwriting was revealed.

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 decay detection of Yali pears using hyperspectral imaging coupled with 2D correlation spectroscopy

The black spot disease caused by Alternaria alternata on Yali pears is a great concern as it compromises their edible quality and commercial value.To realize rapid and non-destructive classification of this disease,hyperspectral imaging(HSI)technology was combined with two-dimensional correlation spectroscopy(2DCOS)analysis.A total of 150 pear samples at different decay grades were prepared.After obtaining the HSI images,the whole sample was demarcated as the region of interest,and the spectral information was extracted.Seven preprocessing methods were applied and compared to build the classification models.Thereafter,using the inoculation day as an external perturbation,2DCOS was used to select the feature-related wavebands for black spot disease identification,and the result was compared to those obtained using competitive adaptive reweighting sampling and the successive projections algorithm.Results demonstrated that the simplified least squares support vector model based on 2DCOS-identified feature wavebands yielded the best performance with the identification accuracy,precision,sensitivity,and specificity of 97.30%,94.60%,96.16%,and 98.21%,respectively.Therefore,2DCOS can effectively interpret the feature-related wavebands,and its combination with HSI is an effective tool to predict black spot disease on Yali pears.

Real-time detection of influenza A virus using semiconductor nanophotonics

Modern miniaturization and the digitalization of characterization instruments greatly facilitate the diffusion of technological advances in new fields and generate innovative applications.The concept of a portable,inexpensive and semi-automated biosensing platform,or lab-on-a-chip,is a vision shared by many researchers and venture industries.Under this scope,we present a semiconductor monolithic integration approach to conduct surface plasmon resonance studies.This technology is already commonly used for biochemical characterization in pharmaceutical industries,but we have reduced the technological platform to a few nanometers in scale on a semiconductor chip.We evaluate the signal quality of this nanophotonic device using hyperspectral-imaging technology,and we compare its performance with that of a standard prism-based commercial system.Two standard biochemical agents are employed for this characterization study:bovine serum albumin and inactivated influenza A virus.Time resolutions of data acquisition varying between 360 and 2.2 s are presented,yielding 2.731025–1.531026 RIU resolutions,respectively.