Modeling method for SSC prediction in pomelo using Vis-NIRS with wavelength selection and latent variable updating

The aim of this study was in-line,rapid,and non-destructive detection for soluble solid content(SSC)in pomelos using visible and near-infrared spectroscopy(Vis-NIRS).However,the large size and thick rind of pomelo affect the stability of spectral acquisition and the biological variabilities affect the robustness of models.Given these issues,in this study,an efficient prototype in-line detection system in transmittance mode was designed and evaluated in comparison with an off-line detection system.Data from the years 2019 and 2020 were used for modeling and the external validation data were obtained by the inline detection system in 2021.The wavelength selection methods of changeable size moving window(CSMW),random frog(RF),and competitive adaptive reweighted sampling(CARS)were used to improve the prediction accuracy of partial least squares regression(PLSR)models.The best performance of internal prediction was obtained by CARS-PLSR and the determination coefficient of prediction(),root mean square error of prediction(RMSEP),and residual predictive deviation(RPD)were 0.958,0.204%,and 4.821,respectively.However,all models obtained large prediction biases in external validation.The latent variable updating(LVU)method was proposed to update models and improve the performance in external validation.Ten samples from the external validation set were randomly selected to update the models.Compared with the recalibration method,LVU could effectively modify the original models which matched the SSC range of the external validation set.The CSMW-PLSR models were more robust in external validations.The off-line model with LVU performed best with a root mean square error of validation(RMSEV)of 0.599%and the in-line model with recalibration obtained RMSEV of 0.864%.These results demonstrated the application potential of the transmittance Vis-NIRS for in-line rapid prediction of SSC in pomelos and the modeling and updating methods could be applied to samples with biological variabilities.

Wavelength Channel Bandwidth of Wavelength-Selective Devices

Narrow bandwidth is a crucial factor in a high performance wavelength selective device(WSD). There are many different expressions to estimate bandwidths of many different WSDs. In this paper, we derive an important rule that the differential time delay between two coupling modes is the most important factor deciding the wavelength channel bandwidth for several different WSDs. This rule reveals that larger differential time delay between two coupling modes results in higher discrimination level of the operating wavelength. Based on the rule, a novel design of wavelength selective coupler that has a bandwidth narrower than normal couplers is presented.

Comparison of algorithms for monitoring wheat powdery mildew using multi-angular remote sensing data

Powdery mildew is a disease that threatens wheat production and causes severe economic losses worldwide. Its timely diagnosis is imperative for preventing and controlling its spread. In this study, the multiangle canopy spectra and disease severity of wheat were investigated at several developmental stages and degrees of disease severity. Four wavelength variable-selected algorithms: successive projection(SPA), competitive adaptive reweighted sampling(CARS), feature selection learning(Relief-F), and genetic algorithm(GA), were used to identify bands sensitive to powdery mildew. The wavelength variables selected were used as input variables for partial least squares(PLS), extreme learning machine(ELM), random forest(RF), and support vector machine(SVM) algorithms, to construct a suitable prediction model for powdery mildew. Spectral reflectance and conventional vegetation indices(VIs) displayed angle effects under several disease severity indices(DIs). The CARS method selected relatively few wavelength variables and showed a relatively homogeneous distribution across the 13 viewing zenith angles.Overall accuracies of the four modeling algorithms were ranked as follows: ELM(0.70–0.82) > PLS(0.63–0.79) > SVM(0.49–0.69) > RF(0.43–0.69). Combinations of features and algorithms generated varied accuracies, with coefficients of determination(R^(2)) single-peaked at different observation angles. The constructed CARS-ELM model extracted a predictable bivariate relationship between the multi-angle canopy spectrum and disease severity, yielding an R^(2)> 0.8 at each measured angle. Especially for larger angles,monitoring accuracies were increased relative to the optimal VI model(40% at-60°, 33% at +60°), indicating that the CARS-ELM model is suitable for extreme angles of-60° and +60°. The results are proposed to provide a technical basis for rapid and large-scale monitoring of wheat powdery mildew.

Application of swarm intelligence algorithms to the characteristic wavelength selection of soil moisture content

Swarm intelligence algorithms own superior performance in solving high-dimensional and multi-objective optimization problems.The application of the swarm intelligence algorithms to visible and near-infrared(VIS-NIR)spectral analysis of soil moisture can contribute to the optimization of the soil moisture prediction model and the development of the real-time soil moisture sensor.In this study,a high-resolution spectrometer was used to obtain spectral data of different levels of soil moisture which were manually configured.Isolation Forest algorithm(iForest)was used to eliminate outliers from the data.Based on the root mean square error of prediction RMSEP of Back Propagation Neural Network(BPNN)model results,a series of new swarm intelligence algorithms,including Manta Ray Foraging Optimization(MRFO),Slime Mould Algorithm(SMA),etc.,were used to select the characteristic wavelengths of soil moisture.The analysis results showed that MRFO owned the best performance if only from the predictive capability perspective and SMA had a better performance when considering the proportion of the selecting wavelengths and the results of the model prediction.By comparing and analyzing the modeling results of traditional intelligence algorithms Genetic Algorithm(GA)and Particle Swarm Optimization(PSO),it was found that the new swarm intelligence had a better performance in selecting the characteristic wavelengths of soil moisture.Integrating the results of all intelligence algorithms used,soil moisture sensitive wavelengths were selected as 490 nm,513 nm,543 nm,900 nm and 926 nm,which provide the basis for the design of real-time soil moisture sensor based on VIS-NIR.

Quantification of glycated hemoglobin indicator HbA1c through near-infrared spectroscopy

A new strategy for quantitative analysis of a major clinical biochemical indicator called glycatedhemoglobin(Hb·A1c)was proposed.The technique was based on the simultaneous near-infrared(NIR)spectral determination of hemoglobin(Hb)and absolute HbAlc content(Hb·HbA1c)inhuman hemolysate samples.Wavelength selections were accomplished using the improvedmoving window partial least square(MWPLS)method for stability.Each model was establishedusing an approach based on randomness,similarity,and stability to obtain objective,stable,andpractical models.The optimal wavebands obtained using MWPLS were 958 to 1036 nm for Hband 1492 to 1858 nm for Hb·HbA1c,which were within the NIR overtone region.The validationroot mean square error and validation correlation coeficients of prediction(V-SEP,V-Rp)were 3.4g L^(-1) and 0.967 for Hb,respectively,whereas the corresponding values for Hb.HbAic were 0.63 g L^(-1) and 0.913.The corresponding V-SEP and V-Rp were 0.40% and 0.829 for the relativepercentage of HbA1c.The experimental results confirm the feasibility for the quantification of HbAlc based on simultaneous NIR spectroscopic analyses of Hb and Hb·HbA1c.

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.

850 nm centered wavelength-swept laser based on a wavelength selection galvo filter

A wavelength-swept laser is constructed using a free space external cavity configuration coupled with a fiber- based ring cavity at the 850 nm region. The external cavity filter employs a galvo-mirror scanner with a diffraction grating for wavelength selection. The filter is connected to a ring cavity through an optical circulator. The ring cavity contains a broadband semiconductor optical amplifier with a high optical output. The perfor- mance of this laser is demonstrated with broad bandwidths and narrow linewidths. The 3 dB linewidth and the bandwidth of this source are 0.05 nm （-20 GHz） and 48 nm, respectively. The maximum output power is 26 mW at 160 mA current.

Compact on-chip 1×2 wavelength selective switch based on silicon microring resonator with nested pairs of subrings

We propose and experimentally demonstrate compact on-chip 1×2 wavelength selective switches(WSSs) based on silicon microring resonators(MRRs) with nested pairs of subrings(NPSs). Owing to the resonance splitting induced by the inner NPSs, the proposed devices are capable of performing selective channel routing at certain resonance wavelengths of the outer MRRs. System demonstration of dynamic channel routing using fabricated devices with one and two NPSs is carried out for 10 Gb∕s non-return-to-zero signal. The experimental results verify the effectiveness of the fabricated devices as compact on-chip WSSs.

Superimposed Fiber Gratings Based Wavelength Selectable Fiber Laser

A novel wavelength selectable fiber ring laser was proposed. Superimposed fiber Bragg gratings with uniform reflectivity were made to serve as comb-like filter. Optical tunable filter combined with the comb filter realized fast and accurately wavelength selectivity. The SMSR at each wavelength is more than 50dB.

Two -Dimensional Wavelength Selective Diffraction by High-Order Three-Dimensional Composite Grating

We propose a wavelength selective diffraction using reflectors placed on three-dimensional grid cross points. Different wavelengths are separated into spots distributed in two-dimensional plane. Compact device with high port counts is attainable.

Analysis of a wavelength selectable cascaded DFB laser based on the transfer matrix method

A novel cascaded DFB laser,which consists of two serial gratings to provide selectable wavelengths,is presented and analyzed by the transfer matrix method.In this method,efficient facet reflectivity is derived from the transfer matrix built for each serial section and is then used to simulate the performance of the novel cascaded DFB laser through self-consistently solving the gain equation,the coupled wave equation and the current continuity equations.The simulations prove the feasibility of this kind of wavelength selectable laser and a corresponding designed device with two selectable wavelengths of 1.51μm and 1.53μm is realized by experiments on InP-based multiple quantum well structure.

Measurement of the wavelength modulation indices with selective reflection spectroscopy

The wavelength modulation indices are measured based on harmonic amplitude ratio of 4famp/6famp (4famp and 6famp are the 4- and 6-th harmonic central peak amplitudes correspondingly) with the Doppler-free selective reflection modulation spectroscopy. The experiments for the 6S1/2(F = 4) → 6P3/2(F' = 5) transition of cesium D2 line with 30-MHz linewidth were carried out. The 4f- and 6f-harmonic signals were detected with two digital lock-in amplifiers separately. The maximum error for modulation indices measurement was ±0.1 within the range of m from 3 to 6. The non-linear modulation behaviour of an external cavity diode laser induced by voltage tuning was studied with this method. The method for modulation indices measurement does not require a solid etalon as usual for measuring the wavelength modulation depth and the absorption linewidth correspondingly.

Modeling for mung bean variety classification using visible and near-infrared hyperspectral imaging

This study was carried out to investigate the feasibility of using visible and near infrared hyperspectral imaging for the variety classification of mung beans.Raw hyperspectral images of mung beans were acquired in the wavelengths of 380-1023 nm,and all images were calibrated by the white and dark reference images.The spectral reflectance values were extracted from the region of interest(ROI)of each calibrated hyperspectral image,and then they were treated as the independent variables.The dependent variables of four varieties of mung beans were set as 1,2,3 and 4,respectively.The extreme learning machine(ELM)model was established using full spectral wavelengths for classification.Modified gram-schmidt(MGS)method was used to identify effective wavelengths.Based on the selected wavelengths,the ELM and linear discriminant analysis(LDA)models were built.All models performed excellently with the correct classification rates(CCRs)covering 99.17%-99.58% in the training sets and 99.17%-100%in the testing sets.Fifteen wavelengths(432 nm,455 nm,468 nm,560 nm,705 nm,736 nm,760 nm,841 nm,861 nm,921 nm,930 nm,937 nm,938 nm,959 nm and 965 nm)were recommended by MGS.The results demonstrated that hyperspectral imaging could be used as a non-destructive method to classify mung bean varieties,and MGS was an effective wavelength selection method.

Simultaneous characterization of multiple properties of solid and liquid phases in crystallization processes using NIR

Near infrared spectroscopy （NIR） is now probably the most popular process analytical technology （PAT） for pharmaceutical and some other industries. However, unlike mid-IR, NIR is known to have difficulties in monitoring crystallization or precipitation processes because the existence of solids could cause distortion of the spectra. This phenomenon, seen as unfavorable previously, is however an indication that NIR spectra contain rich information about both solids and liquids, giving the possibility of using the same instrument for multiple property characterization. In this study, transflectance NIR calibration data was obtained using solutions and slurries of varied solution concentration, particle size, solid concentration and temperature. The data was used to build calibration models for prediction of the multiple properties of both phases. Predictive models were developed for this challenging application using an approach that combines genetic algorithm （GA） and support vector machine （SVM）. GA is used for wavelength selection and SVM for mode building. The new GA-SVM approach is shown to outperform other methods including GA-PLS （partial least squares） and traditional SVM. NIR is thus successfully applied to monitoring seeded and unseeded cooling crystallization processes of L-glutamic acid.