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The goal of this study was to model the total leaf chlorophyll content (LCC tot) of Gannan navel orange leaves using a field imaging spectroscopy system in the visible and near-infrared domain. The spectral range from 400 to 1000 nm with 176 wavebands (a wavelength interval of 3.41 nm) or 360 wavebands (a wavelength interval of 1.67 nm), labeled as ''Datasets_1.67'' and ''Datasets_3.41'', respectively, were used. Although different spectral data types were used, better prediction results for LCC tot were based on Datasets_1.67 for LCC tot prediction. Several prediction models of LCC tot were built based on partial least squares regression (PLSR), artificial neural networks (ANN), ordinary least squares regression (OLSR), and stepwise linear regression (SLR) using full spectral and effective wavelength (EW) data (raw spectral (RS), first derivative spectral (FDS) and second derivative spectral (SDS) data). The determination coefficient (R 2), the root mean square error (RMSE) and the residual predictive deviation (RPD) were used to evaluate the reliability and accuracy of the predicted LCC tot values. As a result, 14 (7 obtained from Datasets_1.67, 7 obtained from Datasets_3.41), 39 (21 obtained from Datasets_1.67, 18 obtained from Datasets_3.41) and 50 (27 obtained from Datasets_1.67, 23 obtained from Datasets_3.41) wavebands were selected from the RS data, FDS data and SDS data, respectively, as the EWs for LCC tot prediction of navel orange leaves. After that, PLSR and ANN predictive models were established using full spectra, and OLSR and SLR predictive models were built using the selected EWs. The experimental results demonstrated that these various regression methods were useful for estimating LCC tot in the order of PLSR models established using full spectra from RS data (F-RS-PLSR) > PLSR models established using full spectra from SDS data (F-SDS-PLSR) > PLSR models established using full spectra from FDS data (F-FDS-PLSR) > SLR models established using EWs by RS data (EWs-RS-SLR). However, models built with ANN and OLSR, where the RPD values were less than 3, cause the models to be inaccurate. Finally, in comparison, the F-RS-PLSR model exhibited the best performance of LCC tot estimation; with the number of principal components (Pcs) = 5, this model provided high values of the R 2 of calibration (C-R 2) = 0.92 and the R 2 of validation (V-R 2) = 0.96, small values of the RMSE of calibration (C-RMSE)=0.05 mg/g and the RMSE of validation (V-RMSE) = 0.19 mg/g, and sufficient the RPD of calibration (C-RPD)=17.00 and the RPD of validation (V-RPD)=3.63 values. Overall, the best modeling method was PLSR. Hence, the PLSR applicability for assessing chlorophyll content in navel orange leaves was demonstrated. INDEX TERMS Chlorophyll, hyperspectral data, navel oranges, partial least squares.

As an important physical property of molecules, absorption energy can characterize the electronic property and structural information of molecules. Moreover, the accurate calculation of molecular absorption energies is highly valuable. Present linear and nonlinear methods hold low calculation accuracies due to great errors, especially irregular complicated molecular systems for structures. Thus, developing a prediction model for molecular absorption energies with enhanced accuracy, efficiency, and stability is highly beneficial. By combining deep learning and intelligence algorithms, we propose a prediction model based on the chaos-enhanced accelerated particle swarm optimization algorithm and deep artificial neural network (CAPSO BP DNN) that possesses a seven-layer 8-4-4-4-4-4-1 structure. Eight parameters related to molecular absorption energies are selected as inputs, such as a theoretical calculating value Ec of absorption energy (B3LYP/STO-3G), molecular electron number Ne, oscillator strength Os, number of double bonds Ndb, total number of atoms Na, number of hydrogen atoms Nh, number of carbon atoms Nc, and number of nitrogen atoms NN; and one parameter representing the molecular absorption energy is regarded as the output. A prediction experiment on organic molecular absorption energies indicates that CAPSO BP DNN exhibits a favourable predictive effect, accuracy, and correlation. The tested absolute average relative error, predicted root-mean-square error, and square correlation coefficient are 0.033, 0.0153, and 0.9957, respectively. Relative to other prediction models, the CAPSO BP DNN model exhibits a good comprehensive prediction performance and can provide references for other materials, chemistry and physics fields, such as nonlinear prediction of chemical and physical properties, QSAR/QAPR and chemical information modelling, etc.

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BACKGROUND Currently, hyperspectral technology has been used in various fields, but its applications for the detection of chylous plasma are lacking. This paper used hyperspectral techniques in combination with machine learning algorithms for the detection of chylous plasma, providing a new diagnostic method. OBJECTIVE This paper proposed a method of plasma chylous degree detection and recognition based on machine learning and hyperspectral technology. A plasma chylous degree detection model was established.It fills the gap of machine learning and hyperspectral technology in the detection of chylous plasma. METHODS The plasma hyperspectral data were preprocessed using the multiple scattering correction (MSC) method and then classified using four classification algorithms, including random forest (RF), K-nearest neighbor KNN), Perceptron, and stochastic gradient descent (SGD) algorithms and the best algorithm was compared.Finally, band selection is carried out to screen the best band subset. RESULTS The results showed that the random forest algorithm had the best effect. Then, the model of plasma chylous degree detection based on random forest was established. Finally, 10 important spectral bands, including 1192.45 nm, 1182.9 nm, 946.98 nm, 1202.01 nm, 1080.93 nm, 1278.41 nm, 1237.03 nm, 991.65 nm, 1020.35 nm, and 1697.8 nm, were selected by band selection. After adjusting the parameters to optimize the model, the prediction accuracy of the whole band was 0.89. CONCLUSIONS This study suggested that hyperspectral technology could identify chylous plasma and could be used to improve its detection efficiency in biomedicine, human function tests, and other aspects.