Mechanical modulation of multifunctional responses in three-dimensional terahertz metamaterials.

Reconfigurable metamaterials have attracted a surge of attention for their formidable capability to dynamically manipulate the electromagnetic wave. Among the multifarious modulation methods, mechanical deformation is widely adopted to tune the electromagnetic response of the stereotype metamaterial owing to its straightforward and continuous controllability on the metamaterial structure. However, previous morphologic reconfigurations of metamaterials are typically confined in planar deformation that renders limited tunable functionalities. Here we have proposed a novel concept of out-of-plane deformation to broaden the functionalities of mechanically reconfigurable metamaterials via introducing a cross-shaped metamaterial. Our results show that the out-of-plane mechanical modulation dramatically enhances the magnetic response of the pristine metamaterial. Furthermore, by uncrossing the bars of cross-shaped meta-atoms, a L-shaped metamaterial is proposed to verify the effectiveness of such a mechanical method on the handedness switching via changing mechanical loading-paths. More importantly, the differential transmission for circularly polarized incidences can be continuously modulated from -0.45 to 0.45, and the polarization states of the transmission wave can be dynamically manipulated under the linearly polarized illumination. Our proposed mechanical modulation principle might open a novel avenue toward the three-dimensional reconfigurable metamaterials and shows their ample applications in the areas of chiroptical control, tunable polarization rotator and converter.

Determination of invert syrup adulterated in acacia honey by terahertz spectroscopy with different spectral features.

BACKGROUND: Invert syrup is a common adulterant in honey falsification, thus generating risk for consumers. Most of the methods developed are tedious and time-consuming for manufactures and consumers. However, terahertz spectroscopy provides analytical information in a simple, rapid, and environmentally friendly manner. Subsequently, 3 kinds of terahertz spectroscopic characteristics data, the absorption coefficient, the slope of the absorption coefficient spectra, and the area of the absorption coefficient spectra, were employed for determination of acacia honey adulterated with invert syrup. RESULTS: Single linear regression (SLR) models with different terahertz spectroscopic features were adopted to predict the syrup adulterant proportion in acacia honey. The best SLR model used the area of the absorption coefficient, displaying an adjusted correlation coefficient of 0.985 and a root-mean-square error of 3.201. Meanwhile, multiple linear regression (MLR) models using a successive projections algorithm for variables selection were implemented. The MLR model considered the integral area of the absorption coefficient spectra, as the inputs yielded the best result with less variables selected, higher R c 2 and R p 2 , lower root-mean-square error of calibration and prediction, as well as higher residual predictive deviation. CONCLUSIONS: The results indicate terahertz spectroscopy combined with the integral area of the absorption coefficient spectra is reliable enough for invert syrup proportion quantification in acacia honey and is also a rapid and nondestructive determination method for other honey adulterants. © 2019 Society of Chemical Industry.

LncRNA MALAT-1 Elevates HMGB1 to Promote Autophagy Resulting in Inhibition of Tumor Cell Apoptosis in Multiple Myeloma.

Long non-coding RNAs (lncRNAs) can participate in the pathological process of multiple myeloma (MM) via regulation of specific gene expression and function. This research aimed to study the role of MALAT-1 and the underlying mechanism in MM. In this study, the expression of MALAT-1 and HMGB1 protein in the bone marrow mononuclear cells from MM patients at different stages and in MM cell lines was determined by qRT-PCR and western blot, respectively. The endogenous expression of MALAT-1 and HMGB1 was modulated using lentivirus vectors transfection. CHX chase assay and RIP analyses were performed to explore the interaction between MALAT-1 and HMGB1 in MM. Nude mouse xenograft was made and used for in vivo experiment study. The expression of MALAT-1 and HMGB1 in the bone marrow mononuclear cells from patients with untreated multiple myeloma was dramatically increased, as well as in MM cell lines, KM3 and U266; while MALAT-1 expression and HMGB1 protein level both decreased significantly in complete remission patients. Furthermore, MALAT-1 knockdown facilitated the degradation of HMGB1 at the post-translational level via increase of the ubiquitination of HMGB1 in MM cells. MALAT-1 was shown to promote autophagy in MM through upregulation of HMGB1. In vivo, MALAT-1 knockdown could inhibit tumor growth significantly in tumor-bearing mice and reduced the protein expressions of HMGB1, Beclin-1, and LC3B in tumor tissues. LncRNA MALAT-1 increases the expression level of HMGB1 in MM thereby promotes autophagy resulting in the inhibition of apoptosis. J. Cell. Biochem. 118: 3341-3348, 2017. © 2017 Wiley Periodicals, Inc.

[Study on NIR Model Transfer between Similar Kinds of Fruits Based on Slope/Bias Algorithm].

In order to expand the application range of the model for a single kind of fruits in the portable near infrared instrument, this paper comes up with a new method for the soluble solid content (SSC) model transfer between different kinds of fruits. This method is focusing on the idea of model transfer between different instruments. Based on the similar physical and chemical properties of apples, peaches and pears, such as the range of SSC content, fruit size and the thickness of peel, a simple Slope/Bias algorithm is applied to the transfer of apple SSC partial least square (PLS) model. After that, it can be used to predict pear & peach SSC value with very little extra samples. It's more convenient and costs less by using this method. For pear samples, by using extra 35 standard samples to transfer apple SSC model, RMSEP reduced from 1.009 °Brix to 0.565 °Brix. For peaches, extra 40 standard samples led to a significant reduce of RMSEP from 1.726 °Brix to 0.677 °Brix after model transfer. To validate the feasibility of this model transfer method, both pear and peach SSC models were tested using the same Slope/Bias algorithm model transfer respectively. A pear SSC model was firstly set up and then transferred with Slope/Bias method. Taking 30 standard apples as samples, RMSEP value reached 0.597°Brix, while taking 40 standard peaches as samples, RMSEP value reached 0.689°Brix. The peach SSC model was transferred in the same way. For apples, using 35 standard samples, RMSEP value reached 0.654°Brix, and for pears, using 30 standard samples, RMSEP value reached 0.439°Brix. These results show that slope/bias algorithm can be used to transfer model between similar kinds of fruits such as apples, pears and peaches. The paper provides innovative ideas for the model transfer among similar kinds of materials, so that the portable near infrared instruments can be used more conveniently and widely.

[Potentiality of synchronous fluorescence technology for determination of reconstituted milk adulteration in fresh milk].

In the present research, synchronous fluorescence technique was used for qualitative and quantitative detection of re- constituted milk mixed into two kinds of milk samples, raw milk and pasteurized milk, respectively. The total accuracy of sample was used to evaluate the performance of the qualitative discrimination models. The correlation coefficient (r), the root mean square error of correction (RMSEC) and the root mean square error of prediction (RMSEC) were used to evaluate the perform- ance of the quantitative analysis models. The constant wavelength difference (Δλ) between the excitation and emission scanning was determined to be 80 nm from three-dimensional fluorescence spectrum of milk. The total discrimination accuracy was 100% by partial least squares discrimination analysis (PLS-DA) for raw milk, pasteurized milk and reconstituted milk samples. When checking whether the raw milk and pasteurized milk were mixed with reconstituted milk, the total accuracy of calibration samples was 100% and the accuracy of prediction samples was 75% and 81.25%, respectively. The effects of qualitative discrimination models were satisfactory. The PLS regression was used for quantitative analysis of the reconstituted milk content mixed in raw milk and pasteurized milk. The correlation coefficients of actual values versus predicted values were 0.911 2 and 0.911 2, re-spectively. The RMSEC was 0.042 2 and 0.0384, respectively. The RMSEP was 0.054 8 and 0.057 5, respectively. The cor- relation coefficients of quantitative analysis models could reach up to 0.9. The results showed that synchronous fluorescence technology could be applied for rapid detection of reconstituted milk mixed in fresh milk

[On-site evaluation of raw milk qualities by portable Vis/NIR transmittance technique].

To ensure the material safety of dairy products, visible (Vis)/near infrared (NIR) spectroscopy combined with che- mometrics methods was used to develop models for fat, protein, dry matter (DM) and lactose on-site evaluation. A total of 88 raw milk samples were collected from individual livestocks in different years. The spectral of raw milk were measured by a porta- ble Vis/NIR spectrometer with diffused transmittance accessory. To remove the scatter effect and baseline drift, the diffused transmittance spectra were preprocessed by 2nd order derivative with Savitsky-Golay (polynomial order 2, data point 25). Changeable size moving window partial least squares (CSMWPLS) and genetic algorithms partial least squares (GAPLS) meth- ods were suggested to select informative regions for PLS calibration. The PLS and multiple linear regression (MLR) methods were used to develop models for predicting quality index of raw milk. The prediction performance of CSMWPLS models were similar to GAPLS models for fat, protein, DM and lactose evaluation, the root mean standard errors of prediction (RMSEP) were 0.115 6/0.103 3, 0.096 2/0.113 7, 0.201 3/0.123 7 and 0.077 4/0.066 8, and the relative standard deviations of prediction (RPD) were 8.99/10.06, 3.53/2.99, 5.76/9.38 and 1.81/2.10, respectively. Meanwhile, the MLR models were also cal- ibrated with 8, 10, 9 and 7 variables for fat, protein, DM and lactose, respectively. The prediction performance of MLR models was better than or close to PLS models. The MLR models to predict fat, protein, DM and lactose yielded the RMSEP of 0.107 0, 0.093 0, 0.136 0 and 0.065 8, and the RPD of 9.72, 3.66, 8.53 and 2.13, respectively. The results demonstrated the usefulness of Vis/NIR spectra combined with multivariate calibration methods as an objective and rapid method for the quality evaluation of complicated raw milks. And the results obtained also highlight the potential of portable Vis/NIR instruments for on-site assessing quality indexes of raw milk.

Abietic acid inhibits acetaminophen-induced liver injury by alleviating inflammation and ferroptosis through regulating Nrf2/HO-1 axis.

Abietic acid has been known to exhibit anti-inflammatory activity. This study was designed to investigate the protective effects of abietic acid on acetaminophen (APAP)-induced liver injury. The data demonstrated that abietic acid significantly ameliorated APAP-induced liver pathological changes, TNF-α and IL-1ß production. APAP could increase malondialdehyde (MDA) and Fe2+ levels, and decrease ATP and glutathione (GSH) levels, as well as glutathione peroxidase 4 (GPX4) and xCT expression. However, these changes induced by APAP were prevented by abietic acid, indicating abietic acid could inhibit APAP-induced ferroptosis. Furthermore, abietic acid inhibited APAP-induced NF-κB activation and increased the expression of Nrf2 and HO-1. Additionally, the inhibitory effects of abietic acid on APAP-induced liver injury were prevented in Nrf2-/- mice. In vitro, the inhibition of abietic acid on APAP-induced inflammation and ferroptosis were reversed when Nrf2 was knockdown. In summary, abietic acidexhibited a therapeutic effectagainst liver injury by attenuating inflammation and ferroptosis.

Hidden-information extraction from layered structures through terahertz imaging down to ultralow SNR.

Noninvasive inspection of layered structures has remained a long-standing challenge for time-resolved imaging techniques, where both resolution and contrast are compromised by prominent signal attenuation, interlayer reflections, and dispersion. Our method based on terahertz (THz) time-domain spectroscopy overcomes these limitations by offering fine resolution and a broadband spectrum to efficiently extract hidden structural and content information from layered structures. We exploit local symmetrical characteristics of reflected THz pulses to determine the location of each layer, and apply a statistical process in the spatiotemporal domain to enhance the image contrast. Its superior performance is evidenced by the extraction of alphabetic characters in 26-layer subwavelength papers as well as layer reconstruction and debonding inspection in the conservation of Terra-Cotta Warriors. Our method enables accurate structure reconstruction and high-contrast imaging of layered structures at ultralow signal-to-noise ratio, which holds great potential for internal inspection of cultural artifacts, electronic components, coatings, and composites with dozens of submillimeter layers.

[Evaluation of walnut by terahertz nondestructive technology].

The deterioration and shell thickness of walnut were studied using the terahertz time domain spectroscopy. Firstly, the THz spectra of moth-eaten, moldy and normal walnuts were compared, and the bad walnuts were properly rejected due to the differences of absorption peaks. Secondly, the transmission-type and reflection-type terahertz time domain spectroscopy system was used simultaneously, and a new formula to calculate shell thickness of walnut was built in the THz system. Then the authors measured the shell thickness based on the detectable refractive index of walnut, and the relative error was 3.7%. Consequently, the quality of walnut was evaluated nondestructively according to physical and chemical indicators from walnut THz spectra respectively.

[Maturity qualitative discrimination of small watermelon fruit].

Dividing watermelons into two categories as not complete mature and fully mature by cluster analyzing the 10 indicators associated with maturity, the two modeling methods PCADA and PLSDA were used, and through the near-infrared spectroscopy, the maturity of small watermelon fruit JINGXIU was qualitatively determined. The PCADA model is the best. Modeling at the top position is better than that of the equatorial parts of the melon. The two models both have a miscarriage of justice, and exists the same sample with a miscarriage of justice. Fruit samples of different physical and chemical composition and structure will have an impact on the spectral information, resulting in miscarriage of justice. Near-infrared diffuse transmittance technique can get better results in detection of small watermelon maturity. But the prediction model should be established to select the appropriate parts of the spectrum acquisition and modeling methods.

[Research on identification of American ginseng and panax ginseng by near infrared spectra of samples' cross section].

In order to identify American ginseng and panax ginseng samples accurately and rapidly, the authors acquired the NIR spectra of the samples' cross-sections. Then the spectra were respectively analyzed according to the samples' physical structure factors and chemical factors. The authors selected appropriate bands and built a physical factor leading model, a chemical factors leading model as well as a comprehensive factor model. The authors found that all the three models' discriminant rates were above 96 percents, which can meet the needs of the rapid detection of raw Chinese medicinal crop materials. While the physical factors model had a simple operation, the discriminant rate was relatively low. The chemical factors model' discriminant rate was higher, but the computation is much more complex. Among the three models, the mixed factor model had the best result with the highest discrimination rate (100 percents) and a smaller number of principal components (4). The effect was the most ideal. It proved that physical factors play an important part in NIR modeling. The cross section method is accurate and convenient which can be used in the quality control in enterprise, realizing the rapid screening of the medicine raw materials.

Detection of nitrofurans residues in honey using surface-enhanced Raman spectroscopy.

Residues of veterinary antibiotics in honey may be damaging to human health. Surface-enhanced Raman scattering spectroscopy (SERS) is an emerging technology widely applied in food safety. SERS has advantages of enabling fingerprint identification and fast detection, as well as does not require complex pretreatment. Considering the overuse of nitrofurans in honeybee breeding, SERS combined with spectral preprocessing was used to detect nitrofurantoin in honey. By using standardized experimental procedures and improved spectral correction methods, the lowest detection limit of nitrofurantoin was 0.1321 mg/kg. A good linear relationship in the partial least squares regression model was found among spiked samples, which allowed prediction of nitrofurantoin content in honey sample ( R C 2 $R_C^2$ = â€¯0.9744; R P 2 $R_P^2$ =  0.976; RMSECV = 1.0353 mg/kg; RMSEP  =  0.9987 mg/kg). Collectively, these results reliably demonstrated that quantification is more accurate when spectral preprocessing is better controlled. Therefore, this study indicates that SERS could be further implemented in fast and onsite detection of nitrofurantoin in honey for improved food safety. PRACTICAL APPLICATION: This article presents a novel SERS-based method for the rapid detection of nitrofurantoin residues in honey. The original spectra were corrected by multiple linear regression based on the fitting baseline. This study aims to develop a rapid onsite detection method for toxic hazardous substance residues in food.

Quantitative determination of acacia honey adulteration by terahertz-frequency dielectric properties as an alternative technique.

The dielectric characteristics in the terahertz region contribute to a revealing insight into the material components and provide intermolecular information. The dielectric properties of adulterated honey, described as the real and imaginary parts of the complex dielectric constant (Re[ε] and Im[ε]), were obtained from 0.3 to 1.5 THz. The relationship between invert syrup proportions and complex dielectric constants at different frequencies implied the possibility of using the dielectric property as an indicator of honey authenticity. The selected effective dielectric variables of Re[ε] and Im[ε] and their combination were chosen by stability competitive adaptive reweighted sampling (SCARS) algorithm and then used to establish PLS models. The accuracy and uncertainty result revealed SCARS-PLS model based on the combination of Re[ε] and Im[ε] is the best model relatively. These findings indicated the potential utility of this rapid, non-destructive, and on-site method for authenticity verification.

[Determination of fat, protein and DM in raw milk by portable short-wave near infrared spectrometer].

Near infrared diffuse reflectance spectroscopy calibrations of fat, protein and DM in raw milk were studied with partial least-squares (PLS) regression using portable short-wave near infrared spectrometer. The results indicated that good calibrations of fat and DM were found, the correlation coefficients were all 0.98, the RMSEC were 0.187 and 0.217, RMSEP were 0.187 and 0.296, the RPDs were 5.02 and 3.20 respectively; the calibration of protein needed to be improved but can be used for practice, the correlation coefficient was 0.95, RMSEC was 0.105, RMSEP was 0.120, and RPD was 2.60. Furthermore, the measuring accuracy was improved by analyzing the correction relation of fat and DM in raw milk This study will probably provide a new on-site method for nondestructive and rapid measurement of milk.

Discrimination of corn variety using Terahertz spectroscopy combined with chemometrics methods.

High-oil corn is a high-quality variety of corn possessing higher oil content with greater caloric energy than normal corn. Hence, controlling the purity and authenticity of high-oil corn is of great importance in current crop research. The aim of this study is to develop a novel method for corn variety discrimination using Terahertz (THz) spectroscopy and signal classification analysis. In brief, the method involves feature extraction and variable selection of raw signals from Terahertz time-domain waveforms (THz-TDW) and absorption spectrum (THz-AS), and the use of classifiers on those treated signals to establish the discrimination models. Principle component analysis (PCA) were used for feature extraction with THz-TDW, while three different methods of variable selection were implemented with THz-AS, including uninformative variables elimination (UVE), uninformative variables elimination-successive projections algorithm (UVE-SPA) and competitive adaptive reweighted sampling (CARS). Then, two classification algorithms, Linear discriminant analysis (LDA) and support vector machine (SVM), were employed and compared in the discrimination models. Bootstrapped Latin partitions (BLP) method with 10 bootstraps and 5 Latin-partitions was applied to validate these models. Our modeling results suggest SVM as the better classification algorithm achieving higher identifying accuracy, such that the PCA-SVM model for THz-TDW has achieved 94.7% accuracy. The results also indicate variable selection as an important step to create an accurate and robust discrimination model for THZ-AS. The CARS-SVM model with radial basic function (RBF) has achieved 100% average accuracy in prediction set, while the UVE-SVM and UVE-SPA-SVM have achieved 91.2% and 99.1% accuracy, respectively. These results demonstrate that high-oil corn and normal corn can be identified successfully by using THz spectroscopy with discriminant analysis, suggesting our techniques to provide an efficient and practical reference for classifying crop varieties in agriculture research, while expanding the application of THz spectroscopy in the related field.

Determination of the Geographical Origin of Coffee Beans Using Terahertz Spectroscopy Combined With Machine Learning Methods.

Different geographical origins can lead to great variance in coffee quality, taste, and commercial value. Hence, controlling the authenticity of the origin of coffee beans is of great importance for producers and consumers worldwide. In this study, terahertz (THz) spectroscopy, combined with machine learning methods, was investigated as a fast and non-destructive method to classify the geographic origin of coffee beans, comparing it with the popular machine learning methods, including convolutional neural network (CNN), linear discriminant analysis (LDA), and support vector machine (SVM) to obtain the best model. The curse of dimensionality will cause some classification methods which are struggling to train effective models. Thus, principal component analysis (PCA) and genetic algorithm (GA) were applied for LDA and SVM to create a smaller set of features. The first nine principal components (PCs) with an accumulative contribution rate of 99.9% extracted by PCA and 21 variables selected by GA were the inputs of LDA and SVM models. The results demonstrate that the excellent classification (accuracy was 90% in a prediction set) could be achieved using a CNN method. The results also indicate variable selecting as an important step to create an accurate and robust discrimination model. The performances of LDA and SVM algorithms could be improved with spectral features extracted by PCA and GA. The GA-SVM has achieved 75% accuracy in a prediction set, while the SVM and PCA-SVM have achieved 50 and 65% accuracy, respectively. These results demonstrate that THz spectroscopy, together with machine learning methods, is an effective and satisfactory approach for classifying geographical origins of coffee beans, suggesting the techniques to tap the potential application of deep learning in the authenticity of agricultural products while expanding the application of THz spectroscopy.

[Study on variable selection of NIR spectral information based on GA and SCMWPLS].

Spectral data compression and informative variable selection are the research focus on the application of NIR, which enable to simplify the model and improve the accuracy of prediction. The research used the pretreatment methods such as the second derivative, normalization and orthogonal signal correction (OSC) to filter irrelevant array according to the concentration of soluble solid content (SSC) based on the Vis/NIR spectroscopy of apricot. SCMWPLS was used to select 880, 894-910 and 932 nm as the regions for constructing prediction PLS model with correlation coefficient (R) of 0.920, standard error of calibration (SEC) of 0.454 and standard error of prediction (SEP) of 0.470 for SSC. Besides, after conducting an independent run for 100 times, GA obtained the regression variables as 888 and 900 nm according to the higher frequency of selection to set up GA. MLR prediction model, and the R, SEC and SEP were 0.905, 0.488 and 0.459 respectively. The results of the two modeling methods are both better than those of full-region PLS model. This demonstrates that OSC enables to filter irrelevant signal array according to the concentration of SSC and reduce the latent variables used for modeling. Also, SCMWPLS and GA can identify the optimal combination of information variables. These methods have a universal significance on building NIR express analysis model with low dimension and high precision.

[Research on fast discrimination between Panax ginseng and Panax quinque folium based on near infrared spectroscopy].

Near infrared spectroscopy combined with pattern recognition techniques were applied to develop a method of fast and nondestructive discrimination between Chinese ginseng and American ginseng. A total of 90 representative ginseng samples including root, fiber and powder were collected. NIR spectra of the samples were obtained directly with wrapped polyethylene packing film. MSC and first derivative were performed after the elimination of notable packing film absorbance in raw spectra. Then the informative wave bands were chosen by moving window partial least-squares regression method. PLS-DA, PCA-DA and SVM discrimination models were founded and their results were compared. SVM was proven to be the most effective method with 100% accurate identification rate for validation set. It indicates that the method founded is precise and convenient and can be practically used in practice for quality control and fast screening of raw herb materials.

[Feasibility of using ethanol liquor NIRS model to predict ethanol in vinous ferment liquid].

The robust NIRS model must be developed by the representative samples and precise chemical values, taking much of work. To reduce the calibration work, the present paper explored the NIRS model developed using ethanol liquor to predict ethanol of the wine samples. The authors used the gene arithmetic (GA) method to select the calibration region (2 245-2 320 nm) which has relatively high correlation with the consistency of ethanol in ethanol liquor and has little interfere by other components in wine. To remove the systematic error between the calibration set of ethanol liquor and the prediction set of turbid vinous ferment liquid, according to the method of slope/bias, the authors selected 21 samples in prediction set which can represent the range of consistency of vinous ferment liquid to revise the ethanol model in order to predict the remaining wine samples well. After the calculation, the authors obtained the bias and the slope to be 0.523 3 and 0.980 8, respectively. Then we predicted the other turbid samples of wine using the ethanol liquor model after being revised by the slope/bias method. And the prediction model for the ethanol of turbid samples was developed, with r, RPD and RSD for the prediction model for ethanol of samples being 0.99%, 11.71% and 3.11%, respectively, indicating that the ethanol liquor model is robust and can serve as the model of vinous ferment liquid to detect the ethanol of the wine. So this method can largely reduce the calibration work during the NIR calibration process, and has the practical feasibility and application value.

[Improving the prediction model of protein in milk powder using GA-PLS combined with PC-ANN arithmetic].

The present paper presents a new NIR analysis method with partial least square regression (PLS) and artificial neural network (ANN) to improve the prediction precision of the protein model for milk powder. First, an efficient method named region selecting by genetic algorithms (RS-GA) was used to select the calibration region, and then the GA-PLS model was made to predict the linear part of the protein content in milk powder. And then in the region selected by RS-GA method, principal component analysis (PCA) was calculated. The principal components were taken as the input of ANN model. The remnant values by subtracting the standard values and the GA-PLS validation values were regarded as the output of ANN. The ANN model was made to predict the nonlinear part of the protein content. The final result of the model was the addition of the two model's validation values, and the root mean squared error of prediction (RMSEP) was used to estimate the mixed model. A full region PLS model (Fr-PLS) was also made, and the RMSEP of the Fr-PLS, GA-PLS and GA-PLS+PC-ANN model was 0.511, 0.440 and 0.235, respectively. The results show that the prediction precision of the protein model for milk powder was largely improved when adding the nonlinear port in the NIR model, and this method can also be used for other complex material to improve the prediction precision.