PLSR Method Research Articles

The spectral inversion model for electrical conductivity in mural plaster following phosphate erosion based on fractional order differentiation and novel spectral indices

The Dunhuang murals are a precious treasure of China’s cultural heritage, yet they have long been affected by salt damage. Traditional methods for detecting salt content are costly, inefficient, and may cause physical harm to the murals. Among current techniques for measuring salt content in murals, hyperspectral remote sensing technology offers a non-invasive, circumventing issues of high costs, low efficiency. Building on this, the study constructs an inversion model for the Electrical Conductivity (EC) values of mural plaster subjected to phosphate erosion, through the integration of Fractional Order Differentiation (FOD), a novel three-band spectral index, and the Partial Least Squares Regression algorithm. The specific research contents include: (1) Initially, in preparation for the experiments, the materials used to create the samples underwent a rigorous desalting process, and phosphate solutions were prepared using deionized water to ensure uniform experimental conditions and the accuracy of the results. These meticulous preprocessing steps guaranteed that the measured EC values exhibited a clear correlation with the phosphate content. Subsequently, by employing qualitative experimental analysis techniques, this study was able to more accurately simulate the real-world scenarios of mural plaster affected by salt damage, enabling a deeper investigation into the mechanisms by which salts inflict microscopic damage to murals. (2) Explores the absorption mechanisms and characteristic spectral bands of the Electrical Conductivity (EC) values measured after the phosphate erosion of mural plaster. By integrating the optimal spectral indices, a univariate linear regression model is constructed, providing a basis for the rapid quantitative measurement of electrical conductivity in murals. (3) By comparing the accuracy of the Phosphate Simple Ratio (PSR) and Phosphate Normalized Difference Index (PNDI) spectral indices based on the linear regression model, the first six orders of the highest accuracy spectral index were selected as the optimal three-band spectral index combination, used as explanatory variables, with mural plaster electrical conductivity as the response variable, employing the PLSR method to construct the mural phosphate content high-spectral feature inversion model. The study’s findings include: (1) Surfaces of samples deteriorated by phosphate erosion formed numerous irregularly shaped crystal clusters, exhibiting uneven characteristics. (2) By comparing the outcomes of different orders of fractional differentiation, it was found that the model performance reached its optimum at a 0.3 order of differentiation for both PSR and PNDI data, with a determination coefficient (Q2) of 0.728. (3) Utilizing PLSR, this study employed the previously determined optimal six-order three-band spectral index combination as explanatory variables, with salt content as the response variable, successfully constructing the high-spectral feature inversion model for mural electrical conductivity with a determination coefficient (Q2) of 0.815. This provides an effective technical means for monitoring the salt damage conditions of precious cultural heritage such as murals.

Algorithm for Determination of Pepper Maturity Classes by Combination of Color and Spectral Indices

Abstract The aim of the present work is to propose methods and tools for classifying sweet pepper into groups according to their degree of maturity based on color and spectral characteristics extracted from color images on the surface of the vegetables. The investigated pepper is two varieties of sweet - red Banji and yellow Liri. Five groups were formed, depending on the degree of maturity, and 16 color and 11 spectral indices were calculated for each of the groups. By successively using the ReliefF and PLSR methods, a selection of informative features and subsequent reduction of the vector formed by them was carried out, thereby aiming to increase the predictive results and minimize the time for data processing. The obtained classification errors between the individual stages of ripening vary according to the type of pepper and depending on which of the two types of maturity the fruits are in - technical or biological. For red sweet pepper, the separation inaccuracy obtained using a discriminant classifier with a quadratic separation function is in the range of 8 - 19%, while for yellow it is from 5 to 23%. The results obtained in the present work for the classification of pepper into groups according to their degree of maturity would support decision-making in selective harvesting and overall more accurate and efficient management of the harvesting process from the point of view of precision agriculture. The work will continue with studies related to the prediction of various compounds indicating changes in the color of peppers, including chlorophylls, carotenes and xanthophylls. In this way, it is possible to increase the accuracy in determining the degree of ripeness, since in pepper the color does not always follow the same pattern of change from green to yellow to orange to red.

Establishment of deep learning model for the growth of tea cutting seedlings based on hyperspectral imaging technique

The shoot and root growth not only serve as important indicators for measuring the quality of tea cutting seedlings, but also can provide scientific basis for agricultural production and fine seedling management. Currently, manual weighing is the main method used to measure shoot and root growth, which is both destructive and inefficient. The study proposes a method for monitoring the shoot and root growth in tea cutting seedlings using hyperspectral imaging. First, mature leaves and shoots spectra were extracted from tea cutting seedlings using Mask R-CNN (Epoch=20 and Learning-rate=0.001). The spectra were then preprocessed by MSC, S-G, 1-D filtering techniques, with feature bands screened by UVE (Optimal factor number=5), CARS (Monte Carlo sampling times=300), and SPA (Epoch=25). Finally, a CNN-GRU (Epoch=100 and Learning-rate=0.01) network was employed to predict shoot and root biomass, and compared with machine learning methods of SVR (Kernel function=Polynomial), RFR (Ntrees=200), and PLSR (Latent variable=16) and deep learning methods (Epoch=100 and Learning-rate=0.01) of CNN and LSTM. The results show that (i) Mask R-CNN can accurately extract the spectral of mature leaves (precision=97.8 %) and shoots (precision=91.5 %), (ii) The spectral feature bands of shoot (number=212) and root (number=105) biomass screened by UVE were more abundant than CARS and SPA, (iii) The UVE+CNN-GRU model (Rp2=0.90, RMSEP = 0.12, RPD=2.43) based on shoots spectrum provided the optimal estimation of shoot biomass. The SPA+LSTM model (Rp2 = 0.65, RMSEP = 0.05, RPD=1.67) based on the mature leaves spectrum provided the optimal estimation of root biomass. These findings demonstrate that hyperspectral imaging technology combined with deep learning algorithms can accurately monitor the growth of cutting seedlings quickly without causing damage. This not only provides a new data sources along with technical means for efficient screening excellent tea varieties, but also improves agricultural production efficiency and resource utilization.

The construction of hotel information management mode under the trend of low carbon tourism

Abstract The management of hotels in the context of low-carbon tourism tends to be more and more information-based, and there is an urgent need to build a management model in line with information technology. In this paper, a hotel informatization management system considering customer satisfaction is established, the system objectives are designed based on the demand analysis of travelers, and the functions of subsystems are designed based on the information management activities of the system. For the analysis module of customer satisfaction, the satisfaction index system of hotel travelers is constructed based on the ACSI model, and the satisfaction index is analyzed using the PLSR method. For the database structure of the system, the system tables were divided into several interrelated information tables. In the system test, this system’s read/write rate improved by 8.26% compared with the Green Cloud system and 7.53% compared with the Golden Swan system. Regarding the fit of satisfaction analysis, this system improved by 5.53%, 18.90%, 12.29%, and 1.33% over the quadratic graph method, the Carnot model, the CCSI model, and the ECSI model, respectively. Regarding the optimization rate of services, this system improves by 8.03% compared to the Opera system. The information-based management system helps improve the hotel’s service quality and management level and also provides a solution for operators to develop users in the hotel industry.

Non-invasive detection of haemoglobin, platelets, and total bilirubin using hyperspectral cameras

Hyperspectral imaging has emerged as a promising high-resolution and real-time imaging technology with potential applications in medical diagnostics and surgical guidance. In this study, we developed a high-speed hyperspectral camera by integrating a Fabry-Perot cavity filter on each CMOS pixel. We used it to non-invasively detect three blood components (haemoglobin, platelet, and total bilirubin). Specifically, we acquired transmission images of the subject's fingers, extracted spectral signals at each wavelength, and used dynamic spectroscopy to obtain non-invasive blood absorption spectra. The prediction models were established using the PLSR method and were modelled and validated based on the standard clinical-biochemical test values. The experimental results demonstrated excellent performance. The best predictions were obtained for haemoglobin, with a high related coefficient (R) of 0.85 or more in both the calibration and prediction sets and a mean absolute percentage error (MAPE) of only 5.7%. The results for total bilirubin were also ideal, with R values exceeding 0.8 in both sets and a MAPE of 10.6%. Although the prediction results for platelets were slightly less satisfactory, the error was still less than 15%, indicating that the results were also acceptable. Overall, our study highlights the potential of hyperspectral imaging technology for the development of portable and affordable devices for blood analysis, which can be used in various settings.

Analysis of Near-Infrared Spectral Properties and Quantitative Detection of Rose Oxide in Wine

This study aims to investigate the near-infrared spectral properties of Rose Oxide (4-Methyl-2-(2-methyl-1-propenyl) tetrahydropyran) in wine, establish a quantitative detection, and build relationships between the chemical groups of Rose Oxide and near-infrared characteristic bands, so as to provide ideas and references for the near-infrared detection of a low-content aroma substance in wine. In total, 133 samples with different wine matrices were analyzed using Fourier transform–near-infrared (FT-NIR) spectroscopy. Min–max normalization (MMN), principal component analysis (PCA), and synergy interval partial least squares regression (Si-PLSR) were used for pre-processing, outlier rejection, analysis of spectral properties, and modeling. Finally, the quantitative detection model was established using the PLSR method and the wine sample containing Rose Oxide was verified externally. Eight subintervals (4000–4400 cm−1, 4400–4800 cm−1, 5600–6000 cm−1, 6000–6400 cm−1, 6400–6800 cm−1, 6800–7200 cm−1, 7200–7600 cm−1, 8400–8800 cm−1) were determined as the characteristic band intervals of Rose Oxide in the NIR region. Among them, 5600–6000 cm−1 was assigned to the first overtone C–H stretching in tetrahydropyran ring and methyl as well as the combination C–H stretching of the CH3 function groups, 6000–6400 cm−1 was assigned to the first overtone C–H stretching of the C–H=group and the combination C=C stretching in isobutyl, and 8400–8800 cm−1 was assigned to the second overtone C–H stretching and C–O stretching in tetrahydropyran ring as well as the C–H stretching vibration in methyl. In addition, 4000–4800 cm−1, 6400–6800 cm−1, and 7200–7600 cm−1 were assigned to the C–H stretching vibration, while 6400–7600 cm−1 was assigned to the C–O stretching vibration. The training result showed that the calibration model (rcv2 of 0.96 and RMSECV of 2.33) and external validation model (rcv2 of 0.84 and RMSECV of 2.72) of Rose Oxide in wine were acceptable, indicating a good predictive ability. The spectral assignment of Rose Oxide provides a new way for the NIR study of other terpenes in wine, and the use of the established Si-PLSR model for the rapid determination of Rose Oxide content in wine is feasible.

Spectral Quantitative Analysis and Research of Fusarium Head Blight Infection Degree in Wheat Canopy Visible Areas

Obtaining complete and consistent spectral images of wheat ears in the visible areas of in situ wheat canopies poses a significant challenge due to the varying growth posture of wheat. Nevertheless, detecting the presence and degree of wheat Fusarium head blight (FHB) in situ is critical for formulating measures that ensure stable grain production and supply while promoting green development in agriculture. In this study, a spectral quantitative analysis model was developed to evaluate the infection degree of FHB in an in situ wheat canopy’s visible areas. To achieve this, a spectral acquisition method was used to evaluate the infection degree of FHB in a wheat canopy’s visible areas. Hyperspectral images were utilized to obtain spectral data from healthy and mildly, moderately, and severely infected wheat ear canopies. The spectral data were preprocessed, and characteristic wavelengths were extracted using twelve types of spectral preprocessing methods and four types of characteristic wavelength extraction methods. Subsequently, sixty-five spectral quantitative prediction models for the infection degree of FHB in the in situ wheat canopy visible areas were established using the PLSR method, based on the original spectral data, preprocessed spectral data, original spectral characteristic wavelengths extracted data, and preprocessed spectral characteristic wavelengths extracted data. Comparative analysis of the models indicated that the MMS + CARS + PLSR model exhibited the best prediction effect and could serve as the spectral quantitative analysis model for the evaluation of the infection degree of FHB in an in situ wheat canopy’s visible areas. The model extracted thirty-five characteristic wavelengths, with a modeling set coefficient of determination (R2) of 0.9490 and a root-mean-square error (RMSE) of 0.2384. The testing set of the coefficient of determination (R2) was 0.9312, with a root-mean-square error (RMSE) of 0.2588. The model can facilitate the spectral quantitative analysis of the infection degree of FHB in the in situ wheat canopy visible areas, thereby aiding in the implementation of China’s targeted poverty alleviation and agricultural power strategy.

Rapid detection of waste cooking oil using low-field nuclear magnetic resonance

The illegal circulation of waste cooking oil (WCO) in the market makes the development of an efficient detection method an urgent task. Therefore, 20 batches of WCOs were collected and compared with 15 kinds of edible oils using low-field nuclear magnetic resonance (LF-NMR) combined with the chemometrics method. LDA, PLS-DA and SVM-DA models were proven to totally discriminate WCO from 15 kinds of edible oils, and T 22 -related parameters were crucial factors in the discrimination processes. WCO was blended at a ratio from 5% to 50% into corn oil (CO), olive oil (OO), peanut oil (PO), rapeseed oil (RO) and soybean oil (SO) in the following adulteration assay. The SVM-DA model showed a better classification effect than the LDA and PLS-DA models in differentiating edible oils from corresponding WCO-adulterated oils, and WCO-adulterated CO was relatively more difficult (80%<validation accuracy<88.6%) to classify than the other groups (82.9%<validation accuracy<100%). Finally, four quantitative models of WCO-adulterated OO, PO, RO and SO were well established using the PLSR method, with R 2 > 0.956, RMSEC<0.035 and RMSEP<0.038. These results illustrate that LF-NMR combined with chemometrics analysis can provide promising rapid screening of WCO. • LF-NMR detection distinguished 20 batches of WCOs from 15 kinds of edible oils. • The parameters of T 22 fraction were crucial factors to discriminate WCOs from edible oils. • SVM-DA model showed better classification effect than the LDA and PLS-DA models. • PLSR model was established to calculate the adulteration level of WCOs.

Exploring relationship of soil PTE geochemical and “VIS-NIR spectroscopy” patterns near Cu–Mo mine (Armenia)

PTE contamination of soils remains one of the global environmental concerns. The ways of detecting and monitoring PTE concentrations in soils varies including traditional field sampling accompanied by sample preparation and chemical analysis and state of the art visible and near-infrared (Vis-NIR) spectroscopic approaches. Among the different Machine Learning (ML) to extract soil information from spectra and to explore the relationship between spectral reflectance data and soil PTE content PLSR method is a well-established one to construct a soil PTE estimation model. This study aimed to explore the relationship of soil PTE geochemical and VIS-NIR spectroscopy characteristics in agricultural soils near Cu–Mo mine area in Armenia. PLSR method is applied to identify the links between the spectra and agricultural soil Ti, V, Cr, Mn, Fe, Co, Ba, Pb, Zn, Cu, Sr, Zr and Mo contents to reveal the potential of VIS-NIR spectroscopy in ex-situ monitoring of Kajaran soils. The results show that different portions of VIS-NIR spectra are responsible for Ti (1100–1200 nm, 2350–2500 nm), V (350–500 nm, 700–750 nm, 1000–1100 nm, 1400–2500 nm), Cr (1300–1400 nm, 1900–2100 nm) and Ba (450–500 nm, 600–800 nm, 1050–1700 nm, 2000–2100 nm, 2350–2400 nm) estimations through PLSR correspondingly. However, among the studied PTEs Ti and V, which shows significant negative correlations in VIS-NIR spectra registered at around 400–600 nm and 850–1150 nm regions, are remarkable and promising with the PLSR estimation results using VIS-NIR spectra Ti (R2Test = 0.74), V (R2Test = 0.71). This study shows that VIS-NIR spectroscopy has a high potential for the estimation of at least several PTE in soils and PLSR modelis reliable for deriving information from there.

A method based on a one-dimensional convolutional neural network for UV-vis spectrometric quantification of nitrate and COD in water under random turbidity disturbance scenario.

This paper proposed a novel spectrometric quantification method for nitrate and COD concentration in water using a double-channel 1-D convolution neural network for relatively long UV-vis absorption spectra data (2600 points). To improve the model's ability to resist turbidity disturbance, a new dataset augmentation method was applied and the absorption spectra of nitrate and COD under different turbidity disturbances were successfully simulated. Compared to the PLSR model, the value of RRMSEP for the CNN model was reduced from 6.1% to 1.4% in nitrate solution and 4.5% to 1.3% in COD solution. Compared to the PLSR model, the regression accuracy of the CNN model was increased from 56% to 93% in nitrate solution and 68% to 91% in COD solution. The test on the actual solution under different turbidity disturbances shows that the 1D-CNN model had a bias rate of less than 2% in both nitrate and COD solutions, while the worst bias rate in the PLSR method was 15%.

The potential of the spatially offset Raman spectroscopy (SORS) for implementing rapid and non-invasive in-situ authentication methods of plastic-packaged commodity foods – Application to sliced cheeses

Key points for the authentication of foodstuffs are the verification of the stated labelling, as well as checking its nature and verifying the absence of fraud. Portable spatially offset Raman spectroscopy (SORS) allows the rapid and straightforward acquisition of Raman spectra of packaged foods without the package opening and can therefore be considered as a non-invasive analytical technique par excellence since it enables in-situ analytical controls even directly on the grocery and supermarket shelves. Cheese is a dairy product available on the market which can be classified depending mainly on two characteristics features: (i) the animal origin of the milk from which it is produced and (ii) the manufacturing processes. This study aims to authenticate plastic-packaged sliced cheeses according to the origin of the milk stated by animal species (cow, sheep and/or goat). The NEAR similarity index was previously applied to verify that cheeses manufactured from the same animal species milk have a high similarity (NEAR >0.8), higher than those found among cheeses produced from milk of different animal species. From the adquired spectra, the one input-class SIMCA approach was used to develop multivariate classification models in order to distinguish the origin of the milk by animal species. The performance parameters of the developed classification models showed values close to 0.9 for sensitivity and precision among others. The PLSR method was used to develop quantification models capable of predicting the total protein and fat contents (major nutrients) in cheese samples having errors of less than 2%. These advances open the door to the development of new rapid and non-invasive analytical methods for in-situ packaged food authentication, demonstrating the potential of SORS for food authentication supported by proper chemometrics.

Study of biophysical properties, synthesis and biological evaluations of newer thiazolidine-2,4-dione conjugates

Thiazolidine-2,4-dione and its derivatives are acting as antimicrobial and antitubercular agents. Computational approach 2D-QSAR is used for prediction of antitubercular activity of the synthetic derivatives. 2D-QSAR generated model using PLSR method which predicted the statistically significant r 2 = 0.3333, q 2 = 0.4000, pred_r 2 = -1.9753 and F test = 3.0000. 2D-QSAR generated equation of pMICs is denoted the antitubercular activity correlated with thermodynamic descriptor T_2_2_O. Pharmacokinetic properties absorption, distribution, metabolism, excretion are also predicted which are useful for design the derivatives. A designed derivatives of ( Z )-2-(5-substituted-2,4-dioxothiazolidin-3-yl)- N -(4-methoxy-6-methyl-1,3,5-triazin-2-yl)acetamide (C 1 -C 10 ) are synthesized and spectrally characterized using IR, 1 H NMR, 13 C NMR and mass spectral data analysis as well as biologically evaluated against antitubercular and antimicrobial activities. From the biologically evaluated derivatives, compounds C 1 and C 4 were found to be active against the different antimicrobial species. Compounds C 7 and C 10 are more progressive than others against antitubercular species.

Structure-Activity Design, Synthesis and Biological Activity of Newer Imidazole- Triazine Clubbed Derivatives as Antimicrobial and Antitubercular Agents

Methods: PLSR method is applied for 2D-QSAR determination of the (Z)-5-ethylidene-3-(4- methoxy-6-methyl-1,3,5-triazin-2-yl)-2-phenyl-3,5-dihydro-4H-imidazol-4-one (B1-B10). The designed compounds were synthesized and spectrally evicted by IR, 1H NMR, 13C NMR, and mass spectra data as well as biologically screened against the different antitubercular and antimicrobial species.

RoBoost-PLS2-R: An extension of RoBoost-PLSR method for multi-response

Recently, a novel robust PLSR method was developed to address the problem of outliers in the data. In this paper, an extension of this method, called RoBoost-PLS2-R is proposed to predict multi-response variables. Robustness and efficiency of this new approach have been validated on two simulated data sets and one real data set containing different outlier scenarios. Its performance was also compared with reference methods (PLS2-R and RSIMPLS) for predicting multi-response variables. Results confirm that RoBoost-PLS2-R greatly reduces prediction errors when data contain outliers. Prediction performances of RoBoost-PLS2-R are close to the optimal model (PLS2-R) calibrated without outliers and also to RSIMPLS method. This method seems to be a reliable and a competitive robust regression tool for predicting multi-response variables.

Rapid determination of patchouli oil quality as a tropical plant using infrared technology

Patchouli alcohol (PA) content is an important indication in the world patchouli oil trade. Testing the PA content of patchouli oil usually takes a long time and expensive, so that it greatly inhibits the activity of buying and selling patchouli oil in the community. Therefore the main objective of this study is to develop NIRS technology to estimate the PA content in Aceh patchouli oil purified rapidly and appropriately using PLSR and PCR methods. The sample in this study was patchouli oil which was purified into 20 types based on PA content. Laboratory test using GC-MS. Near infrared (NIR) spectrum of Patchouli oil samples were acquired and recorded using self-developed FT-IRIPTEK T-1516. The results showed that NIRS technology with PLSR and PCR methods provides a solution to solve the problem of PA content prediction in Aceh’s patchouli oil. SNV pretreatment is proven to improve prediction accuracy and robustness of the prediction model that built. The PLSR-SNV method succeeded in obtaining a prediction model that was categorized as very good model performance, while PCR-SNV method succeeded in obtaining a prediction model that was categorized as good model performance.

PREDICTIVE MODELS OF CHLOROPHYLL CONTENT IN SUGARCANE SEEDLINGS USING SPECTRAL IMAGES

Chlorophyll content is a widely used parameter for nutritional status diagnosis in sugarcane. This study aimed to develop a predictive model of chlorophyll content in sugarcane seedlings using spectral imagery analysis within the electromagnetic spectrum visible range. The experiment was carried out in a split-plot design, with two fertilization rates and three sugarcane cultivars. For chlorophyll analysis, 144 leaves were collected from seedlings. Chlorophyll contents were extracted and measured by SPAD-502 meter. Spectral images within the range of 480 to 710 nm were analyzed using reflectance, absorbance (white source), and fluorescence (source at 405 and 470 nm) responses. Predictive models were developed using multivariate regression methods such as Principal Component Regression and Partial Least Squares Regression. We chose the best model through absorbance response using variable selection and the PLSR method (R2P = 0.718 and RMSEP = 7.665). The wavelengths of 480, 490, 500, 600, 630, and 640 nm were identified as the best for total chlorophyll content determination. The spectral image processing-based method can provide a chlorophyll measurement equivalent to SPAD, with the advantage of having a higher spatial coverage over the entire leaf area. Besides, it can also support automation of the chlorophyll measurement in greenhouses.

Determination of the quality of metronidazole formulations by near-infrared spectrophotometric analysis

In the quality control of medicines and the fight against the phenomenon of poor quality medicines, there is an urgent need for rapid and broad spectrum methods for screening these types of medicines. In the present work, we have used near infrared spectroscopy combined with multivariate data analysis to develop chemometric models for the classification and quantification of metronidazole in Burkina Faso pharmaceutical formulations. For this purpose, drug samples were collected in drugstores located in different Burkina Faso border zones. Four product classes were defined based on the national nomenclature: 3 classes for the generic drugs (C1, C3, and C4) and one class for the reference (C2) drugs. The exploratory analysis using PCA identified two clusters of drugs within class C1. Discrimination was confirmed by the developed and optimised DD-SIMCA model, with only one target class. The quality control of the samples from product class C1 was proven to be very satisfactory with specificities and sensitivities of 100%. The quantification models developed with the PLS-R method were successfully applied for the determination of the active ingredient content in the samples, with acceptable relative bias between 0.15 and 12.7 % with respect to the dose determined by the HPLC method. The RMSEC was estimated at 13.57 (R2, 0.9937), the RMSECV at 18.07 (R2, 0.9888) and the RMSEP at 13.69 (R2, 0.9941).The models developed and the results obtained are promising for routine quality control of similar formulations of metronidazole.

Non-Destructive Prediction of Titratable Acidity and Taste Index Properties of Gala Apple Using Combination of Different Hybrids ANN and PLSR-Model Based Spectral Data.

Non-destructive estimation of the internal properties of fruits and vegetables is very important, because better management can be provided for subsequent operations. Researchers and scientists around the world are focusing on non-destructive methods because if they are developed and commercialized, there will be an impressive change in the food industry. In this regard, this paper aims to present a non-destructive method based on Vis-NIR spectral data. The different stages of the proposed algorithm are: (1) Collection of samples of Gala apples, (2) Spectral data extraction by spectroscopy, (3) Pre-processing of spectral data, (4) Measurement of chemical properties of titratable acidity (TA) and taste index, (5) Selection of key wavelengths using hybrid artificial neural network-firefly algorithm (ANN-FA), (6) Non-destructive estimation of the properties using two methods of hybrid ANN- Particle swarm optimization algorithm and partial least squares regression. For considering the reliability of methods for estimating the chemical properties, the prediction operation was executed in 300 iterations. The results represented that the mean and standard deviation of the correlation coefficient and the root mean square error of hybrid ANN-PSO and PLSR for TA were 0.9095 ± 0.0175, 0.0598 ± 0.0064, 0.834 ± 0.0313 and 0.0761 ± 0.0061 respectively. These values for taste index were 0.918 ± 0.02, 3.2 ± 0.39, 0.836 ± 0.033 and 4.09 ± 0.403, respectively. Therefore, it can be concluded that the hybrid ANN-PSO has a better performance for non-destructive prediction of the two mentioned chemical properties than the PLSR method. In general, the proposed method can predict the chemical properties of TA and taste index non-destructively, which is very useful for mechanized harvesting and management of post-harvest operation.

2D AND 3D QSAR ANALYSIS OF IMIDAZOLE DERIVATIVES AS HEME OXYGENASE INHIBITOR

Selective inhibition of heme oxygenase is an important strategy in design of potent inhibitors of enzyme for the treatment of neonatal jaundice, cancer and many more. QSAR analysis is employed for a given set of compounds containing imidazole pharmacophore in order to establish a relationship between the biological activity and related descriptors, which provides us an idea to gain a potent inhibitor with lesser side effects. In this paper we present results of 2D and 3D QSAR studies of series of 26 molecules containing imidazole pharmacophore as selective heme oxygenase inhibitor using V Life MDS 3.5 Software. The 2D QSAR studies was performed using Partial Least Square Regression method and the 3D QSAR studies was performed using k- Nearest Neighbor Molecular Field Analysis(kNN- MFA) method. The analysis has produced good predictive and statistically significant QSAR models. 2D QSAR studies produced good statistical model with r2 value 0.8487, cross validated r2 value 0.6553 and prd_r2 value 0.7478 by PLSR method while 3D QSAR model gave statistical value of cross validated r2 value 0.5493 and pred_r2 value 0.3358. The results of the QSAR analysis suggested that the 2-D descriptor viz. physicochemical and alignment independent played an important role for heme oxygenase inhibition and the 3-D descriptors electrostatic and steric revealed the relative positions and range for substitution in a molecule. In 3D model, grid suggested that a positive electrostatic potential is favorable for increase in biological activity and the steric field with negative range and the negative range indicates that negative steric potential is favorable for increase in the activity. Thus, the descriptors generated by 2-D and 3-D QSAR analysis were useful in designing of potent molecules.

RETRACTED ARTICLE: Monitoring soil salinization in Manas River Basin, Northwestern China based on multi-spectral index group

ABSTRACT Large-scale and accurate monitoring soil salinization is essential for controlling soil degradation and sustainable agricultural development. The agricultural irrigation area of the Manas River Basin in the arid area of Northwest China was selected as the test area. The soil salinization monitoring model based on spectral index group was constructed by comparing the accuracy of PCR, PLSR and MLR models using the transformation of multi-spectral index group and index screening. The results showed that there was a certain correlation between the 28 spectral index groups, with a maximum correlation coefficient -0.3689 between the original spectral group and the soil salt content was B10 band. After the transformation of original data for the logarithm Ln(R), exponential eR and square root R1/2 respectively, the correlation between each index and soil salinity was significantly improved, with the maximum correlation coefficient was up to -0.7564 of R1/2. The salt content estimation models were constructed by different data transformation using PLSR, PCR and MLR methods, respectively. This study provides a fast and accurate method for monitoring regional soil salinity content and the results can provide a reference for soil salinity grading management in arid and semi-arid areas.