Principle Component Regression Research Articles

Using Macroscopic, Microscopic, and FTIR Spectroscopy combined with Chemometrics to Authenticate Arabica Coffee from Antbush

Limited coffee stocks, high consumption, export demands, and increasing prices may contribute to counterfeiting. In other countries, including India and Africa, coffee forgery using antbush has been extensively established. Due to its abundance in Indonesia, unscrupulous coffee growers allegedly employ antbush (Senna occidentalis) as an adulterant in coffee to increase commercial earnings. This study aims to authenticate coffee from antbush using macroscopic and microscopic differences and FTIR spectroscopy combined with chemometrics. The arabica coffee samples from various regions were oven-roasted to a second crack and milled. The materials were then examined under macroscopic, microscopic, and infrared spectroscopic conditions. The obtained responses were used to monitor the qualitative and quantitative information in the targeted samples. Antbush in coffee samples is successfully identified microscopically by remnants of palisade tissue as well as the structural differences of the endosperm. In addition, FTIR spectroscopy combined with multivariate calibration can accurately estimate the concentration of antbush as adulterants in the target sample. Principle component regression (PCR) provides the best modeling for the relationship between the actual value. FTIR predicted the value of antbush with the lowest RMSEC and RMSEP values of 0.852 and 0.896, respectively, with the coefficient of determination (R2) in calibration and validation models of 0.9996 and 0.9967, respectively. The combination of macroscopic, microscopic, and FTIR spectroscopy offered reliable tools to authenticate arabica coffee from antbush.

Thermal error modelling of gear measuring instrument based on principal component regression

As gear measuring instruments (GMI) are frequently used at the production site, the influence of temperature on their accuracy and stability becomes non-negligible. The existing standards and technologies only consider the error modelling and compensation problem when the GMI is in a uniform temperature field and is not suitable for the production site environment with a large temperature change rate and large temperature gradient. The paper delves into the problems of GMI thermal error modelling at the production site. First, the temperatures of the GMI and its surroundings were measured using multiple temperature sensors. The parallelism thermal errors between centres and the Z-axis (referred to as parallelism thermal errors) and the y-direction spatial thermal errors (referred to as spatial thermal errors) were also measured by the probe and laser interferometer synchronously. Then, the models of them were established based on principle component regression (PCR). As evidenced by the experimental results, the fitting determination coefficient of the models are both greater than 97%, and the prediction determination coefficient is greater than 90%, demonstrating that the models are precise and robust.

RAPID DETECTION OF RAT MEAT ADULTERATION IN BEEF SAUSAGES USING FTIR‑ATR SPECTROSCOPY AND CHEMOMETRICS FOR HALAL AUTHENTICATION

Objective: The objective of this study was to employ Fourier Transform Infrared-Attenuated Total Reflectance (FTIR‑ATR) spectroscopy in combination with chemometrics for the analysis of rat meat adulteration in beef sausages.
 Methods: Lipid components in sausages were extracted using three extraction methods, namely Bligh and Dyer, Folch, and Soxhlet methods. The lipid components extracted were then analysed using FTIR‑ATR spectroscopy, and their spectra obtained were used as variables during chemometrics modeling. Samples were prepared by mixing beef with adulterant of rat meat in the concentration range of 0-100% of rat meat. Each sample was scanned using FTIR-Attenuated Total Reflectance (ATR) spectroscopy in three replicates at 4000-650 cm-1 wavenumber region.
 Results: The absorbance values at wavenumbers regions of 3100-700 cm-1 were used to discriminate lipid components extracted by the Bligh Dyer, Folch, and Soxhlet Method with an accuracy level of 100%. The prediction of rat sausages was successfully determined using multivariate calibrations of Partial Least Square (PLS) and Principle Component Regression (PCR) using optimised conditions.
 Conclusion: FTIR-ATR spectroscopy coupled with chemometrics is a rapid and accurate method for detecting and quantifying rat meat in beef sausages for halal authentication.

“Forecasting particulate matter concentrations by combining statistical models”

Air pollutants have adverse effects on human health and play significant roles in urban planning and specifying air quality ranges. Atmospheric particulate matter is one of the criteria air pollutants that may have a dominant effect on air pollution. The present analysis was made using data collected over five years (2011–2015) in an urban area. Air pollutant concentrations and climate data are analyzed using four models: a multiple linear regression model, a principle component regression model, a logarithmic architecture, a principle component regression model with the variables that only have the highest factor loadings in each principal component. Thus, the proposed model combines both methods the multiple linear regression and the principal component analysis to obtain clearer and more reliable predictions. The prediction model’s accuracy has been verified operating several performance indicators, which revealed acceptable values, demonstrating that the proposed model can be used to predict pollutant concentrations. According to statistical indicators (RMSE, NMSE, CV, FB and IOA), the best prediction models were Model 3 for winter (0.06, 0.001, 0.03, −0.000, and 0.69), Model 4 for spring (0.08, 0.002, 0.04, −0.019, and 0.99), Model1 for summer (3.41, 0.005, 0.07, 0.000, and 0.98), and Model 2 for autumn (11.71, 0.018, 0.13, −0.000, and 0.57). In addition, Model2 generally gave appropirate values for all seasons and can be used as a common model. Finally, combined models based on principal component analysis and multiple linear regression outperformed models with only multiple linear regression in terms of error.

Greenness and whiteness evaluated triple divisor-continuous wavelet transform spectrophotometric and advanced chemometric methods for the simultaneous determination of four photoinitiators in saliva and intravenous injection solution

Migration of photoinitiators from packaging materials and dental products in saliva presents an extensive real damage, particularly that emerges from packed food and dental products in saliva or from polyethylene packaging of the intravenous solutions which is extremely dangerous to human health and to the environment. In line with this, environmentally evaluated, advanced and reliable triple divisor combined with continuous wavelet transform (CWT) spectrophotometric method and nine chemometric methods were established and validated for the concurrent determination of four photoinitiators: Benzophenone, 4-Hydroxyacetophenone, Benzoin, and Diphenyliodonium hexafluorophosphate in saliva and in intravenous injection solutions. To our knowledge, this is the first spectrophotometric method for the analysis of the cited photoinitiators as well as the primary white method for the examination of those photoinitiators. Also, in this paper, the triple divisor method is combined with continuous wavelet transform for the first time. Triple divisor–CWT peak amplitudes were defined at 260.0, 275.8, 262.2 and 231.2 nm over a linear dynamic range of 0.50–13.00, 0.80–10.00, 1.5–30.00 and 1.00–28.00 μg/mL for Benzophenone, 4-Hydroxyacetophenone, Benzoin and Diphenyliodonium hexafluorophosphate, respectively. The nine chemometric methods incorporate the application of principle component regression (PCR), orthogonal partial least square (OPLS), ridge regression (RR) and artificial neural networks (ANN) methods along with different variable selections including genetic algorithm (GA-PCR, GA-OPLS and GA-RR) and interval selections (Interval-PCR and Interval-OPLS). Regarding the adopted chemometrics models, a multivariate multifactor experimental design based on the calibration curve range of the studied photoinitiators was considered to construct a calibration set consisting of eighteen mixtures while using seven mixtures for the validation set. The proposed methods were demonstrated to be sensitive, accurate, precise and reproducible throughout the validation process. The acquired results revealed that the proposed spectrophotometric and chemometric approaches could be effectively applied for the regular analysis of the cited photoinitiators in biological fluids as saliva and in intravenous solutions. The greenness of the proposed methods was assessed using two green metric tools enclosing Analytical Eco-scale & Analytical Greenness Assessment (AGREE), and a White Analytical Chemistry tool (WAC).

Smart Multivariate Spectrophotometric Determination of Two Co-Administered Autoimmune Drugs; Sulfasalazine and Pentoxifylline; in Bulk and Spiked Human Plasma.

Sulfasalazine and pentoxifylline are co-prescribed together to treat psoriasis and pemphigus vulgaris. Sulfasalazine is an anti-inflammatory, immunosuppressant, and antibiotic drug, while pentoxifylline is a vasodilator and immunosuppressant. The spectra of the two drugs and plasma suffer from severe overlap. This work aims to simultaneously determine sulfasalazine and pentoxifylline in their binary mixture and spiked human plasma by the assessment of their UV spectral data. Two model updated chemometric methods were established using principal component regression and partial least-squares regression models. The two models were validated in accordance with the U.S. Food and Drug Administration guidelines for bioanalysis and were applied for the determination of both drugs in synthetic mixtures or spiked human plasma. Accuracy and precision were within the accepted limits. In addition, three different assessment methods were used to evaluate the environmental greenness of the proposed models. The two updated models are simple, rapid, sensitive, and precise, and could be easily applied in QC laboratories for determination of sulfasalazine and pentoxifylline, without any preliminary separation steps or interference from plasma matrix. Two updated chemometric models called principlal component regression and partial least-squares regression were established for determination of sulfasalazine and pentoxifylline in spiked human plasma using UV spectrophotometric data.

On the Experimental Investigation of Bone Fracture Recovery Process Using an Ultra-Wideband Planar Monopole Antenna

In this paper, a noninvasive bone fracture monitoring system is developed using a planar monopole antenna. The proposed antenna provides an ultra-wideband response, and its overall size is 18 × 19 × 0.8 mm3. The proposed ultra-wideband monopole (UWM) antenna has a maximum measured gain of 3.77 dBi, and the maximum SAR value for an input power of 18 dBm is less than 1.6 (W/kg) (1 g). A bovine tibia is experimentally tested using a proposed UWM antenna to monitor the fracture recovery process and then further analysed using principle component and linear regression analysis. In addition, a microcontroller with a wireless communication module is developed to monitor the data in an Android application. The proposed system could be a promising approach for developing a portable, noninvasive monitoring device.

A Hybrid Approach for Regional Low-Flow Frequency Analysis for the Upper Tigris and Euphrates Basin

The Tigris and Euphrates basin is a challenging watershed because its water resources are critical for five countries. Therefore, this research analyzed and estimated the low flow index and its risk of occurrence. Based on 30 unregulated stations in the upper Tigris and Euphrates basin, different low flow indexes were estimated. A multivariate and hybrid principle component regression (PCR) regional model was used to assign the relationship between basin characteristics and low flow indexes in the homogeneous regions. The model performance showed that the principle component regression outperformed the regression models. The jackknife procedure indicated that the model performance for short-duration low flows is weak, but it is relatively good for long-duration low flows. This study suggests that hydrologic drought of the Tigris and Euphrates basin should be investigated more carefully for better water resources management in the changing socio-environmental system of the Middle East.

DigitalExposome: quantifying impact of urban environment on wellbeing using sensor fusion and deep learning

The increasing level of air pollutants (e.g. particulates, noise and gases) within the atmosphere are impacting mental wellbeing. In this paper, we define the term ‘DigitalExposome’ as a conceptual framework that takes us closer towards understanding the relationship between environment, personal characteristics, behaviour and wellbeing using multimodal mobile sensing technology. Specifically, we simultaneously collected (for the first time) multi-sensor data including urban environmental factors (e.g. air pollution including: Particulate Matter (PM1), (PM2.5), (PM10), Oxidised, Reduced, Ammonia (NH3) and Noise, People Count in the vicinity), body reaction (physiological reactions including: EDA, HR, HRV, Body Temperature, BVP and movement) and individuals’ perceived responses (e.g. self-reported valence) in urban settings. Our users followed a pre-specified urban path and collected the data using a comprehensive sensing edge device. The data is instantly fused, time-stamped and geo-tagged at the point of collection. A range of multivariate statistical analysis techniques have been applied including Principle Component Analysis, Regression and Spatial Visualisations to unravel the relationship between the variables. Results showed that Electrodermal Activity (EDA) and Heart Rate Variability (HRV) are noticeably impacted by the level of Particulate Matter in the environment. Furthermore, we adopted Convolutional Neural Network (CNN) to classify self-reported wellbeing from the multimodal dataset which achieved an f1-score of 0.76.

P235 Patients with inflammatory ileal pouch anal anastomosis (IPAA) disorders are characterized by a distinct breath volatile organic compounds (VOC) metabolome profile

Abstract Background Following creation of an IPAA in patients with ulcerative colitis (UC), more than 60% of subjects develop inflammatory complications. The current objective assessment for inflammation of the pouch is limited to surrogate stool and blood biomarkers or endoscopy. The development of non-invasive and accurate biomarkers for the assessment of IPAA inflammation is an area of unmet need. Measurement of exhaled breath volatile organic metabolome compounds (VOCs) has shown promise as a biomarker for the diagnosis and monitoring of inflammatory disorders. We here aimed to characterize the pattern of VOCs in the exhaled breath of patients with an IPAA and assess whether VOC analysis is able to discriminate patients with endoscopically active IPAA inflammation from patients without IPAA inflammation. Methods This is a cross-sectional study of patients with an IPAA created for the management of UC. Exhaled breath samples were collected at time of endoscopic evaluation of the pouch and 97 VOC metabolites assessed via selective ion flow tube mass spectrometry (SIFT-MS). The IPAA cohort was dichotomized using the endoscopic pouch disease activity index (PDAI) into endoscopic PDAI of >= 4 (severe inflammation), or endoscopic PDAI score <= 1 (mild or no inflammation). Principle component analysis (PCA) was conducted to reveal the VOCs with the strongest discriminatory capability and principle component regression (PCR) was performed to assess the association of exhaled breath VOC analysis in differentiating the groups. Results Exhaled breath metabolome analysis was performed on 10 subjects with PDAI >=4 and 7 subjects with PDAI <=1. Demographics are provided in Table 1. PCA indicated robust discrimination of the two groups based on breath VOCs (Figure 1). 10 out of 97 VOCs were up-regulated in the PDAI >=4 group compared to control, including ammonia and hydrogen sulfide. Isopropenyltoluene, tetrachloroethylene, and 3-pentanone provided the highest contribution to differentiate between the cohorts (Figure 2). Receiver operative curve (ROC) analysis of the PCR model indicated an area under the curve (AUC) of 0.81 (0.61-99), suggesting a strong association of breath VOCs with inflammation of the pouch (Figure 1). Conclusion VOC exhaled breath metabolome analysis shows a strong ability to discriminate patients with severe endoscopic pouch inflammation from patients with minimal to no endoscopic inflammation. The differences in reported VOCs point toward metabolic differences in bacterial fermentation, lipid and carbohydrate metabolism, and an increase in reactive oxygen species in patients with endoscopic pouch inflammation. Validation studies addressing the role of VOC analysis for non-invasive disease assessment patients with IPAA are ongoing.

Nonlinearity in the cross-section of stock returns: Evidence from China

We study which characteristics provide independent information for the cross-section of expected returns in the Chinese stock market based on nonlinear predictive functions. Using 100 commonly explored stock characteristics from January 2000 to December 2019, we identify 15 to 19 characteristics that provide incremental predictive information. We find significant alphas based on the most up-to-date four-factor model of Liu et al. (2019) when exploring these characteristics jointly using nonlinear predictive models. A long–short spread portfolio based on out-of-sample predicted returns by a nonlinear model delivers a higher Sharpe ratio than that by a linear model. We document more supportive evidence for the nonlinear model after exploring interaction effects with firm size, earnings-to-price ratio, turnover, state dependency of predictors, and various methods of predictive information aggregation, such as forecast combination, principle component regression, and partial least squares.

An analysis of factors affecting access to credit in the smallholder agricultural sector in T/A Mpinganjira – Mangochi district

The underperformance of Malawi's agricultural sector, particularly in the T/A Mpinganjira – Mangochi area, can be attributed mostly to smallholders' resistance to transitioning from traditional agriculture to a more scientific and technology-driven approach. Access to financial services, particularly finance, is one of the obstacles preventing smallholders from rising to the occasion. Using a logistic regression model inside the principle component regression framework, the aim of this study is to investigate the factors that might affect smallholders' capacity to obtain financing. The results demonstrated the influence of various factors on smallholder farmers' access to financing and their capacity to transition to a more scientific and technologically oriented agriculture sector. These factors included the amount of income from both farms and non-farm sources, pensions and remittances, farm size, availability of family labor, land ownership, savings, and repayment capacity. The results offer vital direction for the institutional setup needed to improve credit availability in Malawi.

Combined effects of runoff increase and sea level rise on the water exchange and saltwater intrusion for an estuary bay in non‐flood season

AbstractEstuary bays are vulnerable to salinization effects due to relative sea level rise and modification in upland hydrology owing to a variety of factors. Much research has evaluated the one‐sided effect of runoff discharge or sea level on the water exchange and saltwater intrusion for an estuary, while their combined effects have not been evaluated, especially for an estuary bay with narrow river mouth. Taking the Wanquan Estuary Bay (WEB), China, as study case, this paper has revealed the long‐term significant increasing trend of freshwater runoff in non‐flood season during years 1956–2017 using the Man‐Kendall trend analysis, robust principle component regression and Theil–Sen's estimator. A 3‐D hydrodynamic‐salinity‐tracer numerical model is built and calibrated based on the finite‐volume community ocean model, and the effects of runoff increase and sea level rise on the water exchange and saltwater intrusion are investigated. This paper shows the following: (1) The freshwater runoff during non‐flood season in WEB shows significant long‐term increasing trend, and the mean changing magnitude is 0.4546 m3/s/a. (2) The increased runoff accelerates the water exchange, and reduces the turn‐over time and saltwater intrusion in the WEB. The sea level rise reduces the water exchange and increase the saltwater intrusion. (3) For the estuary bay with narrow river mouth, the salinity distributions show more vertical stratification characteristics. The freshwater runoff has power relationships with the average turnover time and salinity, and the sea level has linear relationships. (4) The water exchange and salinity of an estuary bay is more vulnerable to the changed runoff than the sea level rise, while the impacts of sea level rise would increase for estuaries with wide river mouth. This paper provides support for the freshwater exploitation and utilization in estuary bay under the background of climate change and sea level rise.

Germplasm variability-assisted near infrared reflectance spectroscopy chemometrics to develop multi-trait robust prediction models in rice.

Rice is a major staple food across the world in which wide variations in nutrient composition are reported. Rice improvement programs need germplasm accessions with extreme values for any nutritional trait. Near infrared reflectance spectroscopy (NIRS) uses electromagnetic radiations in the NIR region to rapidly measure the biochemical composition of food and agricultural products. NIRS prediction models provide a rapid assessment tool but their applicability is limited by the sample diversity, used for developing them. NIRS spectral variability was used to select a diverse sample set of 180 accessions, and reference data were generated using association of analytical chemists and standard methods. Different spectral pre-processing (up to fourth-order derivatization), scatter corrections (SNV-DT, MSC), and regression methods (partial least square, modified partial least square, and principle component regression) were employed for each trait. Best-fit models for total protein, starch, amylose, dietary fiber, and oil content were selected based on high RSQ, RPD with low SEP(C) in external validation. All the prediction models had ratio of prediction to deviation (RPD) > 2 amongst which the best models were obtained for dietary fiber and protein with R2 = 0.945 and 0.917, SEP(C) = 0.069 and 0.329, and RPD = 3.62 and 3.46. A paired sample t-test at a 95% confidence interval was performed to ensure that the difference in predicted and laboratory values was non-significant.

Analysis of beef meatballs with rat meat adulteration using Fourier Transform Infrared (FTIR) spectroscopy in combination with chemometrics

ABSTRACT Indonesia is a country with a majority Muslim population, and the halal food consumed should be assured. One type of food that must be considered halal is food from meat products. Due to the price difference between halal meat and non-halal meat, some unethical producers try to adulterate beef with non-halal meat of rat meat. The research objective was to employ FTIR spectroscopy in combination with chemometrics for the analysis of rat meat adulteration in beef meatballs. Lipid components in meatballs containing beef, rat meat, or its binary mixture were extracted using three extraction methods, namely Bligh and Dyer, Folch, and Soxhlet methods. The lipid components extracted were then analyzed using FTIR spectroscopy and FTIR spectra obtained were used as variables during chemometrics modeling. The absorbance values of FTIR spectra of lipid components extracted by Bligh and Dyer, Folch, and Soxhlet methods at selected wavenumbers regions of 3100–800 cm−1 were selected for discrimination between beef meatballs and meatballs adulterated with rat meat using chemometrics of linear discriminant analysis (LDA). LDA was successfully used to classify lipid components extracted by three lipid extraction methods from beef meatballs and rat meatballs with accuracy levels of 100%. The prediction of rat meatballs was successfully determined using multivariate calibrations of Partial Least Square (PLS) and Principle Component Regression (PCR) using optimized conditions. The difference in lipid composition, as indicated in FTIR spectra profiles of the analyzed samples, is used as a fingerprint technique for the analysis of rat meat in beef meatballs for halal authentication purposes.

Assessment of Population pressure on Urban Housing in Ngwa Road, Ohanku and Obohia Parts of Aba South, Abia State Nigeria

Abstract: The effect of population growth on urban housing is a global challenge but the worst examples are found in the developing world which has led to an increased growth of poorly planned cities in the developing world, and loss of aesthetics. The aim of this research is to assess the level of population pressure on urban housing within Ngwa Road, Ohanku and Obohia environs in Aba South local government area in Abia State. The research evaluated housing demand and supply in the area, condition of houses in the area, factors encouraging population growth in the study area and level of compliance of buildings with town planning laws. The study area has an estimated 2021 population of 138017 as projected from 88,951 in 2016 with a growth rate of 2.94%. The research data was collected through household survey, Aba South Town Planning Authority, National Population Commission and the Aba South Street Naming and House Numbering Office. The simple random sampling technique and the purposive sampling technique were employed by the researcher with a sample size of 399.9 at 0.05 confidence level which was determined using the Taro Yamane method for sample size collection. The Principle Component Analysis (PCA), Principle Component Regression (PCR), standard deviation, weighted mean, percentages/proportions to analyze the data collected. The Principal Component Analysis result showed that component I have the highest loading with eigen value of 6.721 while component II has an eigen value of 3.279. The result showed that ease of access to commercial activities and educational level of heads of households are major factors encouraging population growth in the study area. The research revealed a housing demand and supply cluster estimate of 60.11% which was not considered statistically high. The average response of 93.2% is that houses in the study area are in a poor condition. The Aba South town planning authority revealed a 0.741 standard deviation which shows a high level of non-compliance with town planning laws. The research hypothesis was tested using Spearman Rank Correlation test at 0.05 level of confidence and the null hypothesis was accepted. Looking at the Land-UseLand-Cover images of the area obtained for 3 epochs from 1980-2020, the study area experienced an major growth in rate of housing stock after year 2000 and with increasing population growth, the area may soon be unable to carry the population that will be found there. This research calls the attention of the Abia State government in particular and the federal government in general to address these challenges through recommended public-private partnership and decentralization of pull factors. These recommendat

Development and validation of chemometric-assisted spectrophotometric method for the simultaneous estimation of aceclofenac, paracetamol, and chlorzoxazone with impurities

Background: Analysis of tertiary mixtures of analytes along with their impurities with simple and cost effective manner is always of interest. Utility of chemometric techniques are growing in pharmaceuticals, it improve speediness in the analysis and also provide analytical solutions with reduce the number of steps in the analytical method. In this study UV-Visible spectrophotometry coupled with principle component regression (PCR) and partial least square (PLS) multivariate methods was applied for estimation of three drugs in their formulation. Method: The calibration and validation sets were prepared in linear concentration range of three drugs and major impurities of paracetamol and aceclofenac. The series of sets were prepared using multilevel multifactorial design. Leave- One-Out (LOO) cross validation technique was employed to get essential number of Latent variables (LVs) that provides the greatest predictive ability. The developed method was studied for qualitative and quantitative analysis of titled drugs and validated as per regulatory guidelines. Results: The results showed the values of coefficient of determination (R2) for all drugs and impurities was higher than 0.99 indicating high acceptability. The obtained RMSE values were relatively low. Coefficient of determination and RMSE values indicate good accuracy and precision, respectively. Conclusion: Proposed method was successfully used for analysis of aceclofenac, paracetamol and chlorzoxazone in tablet dosage form and major impurities of aceclofenac, paracetamol in bulk.

Comparison of Chemometric assisted UV Spectrophotometric and RP-HPLC Method for the simultaneous determination of Ofloxacin and Tinidazole in their Combined dosage form

In present work, chemometric-assisted UV spectrophotometric methods as well as RP-HPLC method were developed for the simultaneous estimation of Ofloxacin and Tinidazole in their combined pharmaceutical dosage form. The two chemometric methods i.e. principle component regression (PCR) and partial least square regression (PLS) were successfully applied to quantify each drug in mixture using UV absorption spectra in range of 280 to 320nm at ∆λ of 0.5nm. Chemometric model development was done using 24 binary mixture solutions and 12 solutions were used for validation of model. The chemometric-assisted analysis does not require any prior separation step. In addition, RP-HPLC method was also developed using THERMOSIL C18 column with a mobile phase consisting ofAcetonitrile: Phosphate Buffer (85:15% v/v), flow rate of 1 ml/min and quantification was achieved using UV detector at 300 nm. The methods were successfully applied for the simultaneous determination of these drugs in synthetic mixture. The results obtained for analysis by PCR and PLS methods were compared with RP-HPLC method and a good agreement was found.

Nondestructive Detection of Moisture Content in Walnut Kernel by Near-Infrared Diffuse Reflectance Spectroscopy

The rapid and accurate detection of the moisture content is of great significance to the quality evaluation and oil extraction process of walnut kernel. Near-infrared (NIR) spectroscopy is an ideal method for measuring the moisture content in walnut kernel. In this study, a regression model for moisture content in walnut kernel was developed based on NIR diffuse reflectance spectroscopy using chemometric methods. The different spectral pretreatment methods were adopted to preprocess the original spectral data. The whole spectra band was divided into 5 subbands, 10 subbands, 15 subbands, and 20 subbands to screen specific wavelengths relevant to the walnut kernel moisture content. PLS (partial least square regression), MLR (multivariate linear regression), PCR (principle component regression), and SVR (support vector regression) were used to establish the relationship model between the spectral data and measurement values of the moisture content. In comparison, the optimized modeling conditions were determined as follows: detection wavelength 1349–1490 nm, SNV-FD (standard normal variate transformation and first derivative) preprocessing method, and PLS algorithm. Under these conditions, the square correlation coefficient (R2) and root mean square error of prediction (RMSEP) of the prediction model were 0.9865 and 0.0017, respectively. The results of this study provided a feasible method for the rapid detection of moisture content in walnut kernel. To improve the performance and applicability of the model, it is necessary to continuously expand the size of the sample set.

Trait Based Modelling Approach for Selection of Elite Germplasm Accessions in Soybean [Glycine max (L). Merrill]

Background: Identification of suitable factors that influence significantly to the response is crucial for the traits based breeding program to make a better decision about improvement in productivity. Multiple linear regression (MLR) is the benchmark method commonly using to identify suitable factors for crop improvement. It doesn’t work always due to stringent assumption (Multicollinearity, Linearity) behind the MLR model. Here we tried to develop an efficient model for the selection of major traits that contribute to seed yield in soybean by comparing different models.Methods: Field experiment was conducted using 98 soybean core population through augmented design.18 morphometric traits obtain from soybean core population were considered under the study as regressors.Multiple linear regression (MLR), Principle component Regression (PCR), Regression tree and Random Forest models were compared to select traits based on prediction accuracy.Result: All the models identified the number of pods per plant (NPP) has the most influencing variable to the soybean yield. However random forest has a much higher prediction power (RMSE=4.59, MAPE=0.18) compared to other models under study. The results of random forest revealed that the number of pods per plant, number of branches per plant and other associated characters like plant height at harvest as highly influencing traits for seed yield in soybean.Finally, tried to identify genotypesthat possess superiority about most influencing morphological characters on seed yield using cluster analysis.