Optimization of Parameter Selection for Partial Least Squares Model Development

Comparative chemometric modeling of fresh and dry cannabis inflorescences using FT-NIR spectroscopy: Quantification and classification insights

INTRODUCTION

Cannabis sativa L. inflorescences are rich in cannabinoids and terpenes. Traditional chemical analysis methods for cannabinoids and terpenes, such as liquid and gas chromatography (using UV or MS detectors), are expensive and time-consuming.

OBJECTIVES

This study explores the use of Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometric approaches for classifying cannabis chemovars and predicting cannabinoid and terpene concentrations for the first time in freshly harvested (wet) cannabis inflorescence. The study also compares the performance of FT-NIR spectroscopy on wet versus dry cannabis inflorescences.

MATERIALS AND METHODS

Spectral data from 187 samples across seven cannabis chemovars were analyzed using partial least squares-discriminant analysis (PLS-DA) and partial least squares-regression (PLS-R) models.

RESULTS

The PLS-DA models effectively classified chemovars and major classes using only two latent variables (LVs) with minimal overfitting risk, with sensitivity, specificity, and accuracy values approaching 1. Despite the high water content in wet cannabis inflorescence, the PLS-R models demonstrated good to excellent predictive capabilities for nine cannabinoids and eight terpenes using FT-NIR spectra for the first time, achieving cross-validation and prediction R-squared values greater than 0.7, ratio of performance to interquartile range (RPIQ) exceeding 2, and a RMSECV/RMSEC ratio below 1.24. However, the low-cannabidiolic acid submodel and (-)-Δ9-trans-tetrahydrocannabinol model showed poor predictive performance. Some cannabinoid and terpene prediction models in wet cannabis inflorescence exhibited lower predictive capabilities compared with previously published models for dry cannabis inflorescence.

CONCLUSIONS

These findings suggest that FT-NIR spectroscopy can be a viable rapid on-site analytical tool for growers during the inflorescence flowering stage.

Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systems

The proliferation of Internet of Things (IoT) devices in smart homes has created a demand for efficient computational task management across complex networks. This paper introduces the Dynamic Multi-Criteria Scheduling (DMCS) algorithm, designed to enhance task scheduling in fog-cloud computing environments for smart home applications. DMCS dynamically allocates tasks based on criteria such as computational complexity, urgency, and data size, ensuring that time-sensitive tasks are processed swiftly on fog nodes while resource-intensive computations are handled by cloud data centers. The implementation of DMCS demonstrates significant improvements over conventional scheduling algorithms, reducing makespan, operational costs, and energy consumption. By effectively balancing immediate and delayed task execution, DMCS enhances system responsiveness and overall computational efficiency in smart home environments. However, DMCS also faces limitations, including computational overhead and scalability issues in larger networks. Future research will focus on integrating advanced machine learning algorithms to refine task classification, enhancing security measures, and expanding the framework's applicability to various computing environments. Ultimately, DMCS aims to provide a robust and adaptive scheduling solution capable of meeting the complex requirements of modern IoT ecosystems and improving the efficiency of smart homes.

Evaluating Soluble Solids in White Strawberries: A Comparative Analysis of Vis-NIR and NIR Spectroscopy

In recent years, due to breeding improvements, strawberries with low anthocyanin content and a white rind are now available, and they are highly valued in the market. Strawberries with white skin color do not turn red when ripe, making it difficult to judge ripeness. The soluble solids content (SSC) is an indicator of fruit quality and is closely related to ripeness. In this study, visible-near-infrared (Vis-NIR) spectroscopy and near-infrared (NIR) spectroscopy are used for non-destructive evaluation of the SSC. Vis-NIR (500-978 nm) and NIR (908-1676 nm) data collected from 180 samples of "Tochigi iW1 go" white strawberries and 150 samples of "Tochigi i27 go" red strawberries are investigated. The white strawberry SSC model developed by partial least squares regression (PLSR) in Vis-NIR had a determination coefficient R2p of 0.89 and a root mean square error prediction (RMSEP) of 0.40%; the model developed in NIR showed satisfactory estimation accuracy with an R2p of 0.85 and an RMSEP of 0.43%. These estimation accuracies were comparable to the results of the red strawberry model. Absorption derived from anthocyanin and chlorophyll pigments in white strawberries was observed in the Vis-NIR region. In addition, a dataset consisting of red and white strawberries can be used to predict the pigment-independent SSC. These results contribute to the development of methods for a rapid fruit sorting system and the development of an on-site ripeness determination system.

Rice bean-adzuki bean multitrait near infrared reflectance spectroscopy prediction model: a rapid mining tool for trait-specific germplasm

In the present era of climate change, underutilized crops such as rice beans and adzuki beans are gaining prominence to ensure food security due to their inherent potential to withstand extreme conditions and high nutritional value. These legumes are bestowed with higher nutritional attributes such as protein, fiber, vitamins, and minerals than other major legumes of the Vigna family. With the typical nutrient evaluation methods being expensive and time-consuming, non-invasive techniques such as near infrared reflectance spectroscopy (NIRS) combined with chemometrics have emerged as a better alternative. The present study aims to develop a combined NIRS prediction model for rice bean and adzuki bean flour samples to estimate total starch, protein, fat, sugars, phytate, dietary fiber, anthocyanin, minerals, and RGB value. We chose 20 morphometrically diverse accessions in each crop, of which fifteen were selected as the training set and five for validation of the NIRS prediction model. Each trait required a unique combination of derivatives, gaps, smoothening, and scatter correction techniques. The best-fit models were selected based on high RSQ and RPD values. High RSQ values of >0.9 were achieved for most of the studied parameters, indicating high-accuracy models except for minerals, fat, and phenol, which obtained RSQ <0.6 for the validation set. The generated models would facilitate the rapid nutritional exploitation of underutilized pulses such as adzuki and rice beans, showcasing their considerable potential to be functional foods for health promotion.

Estimation and Identification of Nonlinear Parameter of Motion Index Based on Least Squares Algorithm

Parameter identification is an important branch of automatic control. Due to its special function, it has been widely used in various fields, especially the modeling of complex systems or systems whose parameters are not easy to determine. With the development of control technology, the scale of the control object is getting larger and larger, which makes the calculation amount of the identification algorithm larger and larger. For the nonlinear system with complex structure, especially the nonlinear system containing the product of unknown parameters, the number of parameters of the over-parameterized identification method increases greatly, and the calculation amount of the identification algorithm also increases sharply. Therefore, a parameter estimation method with a small amount of calculation is explored. The results show that the proposed method can overcome the phenomenon of “data saturation”, thus improving the parameter identification results.

Prediction of bioaccessible lead in urban and suburban soils with Vis-NIR diffuse reflectance spectroscopy

The successful use of visible and near-infrared (Vis-NIR) reflectance spectroscopy analysis requires selecting an optimal procedure of data acquisition and an accurate modeling approach. In this study, Vis-NIR with 350-2500 nm wavelengths were applied to detect different forms of lead (Pb) through the spectrally active soil constituents combining principal component regression (PCR) and Partial least-square regression (PLSR) for the Vis-NIR model calibration. Three clouds with different soil spectral properties were divided by the Linear discriminant analysis (LDA) in categories of Pb contamination risks: "low," "health," "ecological," ranging from 200 to 750 mg kg^-1. Farm soils were used for calibration (n = 26), and more polluted garden soils (n = 36) from New York City were used for validation. Total and bioaccessible Pb concentrations were examined with PLSR models and compared with Support Vector Machine (SVM) Regression and Boosting Regression Tree (BRT) models. Performances of all models' predictions were qualitatively evaluated by the Root Mean Square Error (RMSE), Residual Prediction Deviation (RPD), and coefficient of determination (R²). For total Pb, the best predictive models were obtained with BRT (R² = 0.82 and RMSE 341.80 mg kg^-1) followed by SVM (validation, R² = 0.77 and RMSE 337.96 mg kg^-1), and lastly by PLSR (validation, R² = 0.74 and RMSE 499.04 mg kg^-1). The PLSR technique is the most accurate calibration model for bioaccessible Pb with an R² value of 0.91 and RMSE of 68.27 mg kg^-1. The regression analysis indicated that bioaccessible Pb is strongly influenced by organic content, and to a lesser extent, by Fe concentrations. Although PLSR obtained lower accuracy, the model selected many characteristic bands and, thus, provided accurate approach for Pb pollution monitoring.

Predictive quality control for compound liquorice tablets by the intelligent mergence fingerprint method combined with the systematic quantitative fingerprint method

INTRODUCTION

Compound liquorice tablet (CLT) is a herbal compound preparation and is used as a classic antitussive and expectorant in China. It is composed of liquorice extract powder, opioid powder, star anise oil, camphor, and sodium benzoate. The complexity of herbal materials brings a huge challenge in producing compound preparations with stable and uniform quality consistency.

OBJECTIVE

To establish a new intelligent model for predicting the quality of CLT.

METHODS

The HPLC fingerprints of raw materials including liquorice extract powder, powdered opium, star anise oil, and sodium benzoate were tested and merged to generate the intelligent mergence fingerprints, whose correlation with the raw materials and the CLT samples was studied. The consistency of the intelligently merged fingerprints with the standard fingerprints was observed by using the systematic quantitative fingerprint method in order to calculate quality evaluation results.

RESULTS

The intelligent mergence fingerprints covered all the main fingerprint peaks of four raw materials and had a good correlation with the CLT sample fingerprint. There were no significant quality differences either among the six intelligent mergence models obtained by combining different batches of raw materials or between the reference fingerprint of the intelligent mergence connection fingerprints (RFP_IMFC ) and the theoretical standard preparation (RFP_S ).

CONCLUSION

The computer-aided model of intelligent mergence fingerprints could be used to predict the quality of herbal compound preparations based on raw materials. In this way, preproduction quality prediction can be realised in order to avoid low-quality medicinal materials and improve the quality consistency among different batches.

Quality Changes and Shelf-Life Prediction of Cooked Cured Ham Stored at Different Temperatures

ABSTRACT

Cooked cured ham is a ready-to-eat food that is popular among consumers. Stored temperature has a key effect on the quality and shelf life of ham. In this work, the quality changes and shelf-life prediction of cooked cured ham stored at different temperatures were investigated. Sensory evaluation, physical and chemical indicators, and aerobic plate count were determined. Results showed that high storage temperature of cooked ham accelerates quality deterioration. Partial least squares (PLS) regression analysis based on the variable importance for projection identified nine important variables for predicting the shelf life of cooked cured ham. Compared with either PLS or back-propagation artificial neural network, the hybrid PLS-back-propagation artificial neural network model better predicts the shelf life of cooked cured ham by using the nine variables. This study provides a theoretical basis and data support for the quality control of cooked cured ham and a new idea for research on the shelf-life prediction of cooked cured ham.

HIGHLIGHTS

Application of different chemometric assisted models for spectrophotometric quantitative analysis of velpatasvir and sofosbuvir

Four chemometric assisted spectrophotometric models were developed for the quantitative analysis of velpatasvir and sofosbuvir, in their newly FDA approved pharmaceutical dosage form. Due to the existed overlap of the scanned absorption spectra between velpatasvir and sofosbuvir, this resulted in degree of difficulty of the possibility of the conventional spectrophotometric methods to quantify and analyze the cited drugs simultaneously. Classical least squares, principal component regression, partial least squares and genetic algorithm partial least squares were designed and compared for the quantitative analysis of velpatasvir and sofosbuvir in their binary mixture. Experimental design for different concentrations of the studied drugs was done based on the spectral sensitivity of velpatasvir and sofosbuvir and the confirmed ratio of the two drugs in the commercial pharmaceutical dosage form. Optimization of the described models was adopted with the aid of five-levels, two factors experimental design. Successfully quantitative assay of the drugs in Epclusa® tablets was done by the proposed models. Statistically comparative analysis for the obtained models results with another published capillary electrophoresis quantitative analytical method was performed. It is noteworthy mentioning that there was no significant difference between the proposed models and the published method with respect to the accepted statistical parameters.

System optimisation quantitative model of on-line NIR: a case of Glycyrrhiza uralensis Fisch extraction process

INTRODUCTION

The on-line analysis of active pharmaceutical ingredients (APIs) during the extraction process in herbal medicine is a challenge. Establishing a reliable and robust model is a critical procedure for the industrial application of on-line near-infrared (NIR) technology.

OBJECTIVE

To evaluate the advantages of on-line NIR model development using system optimisation strategy, Glycyrrhiza uralensis Fisch was used as a case. The content of liquiritin and glycyrrhizic acid was monitored during pilot scale extraction process of Glycyrrhiza uralensis Fisch in three batches.

METHODS

High-performance liquid chromatography (HPLC) was used as reference method for content determination of liquiritin and glycyrrhizic acid. The quantitative models of on-line NIR were developed by system optimisation of processing trajectory. For comparison, the models were simultaneously developed by stepwise optimisation. Moreover, the modelling parameters obtained through system optimisation and stepwise optimisation were reused in three batches. Root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) were used to assess the model quality.

RESULTS

The average values of RMSEP and RPD of systematic model for liquiritin in three batches were 0.0361, 4.1525 (first batch), 0.0348, 4.7286 (second batch) and 0.0311, 4.9686 (third batch), respectively. In addition, the modelling parameters of systematic model for glycyrrhizic acid in three batches were same, and the average values of RMSEP and RPD were 0.0665 and 5.2751, respectively. The predictive performance and robustness of systematic models for the three batches were better than the comparison models.

CONCLUSION

The work demonstrated that system optimisation quantitative model of on-line NIR could be used to determine the contents of liquiritin and glycyrrhizic acid during Glycyrrhiza uralensis Fisch extraction process.

Foliage Biophysical Trait Prediction from Laboratory Spectra in Norway Spruce Is More Affected by Needle Age Than by Site Soil Conditions

Scaling leaf-level optical signals to the canopy level is essential for airborne and satellite-based forest monitoring. In evergreen trees, biophysical and optical traits may change as foliage ages. This study aims to evaluate the effect of age in Norway spruce needle on biophysical trait-prediction based on laboratory leaf-level spectra. Mature Norway spruce trees were sampled at forest stands in ten headwater catchments with different soil properties. Foliage biophysical traits (pigments, phenolics, lignin, cellulose, leaf mass per area, water, and nitrogen content) were assessed for three needle-age classes. Complementary samples for needle reflectance and transmittance were measured using an integrating sphere. Partial least square regression (PLSR) models were constructed for predicting needle biophysical traits from reflectance—separating needle age classes and assessing all age classes together. The ten study sites differed in soil properties rather than in needle biophysical traits. Optical properties consistently varied among age classes; however, variation related to the soil conditions was less pronounced. The predictive power of PLSR models was needle-age dependent for all studied traits. The following traits were predicted with moderate accuracy: needle pigments, phenolics, leaf mass per area and water content. PLSR models always performed better if all needle age classes were included (rather than individual age classes separately). This also applied to needle-age independent traits (water and lignin). Thus, we recommend including not only current but also older needle traits as a ground truth for evergreen conifers with long needle lifespan.

Novel NIR modeling design and assignment in process quality control of Honeysuckle flower by QbD

Honeysuckle flower is a common edible-medicinal food with significant anti-inflammatory efficacy. Process quality control of its ethanol precipitation is a topical issue in the pharmaceutical field. Near infrared (NIR) spectroscopy is commonly used for process quality analysis. However, establishing a robust and reliable quantitative model of complex process remains a challenge in industrial applications of NIR. In this paper, modeling design based on quality by design concept (QbD) was implemented for the ethanol precipitation process quality control of Honeysuckle flower. According to the 56 models' performances and 25 contour plots, quadratic model was the best with R_adj² increasing from 0.1395 to 0.9085, indicating the strong interaction among spectral pre-processing methods, variable selection methods, and latent factors. SG9 and CARS was an appropriate combination for modeling. Furthermore, spectral assignment method was creatively introduced for variable selection. Another 56 models' performances and 25 contour plots were established. Compared with the chemometric variable selection method, spectral assignment combined with QbD concept made a higher R_pre² and a lower RMSEP. When the latent factors of PLS was small, R_pre² of the model by spectral assignment increased from 0.9605 to 0.9916 and RMSEP decreased from 0.1555 mg/mL to 0.07134 mg/mL. This result suggests that the variable selected by spectral assignment is more representative and precise. This provided a novel modeling guideline for process quality control in PAT.

A Non-destructive Method to Quantify Leaf Starch Content in Red Clover

Grassland-based ruminant livestock production provides a sustainable alternative to intensive production systems relying on concentrated feeds. However, grassland-based roughage often lacks the energy content required to meet the productivity potential of modern livestock breeds. Forage legumes, such as red clover, with increased starch content could partly replace maize and cereal supplements. However, breeding for increased starch content requires efficient phenotyping methods. This study is unique in evaluating a non-destructive hyperspectral imaging approach to estimate leaf starch content in red clover for enabling efficient development of high starch red clover genotypes. We assessed prediction performance of partial least square regression models (PLSR) using cross-validation, and validated model performance with an independent test set under controlled conditions. Starch content of the training set ranged from 0.1 to 120.3 mg g-1 DW. The best cross-validated PLSR model explained 56% of the measured variation and yielded a root mean square error (RMSE) of 17 mg g-1 DW. Model performance decreased when applying the trained model on the independent test set (RMSE = 29 mg g-1 DW, R 2 = 0.36). Different variable selection methods did not increase model performance. Once validated in the field, the non-destructive spectral method presented here has the potential to detect large differences in leaf starch content of red clover genotypes. Breeding material could be sampled and selected according to their starch content without destroying the plant.

Near-Infrared Spectroscopy for Mapping of Human Meniscus Biochemical Constituents

Degenerative changes in meniscus are diagnosed during surgery by means of mechanical testing and visual evaluation. This method is qualitative and highly subjective, providing very little information on the internal state of the meniscus. Thus, there is need for novel quantitative methods that can support decision-making during arthroscopic surgery. In this study, we investigate the potential of near-infrared spectroscopy (NIRS) for mapping the biochemical constituents of human meniscus, including water, uronic acid, and hydroxyproline contents. Partial least squares regression models were developed using data from 115 measurement locations of menisci samples extracted from 7 cadavers and 11 surgery patient donors. Model performance was evaluated using an independent test set consisting of 55 measurement locations within a meniscus sample obtained from a separate cadaver. The correlation coefficient of calibration (ρ_training), test set (ρ_test), and root-mean-squared error of test set (RMSEP) were as follows: water (ρ_training = 0.61, ρ_test = 0.39, and RMSEP = 2.27 percentage points), uronic acid (ρ_training = 0.68, ρ_test = 0.69, and RMSEP = 6.09 basis points), and hydroxyproline (ρ_training = 0.84, ρ_test = 0.58, and error = 0.54 percentage points). In conclusion, the results suggest that NIRS could enable rapid arthroscopic mapping of changes in meniscus biochemical constituents, thus providing means for quantitative assessment of meniscus degeneration.

Ultraviolet-visible diffuse reflectance spectroscopy combined with chemometrics for rapid discrimination of Angelicae Sinensis Radix from its four similar herbs

Ultraviolet-visible diffuse reflectance spectroscopy (UV-Vis DRS) combined with chemometrics was used for the first time to differentiate Angelicae Sinensis Radix (ASR) from four other similar herbs (either from the same genus or of similar appearance). A total of 191 samples, including 40 ASR, 39 Angelicae Pubescentis Radix (APR), 38 Chuanxiong Rhizoma (CR), 35 Atractylodis Macrocephalae Rhizoma (AMR) and 39 Angelicae Dahuricae Radix (ADR), were collected and divided into the training and prediction sets. Principal component analysis (PCA) was used for observing the sample cluster tendency of the calibration set. Different preprocessing methods were investigated and the optimal preprocessing combination was selected according to spectral signal characteristics and three-dimensional PCA (3D PCA) clustering results. The final discriminant model was built using extreme learning machine (ELM). The exploratory studies on the raw spectra and their 3D PCA scores indicate that the classification of the five herbs cannot be achieved by PCA of the raw spectra. Autoscaling, continuous wavelet transform (CWT) and Savitzky-Golay (SG) smoothing can improve the clustering results to different degrees. Furthermore, their combination in the order of CWT + autoscaling + SG smoothing can enhance the spectral resolution and obtain the best clustering result. These results are also validated using ELM models of raw and different preprocessing methods. By using CWT + autoscaling + SG smoothing + ELM, 100% classification accuracy can be achieved in both the calibration set and the prediction set. Therefore, the developed method could be used as a rapid, economic and effective method for discriminating the five herbs used in this study.

A critical review of recent trends, and a future perspective of optical spectroscopy as PAT in biopharmaceutical downstream processing

As competition in the biopharmaceutical market gets keener due to the market entry of biosimilars, process analytical technologies (PATs) play an important role for process automation and cost reduction. This article will give a general overview and address the recent innovations and applications of spectroscopic methods as PAT tools in the downstream processing of biologics. As data analysis strategies are a crucial part of PAT, the review discusses frequently used data analysis techniques and addresses data fusion methodologies as the combination of several sensors is moving forward in the field. The last chapter will give an outlook on the application of spectroscopic methods in combination with chemometrics and model predictive control (MPC) for downstream processes. Graphical abstract.

Performance Evaluation of Two Commercially Available Portable Spectrometers to Non-Invasively Determine Table Grape and Peach Quality Attributes

Near-infrared (NIR) spectroscopy has been used to non-destructively and rapidly evaluate the quality of fresh agricultural produce. In this study, two commercially available portable spectrometers (F-750: Felix Instruments, WA, USA; and SCiO: Consumer Physics, Tel Aviv, Israel) were evaluated in the wavelength range between 740 and 1070 nm to non-invasively predict quality attributes, including the dry matter (DM), and total soluble solids (TSS) content of three fresh table grape cultivars (‘Autumn Royal’, ‘Timpson’, and ‘Sweet Scarlet’) and one peach cultivar (‘Cassie’). Prediction models were developed using partial least-square regression (PLSR) to correlate the NIR absorbance spectra with the invasive quality measurements. In regard to grapes, the best DM prediction models yielded an R2 of 0.83 and 0.81, a ratio of standard error of performance to standard deviation (RPD) of 2.35 and 2.29, and a root mean square error of prediction (RMSEP) of 1.40 and 1.44; and the best TSS prediction models generated an R2 of 0.97 and 0.95, an RPD of 5.95 and 4.48, and an RMSEP of 0.53 and 0.70 for the F-750 and SCiO spectrometers, respectively. Overall, PLSR prediction models using both spectrometers were promising to predict table grape quality attributes. Regarding peach, the PLSR prediction models did not perform as well as in grapes, as DM prediction models resulted in an R2 of 0.81 and 0.67, an RPD of 2.24 and 1.74, and an RMSEP of 1.28 and 1.66; and TSS resulted in an R2 of 0.62 and 0.55, an RPD of 1.55 and 1.48, and an RMSEP of 1.19 and 1.25 for the F-750 and SCiO spectrometers, respectively. Overall, the F-750 spectrometer prediction models performed better than those generated by using the SCiO spectrometer data.

Real-time process quality control of ramulus cinnamomi by critical quality attribute using microscale thermophoresis and on-line NIR

Real-time process quality control of ramulus cinnamomi (cassia twig) is still a challenge in pharmaceutical industry. Rapid critical quality attribute (CQA) determination of ramulus cinnamomi is essential for quality control. Microscale thermophoresis (MST) was used to investigate the CQA of ramulus cinnamomi by the interaction with biomacromolecule. There was a good affinity between cinnamaldehyde and human serum albumin (HSA) with Ka as 2.1722×103mol/L. It was an excellent combination of similarity to ibuprofen with same binding force as discovered as hydrogen bond and van der Waals force. Furthermore, regarding cinnamaldehyde as CQA, on-line near-infrared was used to monitor pilot extraction process of ramulus cinnamomi combined with high performance liquid chromatography (HPLC). Quantitative model was established with Rpre2 as 0.9798 and RMSECV as 0.0993, suggesting the NIR model was so robust and accurate for pilot process quality control. This method provided a perfect guideline for rapid CQA determination and real-time process quality control of Chinese materia medica (CMM) based on a vital CQA.

Chemical fingerprinting and quantitative monitoring of the doping drugs bambuterol and terbutaline in human urine samples using ATR-FTIR coupled with a PLSR chemometric tool

The use of performance-enhancing drugs is prohibited in sports competitions according to the World Anti-Doping Agency (WADA) regulations. Here, ATR-FTIR spectroscopy coupled with a partial least squares regression (PLSR) chemometric tool was used for the detection of the misuse of such substances. Bambuterol and its metabolite terbutaline have been included in the list of prohibited doping agents. Therefore, we used bambuterol and terbutaline as models for the accurate and simultaneous qualitative and quantitative analysis of bambuterol and terbutaline in human urine samples. The method was straightforward and once the urine samples were collected, they could be directly applied to the surface of the ZnSe prism (ATR unit) to get the results within one minute. A calibration set with a partial factorial design was used to develop the PLSR model that could be used to predict the concentration of unknown samples containing the two drugs. The developed method was carefully validated and successfully applied to the urine sample analysis of human volunteers. The drugs were quantified at nanogram level concentrations. A side-by-side comparison of the proposed method with the routine GC-MS method was performed to demonstrate the challenges and opportunities of each method.

ATR-FTIR spectroscopy coupled with chemometric tools could be a suitable alternative to the traditional techniques for quantification of the performance enhancing drugs such as bambuterol and terbutaline in urine samples in and out of competition.

Systematic discovery about NIR spectral assignment from chemical structural property to natural chemical compounds

Spectra-structure interrelationship is still the weakness of NIR spectral assignment. In this paper, a comprehensive investigation from chemical structural property to natural chemical compounds was carried out for NIR spectral assignment. Surprisingly, we discovered that NIR absorption frequency of the skeleton structure with sp2 hybridization is higher than one with sp3 hybridization. Specifically, substituent was another vital factor to be explored, the first theory discovery demonstrated that the absorption intensity of methyl substituted benzene at 2330 nm has a linear relationship with the number of substituted methyl C-H. The greater the number of electrons given to the substituents, the larger the displacement distance of absorption bands is. In addition, the steric hindrance caused by the substituent could regularly reduce the intensity of NIR absorption bands. Furthermore, the characteristic bands and group attribution of 29 natural chemical compounds from 4 types have been systematic assigned. These meaningful discoveries provide guidance for NIR spectral assignment from chemical structural property to natural chemical compounds.

Spatiotemporal predictions of obesity prevalence in Chinese children and adolescents: based on analyses of obesogenic environmental variability and Bayesian model

OBJECTIVE

To find variations in Chinese obesogenic environmental priorities from 2000 to 2011, predict spatiotemporal distribution of obesity prevalence aged 7-17 years in 31 provinces, and provide foundations for policy-makers to reduce obesity in children and adolescents.

METHODS

Based on data examination of provincial obesity prevalence aged 7-17 years from three rounds of China Health and Nutrition Surveys (in 9 [2000], 9 [2006], and 12 [2011] provinces) and corresponding years' environments in 31 provinces from China Statistical Yearbooks and other sources, 12 predictors were selected. We used 30 surveyed provinces in three rounds as training samples to fit three analytic models with partial least-square regressions and prioritized predictors by variable importance projection to find variations. And fitted a spatiotemporal prediction model with Bayesian analysis to infer in space-time.

RESULTS

Variations of obesogenic environmental priorities were found at different times. A Bayesian spatiotemporal prediction model with deviance information criterion of 155.60 and statistically significant (P < 0.05) parameter estimates of intercept (-717.0400, 95% confidence intervals [CI]: -1186.0300, -248.0480), year (0.3584, CI: 0.1245, 0.5924), square of food industry level (0.0003, CI: 0.0002, 0.0004), and log (healthcare) (5.3742, CI: 2.5138, 8.2347) was optimized. Totally inferred average obesity prevalence among children and adolescents were 2.23%, 5.11%, 10.77%, 12.20%, 13.99%, and 17.58% in 31 provinces in China in 2000, 2006, 2011, 2015, 2020, and 2030, respectively. Obesity in north and east of China clusters on predicted maps.

CONCLUSIONS

Obesity prevalence in children and adolescents in China is rapidly increasing, growing at 0.3584% annually from 2000 to 2011. From longitudinal observation, prevalence was significantly influenced by food industry ("Amplifier") and healthcare service ("Balancer"). Targeted interventions in north and east of China are pressing. Further researches on the mechanisms underlying the influence of food industry, healthcare service, and so on in children and adolescents are needed.

Rapid identification and quantification of Panax notoginseng with its adulterants by near infrared spectroscopy combined with chemometrics

Traditional methods for identification of Panax notoginseng (PN) such as high performance liquid chromatography (HPLC) and gas chromatography (GC) are time-consuming, laborious and difficult to realize rapid and online analysis. In this research, the feasibility of identification and quantification of PN with rhizoma curcumae (RC), Curcuma longa (CL) and rhizoma alpiniae offcinarum (RAO) are investigated by using near infrared (NIR) spectroscopy combined with chemometrics. Five chemical pattern recognition methods including hierarchical cluster analysis (HCA), partial least squares-discriminant analysis (PLS-DA), artificial neural networks (ANN), support vector machine (SVM) and extreme learning machine (ELM) are used to build identification model of the dataset with 109 samples of PN and its three adulterants. Then seven datasets of binary, ternary and quaternary adulterations of PN are designed, respectively. Five multivariate calibration methods, i.e., principal component regression (PCR), support vector regression (SVR), partial least squares regression (PLSR), ANN and ELM are used to build quantitative model and compared for each dataset, separately. Finally, in order to further improve the prediction accuracy, SG smoothing, 1st derivative, 2nd derivative, continuous wavelet transform (CWT), standard normal variate (SNV), multiple scatter correction (MSC) and their combinations are investigated. Results show that PLS-DA and SVM can achieve 100% classification accuracy for identification of 109 PN with its three adulterants. PLSR is an optimal calibration method by comprehensive consideration of prediction accuracy, over-fitting and efficiency for the quantitative analysis of seven adulterated datasets. Furthermore, the predictive ability of the PLSR model for PN contents can be improved obvious by pretreating the spectra by the optimal preprocessing method, with correlation coefficients of which all higher than 0.99.

Pharmaceutical Analysis Model Robustness From Bagging-PLS and PLS Using Systematic Tracking Mapping

Our work proved that processing trajectory could effectively obtain a more reliable and robust quantitative model compared with the step-by-step optimization method. The use of systematic tracking was investigated as a tool to optimize modeling parameters including calibration method, spectral pretreatment and variable selection latent factors. The variable was selected by interval partial least-squares (iPLS), backward interval partial least-square (BiPLS) and synergy interval partial least-squares (SiPLS). The models were established by Partial least squares (PLS) and Bagging-PLS. The model performance was assessed by using the root mean square errors of validation (RMSEP) and the ratio of standard error of prediction to standard deviation (RPD). The proposed procedure was used to develop the models for near infrared (NIR) datasets of active pharmaceutical ingredients in tablets and chlorogenic acid of Lonicera japonica solution in ethanol precipitation process. The results demonstrated the processing trajectory has great advantages and feasibility in the development and optimization of multivariate calibration models as well as the effectiveness of bagging model and variable selection to improve prediction accuracy and robustness.

Process Analytical Technology for Advanced Process Control in Biologics Manufacturing with the Aid of Macroscopic Kinetic Modeling

Productivity improvements of mammalian cell culture in the production of recombinant proteins have been made by optimizing cell lines, media, and process operation. This led to enhanced titers and process robustness without increasing the cost of the upstream processing (USP); however, a downstream bottleneck remains. In terms of process control improvement, the process analytical technology (PAT) initiative, initiated by the American Food and Drug Administration (FDA), aims to measure, analyze, monitor, and ultimately control all important attributes of a bioprocess. Especially, spectroscopic methods such as Raman or near-infrared spectroscopy enable one to meet these analytical requirements, preferably in-situ. In combination with chemometric techniques like partial least square (PLS) or principal component analysis (PCA), it is possible to generate soft sensors, which estimate process variables based on process and measurement models for the enhanced control of bioprocesses. Macroscopic kinetic models can be used to simulate cell metabolism. These models are able to enhance the process understanding by predicting the dynamic of cells during cultivation. In this article, in-situ turbidity (transmission, 880 nm) and ex-situ Raman spectroscopy (785 nm) measurements are combined with an offline macroscopic Monod kinetic model in order to predict substrate concentrations. Experimental data of Chinese hamster ovary cultivations in bioreactors show a sufficiently linear correlation (R² ≥ 0.97) between turbidity and total cell concentration. PLS regression of Raman spectra generates a prediction model, which was validated via offline viable cell concentration measurement (RMSE ≤ 13.82, R² ≥ 0.92). Based on these measurements, the macroscopic Monod model can be used to determine different process attributes, e.g., glucose concentration. In consequence, it is possible to approximately calculate (R² ≥ 0.96) glucose concentration based on online cell concentration measurements using turbidity or Raman spectroscopy. Future approaches will use these online substrate concentration measurements with turbidity and Raman measurements, in combination with the kinetic model, in order to control the bioprocess in terms of feeding strategies, by employing an open platform communication (OPC) network—either in fed-batch or perfusion mode, integrated into a continuous operation of upstream and downstream.

Application of different spectrophotometric methods for simultaneous determination of elbasvir and grazoprevir in pharmaceutical preparation

The first three UV spectrophotometric methods have been developed of simultaneous determination of two new FDA approved drugs namely; elbasvir and grazoprevir in their combined pharmaceutical dosage form. These methods include simultaneous equation, partial least squares with and without variable selection procedure (genetic algorithm). For simultaneous equation method, the absorbance values at 369 (λ_max of elbasvir) and 253nm (λ_max of grazoprevir) have been selected for the formation of two simultaneous equations required for the mathematical processing and quantitative analysis of the studied drugs. Alternatively, the partial least squares with and without variable selection procedure (genetic algorithm) have been applied in the spectra analysis because the synchronous inclusion of many unreal wavelengths rather than by using a single or dual wavelength which greatly increases the precision and predictive ability of the methods. Successfully assay of the drugs in their pharmaceutical formulation has been done by the proposed methods. Statistically comparative analysis for the obtained results with the manufacturing methods has been performed. It is noteworthy to mention that there was no significant difference between the proposed methods and the manufacturing one with respect to the validation parameters.

Novelty application of multi-omics correlation in the discrimination of sulfur-fumigation and non-sulfur-fumigation Ophiopogonis Radix

A rapid and sensitive approach to differentiate sulfur-fumigated (SF) Ophiopogonis Radix based on Multi-Omics Correlation Analysis (MOCA) strategy was first established. It was characterized by multiple data-acquisition methods (NIR, HPLC, and UHPLC-HRMS) based metabonomics and multivariate statistical analysis methods. As a result, SF and non-sulfur fumigated (NSF) Ophiopogonis Radix samples were efficaciously discriminated. Moreover, based on the acquired HRMS data, 38 sulfur-containing discriminatory markers were eventually characterized, whose NIR absorption could be in close correlation with the discriminatory NIR wavebands (5000–5200 cm⁻¹) screened by NIR metabonomics coupled with SiPLS and 2D-COS methods. This results were also validated from multiple perspectives, including metabonomics analysis based on the discriminatory markers and the simulation of SF ophiopogonin D and Ophiopogonis Radix sample. In conclusion, our results first revealed the intrinsic mechanism of discriminatory NIR wavebands by means of UHPLC-HRMS analysis. Meanwhile, the established MOCA strategy also provided a promising NIR based differential method for SF Ophiopogonis Radix, which could be exemplary for future researches on rapid discrimination of other SF Chinese herbal medicines.

High-throughput analysis of chemical components and theoretical ethanol yield of dedicated bioenergy sorghum using dual-optimized partial least squares calibration models

BACKGROUND

Due to its chemical composition and abundance, lignocellulosic biomass is an attractive feedstock source for global bioenergy production. However, chemical composition variations interfere with the success of any single methodology for efficient bioenergy extraction from diverse lignocellulosic biomass sources. Although chemical component distributions could guide process design, they are difficult to obtain and vary widely among lignocellulosic biomass types. Therefore, expensive and laborious "one-size-fits-all" processes are still widely used. Here, a non-destructive and rapid analytical technology, near-infrared spectroscopy (NIRS) coupled with multivariate calibration, shows promise for addressing these challenges. Recent advances in molecular spectroscopy analysis have led to methodologies for dual-optimized NIRS using sample subset partitioning and variable selection, which could significantly enhance the robustness and accuracy of partial least squares (PLS) calibration models. Using this methodology, chemical components and theoretical ethanol yield (TEY) values were determined for 70 sweet and 77 biomass sorghum samples from six sweet and six biomass sorghum varieties grown in 2013 and 2014 at two study sites in northern China.

RESULTS

Chemical components and TEY of the 147 bioenergy sorghum samples were initially analyzed and compared using wet chemistry methods. Based on linear discriminant analysis, a correct classification assignment rate (either sweet or biomass type) of 99.3% was obtained using 20 principal components. Next, detailed statistical analysis demonstrated that partial optimization using sample set partitioning based on joint X-Y distances (SPXY) for sample subset partitioning enhanced the robustness and accuracy of PLS calibration models. Finally, comparisons between five dual-optimized strategies indicated that competitive adaptive reweighted sampling coupled with the SPXY (CARS-SPXY) was the most efficient and effective method for improving predictive performance of PLS multivariate calibrations.

CONCLUSIONS

As a dual-optimized methodology, sample subset partitioning combined with variable selection is an efficient and straightforward strategy to enhance the accuracy and robustness of NIRS models. This knowledge should facilitate generation of improved lignocellulosic biomass feedstocks for bioethanol production. Moreover, methods described here should have wider applicability for use with feedstocks incorporating multispecies biomass resource streams.

Online near-infrared analysis coupled with MWPLS and SiPLS models for the multi-ingredient and multi-phase extraction of licorice (Gancao)

Background

This study aims to analyze the active pharmaceutical ingredients (APIs) of licorice (Radix Glycyrrhizae; gancao), including glycyrrhizic acid, liquiritin, isoliquiritin and total flavonoids, in multi-ingredient and multi-phase extraction by online near-infrared technology with fiber optic probes and chemometric analysis.

Methods

High-performance liquid chromatography and ultraviolet spectrophotometry determined the APIs content in different extraction phases by online near-infrared analysis, which included sample set selection by the Kennard–Stone algorithm, optimization of spectral pretreatment methods (i.e., orthogonal signal correction and wavelet denoising spectral correction), and model calibration by the partial least-squares algorithm, moving-window partial least-squares algorithm and synergy interval partial least-squares (SiPLS) algorithm. The relative errors and F values were used to assess the models in different extraction phases.

Results

The root-mean-square error of correction, root-mean-square error of cross-validation and root-mean-square error of prediction of APIs in the SiPLS model was less than 0.07. The F values of glycyrrhizic acid, liquiritin, isoliquiritin and total flavonoids were 10,765, 32,431, 649 and 6080, respectively, which were larger than 6.90 (P < 0.01).

Conclusion

The study demonstrated the feasibility of online NIR analysis in the multi-ingredient and multi-phase extraction of APIs from licorice.

Electronic supplementary material

The online version of this article (doi:10.1186/s13020-015-0069-2) contains supplementary material, which is available to authorized users.