Root Mean Square Error Of Cross-validation Research Articles

Predicting heavy metals in dark sun-cured tobacco by near-infrared spectroscopy modeling based on the optimized variable selections

As a product absorbed by human body, the safety of cigarettes highly depends on the quality of tobacco (Nicotiana tabacum L.) raw materials. The heavy metal is an important indicator of tobacco quality which attracts the widespread concerns. Current detection of heavy metals in tobacco mainly adopts the inductively coupled plasma mass spectrometry (ICP-MS) method, which contains the cumbersome and time-consuming procedures. This study attempted to establish the near-infrared spectroscopy (NIRS) models for the six heavy metals as Zn, As, Cd, Hg, Pb, and Cr in dark sun-cured tobacco based on chemometrics methods. After spectral pretreatments and variable selections, partial least squares (PLS) regression was used to establish the optimized models. Prediction of Pb by competitive adaptive reweighted sampling (CARS) combining PLS method was satisfactory with the Rcv2 of 91.90 %, root mean square error of cross-validation (RMSECV) of 0.554, Rp2 of 92.40 %, and root mean square error of prediction (RMSEP) of 1.120. Quantification of Cd was also positive, with Rp2 of 89.10 % and RMSEP of 2.051. Besides, results of the other four heavy metals were also acceptable. The proposed non-destructive detection method can rapidly acquire the content of heavy metals in raw tobacco leaves from different regions, aiding to manage the tobacco resources reasonably and reduce the harmfulness of cigarettes from the source.

Online accurate detection of soluble solids content in navel orange assisted by automatic orientation correction device

Non-destructive online sorting system for fruit quality are gaining importance in the global agro-industry as they allow for optimising harvesting, storage and marketing management decisions. The most commonly used sensing technology is visible-near infrared (Vis/NIR) spectroscopy. However, in online detection, the reproducibility of spectra is easily affected by fruit placement attitude. Up to now, little or no work considered the problem of attitude correction before spectral acquisition. This work aims at filling this gap. In this study, Vis/NIR diffuse transmission technology was used to detect the soluble solids content (SSC) in navel oranges, the analysis models of SSC in navel orange with consistent-orientation (T1: navel upward, T2: navel forward, and T3: navel leaning with an angle about 30°-60°), mixed-orientation (mix of T1, T2 and T3) and corrected-orientation (T2 correction) were established. Partial least square regression (PLSR) combined with different spectral pretreatment and effective wavelengths (EWs) selection methods were used to establish the optimal SSC analytical models. Root mean square error of cross-validation (RMSECV), correlation coefficient of calibration (RC), root mean square error of prediction (RMSEP), correlation coefficient of prediction (RP), residual predictive deviation (RPD) and the ratio of error range (RER) are used as the evaluation indicators of the model. The results show that the inconsistency of the placement attitude affects the prediction accuracy. The mixed-orientation model contains inconsistent placement information of the fruit orientation, and the robustness of the model is improved, but the prediction accuracy decreased. In the consistent-orientation model, the best analytical results can be obtained with the T2 placement orientation. The automatic orientation correction device based on image processing technology can correct the attitude of randomly placed navel orange samples according to the T2 orientation. The analytical accuracy of the model is further improved. In the corrected-orientation model, the best SSC analytical model can be obtained using NWS-MSC (Norris Williams smoothing - multiplicative scattering correction) pretreatment and VCPA (variable combination population analysis) variable selection methods, and RP, RMSEP, RPD and RER of the optimal prediction model were 0.981, 0.313 °Brix, 5.176 and 16.613 respectively. This method can be extended to the online analysis of other spherical fruits.

Rapid and Nondestructive Determination of Polyacrylonitrile Molecular Weight by Fourier Transform near-Infrared (NIR) Spectroscopy

The molecular weight of polyacrylonitrile (PAN) precursors plays an important role in specifying the properties of prepared carbon fibers. However, it is challenging to rapidly and conveniently determine the accurate molecular weight of PAN. Herein, a practical and facile molecular weight determination model of PAN based on near-infrared spectroscopy (NIR) combined with partial least squares (PLSs) is reported. Using the spectral modeling range from 4000 to 6000 cm−1 with standard normal variate (SNV) or multiplicative scatter correction (MSC) combined with smoothing and first-order differential, the optimized models with main factors number of nine are achieved. The models were systematically evaluated by root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), and root mean squares error of cross-validation (RMSECV). In the determination of the molecular weight of the samples based on these optimized models, the value of the relative standard deviation is only 0.9%, and the value of deviation from the established gel permeation chromatography (GPC) method is between −0.037 × 105 and 0.051 × 105. The results show that the reported NIR method has promising potential for the rapid and nondestructive determination of the molecular weight of PAN in production processes.

Determination and Quantification of Heavy Metals in Sediments through Laser-Induced Breakdown Spectroscopy and Partial Least Squares Regression

Conventional analysis techniques and sample preprocessing methods for identifying trace metals in soil and sediment samples are costly and time-consuming. This study investigated the determination and quantification of heavy metals in sediments by using a Laser-Induced Breakdown Spectroscopy (LIBS) system and multivariate chemometric analysis. Principle Component Analysis (PCA) was conducted on the LIBS spectra at the emission lines of 11 selected elements (Al, Ca, Cd, Cr, Fe, K, Mg, Na, Ni, Pb, and Si). The results showed apparent clustering of four types of sediment samples, suggesting the possibility of application of the LIBS technique for distinguishing different types of sediments. Mainly, the Cd, Cr, and Pb concentrations in the sediments were analyzed. A data-smoothing method—namely, the Savitzky–Golay (SG) derivative—was used to enhance the performance of the Partial Least Squares Regression (PLSR) model. The performance of the PLSR model was evaluated in terms of the coefficient of determination (R2), Root Mean Square Error of Calibration (RMSEC), and Root Mean Square Error of Cross Validation (RMSECV). The results obtained using the PLSR with the SG derivative were improved in terms of the R2 and RMSECV, except for Cr. In particular, the results for Cd obtained with the SG derivative showed a decrease of 25% in the RMSECV value. This demonstrated that the PLSR model with the SG derivative is suitable for the quantitative analysis of metal components in sediment samples and can play a significant role in controlling and managing the water quality of rivers.

Monitoring and Mapping Vineyard Water Status Using Non-Invasive Technologies by a Ground Robot

There is a growing need to provide support and applicable tools to farmers and the agro-industry in order to move from their traditional water status monitoring and high-water-demand cropping and irrigation practices to modern, more precise, reduced-demand systems and technologies. In precision viticulture, very few approaches with ground robots have served as moving platforms for carrying non-invasive sensors to deliver field maps that help growers in decision making. The goal of this work is to demonstrate the capability of the VineScout (developed in the context of a H2020 EU project), a ground robot designed to assess and map vineyard water status using thermal infrared radiometry in commercial vineyards. The trials were carried out in Douro Superior (Portugal) under different irrigation treatments during seasons 2019 and 2020. Grapevines of Vitis vinifera L. Touriga Nacional were monitored at different timings of the day using leaf water potential (Ψl) as reference indicators of plant water status. Grapevines’ canopy temperature (Tc) values, recorded with an infrared radiometer, as well as data acquired with an environmental sensor (Tair, RH, and AP) and NDVI measurements collected with a multispectral sensor were automatically saved in the computer of the autonomous robot to assess and map the spatial variability of a commercial vineyard water status. Calibration and prediction models were performed using Partial Least Squares (PLS) regression. The best prediction models for grapevine water status yielded a determination coefficient of cross-validation (r2cv) of 0.57 in the morning time and a r2cv of 0.42 in the midday. The root mean square error of cross-validation (RMSEcv) was 0.191 MPa and 0.139 MPa at morning and midday, respectively. Spatial–temporal variation maps were developed at two different times of the day to illustrate the capability to monitor the grapevine water status in order to reduce the consumption of water, implementing appropriate irrigation strategies and increase the efficiency in the real time vineyard management. The promising outcomes gathered with the VineScout using different sensors based on thermography, multispectral imaging and environmental data disclose the need for further studies considering new variables related with the plant water status, and more grapevine cultivars, seasons and locations to improve the accuracy, robustness and reliability of the predictive models, in the context of precision and sustainable viticulture.

Rapid Quantification of the Palm Kernel Biokerosene Content in Mixtures with Aviation Kerosene using MIR Spectroscopy and Multivariate Regression by PLS

An alternative methodology was developed to monitor the biokerosene content of palm kernel in blend with kerosene using medium infrared spectroscopy associated with partial least squares (PLS). The efficiency of this methodology was analyzed based on the parameters of accuracy and figures of merit. The values of root-mean-square error of cross-validation (RMSECV), root-mean-square error of calibration (RMSEC) and root-mean-square error of prediction (RMSEP) were in agreement because the RMSEP was higher than RMSECV and RMSEC. In addition, the RMSEP value is considered acceptable according to the Brazilian standard ABNT NBR 15568 because it is less than 1%. The figures of merit were performed in agreement with the requirements established in the standard ASTM E1655-05. The linearity of the model was assessed based on the analysis of the model fit through the correlation of the actual and predicted values of the calibration and prediction sets, where a high correlation between the values was evidenced, with a correlation coefficient (R) exceeding 0.99. The good results of the application of MIR spectroscopy combined with multivariate regression by PLS suggest that this analytical methodology is feasible, efficient and suitable for use by inspection agencies to control the biokerosene content of palm kernel in mixture with diesel.

A Multi-Sensor System for Sea Water Iodide Monitoring and Seafood Quality Assurance: Proof-of-Concept Study.

Iodine is a trace chemical element fundamental for a healthy human organism. Iodine deficiency affects about 2 billion people worldwide causing from mild to severe neurological impairment, especially in children. Nevertheless, an adequate nutritional intake is considered the best approach to prevent such disorders. Iodine is present in seawater and seafood, and its common forms in the diet are iodide and iodate; most iodide in seawater is caused by the biological reduction of the thermodynamically stable iodate species. On this basis, a multisensor instrument which is able to perform a multidimensional assessment, evaluating iodide content in seawater and seafood (via an electrochemical sensor) and discriminating when the seafood is fresh or defrosted quality (via a Quartz Micro balance (QMB)-based volatile and gas sensor), is strategic for seafood quality assurance. Moreover, an electronic interface has been opportunely designed and simulated for a low-power portable release of the device, which should be able to identify seafood over or under an iodide threshold previously selected. The electrochemical sensor has been successfully calibrated in the range 10–640 μg/L, obtaining a root mean square error in cross validation (RMSECV) of only 1.6 μg/L. Fresh and defrosted samples of cod, sea bream and blue whiting fish have been correctly discriminated. This proof-of-concept work has demonstrated the feasibility of the proposed application which must be replicated in a real scenario.

Quantification of soluble solids and individual sugars in apples by Raman spectroscopy: A feasibility study

This study reports the feasibility of using Raman spectroscopy for quantification of soluble solids and individual sugars in apples. Six different commercial apple varieties were measured by Raman spectroscopy at three different steps: 1) Intact apples with skin, 2) apples without skin and 3) juices obtained from apples. Results indicated that it is possible to measure Raman signals to a depth of 8 mm into the apple with a wide area Raman probe. Multivariate calibration models were established to evaluate how well Raman spectra can be used to estimate the quality parameters SSC (%), total sugars, glucose, fructose and sucrose. Estimation accuracy for SSC was comparable with what is achievable with near-infrared spectroscopy: Root mean square error of cross-validation (RMSECV) = 0.66, 0.46 and 0.72 % and coefficients of determination (R2) = 0.70, 0.85 and 0.63 for intact apples, apples without skin and juices, respectively. Sucrose and glucose were well estimated with RMSECV of 2.8, 1.9, 2.1 mg/mL for glucose and 5.8, 3.9 and 3.7 mg/mL for sucrose, for the three sample cases, respectively. Coefficient of determination was higher than 0.82 for all models. Regression coefficients for all calibration models highlighted identifiable Raman bands that could be related to the target sugars.

Rapid spectroscopic method for quantifying gluten concentration as a potential biomarker to test adulteration of green banana flour

The demand for gluten-free banana flour has led manufactures to enforce strict measures for quality control. A need has arisen for the development of more sensitive and reliable methods to test the quality of green banana flour (GBF). The objective of this study was to develop rapid visible to near-infrared (Vis-NIR) based spectroscopic models to detect gluten concentration, as a biomarker to detect wheat flour adulteration in green banana flour (GBF). Spectroscopic data were acquired using a desktop (FOSS®) Vis-NIR spectroscopy ranging from 400 to 2500 nm of the electromagnetic spectrum. The spectral and reference data were submitted to principal component analysis (PCA) and partial least squares regression (PLSR) for the development of gluten adulteration detection models. Calibration models were constructed based on a full cross-validation approach, consisting of 51 samples for the calibration set and 21 samples for the test set. PCA scores plot discriminated gluten adulterated and unadulterated GBF samples with 100% accuracy for the first two principal components (PCs). The optimal prediction model was obtained after a combination of baseline (offset and baseline linear correlation) and standard normal variate (SNV) pre-processing technique. This model showed a 94% coefficient of determination of cross-validation (R2cv) and prediction (R2p); root mean square error of cross-validation (RMSECV) of 3.7 mg/kg, root mean square error of prediction (RMSEP) of 3.9 mg/kg; and RPD value of 4. This work has demonstrated that Vis-NIRS method is a robust and feasible technology that may be used to ensure the safety of banana flour and that this product stays gluten-free by providing good and reliable gluten detection and quantification prediction models.

Nondestructive Analysis of Internal Quality in Pears with a Self-Made Near-Infrared Spectrum Detector Combined with Multivariate Data Processing.

The consumption of pears has increased, thanks not only to their delicious and juicy flavor, but also their rich nutritional value. Traditional methods of detecting internal qualities (e.g., soluble solid content (SSC), titratable acidity (TA), and taste index (TI)) of pears are reliable, but they are destructive, time-consuming, and polluting. It is necessary to detect internal qualities of pears rapidly and nondestructively by using near-infrared (NIR) spectroscopy. In this study, we used a self-made NIR spectrum detector with an improved variable selection algorithm, named the variable stability and cluster analysis algorithm (VSCAA), to establish a partial least squares regression (PLSR) model to detect SSC content in snow pears. VSCAA is a variable selection method based on the combination of variable stability and cluster analysis to select the infrared spectrum variables. To reflect the advantages of VSCAA, we compared the classical variable selection methods (synergy interval partial least squares (SiPLS), genetic algorithm (GA), successive projections algorithm (SPA), and bootstrapping soft shrinkage (BOSS)) to extract useful wavelengths. The PLSR model, based on the useful variables selected by SiPLS-VSCAA, was optimal for measuring SSC in pears, and the correlation coefficient of calibration (Rc), root mean square error of cross validation (RMSECV), correlation coefficient of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) were 0.942, 0.198%, 0.936, 0.222%, and 2.857, respectively. Then, we applied these variable selection methods to select the characteristic wavelengths for measuring the TA content and TI value in snow pears. The prediction PLSR models, based on the variables selected by GA-BOSS to measure TA and that by GA-VSCAA to detect TI, were the best models, and the Rc, RMSECV, Rp and RPD were 0.931, 0.124%, 0.912, 0.151%, and 2.434 and 0.968, 0.080%, 0.968, 0.089%, and 3.775, respectively. The results showed that the self-made NIR-spectrum detector based on a portable NIR spectrometer with multivariate data processing was a good tool for rapid and nondestructive analysis of internal quality in pears.

Comparison of Raman and attenuated total reflectance (ATR) infrared spectroscopy for water quantification in natural deep eutectic solvent.

Natural deep eutectic solvents (NADES) are ionic solutions, of great interest for extraction from biomass, biocatalysis, and nanoparticle synthesis. They are easily synthesised and eco-friendly, have low volatility and high dissolution power, and are biodegradable. However, water content in NADES is a critical parameter, affecting their optimal use and extraction efficiency. Vibrational spectroscopic techniques are rapid, label-free, non-destructive, non-invasive, and cost-effective analytical tools that can probe the molecular composition of samples. A direct comparison between a previous study using attenuated total reflectance infrared (ATR-IR) spectroscopy for water quantification in NADES and the same investigation performed with Raman spectroscopy is presently reported. Three NADES systems, namely betaine-glycerol (BG), choline chloride-glycerol (CCG), and glucose-glycerol (GG), containing a range of water concentrations between 0% (w/w) and 40% (w/w), have been analysed with Raman spectroscopy coupled to partial least squares regression multivariate analysis. The values of root mean square error of cross-validation (RMSECV) obtained from analysis performed on the pre-processed spectra over the full spectral range (150-3750cm-1) are respectively 0.2966% (w/w), 0.4703% (w/w), and 0.2351% (w/w) for BG, GG, and CCG. While the direct comparison to previous ATR-IR results shows essentially similar outcomes for BG, the RMSECV is 33.14% lower and 65.84% lower for CG and CCG. Furthermore, mean relative errors obtained with Raman spectroscopy, and calculated from a set of samples used as independent samples, were 1.452% (w/w), 1.175% (w/w), and 1.188% (w/w). Ultimately, Raman spectroscopy delivered performances for quantification of water in NADES with similar accuracy to ATR-IR. The present demonstration clearly highlights the potential of Raman spectroscopy to support the development of new analytical protocols in the field of green chemistry.

Comparison of Chemometric Models for Quantification of Total Curcuminoids in Powdered Turmeric Using Visible and Near Infrared Spectra

Introduction The combination of a light source emitting a desired wavelength range and a detector capturing that signal can help understand the photonic interaction of matter in different regions of the electromagnetic spectrum. The reflectance spectra of the same analyte will vary based on which region of the electromagnetic spectrum it is captured in. In conjunction with machine learning applications, several parameters of the analyte can be simultaneously analyzed and reported. In this study, we have attempted to compare chemometric models for predicting the quantity of total curcuminoids (sum of bisdemethoxycurcumin, demethoxycurcumin and curcumin) in powdered turmeric samples using diffused reflectance spectroscopy, in two regions of the electromagnetic spectrum: (1) The visible range (360-750 nm), and (2) A small region of the near-infrared (NIR) range (1550-1950 nm). Materials and Methods The diffused reflectance spectra of turmeric powders (n = 66) are captured using two different spectrometers — JAZ from Ocean optics for the visible range, and Spectral Engines for the NIR range. The quantity of total curcuminoids in this data set ranges between 1-4%. The spectra from each instrument are subjected to a series of pre-processing steps to condition the data. The pre-processing steps were optimized to obtain the best-possible accuracy in the subsequent regression models. Principal Component Analysis (PCA) is used to qualitatively analyze the data and visualize the clustering patterns, if any. The processed spectra are then evaluated with Partial Least Squared Regression algorithm (PLSR), using High Performance Liquid Chromatography (HPLC) as the reference data, to predict the quantity of curcuminoids in powdered turmeric samples. The model performance is reported with coefficient of determination (R2 ), root-mean-squared error of cross validation (RMSECV) and root-mean-squared error of prediction (RMSEP). Results and Discussions The spectrum of turmeric has a single broad peak in the visible region (360-750 nm), owing to the electronic transitions in the curcumin molecule. There are two peaks observed in the NIR region (1550-1950 nm), corresponding to the first overtone of the C-H stretching vibrations and second overtone of the C=O stretching vibrations in turmeric. On subjection to PCA, there is a subtle clustering when the spectra are divided into 1% curcumin slabs in the visible range, as seen in Fig. 2(a). However, there is no such separation seen for spectra collected in the NIR region. On building regression models based on this data, the model with the visible spectra of turmeric has a better R2 of 0.921 as compared to 0.83 for the NIR spectra. Similarly RMSECV is found to be 0.219 (visible), 0.273 (NIR) and RMSEP as 0.302 (visible), 0.352 (NIR) (Fig. 3). The model for the visible spectra performs better both qualitatively and quantitatively. Both the models are also validated on a test data set of 7 samples (not a part of the training data), and the predicted results are shown in Fig. 4. The predicted curcuminoid values from 7 samples have a mean deviation of 7.7% and 11% from the model based on visible and NIR spectra of turmeric, respectively, when compared with the actual curcuminoid values from HPLC. Conclusion The chemometric model built with the spectra of turmeric in the visible range performs better than its NIR counterpart with the same set of turmeric samples. This observation implies that the cost of a chemometric sensing system to predict total curcuminoids in turmeric can be reduced by using the visible wavelength range due to the relatively lower cost of detectors and light sources. However, this is a preliminary study with limited number of samples to conclusively say which region of the electromagnetic spectrum is better for analyzing and predicting total curcuminoids in turmeric. Upon further addition of samples and exploring different machine learning approaches, a stronger conclusion can be drawn. References H. Suresh, A. R. Behera, and R. Pratap, ECS Meet. Abstr., MA2020-01, 1858–1858 (2020) https://iopscience.iop.org/article/10.1149/MA2020-01261858mtgabs Figure 1

A new and rapid analysis method for the most important herbal squalene source: Comparison of UV–visible, fluorescence, and FTIR techniques for the quantification of squalene in amaranth seed oil

A new method for the quantification of squalene in amaranth seed oil (ASO) is proposed. The spectroscopic techniques of ultraviolet–visible (UV–vis), fluorescence, and Fourier transform infrared (FTIR) were tested using partial least square (PLS) calibration models. Based on the results obtained from the reference chromatographic method, two ASO samples with the highest and lowest concentrations among totally 40 ASO samples were selected to prepare the calibration set (CS) (n = 20). All ASO samples were also kept as prediction set (PS) for the control of the predictive capacity of the chemometric model to be developed. To quantify the squalene compound in ASO, a totally of 26 PLS calibration models were tested using all spectroscopic techniques with various spectral pretreatments, and the best PLS model was selected by evaluating their statistical values of root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), and R-square. The best calibration model was obtained from normal spectra in FTIR spectroscopy (4000–400 cm−1) with the lowest RMSEC of 0.0930, RMSECV of 0.1301, and the highest R-square of 0.9989 for calibration. The results of this study revealed that FTIR spectroscopy coupled with PLS regression could be used for fast, accurate, and environment-friendly quantification of squalene present in ASO samples.

Determination of the age of dry red wine by multivariate techniques using color parameters and pigments

Vintage is a decisive factor for market price of commercial bottled dry red wines. Aim of this study was to use color parameters and pigments for the prediction of the age of dry red wine by means of partial least squares (PLS) and multiple linear regression (MLR). Both models allow the prediction of the age accurately, with high coefficients of determination (R 2 ), and low enough root mean square error of cross-validation (RMSECV). Although less variables were required to obtain a successful MLR model, its prediction results were as accurate as the model obtained by PLS. The ratio of the first polymeric pigments (TFPP) and malvidin-3-glycoside was demonstrated to be a promising and practical chemical parameter for the rapid and simple age assessment of the dry red wines. • Significant pigmented polymers for wine age is analyzed by HPLC-DAD. • Color parameters and pigments highly correlated with dry red wine age are obtained. • PLS and MLR models are established to predict the vintage of dry red wine. • The predictive ability of dry red wine vintage of best PLS and MLR models is compared.

The application of Raman spectroscopy for monitoring product quality attributes in perfusion cell culture

In-situ Raman spectroscopy provides enhanced capabilities to monitor and control the mammalian cell culture process in real-time, which conforms to the concepts Process Analytical Technology (PAT) and Quality by Design (QbD) raised by U.S. Food and Drug Administration (FDA), and help us to overcome the challenges encountered in the Pharmaceutical R&D. Product quality control was addressed in FDA’s guidance to encourage continuous manufacturing, few work was done to maintain a steady output of drug product in the continuous mammalian cell culture process with the aid of Raman spectroscopy. Here, the state-of-the-art intensified perfusion cell culture - Raman integrated system was set up where product quality attributes on-line monitoring was archived, which could enhance our product quality attributes (PQA) control capabilities as well as ensuring product quality stability in the cell culture process.In this study, an intensified perfusion cell culture system integrated with real-time Raman analyzer was built-up at 3 L bench-top scale for product quality attributes monitoring. Real-time Raman spectrums were correlated with the PQA data of one-step Protein A purified sample after the alternating tangential flow (ATF) column via partial least square (PLS) modeling, results showed that the Raman spectrum could reflect the PQA changes as a function of elapsed time. Thus the real-time monitoring of 3 representative analytical items for cell culture, i.e., Non-reduced Microchip CE-SDS (SDS_Caliper-NR), size-exclusion chromatography (SEC) and N-linked glycan liquid chromatography (N-glycan LC) were evaluated through Raman analyzer. For different analytical items, around 6∼8 kinds of PLS models with different pre-processing methods were evaluated, which considered the effects of Raman shift range, spectrum pre-processing (1st order derivate or 2nd order derivate), utilization of variable importance in projection (V.I.P) on Raman shift range on the model performance. Then the trained models were tested in another intensified perfusion batch with changed process.As for SDS_Caliper-NR main peak, SEC monomer, high molecule weight (HMW) species, Mannose 5 (Man5) percentage, our optimized models could predict these items accurately, with relatively low root mean square error of cross validation (RMSECV) of 0.37 %, 0.44 %, 0.24 % and 0.51 %, respectively. Adapting these models in another 3 L intensified perfusion culture bioreactor with different processes, these models still have predictability, yielded root mean square error of prediction (RMSEP) of 1.88 %, 1.74 %, 0.90 %, 2.79 %, suggesting that Raman spectrums could capture the PQA profiling trends and these models were quite robust against some process changes like pH strategy, perfusion rate, additional feeding. We also found that the 1st order derivate with standard normal variate transformation (SNV), Raman wavenumber from 800 to 1800 cm−1 and V.I.P > 0.8 usually obtained the best performance in the training set, suggesting a golden point for Raman PQA monitoring of cell culture process.This work demonstrated that real-time Raman spectroscopy is an effective PAT tool for on-line product quality attributes monitoring in the cell culture process, especially for continuous perfusion cell culture, allowing us to explore the possibility of PQA on-line tuning in the continuous manufacturing.

Feasibility study on the use of near-infrared spectroscopy for rapid and nondestructive determination of gossypol content in intact cottonseeds

BackgroundGossypol found in cottonseeds is toxic to human beings and monogastric animals and is a primary parameter for the integrated utilization of cottonseed products. It is usually determined by the techniques relied on complex pretreatment procedures and the samples after determination cannot be used in the breeding program, so it is of great importance to predict the gossypol content in cottonseeds rapidly and nondestructively to substitute the traditional analytical method.ResultsGossypol content in cottonseeds was investigated by near-infrared spectroscopy (NIRS) and high-performance liquid chromatography (HPLC). Partial least squares regression, combined with spectral pretreatment methods including Savitzky-Golay smoothing, standard normal variate, multiplicative scatter correction, and first derivate were tested for optimizing the calibration models. NIRS technique was efficient in predicting gossypol content in intact cottonseeds, as revealed by the root-mean-square error of cross-validation (RMSECV), root-mean-square error of prediction (RMSEP), coefficient for determination of prediction (Rp2), and residual predictive deviation (RPD) values for all models, being 0.05∼0.07, 0.04∼0.06, 0.82∼0.92, and 2.3∼3.4, respectively. The optimized model pretreated by Savitzky-Golay smoothing + standard normal variate + first derivate resulted in a good determination of gossypol content in intact cottonseeds.ConclusionsNear-infrared spectroscopy coupled with different spectral pretreatments and partial least squares (PLS) regression has exhibited the feasibility in predicting gossypol content in intact cottonseeds, rapidly and nondestructively. It could be used as an alternative method to substitute for traditional one to determine the gossypol content in intact cottonseeds.

Developing near infrared spectroscopy models for predicting chemistry and responses to stress in Pinus radiata (D. Don)

Incorporating chemical traits in breeding requires the estimation of quantitative genetic parameters, especially the levels of additive genetic variation. This requires large numbers of samples from pedigreed populations. Conventional wet chemistry procedures for chemotyping are slow, expensive and not a practical option. This study focuses on the chemical variation in Pinus radiata, where the near infrared (NIR) spectral properties of the needles, bark and roots before and after exposure to methyl jasmonate (MJ) and artificial bark stripping (strip) treatments were investigated as an alternative approach. The aim was to test the capability of NIR spectroscopy to (i) discriminate samples exposed to MJ and strip assessed 7, 14, 21 and 28 days after treatment from untreated samples, and (ii) quantitatively predict individual chemical compounds in the three plant parts. Using principal components analysis (PCA) on the spectral data, we differentiated between treated and untreated samples for the individual plant parts. Based on partial least squares–discriminant analysis (PLS-DA) models, the best discrimination of treated from non-treated samples with the smallest root mean square error cross-validation (RMSECV) and highest coefficient of determination (r2) was achieved in the fresh needles (r2 = 0.81, RMSECV= 0.24) and fresh inner bark (r2 = 0.79, RMSECV = 0.25) for MJ-treated samples 14 days and 21 days after treatment, respectively. Using partial least squares regression, models for individual compounds gave high (r2), residual predictive deviation (RPD), lab to NIR error (PRL) or range error ratio (RER) for fructose (r2 = 0.84, RPD = 1.5, PRL = 0.71, RER = 7.25) and glucose (r2 = 0.83, RPD = 1.9, PRL = 1.14, RER = 8.50) and several diterpenoids. This provides an optimistic outlook for the use of NIR spectroscopy-based models for the larger-scale prediction of the P. radiata chemistry needed for quantitative genetic studies.

Assessment of Bulk Composition of Heterogeneous Food Matrices Using Raman Spectroscopy.

Raman spectroscopy (RS) has for decades been considered a promising tool for food analysis, but widespread adoption has been held back by, e.g., high instrument costs and sampling limitations regarding heterogeneous samples. The aim of the present study was to use wide area RS in conjunction with surface scanning to overcome the obstacle of heterogeneity. Four different food matrices were scanned (intact and homogenized pork and by-products from salmon and poultry processing) and the bulk chemical parameters such as fat and protein content were estimated using partial least squares regression (PLSR). The performance of PLSR models from RS was compared with near-infrared spectroscopy (NIRS). Good to excellent results were obtained with PLSR models from RS for estimation of fat content in all food matrices (coefficient of determination for cross-validation (R2CV) from 0.73 to 0.96 and root mean square error of cross-validation (RMSECV) from 0.43% to 2.06%). Poor to very good PLSR models were obtained for estimation of protein content in salmon and poultry by-product using RS (R2CV from 0.56 to 0.92 and RMSECV from 0.85% to 0.94%). The performance of RS was similar to NIRS for all analyses. This work demonstrates the applicability of RS to analyze bulk composition in heterogeneous food matrices and paves way for future applications of RS in routine food analyses.

An approach for simultaneous monitoring the content of insensitive agent in the double-base oblate spherical propellant by application of near-infrared spectroscope and partial least squares

The content of insensitive agent is an important parameter that has been shown correlated with the combustion characteristic of double-base oblate spherical propellant (DOSP). This work focused on the feasibility of simultaneous monitoring the content of insensitive agent (dibutyl phthalate (DBP) and N, N′‐dimethyl‐N, N′‐diphenylurea (C2)) in DOSP by using near-infrared (NIR) spectroscope coupled with partial least squares (PLS). The optimal spectral intervals for creating models of DBP and C2 corresponded to 5964 cm−1–4212 cm−1 and 6240 cm−1–4380 cm−1, respectively. It had been demonstrated that derivative tools were more suitable for spectral preprocessing as which had the lowest root mean squares error of cross-validation (RMSECV). The best-performance models of DBP and C2 were built under 4 and 7 PLS factors, respectively. The results showed that the determination coefficients of calibration (Rc2) and the root mean squares error of calibration (RMSEC) were 0.9771 and 0.0173 for DBP; 0.984 and 0.0072 for C2, respectively. Besides, the developed models exhibited excellent ability in prediction with the determination coefficients in prediction (RP2) and the root mean squares error in prediction (RMSEP) of 0.9681 and 0.0275 for DBP, and of 0.9554 and 0.0107 for C2, respectively. The residual predictive deviation (RPD) of prediction set were 5.68 and 5.12 for DBP and C2, respectively. The average relative errors of the proposed and reference methods were 0.652% for DBP, and 0.429% for C2, revealing a good correlation between the reference values and predicted values. Therefore, it concluded that the proposed plan has shown to be an attractive means since its efficient and highly accurate which could provide a better option for quality control in the large-scale production of DOSP.

The use of Raman spectroscopy and chemometrics for the discrimination of lab-produced, commercial, and adulterated cold-pressed oils

In this study, lab-produced cold-pressed oil samples of black cumin (CPBCO), almond (CPAO), and walnut (CPWO) were compared with their commercial substitutes. Additionally, adulteration of cold-pressed oils with corn and sunflower oils was determined. Chemical information and fatty acid composition of the samples were obtained using Raman spectroscopy and gas chromatography, respectively. Raman spectra were analyzed with principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The developed PCA models for the discrimination of CPBCO, CPAO, and CPWO from the adulterated samples explained 93.35%, 96.24, and 94.25% of the cumulative variances, respectively. Root mean squared error of calibration (RMSEC), root mean squared error of cross-validation (RMSECV), and root mean squared error of cross-prediction (RMSEP) values were found to be between 0.06 and 0.21, 0.03–0.11, and 0.05–0.21; 0.08–0.24, 0.06–0.15, and 0.08–0.25; and 0.05–0.18, 0.03–0.10, and 0.09–0.20 for the PLS-DA models of CPBCO, CPAO, and CPWO adulterations. The developed PCA models for the discrimination of CPBCO, CPAO, and CPWO from commercial samples explained 99.64%, 91.27, and 92.11% of the cumulative variances, respectively. These results indicate that Raman spectroscopy coupled with chemometrics could be potentially useful both for the discrimination of cold-pressed oils and for the rapid determination of adulteration with cheaper oils.