Calibration Sample Set Research Articles

A method to reveal bulk and surface crystallinity of Polypropylene by FTIR spectroscopy - Suitable for fibers and nonwovens

The crystallinity of polymers is usually determined via Differential Scanning Calorimetry (DSC), X-ray diffraction (XRD) or density measurements. Even though DSC is an easy and fast way to determine crystallinity, it suffers of several disadvantages in the case of fibers and nonwovens. In DSC studies, the sample preparation of fiber material may be difficult, results are not calibration free and heat is introduced in the measuring process, which might lead to transitions in instable crystal structures in the polymer. XRD is able to overcome these problems but is also time consuming and complicated in terms of sample preparation and evaluation of the measurements. Furthermore, it is not possible to determine surface crystallinity or crystallinity gradients inside the specimen with both methods.Based on approaches found in literature, a method was developed which is capable of measuring the degree of crystallinity in the bulk and at the surface of Polypropylene via Fourier transformed infrared spectroscopy. A broad set of calibration samples with a wide range of degrees of crystallinity but without a crystallinity gradient was produced by cooling samples with cooling rates from 1 to 3500 K min−1. The degree of crystallinity was then determined from the ratio of the maximum peak height at 974 cm−1 and the maximum peak height at 998 cm−1 multiplied by 61.4%.Consecutively, the transferability of the method to the measurement of fibers and nonwovens is demonstrated and verified via procedures which are already well described in literature. This method has an excellent reproducibility, spatial resolution and is capable of determining the degree of crystallinity in less than 5 min. Furthermore, this method is easy to implement and does not require further sample preparation.

Rapid qualitative and quantitative analysis of methamphetamine, ketamine, heroin, and cocaine by near-infrared spectroscopy

Rapid and nondestructive near infrared spectroscopy (NIR) methods have been developed for simultaneous qualitative and quantitative analysis of methamphetamine, ketamine, heroin, and cocaine in seized samples. This is the first systematic report regarding a qualitative and quantitative procedure of applying NIR for drug analysis. A total of 282 calibration samples and 836 prediction samples were used for the building and validating of qualitative and quantitative models. Two qualitative analysis modeling methods for soft independent modeling by class analogy (SIMCA) and supporting vector machine (SVM) were compared. From its excellent performance in rejecting false positive results, SIMCA was chosen. The drug concentrations in the calibration and validation sample sets were analyzed using high-performance liquid chromatography. Based on the use of first-order derivative spectral data after standard normal variate (SNV) transformation correction, in the wavelength range from 10,000 to 4000cm−1, four partial least squares quantitative-analysis models were built. The coefficients of determination for all calibration models were >99.3, and the RMSEC, RMSECV, and RMSEP were all less than 1.6, 2.9, and 3.6%, respectively. The results obtained here indicated that NIR with chemometric methods was accurate for qualitative and quantitative analysis of drug samples. This methodology provided a potentially useful alternative to time-consuming gas chromatography–mass spectroscopy and high-performance liquid chromatography methods.

Quantifying the Prediction Error in Analytical Multivariate Curve Resolution Studies of Multicomponent Systems.

In multivariate curve resolution (MCR) analysis, a range of feasible solutions is often encountered, because of the rotational ambiguities associated with the bilinear decomposition of data matrices. For quantitative purposes, the analysis is usually applied to a carefully designed set of calibration and test samples having uncalibrated interferents. Under the usual minimal constraints (non-negativity, unimodality, species correspondence, etc.), concentration and spectral profiles of the analyte in the test samples are not univocally recovered, unlike those in the calibration samples, especially when profile overlapping with the interferents is significant and selective regions do not exist for the analyte. In this report, a quantitative measure of the prediction errors due to rotational ambiguities is discussed, based on the calculation of the differences between the maximum and minimum area under the analyte concentration profiles calculated by the MCR-BANDS procedure. This methodology can be applied in different analytical scenarios with any number of analytes and interferents. Both absolute and relative quantitative errors due to rotation ambiguities are estimated and discussed in both simulated and experimental examples derived from liquid chromatography with diode array detection. The proposed procedure can be generalized to most of the analytical situations where every instrumentally measured sample produces a data table or data matrix.

The Detection and Quantification of Adulteration in Ground Roasted Asian Palm Civet Coffee Using Near-Infrared Spectroscopy in Tandem with Chemometrics

In the present research, an evaluation of using near infrared (NIR) spectroscopy in tandem with full spectrum partial least squares (FS-PLS) regression for quantification of degree of adulteration in civet coffee was conducted. A number of 126 ground roasted coffee samples with degree of adulteration 0-51% were prepared. Spectral data were acquired using a NIR spectrometer equipped with an integrating sphere for diffuse reflectance measurement in the range of 1300-2500 nm. The samples were divided into two groups calibration sample set (84 samples) and prediction sample set (42 samples). The calibration model was developed on original spectra using FS-PLS regression with full-cross validation method. The calibration model exhibited the determination coefficient R2=0.96 for calibration and R2=0.92 for validation. The prediction resulted in low root mean square error of prediction (RMSEP) (4.67%) and high ratio prediction to deviation (RPD) (3.75). In conclusion, the degree of adulteration in civet coffee have been quantified successfully by using NIR spectroscopy and FS-PLS regression in a non-destructive, economical, precise, and highly sensitive method, which uses very simple sample preparation.

Partial constrained least squares (PCLS) and application in soft sensor

With the combination of extracting latent variables and setting constrained samples, partial constrained least squares (PCLS) is proposed and applied for soft sensor. Similar to constrained least squares (CLS), for the purpose of generating matrix equations with Lagrange multiplier, PCLS assigns some samples in calibration set as constrained ones and others as non-constrained ones. Then, this regression coefficients vector for calibration can be obtained by solving matrix equations with partial least squares (PLS). In moving window method of soft sensor, the sample to be predicted is highly related to the samples in previous adjacent sequential time points, thus those samples can be set as constrained ones and other samples not close to those sequential time points in the window as non-constrained ones. Based on the constrained and non-constrained samples, PCLS can be applied to calibrating a model and estimating the predicted sample. Two batches of datasets containing Sulfur Recovery Unite (SRU) and simulated datasets generated by random walk were tested by the proposed method. The results showed that PCLS is the generation of CLS, while CLS is the special case of PCLS when the number of latent variables equals the total number of variables and constrained samples. Meanwhile, in contrast with least squares (LS), PLS and CLS, PCLS can result in smaller prediction errors. Furthermore, four simulated datasets (SIM1, SIM2, SIM3 and SIM4) with trend and/or random walk, or, without trend and/or random walk, showed PCLS can be applied to the datasets when the samples in sequential time points are correlated to those in the previous adjacent sequential time points.

New cellulose crystallinity estimation method that differentiates between organized and crystalline phases

A new method is proposed for estimation of cellulose crystallinity (CrI) based on 93 cm−1 Raman band in spectra of cellulose I materials. In this method (93-Raman), CrI was determined based on regression that was developed using the ratios of peak-heights of the 93 and 1096 cm−1 Raman bands (I93/I1096). For calibration purposes, a set of eight samples, all derived from cotton microcrystalline cellulose Whatman CC31 were selected. When the peak intensity ratios (I93/I1096) were plotted against the calculated CrIs of the calibration set samples, the plot showed an excellent linear correlation (R2 = 0.9888). The 93-Raman method was used to estimate crystallinities of a number of cellulose materials including poplar wood samples that were hydrothermally treated at various temperatures. The wood 93-Raman CrI data showed that the method is able to differentiate between organized and crystalline phases of cellulose, a capability lacking in many other CrI estimation methods.

Nondestructive determination of soluble solids content of persimmons by using dielectric spectroscopy

ABSTRACTTo explore the feasibility of dielectric spectroscopy in predicting soluble solids content (SSC) of persimmons during postharvest storage period, the dielectric constant spectra and dielectric loss factor spectra of 105 ‘Shui’ persimmons were measured from 20 MHz to 4500 MHz. Based on the joint x-y distances algorithm, the persimmon samples were divided into two sets: 70 samples in calibration set and 35 samples in prediction set. One hundred and seventy-four, 14, and 24 variables were extracted as characteristic variables from full dielectric spectra (FS) by uninformative variables elimination (UVE), successive projection algorithm (SPA), and competitive adaptive reweighted sampling (CARS), respectively. Partial least squares (PLS) and least squares support vector machine (LSSVM) were applied to build SSC prediction models using FS and characteristic variables extracted by UVE, SPA, and CARS. The results indicated that LSSVM models offered better performance than PLS models at same input variables. CARS-LSSVM had the best SSC determination performance with the correlation coefficient and root-mean-square error of prediction set of 0.970 and 0.494°Brix. This study indicates that dielectric spectroscopy technique combined with characteristic variables selection methods is promising for determining SSC of persimmons.

Identification of Adulterated and Non-adulterated Norwegian Salmon Using FTIR and an Improved PLS-DA Method

Norwegian salmon is often adulterated with Heilongjiang salmon at local fish markets. To promote fair price competition at fish markets and protect consumer rights, we developed a quick and accurate identification method to distinguish adulterated and non-adulterated Norwegian salmon using a Fourier transform infrared spectroscopy (FTIR). In this study, Norwegian and Heilongjiang salmon could be readily distinguished using partial least squares discriminant analysis (PLS-DA), but it failed to detect the accurate level of 20 to 80% of adulterated Norwegian salmon samples. In order to improve the PLS-DA model, several pre-processing methods, including standard normal variate (SNV), multiplicative scatter correction (MSC), and normalization, were used to evaluate individually to select the most appropriate correction method. Characteristics of the spectra within the waveband range covering 450 to 4000 cm−1 were also analyzed to determine the optimum sub-waveband range to improve the accuracy of the model. The results of the study showed that using FTIR and the improved PLS-DA model established in this study, the adulterated and non-adulterated Norwegian salmon could be completely distinguished. The accuracy of the adulteration level and the prediction accuracy of the model were also significantly improved when normalization method was used at 450 to 1790 cm−1 sub-wavebands. For the calibration and cross-validation sample sets, the determination coefficients of the improved PLS-DA model were at 0.99 and 0.98, respectively. The mean square errors were 2.3 and 4%, resulting in a 90% accuracy of validation sample sets. This technology should provide fish markets an easy and reliable way to distinguish the adulterated and non-adulterated salmon.

Calibration transfer of a Raman spectroscopic quantification method for the assessment of liquid detergent compositions from at-line laboratory to in-line industrial scale

Calibration transfer or standardisation aims at creating a uniform spectral response on different spectroscopic instruments or under varying conditions, without requiring a full recalibration for each situation. In the current study, this strategy is applied to construct at-line multivariate calibration models and consequently employ them in-line in a continuous industrial production line, using the same spectrometer.Firstly, quantitative multivariate models are constructed at-line at laboratory scale for predicting the concentration of two main ingredients in hard surface cleaners. By regressing the Raman spectra of a set of small-scale calibration samples against their reference concentration values, partial least squares (PLS) models are developed to quantify the surfactant levels in the liquid detergent compositions under investigation. After evaluating the models performance with a set of independent validation samples, a univariate slope/bias correction is applied in view of transporting these at-line calibration models to an in-line manufacturing set-up. This standardisation technique allows a fast and easy transfer of the PLS regression models, by simply correcting the model predictions on the in-line set-up, without adjusting anything to the original multivariate calibration models.An extensive statistical analysis is performed in order to assess the predictive quality of the transferred regression models. Before and after transfer, the R2 and RMSEP of both models is compared for evaluating if their magnitude is similar. T-tests are then performed to investigate whether the slope and intercept of the transferred regression line are not statistically different from 1 and 0, respectively. Furthermore, it is inspected whether no significant bias can be noted. F-tests are executed as well, for assessing the linearity of the transfer regression line and for investigating the statistical coincidence of the transfer and validation regression line. Finally, a paired t-test is performed to compare the original at-line model to the slope/bias corrected in-line model, using interval hypotheses.It is shown that the calibration models of Surfactant 1 and Surfactant 2 yield satisfactory in-line predictions after slope/bias correction. While Surfactant 1 passes seven out of eight statistical tests, the recommended validation parameters are 100% successful for Surfactant 2. It is hence concluded that the proposed strategy for transferring at-line calibration models to an in-line industrial environment via a univariate slope/bias correction of the predicted values offers a successful standardisation approach.

Rapid quantification of multi-components in alcohol precipitation liquid of Codonopsis Radix using near infrared spectroscopy (NIRS).

A near infrared spectroscopy (NIRS) approach was established for quality control of the alcohol precipitation liquid in the manufacture of Codonopsis Radix. By applying NIRS with multivariate analysis, it was possible to build variation into the calibration sample set, and the Plackett-Burman design, Box-Behnken design, and a concentrating-diluting method were used to obtain the sample set covered with sufficient fluctuation of process parameters and extended concentration information. NIR data were calibrated to predict the four quality indicators using partial least squares regression (PLSR). In the four calibration models, the root mean squares errors of prediction (RMSEPs) were 1.22 μg/ml, 10.5 μg/ml, 1.43 μg/ml, and 0.433% for lobetyolin, total flavonoids, pigments, and total solid contents, respectively. The results indicated that multi-components quantification of the alcohol precipitation liquid of Codonopsis Radix could be achieved with an NIRS-based method, which offers a useful tool for real-time release testing (RTRT) of intermediates in the manufacture of Codonopsis Radix.

Nonlinear calibration transfer based on hierarchical Bayesian models and Lagrange Multipliers: Error bounds of estimates via Monte Carlo – Markov Chain sampling

The calibration of analytical systems is time-consuming and the effort for daily calibration routines should therefore be minimized, while maintaining the analytical accuracy and precision. The ‘calibration transfer’ approach proposes to combine calibration data already recorded with actual calibrations measurements. However, this strategy was developed for the multivariate, linear analysis of spectroscopic data, and thus, cannot be applied to sensors with a single response channel and/or a non-linear relationship between signal and desired analytical concentration. To fill this gap for a non-linear calibration equation, we assume that the coefficients for the equation, collected over several calibration runs, are normally distributed. Considering that coefficients of an actual calibration are a sample of this distribution, only a few standards are needed for a complete calibration data set. The resulting calibration transfer approach is demonstrated for a fluorescence oxygen sensor and implemented as a hierarchical Bayesian model, combined with a Lagrange Multipliers technique and Monte-Carlo Markov-Chain sampling. The latter provides realistic estimates for coefficients and prediction together with accurate error bounds by simulating known measurement errors and system fluctuations. Performance criteria for validation and optimal selection of a reduced set of calibration samples were developed and lead to a setup which maintains the analytical performance of a full calibration. Strategies for a rapid determination of problems occurring in a daily calibration routine, are proposed, thereby opening the possibility of correcting the problem just in time.

Three-point multivariate calibration models by correlation constrained MCR-ALS: A feasibility study for quantitative analysis of complex mixtures

When it comes to address quantitative analysis in complex mixtures, Partial Least Squares (PLS) is often referred to as a standard first-order multivariate calibration method. The set of samples used to build the PLS regression model should ideally be large and representative to produce reliable predictions. In practice, however, the large number of calibration samples is not always affordable and the choice of these samples should be handled with care as it can significantly affect the accuracy of the predictive model. Correlation constrained multivariate curve resolution (CC-MCR) is an alternative regression method for first-order datasets where, unlike PLS, calibration and prediction stages are performed iteratively and optimized under constraints until the decomposition meets the convergence criterion. Both calibration and test samples are fitted into a unique bilinear model so that the number of calibration samples is no longer a critical issue. In this paper we demonstrate that under certain conditions CC-MCR models can provide for reasonable predictions in quantitative analysis of complex mixtures even when only three calibration samples are employed. The latter are defined as samples having the minimum, the maximum and the average concentration, providing for a simple and rapid strategy to build reliable calibration model. The feasibility of three-point multivariate calibration approach was assessed with several case studies featuring mixtures of different analytes in presence of interfering species. Satisfactory predictions with relative errors in the range 3–15% were achieved and good agreement with classical PLS models built from a larger set of calibration samples was observed.

Botanical Composition Prediction of Alfalfa–Grass Mixtures using NIRS: Developing a Robust Calibration

Botanical composition of alfalfa (Medicago sativa L.)–grass fresh and ensiled mixtures is a key parameter for assessing forage and diet quality as well as for managing mixed stands. Previous attempts to validate near‐infrared reflectance spectroscopy (NIRS) equations for estimating botanical composition have had mixed results. This study was conducted to develop a robust NIRS method to estimate botanical composition of binary alfalfa–grass mixtures. Alfalfa–grass samples were collected across New York State over four growing seasons, hand separated, and a subset were ensiled separately. Dry samples were coarsely ground, mixed in known proportions, and reground for analysis by NIRS at Dairy One Forage Laboratory, Ithaca, NY. Samples were mixed to range from 0 to 100% alfalfa for NIRS calibration, with a total of 741 individual samples from 3 yr used for calibration of three NIRS instruments and samples from a fourth year used for validation. Grass composition was predicted with good precision and accuracy showing biases of 2.49 and standard errors of prediction (SEP) of 5.06, with R2 of 0.972, using the equation developed across multiple instruments. With selection of a robust set of calibration samples over many environments, NIRS can be used to determine the botanical composition of fresh‐dried or ensiled‐dried alfalfa–grass samples, and replicate scans from multiple instruments can be combined to develop a single calibration that will perform with equal efficiency across different instruments.

Analytical approaches for determination of bromine in sediment core samples by X-ray fluorescence spectrometry

Bromine has been recognized as a valuable indicator for paleoclimatic studies. Wavelength dispersive X-ray fluorescence (WDXRF) and total reflection X-ray fluorescence (TXRF) methods were applied to study the bromine distributions in lake sediment cores. Conventional WDXRF technique usually requires relatively large mass of a sediment sample and a set of calibration samples. Some analytical approaches were developed to apply WDXRF to small sediment core samples in the absence of adequate calibration samples with a known Br content. The mass of a sample to be analyzed was reduced up to 200–300mg and the internal standard method with correction using fundamental parameters was developed for Br quantification. TXRF technique based on the direct analysis of a solid suspension using 20mg of sediment sample by internal standard method was additionally tested. The accuracy of the WDXRF and TXRF techniques was assessed by the comparative analysis of reference materials of sediments, soil and biological samples. In general, good agreement was achieved between the reference values and the measured values. The detection limits of Br were 1mg/kg and 0.4mg/kg for WDXRF and TXRF respectively. The results of the Br determination obtained with different XRF techniques were comparable to each other and used for paleoclimatic reconstructions.

Determination of adulterated neem and flaxseed oil compositions by FTIR spectroscopy and multivariate regression analysis

Natural oils are increasingly being used in the food, cosmetic, and agrochemical industries in recent years. However, natural oils with high market values are often counterfeited and adulterated with cheap, poor quality oils, with serious economic implications for the food and cosmetic industries, and potential health implications to consumers. This study reports the first combined use of Fourier transform infrared spectroscopy (FTIR) and partial-least-square (PLS) multivariate regression analysis for rapid, accurate, and low cost determination of the % compositions of two natural oils (neem oil (NO) and flaxseed oil (FO)) adulterated either with edible vegetable oil (VO) or extra virgin olive oil (EVOO). The FTIR of the calibration sample sets containing adulterated NO and FO with VO and EVOO at a concentration range of 1–99% w/w were measured and subjected to PLS multivariate regression analyses. The obtained FTIR spectra profile of the adulterated samples are highly dependent on the type of natural oil as well as the type of adulterant oil. The developed PLS models were subsequently used to determine the % compositions of independently prepared validation samples of NO and FO adulterated with VO and EVOO. The figures-of-merit of the PLS regression models were excellent and with good linearity (R2 > 0.998814). The score plots of the PLS regressions revealed interesting and useful information for pattern recognition of adulterated NO and FO samples. The PLS regression models correctly determined % compositions of adulterated NO and FO with VO with low root-mean-square-relative-percent errors (RMS%RE) of determination of 3.02%, and 4.24%, with an overall RMS%RE of 3.63%. The models also correctly determined % compositions of adulterated NO and FO with EVOO with RMS%RE of 7.13% and 2.00%, with an overall RMS%RE of 4.56%. The simplicity and high accuracy of this low-cost study presents an attractive model with potential real-world applications in quality control and quality assurance for consumer products in the food, cosmetics, and agrochemical industries.

Near-Infrared Spectroscopy Combined with Absorbance Upper Optimization Partial Least Squares Applied to Rapid Analysis of Polysaccharide for Proprietary Chinese Medicine Oral Solution

Near-infrared (NIR) spectroscopy was applied to reagent-free quantitative analysis of polysaccharide of a brand product of proprietary Chinese medicine (PCM) oral solution samples. A novel method, called absorbance upper optimization partial least squares (AUO-PLS), was proposed and successfully applied to the wavelength selection. Based on varied partitioning of the calibration and prediction sample sets, the parameter optimization was performed to achieve stability. On the basis of the AUO-PLS method, the selected upper bound of appropriate absorbance was 1.53 and the corresponding wavebands combination was 400 - 1880 & 2088 - 2346 nm. With the use of random validation samples excluded from the modeling process, the root-mean-square error and correlation coefficient of prediction for polysaccharide were 27.09 mg·L-1 and 0.888, respectively. The results indicate that the NIR prediction values are close to those of the measured values. NIR spectroscopy combined with AUO-PLS method provided a promising tool for quantification of the polysaccharide for PCM oral solution and this technique is rapid and simple when compared with conventional methods.

Evaluation of oil yield of oil shale by infrared spectrometry coupled with ultrasound-assisted extraction

The oil yield of oil shale was evaluated by Fourier transform infrared(FTIR) spectrometry coupled with ultrasound-assisted extraction. The extraction conditions, including the amount of sample, extraction time and extraction temperature, were examined and optimized. Twenty-four oil shale samples were collected and divided into calibration set and prediction set randomly with a ratio of 2:1. The oil yields of all the samples were determined by the routine method(low-temperature retorting) for reference. The linear regression(LR) equations of oil yield vs. the total area of the spectrum peaks in a wavenumber range of 3100–2800 cm–1 as well as the sum of absorbance of three absorption peaks(2855, 2927 and 2955 cm–1), and the multiple linear regression(MLR) model of oil yield vs. the absorbances of the three absorption peaks were constructed with the samples in calibration set and applied to the evaluation of the oil yields of the samples in prediction set, respectively. The results show that the MLR model provides more accurate predictions than the other LR two equations. The determination coefficient(Rp2), the root-mean-square error of prediction(RMSEP) and the residual prediction deviation(RPD) of the MLR model are 0.9616, 0.6458 and 3.6, respectively. The present method is a rapid and effective alternative to the routine low-temperature retorting method.

Nondestructive Measurement of Soluble Solids Content of Kiwifruits Using Near-Infrared Hyperspectral Imaging

This study aims to determine the soluble solids content (SSC) of intact kiwifruits of varieties “Xixuan” and “Huayou” by using near-infrared (NIR) hyperspectral imaging and to investigate which model, developed for a single variety or for two varieties together, had better SSC determination performance. The NIR hyperspectral reflectance images of 200 kiwifruits (100 kiwifruits for each variety) were obtained over the wavelength of 865.11–1,711.71 nm. Mean spectra were extracted from the regions of interest in hyperspectral images of each kiwifruit. The samples were divided into calibration set and prediction set based on the joint x–y distance sample set partitioning method. There were 67 samples in calibration set and 33 samples in prediction set when a single variety was used to establish SSC calibration model, and there were 134 samples (67 “Xixuan” and 67 “Huayou”) in calibration set and 66 samples (33 “Xixuan” and 33 “Huayou”) in prediction set when the two varieties were used together. Successive projections algorithm (SPA) was applied to extract the effective wavelengths (EWs) from full spectra (FS). Nine EWs were selected when a single variety (“Xixuan” or “Huayou”) was used in modeling, and 19 EWs were selected when the two varieties were used together. SSC calibration models were developed based on the partial least squares (PLS) regression and least square support vector machine (LSSVM) modeling methods using the full spectra and extracted EWs as inputs, respectively. The results showed that both calibration and prediction performances of LSSVM models were better than those of PLS. The best SSC determination model for “Xixuan,” “Huayou,” and two varieties together were SPA-LSSVM, FS-LSSVM, and FS-LSSVM with the correlation coefficient of prediction set of 0.766, 0.971, and 0.911, and the root-mean-square error of prediction set of 0.968, 0.589, and 1.137, respectively. The study demonstrates the feasibility of using NIR hyperspectral reflectance imaging technique as a noninvasive method for predicting SSC of kiwifruits and indicates that developing a model for a specific variety is helpful to decreasing prediction error and to improving calculation speed.

Investigating the impact of biomass quality on near-infrared models for switchgrass feedstocks

The aim of this study was to determine the impact of incorporating switchgrass samples that have been in long term storage on the development of near-infrared (NIR) multivariate calibration models and their predictive capabilities. Stored material contains more variation in their respective spectral signatures due to chemical changes in the bales with storage time. Partial least squares (PLS) regression models constructed using NIR spectra of stored switchgrass possessed an instability that interfered with the correlation between the spectral data and measured chemical composition. The models were improved using calibration sample sets of equal parts stored and fresh switchgrass to more accurately predict the chemical composition of stored switchgrass. Acceptable correlation values (r calibration ) were obtained using a calibration sample set composed of 25 stored samples and 25 samples of fresh switchgrass for cellulose (0.91), hemicellulose (0.74), total carbohydrates (0.76), lignin (0.98), extractives (0.92), and ash (0.87). Increasing the calibration sample set to 100 samples of equal parts stored to senesced material resulted in statistically increased ( p = 0.05) correlations for total carbohydrates (0.89) and ash (0.96). When these models were applied to a separate validation set (equal to 10% of the calibration sample set), high correlation coefficients (r) for predicted versus measured constituent content were observed for cellulose (0.94), total carbohydrates (0.98), lignin (0.91), extractives (0.97), and ash (0.90). For optimization of processing economics, the impact of feedstock storage must be investigated for implementation in conversion processes. While NIR is a well-known high-throughput technique for characterization of senesced switchgrass, the selection of appropriate calibration samples and consequent multivariate models must be taken into careful consideration for NIR application in a biomass storage facility for rapid chemical compositional determination.

Determination of Dry Matter and Soluble Solids of Durian Pulp Using Diffuse Reflectance near Infrared Spectroscopy

Fourier transform near infrared spectroscopy was used as a non-invasive technique for the determination of dry matter and soluble solids of durian pulp. A set of 25 fruit was randomly harvested every 10 days, starting from 80 days until 127 days after the onset of fruit development covering six levels of maturity (80 days, 90 days, 100 days, 110 days, 120 days and 127 days). After applying ethephon on the fruit stems, the fruits were kept for 3 days at room temperature and allowed to ripen. Only the pulp of the durian was scanned. The dry matter and soluble solids reference values of the samples were determined by a hot-air-oven method and using a refractometer, respectively. Prediction models using half the samples related the spectral data, and dry matter and soluble solids data were subsequently established using partial least-squares regression and validated using the other half of the samples in a prediction set. A full cross-validation was also generated using all 149 samples. Both the half-of-the-samples model and the all-sample model were then validated using a true validation set of samples collected in a later year. When tested against the validation half of the samples, the half-of- the-samples model predicted dry-matter content with a coefficient of determination ( r2) and root mean square error of prediction ( RMSEP) of 0.89 and 3.60%, respectively, and for soluble solids content 0.55 and 1.63 °Brix (Bx), respectively. When tested on samples from a later season, the model for dry-matter content returned an r2, RMSEP and bias of 0.26, 6.10% and −2.16%, respectively, and for soluble solids content 0.27, 1.25 °Bx and 1.09 °Bx, respectively. The cross-validated model for dry matter yielded a slightly better r2 and root mean square error of cross-validation ( RMSECV) of 0.90 and 3.58%, respectively, however, the model for soluble solids did not provide a better r2 and RMSECV: 0.51 and 1.81 °Bx, respectively. When tested on samples from a later season, the cross-validated models gave, r2, RMSEP and bias of 0.15, 5.17% and −1.49%, respectively, for dry-matter content, and for soluble solids content 0.37, 1.32 °Bx and 1.23 °Bx, respectively. The poor results obtained when predicting dry matter in samples in later seasons indicate that samples from several seasons must be included in the set of calibration samples. This is the first report on the application of NIR spectroscopy to evaluate the dry matter and soluble solids of durian pulp and could be useful to customers, exporters, importers and also postharvest technologists. However, prediction accuracy was not demonstrated in the model for durian pulp soluble solids, possibly because of the effect of ethephon applied after harvesting to induce ripening within 3 days to make the fruit suitable for consumption. In addition, it was found that the vibration bands of cellulose and fat, and those of aromatic, CH2 and sucrose highly affected the predictions of dry matter and soluble solids in the durian pulp, respectively.