Partial Least Squares Calibration Models Research Articles

Forecasting wine phenolic composition from infrared spectra of grapes extracts and monitoring of fermentations with optimised time-specific prediction models

Incorporating monitoring and forecasting technologies has the potential to enhance the different aspects of winemaking. By monitoring certain chemical parameters, it is possible to evaluate the progress of the fermentation and phenolic extraction, while also detecting deviations from expected behaviour. This study aims to investigate the development of a rapid forecasting method and different strategies to improve the accuracy of process monitoring. The first part involves the introduction of partial least squares (PLS) calibration models able to forecast the trajectories of phenolic extraction during fermentation using grape extract spectra. Additionally, novel methods for reoptimizing calibration models are proposed, with the intention of increasing the prediction accuracy. Whilst this method is transferable to PLS models, principal component regression (PCR) models were used instead as a more simplistic way to test the new methods. First, dynamic PCR linear regression coefficients were adapted over time to capture the compound evolution in the wine matrix. Secondly, PCR calibration models were reoptimized using interpolated spectral and reference data. The forecasting PLS calibration models have shown promising results for total anthocyanin content (mg/L), colour density, polymeric pigments (mg/L), MCP tannins (mg/L), and total phenolic index with overall R2 values on the validation set above 0.6, and low RSMEV values. Using interpolated data to re-optimise models was found to be more effective than global models or those using the calculated regression coefficients methods for predicting the concentration of a particular compound at a certain time point. While R2 values are suboptimal for all considered PCR models, in 72% of the cases analysed, the interpolation method for re-optimisation showed RMSEV values between 6% and 83% lower than those reported for a more traditional PCR model and other re-optimisation strategies. Interpolated models have shown consistency in predicting values, along with good performance metrics, indicating that they could be applied to samples for which calibration models do not exist.

Raman spectroscopy combined with partial least squares (PLS) based on hybrid spectral preprocessing and backward interval PLS (biPLS) for quantitative analysis of four PAHs in oil sludge

Polycyclic aromatic hydrocarbons (PAHs) contained in a large amount of oily sludge produced in petroleum and petrochemical production has become one of the main environmental protection concerns in the industry. The accurate determination of PAHs is of great significance in the field of petroleum geochemistry and environmental protection. In this study, Raman spectroscopy combined with partial least squares (PLS) based on different hybrid spectral preprocessing methods and variable selection strategies was proposed for quantitative analysis of phenanthrene, fluoranthrene, fluorene and naphthalene (Phe, Flt, Flu and Nap) in oil sludge. At first, PAHs in oily sludge was extracted by solid–liquid extraction with methanol as extractant, and Raman spectra of 21 oily sludge samples were collected by portable Raman spectrometer. And then, the influence of first derivative (D1st), wavelet transform (WT) and their hybrid spectral preprocessing on the predictive performance of the PLS calibration model was discussed. Thirdly, biPLS (backward interval partial least squares) was used to optimize the input variables before and after the hybrid spectral preprocessing methods, and the influence of biPLS and the hybrid spectral preprocessing sequence on the predictive performance of the PLS calibration model was discussed. Finally, the predictive performance of the PLS calibration model was optimized according to the results of leave-one-out cross-validation (LOOCV) method. The results show that the biPLS-D1st-WT-PLS calibration model established by using biPLS first to select the characteristic variables, followed by hybrid spectral preprocessing of the characteristic variables, has better prediction performance for Flt (determination coefficient of prediction (R2P) = 0.9987, and the mean relative error of prediction (MREP) = 0.0606). For Phe, Flu and Nap, the WT-biPLS-PLS calibration model has a better predictive effect (R2P are 0.9995, 0.9996 and 0.9983, and MREP are 0.0426, 0.0719 and 0.0497, respectively). In general, portable Raman spectroscopy combined with PLS calibration model based on different hybrid spectral preprocessing and variable selection strategies has achieved good prediction results for quantitative analysis of four PAHs in oily sludge. It is a new strategy to firstly select the characteristic variables of the original spectra, and secondly to preprocess the characteristic variables by the hybrid spectral preprocessing, which will provide a new idea for the establishment of quantitative analysis methods for PAHs in oily sludge.

FTIR-based prediction of collagen content in hydrolyzed protein samples

Fourier transform infrared spectroscopy (FTIR) is a powerful analytical tool that has been used for protein and peptide characterization for decades. In the present study, the objective was to investigate if FTIR can be used to predict collagen content in hydrolyzed protein samples. All samples were obtained from enzymatic protein hydrolysis (EPH) of poultry by-products providing a span in collagen content from 0.3% to 37.9% (dry weight), and the FTIR analysis was performed using dry film FTIR. Since nonlinear effects were revealed by calibration using standard partial least squares (PLS) regression, Hierarchical Cluster-based PLS (HC-PLS) calibration models were constructed. The HC-PLS model provided a low prediction error when validated using an independent test set (RMSE = 3.3% collagen), while validation using real industrial samples also showed satisfying results (RMSE = 3.2%). The results corresponded well with previously published FTIR-based studies of collagen, and characteristic spectral features for collagen were well identified in the regression models. Covariance between collagen content and other EPH related processing parameters could also be ruled out in the regression models. To the authors' knowledge, this is the first time that collagen content has been systematically studied in solutions of hydrolysed proteins using FTIR. This is also one of few examples where FTIR is successfully used to quantify protein composition. The dry-film FTIR approach presented in the study is expected to be an important tool in the growing industrial segment that is based on sustainable utilization of collagen-rich biomass.

Rapid Determination of the Total Petroleum Hydrocarbon Content of Soils by Handheld Fourier Transform Near-Infrared Spectroscopy.

For successful soil remediation and hydrocarbon exploration operations, determining the total petroleum hydrocarbon (TPH) content of soils is an indispensable process step. This paper reports on the performance of a handheld Fourier transform near-infrared (FT-NIR) spectrometer for rapid and quantitative determination of TPH content of soils from two different sites by diffuse reflection measurements. For rapid decisions for exploration work or environmental site assessment projects, a quick─preferably on-site─determination of TPH content is valuable. Diffuse reflection NIR spectra were recorded from soil samples of two different sites with TPH reference values ranging from 350 to 30,000 ppm, as determined by capillary gas chromatography and flame ionization detection with hydrocarbon fingerprinting C1-C44. However, this paper not only addresses the development of site-specific partial-least squares (PLS) calibrations but also demonstrates the locally-weighted PLS (LW-PLS) technique, which can be used to develop global, site-independent PLS calibrations without significant penalty in calibration performance. As a first step, the diffuse reflection spectra were used to develop conservative, site-specific PLS calibration models with root-mean-square calibration/cross-validation errors (RMSEC/RMSECV) of 1043/1106 and 741/785 ppm TPH, respectively, and the average absolute prediction errors for samples not contained in the calibration set were 451 and 293 ppm for the two sites, respectively. In a further step, significant degradation of the RMSE values of a conservative PLS model based on the NIR spectra of both sites was then compared to the application of the LW-PLS method, with only a slight loss of the prediction accuracy relative to the site-independent models. This study confirms the ability of next-generation portable FT-NIR spectrometers to predict low TPH levels in various soil types through both─soil-specific and site-independent─calibrations, giving these spectrometers the potential to become rapid screening tools in the field.

Rapid quantitative analysis of rare earth elements Lu and Y in rare earth ores by laser induced breakdown spectroscopy combined with iPLS-VIP and partial least squares.

Rare earth ores are complex in composition and diverse in mineral composition, requiring high technical requirements for the selection of rare earth ores. It is of great significance to explore the on-site rapid detection and analysis methods of rare earth elements in rare earth ores. Laser induced breakdown spectroscopy (LIBS) is an important tool to detect rare earth ores, which can be used for in situ analyses without complicated sample preparation. In this study, a rapid quantitative analysis method for rare earth elements Lu and Y in rare earth ores was established by LIBS combined with an iPLS-VIP hybrid variable selection strategy and partial least squares (PLS) method. First, the LIBS spectra of 25 samples were studied using laser induced breakdown spectrometry. Second, taking the spectrum processed by wavelet transform (WT) as the input variables, PLS calibration models based on interval partial least squares (iPLS), variable importance projection (VIP) and iPLS-VIP hybrid variable selection were constructed to quantitatively analyze rare earth elements Lu and Y, respectively. The results show that the WT-iPLS-VIP-PLS calibration model has better prediction performance for rare earth elements Lu and Y, and the optimal coefficient of determination (R2) of Lu and Y were 0.9897 and 0.9833, the root mean square error (RMSE) were 0.8150 μg g-1 and 97.1047 μg g-1, and the mean relative error (MRE) were 0.0754 and 0.0766, respectively. It shows that LIBS technology combined with the iPLS-VIP and PLS calibration model provides a new method for in situ quantitative analysis of rare earth elements in rare earth ores.

Determination of total phenolic content and selected phenolic compounds in sweet wines by fluorescence spectroscopy and multivariate calibration

The aim of this work was to develop a simple, fast and reagent-free method for the prediction of total phenolic content (TPC) and selected phenolic compounds in Slovak botrytized Tokaj wines based on spectral data and partial least squares (PLS) regression. The significant variables for the prediction using PLS were obtained by evaluating the variable importance in projection (VIP). Folin-Ciocalteu and HPLC, respectively, were used as reference methods for the determination of TPC and selected phenolic compounds. Using UV–vis and excitation-emission matrix (EEM) fluorescence spectra recorded on the bulk and diluted samples, the calibration models for TPC were developed and compared. The best PLS model with relative predictive deviation (RPD) of 5.8, coefficient of determination (R2) of 0.972 and root mean squares error (RMSE) of 20.2 mg /L was based on the variables selected from unfolded EEM fluorescence spectra of diluted samples. Using unfolded EEM fluorescence spectra recorded on the diluted samples and VIP, the PLS calibration models for gallic, protocatechic, caffeic and p-coumaric acids and (+)-catechin were obtained with RPD > 4.0 and R2 > 0.9. The RMSE values were 0.7, 0.5, 0.3, 0.2 and 1.0 mg/L for gallic, protocatechic, caffeic and p-coumaric acids and (+)-catechin, respectively.

Evaluation of gasoline fuel quality using FTIR spectroscopy and multivariate technique: a case study in Aizawl city

Fuel quality control had become increasingly important due to the direct relationship between fuel quality and vehicle emissions. Variability of gasoline fuel from one refilling station to another is analyze using Fourier Transform Infrared spectroscopy (FTIR) and Principal Component Analysis (PCA). The result indicates that HC chain length, oxygenate, toluene and aromatic content are the major variables that cause sample variation and grouping. Determination of kerosene and Methyl tert-Butyl Ether (MTBE) concentration in gasoline fuel is done by using partial least squares (PLS) regression multivariate technique as applied to FTIR-ATR spectral data of the test samples. PLS calibration models were developed for the prediction of kerosene and MTBE and validated with independent set of known samples. Accordingly, among 179 tested samples, 103 samples contain kerosene (% v/v) less than 10%, 61 samples contain 11–20%, 11 samples contain 21–30% and 4 samples exceed 30% resulting that majority of gasoline fuel in the study area are adulterated. It is observed that 17 samples are MTBE blended with varying concentrations in the range of 1–7.34% v/v. This study provides an inexpensive and effective method to investigate gasoline quality, detection of adulterants and oxygenator in areas where adulteration of fuel is quite popular.

Determination of Pork Adulteration in Roasted Beef Meatballs using Fourier Transform Infrared Spectroscopy in Combination with Chemometrics

The pork adulteration in food is a matter of concern for Muslims worldwide. This study aimed to quantitatively analyze and classify pork and beef in roasted meatballs using the Fourier Transform Infrared (FTIR) spectroscopy method combined with chemometrics. To obtain the FTIR spectral data, pure beef meatballs and beef meatballs added with pork at various concentrations were analyzed in the mid-infrared region corresponding to wavenumbers ranging from 4000 to 400 cm-1. Samples of beef meatballs randomly selected from the market were analyzed in the optimum condition in terms of wavenumbers capable of providing best modeling. Qualitative and quantitative analyses were performed on the optimum wavenumbers of 1100-900 cm-1 using chemometrics of Partial Least Square (PLS) and Principal Component Analysis (PCA), respectively. The PLS calibration model for the relationship between actual values of pork (x-axis) and predicted FTIR values (y-axis) of roasted meatballs resulted in the equation of y = 0.99965x + 0.0221, with a coefficient of determination (R2-value) of 0.99965 and root mean square error of calibration (RMSEC) of 0.7137%. The PCA indicated differences in quadrants between pure beef and pork roasted meatballs. Most samples of marketed beef meatballs lied in the beef meatball quadrant. However, one sample of marketed beef meatballs was in the pork meatball quadrant; thus, it contained pork. To conclude, the FTIR spectroscopy method combined with chemometrics could qualitatively and quantitatively identify the ingredient of pork in roasted beef meatballs.

IR spectroscopy coupled with chemometrics used as a simple and rapid method to determine the caffeine content of tea products


 Introduction: Tea is a popular beverage that comes from Camellia sinensis. Tea is generally categorised into four types: black tea, oolong tea, green tea, and white tea. These four types are distinguished based on the presence or absence of a fermentation process during their processing. One of the compounds that play a role in providing freshness to tea is caffeine.     
 Aims: The purpose of this study was to determine the caffeine content in the tea samples that are on the market.   
 Methods: This was done using the near-infrared (NIR)-chemometric method and using the TLC-Densitometry method as a comparison. Infrared (IR) spectroscopy combined with chemometrics has been developed as a simple method to analyse the caffeine content in a  tea sample. IR spectra of tea samples were correlated with caffeine content using chemometrics.   
 Results:  In this study, the partial least squares (PLS) model of the NIR model that showed the best calibration with r-square was 0.958, and the root mean squared error of calibration (RMSEC) value was 0.070. The PLS calibration model of the NIR models was further used to predict the unknown caffeine content in commercial samples. The significance of the caffeine content that had been measured with NIR and TLC-Densitometry was evaluated using a two paired sample t-test.   
 Conclusion: The caffeine content measured with both methods gave no significant difference.

Raman Spectrometry as a Tool for an Online Control of a Phototrophic Biological Nutrient Removal Process

Real-time bioprocess monitoring is crucial for efficient operation and effective bioprocess control. Aiming to develop an online monitoring strategy for facilitating optimization, fault detection and decision-making during wastewater treatment in a photo-biological nutrient removal (photo-BNR) process, this study investigated the application of Raman spectroscopy for the quantification of total organic content (TOC), volatile fatty acids (VFAs), carbon dioxide (CO2), ammonia (NH3), nitrate (NO3), phosphate (PO4), total phosphorus (total P), polyhydroxyalkanoates (PHAs), total carbohydrates, total and volatile suspended solids (TSSs and VSSs, respectively). Specifically, partial least squares (PLS) regression models were developed to predict these parameters based on Raman spectra, and evaluated based on a full cross-validation. Through the optimization of spectral pre-processing, Raman shift regions and latent variables, 8 out of the 11 parameters that were investigated—namely TOC, VFAs, CO2, NO3, total P, PHAs, TSSs and VSSs—could be predicted with good quality by the respective Raman-based PLS calibration models, as shown by the high coefficient of determination (R2 > 90.0%) and residual prediction deviation (RPD > 5.0), and relatively low root mean square error of cross-validation. This study showed for the first time the high potential of Raman spectroscopy for the online monitoring of TOC, VFAs, CO2, NO3, total P, PHAs, TSSs and VSSs in a photo-BNR reactor.

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.

Sampling optimization for blend monitoring of a low dose formulation in a tablet press feed frame using spatially resolved near-infrared spectroscopy

In-line measurements of low dose blends in the feed frame of a tablet press were performed for API concentration levels as low as 0.10% w/w. The proposed methodology utilizes the advanced sampling capabilities of a Spatially Resolved Near-Infrared (SR-NIR) probe to develop Partial Least-Squares calibration models. The fast acquisition speed of multipoint spectra allowed the evaluation of different numbers of co-adds and feed frame paddle speeds to establish the optimum conditions of data collection to predict low potency blends. The interaction of the feed frame paddles with the SR-NIR probe was captured with high resolution and allowed the implementation of a spectral data selection criterion to remove the effect of the paddles from the calibration and testing process. The method demonstrated accuracy and robustness when predicting drug concentrations across different feed frame paddle speeds.

Near infrared spectral calibration model transfer without standards by screening spectral points with scale invariant feature transform from master samples spectra

In order to realize calibration model transfer of near infrared (NIR) spectra without standards, scale invariant feature transform (SIFT) algorithm was applied to extract characteristic spectral points of NIR spectra in this study. Three sets of spectral points were selected by SIFT from the spectra of precision detection (SPD) of a radix scutellariae sample by continuously testing the sample three times. Aiming at obtaining high consistency of the three sets, the orthogonal table L9 (34) was used to optimize the parameters of SIFT. Basing on the NIR spectra of several representative radix scutellariae samples, a series of spectral point sets were screened by SIFT with the optimized parameters. Three methods of further treating the spectral points sets to optimize the combination of the spectral points and provided three spectral point sets, which were recorded as Ui, Uu and Uur, respectively. The partial least square (PLS) calibration models for predicting baicalin content of radix scutellariae were built on whole wavelengths, Ui, Uu and Uur at different number of latent variables (nLVs), respectively. Compared with other PLS models, the models of SIFTur-PLS built on Uur, which was obtained by taking union of the firstly selected spectral point sets, then eliminating the points with high deviance of SPD and those with high correlativity from the union, are most robust and always give lower or lowest prediction errors for both master and slave samples at many nLVs. It is a good way to filter stable, highly independent and characteristic spectral points to build robust PLS calibration models by combining SIFT algorithm with standard deviance analysis of SPD and correlative analysis. The models can be directly shared by the slave instrument, without needing transfer sets, and without requiring to correct the spectra of slave instruments or spectral calibration models.

Quantitative analysis of polycyclic aromatic hydrocarbons in soil by infrared spectroscopy combined with hybrid variable selection strategy and partial least squares

Infrared spectroscopy (IR) combined with multivariate calibration technology can be used as a potential method to quantitative analysis of polycyclic aromatic hydrocarbons (PAHs) in soil, which provides a rapid data support for soil risk assessment. However, IR spectrum contains lots of useless information, its predictive performance is poor. Variable selection is an effective strategy to eliminate irrelevant wavelengths and enhance predictive performance. In this study, IR combined with partial least squares (PLS) was proposed to quantify anthracene and fluoranthene in soil. In order to improve the predictive performance of the PLS calibration model, the synergy interval PLS (siPLS) method was first used for “rough selection” to select feature bands; on this basis, “fine selection” was performed to extract the feature variables. In “fine selection”, three different feature variables selection methods, such as successive projection algorithm (SPA), genetic algorithm (GA), and particle swarm optimization (PSO), were compared for their performance in extracting effective variables. The results show that the siPLS-GA calibration model receive a lowest root mean square error (RMSE) and a largest determination coefficient (R2). Results of external validation demonstrate an excellent predictive performance of siPLS-GA calibration model, with the R2 = 0.9830, RMSE = 0.5897 mg/g and R2 = 0.9849, RMSE = 0.4739 mg/g for anthracene and fluoranthene, respectively. In summary, siPLS combined with GA can accurately extract the effective information of the target substance and improve the predictive performance of the PLS calibration model based on IR spectroscopy.

Chemical Profile and Observing Honey Adulteration Using Fourier Transform Infrared (FTIR) Spectroscopy and Multivariate Calibration

Honey is a complex mixture that contains nutrients and bioactive composition and chemical compositionthat is needed by the human body. Honey is also rich in antioxidants because it is prone to falsification given its manyproperties. This study discusses the chemical and nutritional profiles and the observation of counterfeiting in honey usingthe infrared septicroscopy method. The honey used is obtained from providers of native honey from Kalimantan forestswith 3 kinds of honey brands, namely Mahuka A (MHA), Mahuka B (MHB) and Mahuka C (MHC). Tests of chemicaland nutritional profiles included Water content, ash content, protein content, fat content and calcium levels followed byhoney counterfeiting observation using the Fourier Transform Infrared (FTIR) method with a combination of Partial leastsquare (PLS) calibration model and Principle Component Regression (PCR). The results of all honey samples havevarying values in the water content between 26.75 - 31.00%. Ash content of 0.10 -0.16%. Protein content 0.24 - 0.88%fat content 0.08 -0.44% carbohydrate content. Observation of FTIR honey counterfeiting is used to replace the authenticity of Honey (MH). FTIR combined with Partial Least Square (PLS) was optimized in the subsequent testing ofa mixture of sucrose (MCS) with native honey (MH). Calibration models were taken in a combination of regions 1423 -1825 cm-1. A high coefficient of determination (R2) of 0.9960 with a calibration value (RMSEC) of the root error of thesquare root low of 0.0898% v / v was successfully understood in the MHA on the PLS model. high R2 values and lowRMSEC and RMSEP values on calibration and validation assessments with both accuracy and precision models used.

NIR associated to PLS and SVM for fast and non-destructive determination of C, N, P, and K contents in poultry litter.

Using near-infrared (NIR) spectroscopy for poultry litter characterization can be a rapid, non-destructive, and low-cost alternative. This study aims to estimate the C, N, P, and K content in poultry litter samples using for first time NIR spectroscopy. For these purposes, the building models were carried out using Partial Least Squares (PLS) and Support Vector Machines (SVM) methods. A total of 160 litter samples were analyzed in poultry houses of different rearing systems, seeking the highest possible variability in their chemical composition. NIR spectroscopy, combined with PLS and SVM methods, is an alternative method for non-destructive C, N, P, and K determination in poultry samples. The regression models using SVM provide better accuracy for all elements, laying the basis for the nonlinear regression approach's application. The K determination on poultry litter using NIR was possible only by the SVM model (R2=0.8620 and RPD=2.7330). Conclusively, the predictive ability was improved using the SVM method.

Competitive adaptive reweighted sampling assisted partial least square analysis of excitation-emission matrix fluorescence spectroscopic data sets of certain polycyclic aromatic hydrocarbons.

Excitation-emission matrix fluorescence (EEMF) spectroscopy is a simple and sensitive analytical technique. EEMF spectrum is essentially a collection of emission and excitation spectra acquired as increasing functions of excitation and emission wavelengths, respectively. EEMF spectral data sets produced per sample are highly correlated and larger in amount that need the assistance of chemometric techniques such partial least square (PLS) analysis if one desire to build robust calibration model. The objective of the PLS algorithm is to explain maximum variation of the spectral and concentration data matrices and to maximise the correlation between them. The application of a suitable variable selection technique can significantly improve the performance of PLS calibration model. Towards this, the present work proposes application of competitive adaptive reweighted sampling (CARS) as a variable selection approach prior to PLS analysis of EEMF spectral data sets. The utility of proposed approach was successfully demonstrated by analysing the significantly overlapped EEMF spectral data set of aqueous mixtures of Anthracene, Chrysene, Fluoranthene and Pyrene that are highly carcinogenic and mutagenic in nature. The developed procedure was also successfully used for the analysis of Chrysene and Pyrene mixtures in gasoline spiked ground water samples. The CARS assisted PLS model was also compared with full spectrum PLS, genetic algorithm assisted PLS, ant colony optimisation assisted PLS and N-way PLS models. The obtained results of the present work clearly indicated that application of PLS algorithm on CARS optimised EEMF spectral variables significantly improved the performance of the calibration models.

Data Fusion of Raman and Near-Infrared Spectroscopies for the Rapid Quantitative Analysis of Methanol Content in Methanol–Gasoline

Rapid analysis of methanol content in methanol–gasoline is of great significance to monitor the methanol–gasoline quality. In this work, two different data-fusion strategies based on Raman and near-infrared (NIR) spectroscopies coupled with partial least square (PLS) were constructed and applied for a rapid and accurate analysis of the methanol content in methanol–gasoline. The Raman and NIR spectra of 49 methanol–gasoline samples were recorded, and the characteristic peaks of the methanol–gasoline samples in Raman and NIR spectroscopies were identified. For spectral data fusion, two different data-fusion strategies based on Raman and NIR spectroscopies coupled with PLS were proposed; normalization was used for low-level data fusion, and variable importance in projection (VIP) was used for mid-level data fusion. The different spectra pretreatment methods, latent variables, and variable importance thresholds of VIP were explored and optimized by 5-fold cross-validation (CV) to optimize the PLS calibration model for methanol content analysis. To further prove the predictive performance and stability of the PLS calibration model based on two data-fusion strategies, four PLS calibration models based on Raman, NIR, and two data-fusion strategies were applied to the quantitative analysis of methanol content in methanol–gasoline. The results show that the predictive performance of PLS calibration models based on the two data-fusion strategies is improved, and the PLS calibration model based on mid-level data fusion strategy gave an excellent predictive performance in methanol content analysis, with coefficients of determination of cross-validation (Rcᵥ²) and validation set (Rᵥ²) of 0.9988 and 0.9905, respectively, and root mean square error of cross-validation (RMSECV) and validation set (RMSEV) of 0.0068 and 0.0288%, respectively. Therefore, data fusion based on Raman and NIR spectroscopies coupled with PLS can give a rapid and accurate quantitative analysis of the methanol content in methanol–gasoline.

Adaptation of visible and short wave Near Infrared (VIS-SW-NIR) common PLS model for quantifying paddy hardness

Hardness of paddy is an indicator of preserved quality of rice as it maintains the integrity of grain to avoid falling-off during milling. Most of the prevailing methods carter to determine the hardness are destructive and time consuming. Though, use of Near Infrared (NIR) is nondestructive, its conventional approach implies separate Partial Least Square (PLS) model for each new paddy type to be predicted successfully. Therefore, this study evaluates NIR spectroscopy (588–1091 nm) to be used as a common PLS model for quantifying a range of paddy varieties and processing conditions in rapid and non-destructive mode. The acquired NIR spectra were used to construct PLS calibration models by using Pirouette 4.5 software. As a result, the lowest standard error of validation (SEV = 1.711) and highest correlation coefficient of validation (R2 = 0.936) were obtained. The research has successfully demonstrated that the potential possibility of NIR spectroscopy to be used as a common PLS model for quantifying hardness of paddy from new varieties and conditions.

Rapid Determination of Nutritional Parameters of Pasta/Sauce Blends by Handheld Near-Infrared Spectroscopy.

Nowadays, near infrared (NIR) spectroscopy has experienced a rapid progress in miniaturization (instruments < 100 g are presently available), and the price for handheld systems has reached the < $500 level for high lot sizes. Thus, the stage is set for NIR spectroscopy to become the technique of choice for food and beverage testing, not only in industry but also as a consumer application. However, contrary to the (in our opinion) exaggerated claims of some direct-to-consumer companies regarding the performance of their “food scanners” with “cloud evaluation of big data”, the present publication will demonstrate realistic analytical data derived from the development of partial least squares (PLS) calibration models for six different nutritional parameters (energy, protein, fat, carbohydrates, sugar, and fiber) based on the NIR spectra of a broad range of different pasta/sauce blends recorded with a handheld instrument. The prediction performance of the PLS calibration models for the individual parameters was double-checked by cross-validation (CV) and test-set validation. The results obtained suggest that in the near future consumers will be able to predict the nutritional parameters of their meals by using handheld NIR spectroscopy under every-day life conditions.