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The utilization of near-infrared (NIR) spectroscopy, in conjunction with chemometric techniques, has been widely used in a variety of sectors, including agricultural production and pharmaceutical production. Nevertheless, the laborious and arduous procedure of gathering samples and evaluating their physicochemical properties leads to relatively limited training set sizes for modeling. This problem severely limits the optimization and practical application of NIR spectrum analysis models. The Safer Active Semi-Supervised Sample Augmentation Learning Model (Safer-AS3A) proposed in this paper tries to address the problem by incorporating active learning (AL) and semi-supervised learning (SSL) techniques. Experiments were conducted on two sets of publicly available NIR spectral datasets, and the Safer-AS3A model was compared to other models with similar characteristics. The experimental results indicate that the Safer-AS3A model proposed in our study outperforms comparable models in terms of accuracy and robustness when dealing with scenarios having a limited number of labeled samples. Furthermore, after the training set was expanded with the Safer-AS3A model, the Partial least squares regression (PLSR), Bayesian ridge regression (BRR), and Support vector regression (SVR) models on the Diesel dataset improved their R 2 on the test set by 5.923 %, 3.018 %, and 7.331 %, respectively, compared to the models using only the labeled sample set. On the other hand, the Ridge regression (RR), BRR, and SVR models on the test set on the Shoot dataset improved the R 2 by 4.169 %, 4.449 %, and 11.597 %, respectively. Overall, the Safer-AS3A model can effectively expand the NIR spectral dataset and considerably improve the performance of the NIR spectral analysis model. Using the AL method, the SSL method, and the co-training method together, a novel and effective method is presented for generating high-quality pseudo-labels. This method opens up new avenues for enhancing the efficiency and precision of NIR spectrum analysis. It also provides novel perspectives on sample diversification and prospective applications in other disciplines. [Display omitted] • A deep active learning method is developed to analyze near-infrared spectroscopy data. • The stability principle and voting mechanism are added to the confidence evaluation of pseudo-labeling. • The co-training method effectively alleviates the problem of insufficient labeled spectral samples. [ABSTRACT FROM AUTHOR]

Near infrared spectroscopy (NIRS) is an analytical technique for determining the chemical composition or structure of a given sample. For several decades, NIRS has been a frequently used analysis tool in agriculture, pharmacology, medicine, and petrochemistry. The popularity of NIRS is constantly growing as new application areas are discovered. Contrary to mid infrared spectral region, the absorption bands in near infrared spectral region are often non-specific, broad, and overlapping. Analysis of NIR spectra requires multivariate methods which are highly subjective to noise arising from instrumentation, scattering effects, and measurement setup. NIRS measurements are also frequently performed outside of a laboratory which further contributes to the presence of noise. Therefore, preprocessing is a critical step in NIRS as it can vastly improve the performance of multivariate models. While extensive research regarding various preprocessing methods exists, selection of the best preprocessing method is often determined through trial-and-error. A more powerful approach for optimizing preprocessing in NIRS models would be to automatically compare a large number of preprocessing techniques (e.g., through grid-search or hyperparameter tuning). To enable this, we present, nippy, an open-source Python module for semi-automatic comparison of NIRS preprocessing methods (available at https://github.com/uef-bbc/nippy). We provide here a brief overview of the capabilities of nippy and demonstrate the typical usage through two examples with public datasets. Image 1 • Spectral preprocessing affects the performance of chemometric models. • Selection of best preprocessing strategy depends heavily on the intended application. • nippy module can rapidly explore different preprocessing combinations. • Semi-automatic preprocessing tuning for spectroscopic models. [ABSTRACT FROM AUTHOR]

In a qualitative analysis of near-infrared spectroscopy (NIRS), when the samples to be analyzed are difficult to obtain or there are few counterexamples, the robustness of the models is poor, resulting in the decline of the generalization ability of the models. In this case, the effective method is to construct virtual samples to achieve the balance of categories. In this contribution, three virtual spectrum construction strategies including Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), and Deep Convolutional Generative Adversarial Network (DCGAN) were explored to deal with the problem of insufficient or imbalanced sample numbers in NIRS analysis. The strategies were tested with the melamine and Yali pears two spectral datasets. The PLS-DA and Correct Recognition Rate (CRR) were used for discriminant model construction and accuracy evaluation, respectively. The results show that SMOTE, ADASYN, and DCGAN processing strategies can all improve the global CRR (CRR glob). The SMOTE and ADASYN can improve the CRR for majority class sample (CRR maj), but the CRR for minority class sample (CRR min) has decreased. For the DCGAN method, the CRR glob , CRR maj , and CRR min were all improved. The standard deviation of the results of the multiple parallel calculations demonstrates the robustness of DCGAN generation method. Therefore, the DCGAN method has good reliability and practicability, and can increase the robustness and generalization ability of the NIRS model. [Display omitted] • Exploring virtual spectrum construction strategies. • Evaluate the plausibility and diversity of the virtual spectrum. • Improve the robustness and generalization ability of the NIRS model. • Test model performance on public datasets and experimental data. [ABSTRACT FROM AUTHOR]

Near infrared spectroscopy is an overtone spectroscopy regarded as a quick and non-destructive method that provides analytical solutions for components that represent approximately 1% or more of the total mass of the investigated composite samples. Aquaphotomics offers the possibility for disentanglement of information remaining hidden in the spectra when conventional data evaluation methods are used, since this concept utilizes changes of the water structure induced by the measured solute as specific molecular vibrations at water bands. Here, near infrared technique and aquaphotomics are applied for non-destructive identification and quantification of mono- and di-saccharide solutes at 100–0.02 mM concentration that is accepted as unachievable with near infrared spectroscopy. The results presented in this study support the aquaphotomics' water molecular mirror concept that explores spectral changes related to water molecular rearrangements caused by minute changes of the solutes in the aqueous systems. The method provides quick and accurate alternative for classical analytical measurements of saccharides even at millimolar concentration levels. [ABSTRACT FROM AUTHOR]

The successive projections algorithm (SPA) is widely used to select variables for multiple linear regression (MLR) modeling. However, SPA used only once may not obtain all the useful information of the full spectra, because the number of selected variables cannot exceed the number of calibration samples in the SPA algorithm. Therefore, the SPA-MLR method risks the loss of useful information. To make a full use of the useful information in the spectra, a new method named “consensus SPA-MLR” (C-SPA-MLR) is proposed herein. This method is the combination of consensus strategy and SPA-MLR method. In the C-SPA-MLR method, SPA-MLR is used to construct member models with different subsets of variables, which are selected from the remaining variables iteratively. A consensus prediction is obtained by combining the predictions of the member models. The proposed method is evaluated by analyzing the near infrared (NIR) spectra of corn and diesel. The results of C-SPA-MLR method showed a better prediction performance compared with the SPA-MLR and full-spectra PLS methods. Moreover, these results could serve as a reference for combination the consensus strategy and other variable selection methods when analyzing NIR spectra and other spectroscopic techniques. [ABSTRACT FROM AUTHOR]

Highlights: [•] We present a comprehensive comparative study of nonlinear calibration techniques. [•] The methods are connected in terms of traditional calibration by ridge regression. [•] Three real-life near infrared (NIR) data sets are used for comparison of the methods. [•] Different practical aspects of the methods are discussed for spectral analysis. [ABSTRACT FROM AUTHOR]

Abstract: Analytical methods for confirmation of food authenticity claims should be rapid, economic, non-destructive and should not require highly skilled personnel for their deployment. All such conditions are satisfied by spectroscopic techniques. In order to be extensively implemented in routine controls, an ideal method should also give a response independent of the particular equipment used. In the present study, near-infrared (NIR) spectroscopy was used for verifying authenticity of commercial olives in brine of cultivar Taggiasca. Samples were analysed in two laboratories with different NIR spectrometers and a mathematical spectral transfer correction – the boxcar signal transfer (BST) – was developed, allowing to minimise the systematic differences existing between signals recorded with the two instruments. Class models for the verification of olive authenticity were built by the unequal dispersed classes (UNEQ) method, after data compression by disjoint principal component analysis (PCA). Models were validated on an external test set. [Copyright &y& Elsevier]

Abstract: In this paper a new model based on frequency self deconvolution (FSD) is proposed for the quantitative analysis of a near infrared (NIR) spectrum. The model couples FSD and partial least square regression (PLS). The grid search optimization method is used to select the optimal values of the full width at half height (FWHH) and the truncation point of the apodization function. The proposed FSD–PLS provides a significant improvement in the prediction ability of the PLS model. Furthermore, a modification of the new FSD–PLS method is introduced to enable the removal of the baseline variations from the NIR spectra. The proposed models were validated using absorbance spectra of mixtures composed from glucose, urea and triacetin in a phosphate buffer solution where the concentrations of the components are selected to be within their physiological range in blood. The whole experiments were carried out in a non-controlled environment to show that the model can suppress effectively most of the experimental variations. The results show that the standard error of prediction (SEP) decreases from 35.58mgdL−1 using 8 factors for the PLS model to 15.53mgdL−1 by using 12 factors for the modified FSD–PLS model. The proposed models are also shown to yield a slightly improved performance than a newly developed second derivative-PLS model without incurring the shortcoming associated with the derivative approach in not providing interpretable results and in degrading the SNR of the spectra at a faster rate. [Copyright &y& Elsevier]

Abstract: A spectral similarity measure was developed that can differentiate subtle differences between two spectra. The spectra are digitalized into a vector. The difference between the two spectra is defined by a difference vector, which is one spectrum minus the other. The spectral similarity measure is transformed into a hypothesis test of the similarities and differences between the two spectra. The scalar mean of the difference vector is used as the statistical variable for the hypothesis test. A threshold for the hypothesis that the spectra are different was proposed. The Bayesian prior odds ratio was estimated from multiple spectra of the same sample. The posterior odds ratio was used to quantity the spectral similarity measure of the two spectra. Diffuse reflectance near-infrared spectra of tobacco samples of two formulations were used to demonstrate this method. The results show that this new method can detect subtle differences between the spectra. [Copyright &y& Elsevier]

Abstract: The potential of the near infrared spectroscopy (NIRS) technique for the analysis of red paprika for aflatoxin B1, ochratoxin A and total aflatoxins is explored. As a reference, the results from a chromatographic method with fluorescence detection (HPLC-FD) following an immunoaffinity cleanup (IAC) were employed. For the NIRS measurement, a remote reflectance fibre-optic probe was applied directly onto the samples of paprika. There was no need for pre-treatment or manipulation of the sample. The modified partial least squares (MPLS) algorithm was employed as a regression method. The multiple correlation coefficients (RSQ) and the prediction corrected standard errors (SEP(C)) were respectively 0.955 and 0.2μgkg−1, 0.853 and 2.3μgkg−1, 0.938 and 0.3μgkg−1 for aflatoxin B1, ochratoxin A and total aflatoxins, respectively. The capacity for prediction of the developed model measured as ratio performance deviation (RPD) for aflatoxin B1 (5.2), ochratoxin A (2.8) and total aflatoxins (4.4) indicate that NIRS technique using a fibre-optic probe offers an alternative for the determination of these three parameters in paprika, with an advantageously lower cost and higher speed as compared with the chemical method. Content of aflatoxin B1 and total aflatoxins are the parameters currently employed by the food regulations to limit the levels of the four aflatoxins in many foodstuffs. In addition, aflatoxin B1 itself is an excellent indicator for aflatoxins’ contamination since it is always the most abundant and toxic. [Copyright &y& Elsevier]

Abstract: A rapid and nondestructive near infrared (NIR) method combined with chemometrics was used to discriminate Ganoderma lucidum according to cultivation area. Raw, first, and second derivative NIR spectra were compared to develop a robust classification rule. The chemical properties of G. lucidum samples were also investigated to find out the difference between samples from six varied origins. It could be found that the amount of polysaccharides and triterpenoid saponins in G. lucidum samples was considerably different based on cultivation area. These differences make NIR spectroscopic method viable. Principal component analysis (PCA), discriminant partial least-squares (DPLS) and discriminant analysis (DA) were applied to classify the geographical origins of those samples. The results showed that excellent classification could be obtained after optimizing spectral pre-treatment. For the discriminating of samples from three different provinces, DPLS provided 100% correct classifications. Moreover, for samples from six different locations, the correct classifications of the calibration as well as the validation data set were 96.6% using the DA method after the SNV first derivative spectral pre-treatment. Overall, NIR diffuse reflectance spectroscopy using pattern recognition was shown to have significant potential as a rapid and accurate method for the identification of herbal medicines. [Copyright &y& Elsevier]

Abstract: Near infrared spectroscopy (NIRS) is an analytical technique that can be very useful for stability studies in particular because of its non destructive analytical capability. However, the spectral interpretation and treatment of this kind of multivariate data remains difficult without the use of chemometrics. In this article, a recent chemometrics method, analysis of variance – principal component analysis (ANOVA-PCA), was used for NIRS stability studies of sunflower and bread wheat external reference materials (ERM). It provided a practical tool for the study of the significance of various storage conditions according to an experimental design. Thus, the effect of the temperature, the nature of the atmosphere in the packaging and the storage duration were tested. ANOVA-PCA highlighted the influence of temperature and storage duration on the stability of the sunflower materials. For the bread wheat materials, the storage conditions did not have a significant effect on stability. Consequently, by applying ANOVA-PCA to near infrared spectral data, the sunflower materials were found to be considered stable for the time length of the study, i.e. 18 months stored in a cold room, while the bread wheat materials were found to be considered stable for the time length of the study, i.e. 12 months under the same conditions. [Copyright &y& Elsevier]

Abstract: Near infrared (NIR) spectra of a sample can be treated as a signature, allowing samples to be grouped on basis of their spectral similarities. Near infrared spectroscopy (NIRS) combined with probabilistic neural networks (PNN) have been used to discriminate producing area and variety of loquats. Two varieties of loquats (‘Dahongpao’ and ‘Jiajiaozhong’) picked from two producing areas of ‘Tangxi’ and ‘Cunan’ in Zhejiang province were analyzed in this study. Principal component analysis (PCA) was applied before PNN modeling and the results indicated that the dimension of the vast spectral data can be effectively reduced. For each model, half samples were used to train the network and the remaining half were used to test the network. The results of the PCA–PNN models for discriminating the variety of samples from the same producing area or for discriminating the producing area of the same variety samples were much better than those of the PCA–PNN models for discriminating variety or producing area of all loquat samples. The results of this study show that NIRS combined with PCA–PNN is a feasible way for qualitative analysis of discriminating fruit producing areas and varieties. [Copyright &y& Elsevier]

Abstract: Near infrared spectroscopy (NIRS) was used in combination with partial least squares (PLS) calibration to determine low concentrated analytes. The effect of the orthogonal signal correction (OSC) and net analyte signal (NAS) pretreatments on the models obtained at concentrations of analyte near its detection limit was studied. Both pretreatments were found to accurately resolve the analyte signal and allow the construction of PLS models from a reduced number of factors; however, they provided no substantial advantage in terms of %RSE for the prediction samples. Multiple methodologies for the estimation of detection limits could be found in the bibliography. Nevertheless, detection limits were determined by a multivariate method based on the sample-specific standard error for PLS regression, and compared with the univariate method endorsed by ISO 11483. The two methods gave similar results, both being effective for the intended purpose of estimating detection limits for PLS models. Although OSC and NAS allow isolating the analyte signal from the matrix signal, they provide no substantial improvement in terms of detection limits. The proposed method was used to the determine 2-ethylhexanol at concentrations from 20 to 1600ppm in an industrial ester. The detection limit obtained, round 100ppm, testifies to the ability of NIR spectroscopy to detect low concentrated analytes. [Copyright &y& Elsevier]

Abstract: This paper proposes the use of the least-squares support vector machine (LS-SVM) as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants (starch, whey or sucrose) found in powdered milk samples, using near-infrared spectroscopy with direct measurements by diffuse reflectance. Due to the spectral differences of the three adulterants a nonlinear behavior is present when all groups of adulterants are in the same data set, making the use of linear methods such as partial least squares regression (PLSR) difficult. Excellent models were built using LS-SVM, with low prediction errors and superior performance in relation to PLSR. These results show it possible to built robust models to quantify some common adulterants in powdered milk using near-infrared spectroscopy and LS-SVM as a nonlinear multivariate calibration procedure. [Copyright &y& Elsevier]

Abstract: The combination of mass spectrometry (MS) based electronic nose (eNose) with visible (VIS) and near infrared spectroscopy (NIR) was explored as an objective tool to measure sensory attributes in commercial Riesling wines grown in Australia. Calibration models were developed between instrumental data and sensory scores using partial least squares (PLS) regression with full cross validation. Good correlations (r >0.70, root mean square standard error in cross validation (RMSECV): 0.66) were found for developed and floral; intermediate (0.70> r >0.60, RMSECV: 0.84 and 0.63) for tropical and low (r<0.50, RMSECV: 0.98) for green characters measured by a sensory panel and the combination of both techniques. The results suggested that data from instrumental techniques coupled with chemometrics might be related with sensory scores measured by a trained panel. The study is considered a starting point in order to evaluate useful sources of information generated by different instrumental techniques with the objective to select combination of sensors for specific wine quality attributes. [Copyright &y& Elsevier]

Abstract: The Vitamin E (α-, (β+γ)- and δ-tocopherol) contents present in feeds of different textures used in animal feed were analysed using near infrared spectroscopy (NIRS) technology together with a remote reflectance fibre-optic probe. The range of Vitamin E was 0.06 and 1.87mg/100g. The regression method employed was modified partial least squares (MPLS). The equations developed using the fiber-optic probe for 44 samples to determine the content of Vitamin E in feeds, in prediction, had multiple correlation coefficients (RSQ) and corrected standard errors (SEP (C)): 0.792, 0.223mg/100g for α-tocopherol, of 0.766 and 0.022mg/100g for (β+γ)-tocopherol, and of 0.789 and 0.112mg/100g for δ-tocopherol. The predicted values of Vitamin E in feeds using NIRS technology applying the fibre-optic probe directly on the sample with neither previous treatment nor manipulation are comparable to the values obtained using the chemical method that includes alkaline hydrolysis and extraction with hexane of the vitamin from the unsaponifiable fraction before chromatographic determination. [Copyright &y& Elsevier]

Abstract: The use of process analytical technologies by the pharmaceutical industry is a response to its growing need for improved productivity in order to face the increasing competition in this field. In this work, we explored the use of near infrared spectroscopy (NIRS) for the determination of physical (tablet hardness) and chemical parameters (active principle and content uniformity) in intact individual pharmaceutical tablets. Quantization was done by using a Partial Least Squares 1 (PLS1) calibration model constructed from laboratory calibration samples that were prepared by mixing the active principle and excipients, and pressing the mixture into tablets. The compaction pressure to be applied to the powder was previously determined by using another PLS1 model that allows calculating it from production tablets. The NIRS method used to quantify the active principle is simpler as the calibration set encompasses the variability sources present in production samples; also, it allows individual tablets to be analysed. The proposed method was validated in accordance with the International Conference on Harmonization (ICH) and The European Agency for the Evaluation of Medicinal Products (EMEA) guidelines, and found to be fit for its intended purpose. [Copyright &y& Elsevier]

Abstract: The strengths of hard and soft modelling were exploited by using both types of methods in combination to monitor alcoholic fermentations under Saccharomyces cerevisiae yeasts. Experimental work was performed in two steps. In the first, fermentation processes were conducted under identical conditions except for the initial glucose concentration in order to tests various previously reported empirical hard modelling methods. The product inhibition model of Hinshelwood was found to provide the best results in terms of goodness of fit and consistency in parameter values between runs under these conditions. In the second step, fermentation processes conducted at variable temperature and pH were monitored in-line by using an immersion NIR probe. The results were processed by using multivariate curve resolution–alternating least-squares (MCR–ALS) methodology in combination with the hard modelling information obtained in the first step as spectral equality constraints. Notwithstanding the complexity of the fermentation matrix introduced by variability in the species involved in yeast metabolism, the extracted profiles exhibited highly significant correlation with the reference values provided by a validated reference method for the determination of glucose, ethanol and biomass. The results testify to the efficiency of the joint use of soft and empirical hard modelling for studying evolving biological systems and opens up new avenues for application to other bioprocesses. [Copyright &y& Elsevier]

Abstract: The utility of near infrared spectroscopy as a non-invasive technique for the assessment of internal eating quality parameters of stonefruit (peaches, nectarines and plums) was assessed. Calibration model performance for the attributes of total soluble solids (TSS) was encouraging (typical R 2 >0.88, RMSECV 0.53–0.88%TSS, SDRCV 2.9–3.7). Model performance was acceptable using a combined multi-variety peach–nectarine data set, but it was advantageous to maintain a separate multi-variety plum model. Model robustness to temperature was achieved by including into the calibration set samples scanned at a range of temperatures, with less than 5% of total population required to be treated in this way. Similarly, where models incorporated the range of TSS seen in the validation population, prediction performance was good. Model performance was stable over several seasons in terms of R 2 (typical R 2 >0.8), with bias corrected SEP varying in proportion to population S.D. Prediction bias for new populations could be corrected by model updating or direct bias adjustment. [Copyright &y& Elsevier]

Abstract: Express-methods for detection of counterfeit drugs are of vital necessity. Visual control, dissociating tests or simple color reaction tests reveal only very rough forgeries. The feasibility of information-rich NIR-measurements as an analytical method together with multivariate calibration for mathematical data processing for false drugs detection is demonstrated. Also, multivariate hyperspectral image analysis is applied providing additional diagnostic information. Hyperspectral imaging is becoming a useful diagnostic tool for identifying non-homogeneous spatial regions of drug formulation. Two types of drugs are used to demonstrate the applicability of these approaches. [Copyright &y& Elsevier]

Abstract: The functional properties of fats are determined by the distribution pattern of fatty acid residues in their acylglycerols, which may be modified by ester interchange (transesterification). In the margarine industry, the time course of the transesterification of fat blends is monitored by assaying for the amount of the solid fraction at different temperatures (SFC-solid fat content) currently measured by nuclear magnetic resonance (NMR). The aim of this study was to evaluate the feasibility of near infrared spectroscopy (NIRS) to quantify the SFC of different fat blends using NMR data for calibration. SFC values of 128 samples, consisting of different blends of palm stearin, palm kernel oil and concentrates of triglycerides enriched with ω-3 polyunsaturated fatty acids were assayed by NMR prior to (64 samples) and following inorganic (10 samples) or lipase-catalysed transesterification (54 samples). Prior to SFC measurement by NMR, sample preparation takes about 90min. With NIRS technique, a faster determination is achieved since NIR spectra for SFC estimations are directly obtained on the sample at room temperature. High correlations were obtained for cross-validation of the data estimated by NIRS models and NMR for SFC assays at 10°C (R 2 =0.91, RMSECV=2.4), 20°C (R 2 =0.96, RMSECV=1.7), 30°C (R 2 =0.96, RMSECV=1.3) and 35°C (R 2 =0.96, RMSECV=1.3) of the different blends tested. The obtained results show that NIRS is a reliable technique to replace NMR for SFC estimation. [Copyright &y& Elsevier]

Abstract: Diffuse reflectance infrared fourier transform spectroscopy (DRIFTS) in the near-infrared (NIR) range is a technique able to determine moisture, ash, volatile matter, fixed carbon, heating value and percentage of carbon, hydrogen, nitrogen and sulfur in coal samples. In this paper, spectra from 142 coal samples of different origins were acquired in absorbance, reflectance and Kubelka–Munk units. Physical effects due to particle size were minimized after applying different pre-treatments to each spectra. The resultant spectra were correlated to the above mentioned coal properties using partial least squares regression (PLS). Moreover, a principal component analysis (PCA) of the full set of samples suggested the use of more homogeneous sample sets. The results obtained for a homogeneous set improved the prediction ability of the procedure. [Copyright &y& Elsevier]

Abstract: A study of the feasibility of near infrared reflectance spectroscopy (NIRS) for analytical monitoring in wineries is presented, in which equations for the determination or screening of the commonest enological parameters are proposed. The training and validation sets to develop NIR general equations were built with samples (180) from different apellation d’origine, different wine types, etc. By the calibration step (partial least squares regression and cross-validation were used for multivariate calibration), major components such as ethanol, volumic mass, total acidity, pH, glycerol, colour, tonality and total polyphenol index are accurate determined by the proposed equations as compared with the reference data obtained by the official and standard methods—determination coefficients (R2) were higher than 0.800 (and higher than 0.900 most times) and standard error cross-validation (SECV) values were close to those of the reference methods. The proposed method also offers screening capability for components such as volatile acidity (R2 = 0.481), organic acids (R2 = 0.432 for malic acid, R2 = 0.544 for tartaric acid, R2 = 0.541 for gluconic acid)—with the exception of the accurate determination of lactic acid (0.860 and 0.35gl-1 for R2 and SECV, respectively)—reducing sugars (R2 = 0.705) and total sulphur dioxide (R2 = 0.615). In equations validation, the correlation between the reference and NIRS methods was tested, and slope and bias values statistically not different from 1 and 0, respectively, were obtained for most parameters. [Copyright &y& Elsevier]

Two orthogonal signal correction methods (OSC and DOSC) were applied on a set of 83 roasted coffee NIR spectra from varied origins and varieties in order to remove information unrelated to a specific chemical response (caffeine), which was selected due to its high discriminant ability to differentiate between arabica and robusta coffee varieties. These corrected NIR spectra, as well as raw NIR spectra and three chemical quantities (caffeine, chlorogenic acids and total acidity), were used to develop separate classification models accordingly using the potential functions method as a class-modelling technique in order to evaluate their respective capacities to discriminate between coffee varieties and the influence of these pre-processing methods on the classification of the coffee samples into their corresponding variety class. The transformation of roasted coffee NIR spectra by means of an orthogonal signal correction method, taking into account in this correction a chemical response closely related to the sample origin, prompted a notable improvement in the specificity of the constructed classification models. [Copyright &y& Elsevier]

Near infrared (NIR) spectroscopy was used to simultaneously predict the concentrations of malvidin-3-glucoside (M3G), pigmented polymers (PP) and tannins (T) in red wine. A total of 495 samples from 32 commercial scale red wine fermentations over two vintages using two grape varieties (Cabernet Sauvignon and Shiraz), and also including as additional variables two types of fermenters, two different yeasts, and three fermentation temperatures were used. Samples were scanned in transmission mode (400–2500 nm) using a monochromator instrument (NIRSystems6500). Calibration equations were developed from high performance liquid chromatography (HPLC) and NIR data using partial least squares (PLS) regression with internal cross validation. Using PLS regression, very good calibration statistics (Rcal2>0.80) were obtained for the prediction of M3G, PP and T with standard deviation (S.D.)/standard error in cross validation (SECV) ratio (residual predictive deviation, RPD)) ranging from 1.8 to 5.8. It was concluded that near infrared spectroscopy could be used as rapid alternative method for the prediction of the concentration of phenolic compounds in red wine fermentations. [Copyright &y& Elsevier]

The polymorphic purity of drug is of high pharmaceutical interest as it often dictates its bioavailability. In this work, we developed a rapid, efficient method for the characterization and determination of azithromycin polymorphs using near-infrared (NIR) spectrometry. The drug is characterized by comparison with a NIR spectral library that permits one to determine whether the amount of crystalline form contained in an amorphous azithromycin sample exceeds allowed levels. While the crystalline form is a hydrate, the amorphous form is anhydrous; however, the absorption of a small amount of moisture by the drug reduces the spectral differences between the two forms and hinders the establishment of an accurate calibration model. In this work, we determined the crystalline form by using a partial least-squares regression model (PLS1) for calibration and examined the influence of factors such as spectral treatment, wavelength range and moisture content on the results. The high correlation between the spectra for the two forms enabled the development of a PLS2 model for determining both species jointly. The proposed method was validated with a view to its subsequent use in the analytical control of azithromycin. [Copyright &y& Elsevier]