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Highlights • Pear parenchyma cell walls have the highest affinity for procyanidins. • Pear skin cell walls have the lowest affinity for procyanidins. • Overripening promotes binding of procyanidins for all pear cell walls. Abstract Procyanidins and cell walls were extracted from pear at ripe and overripe stages in order to investigate the impact of ripening stage on their association. Procyanidin composition and structure remained stable at the overripe stage. Mid Infrared Spectroscopy (MIR) discriminated cell wall-procyanidin complex from initial purified cell wall material (CWM). Interactions between procyanidins and CWM isolated from the whole flesh (FL), parenchyma cells (PC), stone cells (ST) and skin (SK) at ripe and overripe stage were characterized using UV–Vis spectrometry using Langmuir isotherm formulation and Isothermal Titration Microcalorimetry (ITC). The affinity between procyanidins and CWM decreased as follows: PC > FL > ST > SK. The proportion of bound procyanidins increased at the overripe stage for all CWM and the maximal saturation level was obtained for overripe FL and ST. ITC indicated that associations between pear cell walls and procyanidins involved hydrogen bonds and mainly hydrophobic interactions for overripe PC. [ABSTRACT FROM AUTHOR]

Common Dimension (ComDim) chemometrics method for multi-block data analysis was employed to evaluate the impact of different added salts and ripening times on physicochemical, color, dynamic low amplitude oscillatory rheology, texture profile, and molecular structure (fluorescence and MIR spectroscopies) of five Cantal-type cheeses. Firstly, Independent Components Analysis (ICA) was applied separately on fluorescence and MIR spectra in order to extract the relevant signal source and the associated proportions related to molecular structure characteristics. ComDim was then applied on the 31 data tables corresponding to the proportion of ICA signals obtained for spectral methods and the global analysis of cheeses by the other techniques. The ComDim results indicated that generally cheeses made with 50% NaCl or with 75:25% NaCl/KCl exhibit the equivalent characteristics in structural, textural, meltability and color properties. The proposed methodology demonstrates the applicability of ComDim for the characterization of samples when different techniques describe the same samples. [ABSTRACT FROM AUTHOR]

The aim of the present study was to investigate the ability of MIR and texture analyzer to evaluate the quality of pound cake samples produced with palm oil and rapeseed oil throughout storage. The MIR spectra analyzed by using principal component analysis (PCA) showed a clear separation of pound cakes as a function of the storage time and the nature of the used oil in the recipe. By applying partial least square regression (PLSR), excellent prediction was obtained for hardness (R 2  = 0.91; RPD = 2.26), while an approximate qualitative prediction was found for springiness (R 2  = 0.73; RPD = 2.07), cohesiveness (R 2  = 0.67; RPD = 1.31) and resilience (R 2  = 0.65; RPD = 1.24). It could be concluded that the MIR spectroscopy could be used as a rapid and non-destructive technique for monitoring texture of pound cakes throughout storage as well as for the prediction of their hardness. [ABSTRACT FROM AUTHOR]

A novel method for quantitative prediction and variable-selection on spectroscopic data, called Durbin-Watson partial least-squares regression (dwPLS), is proposed in this paper. The idea is to inspect serial correlation in infrared data that is known to consist of highly correlated neighbouring variables. The method selects only those variables whose intervals have a lower Durbin-Watson statistic (dw) than a certain optimal cutoff. For each interval, dw is calculated on a vector of regression coefficients. Adulteration of cold-pressed linseed oil (L), a well-known nutrient beneficial to health, is studied in this work by its being mixed with cheaper oils: rapeseed oil (R), sesame oil (Se) and sunflower oil (Su). The samples for each botanical origin of oil vary with respect to producer, content and geographic origin. The results obtained indicate that MIR-ATR, combined with dwPLS could be implemented to quantitative determination of edible-oil adulteration. [ABSTRACT FROM AUTHOR]

The most common methods for acrylamide analysis in foods require the use of LC–MS/MS and GC–MS. Although these methods have great analytical performance, they need intensive sample preparation, highly specialised instrumentation, and are time consuming. In this study, portable and handheld infrared spectrometers were evaluated as rapid methods for screening acrylamide in potato chips and their performances were compared to those of benchtop infrared systems. The acrylamide content of 64 commercial potato chips (169–2453 μg/kg) was determined by LC–MS/MS. Spectral data were collected using mid-infrared (MIR) and near-infrared (NIR) spectrometers. Partial least squares regression (PLSR) calibration models were developed to predict acrylamide levels. Overall, good linear correlation was found between the predicted acrylamide levels and actual measured acrylamide concentrations by LC–MS/MS ( r Pred > 0.90 and SEP < 100 μg/kg). Our results indicate that portable and handheld spectrometers can be used as simple and rapid alternatives for acrylamide analysis in potato chips. [ABSTRACT FROM AUTHOR]

Abstract: Camellia oil is often the target for adulteration or mislabeling in China because of it is a high priced product with high nutritional and medical values. In this study, the use of attenuated total reflectance infrared spectroscopy (MIR-ATR) and fiber optic diffuse reflectance near infrared spectroscopy (FODR-NIR) as rapid and cost-efficient classification and quantification techniques for the authentication of camellia oils have been preliminarily investigated. MIR spectra in the range of 4000–650cm−1 and NIR spectra in the range of 10,000–4000cm−1 were recorded for pure camellia oils and camellia oil samples adulterated with varying concentrations of soybean oil (5–25% adulterations in the weight of camellia oil). Identifications is successfully made base on the slightly difference in raw spectra in the MIR ranges of 1132–885cm−1 and NIR ranges of 6200–5400cm−1 between the pure camellia oil and those adulterated with soybean oil with soft independent modeling of class analogy (SIMCA) pattern recognition technique. Such differences reflect the compositional difference between the two oils with oleic acid being the main ingredient in camellia oil and linoleic acid in the soybean oil. Furthermore, a partial least squares (PLS) model was established to predict the concentration of the adulterant. Models constructed using first derivative by combination of standard normal variate (SNV), variance scaling (VS), mean centering (MC) and Norris derivative (ND) smoothing pretreatments yielded the best prediction results With MIR techniques. The R value for PLS model is 0.994.The root mean standard error of the calibration set (RMSEC) is 0.645, the root mean standard error of prediction set (RMSEP) and the root mean standard error of cross validation (RMSECV) are 0.667 and 0.85, respectively. While with NIR techniques, NIR data without derivative gave the best quantification results. The R value for NIR PLS model is 0.992. The RMSEC, RMSEP and RMSECV are 0.70, 1.78 and 1.79, respectively. Overall, either of the spectral method is easy to perform and expedient, avoiding problems associated with sample handling and pretreatment than the conventional technique. [Copyright &y& Elsevier]

• A quick and easy method using MIR to differentiate between true and false cinnamon was developed. • Eugenol, cinnamaldehyde and coumarin are the main chemical markers in the differentiation of true and false cinnamon. • Superiority of the antioxidant capacity of true cinnamon confirmed with more than 100 samples. • Multivariate index can indicate whether the cinnamon samples are true or false. • The PCA and PLS-DA models provide satisfactory separation between the cinnamon samples. The objective of this study was to identify useful chemical markers for the differentiation of true and false cinnamon; to develop a fast and efficient method of differentiation using MIR associated with chemometric methods. A total of 129 cinnamon samples from 3 countries (Brazil, Sri Lanka, and Paraguay) were purchased, being differentiated by visual analysis. The bioactive compounds, antioxidant activity, HPLC, and MIR analysis were performed. The data obtained were submitted to PCA and PLS-DA. The results showed that the samples of true cinnamon have a higher concentration of eugenol, cinnamaldehyde, and antioxidant capacity, as well as a lower coumarin content. The PCA showed the separation of two groups of cinnamon samples. PLS-DA was effective in differentiating the studied samples with an accuracy of 94.44% and 100% for the composition and MIR analysis, respectively. The MIR proved to be an alternative for the rapid differentiation of true and false cinnamon. [ABSTRACT FROM AUTHOR]

• Major components in cow and camel milk were analyzed by infrared spectroscopy. • NIR and MIR spectroscopy gave different results. • Identification of bias requires validation with reference methods. Milk samples (150 cow and 217 camel milk samples) were analyzed for protein, fat, lactose and total solids by near and mid-infrared transmission spectroscopy. Excellent positive correlations between the two methods were obtained for both types of milk (p < 0.001); for protein (r ≥ 0.96), fat (r ≥ 0.99), lactose (r = 0.82) and total solids (r = 0.90). The mean of the relative difference ((MIR values – NIR values)/0.5 (MIR values + NIR values) × 100%) for cow and camel milk were, for protein (+8.2 & +13.4%), fat (− 9.3 & +0.9%), lactose (− 5.4 & − 0.7%) and total solids (− 2.2 & − 3.4%), respectively. The difference between the two methods may be due to the effects of differences in milk homogeneity, especially with respect to casein micelles and fat globules. [ABSTRACT FROM AUTHOR]

• MIR could be a useful method for classifying the pectin content in orange juice. • SVM and kNN regression were able to model the MIR spectra data. • MIR spectroscopy measurements coupled to machine learning models can be used as a screening method in orange juice factories. This study represents a rapid and non-destructive approach based on mid-infrared (MIR) spectroscopy, time domain nuclear magnetic resonance (TD-NMR), and machine learning classification models (ML) for monitoring soluble pectin content (SPC) changes in orange juice. Current reference methods of SPC in orange juice are laborious, requiring several extractions with successive adjustments hindering rapid process intervention. 109 fresh orange juices samples, representing different harvests, were analysed using MIR, TD-NMR and reference method. Unsupervised algorithms were applied for natural clustering of MIR and TD-NMR data in two groups. Analyses of variance of the two MIR and TD-NMR datasets show that only the MIR groups were different at 95% confidence for SPC average values. This approach allows build classification models based on MIR data achieving 85% and 89% of accuracy. Results demonstrate that MIR/ML can be a suitable strategy for the quick assessment of SPC trends in orange juices. [ABSTRACT FROM AUTHOR]

For more than a decade, Fourier-transform infrared (FTIR) spectroscopy combined with partial least squares (PLS) regression has been used as a fast and reliable method for simultaneous estimation of multiple parameters in wine. In this study, different FTIR instruments (single bounce attenuated total reflection, transmission with variable and defined pathlength) and different variable selection techniques (full spectrum PLS, genetic algorithm PLS, interval PLS, principal variable PLS) were compared on an identical sample set of international wines and ten wine parameters. Results suggest that the single bounce attenuated total reflection technique is well suited for the analysis of ethanol, relative density and sugars, but less accurate in the analysis of organic acid content. The transmission instrument with variable pathlength shows good validation results for the analysis of organic acids, but less accurate results for the analysis of ethanol and relative density as compared to the other instruments. The transmission instrument with defined pathlength was well suited for the analysis for all parameters investigated in this study. Variable selection improved model robustness and calibration results, with genetic algorithm PLS being the most effective technique., (Copyright © 2013 Elsevier Ltd. All rights reserved.)