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A person's age estimation from biological evidence is a crucial aspect of forensic investigations, aiding in victim identification and criminal profiling. In this study, we present a novel approach of utilizing Attenuated Total Reflection Fourier Transform Infrared (ATR FT‐IR) spectroscopy to predict the age of donors based on nail samples. A diverse dataset comprising nails from donors spanning different age groups was analyzed using ATR FT‐IR, with subsequent multivariate analysis techniques used for age prediction. The developed partial least squares regression (PLS‐R) model demonstrated promising accuracy in age estimation, with a root mean square error of prediction (RMSEP) equal to 11.1 during external validation. Additionally, a partial least squares discriminant analysis (PLS‐DA) classification model achieved high accuracy of 88% in classifying donors into younger and older age groups during external validation. This proof‐of‐concept study highlights the potential of ATR FT‐IR spectroscopy as a non‐destructive and efficient tool for age estimation in forensic investigations, offering a new approach to forensic analysis with practical implications. [ABSTRACT FROM AUTHOR]

The aim of the present study was to compare different spectroscopic techniques using the example of adulteration of pumpkin seed oil with rapeseed oil in combination with a multivariate regression method. A total of 124 pure seed oils and 96 adulterated samples (adulteration levels from 0.5 to 90.0% w/w) were analyzed using mid infrared, Raman, and 1H-nuclear magnetic resonance spectroscopy. To build quantification models, partial least squares regression (PLS-R) was used. The regression performance parameters, latent variables, and the detection limits (in terms of root mean square error of PLS prediction) calculated when applying the different spectroscopic approaches were compared. For the studied example (pumpkin seed oil adulterated with refined rapeseed oil), the lowest detection limit (3.4% w/w) was obtained for 1H-nuclear magnetic resonance spectroscopy. For the mid infrared and Raman spectroscopy, detection limits of 4.8% w/w and 9.2% w/w, respectively, were obtained, which might be used as screening methods. [ABSTRACT FROM AUTHOR]

Since aquatic microbial communities promptly respond to environmental changes, it is now evident that they can complement traditional taxa such as fish, macroinvertebrates and algae as bioindicators of water quality. The aim of this study was to correlate the physico-chemical parameters of water with the microbial community structure and the occurrence of putative bioindicator taxa. Thirty-five water samples were collected throughout Croatia and their physico-chemical parameters, including the concentration of trace elements using the high-resolution inductively coupled plasma mass spectrometry (HR-ICP-MS), and the composition of the microbial communities by high-throughput sequencing of the 16S rRNA marker gene, were analysed in parallel. Partial least squares regression (PLS-R) modelling revealed that a number of microbial taxa were positively correlated with some of the water parameters. For example, some taxa from the phylum Proteobacteria were positively correlated with the ion content of the water (e.g. Erythrobacter, Rhodobacteraceae, Alteromonadaceae), while some Firmicutes taxa, such as the well-known faecal indicators Enterococcus and Clostridium, were correlated with nutrient content (ammonium and total phosphorus). Among the trace elements, uranium was positively correlated with a highest number of microbial taxa. The results obtained will aid in development of protocols for eDNA-based biological assessment of water quality. [ABSTRACT FROM AUTHOR]

UV hyperspectral imaging (225 nm–410 nm) was used to identify and quantify the honeydew content of real cotton samples. Honeydew contamination causes losses of millions of dollars annually. This study presents the implementation and application of UV hyperspectral imaging as a non-destructive, high-resolution, and fast imaging modality. For this novel approach, a reference sample set, which consists of sugar and protein solutions that were adapted to honeydew, was set-up. In total, 21 samples with different amounts of added sugars/proteins were measured to calculate multivariate models at each pixel of a hyperspectral image to predict and classify the amount of sugar and honeydew. The principal component analysis models (PCA) enabled a general differentiation between different concentrations of sugar and honeydew. A partial least squares regression (PLS-R) model was built based on the cotton samples soaked in different sugar and protein concentrations. The result showed a reliable performance with R2cv = 0.80 and low RMSECV = 0.01 g for the validation. The PLS-R reference model was able to predict the honeydew content laterally resolved in grams on real cotton samples for each pixel with light, strong, and very strong honeydew contaminations. Therefore, inline UV hyperspectral imaging combined with chemometric models can be an effective tool in the future for the quality control of industrial processing of cotton fibers. [ABSTRACT FROM AUTHOR]

The success rate of matrix acidizing in hydraulic fractured sandstone formation is less than 55%, much lower compared to the more than 91% success rate in carbonate formation. The need for alternative approaches to help the success ratio in matrix acidizing is crucial. This paper demonstrates a modeling technique to improve the success ratio of matrix acidizing in a hydraulic fractured sandstone formation. Supervised machine learning with 4 models of a neural network, logistic regression, tree, and random forest was selected to predict the successfulness of matrix acidizing in hydraulic fracturing. In parallel, multivariate analysis of principal component regression and partial least square regression approach were utilized to predict the oil gain of the job. For qualitative prediction, the results showed that the random forest was the best model to predict the successfulness of the job with the area under the curve (AUC) of 0.68 and precision of 0.73 in the training model with 70% of the data. Subsequently, the validation test with the rest of the data (30% data) gave 0.51 AUC and 61% precision. For quantitative prediction, the net oil gain was evaluated by using principal component regression (PCR) and partial least square regression (PLSR). The PCR and PLS-R model gave a coefficient of determination (R square) of 0.22 and 0.35, respectively. The p-value of PLS-R was 0.047 (95% confidence interval) which indicates that the model is significant. The results of this work demonstrate the potential application of supervised machine learning, principal component regression, and partial least square regression to improve candidate selection of oil wells for matrix acidizing especially in hydraulic fractured wells with limited design data. [ABSTRACT FROM AUTHOR]

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Azo compounds such as the Sudan dyes I–IV are frequently used illegally as colorants and added to a wide range of foods. These compounds have been linked to a number of food safety hazards. Several methods have been proposed to detect food contamination by azo compounds and most of these are laboratory based; however, the development of reliable and portable methods for the detection and quantification of food contaminated by these chemicals in low concentration is still needed due to their potentially carcinogenic properties. In this study, we investigated the ability of surface enhanced Raman scattering (SERS) combined with chemometrics to quantify Sudan I–IV dyes. SERS spectra were acquired using a portable Raman device and gold nanoparticles were employed as the SERS substrate. As these dyes are hydrophobic, they were first dissolved in water: acetonitrile (1:10, v/v) as single Sudan dyes (I–IV) at varying concentrations. SERS was performed at 785 nm and the spectra were analyzed by using partial least squares regression (PLS-R) with double cross-validations. The coefficient of determination (Q2) were 0.9286, 0.9206, 0.8676 and 0.9705 for Sudan I to IV, respectively; the corresponding limits of detection (LOD) for these dyes were estimated to be 6.27 × 10−6, 5.35 × 10−5, 9.40 × 10−6 and 1.84 × 10−6 M. Next, quadruplex mixtures were made containing all four Sudan dyes. As the number of possible combinations needed to cover the full concentration range at 5% intervals would have meant collecting SERS spectra from 194,481 samples (214 combinations) we used a sustainable solution based on Latin hypercubic sampling and reduced the number of mixtures to be analyzed to just 90. After collecting SERS spectra from these mixture PLS-R models with bootstrapping validations were employed. The results were slightly worse in which the Q2 for Sudan I to IV were 0.8593, 0.7255, 0.5207 and 0.5940 when PLS1 models (i.e., one model for one dye) was employed and they changed to 0.8329, 0.7288, 0.5032 and 0.5459 when PLS2 models were employed (i.e., four dyes were modelled simultaneously). These results showed the potential of SERS to be used as a high-throughput, low-cost, and reliable methods for detecting and quantifying multiple Sudan dyes in low concentration from illegally adulterated samples. [ABSTRACT FROM AUTHOR]

The potential of Fourier transform infrared (FT-IR) spectroscopy, multispectral imaging (MSI), and electronic nose (E-nose) was explored in order to determine the microbiological quality of gilthead sea bream (Sparus aurata) fillets. Fish fillets were maintained at four temperatures (0, 4, 8, and 12 °C) under aerobic conditions and modified atmosphere packaging (MAP) (33% CO2, 19% O2, 48% N2) for up to 330 and 773 h, respectively, for the determination of the population of total viable counts (TVC). In parallel, spectral data were acquired by means of FT-IR and MSI techniques, whereas the volatile profile of the samples was monitored using an E-nose. Thereafter, the collected data were correlated to microbiological counts to estimate the TVC during fish fillet storage. The obtained results demonstrated that the partial least squares regression (PLS-R) models developed on FT-IR data provided satisfactory performance in the estimation of TVC for both aerobic and MAP conditions, with coefficients of determination (R2) for calibration of 0.98 and 0.94, and root mean squared error of calibration (RMSEC) values of 0.43 and 0.87 log CFU/g, respectively. However, the performance of the PLS-R models developed on MSI data was less accurate with R2 values of 0.79 and 0.77, and RMSEC values of 0.78 and 0.72 for aerobic and MAP storage, respectively. Finally, the least satisfactory performance was observed for the E-nose with the lowest R2 (0.34 and 0.17) and the highest RMSEC (1.77 and 1.43 log CFU/g) values for aerobic and MAP conditions, respectively. The results of this work confirm the effectiveness of FT-IR spectroscopy for the rapid evaluation of the microbiological quality of gilthead sea bream fillets. [ABSTRACT FROM AUTHOR]

In the quality control of medicines and the fight against the phenomenon of poor quality medicines, there is an urgent need for rapid and broad spectrum methods for screening these types of medicines. In the present work, we have used near infrared spectroscopy combined with multivariate data analysis to develop chemometric models for the classification and quantification of metronidazole in Burkina Faso pharmaceutical formulations. For this purpose, drug samples were collected in drugstores located in different Burkina Faso border zones. Four product classes were defined based on the national nomenclature: 3 classes for the generic drugs (C1, C3, and C4) and one class for the reference (C2) drugs. The exploratory analysis using PCA identified two clusters of drugs within class C1. Discrimination was confirmed by the developed and optimised DD-SIMCA model, with only one target class. The quality control of the samples from product class C1 was proven to be very satisfactory with specificities and sensitivities of 100%. The quantification models developed with the PLS-R method were successfully applied for the determination of the active ingredient content in the samples, with acceptable relative bias between 0.15 and 12.7 % with respect to the dose determined by the HPLC method. The RMSEC was estimated at 13.57 (R2, 0.9937), the RMSECV at 18.07 (R2, 0.9888) and the RMSEP at 13.69 (R2, 0.9941).The models developed and the results obtained are promising for routine quality control of similar formulations of metronidazole.

The potential of Fourier transform infrared (FT-IR) spectroscopy, multispectral imaging (MSI), and electronic nose (E-nose) was explored in order to determine the microbiological quality of gilthead sea bream (Sparus aurata) fillets. Fish fillets were maintained at four temperatures (0, 4, 8, and 12 °C) under aerobic conditions and modified atmosphere packaging (MAP) (33% CO2, 19% O2, 48% N2) for up to 330 and 773 h, respectively, for the determination of the population of total viable counts (TVC). In parallel, spectral data were acquired by means of FT-IR and MSI techniques, whereas the volatile profile of the samples was monitored using an E-nose. Thereafter, the collected data were correlated to microbiological counts to estimate the TVC during fish fillet storage. The obtained results demonstrated that the partial least squares regression (PLS-R) models developed on FT-IR data provided satisfactory performance in the estimation of TVC for both aerobic and MAP conditions, with coefficients of determination (R2) for calibration of 0.98 and 0.94, and root mean squared error of calibration (RMSEC) values of 0.43 and 0.87 log CFU/g, respectively. However, the performance of the PLS-R models developed on MSI data was less accurate with R2 values of 0.79 and 0.77, and RMSEC values of 0.78 and 0.72 for aerobic and MAP storage, respectively. Finally, the least satisfactory performance was observed for the E-nose with the lowest R2 (0.34 and 0.17) and the highest RMSEC (1.77 and 1.43 log CFU/g) values for aerobic and MAP conditions, respectively. The results of this work confirm the effectiveness of FT-IR spectroscopy for the rapid evaluation of the microbiological quality of gilthead sea bream fillets.

Softwood lignin consists mainly of guaiacyl (G) units and low amounts of hydroxyphenyl (H) units. Even in a small percentage, the ratio of H to G (H/G) and the intraspecific variation are crucial wood lignin properties. Analytical pyrolysis (Py) was already successfully used as a reference method to develop a model based on near-infrared (NIR) spectroscopy for the determination of the H/G ratio on Pinus pinaster (Pnb) wood samples. The predicted values of the Pinus sylvestris (Psyl) samples by this model were well correlated (R = 0.91) with the reference data (Py), but with a bias that increased with increasing H/G ratio. Partial least squares regression (PLS-R) models were developed for the prediction of the H/G ratio, dedicated models for Psyl wood samples and common models based on both species (Pnb and Psyl). All the calibration models showed a high coefficient of determination and low errors. The coefficient of determination of the external validation of the dedicated models ranged from 0.92 to 0.96 and for the common models ranged from 0.83 to 0.93. However, the comparison of the predictive ability of the dedicated and common models using the Psyl external validation set showed almost identical predicted values. [ABSTRACT FROM AUTHOR]

• A sustainable K 2 CO 3 /SFB catalyst was synthesized using an efficient ultrasonic-assisted wet impregnation method. • Its structural and chemical properties were confirmed through comprehensive characterization techniques. • Optimal conditions for the transesterification of castor oil with methanol were determined, resulting in a 99.63 % biodiesel yield. • Reaction kinetics revealed a rate constant of 1.05 h−1 and e act of 18.72 kJ/mol. • The catalyst's loading requirement was reduced to only 20 %, leading to cost savings and a reaction time of 2 h. • After 6 cycles, a 92.31 % activity level was retained, showcasing sustainability and efficiency. • DFT calculations confirmed −0.1 eV adsorption energy at the ca active site, promoting enhanced reaction rates via 0.1 e − transfer. A cost-effective and recyclable catalyst, K 2 CO 3 supported on calcined salmon fish bone (K 2 CO 3 /SFB), was synthesized using an ultrasonic-assisted wet impregnation method. Various characterization techniques, including XRD, XPS, BET, FESEM, TGA, and FT-IR, were used to analyze the prepared catalysts. The catalytic efficiency of the sonicated K 2 CO 3 /SFB catalysts was evaluated in the transesterification reaction between Castor oil and methanol to produce biodiesel. The study also investigated the impact of calcination temperature and K 2 CO 3 loading on the catalyst's performance, revealing that the highest catalytic activity was achieved with K 2 CO 3 calcined at 773 K with a loading of 20 wt%. Optimized conditions for biodiesel production included a loading of catalyst at 10 wt%, a mol proportioan of 10 to 1 for methanol/oil, and a reaction temperature set at 338 K over a period of 2 h, resulting in a process yield of 99.63 %. The catalyst exhibited good stability over six reaction cycles, with only an 8 % decrease in activity. The kinetics studies were investigated with PLS-R, assuming fatty acid methyl esters as the main product and mono- and diglycerides as intermediate and established 95 % confidence intervals and found a rate constant of 1.05 h−1 for the reaction and an energy required for activation. of 18.72 kJ/mol with the optimized catalyst. Additionally, DFT-D3 calculations showed strong interaction between the catalyst and triglyceride, with charge transfer enhancing the reaction rate, and p orbitals from the catalyst playing a significant role in hybridization with triglyceride's carbon and oxygen atoms. [Display omitted] [ABSTRACT FROM AUTHOR]

The functionality of soybeans is an important factor in the selection and utilization of excellent soybean cultivars, and isoflavones are representative functional substances in soybeans, which exhibit effects on antioxidants, estrogen activity, and cancer, and prevent cardiovascular diseases. This study analyzed ABTS, DPPH, estrogen, ER (ER) alpha, UCP-1, and NO inhibition activities in 48 types of soybean cultivars, as well as the relationship with 19 isolated types of individual isoflavone derivatives. Statistical analysis was conducted to find individual isoflavone derivatives affecting physiological activities, revealing the high correlation of three types of derivatives: genistein 7-O-(6″-O-acetyl)glucoside (6″-O-acetylgenistin), genistein 7-O-(2″-O-apiosyl)glucoside, and glycitein. Based on these results, 15 types of soybean cultivars were selected (one control type, seven yellow types, six black types, and one green type), which have both high physiological activities and a high content of individual isoflavone derivatives. In addition, these high correlations were further verified through a genome-wide association study (GWAS) to determine the association between activities, substances, and genetic characteristics. This study comprehensively describes the relationship between the specific physiological activities of soybean resources, individual isoflavone derivative substances, and SNPs, which will be utilized for in-depth research, such as selection of excellent soybean resources with specific physiological activities.

Near-infrared spectroscopic (NIR) technique was used, for the first time, to predict volatile phenols content, namely guaiacol, 4-methyl-guaiacol, eugenol, syringol, 4-methyl-syringol and 4-allyl-syringol, of aged wine spirits (AWS). This study aimed to develop calibration models for the volatile phenol’s quantification in AWS, by NIR, faster and without sample preparation. Partial least square regression (PLS-R) models were developed with NIR spectra in the near-IR region (12,500–4000 cm−1) and those obtained from GC-FID quantification after liquid-liquid extraction. In the PLS-R developed method, cross-validation with 50% of the samples along a validation test set with 50% of the remaining samples. The final calibration was performed with 100% of the data. PLS-R models with a good accuracy were obtained for guaiacol (r2 = 96.34; RPD = 5.23), 4-methyl-guaiacol (r2 = 96.1; RPD = 5.07), eugenol (r2 = 96.06; RPD = 5.04), syringol (r2 = 97.32; RPD = 6.11), 4-methyl-syringol (r2 = 95.79; RPD = 4.88) and 4-allyl-syringol (r2 = 95.97; RPD = 4.98). These results reveal that NIR is a valuable technique for the quality control of wine spirits and to predict the volatile phenols content, which contributes to the sensory quality of the spirit beverages.

Graphical abstract Highlights • Biodiesel reaction mechanism studied in real-time using low-field NMR spectroscopy. • The reaction showed both mass transfer and kinetic controlled behaviors. • Different reaction conditions impacted reaction mechanism and percentage yield. • Peak fitting and PLS-R methods compared and 95% confidence intervals obtained. Abstract The production of bio-fuels as a substitute for crude oil is steadily increasing. The reactions producing bio-fuels need detailed analysis concerning mechanism and reaction kinetics to increase yield. Standard analytical methods provide a way to monitor the reaction in real-time but detailing the mechanism through chemical shift fingerprinting is not possible using these methods. The reaction kinetics during biofuel production can be followed in real-time by nuclear magnetic resonance (NMR) spectroscopy by passing reaction mixture through the magnet. The present work reports the use of desktop NMR spectroscopy for a real-time study of the transesterification of triglycerides (vegetable oil) with methanol for formation of methyl esters (biodiesel) to detail catalytic activity, reaction mechanism and kinetics. The reaction was investigated for different catalyst concentrations, different molar ratios of reactants, and different temperatures. The changes in the chemical shift of the hydroxyl protons in the reaction mixture resulting from changing catalyst concentration and temperature provide information about the role of the catalyst in the aqueous and organic phases. The reaction was determined to be mass transfer controlled in the initial stage and kinetically controlled at later stage depending upon the reaction conditions. Analysis of the reaction for different molar ratios of oil and biodiesel and for increasing methanol concentration suggests the formation of dimers. The time variation of the methyl ester (bio-diesel) concentration was determined by partial least squares regression (PLS-R) using high-field NMR spectroscopy as reference. To obtain rate constants for each reaction the kinetics were modeled assuming fatty acid methyl esters as major product, and mono and diglycerides as intermediates. The 95% confidence intervals were derived by a Monte-Carlo analysis. The reaction kinetics are compared to those obtained by peak fitting of low-field spectra. [ABSTRACT FROM AUTHOR]

The aim of the present study was to assess the microbiological quality of farmed sea bass (Dicentrarchus labrax) fillets stored under aerobic conditions and modified atmosphere packaging (MAP) (31% CO2, 23% O2, 46% Ν2,) at 0, 4, 8, and 12 °C using Fourier transform infrared (FTIR) spectroscopy and multispectral imaging (MSI) in tandem with data analytics, taking into account the results of conventional microbiological analysis. Fish samples were subjected to microbiological analysis (total viable counts (TVC), Pseudomonas spp., H2S producing bacteria, Brochothrix thermosphacta, lactic acid bacteria (LAB), Enterobacteriaceae, and yeasts) and sensory evaluation, together with FTIR and MSI spectral data acquisition. Pseudomonas spp. and H2S-producing bacteria were enumerated at higher population levels compared to other microorganisms, regardless of storage temperature and packaging condition. The developed partial least squares regression (PLS-R) models based on the FTIR spectra of fish stored aerobically and under MAP exhibited satisfactory performance in the estimation of TVC, with coefficients of determination (R2) at 0.78 and 0.99, respectively. In contrast, the performances of PLS-R models based on MSI spectral data were less accurate, with R2 values of 0.44 and 0.62 for fish samples stored aerobically and under MAP, respectively. FTIR spectroscopy is a promising tool to assess the microbiological quality of sea bass fillets stored in air and under MAP that could be effectively employed in the future as an alternative method to conventional microbiological analysis.

FT-NIR models were developed for the non-destructive prediction of soluble solid content (SSC), titratable acidity (TA), firmness and weight of two commercially important apricot cultivars, "Hacıhaliloğlu" and "Kabaaşı" from Turkey. The models constructed for SSC prediction gave good results. We could also establish a model which can be used for rough estimation of the apricot weight. However, it could not be possible to predict accurately TA and firmness of the apricots with FT-NIR spectroscopy. The study was further extended over 3 years for the SSC prediction. Validation of the both mono and multi-cultivar models showed that model performances may exhibit important variations across different harvest seasons. The robustness of the models was improved when the data of two or three seasons were used. It was concluded that in order to developed reliable SSC prediction models for apricots the spectral data should be collected over several harvest seasons. [ABSTRACT FROM AUTHOR]

The current research attempts to provide an alternative tool for grape maturity measurement related to the wine composition since, the classical parameters (weight grape berries, sugar content, titratable acidity), commonly used in the winemaking industry, do not provide any sensorial information. In this context, the evolution of terpenic compounds (TC) during ripening of four V. vinifera L. grape varieties - Bual, Malvasia, Sercial (white grapes) and Tinta Negra (red grapes), was investigated, in addition to the establishment of terpenic pattern, using headspace solid phase microextraction (HS-SPME) combined with GC–MS. Using the optimal analytical conditions were identified 62 TC in the investigated V. vinifera L. grapes. The integration of chromatographic and chemometric data provides a powerful strategy to identify potential maturity markers. The maximum potential of mono- and sesquiterpenic compounds was reached at maturity, whereas the highest levels of norisoprenoids were observed at véraison . Partial Least Squares Regression (PLS-R) was employed to describe the relationship between classical parameters and TC. Based on PLS-R models, three monoterpenic (linalool, α-terpineol, carvomenthol), one sesquiterpenic (bicyclogermacrene) and two norisoprenoids compounds (vitispirane I, β-damascenone) could be used to define the optimum harvest date. The obtained results represent a very important tool to support, in an objective way, the winemakers decision for long-term strategic planning based on the sensory potentialities of grape varieties and consequently improving the excellence of Madeira wine. [ABSTRACT FROM AUTHOR]

The potential of using FT-NIR spectroscopy for the rapid and non-destructive measurement of the moisture, water activity, firmness and SO 2 content of the intact sulphured-dried apricots (SDA) was investigated for the first time in the literature. The partial least squares regression (PLS-R) models constructed using FT-NIR spectra were very successful in predicting the moisture content ( R 2 p = 0.986, RMSEP = 1.22%, RPD = 9.15) and water activity ( R 2 p = 0.987, RMSEP = 0.016, RPD = 9.37) of SDAs. Satisfactory results were also obtained for the models developed for the prediction of the firmness ( R 2 p = 0.845, RMSEP = 0.445, RPD = 2.55) and SO 2 content ( R 2 p = 0.804, RMSEP = 349 mg kg −1 , RPD = 2.27). These results clearly demonstrate that the major quality parameters of SDA can be simultaneously measured in a short time by FT-NIR spectroscopy without any need for the sample preparation or skilled laboratory personnel. [ABSTRACT FROM AUTHOR]

Styrene-butadiene rubber (SBR) is a major source material for the fabrication of elastomer products. Depending on its origin, differences are observed between SBR samples by tabletop NMR spectroscopy that relate to the constitution of the macromolecular chains. This study reports experimental results from the analysis of 108 SBR samples by low-field 1 H and 13 C NMR spectroscopy at 1 T in combination with partial least squares regression to develop a methodology for quality control of raw rubber. Partial least squares regression (PLS-R) models were developed for quantifying the individual monomer units present in SBR which are impossible to quantify directly because of peak overlap in a 1 H NMR spectrum obtained at 1 T. The spectra revealed differences between samples from the same and different manufacturing batches of the same and different manufacturers in a qualitative and quantitative fashion. The range of samples included regular and oil-extended solution and emulsion polymerized SBR. Referring to high-field spectra acquired at 9.4 T the peaks in the low-field 13 C NMR spectra could be assigned for determining the rubber microstructure, and the content of different repeat units could be quantified by partial least squares regression. Over 12 repeatable measurements the standard deviation in mass % was 0.03%, 0.06%, 0.05%, 0.33% and 0.37% for the contents of styrene, 1,2-butadiene, 1,4-butadiene, trans -1,4-butadiene and cis -1,4-butadiene units, respectively. Among 7 different sampling points in a delivery, the standard deviation was 0.51% for 1,2-butadiene, 0.88% for styrene, 0.56% for 1,4-butadiene unit, 0.42% for trans -1,4-butadiene and 0.68% for cis -1,4-butadiene units. The root-mean-square error of prediction (RMSEP) for styrene, 1,2-butadiene, 1,4-butadiene, and trans -1,4-butadiene was 0.15, 0.29, 0.29, and 0.28 with R 2 values of 0.93, 0.92, 0.92, and 0.95, respectively, demonstrating the potential of low-field NMR spectroscopy with compact instruments for quality control of raw rubber when used in combination with effective data analysis procedures such as chemometrics. [ABSTRACT FROM AUTHOR]

Rapid assessments of Total Acid Number (TAN) and Total Base Number (TBN) in crude oil samples are significant in the oil industry. The analysis of TAN and TBN was conducted using the standard methods of the American Society for Testing and Materials (ASTM). However, the standard methods are costly and require a large number of samples for analysis. Therefore, providing analytical methods for the rapid crude oil analysis is very essential. In the current research, we report an application of Attenuated Total Reflection Fourier-Transform Infrared (ATR-FTIR) spectroscopy, based on robust Principal Component Analysis-Multivariate Adaptive Regression Splines (rPCA-MARS), for crude oil analysis. In the rPCA-MARS model, the ATR-FTIR spectral matrix data was decomposed into PCs scores using the rPCA method as an input data for the MARS model. The multivariate calibration models were applied to the analysis of crude oil samples based on the quantitative determination of total acid number (TAN) and total base number (TBN). An analytical method was developed for determination of TAN and TBN of crude oil samples. The ATR-FTIR spectroscopy coupled with multivariate calibration methods can be applied as a novel analytical method for crude oil samples. The result of partial least square regression (PLS-R), principle component analysis (PCR), Piecewise-Linear rPCA-MARS and Piecewise-cubic rPCA-MARS models were compared for analysis of total acid and base number of crude oil samples. The squared correlation coefficient (R2) and root mean square error (RMSE) for calibration and prediction sets were calculated to evaluate multivariate calibration models. The importance of methods was studied and discussed. The mean square error (MSE) values of Piecewise-Linear rPCA-MARS for TAN and TBN were 1.587 * 10−4 and 0.009, respectively. The Piecewise-Linear rPCA-MARS model can be successfully applied for the analysis of TAN and TBN of crude oil samples based on the obtained results. The fast and easy prediction capability of the proposed Piecewise-Linear rPCA-MARS model, as compared to standard methods, for determining TAN and TBN without any sample preparation step is important in the oil industry [Display omitted] • ATR-FTIR spectral data was applied to determine the TAN and TBN of oil samples. • Application of Piecewise-Linear rPCA-MARS model for the determination of the total acid and base number of crude oil samples using ATR-FTIR spectroscopy. • PLS-R and PCR models combined with ATR-FTIR spectroscopy were developed for crude oil analysis based on the TAN and TBN of crude oil samples. • Statistical analysis was applied to investigate the relationship between the ATR-FTIR data and the TAN and TBN of crude oil samples. [ABSTRACT FROM AUTHOR]

We utilized Micro-Photoluminescence (μPL) and time-resolved photoluminescence (TRPL) spectroscopies with a femtosecond laser (440 nm) to assess the optical properties of two types of WS 2 monolayers (MLs) obtained through mechanical exfoliation of different crystals. Based on cryogenic μPL spectra (4 K), the monolayers were classified as low-defect-concentration (LDC) and high-defect-concentration (HDC). By subjecting the MLs to laser excitation at various photon fluxes under ambient and vacuum conditions at room temperature, we characterized their optical properties. The spectra exhibited two main regions corresponding to optical transitions of neutral excitons (X) and trions (T). Notably, two distinct regimes were observed for quasi-particle decay: X-X annihilation at low photon fluxes and Mott transition at high photon fluxes. The photoluminescence quantum yield (PLQY) was measured under ambient and vacuum conditions, with the presence of physisorbed O 2 on the ML surface influencing the results. Additionally, we employed a novel analytical approach combining PL and TRPL spectra with chemometric methods for the regression analysis of the two types of WS2 MLs. Utilizing partial least squares regression and principal component regression models, we estimated the decay times of X and T quasi-particles based on the spectral datasets. [Display omitted] • Two distinct types of WS 2 MLs are obtained by exfoliation of two various crystals. • The MLs are categorized as LDC and HDC based on μPL spectra taken at 4 K. • The decays of quasi-particles include X-X annihilation and Mott transition regimes. • μPL and TRPL spectra are combined with chemometric methods. • PLS-R and PCR approaches are applied to estimate the optical properties of the MLs. [ABSTRACT FROM AUTHOR]

Broomrape (Orobanche and Phelipanche spp.) parasitism is a severe problem in many crops worldwide, including in the Mediterranean basin. Most of the damage occurs during the sub-soil developmental stage of the parasite, by the time the parasite emerges from the ground, damage to the crop has already been done. One feasible method for sensing early, below-ground parasitism is through physiological measurements, which provide preliminary indications of slight changes in plant vitality and productivity. However, a complete physiological field survey is slow, costly and requires skilled manpower. In recent decades, visible to-shortwave infrared (VIS-SWIR) hyperspectral tools have exhibited great potential for faster, cheaper, simpler and non-destructive tracking of physiological changes. The advantage of VIS-SWIR is even greater when narrow-band signatures are analyzed with an advanced statistical technique, like a partial least squares regression (PLS-R). The technique can pinpoint the most physiologically sensitive wavebands across an entire spectrum, even in the presence of high levels of noise and collinearity. The current study evaluated a method for early detection of Orobanche cumana parasitism in sunflower that combines plant physiology, hyperspectral readings and PLS-R. Seeds of susceptible and resistant O. cumana sunflower varieties were planted in infested (15 mg kg-1 seeds) and non-infested soil. The plants were examined weekly to detect any physiological or structural changes; the examinations were accompanied by hyperspectral readings. During the early stage of the parasitism, significant differences between infected and non-infected sunflower plants were found in the reflectance of near and shortwave infrared areas. Physiological measurements revealed no differences between treatments until O. cumana inflorescences emerged. However, levels of several macro- and microelements tended to decrease during the early stage of O. cumana parasitism. Analysis of leaf cross-sections revealed differences in range and in mesophyll structure as a result of different levels of nutrients in sunflower plants, manifesting the presence of O. cumana infections. The findings of an advanced PLS-R analysis emphasized the correlation between specific reflectance changes in the SWIR range and levels of various nutrients in sunflower plants. This work demonstrates potential for the early detection of O. cumana parasitism on sunflower roots using hyperspectral tools.

The predictive power of the two major water bands centered at 6900 cm - 1 and 5200 cm - 1 in the near-infrared (NIR) region was compared to carbohydrate-related spectral areas located in the first overtone (around 6000 cm - 1 ) and combination (around 4500 cm - 1 ) region using glucose in aqueous solutions as a model substance. For the purpose of optimal coverage of stronger as well as weaker absorbing NIR regions, cells with three different declared optical pathlengths were employed. The sample set consisted of multiple separately prepared batches in the range of 50−200 mmol/L. Moreover, the samples were divided into a calibration set for the construction of the partial least squares regression (PLS-R) models and a test set for the validation process with independent samples. The first overtone and combination region showed relative prediction errors between 0.4−1.6% with only one PLS-R factor required. On the other hand, the errors for the water bands were found between 1.6−8.3% and up to three PLS-R factors required. The best PLS-R models resulted from the cell with 1 mm optical pathlength. In general, the results suggested that the carbohydrate-related regions in the first overtone and combination region should be preferred over the regions of the two dominant water bands.

Broomrape (Orobanche and Phelipanche spp.) parasitism is a severe problem in many crops worldwide, including in the Mediterranean basin. Most of the damage occurs during the sub-soil developmental stage of the parasite, by the time the parasite emerges from the ground, damage to the crop has already been done. One feasible method for sensing early, below-ground parasitism is through physiological measurements, which provide preliminary indications of slight changes in plant vitality and productivity. However, a complete physiological field survey is slow, costly and requires skilled manpower. In recent decades, visible to-shortwave infrared (VIS-SWIR) hyperspectral tools have exhibited great potential for faster, cheaper, simpler and non-destructive tracking of physiological changes. The advantage of VIS-SWIR is even greater when narrow-band signatures are analyzed with an advanced statistical technique, like a partial least squares regression (PLS-R). The technique can pinpoint the most physiologically sensitive wavebands across an entire spectrum, even in the presence of high levels of noise and collinearity. The current study evaluated a method for early detection of Orobanche cumana parasitism in sunflower that combines plant physiology, hyperspectral readings and PLS-R. Seeds of susceptible and resistant O. cumana sunflower varieties were planted in infested (15 mg kg-1 seeds) and non-infested soil. The plants were examined weekly to detect any physiological or structural changes; the examinations were accompanied by hyperspectral readings. During the early stage of the parasitism, significant differences between infected and non-infected sunflower plants were found in the reflectance of near and shortwave infrared areas. Physiological measurements revealed no differences between treatments until O. cumana inflorescences emerged. However, levels of several macro- and microelements tended to decrease during the early stage of O. cumana parasitism. Analysis of leaf cross-sections revealed differences in range and in mesophyll structure as a result of different levels of nutrients in sunflower plants, manifesting the presence of O. cumana infections. The findings of an advanced PLS-R analysis emphasized the correlation between specific reflectance changes in the SWIR range and levels of various nutrients in sunflower plants. This work demonstrates potential for the early detection of O. cumana parasitism on sunflower roots using hyperspectral tools. [ABSTRACT FROM AUTHOR]

For the damage assessment of reinforced concrete structures the quantified ingress profiles of harmful species like chlorides, sulfates and alkali need to be determined. In order to provide on-site analysis of concrete a fast and reliable method is necessary. Low transition probabilities as well as the high ionization energies for chlorine and sulfur in the near-infrared range makes the detection of Cl I and S I in low concentrations a difficult task. For the on-site analysis a mobile LIBS-system ( λ = 1064 nm, E pulse ≤ 3 mJ, τ = 1.5 ns) with an automated scanner has been developed at BAM. Weak chlorine and sulfur signal intensities do not allow classical univariate analysis for process data derived from the mobile system. In order to improve the analytical performance multivariate analysis like PLS-R will be presented in this work. A comparison to standard univariate analysis will be carried out and results covering important parameters like detection and quantification limits (LOD, LOQ) as well as processing variances will be discussed (Allegrini and Olivieri, 2014 [1]; Ostra et al., 2008 [2]). It will be shown that for the first time a low cost mobile system is capable of providing reproducible chlorine and sulfur analysis on concrete by using a low sensitive system in combination with multivariate evaluation. [ABSTRACT FROM AUTHOR]

Fourier transform infrared spectroscopy based on attenuated total reflectance sampling technique, combined with multivariate analysis methods was used to monitor the adulteration of pure sunflower oil (SO) with thermally deteriorated oil (TDO). Contrary to published research, in this work, SO was thermally deteriorated in the absence of foodstuff. SO samples were exposed to temperatures between 125 and 225°C from 6 to 24 h. Quantification of adulteration of SO with TDO, based on principal components regression (PCR), partial least squares regression (PLS-R), and linear discriminant analysis (LDA) applied to mid-infrared spectra and to their first and second derivatives is reported for the first time. Infrared frequencies associated with the biochemical differences between TDO samples deteriorated in different conditions were investigated by principal component analysis (PCA). LDA was effective in the twofold classification presence/absence of TDO in adulterated SO (with 5% V/V of less of TDO). It provided 93.7% correct classification for the calibration set and 91.3% correct classification when cross-validated. A detection limit of 1% V/V of TDO in SO was determined. Investigation of an external set of samples allowed the evaluation of the predictability of the models. The regression coefficient (R2) for prediction was 0.95 and 0.96 and the RMSE was 2.1 and 1.9% V/V when using the PCR or PLS-R models, respectively, and the first derivative of spectra. To the best of our knowledge, no investigation of adulteration of SO with TDO based on PCR, PLS-R, and LDA has been reported so far.

Yield loss in crops is often associated with plant disease or external factors such as environment, water supply and nutrient availability. Improper agricultural practices can also introduce risks into the equation. Herbicide drift can be a combination of improper practices and environmental conditions which can create a potential yield loss. As traditional assessment of plant damage is often imprecise and time consuming, the ability of remote and proximal sensing techniques to monitor various bio-chemical alterations in the plant may offer a faster, non-destructive and reliable approach to predict yield loss caused by herbicide drift. This paper examines the prediction capabilities of partial least squares regression (PLS-R) models for estimating yield. Models were constructed with hyperspectral data of a cotton crop sprayed with three simulated doses of the phenoxy herbicide 2,4-D at three different growth stages. Fibre quality, photosynthesis, conductance, and two main hormones, indole acetic acid (IAA) and abscisic acid (ABA) were also analysed. Except for fibre quality and ABA, Spearman correlations have shown that these variables were highly affected by the chemical. Four PLS-R models for predicting yield were developed according to four timings of data collection: 2, 7, 14 and 28 days after the exposure (DAE). As indicated by the model performance, the analysis revealed that 7 DAE was the best time for data collection purposes (RMSEP = 2.6 and R 2 = 0.88), followed by 28 DAE (RMSEP = 3.2 and R 2 = 0.84). In summary, the results of this study show that it is possible to accurately predict yield after a simulated herbicide drift of 2,4-D on a cotton crop, through the analysis of hyperspectral data, thereby providing a reliable, effective and non-destructive alternative based on the internal response of the cotton leaves. [ABSTRACT FROM AUTHOR]

Controlling and monitoring of emissions from municipal solid waste (MSW) landfills is important to reduce environmental damage and health risks. Therefore, simple and meaningful monitoring tools are required. This paper presents how Fourier Transform Infrared (FT-IR) Spectroscopy can be used to monitor leachate from various landfill sites. The composition of percolated leachate provides information about reactivity or stability of organic matter in landfills. Chemical compounds of investigated leachate are depicted by distinct spectral pattern. Partial least squares regression (PLS-R) models, a multivariate analysis tool, were developed based on infrared spectra to determine simultaneously conventional parameters such as ammonium, nitrate, sulfate, and dissolved organic carbon. The developed models are appropriate for application in waste management practice with respect to their excellent coefficients of determination, namely R 2 = 0.99, 0.99, 0.98, and 0.98, their low errors of cross-validation and their high ratios of performance to deviation (RPD = 9.3, 12.5, 6.5, 7.3). Thus, FT-IR spectroscopy turned out to be a reliable, time-saving tool to determine four parameters relevant for landfill aftercare monitoring by one single easy adaptable measurement. [ABSTRACT FROM AUTHOR]

Soil humus dissimilation in 8 kinds of pure forests was detected and its relationship with soil biological and chemical properties and leaf litter characteristics was assessed using partial least squares regression (PLS-R). The results indicated that: The particular soil properties in pure forest rather than the litter characteristics exhibited the dominant impacts on humus accumulation and degree of humifications. High soil microbial biomass carbon (MC), alkaline N and available P contents, soil phosphatase, dehydrogenase and urease activities, and litter accumulation were associated with high humic acid accumulation, while high soil available Fe and litter Mn contents were opposite. High soil MC, alkaline N, available Zn and P contents, and dehydrogenase and phosphatase activities were in favour of fulvic acid accumulation, while the high litter Mn content were opposite. High soil MC and alkaline N contents, dehydrogenase and phosphatase activities and high litter N content were associated with the accumulation of humin, in contrast, increase in soil available K and Zn contents and sucrase activity hindered this process. Increases in soil available K content, urease and peroxidase activities and litter accumulation and C/P ratio increased the degree of humifications of soil, while the increases in soil available Fe and Cu contents, catalase activity and litter P content significantly decreased it. [ABSTRACT FROM AUTHOR]

The emergence of omics approaches in environmental research has enhanced our understanding of the mechanisms underlying toxicity; however, extrapolation from molecular effects to whole-organism and population level outcomes remains a considerable challenge. Using environmentally relevant, sublethal, concentrations of two metals (Cu and Ni), both singly and in binary mixtures, we integrated data from traditional chronic, partial life-cycle toxicity testing and metabolomics to generate a statistical model that was predictive of reproductive impairment in a Daphnia pulex-pulicaria hybrid that was isolated from an historically metal-stressed lake. Furthermore, we determined that the metabolic profiles of organisms exposed in a separate acute assay were also predictive of impaired reproduction following metal exposure. Thus we were able to directly associate molecular profiles to a key population response – reproduction, a key step towards improving environmental risk assessment and management. [ABSTRACT FROM AUTHOR]

• RGB-based data facilitate biomass LAI and grain yield prediction in chickpea. • Data fusion improves the prediction accuracy and robustness. • The SVM model adequately handled non-linear biomass and LAI data sets. • UAS with RGB camera is a suitable tool for monitoring chickpea growth status. Chickpea (Cicer arietinum L.) is a key legume crop grown in many semi-arid areas. Traditionally, chickpea is a rainfed spring crop, but in certain countries it has become an irrigated crop. The main objective of this study was to evaluate the ability of Unmanned Aerial Systems (UAS) imaging platform with an integrated RGB camera to provide estimations of leaf area index (LAI), biomass, and yield for chickpea during the irrigation period. Two field trials were conducted in 2019 and 2020, in which chickpea plants were subjected to five and six irrigation regimes, respectively. Eight vegetation indexes (VIs) and three morphological parameters were estimated from the RGB images. In parallel, biomass was determined, LAI was measured manually, and yield was determined at full maturity. In total, 294 plant samples were acquired and analyzed over the two years. Firstly, each of the VIs and morphological parameters were correlated separately against the two biophysical parameters and yield. Then, all the VIs and morphological parameters were analyzed together, and two statistical models, partial least squares regression (PLS-R) and support vector machine (SVM); were used to predict biomass and LAI. The yield was predicted using multi-linear regression (MLR). When each index or morphological parameter was analyzed separately, plant height and some of the VIs provided adequate predictions of the biophysical parameters in 2019 (R2 values ≥ 0.50) but failed (R2 values ≤ 0.25) in 2020. The integration of the VIs with the morphological parameters and the use of PLS-R and SVM models increased the accuracy level for both biophysical parameters (R2 ranged from 0.31 to 0.96) and mitigated the lack of consistency between the years. The SVM model was superior to the PLS-R model in both biophysical parameters. The R2 values for the combined 2019 and 2020 biomass model increased, at the model-testing stage, from 0.62 to 0.96 and the RMSE values dropped from 1778 to 490 kg ha−1. The ability of the SVM model to estimate chickpea biomass and LAI can provide convenient support for different management decisions, including timing and amount of irrigation and harvest date. [ABSTRACT FROM AUTHOR]

Physicochemical characteristics of seventy wheat flours of different species namely Einkorn (Triticum monococcum), Spelt (Triticum spelta) and common wheat (Triticum aestivum) were analyzed using different standard methods. The wheat grains were analyzed for moisture, ash, protein, wet gluten, sedimentation index, pH, acidity, fat, starch, falling number, damage starch and Glutograph parameters stretching and relaxation. The relationship between physicochemical characteristics and wheat samples were analyzed using the principal component analysis. For almost all the physicochemical data except moisture and damage starch were obtained significant differences between species. These differences exist in especially between common wheat samples and ancient wheat species. The highest protein, wet gluten, fat, ash, acidity were obtained for ancient species whereas the common wheat were richer in starch and presented the highest stretching and relaxation Glutograph values. Also, the Fourier transforms infrared (FTIR) spectroscopy was used as a nondestructive method to analyze wheat flour composition. On basis of peaks the chemical values of grain were determined. The FTIR spectra was subjected to different spectral pre-treatment in order to improve the prediction of some physicochemical parameters (moisture content, protein content, starch content, gluten content, FN and UCDc) of wheat using partial least squares regression (PLS-R). 1st and 2nd derivates pre-treatments of spectra provided the most suitable model for the prediction of the studied parameters. • FTIR can be used as reliable tool to predict the quality control of wheat. • The physicochemical characteristics of wheat varies between species. • Moisture, protein, starch, gluten, FN and UCDc were predicted using FTIR spectra bands coupled with PLS-R analysis. [ABSTRACT FROM AUTHOR]

[Display omitted] • ATR-FTIR spectroscopy combined with chemometric methods were developed for crude oil analysis. • Analytical methods were developed for the sulfur content quantitative determination and qualitative classification in crude oil samples. • The Sulfur content was measured by X-ray fluorescence spectroscopy. • SVM-R and PLS-R models were used for quantitative determination of sulfur content in crude oils. • PLS-DA and SVM-DA algorithms provide 96% classification accuracy and error rate of 0.0384 for the prediction set. Determining and classifying the sulfur content of crude oil has long been of great importance because of its adverse economic and environmental effects. In this study, the total sulfur concentration in crude oil samples was determined and classified using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and chemometric methods. The methods designed for the analysis of crude oils are rapid, economical and non-destructive in the production process of the petroleum industry. Two sets of 70 and 31 samples in regression models were considered for the calibration and prediction sets, respectively. The calibration models were developed using the partial least squares regression model (PLS-R) and support vector machine regression model (SVM-R). Different pre-processing methods were also evaluated for the development of models. The preprocessing methods based on baseline correction, standard normal variate (SNV) and the auto scale were selected for regression and classification models. The use of SVM-R as a non-linear regression provided a model with significantly better root mean square error of prediction (RMSEP) values than the PLS-R model as a linear model. The ATR-FTIR spectral data were also applied by supervised classification method using the partial least squares-discriminant analysis (PLS-DA) and support vector machine-discriminant analysis (SVM-DA) for classifying crude oils based on sulfur content. The samples were classified into two classes according to the sulfur content into sweet and sour crude oil. The result of the classification found an accuracy of 96% and a classification error of 0.0384 for the prediction set in the PLS-DA algorithm. The results indicated that ATR-FTIR spectroscopy associated with multivariate calibration and classification models is a rapid and reliable approach for parallel quantification and qualification of the sulfur content present in crude oils. [ABSTRACT FROM AUTHOR]

Physiological measurements are considered to be the most accurate way of assessing plant water status, but they might also be time-consuming, costly and intrusive. Since visible (VIS)-to-shortwave infrared (SWIR) imaging spectrometers are able to monitor various bio-chemical alterations in the leaf, such narrow-band instruments may offer a faster, less expensive and non-destructive alternative. This requires an intelligent downsizing of broad and noisy hyperspectra into the few most physiologically-sensitive wavelengths. In the current study, hyperspectral signatures of water-stressed grapevine leaves ( Vitis vinifera L. cv. Cabernet Sauvignon) were correlated to values of midday leaf water potential ( Ψ l ), stomatal conductance ( g s ) and non-photochemical quenching (NPQ) under controlled conditions, using the partial least squares-regression (PLS-R) technique. It was found that opposite reflectance trends at 530–550 nm and around 1500 nm – associated with independent changes in photoprotective pigment contents and water availability, respectively – were indicative of stress-induced alterations in Ψ l , g s and NPQ. Furthermore, combining the spectral responses at these VIS and SWIR regions yielded three normalized water balance indices (WABIs), which were superior to various widely-used reflectance models in predicting physiological values at both the leaf and canopy levels. The potential of the novel WABI formulations also under field conditions demonstrates their applicability for water status monitoring and irrigation scheduling. [ABSTRACT FROM AUTHOR]

A growing body of knowledge is available on the cellular regulation of overflow metabolism in mammalian hosts of recombinant protein production. However, to develop strategies to control the regulation of overflow metabolism in cell culture processes, the effect of process parameters on metabolism has to be well understood. In this study, we investigated the effect of pH and temperature shift timing on lactate metabolism in a fed-batch Chinese hamster ovary (CHO) process by using a Design of Experiments (DoE) approach. The metabolic switch to lactate consumption was controlled in a broad range by the proper timing of pH and temperature shifts. To extract process knowledge from the large experimental dataset, we proposed a novel methodological concept and demonstrated its usefulness with the analysis of lactate metabolism. Time-resolved metabolic flux analysis and PLS-R VIP were combined to assess the correlation of lactate metabolism and the activity of the major intracellular pathways. Whereas the switch to lactate uptake was mainly triggered by the decrease in the glycolytic flux, lactate uptake was correlated to TCA activity in the last days of the cultivation. These metabolic interactions were visualized on simple mechanistic plots to facilitate the interpretation of the results. Taken together, the combination of knowledge-based mechanistic modeling and data-driven multivariate analysis delivered valuable insights into the metabolic control of lactate production and has proven to be a powerful tool for the analysis of large metabolic datasets. [ABSTRACT FROM AUTHOR]

Fourier transform infrared spectroscopy based on attenuated total reflectance sampling technique, combined with multivariate analysis methods was used to monitor the adulteration of pure sunflower oil (SO) with thermally deteriorated oil (TDO). Contrary to published research, in this work, SO was thermally deteriorated in the absence of foodstuff. SO samples were exposed to temperatures between 125 and 225°C from 6 to 24 h. Quantification of adulteration of SO with TDO, based on principal components regression (PCR), partial least squares regression (PLS-R), and linear discriminant analysis (LDA) applied to mid-infrared spectra and to their first and second derivatives is reported for the first time. Infrared frequencies associated with the biochemical differences between TDO samples deteriorated in different conditions were investigated by principal component analysis (PCA). LDA was effective in the twofold classification presence/absence of TDO in adulterated SO (with 5% V/V of less of TDO). It provided 93.7% correct classification for the calibration set and 91.3% correct classification when cross-validated. A detection limit of 1% V/V of TDO in SO was determined. Investigation of an external set of samples allowed the evaluation of the predictability of the models. The regression coefficient (R2) for prediction was 0.95 and 0.96 and the RMSE was 2.1 and 1.9% V/V when using the PCR or PLS-R models, respectively, and the first derivative of spectra. To the best of our knowledge, no investigation of adulteration of SO with TDO based on PCR, PLS-R, and LDA has been reported so far. [ABSTRACT FROM AUTHOR]

The aim of this work was to evaluate the potential of Fourier transform infrared (FTIR) spectroscopy as a rapid and accurate technique to detect and predict the onset of spoilage in fresh chicken breast fillets stored at 3, 8, and 30 °C. Chicken breasts were excised from carcasses at 6 h post-mortem; cut in fillets; packed in air; stored at 3, 8, and 30 ºC; and periodically examined for FTIR, pH, microbiological analysis, and sensory assessment of freshness. Partial least squares regression allowed estimations of total viable counts (TVC), lactic acid bacteria (LAB), Pseudomonas spp., Brochothrix thermosphacta, Enterobacteriaceae counts and pH, based on FTIR spectral data. Analysis of an external set of samples allowed the evaluation of the predictability of the method. The correlation coefficients (R) for prediction were 0.798, 0.832, 0.789, 0.810, 0.857, and 0.880, and the room mean square error of prediction were 0.789, 0.658, 0.715, 0.701, 0.756 log cfu g and 0.479 for TVC, LAB, Pseudomonas spp., B. thermosphacta, Enterobacteriaceae, and pH, respectively. The spectroscopic variables that can be linked and used by the models to predict the spoilage/freshness of the samples, pH, and microbial counts were the absorbency values of 375 wave numbers from 1,700 to 950 cm. A principal component analysis led to the conclusion that the wave numbers that ranges from 1,408 to 1,370 cm and from 1,320 to 1,305 cm are strongly connected to changes during spoilage. These wave numbers are linked to amides and amines and may be considered potential wave numbers associated with the biochemical changes during spoilage. Discriminant analysis of spectral data was successfully applied to support sensory data and to accurately bound samples freshness. According to the results presented, it is possible to conclude that FTIR spectroscopy can be used as a reliable, accurate, and fast method for real time freshness evaluation of chicken breast fillets during storage. [ABSTRACT FROM AUTHOR]

Abstract: This study investigates the use of Hotelling’s T 2 control charts as the basis of a process monitor for sewage sludge anaerobic digestion. Fourier transform near infrared spectroscopy was used to produce partial least squares regression models of volatile fatty acids, bicarbonate alkalinity and volatile solids. These were utilised in a series of principle component analysis models along with spectral data from digestate and feedstock samples to produce a pseudo steady state model, which was then used with an independent test set to evaluate the system. The system was able to identify disturbances to the digester due to a temporary alteration of the type of feedstock to the digester and separately, halving of the hydraulic retention time of the digester. It could also provide advance warning of disturbances to the digester. This technique could be used to improve the performance of sewage sludge anaerobic digesters by enabling optimisation of the process. [Copyright &y& Elsevier]

Abstract: Chemiluminescence emissions from OH∗, CH∗, , and formed within the reaction zone of premixed flames depend upon the fuel–air equivalence ratio in the burning mixture. In the present paper, a new partial least square regression (PLS-R) based multivariate sensing methodology is investigated and compared with an OH∗/CH∗ intensity ratio-based calibration model for sensing equivalence ratio in atmospheric methane–air premixed flames. Five replications of spectral data at nine different equivalence ratios ranging from 0.73 to 1.48 were used in the calibration of both models. During model development, the PLS-R model was initially validated with the calibration data set using the leave-one-out cross validation technique. Since the PLS-R model used the entire raw spectral intensities, it did not need the nonlinear background subtraction of emission that is required for typical OH∗/CH∗ intensity ratio calibrations. An unbiased spectral data set (not used in the PLS-R model development), for 28 different equivalence ratio conditions ranging from 0.71 to 1.67, was used to predict equivalence ratios using the PLS-R and the intensity ratio calibration models. It was found that the equivalence ratios predicted with the PLS-R based multivariate calibration model matched the experimentally measured equivalence ratios within 7%; whereas, the OH∗/CH∗ intensity ratio calibration grossly underpredicted equivalence ratios in comparison to measured equivalence ratios, especially under rich conditions (φ >1.2). The practical implications of the chemiluminescence-based multivariate equivalence ratio sensing methodology are also discussed. [Copyright &y& Elsevier]

Abstract: Vinasses coming from the five CBOs of Galicia, north-western Spain, were characterized, and successfully employed as economic nutritional supplements for xylitol production by Debaryomyces hansenii. All fermentations can be modelled showing kinetic patterns fairly described by the mathematical models. No negative effect of the phenolic compounds in the liquid phase on the initial volumetric rate of product formation (rP 0) was observed. Multiple linear regression analysis was used to describe the effect of metals and initial xylose acting on P max and YP / S . Zn was the most influential variable. Besides, partial least-squares regression models show a clear separation, based on the first two principal components, between the whole vinasses and the liquid fractions, which provided the higher P max, with the exception of CBO 4, where P max =40.4g/L, was achieved using the solid and liquid fraction. [Copyright &y& Elsevier]