Decoding the aroma characteristics of icewine by partial least-squares regression, aroma reconstitution, and omission studies

The appeal of icewine is attributable to its distinct aroma characteristics, such as 'honey', 'caramel', and 'dried fruit', but little is known about the chemical basis of these aroma attributes. A set of icewines with different aroma intensities were selected by a panel of wine experts. Detailed volatile compound analyses and sensory descriptive analyses were performed on the selected icewines. Using partial least-squares regression, several lactones, esters, terpenes, furanones, and β-damascenone were positively correlated with 'honey', 'caramel', and 'dried fruit' aromas. Aroma reconstitution studies confirmed that terpenes could significantly enhance the 'honey' aroma, but weaken the 'caramel' aroma, while lactones and furanones could significantly enhance the 'caramel' and 'dried fruit' aromas. In addition, this study demonstrated that terpenes, lactones, and furanones interacted synergistically with each other to cause the sensory perception of the characteristic aromas of icewine.

Sociodemographic factor associations with maternal and placental outcomes: A cluster and partial least squares regression analysis

INTRODUCTION

Maternal social disadvantage adversely affects maternal and offspring health, with limited research on placental outcomes. Therefore, we examined maternal sociodemographic factor associations with placental and birth outcomes in general (Lifeways Cross-Generation Cohort) and at-risk (PEARS Study of mothers with overweight or obesity) populations of pregnant women.

METHODS

TwoStep cluster analysis profiled Lifeways mothers (n = 250) based on their age, parity, marital status, household income, private healthcare insurance, homeowner status, and education. Differences in placental and birth outcomes (untrimmed placental weight (PW), birthweight (BW) and BW:PW ratio) between clusters were assessed using one-way ANOVA and chi-square tests. Partial least squares regression analysed individual effects of sociodemographic factors on placental and birth outcomes in Lifeways and PEARS mothers (n = 461).

RESULTS

Clusters were classified as "Married Homeowners" (n = 140, 56 %), "Highest Income" (n = 58, 23.2 %) and "Renters" (n = 52, 20.8 %) in the Lifeways Cohort. Renters were younger, more likely to smoke, have a means-tested medical card and more pro-inflammatory diets compared to other clusters (p < 0.01). Compared to Married Homeowners, renters' offspring had lower BW (-259.26 g, p < 0.01), shorter birth length (-1.31 cm, p < 0.01) and smaller head circumference (-0.59 cm, p = 0.02). PLS regression analyses identified nulliparity as having the greatest negative effect on PW (Lifeways and PEARS) while being a homeowner had the greatest positive effect on PW (Lifeways).

CONCLUSION

Certain combinations of sociodemographic factors (particularly homeownership) were associated with less favourable lifestyle factors, and with birth, but not placental outcomes. When explored individually, parity contributed to the prediction of placental and birth outcomes in both cohorts of pregnant women.

Exploring UPLC-QTOF-MS-based targeted and untargeted approaches for understanding wine mouthfeel: A sensometabolomic approach

This study aimed to establish relationships between wine composition and in-mouth sensory properties using a sensometabolomic approach. Forty-two red wines were sensorially assessed and chemically characterised using UPLC-QTOF-MS for targeted and untargeted analyses. Suitable partial least squares regression models were obtained for "dry", "sour", "oily", "prickly", and "unctuous". "Dry" was positively contributed by flavan-3-ols, anthocyanin derivatives (AntD), valine, gallic acid and its ethyl ester, and peptides, and negatively by sulfonated flavan-3-ols, anthocyanin-ethyl-flavan-3-ols, tartaric acid, flavonols (FOL), hydroxycinnamic acids (HA), protocatechuic ethyl ester, and proline. The "sour" model included molecules involved in "dry" and "bitter", ostensibly as a result of cognitive interactions. Derivatives of FOLs, epicatechin gallate, and N-acetyl-glucosamine phosphate contributed positively to "oily", as did vanillic acid, HAs, pyranoanthocyanins, and malvidin-flavan-3-ol derivatives for "prickly", and sugars, glutathione disulfide, AntD, FOL, and one HA for "unctuous". The presented approach offers an interesting tool for deciphering the sensory-active compounds involved in mouthfeel perception.

Myrtucommulones and Related Acylphloroglucinols from Myrtaceae as a Promising Source of Multitarget SARS-CoV-2 Cycle Inhibitors

The LABEXTRACT plant extract bank, featuring diverse members of the Myrtaceae family from Brazilian hot spot regions, provides a promising avenue for bioprospection. Given the pivotal roles of the Spike protein and 3CLpro and PLpro proteases in SARS-CoV-2 infection, this study delves into the correlations between the Myrtaceae species from the Atlantic Forest and these targets, as well as an antiviral activity through both in vitro and in silico analyses. The results uncovered notable inhibitory effects, with Eugenia prasina and E. mosenii standing out, while E. mosenii proved to be multitarget, presenting inhibition values above 72% in the three targets analyzed. All extracts inhibited viral replication in Calu-3 cells (EC50 was lower than 8.3 µg·mL-1). Chemometric analyses, through LC-MS/MS, encompassing prediction models and molecular networking, identified potential active compounds, such as myrtucommulones, described in the literature for their antiviral activity. Docking analyses showed that one undescribed myrtucommulone (m/z 841 [M - H]-) had a higher fitness score when interacting with the targets of this study, including ACE2, Spike, PLpro and 3CLpro of SARS-CoV-2. Also, the study concludes that Myrtaceae extracts, particularly from E. mosenii and E. prasina, exhibit promising inhibitory effects against crucial stages in SARS-CoV-2 infection. Compounds like myrtucommulones emerge as potential anti-SARS-CoV-2 agents, warranting further exploration.

An Adaptive Partial Least-Squares Regression Approach for Classifying Chicken Egg Fertility by Hyperspectral Imaging

Partial least-squares (PLS) regression is a well known chemometric method used for predictive modelling, especially in the presence of many variables. Although PLS was not initially developed as a technique for classification tasks, scientists have reportedly used this approach successfully for discrimination purposes. Whereas some non-supervised learning approaches, including, but not limited to, PCA and k-means clustering, do well in identifying/understanding grouping and clustering patterns in multidimensional data, they are limited when the end target is discrimination, making PLS a preferable alternative. Hyperspectral imaging data on a total of 672 fertilized chicken eggs, consisting of 336 white eggs and 336 brown eggs, were used in this study. Hyperspectral images in the NIR region of the 900-1700 nm wavelength range were captured prior to incubation on day 0 and on days 1-4 after incubation. Eggs were candled on incubation day 5 and broken out on day 10 to confirm fertility. While a total number of 312 and 314 eggs were found to be fertile in the brown and white egg batches, respectively, the total number of non-fertile eggs in the same set of batches was 23 and 21, respectively. Spectral information was extracted from a segmented region of interest (ROI) of each hyperspectral image and spectral transmission characteristics were obtained by averaging the spectral information. A moving-thresholding technique was implemented for discrimination based on PLS regression results on the calibration set. With true positive rates (TPRs) of up to 100% obtained at selected threshold values of between 0.50 and 0.85 and on different days of incubation, the results indicate that the proposed PLS technique can accurately discriminate between fertile and non-fertile eggs. The adaptive PLS approach was, thereby, presented as suitable for handling hyperspectral imaging-based chicken egg fertility data.

The Use of Data Mining for Obtaining Deeper Insights into the Fabrication of Prednisolone-Loaded Chitosan Nanoparticles

The present work explores a data mining approach to study the fabrication of prednisolone-loaded chitosan nanoparticles and their properties. Eight PLC formulations were prepared using an automated adaptation of the antisolvent precipitation method. The PLCs were characterized using dynamic light scattering, infrared spectroscopy, and drug release studies. Results showed that that the effective diameter, loading capacity, encapsulation efficiency, zeta potential, and polydispersity of the PLCs were influenced by the concentration and molecular weight of chitosan. The drug release studies showed that PLCs exhibited significant dissolution enhancement compared to pure prednisolone crystals. Principal components analysis and partial least squares regression were applied to the infrared spectra and the DLS data to extract higher-order interactions and correlations between the critical quality attributes and the diameter of the PLCs. Principal components revealed that the spectra clustered according to the type of material, with PLCs forming a separate cluster from the raw materials and the physical mix. PLS was successful in predicting the ED of the PLCs from the FTIR spectra with R2 = 0.98 and RMSE = 27.18. The present work demonstrates that data mining techniques can be useful tools for obtaining deeper insights into the fabrication and properties of PLCs, and for optimizing their quality and performance. It also suggests that FTIR spectroscopy can be a rapid and non-destructive method for predicting the ED of PLCs.

A computational study of potent series of selective estrogen receptor degraders for breast cancer therapy

A detailed multistep framework combining quantitative structure-activity relationship, global reactivity, absorption, distribution, metabolism and elimination properties, molecular docking and molecular dynamics simulation (MD) on a series of Selective Estrogen Receptor Down-Regulators (SERDs) interacting with Estrogen Receptor α (ERα) has been performed. The partial least squares regression method derived an empirical model with better predictive capability. The results of global reactivity descriptors revealed that all the compounds are considered strong electrophiles, allowing them to participate in polar reactions more easily. The Brain Or IntestinaL EstimateD permeation diagram revealed that compounds 49 and 31 were predicted to be well absorbed by the human gastrointestinal tract and would not enter the brain. The elucidation of the binding mode between the most active compounds that comply with Lipinski's and Veber's rules from the dataset and ERα targets was explored by molecular docking. The MD simulations were performed for 100 ns on the best compounds, which indicated their stability state under dynamics simulations. These findings are expected to help predict the anticancer activities of the studied SERD compounds and better understand their binding mechanism with ERα targets.Communicated by Ramaswamy H. Sarma.

Comparing the predictions by NIR spectroscopy based multivariate models for distillation fractions of crude oils by F-test

The whole range of distillation fractions in industrially relevant crude oil samples is predicted by using two multivariate models based on near-infrared (NIR) spectra. The first versions of the models as well as the respective model updates are considered, with the updates largely aimed at expanding the models. The prediction results are compared across all the fractions and F-test is used to critically compare the performance of the models and the effectiveness of the limited updates. The results suggest that both multivariate methods perform very comparably, and the updates do not lead to statistically significant changes, which differs from what one could conclude from the nominal prediction errors. The near-equivalency of the prediction accuracy of the updated models is additionally illustrated by perusing predictions of a number of batches from one sour and one sweet crude arriving at the refinery during a four month period.

Characterizing Spray-Dried Powders through NIR Spectroscopy: Effect of Two Preparation Strategies for Calibration Samples and Comparison of Two Types of NIR Spectrometers

Emerging portable near infrared (NIR) spectroscopic approaches coupled with data analysis and chemometric techniques provide opportunities for the rapid characterization of spray-dried products and process optimization. This study aimed to enhance the understanding of applying NIR spectroscopy in spray-dried samples by comparing two sample preparation strategies and two spectrometers. Two sets of whey protein-maltodextrin matrixes, one with a protein content gradient and one with a consistent protein content, were spray-dried, and the effect of the two preparation strategies on NIR calibration model development was studied. Secondly, a portable NIR spectrometer (PEAK) was compared with a benchtop NIR spectrometer (CARY) for the moisture analysis of prepared samples. When validating models with the samples with focused protein contents, the best PLS protein models established from the two sample sets had similar performances. When comparing two spectrometers, although CARY outperformed PEAK, PEAK still demonstrated reliable performance for moisture analysis, indicating that it is capable as an inline sensor.

Analysis of White Mulberry Leaves and Dietary Supplements, ATR-FTIR Combined with Chemometrics for the Rapid Determination of 1-Deoxynojirimycin

Diabetes mellitus is a metabolic disease affecting more people every year. The treatment of diabetes and its complications involve substantial healthcare expenditures. Thus, there is a need to identify natural products that can be used as nutraceuticals to prevent and treat early-stage diabetes. White mulberry (Morus alba L.) is a plant that has been used in traditional Chinese medicine for thousands of years due to its many beneficial biological properties. White mulberry leaves are a source of 1-deoxynojirimycin (DNJ), which, due to its ability to inhibit α-glucosidase, can be used to regulate postprandial glucose concentration. In addition to consuming dried white mulberry leaves as herbal tea, many functional foods also contain this raw material. The development of the dietary supplements market brings many scientific and regulatory challenges to the safety, quality and effectiveness of such products containing concentrated amounts of nutraceuticals. In the present study, the quality of 19 products was assessed by determining the content of DNJ, selected (poly)phenols and antioxidant activity (DPPH^• assay). Nine of these products were herbal teas, and the other samples were dietary supplements. These results indicate the low quality of tested dietary supplements, the use of which (due to the low content of nutraceuticals) cannot bring the expected beneficial effects on health. Moreover, a method for determining the content of DNJ (the essential component for antidiabetic activity) based on ATR-FTIR spectroscopy combined with PLS regression has been proposed. This might be an alternative method to the commonly used chromatographic process requiring extraction and derivatization of the sample. It allows for a quick screening assessment of the quality of products containing white mulberry leaves.

Big-sized trees and species-functional diversity pathways mediate divergent impacts of environmental factors on individual biomass variability in Sri Lankan tropical forests

In tropical forests, several studies have explored the effects of environmental factors and tree species diversity as well as functional trait diversity and trait composition on aboveground biomass (AGB) stock. However, these abiotic and biotic effects on individual biomass variability (BioVar) are still largely unexplored, which limits our understanding of the plant-plant interactions for species coexistence. Here, we used the Partial Least Squares Structural Equation Models (PLS-SEMs), and other complementary analyses, on data from 189 tropical forest plots in Sri Lanka, to test the linkages amongst climate (a latent variable of solar radiation and potential evapotranspiration), soil (pH and cation exchange capacity), plot (plot size and stand density) conditions, big-sized trees, species-functional diversity, and BioVar. The PLS-SEMs showed that climate conditions decreased BioVar directly but increased indirectly via integrative promoting direct effects on soil conditions, species-functional diversity and big-sized trees. In contrast, soil conditions increased BioVar directly but decreased indirectly via integrative suppressing direct effects on species-functional diversity and big-sized trees. Interestingly, we found that the divergent indirect effects of climate and soil conditions on BioVar via big-sized trees mattered when the direct effect of big-sized trees on species-functional diversity was considered as compared to the reverse effect in PLS-SEMs. Also, the indirect positive effect of plot properties on BioVar was nearly equal to the direct effect because plot properties affected big-sized trees as similar as or lower than species-functional diversity. The positive effect of species-functional diversity on BioVar was mediated by the structural attributes of big-sized trees, indicating increased plant species co-existence. This study suggests that individual tree biomass variability (i.e., BioVar) should be considered for managing natural tropical forests in the context of the plant-plant interactions for species coexistence.

Generic Chemometric Models for Metabolite Concentration Prediction Based on Raman Spectra

Chemometric models for on-line process monitoring have become well established in pharmaceutical bioprocesses. The main drawback is the required calibration effort and the inflexibility regarding system or process changes. So, a recalibration is necessary whenever the process or the setup changes even slightly. With a large and diverse Raman dataset, however, it was possible to generate generic partial least squares regression models to reliably predict the concentrations of important metabolic compounds, such as glucose-, lactate-, and glutamine-indifferent CHO cell cultivations. The data for calibration were collected from various cell cultures from different sites in different companies using different Raman spectrophotometers. In testing, the developed “generic” models were capable of predicting the concentrations of said compounds from a dilution series in FMX-8 mod medium, as well as from an independent CHO cell culture. These spectra were taken with a completely different setup and with different Raman spectrometers, demonstrating the model flexibility. The prediction errors for the tests were mostly in an acceptable range (<10% relative error). This demonstrates that, under the right circumstances and by choosing the calibration data carefully, it is possible to create generic and reliable chemometric models that are transferrable from one process to another without recalibration.

Time-related multivariate strategy for the comprehensive evaluation of microbial chemical data

INTRODUCTION

In microbial metabolomics, the use of multivariate data analysis (MDVA) has not been comprehensively explored regarding the different techniques available and the information that each gives about the metabolome. To overcome these limitations, here we show the use of Fusarium oxysporum cultured in the presence of exogenous alkaloids as a model system to demonstrate a comprehensive strategy for metabolic profiling.

MATHERIALS AND METHODS

F. oxysporum was harvested on different days of incubation after alkaloidal addition, and the chemical profiles were compared using LC-MS data and MDVA. We show significant innovation to evaluate the chemical production of microbes during their life cycle by utilizing the full capabilities of Partial Least Square (PLS) with microbial-specific modeling that considers incubation days, media culture availability, and growth rate in solid media.

RESULTS AND DISCUSSCION

Results showed that the treatment of the Y-data and the use of both PLS regression and discrimination (PLSr and PLS-DA) inferred complemental chemical information. PLSr revealed the metabolites that are produced/consumed during fungal growth, whereas PLS-DA focused on metabolites that are only consumed/produced at a specific period. Both regression and classificatory analysis were equally important to identify compounds that are regulated and/or selectively produced as a response to the presence of the alkaloids. Lastly, we report the annotation of analogs from the piperidine alkaloids biotransformed by F. oxysporum as a defense response to the toxic plant metabolites. These molecules do not show the antimicrobial potential of their precursors in the fungal extracts and were rapidly produced and consumed within 4 days of microbial growth.

A sample selection method specific to unknown test samples for calibration and validation sets based on spectra similarity

As is known to all, the construction of calibration and validation sets is of great importance for how to select representative samples into subsets so that the calibration model can be built, evaluated and predicted effectively for model development. In this study, a method was proposed for the calibration and validation sets constructed by selecting samples maximally similar to the test samples based on the spectra data. Both the Euclidean distance and Mahalanobis distance were attempted to estimate the spectra similarity. The method to select samples for calibration is more suitable and specific to unknown test samples in practical applications, thus improving the measurement accuracy. In addition, the optimization of calibration set size was carried out to avoid the influence of unnecessary samples. Two data sets of Salvia miltiorrhiza (S. miltiorrhiza) and corn by near infrared spectroscopy (NIR) were used to test the performance of the proposed method compared with two typical sample-selection algorithms, Kennard-Stone (KS) and sample set partitioning based on joint x-y distances (SPXY). The experimental results indicated that the proposed method could select a more targeted set of samples for the unknown test samples and had the superior predictive performance to the KS and SPXY methods.

Theoretical Simulation of Near-Infrared Spectrum of Piperine: Insight into Band Origins and the Features of Regression Models

We investigated the near-infrared spectrum of piperine using quantum mechanical calculations. We evaluated two efficient approaches, DVPT2//PM6 and DVPT2//ONIOM [PM6:B3LYP/6-311++G(2df, 2pd)] that yielded a simulated spectrum with varying accuracy versus computing time factor. We performed vibrational assignments and unveiled complex nature of the near-infrared spectrum of piperine, resulting from a high level of band convolution. The most meaningful contribution to the near-infrared absorption of piperine results from binary combination bands. With the available detailed near-infrared assignment of piperine, we interpreted the properties of partial least square regression models constructed in our earlier study to describe the piperine content in black pepper samples. Two models were compared with spectral data sets obtained with a benchtop and a miniaturized spectrometer. The two spectrometers implement distinct technology which leads to a profound instrumental difference and discrepancy in the predictive performance when analyzing piperine content. We concluded that the sensitivity of the two instruments to certain types of piperine vibrations is different and that the benchtop spectrometer unveiled higher selectivity. Such difference in obtaining chemical information from a sample can be one of the reasons why the benchtop spectrometer performs better in analyzing the piperine content of black pepper. This evidenced direct correspondence between the features critical for applied near-infrared spectroscopic routine and the underlying vibrational properties of the analyzed constituent in a complex sample.

Influence of Sample Matrix on Determination of Histamine in Fish by Surface Enhanced Raman Spectroscopy Coupled with Chemometric Modelling

Histamine fish poisoning is a foodborne illness caused by the consumption of fish products with high histamine content. Although intoxication mechanisms and control strategies are well known, it remains by far the most common cause of seafood-related health problems. Since conventional methods for histamine testing are difficult to implement in high-throughput quality control laboratories, simple and rapid methods for histamine testing are needed to ensure the safety of seafood products in global trade. In this work, the previously developed SERS method for the determination of histamine was tested to determine the influence of matrix effect on the performance of the method and to investigate the ability of different chemometric tools to overcome matrix effect issues. Experiments were performed on bluefin tuna (Thunnus thynnus) and bonito (Sarda sarda) samples exposed to varying levels of microbial activity. Spectral analysis confirmed the significant effect of sample matrix, related to different fish species, as well as the extent of microbial activity on the predictive ability of PLSR models with R² of best model ranging from 0.722–0.945. Models obtained by ANN processing of factors derived by PCA from the raw spectra of the samples showed excellent prediction of histamine, regardless of fish species and extent of microbial activity (R² of validation > 0.99).

Determination of a quantitative relationship between material properties, process settings and screw feeding behavior via multivariate data-analysis

In this study, a quantitative relationship between material properties, process settings and screw feeding responses of a high-throughput feeder was established via multivariate models (PLS). Thirteen divergent powders were selected and characterized for 44 material property descriptors. During volumetric feeder trials, the maximum feed capacity (FCC_max), the relative standard deviation on the maximum feed capacity (RSD_FCmax), the short term variability (STRSD) and feed capacity decay (FC_decay) were determined. The gravimetric feeder trials generated values for the mass flow rate variability (RSD_LC), short term variability (STRSD) and refill responses (V_refill and RSD_refill). The developed PLS models elucidated that the material properties and process settings were clearly correlated to the feeding behavior. The extended volumetric feeder trials pointed out that there was a significant influence of the chosen screw type and screw speed on the feeding process. Furthermore, the process could be optimized by reducing the feeding variability through the application of optimized mass flow filters, high frequency vibrations, independent agitator control and optimized top-up systems. Overall, the models could allow the prediction of the feeding performance for a wide range of materials based on the characterization of a subset of material properties greatly reducing the number of required feeding experiments.

Estimating canopy nitrogen concentration of sugarcane crop using in situ spectroscopy

Estimating nitrogen (N) concentration in situ is fundamental for managing the fertilization of the sugarcane crop. The purpose of this work was to develop estimation models that explain how N varies over time as a function of three spectral data transformations in two stages (plant cane and first ratoon) under variable rates of N application. A randomized complete-block experimental design was applied, with four levels of N fertilization: 0, 80, 160, and 240 kg N ha⁻¹. Six sampling events were carried out during the rapid growth stage, where the canopy reflectance spectra with a hyperspectral sensor were measured, and tissue samples for N determination in plant cane and first ratoon were taken, from 60 days after emergence (DAE) and 60 days after harvest (DAH), respectively, until days 210 DAE and 210 DAH. To build the models, partial least squares regression analysis was used and was trained by three transformations of the spectral data: (i) average reflectance spectrum (R), (ii) multiple scatter correction and Savitzky-Golay filter MSC-SG) reflectance spectrum, and (iii) calculated vegetation indices (VIs).

Characterizing the growing microorganisms at species level in 46 anaerobic digesters at Danish wastewater treatment plants: A six-year survey on microbial community structure and key drivers

Anaerobic digestion (AD) is a key technology at many wastewater treatment plants (WWTPs) for converting primary and surplus activated sludge to methane-rich biogas. However, the limited number of surveys and the lack of comprehensive datasets have hindered a deeper understanding of the characteristics and associations between key variables and the microbial community composition. Here, we present a six-year survey of 46 anaerobic digesters, located at 22 WWTPs in Denmark, which is the first and largest known study of the microbial ecology of AD at WWTPs at a regional scale. For three types of AD (mesophilic, mesophilic with thermal hydrolysis pretreatment, and thermophilic), we present the typical value range of 12 key parameters including operational variables and performance parameters. High-resolution bacterial and archaeal community analyses were carried out at species level using amplicon sequencing of >1,000 samples and the new ecosystem-specific MiDAS 3 reference database. We detected 42 phyla, 1,600 genera, and 3,584 species in the bacterial community, where 70% of the genera and 93% of the species represented environmental taxa that were only classified based on MiDAS 3 de novo placeholder taxonomy. More than 40% of the bacterial species were found not to grow in the mesophilic and thermophilic digesters and were only present due to immigration with the feed sludge. Ammonium concentration was the main driver shaping the bacterial community while temperature and pH were main drivers for the archaea in the three types of ADs. Sub-setting for the growing microbes improved significantly the correlation analyses and revealed the main drivers for the presence of specific species. Within mesophilic digesters, feed sludge composition and other key parameters (organic loading rate, biogas yield, and ammonium concentration) correlated with specific growing species. This survey provides a comprehensive insight into community structure at species level, providing a foundation for future studies of the ecological significance/characteristics and function of the many novel or poorly described taxa.

Spectroscopic and Molecular Methods to Differentiate Gender in Immature Date Palm (Phoenix dactylifera L.)

Phoenix dactylifera (date palm) is a well-known nutritious and economically important fruit tree found in arid regions of the Middle East and North Africa. Being diploid, it has extremely high divergence in gender, where sex differentiation in immature date palms (Phoenix dactylifera L.) has remained an enigma in recent years. Herein, new robust infrared (near-infrared reflectance spectroscopy (NIRS) and Fourier transform infrared attenuated total reflectance (FTIR/ATR)) and nuclear magnetic resonance (NMR) spectroscopy methods coupled with extensive chemometric analysis were used to identify the sex differentiation in immature date palm leaves. NIRS/FTIR reflectance and 1H-NMR profiling suggested that the signals of monosaccharides (glucose and fructose) and/or disaccharides (maltose and sucrose) play key roles in sex differentiation. The three kinds of spectroscopic data were clearly differentiated among known and unknown male and female leaves via principal component and partial least square discriminant analyses. Furthermore, sex-specific genes and molecular markers obtained from the lower halves of LG12 chromosomes showed enhanced transcript accumulation of mPdIRDP52, mPdIRDP50, and PDK101 in females compared with in males. The phylogeny showed that the mPdIRD033, mPdIRD031, and mPdCIR032 markers formed distinctive clades with more than 70% similarity in gender differentiation. The three robust analyses provide an alternative tool to differentiate sex in date palm trees, which offers a solution to the long-standing challenge of dioecism and could enhance in situ tree propagation programs.

Analysis of corn and sorghum flour mixtures using laser-induced breakdown spectroscopy

BACKGROUND

In a world constantly challenged by climate change, corn and sorghum are two important grains because of their high productivity and adaptability, and their multifunctional use for different purposes such as human food, animal feed, and feedstock for many industrial products and biofuels. Corn and sorghum can be utilized interchangeably in certain applications; one grain may be preferred over the other for several reasons. The determination of the composition corn and sorghum flour mixtures may be necessary for economic, regulatory, environmental, functional, or nutritional reasons.

RESULTS

Laser-induced breakdown spectroscopy (LIBS) in combination with chemometrics, was used for the classification of flour samples based on the LIBS spectra of flour types and mixtures using partial least squares discriminant analysis (PLS-DA) and the determination of the sorghum ratio in sorghum / corn flour mixture based on their elemental composition using partial least squares (PLS) regression. Laser-induced breakdown spectroscopy with PLS-DA successfully identified the samples as either pure corn, pure sorghum, or corn-sorghum mixtures. Moreover, the addition of various levels of sorghum flour to mixtures of corn-sorghum flour were used for PLS analysis. The coefficient of determination values of calibration and validation PLS models are 0.979 and 0.965, respectively. The limit of detection of the PLS models is 4.36%.

CONCLUSION

This study offers a rapid method for the determination of the sorghum level in corn-sorghum flour mixtures and the classification of flour samples with high accuracy, a short analysis time, and no requirement for time-consuming sample preparation procedures. © 2020 Society of Chemical Industry.

Estimating the out-of-the-loop phenomenon from visual strategies during highly automated driving

During highly automated driving, drivers no longer physically control the vehicle but they might need to monitor the driving scene. This is true for SAE level 2, where monitoring the external environment is required; it is also true for level 3, where drivers must react quickly and safely to a take-over request. Without such monitoring, even if only partial, drivers are considered out-of-the-loop (OOTL) and safety may be compromised. The OOTL phenomenon may be particularly important for long automated driving periods during which mind wandering can occur. This study scrutinized drivers' visual behaviour for 18 min of highly automated driving. Intersections between gaze and 13 areas of interest (AOIs) were analysed, considering both static and dynamic indicators. An estimation of self-reported mind wandering based on gaze behaviour was performed using partial least squares (PLS) regression models. The outputs of the PLS regressions allowed defining visual strategies associated with good monitoring of the driving scene. This information may enable online estimation of the OOTL phenomenon based on a driver's spontaneous visual behaviour.

FT-NIRS Coupled with PLS Regression as a Complement to HPLC Routine Analysis of Caffeine in Tea Samples

Daily consumption of caffeine in coffee, tea, chocolate, cocoa, and soft drinks has gained wide and plentiful public and scientific attention over the past few decades. The concentration of caffeine in vivo is a crucial indicator of some disorders—for example, kidney malfunction, heart disease, increase of blood pressure and alertness—and can cause some severe diseases including type 2 diabetes mellitus (DM), stroke risk, liver disease, and some cancers. In the present study, near-infrared spectroscopy (NIRS) coupled with partial least-squares regression (PLSR) was proposed as an alternative method for the quantification of caffeine in 25 commercially available tea samples consumed in Oman. This method is a fast, complementary technique to wet chemistry procedures as well as to high-performance liquid chromatography (HPLC) methods for the quantitative analysis of caffeine in tea samples because it is reagent-less and needs little or no pre-treatment of samples. In the current study, the partial least-squares (PLS) algorithm was built by using the near-infrared NIR spectra of caffeine standards prepared in tea samples scanned by a Frontier NIR spectrophotometer (L1280034) by PerkinElmer. Spectra were collected in the absorption mode in the wavenumber range of 10,000–4000 cm⁻¹, using a 0.2 mm path length and CaF₂ sealed cells with a resolution of 2 cm⁻¹. The NIR results for the contents of caffeine in tea samples were also compared with results obtained by HPLC analysis. Both techniques provided good results for predicting the caffeine contents in commercially available tea samples. The results of the proposed study show that the suggested FT-NIRS coupled with PLS regression algorithun has a high potential to be routinely used for the quick and reproducible analysis of caffeine contents in tea samples. For the NIR method, the limit of quantification (LOQ) was estimated as 10 times the error of calibration (root mean square error of calibration (RMSECV)) of the model; thus, RMSEC was calculated as 0.03 ppm and the LOQ as 0.3 ppm.

Detecting varroosis using a gas sensor system as a way to face the environmental threat

Colony Collapse Disorder (CCD) is an environmental threat on a global scale due to the irreplaceable role of bees in crop pollination. Varroa destructor (V.d.), a parasite that attacks honeybee colonies, is one of the primary causes of honey bee population decline and the most serious threat to the beekeeping sector. This work demonstrates the possibility of quantitatively determining bee colony infestation by V.d. using gas sensing. The results are based on analysing the experimental data acquired for eighteen bee colonies in field conditions. Their infestation rate was in the 0 to 24.76% range. The experimental data consisted of measurements of beehive air with a semiconductor gas sensor array and the results of bee colony V.d. infestation assessment using a flotation method. The two kinds of data were collected in parallel. Partial Least Square regression was applied to identify the relationship between the highly multivariate measurement data provided by the gas sensor array and the V.d. infestation rate. The quality of the developed quantitative models was very high, as demonstrated by the coefficient of determination exceeding R² = 0.99. Moreover, the prediction error was <0.6% for V.d. infestation rate predictions based on the measurement data that was unknown to the model. The presented work has considerable novelty. To our knowledge, the ability to determine the V.d. infestation rate of bee colony quantitatively based on beehive air measurements using a semiconductor gas sensor array has not been previously demonstrated.

Determination of pH and acidity in green coffee using near-infrared spectroscopy and multivariate regression

BACKGROUND

Coffee is a raw material of global interest. Due to its relevance, this work evaluated the performance of calibration models constructed from spectral data obtained using near-infrared spectroscopy (FT-NIR) to determine the pH values and acidity in coffee beans in a practical and non-destructive way. Partial least squares regression was used during the calibration and the cross-validation to optimize the number of latent variables. The predictive capacity of the spectral pre-processing methods was also accessed.

RESULTS

The results obtained showed that the best methods of pre-processing were the first derivative for the pH variable and the standard normal variate for the acidity, which produced models with correlations of 0.78 and 0.92, ratios of prediction to deviation of 2.061 and 2.966 and biases of -0.00011 and -0.152 to test set validation, respectively. The average errors between predicted and experimental values were lower than 7%.

CONCLUSIONS

FT-NIR was successfully applied to predict properties related to the quality of coffee. The method was demonstrated to be a fast and non-destructive tool which allows the rapid inline evaluation of samples facilitating industrial and commercial processing. © 2020 Society of Chemical Industry.

Retention Modelling of Phenoxy Acid Herbicides in Reversed-Phase HPLC under Gradient Elution

Phenoxy acid herbicides are used worldwide and are potential contaminants of drinking water. Reversed phase high-performance liquid chromatography (RP-HPLC) is commonly used to monitor phenoxy acid herbicides in water samples. RP-HPLC retention of phenoxy acids is affected by both mobile phase composition and pH, but the synergic effect of these two factors, which is also dependent on the structure and pKa of solutes, cannot be easily predicted. In this paper, to support the setup of RP-HPLC analysis of phenoxy acids under application of linear mobile phase gradients we modelled the simultaneous effect of the molecular structure and the elution conditions (pH, initial acetonitrile content in the eluent and gradient slope) on the retention of the solutes. In particular, the chromatographic conditions and the molecular descriptors collected on the analyzed compounds were used to estimate the retention factor k by Partial Least Squares (PLS) regression. Eventually, a variable selection approach, Genetic Algorithms, was used to reduce the model complexity and allow an easier interpretation. The PLS model calibrated on the retention data of 15 solutes and successively tested on three external analytes provided satisfying and reliable results.

Potentiality of using front face fluorescence spectroscopy for quantitative analysis of cow milk adulteration in buffalo milk

In current study, synchronous front-face fluorescence spectroscopy together with partial least squares regression (PLSR) is used to predict the adulteration of cow and buffalo milk quantitatively. Fresh (unprocessed milk) samples of cow and buffalo were collected from local dairy farms. Fluorescence emission from milk samples mixed in different concentrations, show intensity variations at wavelengths 370-380 nm, 410 nm, 442 nm and 520-560 nm. Among them, the emissions at band position of 442 nm and 525 nm are highly selective between the two species and could help in finding adulteration of cow milk in buffalo milk and vice versa. The emissions at these wavelength positions correspond to fat-soluble vitamin-A as well as β-carotene. PLS regression is used as a statistical prediction model, which is developed by training with the emission spectra of milk samples having known level of adulterations. The developed model predicts the unknown level of adulterations by means of their spectral data. The goodness of the model is determined by the correlation coefficient R-square (r²) value, which in our case is 0.99. Furthermore, the model root mean square error in cross validation (RMSECV) and in prediction (RMSECP) remains 1.16 and 6.24 respectively. This approach can effectively be applied to determine milk adulterations among other species as well as in detecting external agents (fraudulent) added into milk and other dairy products by further studies.

Soil Nutrient Detection for Precision Agriculture Using Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and Multivariate Regression Methods (PLSR, Lasso and GPR)

Precision agriculture (PA) strongly relies on spatially differentiated sensor information. Handheld instruments based on laser-induced breakdown spectroscopy (LIBS) are a promising sensor technique for the in-field determination of various soil parameters. In this work, the potential of handheld LIBS for the determination of the total mass fractions of the major nutrients Ca, K, Mg, N, P and the trace nutrients Mn, Fe was evaluated. Additionally, other soil parameters, such as humus content, soil pH value and plant available P content, were determined. Since the quantification of nutrients by LIBS depends strongly on the soil matrix, various multivariate regression methods were used for calibration and prediction. These include partial least squares regression (PLSR), least absolute shrinkage and selection operator regression (Lasso), and Gaussian process regression (GPR). The best prediction results were obtained for Ca, K, Mg and Fe. The coefficients of determination obtained for other nutrients were smaller. This is due to much lower concentrations in the case of Mn, while the low number of lines and very weak intensities are the reason for the deviation of N and P. Soil parameters that are not directly related to one element, such as pH, could also be predicted. Lasso and GPR yielded slightly better results than PLSR. Additionally, several methods of data pretreatment were investigated.

Control of ascorbic acid in fortified powdered soft drinks using near-infrared spectroscopy (NIRS) and multivariate analysis

Powdered soft drinks (PSDs), fortified with antioxidants such as ascorbic acid (AA), are normally controlled by titration or chromatographic methods. This study evaluated the feasibility of using near-infrared spectroscopy (NIRS) and multivariate analysis to predict AA contents in PSDs as an alternative not-destructive method. The AA content of sixty-seven samples of commercial fortified grape and passion fruit PSDs was analyzed by the standard method (titration) and showed significant variance between flavors within the same brand. In addition, 75% of the samples required from 0.3 to 10.2 more cups of grape than passion fruit flavor to supply the AA Reference Nutrient Intake for children and adults. Spectral and reference data sets were split into calibration and validation sets. Partial least squares regression models were built and validated for the determination of AA in both PSDs. The model's basic statistics for grape flavor PSDs (RMSEC = 0.49 mg g^-1, R_cal ² = 0.84; RMSECV = 0.67 mg g^-1, R_CV ² = 0.70; RMSEP = 0.50 mg g^-1, R_pred ² = 0.84), and that for passion fruit flavor PSDs (RMSEC = 0.24 mg g^-1, R_cal ² = 0.95; RMSECV = 0.56 mg g^-1, R_CV ² = 0.76; RMSEP de 0.57 mg g^-1, R_pred ² = 0.72) indicated that NIRS-PLS methodology produced reasonable results. The limits of detection and quantification obtained showed that the method is useful to detect and quantify AA in the studied samples. A new set of grape drinks was used for external prediction and the RMSEP was 0.62 mg g^-1, R_pred ² was 0.72. Based on the results, NIRS-multivariate analysis proved to be useful for quality control of AA in commercialized grape and passion fruit in PSDs and a faster, objective and environmentally friendly method alternative to standard methods.

Determining the number of components in PLS regression on incomplete data set

Partial least squares regression - or PLS regression - is a multivariate method in which the model parameters are estimated using either the SIMPLS or NIPALS algorithm. PLS regression has been extensively used in applied research because of its effectiveness in analyzing relationships between an outcome and one or several components. Note that the NIPALS algorithm can provide estimates parameters on incomplete data. The selection of the number of components used to build a representative model in PLS regression is a central issue. However, how to deal with missing data when using PLS regression remains a matter of debate. Several approaches have been proposed in the literature, including the Q2 criterion, and the AIC and BIC criteria. Here we study the behavior of the NIPALS algorithm when used to fit a PLS regression for various proportions of missing data and different types of missingness. We compare criteria to select the number of components for a PLS regression on incomplete data set and on imputed data set using three imputation methods: multiple imputation by chained equations, k-nearest neighbour imputation, and singular value decomposition imputation. We tested various criteria with different proportions of missing data (ranging from 5% to 50%) under different missingness assumptions. Q2-leave-one-out component selection methods gave more reliable results than AIC and BIC-based ones.

Aroma profile and volatile composition of black ripe olives (Manzanilla and Hojiblanca cultivars)

The aroma profile and volatile composition of 8 samples of black ripe olives from Manzanilla and Hojiblanca cultivars were analyzed with the aim to characterize this type of table olive. The aroma of samples was described by a sensory panel using quantitative descriptive analysis (QDA), whereas the volatiles were analyzed by headspace solid-phase microextraction (HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS). Eleven odor descriptors (briny, sautéed mushroom, earthy/soil-like, oak barrel, nutty, artificial fruity/floral, natural fruity/floral, vinegary, alcohol, fishy/ocean-like, and cheesy) were evaluated, of which only one descriptor (briny) showed a significant difference between cultivars. A total of 74 volatile compounds were identified in the headspace of samples, of which 12 were identified as significant volatiles contributing to the discrimination between Manzanilla and Hojiblanca black ripe olives. Partial least squares (PLS) regression was able to predict one odor descriptor (nutty) with sufficient accuracy and allowed identifying the volatiles that highly contributed to three odor descriptors of black ripe olives (nutty, natural fruity/floral, and cheesy).

Non-destructive determination of strawberry fruit and juice quality parameters using ultraviolet, visible, and near-infrared spectroscopy

BACKGROUND

The development of rapid methods for the determination of the soluble solids content (SSC) and total phenolic content (TPC) in fruit juices is of great interest. Soluble solids content is related to sensory attributes, whereas TPC is related to the antioxidant capacity of juices. The aim of this study was to develop and optimize the calibration models for the prediction of the SSC and TPC of strawberry juices from the spectra of fruit and juices.

RESULTS

Near infrared (NIR) spectra were measured for strawberry fruit and ultraviolet (UV), visible (VIS), and NIR spectra were measured for juices. The partial least squares regression models were validated using the test sample set and their predictive ability was evaluated on the basis of determination coefficients (R² _P ) and root mean square error of prediction (RMSEP). For SSC the models with high predictive ability were obtained using spectra of fruit (R² _P = 0.929, RMSEP = 0.46%) or juices (R² _P = 0.979, RMSEP = 0.25%) in the NIR range. The optimal models for TPC were obtained using NIR spectra of fruit (R² _P = 0.834, RMSEP = 130.8 mg GA L^-1 ) or UV-VIS-NIR spectra of juices (R² _P = 0.844, RMSEP = 126.7 mg GA L^-1 ).

CONCLUSION

The results show the potential of spectroscopy for predicting quality parameters of strawberry juices from the juice spectra itself or non-destructively from the fruit spectra. They may contribute to the development of fruit sorting systems to optimize their use in juice production, as well as fast-screening methods for quality control of juices. © 2019 Society of Chemical Industry.

N-Glycan Profile as a Tool in Qualitative and Quantitative Analysis of Meat Adulteration

Adulteration of meat and meat products causes concerns to consumers. It is necessary to develop novel robust and sensitive methods that can authenticate the origin of meat by qualitative and quantitative means to minimize the drawbacks of the existing methods. This study has shown that the protein N-glycosylation profiles of different meats are species specific and thus can be used for meat authentication. Based on the N-glycan pattern, the investigated five meat species (beef, chicken, pork, duck, and mutton) can be distinguished by principal component analysis, and partial least square regression was performed to build a calibration and validation model for the prediction of adulteration ratio. Using this method, beef samples adulterated with a lower-value duck meat could be detected down to the addition ratio as low as 2.2%. The most distinguishing N-glycans from beef and duck were elucidated for the detailed structures.

Metabolic markers for the yield of lipophilic indole alkaloids in dried woad leaves (Isatis tinctoria L.)

The pharmacologically active dichloromethane extracts of dried woad leaves (Isatis tinctoria L.), and the methanol extracts of comparable fresh leaves of the same plants, were analyzed by LC-MSⁿ. The fresh leaf metabolite profile revealed a complex pattern of indolic compounds. Besides the known indigo precursors, isatan A, isatan B and indican, seven previously unreported indole derivatives were characterized: acetylindican, malonylindican, two dioxindole glucosides, dioxindole malonylglucoside, 6-hydroxyindole-3-carboxylic acid 6-O-glucoside and 6-hydroxyindole-3-carboxylic acid glucose ester. The integration of 122 compounds in fresh leaves and of five selected compounds (indoxyl, isatin, indigo, indirubin, and tryptanthrin) in dried leaves, formed the input data for a stepwise modelling procedure generating five predictive linear models. The structure of the predictive models and a cross validation provide evidence that the models could predict well or moderately well the accumulation of the selected lipophilic compounds, and were simple enough to be used in a woad cultivation program. PLS regression models relating each of the five selected dry leaf indolics to the fresh leaf metabolome were then fitted in order to deduct potential precursors and mechanisms leading to the formation of these lipophilic indolics in drying woad leaves. The models suggested glucobrassicin, isatan A and isatan B as the main candidate precursors of these compounds, besides a minor contribution of other fresh leaf indolics, including malonylindican, actylindican and dioxindole malonylglucoside. Dioxindole malonylglucoside was identified here as isatan C. The models further suggested that the accumulation of phenylpropanoid antioxidants in woad leaves has a negative impact on the formation of indoxyl, isatin, indigo, indirubin and tryptanthrin.

Bulk Protein and Oil Prediction in Soybeans Using Transmission Raman Spectroscopy: A Comparison of Approaches to Optimize Accuracy

Rapid measurements of protein and oil content are important for a variety of uses, from sorting of soybeans at the point of harvest to feedback during soybean meal production. In this study, our goal is to develop a simple protocol to permit rapid and robust quantitative prediction of soybean constituents using transmission Raman spectroscopy (TRS). To develop this approach, we systematically varied the various elements of the measurement process to provide a diverse test bed. First, we utilized an in-house-built benchtop TRS instrument such that suitable optical configurations could be rapidly deployed and analyzed for experimental data collection for individual soybean grains. Second, we also utilized three different soybean varieties with relatively low (33.97%), medium (36.98%), and high protein (41.23%) contents to test the development process. Third, samples from each variety were prepared using whole bean and three different sample treatments (i.e., ground bean, whole meal, and ground meal). In each case, we modeled the data obtained using partial least squares (PLS) regression and assessed spectral metric-based multiple linear regression (metric-MLR) approaches to build robust prediction models. The metric-MLR models showed lower root mean square errors (RMSEPs), and hence better prediction, compared to corresponding classical PLS regression models for both bulk protein and oil for all treatment types. Comparing different sample preparation approaches, a lower RMSEPs was observed for whole meal treatment and thus the metric-MLR modeling with ground meal treatment was considered to be optimal protocol for bulk protein and oil prediction in soybean, with RMSEP values of 1.15 ± 0.04 (R²= 0.87) and 0.80 ± 0.02 (R²= 0.87) for bulk protein and oil, respectively. These predictions were nearly two- to threefold better (i.e., lower RMSEPs) than the corresponding NIR spectroscopy measurements (i.e., secondary gold standards in grain industry). For content prediction in whole soybean, incorporating physical attributes of individual grains in metric-MLR approach show up to 22% improvement in bulk protein and a relatively mild (up to ∼5%) improvement in bulk oil prediction. The unique combination of metric-MLR modeling approach (which is rare in the field of grain analysis) and sample treatments resulted in improved prediction models; using the physical attributes of individual grains is suggested as a novel measure for improving accuracy in prediction.

The potential of Mid-Infrared spectroscopy for prediction of wood density and vulnerability to embolism in woody angiosperms

Xylem resistance to embolism formation determines the species-specific drought tolerance and the survival prospects of plants under extreme climatic conditions. Fourier Transform-Infrared (FTIR) spectroscopy is a cost-effective and rapid analytical tool with potential beyond its current use in plant physiology. We tested the use of FTIR spectroscopy as a method for estimating wood density (WD) and xylem resistance to embolism formation (P50) in 24 angiosperm species. Higher WD was associated with more negative P50 (r2 = 0.41). Partial least squares regression was applied to establish models of FTIR spectra and the reference data. They showed a high predictive quality for WD (r2 = 0.73), whereas the prediction of P50 was weaker (r2 = 0.49). By including WD in the model as an additional factor influencing P50, its predictive power significantly increased (r2 = 0.59). The spectral range in the model elaboration has been also narrowed (bands of lignin, cellulose, hemicellulose), but this did not influence the model descriptors, suggesting that for P50 prediction broad spectral range is more informative than narrow band regions reflecting main wood constituents. In conclusion, FTIR spectroscopy associated with WD measurements has proven to be a promising alternative to traditional methods for screening of individual- or species-specific resistance to embolism in angiosperms.

In vitro cytotoxicity and structure-activity relationship approaches of ent-kaurenoic acid derivatives against human breast carcinoma cell line

In this study, ent-kaurenoic acid derivatives were obtained by microbial transformation methodologies and tested against breast cancer cell lines (MCF-7). A multivariate quantitative-structure activity relationship (QSAR) analysis was performed taking into account both microbial transformation derivatives and other analogues previously reported in literature to give some insight into the main features behind the cytotoxic activity displayed by kaurane-type diterpenes against MCF-7 cells. The partial least square regression (PLS) method was employed in the training set and the best PLS model was built with a factor describing 69.92% of variance and three descriptors (logP, ε_HOMO and ε_HOMO-1) selected by the Ordered Predictors Selection (OPS) algorithm. The QSAR model provided reasonable regression (Q² = 0.64, R² = 0.72, SEC = 0.29 and SEV = 0.33). The model was validated by leave-N-out cross-validation, y-randomization and external validation (R²_pred = 0.89 and SEP = 0.27). The selected descriptors indicated that the activity was mainly related to electronic parameters (HOMO and HOMO-1 molecular orbital energies), as well as to logP. These findings suggest that higher activity values are directly related with both higher logP and frontier orbital energy values. The positive relationship between these orbitals and the activity suggests that the ent-kaurenoic acid analogues interaction with the target involves charge displacement, which is entirely consistent with the literature. Based on these findings, three compounds were proposed and one of them was synthesized and tested. The experimental result confirmed the activity predicted by the model.

Validation of Fluorescence Spectroscopy to Detect Adulteration of Edible Oil in Extra Virgin Olive Oil (EVOO) by Applying Chemometrics

Due to high price and nutritional values of extra virgin olive oil (EVOO), it is vulnerable to adulteration internationally. Refined oil or other vegetable oils are commonly blended with EVOO and to unmask such fraud, quick, and reliable technique needs to be standardized and developed. Therefore, in this study, adulteration of edible oil (sunflower oil) is made with pure EVOO and analyzed using fluorescence spectroscopy (excitation wavelength at 350 nm) in conjunction with principal component analysis (PCA) and partial least squares (PLS) regression. Fluorescent spectra contain fingerprints of chlorophyll and carotenoids that are characteristics of EVOO and differentiated it from sunflower oil. A broad intense hump corresponding to conjugated hydroperoxides is seen in sunflower oil in the range of 441-489 nm with the maximum at 469 nm whereas pure EVOO has low intensity doublet peaks in this region at 441 nm and 469 nm. Visible changes in spectra are observed in adulterated EVOO by increasing the concentration of sunflower oil, with an increase in doublet peak and correspondingly decrease in chlorophyll peak intensity. Principal component analysis showed a distinct clustering of adulterated samples of different concentrations. Subsequently, the PLS regression model was best fitted over the complete data set on the basis of coefficient of determination (R²), standard error of calibration (SEC), and standard error of prediction (SEP) of values 0.99, 0.617, and 0.623 respectively. In addition to adulterant, test samples and imported commercial brands of EVOO were also used for prediction and validation of the models. Fluorescence spectroscopy combined with chemometrics showed its robustness to identify and quantify the specified adulterant in pure EVOO.

The relative weight of ontogeny, topology and climate in the architectural development of three North American conifers

Knowledge of plant architecture allows retrospective study of plant development, hence provides powerful tools, through modelling and simulation, to link this development with environmental constraints, and then predict its response to global change. The present study aims to determine some of the main endogenous and exogenous variables driving the architectural development of three North American conifers. We measured architectural traits retrospectively on the trunk, branches and twigs of whole tree crowns for each species: annual shoot length (ASL), needle length, branching patterns and reproduction organs (male and female). We fitted a partial least square (PLS) regression to explain each architectural trait with respect to topological, ontogenic and climatic variables. Results showed a significant weight of these three groups of variables for previous and current year, corresponding, respectively, to organogenesis and elongation. Topological and ontogenic variables had the greatest weight in models. Particularly, all architectural traits were strongly correlated with ASL. We highlighted a negative architectural response of two species to higher than average temperatures, whereas the third one took advantage of these higher temperatures to some degree. Tree architectural development weekly but significantly improved with higher precipitation. Our study underlines the strong weight of topology and ontogeny in tree growth patterns at twig and branch scales. The correlation between ASL and other tree architectural traits should be integrated into architectural development models. Climate variables are secondary in importance at the twig scale. However, interannual climate variations influence all axis categories and branching orders and therefore significantly impact crown development as a whole. This latter impact may increase with climate change, especially as climate affects architectural traits over at least 2 years, through organogenesis and elongation.

Metabolomics Discovers Early-Response Metabolic Biomarkers that Can Predict Chronic Reproductive Fitness in Individual Daphnia magna

Chemical risk assessment remains entrenched in chronic toxicity tests that set safety thresholds based on animal pathology or fitness. Chronic tests are resource expensive and lack mechanistic insight. Discovering a chemical's mode-of-action can in principle provide predictive molecular biomarkers for a toxicity endpoint. Furthermore, since molecular perturbations precede pathology, early-response molecular biomarkers may enable shorter, more resource efficient testing that can predict chronic animal fitness. This study applied untargeted metabolomics to attempt to discover early-response metabolic biomarkers that can predict reproductive fitness of Daphnia magna, an internationally-recognized test species. First, we measured the reproductive toxicities of cadmium, 2,4-dinitrophenol and propranolol to individual Daphnia in 21-day OECD toxicity tests, then measured the metabolic profiles of these animals using mass spectrometry. Multivariate regression successfully discovered putative metabolic biomarkers that strongly predict reproductive impairment by each chemical, and for all chemicals combined. The non-chemical-specific metabolic biomarkers were then applied to metabolite data from Daphnia 24-h acute toxicity tests and correctly predicted that significant decreases in reproductive fitness would occur if these animals were exposed to cadmium, 2,4-dinitrophenol or propranolol for 21 days. While the applicability of these findings is limited to three chemicals, they provide proof-of-principle that early-response metabolic biomarkers of chronic animal fitness can be discovered for regulatory toxicity testing.

Classification and compositional characterization of different varieties of cocoa beans by near infrared spectroscopy and multivariate statistical analyses

Effective and fast methods are important for distinguishing cocoa varieties in the field and in the processing industry. This work proposes the application of NIR spectroscopy as a potential analytical method to classify different varieties and predict the chemical composition of cocoa. Chemical composition and colour features were determined by traditional methods and then related with the spectral information by partial least-squares regression. Several mathematical pre-processing methods including first and second derivatives, standard normal variate and multiplicative scatter correction were applied to study the influence of spectral variations. The results of chemical composition analysis and colourimetric measurements show significant differences between varieties. NIR spectra of samples exhibited characteristic profiles for each variety and principal component analysis showed different varieties in according to spectral features.

New robust sensitive fluorescence spectroscopy coupled with PLSR for estimation of quercetin in Ziziphus mucronata and Ziziphus sativa

Flavonoids are natural antioxidants derived from plants and commonly found in a variety of foods to sequester free radicals. Quercetin, belonging to flavonol subclass of flavonoids, has received considerable attention because of its wide uses as a nutritional supplement as well as a phytochemical remedy for a number of diseases. In the current study, quantification of quercetin was carried out in two medicinally important flavonoid rich plant Ziziphus mucronata and Ziziphus sativa. Emission spectroscopy was utilized as a new method coupled with Partial Least Squares Regression (PLSR) and the cross validation was done by UV-Visible spectroscopy. The results indicated the higher quercetin content in Z. mucronata (1.50±0.034%) than Z. sativa (1.21±0.052%), and were further verified through Folin-Ciocalteu Colorimetric method (Z. mucronata; 1.41±0.26% and Z. sativa; 1.13±0.136%). In this study the sensitivity was explained in term of slope i.e. Slope=0.9973.

Volatile Compounds Related to 'Stone Fruit' Aroma Attributes in Viognier and Chardonnay Wines

A 'stone fruit' aroma is important in many white wine varieties and styles, but little is known about the chemical basis of this wine aroma attribute. A set of Viognier and Chardonnay wines that featured 'stone fruit' aroma attributes were selected by a panel of wine experts. The selected wines were characterized by sensory descriptive analysis and detailed volatile chemical composition analyses. This comprehensive data also allowed Viognier wine to be profiled for the first time. By partial least-squares regression, several esters and fatty acids and benzaldehyde were indicated as contributing to the 'peach' attribute; however, a reconstitution sensory study was unsuccessful in mimicking this attribute. A mixture of γ-lactones, monoterpenes, and aldehydes were positively correlated to the 'apricot' aroma, which were generally higher in the Viognier wines. Reconstitution studies confirmed that the monoterpenes linalool, geraniol, and nerol were the most important compounds for the mixture being perceived as having an 'apricot' aroma.

Quantification of AKBA in Boswellia sacra Using NIRS Coupled with PLSR as an Alternative Method and Cross-Validation by HPLC

INTRODUCTION

3-O-Acetyl-11-keto-β-boswellic acid (AKBA), one of the pentacyclic triterpenoids, is the main biologically active constituent in the resin of Boswellia sacra and has received significant pharmacological interest in recent years.

OBJECTIVE

It was aimed to develop a robust method to quantify the AKBA content in methanolic extracts of different parts of B. sacra plants and in various fractions of its resin exudates through near-infrared spectroscopy (NIRS) coupled with partial least squares regression (PLSR).

MATERIAL AND METHODS

The near-infrared (NIR) spectra were used to measure the AKBA standards and B. sacra samples at a wavelength range between 700 and 2500 nm in absorption mode. A PLSR model was built from the obtained spectral data using 70% of the AKBA working standard solutions (training set), ranging from 0.1 ppm to 100 ppm. The final PLSR showed a R² value of 99% with a root mean square error of cross-validation (RMSECV) value of 0.39% and a R² value of 99%.

RESULTS

The results showed that a 50% CHCl₃ /n-hexane sub-fraction has the highest concentration of AKBA (14.8%), followed by 55% CHCl₃ /n-hexane (13.6%), and 40% CHCl₃ /n-hexane (6.1%).

CONCLUSION

As the results achieved with the proposed NIRS methodology are in close agreement to the results of AKBA analysis using HPLC, we suggest that our proposed NIRS method is a fast alternative and non-destructive method for the analysis of AKBA in different samples of B. sacra. Copyright © 2017 John Wiley & Sons, Ltd.

DRIFTS-based multivariate calibration and prediction of low-concentration polymorphic impurities in multiple lots of an active pharmaceutical ingredient, and outlier criteria

Mixtures of two polymorphic impurities with one lot of the desired form of an Active Pharmaceutical Ingredient (API), mostly binary mixtures, with up to 2% wt/wt of an impurity, were used for multivariate modeling via Diffuse Reflectance Infrared Fourier Transform (DRIFT) spectra. The two obtained cross-validated models, significantly differing in accuracy, were used to predict the concentrations of these impurities in independent API lots. The predictions were found to be biased and with outliers, as revealed by the Q residuals criterion but not the other two outlier criteria. Updating the models with the spectra from the mixtures using multiple API lots produced very different calibration results: the model of the impurity with the strong IR response became noticeably worse, while the model of the impurity with less responsive IR signal changed only marginally. The updated models performed much better in the prediction as the bias for both polymorphs was reduced and the outlier-related issues mostly disappeared.

Harvesting and blending options for lower alcohol wines: a sensory and chemical investigation

BACKGROUND

Lower alcohol wines often have a poor reputation among consumers, in part due to their unsatisfactory flavours such as reduced overall aroma intensity or herbaceous characters. The aim of this study, performed on Verdelho and Petit Verdot, was to quantify the effectiveness of a monovarietal blend in which wines made from less ripe grapes were blended with an equivalent volume of a wine vinified from riper fruit to produce wines with a lower alcohol content and desirable ripe fruit flavours.

RESULTS

Eleven and 13 attributes, for Verdelho and Petit Verdot, respectively, were selected during sensory descriptive analysis. Intensities of perceived 'acidity', 'sweetness' and 'alcohol' attributes were significantly different (P ≤ 0.05) between the blend (8.8 ± 0.1% v/v) and mature Verdelho (10.3 ± 0.1% v/v) wines, while no significant differences were found between the Petit Verdot blend (11.0 ± 0.1% v/v) and mature (12.6 ± 0.2% v/v) treatments. Volatile composition of wines was assessed using HS-SPME-GC-MS. Partial least square regression suggested relationships between sensory descriptors and chemical attributes in the wines, as well as the modifications of sensory and compositional profiles following blending.

CONCLUSIONS

The blending practice described allowed the production of wines with lower alcohol content while retaining similar sensory profiles of the later harvested, riper fruit wines. © 2017 Society of Chemical Industry.

Application of NIR Spectroscopy Coupled with PLS Regression for Quantification of Total Polyphenol Contents from the Fruit and Aerial Parts of Citrullus colocynthis

INTRODUCTION

Citrullus colocynthis (L.) Schrad is extensively used to treat diabetes, obesity, fever, cancer, amenorrhea, jaundice, leukemia, rheumatism, and respiratory diseases. Chemical studies have indicated the presence of several cucurbitacins, flavones, and other polyphenols in this plant. These phytochemical constituents are responsible for the interesting antioxidant and other biological activities of C. colocynthis.

OBJECTIVE

In the present study, for the first time, near infrared (NIR) spectroscopy coupled with partial least square (PLS) regression analysis was used to quantify the polyphenolic phytochemicals of C. colocynthis.

METHODOLOGY

The fruit and aerial parts of the C. colocynthis were extracted individually in methanol followed by fractionation in n-hexane, chloroform, ethyl acetate, n-butanol, and water. Near infrared (NIR) spectra were obtained in absorption mode in the wavelength range 700-2500 nm. The PLS regression model was then built from the obtained spectral data to quantify the total polyphenol contents in the selected plant samples.

RESULTS

The PLS regression model obtained had a R² value of 99% with a 0.98 correlationship value and a good prediction with a root mean square error of prediction (RMSEP) value of 1.89% and correlation of 0.98. These results were further confirmed through UV-vis spectroscopy and it is found that the ethyl acetate fraction has the maximum value for polyphenol contents (101.7 mg/100 g; NIR, 100.4 mg/100 g; UV-vis).

CONCLUSIONS

The polyphenolic phytochemicals of the fruit and aerial parts of C. colocynthis have been quantified successfully by using multivariate analysis in a non-destructive, economical, precise, and highly sensitive method, which uses very simple sample preparation. Copyright © 2017 John Wiley & Sons, Ltd.

The impact of freshwater metal concentrations on the severity of histopathological changes in fish gills: A statistical perspective

The purpose of this study was to relate the severity of histopathological changes in fish gills with changes in metal concentrations of freshwater samples, and to use the relationships as premature warnings of impairment in aquatic fauna populations. The investigated species were the native barbel (Luciobarbus bocagei) and boga (Pseudochondrostoma sp.), and the introduced trout (Oncorhynchus mykiss), collected from 6 northern Portuguese rivers in a total of 249 individuals. The sampling sites have been linked to different ecological status by the official authorities. The sampling has been repeated 4 times to cover different hydrologic and environmental conditions. The analyzed metals were aluminum, arsenic, cadmium, cobalt, chromium, copper, manganese, nickel, lead and zinc. For each fish, 30 filaments of a gill arch were observed in a light microscope, and the histopathological changes evaluated according to a 6-degree gradation scale that combines the extent and severity of each lesion. The relationships between the histopathological and the chemical results were investigated by the non-parametric Goodman Kruskal gamma correlation and Partial Least Squares regression (PLS). The statistical results highlighted the importance of filament epithelium proliferation (FEP) as key biomarker to the toxicity of sub lethal concentrations of metals, because FEP was significantly correlated with all analyzed metals and explained through PLS regression by concentration changes of Cu, Zn, Mn, Cr and As. A refined regression analysis, where histopathological data on the 3 species were processed in separate, revealed that FEP severity is especially sensitive to changes in metal concentrations in boga. Thus, monitoring studies on the ecological status of northern Portuguese rivers would benefit in time and cost if FEP is used as biomarker and boga as species. Naturally, the option for this species depends on the availability of boga individuals along the stream reaches selected for the monitoring programs.

Application of NIRS coupled with PLS regression as a rapid, non-destructive alternative method for quantification of KBA in Boswellia sacra

In the present study, for the first time, NIR spectroscopy coupled with PLS regression as a rapid and alternative method was developed to quantify the amount of Keto-β-Boswellic Acid (KBA) in different plant parts of Boswellia sacra and the resin exudates of the trunk. NIR spectroscopy was used for the measurement of KBA standards and B. sacra samples in absorption mode in the wavelength range from 700-2500nm. PLS regression model was built from the obtained spectral data using 70% of KBA standards (training set) in the range from 0.1ppm to 100ppm. The PLS regression model obtained was having R-square value of 98% with 0.99 corelationship value and having good prediction with RMSEP value 3.2 and correlation of 0.99. It was then used to quantify the amount of KBA in the samples of B. sacra. The results indicated that the MeOH extract of resin has the highest concentration of KBA (0.6%) followed by essential oil (0.1%). However, no KBA was found in the aqueous extract. The MeOH extract of the resin was subjected to column chromatography to get various sub-fractions at different polarity of organic solvents. The sub-fraction at 4% MeOH/CHCl₃ (4.1% of KBA) was found to contain the highest percentage of KBA followed by another sub-fraction at 2% MeOH/CHCl₃ (2.2% of KBA). The present results also indicated that KBA is only present in the gum-resin of the trunk and not in all parts of the plant. These results were further confirmed through HPLC analysis and therefore it is concluded that NIRS coupled with PLS regression is a rapid and alternate method for quantification of KBA in Boswellia sacra. It is non-destructive, rapid, sensitive and uses simple methods of sample preparation.

Microtiter plate cultivation of oleaginous fungi and monitoring of lipogenesis by high-throughput FTIR spectroscopy

Background

Oleaginous fungi can accumulate lipids by utilizing a wide range of waste substrates. They are an important source for the industrial production of omega-6 polyunsaturated fatty acids (gamma-linolenic and arachidonic acid) and have been suggested as an alternative route for biodiesel production. Initial research steps for various applications include the screening of fungi in order to find efficient fungal producers with desired fatty acid composition. Traditional cultivation methods (shake flask) and lipid analysis (extraction-gas chromatography) are not applicable for large-scale screening due to their low throughput and time-consuming analysis. Here we present a microcultivation system combined with high-throughput Fourier transform infrared (FTIR) spectroscopy for efficient screening of oleaginous fungi.

Results

The microcultivation system enables highly reproducible fungal fermentations throughout 12 days of cultivation. Reproducibility was validated by FTIR and HPLC data. Analysis of FTIR spectral ester carbonyl peaks of fungal biomass offered a reliable high-throughput at-line method to monitor lipid accumulation. Partial least square regression between gas chromatography fatty acid data and corresponding FTIR spectral data was used to set up calibration models for the prediction of saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, unsaturation index, total lipid content and main individual fatty acids. High coefficients of determination (R² = 0.86–0.96) and satisfactory residual predictive deviation of cross-validation (RPD_CV = 2.6–5.1) values demonstrated the goodness of these models.

Conclusions

We have demonstrated in this study, that the presented microcultivation system combined with rapid, high-throughput FTIR spectroscopy is a suitable screening platform for oleaginous fungi. Sample preparation for FTIR measurements can be automated to further increase throughput of the system.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-017-0716-7) contains supplementary material, which is available to authorized users.