Vis-NIR spectroscopy based rapid and non-destructive method to quantitate microplastics: An emerging contaminant in farm soil

Microplastics (MPs) is the second most important environmental issue and can potentially enter into food chain through farmland contamination and other means. There are no standardized extraction methods for quantification of MPs in soil. The embedded errors and biases generated serious problems regarding the comparability of different studies and leading to erroneous estimation. To address this gap, present study was formulated to develop an efficient method for MPs analysis suitable for a wide range of soil and organic matrices. A method based on Vis-NIR (Visible-Near Infra Red) spectroscopy is developed for four different soil belonging to Alfisol, Inceptisol, Mollisol and Vertisol and two organic matter matrices (FYM and Sludge). The developed method was found as rapid, reproducible, non-destructive and accurate method for estimation of all three-density groups of MPs (Low, Medium and High) with a prediction accuracy ranging from 1.9 g MPs/kg soil (Vertisol) to 3.7 g MPs/kg soil (Alfisol). Two different regression models [Partial Least Square Regression (PLSR) and Principal Component Regression (PCR)] were assessed and PLSR was found to provide better information in terms of prediction accuracy and minimum quantification limit (MQL). However, PCR performed better for organic matter matrices than PLSR. The method avoids any complicated sample preparation steps except drying and sieving thus saving time and acquisition of reflectance spectrum for single sample is possible within 18 s. Owing to have the minimum quantification limit ranging from 1.9-3.7 g/kg soil, the vis-NIR based method is perfectly suitable for estimation of MPs in soil samples collected from plastic pollution hotspots like landfill sites, regular based sludge amended farm soils. Additionally, the method can be adapted by small scale compost industries for assessing MPs load in product like city compost which are applied at agricultural fields and will be helpful in quantifying possible MPs at the sources itself.

Quantitative analysis of low-content impurity crystal forms in canagliflozin tablets by NIR solid-state analysis technique

Canagliflozin (CFZ) tablets was a commercially new class of anti-diabetic drug, CFZ had various anhydrate crystal forms and two hydrate crystal forms (Canagliflozin hemihydrate (Hemi-CFZ) and Canagliflozin monohydrate (Mono-CFZ) crystal form). The active pharmaceutical ingredients (APIs) of commercially available CFZ tablets were Hemi-CFZ, was easily convert to CFZ or Mono-CFZ under the influence of temperature, pressure, humidity and other factors in tablets processing, storage, and transportation, thus affected bioavailability and efficacy of tablets. Therefore, quantitative analysis of low-content CFZ and Mono-CFZ in tablets was essential to control tablets' quality. The main objective of this study was to explore the feasibility and in-depth explain its quantitative analysis mechanism of NIR for quantitative analysis of low-content CFZ/Mono-CFZ in CFZ tablets. PLSR models for low-content CFZ/Mono-CFZ were established by NIR solid-state analysis technique in different resolutions with different wavenumber regions combined with various pretreatments methods (such as Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV), Savitzky-Golay First Derivative (SG1st), Savitzky-Golay Second Derivative (SG2nd) and Wavelet Transform (WT)), and the PLSR models were verified. The feasibility of NIR spectroscopy for quantitative analysis of low-content CFZ and Mono-CFZ in CFZ tablets was discussed and analyzed from multiple perspectives, which included the distribution of effective information on the spectrum, the influence of resolution on PLSR models performance, the variance contribution/cumulative variance contribution of PLSR model principal components (PCs), the relation of PCI loadings, scores of the spectra and CFZ/Mono-CFZ content, and the mechanism of quantitative analysis was in-depth explained simultaneously. Eventually the most suitable PLSR models in 0.0000-10.0000 % w/w % obtained. That can provide theoretical support for quantitative analysis of low-content impurity crystal during the production, storage and transportation of CFZ tablets, thus provide reference methods for quality control of CFZ tablets and a reliable reference method for quantitative analysis of impurity crystal forms and quality control of similar drugs.

Towards quantifying biomarkers for respiratory distress in preterm infants: Machine learning on mid infrared spectroscopy of lipid mixtures

Neonatal respiratory distress syndrome (nRDS) is a challenging condition to diagnose which can lead to delays in receiving appropriate treatment. Mid infrared (IR) spectroscopy is capable of measuring the concentrations of two diagnostic nRDS biomarkers, lecithin (L) and sphingomyelin (S) with the potential for point of care (POC) diagnosis and monitoring. The effects of varying other lipid species present in lung surfactant on the mid IR spectra used to train machine learning models are explored. This study presents a lung lipid model of five lipids present in lung surfactant and varies each in a systematic approach to evaluate the ability of machine learning models to predict the lipid concentrations, the L/S ratio and to quantify the uncertainty in the predictions using the jackknife + -after-bootstrap and variant bootstrap methods. We establish the L/S ratio can be determined with an uncertainty of approximately ±0.3 mol/mol and we further identify the 5 most prominent wavenumbers associated with each machine learning model.

Variation of the Start Date of the Vegetation Growing Season (SOS) and Its Climatic Drivers in the Tibetan Plateau

Climate change inevitably affects vegetation growth in the Tibetan Plateau (TP). Understanding the dynamics of vegetation phenology and the responses of vegetation phenology to climate change are crucial for evaluating the impacts of climate change on terrestrial ecosystems. Despite many relevant studies conducted in the past, there still remain research gaps concerning the dominant factors that induce changes in the start date of the vegetation growing season (SOS). In this study, the spatial and temporal variations of the SOS were investigated by using a long-term series of the Normalized Difference Vegetation Index (NDVI) spanning from 2001 to 2020, and the response of the SOS to climate change and the predominant climatic factors (air temperature, LST or precipitation) affecting the SOS were explored. The main findings were as follows: the annual mean SOS concentrated on 100 DOY-170 DOY (day of a year), with a delay from east to west. Although the SOS across the entire region exhibited an advancing trend at a rate of 0.261 days/year, there were notable differences in the advancement trends of SOS among different vegetation types. In contrast to the current advancing SOS, the trend of future SOS changes shows a delayed trend. For the impacts of climate change on the SOS, winter Tmax (maximum temperature) played the dominant role in the temporal shifting of spring phenology across the TP, and its effect on SOS was negative, meaning that an increase in winter Tmax led to an earlier SOS. Considering the different conditions required for the growth of various types of vegetation, the leading factor was different for the four vegetation types. This study contributes to the understanding of the mechanism of SOS variation in the TP.

Portable Raman spectroscopy coupled with PLSR analysis for monitoring and predicting of the quality of fresh-cut Chinese yam at different storage temperatures

Portable Raman spectroscopy coupled with partial least squares regression (PLSR) model was performed for monitoring and predicting four quality indicators, moisture content, water activity, polysaccharide content and microbial content of the fresh-cut Chinese yam at different storage temperatures. The variations in the four key indicators were first depicted through a spider web diagram as the storage temperature changed. More importantly, the four key indicators can be accurately monitored and predicted through optimized PLSR models combining with Raman spectroscopy. Among all of the PLSR models for the four indicators, the regression model for moisture content was relatively the best. In addition, storage temperature played a significant role on the model performance of PLSR. The model performance for all indicators at room temperature and high temperature was better than the corresponding PLSR models at refrigeration and freezing conditions. Especially at 25 ℃, the R2 in the calibration set basically reached 0.9. These observations indicated that portable Raman spectroscopy, a simple and easy-to-use detection technique, can monitor and predict the multiple quality indicators of fresh-cut Chinese yam combined with effectively PLSR model, which would be conducive to their applications in food industry.

Assessment of land use/ land cover change derived catchment hydrologic response: An integrated parsimonious hydrological modeling and alteration analysis based approach

Land use/land cover (LULC) change, often a consequence of natural or anthropogenic drivers, plays a decisive role in governing global catchment dynamics, and subsequent impact on regional hydrology. Insight into the complex relationship between the drivers of LULC change and catchment hydrology is of utmost importance to decision makers. Contemplating the dynamic rainfall-runoff response of the Indian catchments, this study proposes an integrated modeling-based approach to identify the drivers and relative contribution to catchment hydrology. The proposed approach was evaluated in the tropical climate Nagavali River Basin (NRB) (9512 km2) of India. The Soil and Water Assessment Tool (SWAT) hydrological model, which uses daily-scale rainfall, temperature, wind speed, relative humidity, solar radiation, and streamflow information was integrated with the Indicators of Hydrologic Alteration (IHA) technique to characterize the plausible changes in the flow regime of the NRB. Subsequently, the Partial Least Squares Regression (PLSR) based modeling analysis was performed to quantify the relative contribution of individual LULC components on the catchment water balance. The outcomes of the study revealed that forest land has been significantly converted to agricultural land (45-59%) across the NRB resulting in mean annual streamflow increase of 3.57 m3/s during the monsoon season. The affinity between land use class and streamflow revealed that barren land (CN = 83-87) exhibits the maximum positive response to streamflow followed by the built-up land (CN = 89-91) and fallow land (CN = 88-93). The period 1985-1995 experienced an increased ET scenario (911-1050 mm), while the recent period (2005-2020) experienced reduced ET scenario owing to conversion of forest to agricultural land. Certainly, the study endorses adopting the developed methodology for understanding the complex land use and catchment-scale hydrologic interactions across global-scales for early watershed management planning.

Trace level detection of melamine and cyanuric acid extracted from pet liquid food (milk) using a SERS Au nanogap substrate

This study reported an application of Au nanogap substrates for surface-enhanced Raman scattering (SERS) measurements to quantitatively analyze melamine and its derivative products at trace levels in pet liquid food (milk) combined with a waveband selection approach, namely variable importance in projection (VIP). Six different concentrations of melamine, cyanuric acid, and melamine combined with cyanuric acid were created, and SERS spectra were acquired from 550 to 1620cm-1. Detection was possible up to 200 pM for melamine-contaminated samples, and 400 pM concentration detection for other two groups. The VIP-PLSR models obtained correlation coefficient (R2) values of 0.997, 0.985, and 0.981, with root mean square error of prediction (RMSEP) values of 18.492 pM, 19.777 pM, and 15.124 pM for prediction datasets. Additionally, partial least square discriminant analysis (PLS-DA) was used to classify both pure and different concentrations of spiked samples. The results showed that the maximum classification accuracy for melamine was 100%, for cyanuric acid it was 96%, and for melamine coupled with cyanuric acid it was 95%. The results obtained clearly demonstrated that the Au nanogap substrate offers low-concentration, rapid, and efficient detection of hazardous additive chemicals in pet consuming liquid food.

Characterization of Evodia rutaecarpa (Juss) Benth honey: volatile profile, odor-active compounds and odor properties

BACKGROUND

Aroma is one of the most important quality criterion of different honeys and even defines their merchant value. The composition of volatile compounds, especially the characteristic odor-active compounds, contributes significantly to the aroma of honey. Evodia rutaecarpa (Juss) Benth honey (ERBH) is a special honey in China with unique flavor characteristics. However, no work in the literature has investigated the volatile compounds and characteristic odor-active compounds of ERBHs. Therefore, it is imperative to conduct systematic investigation into the volatile profile, odor-active compounds and odor properties of ERBHs.

RESULTS

The characteristic fingerprint of ERBHs was successfully constructed with 12 characteristic peaks and a similarity range of 0.785-0.975. In total, 297 volatile compounds were identified and relatively quantified by headspace solid-phase microextraction coupled with gas chromatography quadrupole time-of-flight mass spectrometry, of which 61 and 31 were identified as odor-active compounds by relative odor activity values and GC-olfactometry analysis, respectively, especially the common 22 odor-active compounds (E)-β-damascenone, phenethyl acetate, linalool, cis-linalool oxide (furanoid), octanal, hotrienol, trans-linalool oxide (furanoid), 4-oxoisophorone and eugenol, etc., contributed significantly to the aroma of ERBHs. The primary odor properties of ERBHs were floral, followed by fruity, herbaceous and woody aromas. The partial least-squares regression results showed that the odor-active compounds had good correlations with the odor properties.

CONCLUSION

Identifying the aroma differences of different honeys is of great importance. The present study provides a reliable theoretical basis for the quality and authenticity of ERBHs. © 2023 Society of Chemical Industry.

Brain age prediction across the human lifespan using multimodal MRI data

Measuring differences between an individual's age and biological age with biological information from the brain have the potential to provide biomarkers of clinically relevant neurological syndromes that arise later in human life. To explore the effect of multimodal brain magnetic resonance imaging (MRI) features on the prediction of brain age, we investigated how multimodal brain imaging data improved age prediction from more imaging features of structural or functional MRI data by using partial least squares regression (PLSR) and longevity data sets (age 6-85 years). First, we found that the age-predicted values for each of these ten features ranged from high to low: cortical thickness (R = 0.866, MAE = 7.904), all seven MRI features (R = 0.8594, MAE = 8.24), four features in structural MRI (R = 0.8591, MAE = 8.24), fALFF (R = 0.853, MAE = 8.1918), gray matter volume (R = 0.8324, MAE = 8.931), three rs-fMRI feature (R = 0.7959, MAE = 9.744), mean curvature (R = 0.7784, MAE = 10.232), ReHo (R = 0.7833, MAE = 10.122), ALFF (R = 0.7517, MAE = 10.844), and surface area (R = 0.719, MAE = 11.33). In addition, the significance of the volume and size of brain MRI data in predicting age was also studied. Second, our results suggest that all multimodal imaging features, except cortical thickness, improve brain-based age prediction. Third, we found that the left hemisphere contributed more to the age prediction, that is, the left hemisphere showed a greater weight in the age prediction than the right hemisphere. Finally, we found a nonlinear relationship between the predicted age and the amount of MRI data. Combined with multimodal and lifespan brain data, our approach provides a new perspective for chronological age prediction and contributes to a better understanding of the relationship between brain disorders and aging.

Latent variable modeling of gamma-ray background in repeated measurements

We propose a novel approach for background subtraction in repeated gamma-ray spectrometric measurements. This entirely data-driven method eliminates the need for Monte Carlo detector simulation. To accomplish this, we utilized the framework of Latent Variable Modeling, incorporating various matrix factorization techniques and artificial neural networks. Subsequently, we applied this method to estimate radionuclide activity through spectrum unmixing. Significant improvements in sensitivity, surpassing traditional methods, were observed for the test case scenario of aerosol filter measurements.

Evaluation of the differences between low-salt solid-state fermented soy sauce and high-salt diluted-state fermented soy sauce in China: from taste-active compounds and aroma-active compounds to sensory characteristics

BACKGROUND

The present study aimed to determine the components related to sensory properties in soy sauce and to characterize the differences between low-salt solid-state fermented soy sauce (LSFSS) and high-salt diluted-state fermented soy sauce (HDFSS). The taste and aroma active components of 18 commercially available soy sauces (eight types of LSFSS and 10 types of HDFSS) were characterized. The relationship between these compounds, soy sauce samples, and sensory properties was modeled by partial least squares regression.

RESULTS

The analysis showed that the 11 taste-active components, including glutamic acid, glycine, alanine, threonine, malic acid, citric acid, tartaric acid, acetic acid, lactic acid, reducing sugar and salt, contributed greatly to the taste of soy sauce. In addition, umami, saltiness and sweetness are the characteristic tastes of HDFSS, whereas sourness and bitterness were the characteristic tastes of LSFSS. At the same time, seven aroma-active compounds, namely 4-ethyl-2-methoxyphenol, ethanol, 3-methyl-1-butanol, ethyl acetate, 2-phenethyl alcohol, 3-methyl thiopropanol and 2-ethyl-4-hydroxy-5-methylfuran-3-one, played a decisive role in the flavor of soy sauce. In addition, HDFSS presented the aroma attributes of smoky, alcoholic, floral, fruity and caramel-like, whereas LSFSS mainly presented sour and malty aroma attributes.

CONCLUSION

The present study reveals new insight into the relationship between the chemical composition and sensory characteristics of soy sauce, which is of great significance for developing an objective measurement system and providing a theoretical basis to improve the sensory quality of soy sauce. © 2023 Society of Chemical Industry.

Sensory-Guided Establishment of Sensory Lexicon and Investigation of Key Flavor Components for Goji Berry Pulp

Many customers prefer goji berry pulp, well-known for its high nutritional content, over fresh goji berries. However, there is limited research on its sensory lexicon and distinctive flavor compounds. This study focused on developing a sensory lexicon for goji berry pulp and characterizing its aroma by sensory and instrumental analysis. Sensory characteristics of goji berry pulp were evaluated by our established lexicon. A total of 83 aromatic compounds in goji berry pulp were quantified using HS-SPME-GC-Orbitrap-MS. By employing OAV in combination, we identified 17 aroma-active compounds as the key ingredients in goji berry pulp. Then, we identified the potentially significant contributors to the aroma of goji berry pulp by combining principal component analysis and partial least squares regression (PLSR) models of aroma compounds and sensory attributes, which included 3-ethylphenol, methyl caprylate, 2-hydroxy-4-methyl ethyl valerate, benzeneacetic acid, ethyl ester, hexanal, (E,Z)-2,6-nonadienal, acetylpyrazine, butyric acid, 2-ethylhexanoic acid, 2-methyl-1-propanol, 1-pentanol, phenylethyl alcohol, and 2-nonanone. This study provides a theoretical basis for improving the quality control and processing technology of goji berry pulp.

Compositional analysis of copper and iron-based alloys using LIBS coupled with chemometric method

The present manuscript deals with the utility of the calibration-free LIBS and calibration curve methods for the compositional study of different alloys using laser-induced breakdown spectroscopy (LIBS). In the process of alloying in the smelting industry, metal concentration in different alloys affects the physical and chemical properties of the final products. Therefore, LIBS can be used as an efficient quantitative analysis tool for online monitoring of the quality of the products. This is because LIBS can be performed online, in situ, without any pre-processing, and need no sample preparation for the compositional analysis of any type of materials present in any phase (solid, liquid, gas or even molten alloys in the industries). In the present study, four alloys (three copper and one iron-based alloy) consisting of Cu, Al, Zn, Ni, Fe, Cr and Mn as major and Sn and Si as minor elements were selected for the study using calibration-free laser-induced breakdown spectroscopy (CF-LIBS) and calibration curve method i.e. partial least square regression (PLSR). For the CF-LIBS method, the temporal delay has been optimized in order to satisfy the optically thin and local thermal equilibrium (LTE) condition of the plasma. For the PLSR method, different regions of the strongest emission lines of constituents have been selected for quantitative analysis. The study of time-resolved LIBS spectra and the variation of plasma parameters with respect to the delay time is also discussed. The utility of the combined technique of CF-LIBS with the PLSR method for rapid monitoring and quality assessment of desired material/products without any sample pretreatment, thus reducing the cost of the analysis, is presented in this paper.

Rapid quantification of the adulteration of pomegranate juices by Raman spectroscopy and chemometrics

The juice drink industry has repeatedly been exposed to adulteration. Unscrupulous producers, for example, use cheap juice for substitution in the pursuit of more significant economic benefits, which presents a tremendous challenge for the control of the quality of drinks. The objective of this study was to apply Raman spectroscopy combined with chemometrics to rapidly quantify the adulteration concentration of apple juice or grape juice in pomegranate juice. Two supervised learning algorithms: partial least squares regression (PLSR) and support vector machine regression (SVR) were used to analyze the Raman spectra of 114 samples. The coefficient of determination (R2), root mean square error (RMSE), and residual prediction deviation (RPD) of the prediction set when using PLSR and SVR to predict the adulterated concentration of apple juice in pomegranate juice were 0.9357 and 0.9465, 6.446% and 5.974%, 3.945 and 4.322, respectively. The R2, RMSE, and RPD of the prediction set when using PLSR and SVR to predict the adulteration concentration of grape juice in pomegranate juice were 0.9501 and 0.9502, 6.334% and 5.571%, and 4.475 and 4.481, respectively. It was concluded that Raman spectroscopy combined with chemometrics has excellent potential for application as a rapid quantitative method to detect adulterated concentrations of pomegranate juice.

Microbial diversity on grape epidermis and wine volatile aroma in spontaneous fermentation comprehensively driven by geography, subregion, and variety

On their journey from the wine grape to the resulting wine, microbiota from grape surfaces controlled by multiple factors is transferred to wine spontaneous fermentation process with indisputable consequences for wine quality parameters. The associated microbiota was regionally distinct (defined to microbial terroir) but how these microbial patterns with significantly regional distinctiveness quantitatively drive the wine regional characteristics are not definite within a complete grape ecosystem at different geographical (> 300 km), subregional (< 10 km), and varietal scales. Here, we collected 24 samples (containing two grape varieties) from four subregions of two regions in Xinjiang wine production area to investigate fungal distribution patterns and the association with wine chemical composition at different evaluation scales. Meanwhile, the relationships were established between geographical, subregional, varietal community of fungi, and wine volatile aroma using partial least squares regression (PLSR) and structural equation modeling (SEM). Results show that microbial and volatile samples present the significantly regional difference inside the complete ecosystem. Microbiota showed a stronger heterogeneity at geography scales, which drove the distributions of subregional and varietal microbiota thereby influencing the volatile composition of finished wines. Moreover, geographical microbiota seems to weaken the effects of varietal community on wine aroma compounds. Microbial communities respond to environmental changes within a completely set grape-related ecosystem at different scales, and these responses resulted in the wine regional distinctiveness based on the volatile profiles. Our findings further confirmed the important role of microbial terroir in shaping wine styles and provided the new cerebration for the terroir drivers of microbiota.

In-Field Implementation of Near-Infrared Quantitative Methods for Analysis of Medicines in Tropical Environments

Near-infrared (NIR) spectroscopy is actually a well-established technique that demonstrates its performance in the frame of detection of poor-quality medicines. The use of low-cost handheld NIR spectrophotometers in low-resource contexts can allow an inexpensive and more rapid detection compared to laboratory methods. Considering these points, it was decided to develop, validate, and transfer methods for the quantification of ciprofloxacin and metronidazole tablet samples using a NIR handheld spectrophotometer in transmission mode (NIR-M-T1) coupled to chemometrics such as partial least squares regression (PLSR) algorithm. All of the models were validated with the total error approach using an accuracy profile as a decision tool, with ±10% specifications and a risk α set at 5%. Quantitative PLSR models were first validated in Belgium, which is a temperate oceanic climate zone. Second, they were transferred to Cameroon, a tropical climate zone, where issues regarding the prediction of new validation series with the initial models were highlighted. Two augmentation strategies were then envisaged to make the predictive models robust to environmental conditions, incorporating the potential variability linked to environmental effects in the initial calibration sets. The resulting models were then used for in-field analysis of ciprofloxacin and metronidazole tablet samples collected in three cities in Cameroon. The contents results obtained for each sample with the two strategies were close and not statistically different. Nevertheless, the first one is easier to implement and the second is the best regarding model diagnostic measures and accuracy profiles. Two samples were found to be noncompliant in terms of content, and these results were confirmed using high-performance liquid chromatography taken as the reference method.

Crude Oil Density Prediction Improved by Multiblock Analysis of Fourier Transform Ion Cyclotron Resonance Mass Spectrometry, Fourier Transform Infrared, and Near-Infrared Spectroscopy Data

Crude oils are among the world's most complex organic mixtures containing a large number of unique components and many analytical techniques lack resolving power to characterize. Fourier transform ion cyclotron resonance mass spectrometry offers a high mass accuracy, making a detailed analysis of crude oils possible. Infrared (IR) spectroscopic methods such as Fourier transform IR spectroscopy (FT-IR) and near-IR, can also be used for crude oil characterization. The three methods measure different properties of the samples, and different data sources can often be combined to improve the prediction accuracy of models. In this study, partial least squares regression (PLSR) models for each of the three methods (single-block PLSR) were compared to multiblock PLSR and sequential and orthogonalized PLSR (SO-PLSR), with the aim of predicting the density of crude oils. Variable importance in projection was used to identify the important variables for each method, as spectroscopic data often contain irrelevant variation. The variables were interpreted to evaluate their underlying chemistry and to check whether consistency could be found between the variables selected from the spectroscopic data for the single-block and multiblock methods. Combining the different blocks of data increased the prediction abilities of the models both before and after variable selection, and SO-PLSR using a reduced data set resulted in the best-performing prediction model.

Chemometric analysis applied to 1 H NMR and FTIR data for a quality parameter distinction of red fruit (Pandanus conoideus, lam.) oil products

INTRODUCTION

Red fruit oil (RFO) is a natural product extracted from Pandanus conoideus Lam. fruit, a native plant from Papua, Indonesia. Recent studies indicate that RFO is popularly consumed as herbal medicine. Therefore, the quality of RFO must be assured.

OBJECTIVES

This study aimed to develop a chemometric analysis applied to 1 H nuclear magnetic resonance (NMR) and Fourier transform infrared (FTIR) data for important quality parameter distinction of red fruit oil (RFO), especially regarding the degree of unsaturation and the amount of free fatty acids (FFA).

MATERIALS AND METHODS

Forty samples consisting of one crude RFO, thirty-three commercial RFOs, and three oils as blends, including olive oil, virgin coconut oil, and black seed oil, were analysed by 1 H NMR and FTIR spectroscopy. After appropriate preprocessing of the spectra, principal component analysis (PCA) and partial least squares regression (PLSR) were used for model development.

RESULTS

The essential signals for modelling the degree of unsaturation are the signal at δ = 5.37-5.27 ppm (1 H NMR) and the band at 3000-3020 cm-1 (FTIR). The FFA profile represents the signal at δ = 2.37-2.20 ppm (1 H NMR) and the band at 1680-1780 cm-1 (FTIR). PCA allows the visualisation grouping on both methods with > 98% total principal component (PC) for the degree of unsaturation and > 88% total PC for FFA values. In addition, the PLSR model provides an acceptable coefficient of determination (R2 ) and errors in calibration, prediction, and cross-validation.

CONCLUSION

Chemometric analysis applied to 1 H NMR and FTIR spectra of RFO successfully grouped and predicted product quality based on the degree of unsaturation and FFA value categories.

FT-MIR-ATR Associated with Chemometrics Methods: A Preliminary Analysis of Deterioration State of Brazil Nut Oil

Brazil nut oil is highly valued in the food, cosmetic, chemical, and pharmaceutical industries, as well as other sectors of the economy. This work aims to use the Fourier transform infrared (FTIR) technique associated with partial least squares regression (PLSR) and principal component analysis (PCA) to demonstrate that these methods can be used in a prior and rapid analysis in quality control. Natural oils were extracted and stored for chemical analysis. PCA presented two groups regarding the state of degradation, subdivided into super-degraded and partially degraded groups in 99.88% of the explained variance. The applied PLS reported an acidity index (AI) prediction model with root mean square error of calibration (RMSEC) = 1.8564, root mean square error of cross-validation (REMSECV) = 4.2641, root mean square error of prediction (RMSEP) = 2.1491, R2cal (calibration correlation coefficient) equal to 0.9679, R2val (validation correlation coefficient) equal to 0.8474, and R2pred (prediction correlation coefficient) equal to 0, 8468. The peroxide index (PI) prediction model showed RMSEC = 0.0005, REMSECV = 0.0016, RMSEP = 0.00079, calibration R2 equal to 0.9670, cross-validation R2 equal to 0.7149, and R2 of prediction equal to 0.9099. The physical-chemical analyses identified that five samples fit in the food sector and the others fit in other sectors of the economy. In this way, the preliminary monitoring of the state of degradation was reported, and the prediction models of the peroxide and acidity indexes in Brazil nut oil for quality control were determined.

Assessment of Maturity of Plum Samples Using Fourier Transform Near-Infrared Technique Combined with Chemometric Methods

The FT-NIR technique was used for rapid and non-destructive determination of plum ripeness. The dry matter (DM), titratable acidity (TA), total soluble solids (TSS) and calculated maturity index (MI: TSS/TA) were used as reference values. The PLS correlations were validated via five-fold cross-validation (RMSECV for different parameters: DM: 0.66%, w/w; TA = 0.07%, w/w; TSS = 0.72%, w/w; MI = 1.39) and test set validation (RMSEP for different parameters: DM: 0.65%, w/w TA = 0.07%, w/w; TSS = 0.61%, w/w; MI = 1.50). Different classification algorithms were performed for TA, TSS and MI. Linear, quadratic and Mahalanobis discriminant analysis (LDA, QDA, MDA) were found to be the best sample detection methods. The accuracy of the classification methods was 100% for all investigated parameters and cultivars.

Investigation of aroma characteristics of seven Chinese commercial sunflower seed oils using a combination of descriptive Analysis, GC-quadrupole-MS, and GC-Orbitrap-MS

The aroma characteristics of seven commercial Chinese sunflower seed oils were investigated in this study using descriptive analysis, headspace solid-phase microextraction coupled with GC-quadrupole-MS (LRMS, low-resolution mass spectrometry), and GC-Orbitrap-MS (HRMS, high-resolution mass spectrometry). GC-Orbitrap-MS quantified 96 compounds, including 18 alcohols, 12 esters, 7 ketones, 20 terpenoids, 11 pyrazines, 6 aldehydes, 6 furans, 6 benzene ring-containing compounds, 3 sulfides, 2 alkanes, and 5 nitrogen-containing compounds. Moreover, 22 compounds including 5 acids, 1 amide, and 16 aldehydes were quantified using GC-Quadrupole-MS. To our knowledge, 23 volatile compounds were reported for the first time in sunflower seed oil. All the seven samples were found to have a 'roasted sunflower seeds' note, 'sunflower seeds aroma' note and 'burnt aroma' note and only five of them had 'fried instant noodles' note, three had 'sweet' note and two had 'puffed food' note. Partial least squares regression was used to screen the candidate key volatiles that caused the aroma differences among these seven samples. It was observed that 'roasted sunflower seeds' note was positively correlated with 1-octen-3-ol, n-heptadehyde and dimethyl sulfone, whereas the 'fried instant noodles' and 'puffed food' demonstrated a positive correlation with pentanal, 3-methylbutanal, hexanal, (E)-2-hexenal and 2-pentylfuran. Our findings provide information to the producers and developers for quality control and improvement of sunflower seed oil.

Soil metabolomics: A powerful tool for predicting and specifying pesticide sorption

Sorption regulates the dispersion of pesticides from cropped areas to surrounding water bodies as well as their persistence. Assessing the risk of water contamination and evaluating the efficiency of mitigation measures, requires fine-resolution sorption data and a good knowledge of its drivers. This study aimed to assess the potential of a new approach combining chemometric and soil metabolomics to estimate the adsorption and desorption coefficients of a range of pesticides. It also aims to identify and characterise key components of soil organic matter (SOM) driving the sorption of these pesticides. We constituted a dataset of 43 soils from Tunisia, France and Guadeloupe (West Indies), covering extensive ranges of texture, organic carbon and pH. We performed untargeted soil metabolomics by liquid chromatography coupled with high-resolution mass spectrometry (UPLC-HRMS). We measured the adsorption and desorption coefficients of three pesticides namely glyphosate, 2,4-D and difenoconazole for these soils. We developed Partial Least Square Regression (PLSR) models for the prediction of the sorption coefficients from the RT-m/z matrix and conducted further ANOVA analyses to identify, annotate and characterise the most significant constituents of SOM in the PLSR models. The curated metabolomics matrix yielded 1213 metabolic markers. The prediction performance of the PLSR models was generally high for the adsorption coefficients Kd_ads (0.3 < R² < 0.8) and for the desorption coefficients Kf_des (0.6 < R² < 0.8) but low for n_des (0.03 < R² < 0.3). The most significant features in the predictive models were annotated with a confidence level of 2 or 3. The molecular descriptors of these putative compounds suggest that the pool of SOM compounds driving glyphosate sorption is reduced compared to 2,4-D and difenoconazole, and these compounds are generally more polar. This approach can provide estimates of the adsorption and desorption coefficients of pesticides, including polar pesticide, for contrasted pedoclimates.

Tropical altitudinal gradient soil organic carbon and nitrogen estimation using Specim IQ portable imaging spectrometer

The largest actively cycling terrestrial carbon pool, soil, has been disturbed during latest centuries by human actions through reduction of woody land cover. Soil organic carbon (SOC) content can reliably be estimated in laboratory conditions, but more cost-efficient and mobile techniques are needed for large-scale monitoring of SOC e.g. in remote areas. We demonstrate the capability of a mobile hyperspectral camera operating in the visible-near infrared wavelength range for practical estimation of soil organic carbon (SOC) and nitrogen content, to support efficient monitoring of soil properties. The 191 soil samples were collected in Taita Taveta County, Kenya representing an altitudinal gradient comprising five typical land use types: agroforestry, cropland, forest, shrubland and sisal estate. The soil samples were imaged using a Specim IQ hyperspectral camera under controlled laboratory conditions, and their carbon and nitrogen content was determined with a combustion analyzer. We use machine learning for estimating SOC and N content based on the spectral images, studying also automatic selection of informative wavelengths and quantification of prediction uncertainty. Five alternative methods were all found to perform well with a cross-validated R² of approximately 0.8 and an RMSE of one percentage point, demonstrating feasibility of the proposed imaging setup and computational pipeline.

Detection and Quantification of Adulteration in Krill Oil with Raman and Infrared Spectroscopic Methods

Raman and infrared spectroscopy, used as individual and low-level fused datasets, were evaluated to identify and quantify the presence of adulterants (palm oil, PO; ω-3 concentrates in ethyl ester, O3C and fish oil, FO) in krill oil. These datasets were qualitatively analysed with principal component analysis (PCA) and classified as adulterated or unadulterated using support vector machines (SVM). Using partial least squares regression (PLSR), it was possible to identify and quantify the adulterant present in the KO mixture. Raman spectroscopy performed better (r² = 0.98; RMSEP = 2.3%) than IR spectroscopy (r² = 0.91; RMSEP = 4.2%) for quantification of O3C in KO. A data fusion approach further improved the analysis with model performance for quantification of PO (r² = 0.98; RMSEP = 2.7%) and FO (r² = 0.76; RMSEP = 9.1%). This study demonstrates the potential use of Raman and IR spectroscopy to quantify adulterants present in KO.

Exploring relationship of soil PTE geochemical and "VIS-NIR spectroscopy" patterns near Cu-Mo mine (Armenia)

PTE contamination of soils remains one of the global environmental concerns. The ways of detecting and monitoring PTE concentrations in soils varies including traditional field sampling accompanied by sample preparation and chemical analysis and state of the art visible and near-infrared (Vis-NIR) spectroscopic approaches. Among the different Machine Learning (ML) to extract soil information from spectra and to explore the relationship between spectral reflectance data and soil PTE content PLSR method is a well-established one to construct a soil PTE estimation model. This study aimed to explore the relationship of soil PTE geochemical and VIS-NIR spectroscopy characteristics in agricultural soils near Cu-Mo mine area in Armenia. PLSR method is applied to identify the links between the spectra and agricultural soil Ti, V, Cr, Mn, Fe, Co, Ba, Pb, Zn, Cu, Sr, Zr and Mo contents to reveal the potential of VIS-NIR spectroscopy in ex-situ monitoring of Kajaran soils. The results show that different portions of VIS-NIR spectra are responsible for Ti (1100-1200 nm, 2350-2500 nm), V (350-500 nm, 700-750 nm, 1000-1100 nm, 1400-2500 nm), Cr (1300-1400 nm, 1900-2100 nm) and Ba (450-500 nm, 600-800 nm, 1050-1700 nm, 2000-2100 nm, 2350-2400 nm) estimations through PLSR correspondingly. However, among the studied PTEs Ti and V, which shows significant negative correlations in VIS-NIR spectra registered at around 400-600 nm and 850-1150 nm regions, are remarkable and promising with the PLSR estimation results using VIS-NIR spectra Ti (R²_Test = 0.74), V (R²_Test = 0.71). This study shows that VIS-NIR spectroscopy has a high potential for the estimation of at least several PTE in soils and PLSR modelis reliable for deriving information from there.

Towards the non-invasive assessment of staling in bovine hides with hyperspectral imaging

Microbial spoilage or staling of bovine hides during storage leads to poor leather quality and increased chemical consumption during processing. Conventional microbiological examinations of hide samples which require time-consuming microbe culture cannot be employed as a practical staling detection approach for leather production. Hyperspectral imaging (HSI), featuring fast data acquisition and implementation flexibility has been considered ideal for in-line detection of microbial contamination in Agri- food products. In this study, a linescan hyperspectral imaging system working in a spectral range of 550 nm to 1700 nm was utilized as a rapid and non-destructive technique for predicting the aerobic plate counts (APC) on raw hide samples during storage. Fresh bovine hide samples were stored at 4 °C and 20 °C for 3 days. Every day, hyperspectral images were acquired on both sides for each sample. The APCs were determined simultaneously by conventional microbiological plating method. Leather quality was evaluated by microscopic inspection of grain surfaces, which indicate the acceptable threshold of microbe load on hide samples for leather processing. Partial least squares regression (PLSR) was applied to fit the spectral information extracted from the samples to the logarithmic values of APC to develop microbe load prediction models. All models showed good prediction accuracy, yielding a R_cv² in the range of 0.74-0.92 and standard error of cross validation (SECV) in the range of 0.61-0.76 %. The prediction capability of the HSI was explored using the model developed with SNV + smoothened pre-processing to spatially predict plate count in the samples. Models established in this study successfully predicted the staling states characterised by bacterial loads on hide samples with low prediction errors. Models, visually, showed the differences in microbial load across the storage time and temperatures. Results illustrate that HSI can be potentially implemented as a non-invasive tool to predict microbe loads in bovine hides before leather processing, so that real-time grading of hides based on staling states can be achieved. This will reduce the cost of leather production and waste management and pave the way for allocating material supply for different production purposes.

An efficient methodology for modeling to predict wine aroma expression based on quantitative data of volatile compounds: A case study of oak barrel-aged red wines

Correlating aroma expression with volatile compounds has long been an ambition in researches of flavor chemistry. To propose a reliable methodology to depict wine aroma, 76 oak barrel-aged dry red wines were investigated through the combination of machine learning algorithm and multivariate analysis. Aromatic characteristic was evaluated by quantitative descriptive analysis (QDA), while non- or oak derived volatiles were detected by HS-SPME-GC-MS and targeted SPE-GC-QqQ-MS/MS, respectively. Results showed that variable importance for projection values (VIPs) from partial least-squares regression (PLSR) and mean decrease accuracy (MDA) from random forest were efficient parameters for feature selection. The correlating accuracy of the optimal PLSR model to predict intensities of different aroma characteristics through selected volatile compounds could achieve 0.754 to 0.943, representing potential application to manage wine aroma by chemical assay in winemaking. From the perspective of mathematical modeling in the real wine matrix, the network analysis between aroma characteristics and key volatile compounds indicated that the expression of oak aroma was not only directly contributed by volatiles derived from oak wood, but also influenced by ethyl esters, including ethyl acetate, ethyl butanoate, ethyl hexanoate, ethyl decanoate, and ethyl nonanoate.

Non-destructive prediction of yak meat freshness indicator by hyperspectral techniques in the oxidation process

This study examined the potential of hyperspectral techniques for the rapid detection of characteristic indicators of yak meat freshness during the oxidation of yak meat. TVB-N values were determined by significance analysis as the characteristic index of yak meat freshness. Reflectance spectral information of yak meat samples (400-1000 nm) was collected by hyperspectral technology. The raw spectral information was processed by 5 methods and then principal component regression (PCR), support vector machine regression (SVR) and partial least squares regression (PLSR) were used to build regression models. The results indicated that the full-wavelength based on PCR, SVR, and PLSR models were shown greater performance in the prediction of TVB-N content. In order to improve the computational efficiency of the model, 9 and 11 characteristic wavelengths were selected from 128 wavelengths by successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS), respectively. The CARS-PLSR model exhibited excellent predictive power and model stability.

Ionomics and metabolomics analysis reveal the molecular mechanism of metal tolerance of Pteris vittata L. dominating in a mining site in Thai Nguyen province, Vietnam

This study aims to find the interaction between ionome and metabolome profiles of Pteris vittata L., an arsenic hyperaccumulator plant, to reveal its metal tolerance mechanism. Therefore, at the Pb-Zn mining sites located in Thai Nguyen province, Vietnam, where these species dominate, soil and plant samples were collected. Their multi-element compositions were analyzed using inductively coupled plasma mass spectrometry (ICP-MS) and thus referred to as the "ionomics" approach. In parallel, the widely targeted metabolomics profiles of these plant samples were performed using liquid chromatography-tandem mass spectrometry (UPLC-QqQ-MS). Nineteen elements, including both metals and nonmetals, were detected and quantified in both tissues of thirty-five plant individuals. A comparison of these elements' levels in two tissues showed that above-ground parts accumulated more As and inorganic P, whereas Zn, Pb, and Sb were raised mostly in the under-ground samples. The partial least squares regression (PLSR) model predicting the level of each element by the whole metabolome indicated that the enhancement of flavonoids content plays an essential contribution in adaptation with the higher levels of Pb, Ag, and Ni accumulated in the aerial part, and Mn, Pb in subterranean part. Moreover, the models also highlighted the effect of Mn and Pb on the metabolic induction of adenosine derivatives in subterranean parts. At the same time, the model presented the most contribution of As to the metabolisms of the amino acids of this tissue. On those accounts, the developed integration approach linking the ionomics and metabolomics data of P. vittata improved the understanding of the molecular mechanism of hyperaccumulation characteristics and provided markers that could be targeted in future investigations.

Temporal instability of partial least squares regressions for estimating leaf photosynthetic traits from hyperspectral information

Partial least squares regression (PLSR) is applied increasingly often to predict plant photosynthesis from reflectance spectra. While its applicability across different areas has been examined in previous studies, its stability across time has yet to be evaluated. In this study, we assessed a series of PLSR models built upon three different band selection approaches (iterative stepwise, genetic algorithm, and uninformative variable elimination), in combination with different spectral transforms (original and first-order derivative spectra), for their stabilities in predicting the maximum carboxylation rate (Vcmax) and maximum electron transport rate (Jmax) from hyperspectral reflectance spectra at different temporal scales (seasonal and interannual). The results showed that both photosynthetic parameters can be estimated from leaf hyperspectral reflectance with moderate to good accuracy across different growing stages (R² = 0.45-0.84) and years (R² = 0.37-0.97). We further found that the iterative stepwise selection of informative bands when building PLSR models could greatly improve its predictive capacity compared with that of other PLSR models, especially those based on first-order derivative spectra. However, the selected bands of the models for both photosynthetic parameters were, unfortunately not consistent. Furthermore, we could not have identified any model with fixed spectra performed consistently across different seasonal stages and across different years. However, the blue spectral regions were popularly selected throughout the growing stages and in different years. The results demonstrate that leaf spectra-trait estimation using PLSR models varies with time and thus cast doubt over the use of a specific PLSR model to infer leaf traits across different temporal-spatial contexts. The development of a general applicable PLSR model is still in the works.

Prediction of Protein Concentration in Pea (Pisum sativum L.) Using Near-Infrared Spectroscopy (NIRS) Systems

Breeding for increased protein concentration is a priority in field peas. Having a quick, accurate, and non-destructive protein quantification method is critical for screening breeding materials, which the near-infrared spectroscopy (NIRS) system can provide. Partial least square regression (PLSR) models to predict protein concentration were developed and compared for DA7250 and FT9700 NIRS systems. The reference protein data were accurate and exhibited a wider range of variation (15.3−29.8%). Spectral pre-treatments had no clear advantage over analyses based on raw spectral data. Due to the large number of samples used in this study, prediction accuracies remained similar across calibration sizes. The final PLSR models for the DA7250 and FT9700 systems required 10 and 13 latent variables, respectively, and performed well and were comparable (R2 = 0.72, RMSE = 1.22, and bias = 0.003 for DA7250; R2 = 0.79, RMSE = 1.23, and bias = 0.055 for FT9700). Considering three groupings for protein concentration (Low: <20%, Medium: ≥20%, but ≤25%, and High: >25%), none of the entries changed from low to high or vice versa between the observed and predicted values for the DA7250 system. Only a single entry moved from a low category in the observed data to a high category in the predicted data for the FT9700 system in the calibration set. Although the FT9700 system outperformed the DA7250 system by a small margin, both systems had the potential to predict protein concentration in pea seeds for breeding purposes. Wavelengths between 950 nm and 1650 nm accounted for most of the variation in pea protein concentration.

A model for colour preference behaviour of spring migrant aphids

Aphids are economically and ecologically important herbivorous insects. A critical step in their life cycle is the visually guided host finding behaviour. To elucidate the role of colour in host finding of aphid spring migrants we conducted large colour trap experiments in the field and analysed aphid catch data, using trap spectral reflectance data as input. Based on known and putative photoreceptor sensitivities we developed and optimized a simple empirical colour choice model for spring migrants of different aphid species which confirmed and explained the yellow preference of these insects. In a further step, we applied multivariate statistical methods to behavioural and reflectance data, but without data on photoreceptor sensitivities, to find the wavelengths of greatest importance for the aphids' behavioural responses. This analysis confirmed the position of the green photoreceptor peak previously obtained independently with electrophysiological methods. In a final step, we applied the colour preference model to a dataset of leaf spectra. This showed that aphid visual preference would be dependent on the plants' nutritional status, with lower nitrogen input being associated with stronger preference, despite known benefits of high nitrogen levels for aphid reproduction and fitness. Ecological and evolutionary implications of these results are discussed. This article is part of the theme issue 'Understanding colour vision: molecular, physiological, neuronal and behavioural studies in arthropods'.

Quantitative analysis of blended corn-olive oil based on Raman spectroscopy and one-dimensional convolutional neural network

Blended vegetable oil is a vital product in the vegetable oil market, and quantifying high-value vegetable oil is of great significance to protect the rights and interests of consumers. In this study, we established a one-dimensional convolutional neural network (1D CNN) quantitative identification model based on Raman spectra to identify the amount of olive oil in a corn-olive oil blend. The results show that the 1D CNN model based on 315 extended average Raman spectra can quantitatively identify the content of olive oil, with R²p and RMSEP values of 0.9908 and 0.7183 respectively. Compared with partial least squares regression (PLSR) and support vector regression (SVR), although the index is not optimal, it provides a new analytical method for the quantitative identification of vegetable oil.

Japanese dace (Tribolodon hakonensis) fish freshness estimation using front-face fluorescence spectroscopy coupled with chemometric analysis

Although fish and its related products are good sources of protein and unsaturated fatty acids, like omega-3 in the human diet, their shelf-life is limited by biochemical and microbial changes. In this study, a front-face fluorescence spectroscopy technique was used to acquire Excitation-emission matrices (EEM) to monitor Japanese dace (Tribolodon hakonensis) fish freshness degradation during storage. EEM of Japanese dace fish parts (intact eyeball and surface-containing scales), excitation from 220 to 585 nm and emissions from 250 to 600 nm, were measured at different times during storage. To simplify the acquired complex spectra datasets from each fish part, the variables were reduced to those that were only significant/important (those with higher positive or negative correlation) for K value prediction, and as an index of freshness. Partial least square regression (PLSR) results demonstrated that combining the fluorescence EEM of the eyeball and surface-containing scales the best monitoring of fish freshness; excitation at 280 and 350 nm for both the eyeball and surface-containing scales, with 2.84 and 0.96 as RMSE and R², respectively. These findings demonstrate that multiple excitation fluorescence approaches can be convenient for the freshness evaluation of fish.

Estimation of amino acid contents in maize leaves based on hyperspectral imaging

Estimation of the amino acid content in maize leaves is helpful for improving maize yield estimation and nitrogen use efficiency. Hyperspectral imaging can be used to obtain the physiological and biochemical parameters of maize leaves with the advantages of being rapid, non-destructive, and high throughput. This study aims to estimate the multiple amino acid contents in maize leaves using hyperspectral imaging data. Two nitrogen (N) fertilizer experiments were carried out to obtain the hyperspectral images of fresh maize leaves. The partial least squares regression (PLSR) method was used to build the estimation models of various amino acid contents by using the reflectance of all bands, sensitive band range, and sensitive bands. The models were then validated with the independent dataset. The results showed that (1) the spectral reflectance of most amino acids was more sensitive in the range of 400-717.08 nm than other bands. The estimation accuracy was better by using the reflectance of the sensitive band range than that of all bands; (2) the sensitive bands of most amino acids were in the ranges of 505.39-605 nm and 651-714 nm; and (3) among the 24 amino acids, the estimation models of the β-aminobutyric acid, ornithine, citrulline, methionine, and histidine achieved higher accuracy than those of other amino acids, with the R ², relative root mean square error (RE), and relative percent deviation (RPD) of the measured and estimated value of testing samples in the range of 0.84-0.96, 8.79%-19.77%, and 2.58-5.18, respectively. This study can provide a non-destructive and rapid diagnostic method for genetic sensitive analysis and variety improvement of maize.

Non-Destructive Analysis of Chlorpheniramine Maleate Tablets and Granules by Chemometrics-Assisted Attenuated Total Reflectance Infrared Spectroscopy

Non-destructive analysis of chlorpheniramine maleate (CPM), pharmaceutical tablets, and granules was conducted by chemometrics-assisted attenuated total reflectance infrared spectroscopy (ATR-IR). For tablets, an optimum PLSR model with eight latent factors was obtained from area-normalized and standard normal variate (SNV) pretreated ATR-IR spectral data with correlation coefficients (R²) of calibration and cross-validation of 0.9716 and 0.9602, respectively. The model capability for the 42 test set samples was proven with R² between the reference and model prediction values of 0.9632, and a root-mean-square error of prediction (RMSEP) of 1.7786. The successive PLSR model for granules was constructed from SNV and first derivative pretreated ATR-IR spectral data with two latent factors and correlation coefficients (R²) of calibration and cross-validation of 0.9577 and 0.9450, respectively.

Field hyperspectral data and OLI8 multispectral imagery for heavy metal content prediction and mapping around an abandoned Pb-Zn mining site in northern Tunisia

Mining and smelting releases toxic contaminants such as zinc (Zn), lead (Pb) or cadmium (Cd) into the soil thereby poisoning it and rendering it unproductive. Remotely alternatives have been widely employed in the attempt of estimating heavy metal content within soils. The present study provides a methodological approach based on VNIR-SWIR field hyperspectral data and multispectral Landsat OLI 8 imageries for the prediction and mapping of Pb, Zn and Cd heavy metal contents around the abandoned Jebel Ressas mine site in Northern Tunisia. Thus, eighty-seven soil and tailing samples were collected from the study site and VNIR-SWIR field reflectances were measured on the same collection points, as well. All samples were analysed by atomic absorption for the estimation of heavy metal concentrations. The partial least squares regression PLSR was conducted considering the measured heavy metal concentrations and using multi-scale data: VNIR-SWIR field hyperspectral data and multispectral Landsat OLI 8 imagery. Standard normal variable (SNV) and multiple scatter correction (MSC) preprocessing methods were applied for further mapping improvement. Thus, this work aims to automate the estimation of the heavy metal contents in contaminated soils, by carrying out: a modeling approach based on the PLSR using VNIR-SWIR field hyperspectral data, ii) the mapping of Pb and Zn contents thanks to the exploitation of Landsat OLI8 multispectral imagery and iii) the application of both MSC and SNV preprocessing methods to optimize the performance of the developed models, when using such spectrally and spatially degraded data.

Fatty Acid Determination in Human Milk Using Attenuated Total Reflection Infrared Spectroscopy and Solvent-Free Lipid Separation

This study introduces the first mid-infrared (IR)-based method for determining the fatty acid composition of human milk. A representative milk lipid fraction was obtained by applying a rapid and solvent-free two-step centrifugation method. Attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy was applied to record absorbance spectra of pure milk fat. The obtained spectra were compared to whole human milk transmission spectra, revealing the significantly higher degree of fatty acid-related spectral features in ATR FT-IR spectra. Partial least squares (PLS)-based multivariate regression equations were established by relating ATR FT-IR spectra to fatty acid reference concentrations, obtained with gas chromatography-mass spectrometry (GC-MS). Good predictions were achieved for the most important fatty acid sum parameters: saturated fatty acids (SAT, R²_CV = 0.94), monounsaturated fatty acids (MONO, R²_CV = 0.85), polyunsaturated fatty acids (PUFA, R²_CV = 0.87), unsaturated fatty acids (UNSAT, R²_CV = 0.91), short-chain fatty acids (SCFA, R²_CV = 0.79), medium-chain fatty acids (MCFA, R²_CV = 0.97), and long-chain fatty acids (LCFA, R²_CV = 0.88). The PLS selectivity ratio (SR) was calculated in order to optimize and verify each individual calibration model. All mid-IR regions with high SR could be assigned to absorbances from fatty acids, indicating high validity of the obtained models.

High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean (Glycine max) nested association mapping population

Photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500-2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R2 from 0.56 to 0.96 and root mean square error approximately <10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis.

Prediction of Photosynthetic, Biophysical, and Biochemical Traits in Wheat Canopies to Reduce the Phenotyping Bottleneck

To achieve food security, it is necessary to increase crop radiation use efficiency (RUE) and yield through the enhancement of canopy photosynthesis to increase the availability of assimilates for the grain, but its study in the field is constrained by low throughput and the lack of integrative measurements at canopy level. In this study, partial least squares regression (PLSR) was used with high-throughput phenotyping (HTP) data in spring wheat to build predictive models of photosynthetic, biophysical, and biochemical traits for the top, middle, and bottom layers of wheat canopies. The combined layer model predictions performed better than individual layer predictions with a significance as follows for photosynthesis R ² = 0.48, RMSE = 5.24 μmol m^-2 s^-1 and stomatal conductance: R ² = 0.36, RMSE = 0.14 mol m^-2 s^-1. The predictions of these traits from PLSR models upscaled to canopy level compared to field observations were statistically significant at initiation of booting (R ² = 0.3, p < 0.05; R ² = 0.29, p < 0.05) and at 7 days after anthesis (R ² = 0.15, p < 0.05; R ² = 0.65, p < 0.001). Using HTP allowed us to increase phenotyping capacity 30-fold compared to conventional phenotyping methods. This approach can be adapted to screen breeding progeny and genetic resources for RUE and to improve our understanding of wheat physiology by adding different layers of the canopy to physiological modeling.

Identification of Stingless Bee Honey Adulteration Using Visible-Near Infrared Spectroscopy Combined with Aquaphotomics

Honey is a natural product that is considered globally one of the most widely important foods. Various studies on authenticity detection of honey have been fulfilled using visible and near-infrared (Vis-NIR) spectroscopy techniques. However, there are limited studies on stingless bee honey (SBH) despite the increase of market demand for this food product. The objective of this work was to present the potential of Vis-NIR absorbance spectroscopy for profiling, classifying, and quantifying the adulterated SBH. The SBH sample was mixed with various percentages (10−90%) of adulterants, including distilled water, apple cider vinegar, and high fructose syrup. The results showed that the region at 400−1100 nm that is related to the color and water properties of the samples was effective to discriminate and quantify the adulterated SBH. By applying the principal component analysis (PCA) on adulterants and honey samples, the PCA score plot revealed the classification of the adulterants and adulterated SBHs. A partial least squares regression (PLSR) model was developed to quantify the contamination level in the SBH samples. The general PLSR model with the highest coefficient of determination and lowest root means square error of cross-validation (RCV2=0.96 and RMSECV=5.88 %) was acquired. The aquaphotomics analysis of adulteration in SBH with the three adulterants utilizing the short-wavelength NIR region (800−1100 nm) was presented. The structural changes of SBH due to adulteration were described in terms of the changes in the water molecular matrix, and the aquagrams were used to visualize the results. It was revealed that the integration of NIR spectroscopy with aquaphotomics could be used to detect the water molecular structures in the adulterated SBH.

Disposable Coverslip for Rapid Throughput Screening of Malaria Using Attenuated Total Reflection Spectroscopy

Malaria is considered to be one of the most catastrophic health issues in the whole world. Vibrational spectroscopy is a rapid, robust, label-free, inexpensive, highly sensitive, nonperturbative, and nondestructive technique with high diagnostic potential for the early detection of disease agents. In particular, the fingerprinting capability of attenuated total reflection spectroscopy is promising as a point-of-care diagnostic tool in resource-limited areas. However, improvements are required to expedite the measurements of biofluids, including the drying procedure and subsequent cleaning of the internal reflection element to enable high throughput successive measurements. As an alternative, we propose using an inexpensive coverslip to reduce the sample preparation time by enabling multiple samples to be collectively dried together under the same temperature and conditions. In conjunction with partial least squares regression, attenuated total reflection spectroscopy was able to detect and quantify the parasitemia with root mean square error of cross-validation and R² values of 0.177 and 0.985, respectively. Here, we characterize an inexpensive, disposable coverslip for the high throughput screening of malaria parasitic infections and thus demonstrate an alternative approach to direct deposition of the sample onto the internal reflection element.

Identifying aroma-active compounds in coffee-flavored dairy beverages

Coffee aroma is a complex mixture of volatile compounds. This study characterized the important aroma-active compounds associated with consumer liking in formulated coffee-flavored dairy beverages. Nine coffee-flavored dairy beverages were formulated: low fat-low coffee; medium fat-low coffee; high fat-low coffee; low fat-medium coffee; medium fat-medium coffee; high fat-medium coffee; low fat-high coffee; medium fat-high coffee; and high fat-high coffee. Regular coffee consumers, (n = 231) used a nine-point hedonic scale to rate acceptance of aroma. Volatile compounds were extracted by head space-solid phase micro-extraction (HS-SPME) and analyzed by gas chromatography-mass spectrometry-olfactometry (GC-MS-O) using a modified frequency (MF) approach. Fifty-two aroma-active compounds were detected. Thirty-one aroma-active compounds were considered important compounds with MF-value ≥ 50%. The total number of aroma-active compounds and their intensity were affected because of fat and coffee concentration. Partial least squares regression (PLSR) was performed to determine the relationship between aroma-active compounds and liking. PLSR analysis identified three groups of compounds regarding liking. Twenty-five compounds were associated with positive liking, for example, 2-(methylsulfanylmethyl) furan (coffee like). Sixteen compounds were negatively associated with liking, for example, 2-methoxyphenol (bacon, medicine like). Eleven detected compounds had no association with liking, for example, butane-2,3-dione (butter, fruit like). Practical Application: The result of this study may be applied to formulate coffee-flavored dairy beverages to maximize consumer acceptance and aroma-liking. This study suggested too low coffee concentration is not desirable. Too much fat affects aroma release and/or alters the characteristic coffee flavor which negatively affects consumer acceptance.

Combining Hyperspectral Reflectance Indices and Multivariate Analysis to Estimate Different Units of Chlorophyll Content of Spring Wheat under Salinity Conditions

Although plant chlorophyll (Chl) is one of the important elements in monitoring plant stress and reflects the photosynthetic capacity of plants, their measurement in the lab is generally time- and cost-inefficient and based on a small part of the leaf. This study examines the ability of canopy spectral reflectance data for the accurate estimation of the Chl content of two wheat genotypes grown under three salinity levels. The Chl content was quantified as content per area (Chl _area, μg cm^-2), concentration per plant (Chl _plant, mg plant^-1), and SPAD value (Chl _SPAD). The performance of spectral reflectance indices (SRIs) with different algorithm forms, partial least square regression (PLSR), and stepwise multiple linear regression (SMLR) in estimating the three units of Chl content was compared. Results show that most indices within each SRI form performed better with Chl _area and Chl _plant and performed poorly with Chl _SPAD. The PLSR models, based on the four forms of SRIs individually or combined, still performed poorly in estimating Chl _SPAD, while they exhibited a strong relationship with Chl _plant followed by Chl _area in both the calibration (Cal.) and validation (Val.) datasets. The SMLR models extracted three to four indices from each SRI form as the most effective indices and explained 73-79%, 80-84%, and 39-43% of the total variability in Chl _area, Chl _plant, and Chl _SPAD, respectively. The performance of the various predictive models of SMLR for predicting Chl content depended on salinity level, genotype, season, and the units of Chl content. In summary, this study indicates that the Chl content measured in the lab and expressed on content (μg cm^-2) or concentration (mg plant^-1) can be accurately estimated at canopy level using spectral reflectance data.

NIRS and Aquaphotomics Trace Robusta-to-Arabica Ratio in Liquid Coffee Blends

Coffee is both a vastly consumed beverage and a chemically complex matrix. For a long time, an arduous chemical analysis was necessary to resolve coffee authentication issues. Despite their demonstrated efficacy, such techniques tend to rely on reference methods or resort to elaborate extraction steps. Near infrared spectroscopy (NIRS) and the aquaphotomics approach, on the other hand, reportedly offer a rapid, reliable, and holistic compositional overview of varying analytes but with little focus on low concentration mixtures of Robusta-to-Arabica coffee. Our study aimed for a comparative assessment of ground coffee adulteration using NIRS and liquid coffee adulteration using the aquaphotomics approach. The aim was to demonstrate the potential of monitoring ground and liquid coffee quality as they are commercially the most available coffee forms. Chemometrics spectra analysis proved capable of distinguishing between the studied samples and efficiently estimating the added Robusta concentrations. An accuracy of 100% was obtained for the varietal discrimination of pure Arabica and Robusta, both in ground and liquid form. Robusta-to-Arabica ratio was predicted with R²CV values of 0.99 and 0.9 in ground and liquid form respectively. Aquagrams results accentuated the peculiarities of the two coffee varieties and their respective blends by designating different water conformations depending on the coffee variety and assigning a particular water absorption spectral pattern (WASP) depending on the blending ratio. Marked spectral features attributed to high hydrogen bonded water characterized Arabica-rich coffee, while those with the higher Robusta content showed an abundance of free water structures. Collectively, the obtained results ascertain the adequacy of NIRS and aquaphotomics as promising alternative tools for the authentication of liquid coffee that can correlate the water-related fingerprint to the Robusta-to-Arabica ratio.

The qualitative and quantitative assessment of xiaochaihu granules based on e-eye, e-nose, e-tongue and chemometrics

Xiaochaihu granules (XCHG), a famous Chinese patent medicine with high sales, have more than 100 approved number by China Food and Drug Administration (CFDA). Therefore, it is important to evaluate the quality of XCHG from different pharmaceutical companies. The data fusion of electronic eye (e-eye), electronic nose (e-nose) and electronic tongue (e-tongue) combined with chemometrics were applied for qualitative identification and quantitative prediction of XCHG quality. Firstly, main chemical constituents, such as saikosaponin b2, baicalin and glycyrrhizin were quantified with ultra-high-performance liquid chromatography (UHPLC). Secondly, the characteristic features of odor, color, and taste of XCHG were measured by e-nose, e-eye and e-tongue, and the Pearson correlation between constituents and e-signals was analyzed. Thirdly, partial least squares discrimination analysis (PLS-DA) of e-eye, e-nose and e-tongue were classified by the hierarchical clustering analysis (HCA) results of the main constituents of XCHG separately. Finally, partial least-squares regression (PLSR) was used to build the prediction model between components and data fusion of e-eye, e-nose and e-tongue. The results showed that saikosaponin b2, baicalin and glycyrrhizin were the three main components in XCHG samples. in which saikosaponin b2 ranged from 0.280 to 2.186 mg (relative standard deviation (RSD), 62.10 %), baicalin range from 25.883 mg to 49.108 mg (RSD, 16.64 %), and glycyrrhizin ranged from 0.897 mg to 6.052 mg (RSD, 40.32 %) of 31 batches of XCHG in each bag. Pearson correlation results showed that the main constituents were related to the core e-signals of XCHG, such as Eab, bitterness and R2 (odor sensitive to nitrogen oxide). Data fusion of e-eye, e-nose and e-tongue with main constitutes of XCHG using the PLSR model showed that the root mean square error (RMSE) values were 0.320 and 0.090 for saikosaponin b2 and licoricesaponin G2 (P < 0.000). The saikosaponin b2 and licoricesaponin G2 contents in XCHG could be predicted with integrated data of e-nose, e-eye, and e-tongue using the PLSR model.

Variation in Volatile Flavor Compounds of Cooked Mutton Meatballs during Storage

Solid phase microextraction (SPME) and Solvent-Assisted Flavor Evaporation (SAFE) were used to analyze the flavor changes of cooked mutton meatballs during storage by gas chromatography-olfactometrymass spectrometry (GC-O-MS), sensory evaluation and Partial Least Squares Regression (PLSR). With the increase of storage time, the concentrations of various volatile compounds in cooked mutton meatballs decreased to varying degrees at the later stage of storage, indicating that the aroma was gradually weakened, which was consistent with the results of sensory evaluation. At 30 days of storage, the overall aroma profile was more prominent, and at the later stage of storage, the sulfur odor was more prominent. The correlation of PLSR further confirmed the credibility of the results. Compared with the SPME and SAFE extraction methods, SPME extracted more flavor substances, and the SAFE extraction rate was higher, which indicated that the combination of several methods was needed for aroma extraction. An analysis of the dilution results and odor activity value (OAV) showed that the key aroma components during storage were 1-octene-3-ol, linalool, methylallyl sulfide, diallyl disulfide, 2-pinene, hexanal and butyric acid.

A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression

Partial least squares regression (PLSR) modelling is a statistical technique for correlating datasets, and involves the fitting of a linear regression between two matrices. One application of PLSR enables leaf traits to be estimated from hyperspectral optical reflectance data, facilitating rapid, high-throughput, non-destructive plant phenotyping. This technique is of interest and importance in a wide range of contexts including crop breeding and ecosystem monitoring. The lack of a consensus in the literature on how to perform PLSR means that interpreting model results can be challenging, applying existing models to novel datasets can be impossible, and unknown or undisclosed assumptions can lead to incorrect or spurious predictions. We address this lack of consensus by proposing best practices for using PLSR to predict plant traits from leaf-level hyperspectral data, including a discussion of when PLSR is applicable, and recommendations for data collection. We provide a tutorial to demonstrate how to develop a PLSR model, in the form of an R script accompanying this manuscript. This practical guide will assist all those interpreting and using PLSR models to predict leaf traits from spectral data, and advocates for a unified approach to using PLSR for predicting traits from spectra in the plant sciences.

Modeling relationship between protein oxidation and denaturation and texture, moisture loss of bighead carp (Aristichthys Nobilis) during frozen storage

To evaluate the effects of protein oxidation and denaturation on the fish texture and moisture loss during frozen storage, we measured the changes of protein oxidation and denaturation (salt-soluble protein (SSP), total sulfhydryl (SH), disulfide (SS), carbonyl contents and Ca²⁺ -ATPase activity), texture (hardness), and moisture loss (drip loss) of bighead carp fillets stored at -12, -20 and -28°C during 16 weeks. These data were employed to develop partial least squares regression (PLSR) model, radial basis function neural network (RBFNN) model, PLSR-RBFNN (PR) model and RBFNN-PLSR (RP) model. The results showed that the RP model provided no enhancement to RBFNN model because it had the exactly same root mean square error (RMSE) and R² . PLSR model showed better performance than other models when predicting hardness. More appropriate linear or linearity-dominant hybrid model needed to be explored to establish the relationship between protein oxidation and denaturation and texture. The PR model performed better than other models in predicting drip loss with its lower RMSE and higher R² , which revealed both linear and nonlinear relationship between protein oxidation and denaturation and moisture loss. Therefore, the PR model was a promising and encouraging tool to provide a more comprehensive understanding of the relationship between protein oxidation and denaturation and moisture loss of fish during frozen storage. PRACTICAL APPLICATION: The study explored the effects of protein oxidation and denaturation on the texture and moisture loss of bighead carp during frozen storage (-12 to -28°C). PLSR model showed better performance than other models when predicting the relationship between protein oxidation and denaturation and texture. The PR model was an available tool for manufacturers to predict the relationship between protein oxidation and denaturation and moisture loss.

Sorghum Grains Grading for Food, Feed, and Fuel Using NIR Spectroscopy

Near-infrared spectroscopy (NIR) is a non-destructive, fast, and low-cost method to measure the grain quality of different cereals. However, the feasibility for determining the critical biochemicals, related to the classifications for food, feed, and fuel products are not adequately investigated. Fourier-transform (FT) NIR was applied in this study to determine the eight biochemicals in four types of sorghum samples: hulled grain flours, hull-less grain flours, whole grains, and grain flours. A total of 20 hybrids of sorghum grains were selected from the two locations in China. Followed by FT-NIR spectral and wet-chemically measured biochemical data, partial least squares regression (PLSR) was used to construct the prediction models. The results showed that sorghum grain morphology and sample format affected the prediction of biochemicals. Using NIR data of grain flours generally improved the prediction compared with the use of NIR data of whole grains. In addition, using the spectra of whole grains enabled comparable predictions, which are recommended when a non-destructive and rapid analysis is required. Compared with the hulled grain flours, hull-less grain flours allowed for improved predictions for tannin, cellulose, and hemicellulose using NIR data. This study aimed to provide a reference for the evaluation of sorghum grain biochemicals for food, feed, and fuel without destruction and complex chemical analysis.